Construction collaborative management method and system based on BIM (Building Information Modeling) and Internet of Things

Abstract

The invention relates to the technical field of building informatization, and discloses a construction collaborative management method and system based on BIM and Internet of Things. The method comprises the steps of building a BIM model, collecting field real-time data, generating a dynamic construction state through association, performing collaborative analysis and forming a scheduling plan, and finally realizing closed-loop management and control through feedback of the Internet of Things. The system comprises a BIM model construction module, a field data acquisition module and a collaborative analysis and scheduling module which are in collaborative linkage to realize information integration, state perception and dynamic optimization control in the whole process of building construction. The method can effectively improve the precision and efficiency of the engineering construction process, enhances the intelligent management and control capability of the construction site, and has good engineering practicability and popularization value.

Description

Technical Field

[0001] The present invention relates to the field of building information technology, and specifically to a construction collaborative management method and system based on BIM and the Internet of Things, which is particularly suitable for application scenarios in which collaborative scheduling and closed-loop control of construction site progress, resources, and safety are carried out. Background Art

[0002] As construction projects become increasingly complex, traditional construction management methods face numerous limitations in terms of progress control, resource coordination, and on-site status response. Although Building Information Modeling (BIM) technology is widely used in the engineering design phase, the "static data island" problem still exists during the construction phase. Model data is difficult to link with the real-time status of the construction site, and there is a lack of dynamic response capabilities to progress, resources, and the environment. This creates a disconnect between plans and reality, making it difficult to support on-site management decisions. Currently, construction sites have begun deploying IoT devices such as temperature and humidity sensors and personnel positioning terminals to collect various types of real-time data. However, this data is often unstructured and fails to effectively connect with engineering information systems, making it impossible to achieve automated identification, intelligent analysis, and visual display. As a result, the data's value is not fully realized.

[0003] Traditional construction scheduling relies on manual experience or static plans to coordinate resources and schedule tasks, lacking the ability to respond to dynamic on-site changes in real time. When anomalies such as mismatches in personnel, equipment, or materials arise, it's difficult to adjust plans in a timely manner. Long feedback cycles and weak optimization mechanisms can easily lead to construction delays and safety risks. Summary of the Invention

[0004] In order to achieve the above-mentioned purpose of the invention, the present invention provides the following technical solution: a construction collaborative management method based on BIM and the Internet of Things, comprising the following steps:

[0005] S1. Build a Building Information Model (BIM) model and input basic engineering data through the project parameter initialization module to form a multi-dimensional engineering information set;

[0006] S2. Collecting real-time data from the construction site using an IoT collection device, and associating the data with the BIM model to generate dynamic construction status information;

[0007] S3. Conduct joint collaborative analysis of construction progress, resource allocation, and safety status based on dynamic construction status information to form a collaborative construction scheduling plan;

[0008] S4. Push the scheduling plan to the construction execution end, and provide real-time feedback on the execution status through IoT devices to achieve closed-loop control.

[0009] Preferably, constructing the BIM model in step S1 includes:

[0010] Input building geometry data, structural node information and construction process parameters according to the building design drawings in the BIM modeling platform;

[0011] Import construction schedules, personnel allocation tables, and equipment configuration lists through a multi-dimensional data entry interface;

[0012] Automatically generate a unified spatial and temporal engineering information model based on set rules and store it in the engineering data set central control library.

[0013] Preferably, collecting real-time data of the construction site in step S2 includes:

[0014] Temperature and humidity sensors, positioning sensors, and personnel identification terminals deployed at construction sites collect environmental parameters, personnel location, and identity information;

[0015] Using wireless gateways to transmit data to on-site edge computing nodes for preliminary filtering and processing;

[0016] The processed data is bound to the component nodes in the BIM model through a dedicated interface to generate structured construction status data, which is then stored in the dynamic information database.

[0017] Preferably, the joint collaborative analysis in step S3 includes:

[0018] Construct a multi-dimensional construction status matrix, including personnel-task mapping, equipment-construction section association, and material-time period allocation;

[0019] Compare completed nodes in the BIM model with on-site data to identify areas with resource mismatches, construction interference, and schedule deviations;

[0020] The following optimization formula is used to calculate the multi-objective scheduling plan:

[0021]

[0022] in, and denote the actual and planned completion time of task i, respectively. Indicates the amount of resources used, Indicates the amount of available resources, C i represents the construction conflict penalty factor of the i-th task, α, β, and γ are weight factors;

[0023] Preferably, the closed-loop control in step S4 includes:

[0024] The scheduling plan is encoded and pushed to construction execution equipment, such as hoisting machinery, concrete mixing equipment, and personnel terminals;

[0025] Receive execution status information, including task completion, abnormal events, and timing deviations, through the construction terminal feedback module;

[0026] The system automatically compares the plan with the actual status, performs early warning analysis on deviations, and generates corrective scheduling suggestions to achieve continuous optimization.

[0027] The present invention also provides a construction collaborative management system based on BIM and the Internet of Things, including the following modules:

[0028] BIM model building module, used to create and maintain engineering information models that include geometric information, time schedules, and resource allocation;

[0029] On-site data acquisition module, used to collect and structure real-time dynamic data of the construction site, achieving synchronization between the physical world and the virtual model;

[0030] The collaborative analysis and scheduling module is used to jointly analyze construction plans, resource status and on-site information and generate optimal scheduling strategies, forming an adaptive collaborative management and control closed loop.

[0031] The BIM model construction module includes:

[0032] Drawing parsing unit, used to automatically parse CAD drawings and Revit files to generate preliminary geometric models;

[0033] Data fusion engine, used to inject construction schedule, personnel information and material list data into the model and establish corresponding relationships;

[0034] The version control component is used to record the evolution and update operations of the BIM model at each stage, ensuring the consistency and traceability of the models at different stages.

[0035] The field data acquisition module includes:

[0036] A multi-source sensing device cluster, including temperature and humidity sensors, personnel positioning equipment, and equipment status collectors;

[0037] Edge computing nodes, used for real-time analysis, anomaly screening, and format standardization of raw data;

[0038] The model associator module is used to bind the collected data with the components and task nodes in the BIM model, so as to visualize, model and structure the on-site status.

[0039] The collaborative analysis and scheduling module includes:

[0040] Scheduling analysis engine, which receives construction site status data and performs resource matching and time deduction for planned tasks;

[0041] The conflict identification submodule identifies potential conflict areas in construction based on the task logic diagram and resource dependency relationships;

[0042] Feedback response mechanism is used to dynamically adjust scheduling results and push them to the construction terminal, while recording on-site response data for subsequent optimization and model training.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention transforms the model from a static design information carrier into a real-time construction status mapping platform by constructing a dynamic association mechanism between the BIM model and the data collected by the Internet of Things. By driving the update of the BIM model with real-time data, not only the visualization level of on-site construction progress, resource utilization and safety status is improved, but also the scheduling and decision-making have a data support basis, realizing the digital and intelligent management and control of the construction process. The multi-dimensional construction status information is comprehensively analyzed through the collaborative analysis and scheduling module, and the optimal scheduling formula is used to realize the generation of the optimal construction plan under multi-objective constraints. At the same time, the real-time feedback and exception capture of the construction execution status are realized through the Internet of Things equipment, and combined with the dynamic adjustment mechanism of the scheduling plan, a closed-loop control link for construction management is constructed, which significantly reduces the resource mismatch rate, progress deviation and management lag, and improves the stability and efficiency of project execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic flow chart of the method steps provided for this application;

[0046] Figure 2 Schematic diagram of the system modules provided for this application. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0048] refer to Figure 1 The embodiment of the present invention provides a construction collaborative management method based on BIM and the Internet of Things, comprising the following steps:

[0049] Step 1: Build a Building Information Model (BIM) model and input basic engineering data through the project parameter initialization module to form a multi-dimensional engineering information set.

[0050] In step one, the first step is to import architectural design drawings through the BIM platform. The system automatically extracts the building's geometric information, including parameters of basic components such as walls, beams, columns, and floors. At the same time, the structural node connection method and the logical sequence of the construction process are manually entered. Then, using the platform's integrated multidimensional data import interface, the pre-established construction schedule, staffing table, and equipment configuration list are imported into the system in a structured format. Secondly, based on the set modeling rules and process logic, a multidimensional BIM engineering model that integrates the construction time dimension and space dimension is automatically generated. This model not only expresses the spatial relationship of physical components, but also encodes dynamic information such as construction timing information and resource dependencies. The final generated BIM model is stored in a standard format in the engineering data set central control library, providing data support for subsequent data binding and collaborative analysis.

[0051] Step 2: Collect real-time data from the construction site based on the Internet of Things collection device, and associate the data with the BIM model to generate dynamic construction status information.

[0052] In step two, temperature and humidity sensors are first deployed on key working surfaces of the construction site to collect environmental climate parameters such as temperature, humidity, and wind speed. UWB high-precision positioning tags and positioning base stations are installed in personnel passages and key construction areas to achieve high-frequency sampling of construction personnel's real-time location information. At the same time, NFC identity recognition chips are embedded in the smart badges worn by construction personnel to automatically identify personnel identities, construction work types, and team affiliations in each operation link. All collected raw data are encapsulated with a unified time sequence identifier and aggregated to the edge computing nodes set up on site through the wireless Mesh network. The edge nodes have an embedded data preprocessing module, which first executes processing logic such as format conversion, noise removal, and time synchronization to ensure data integrity and validity.

[0053] After preprocessing, the system uses a pre-set component mapping table to bind the processed data to spatial component nodes in the BIM model based on the sensor code and device ID. This binding process automatically matches data to the component by parsing the component ID and spatial coordinates, for example, binding temperature data to formwork components and personnel locations to work surfaces. The bound data is structured into a four-tuple consisting of component ID, parameter type, sample value, and timestamp, and is uniformly stored in a dynamic construction information database. This information serves as a key basis for subsequent progress analysis, anomaly identification, and scheduling optimization.

[0054] Step 3: Based on the dynamic construction status information, conduct a joint collaborative analysis of the construction progress, resource allocation, and safety status to form a collaborative construction scheduling plan.

[0055] In step three, the system first extracts daily key data from the dynamic information database based on the construction task dimension, including the actual attendance of each operator, equipment usage frequency, construction node status, and material input records. Based on this data, it constructs a three-dimensional status matrix. This matrix is indexed by task number, with horizontal dimensions including personnel allocation, equipment distribution, material ratios, and other information. The vertical dimension is organized according to time segments, forming a construction status data map that can be updated over time. By comparing it with the predefined construction process and resource plan in the BIM model, the system can quickly identify problems in the current construction, such as duplicate resource calls, process conflicts, uneven personnel scheduling, or inconsistent material delivery timelines.

[0056] To address these issues, the system integrates a multi-objective dynamic scheduling optimization algorithm. The optimization algorithm aims to minimize construction time offsets, maximize resource utilization balance, and minimize construction conflict penalties. The following scheduling optimization model is established:

[0057]

[0058] in, and denote the actual and planned completion time of task i, respectively. Indicates the amount of resources used, Indicates the amount of available resources, C i represents the construction conflict penalty factor of the i-th task, and α, β, and γ are weight factors.

[0059] Step 4: Push the scheduling plan to the construction execution end, and use the Internet of Things devices to provide real-time feedback on the execution status to achieve closed-loop control.

[0060] In step four, the system first converts the latest construction plan output by the scheduling engine into a standard task coding format and distributes it to each construction unit terminal through a data distribution interface. Specifically, after receiving the component lifting sequence instructions, the lifting equipment executes them according to the preset order in the lifting path. The concrete mixing device receives the concrete pouring area and pouring time period. Construction personnel receive instructions on the construction area, task type, and work content through handheld terminals. Each type of terminal has an embedded task feedback module for automatically collecting and transmitting construction progress, execution status, and abnormal events, such as alarm signals for unsuccessful component lifting, temporary absence of operators, and abnormal equipment shutdown.

[0061] After the construction execution status is returned to the system platform in real time, the platform compares the difference between the actual status and the planned status through the data comparison module, and divides it into three categories according to the degree of difference: normal execution, slight deviation and severe deviation. The system will perform regression analysis and abnormal clustering judgment on the deviation data. If the deviation value exceeds the preset tolerance range, the system will automatically trigger the early warning mechanism and push the deviation type, cause diagnosis and correction suggestions. For slight deviations, the system will give priority to using the time window fine-tuning method to correct the resource call rhythm; for severe deviations, it will re-call the scheduling engine to reconstruct the construction plan and dynamically push the new plan. The formation of the entire feedback closed loop not only enhances the response speed and execution stability of the construction site, but also establishes a mechanism for adaptive adjustment and continuous optimization in a data-driven manner, realizing the full-process closed-loop construction management of planning-execution-feedback-re-optimization.

[0062] refer to Figure 2 The embodiment of the present invention provides a construction collaborative management system based on BIM and the Internet of Things, including the following modules:

[0063] BIM model building module is used to create and maintain engineering information models that include geometric information, time schedules, and resource allocation.

[0064] In the BIM model construction module, the drawing parsing unit first receives construction drawings and modeling files provided by the project construction unit, including 2D CAD drawings and 3D Revit model files. The drawing parsing unit incorporates an automatic geometry extraction algorithm that analyzes component outlines, dimensions, and structural levels according to architectural design specifications, generating a preliminary 3D component assembly model. For complex structural areas that cannot be directly parsed, the module supports geometric correction through manual verification and parameter re-entry to ensure the spatial integrity of the model.

[0065] The data fusion engine then activates, binding the construction schedule, personnel roster (including trade type, team, and work time period), and materials and equipment list data (including component number, material type, and supplier batch) provided by the project management platform. This process establishes a logical one-to-one correspondence between component entities and the three-dimensional information of the task plan, personnel, and materials by constructing a mapping table between component IDs and external data fields, achieving semantic fusion of multi-source data.

[0066] On this basis, the version control component generates a unique version number for each model change (such as adding a new task node, replacing a component, and updating data), and stores the modified content, operator, timestamp, and differences before and after in a structured manner.

[0067] The on-site data acquisition module is used to collect and structure real-time dynamic data of the construction site to achieve synchronization between the physical world and the virtual model.

[0068] In the on-site data acquisition module, temperature and humidity sensors are installed in the ventilation shafts of each section inside the construction site to collect air parameters on a 5-minute cycle. The personnel positioning equipment uses a UWB positioning base station and tag solution. Each construction worker wears a positioning badge with a positioning accuracy controlled within 30 centimeters, enabling real-time worker trajectory tracking and work surface aggregation analysis. The equipment status collector is installed on the concrete sprayer and cable laying device to collect key parameters such as operating current, vibration amplitude and fault alarm data.

[0069] The above raw data are uniformly sent to the edge computing node through a low-power wireless communication module (LoRa). A quad-core processor and a lightweight data stream management engine are deployed in the node to complete the processing processes such as format standardization, numerical range anomaly screening, data redundancy elimination, and sampling time alignment in real time, and the data is structured and stored according to the device code-sampling index-timestamp. The data after edge analysis is sent to the model associator module, and through the preset component mapping relationship, the field status data is dynamically bound to the specific component node or construction task process node in the BIM model to realize the real-time status visualization of the model. On the Web visualization platform, managers can intuitively see the environmental parameters, construction status, responsible persons and other information corresponding to each component, and reflect the construction progress in real time through heat maps, flow trajectories, etc.

[0070] The collaborative analysis and scheduling module is used to jointly analyze construction plans, resource status and on-site information and generate optimal scheduling strategies, forming an adaptive collaborative management and control closed loop.

[0071] In the collaborative analysis and scheduling module, the system regularly retrieves the latest construction site status data from the on-site data acquisition module daily, including information such as task completion, personnel deployment, equipment usage load, and weather conditions. After receiving this data, the scheduling analysis engine first maps it to the current BIM model task node. By constructing a three-dimensional task-resource-time status matrix, it assesses the current construction execution deviation and risk level. The engine uses an improved genetic algorithm for scheduling optimization, using the multi-objective functions of minimizing total construction time, minimizing conflicts in the use of key resources, and maximizing construction team work continuity to reconstruct the construction task priority sequence and the allocation of personnel and equipment.

[0072] The system's embedded conflict identification submodule traverses the construction task logic diagram and component dependency graph, using topological analysis to detect potential conflict areas such as resource contention, overlapping areas, and errors in construction process sequences. Upon detection, the system annotates the conflict points and feeds them back to the construction plan interface for manual confirmation by the project manager or automatic system adjustments. The final scheduling results are pushed to work terminals such as smart wearable devices, machine control panels, and scheduling screens via a feedback response mechanism. The scheduling results contain the execution instructions, scheduling rationale, and expected feedback time.

[0073] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions therein may be combined with each other.

[0074] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present invention specification and drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A construction collaborative management method based on BIM and the Internet of Things, characterized by: The following steps are involved: S1. Build a Building Information Model (BIM) model and input basic engineering data through the project parameter initialization module to form a multi-dimensional engineering information set; S2. Collecting real-time data from the construction site using an IoT collection device, and associating the data with the BIM model to generate dynamic construction status information; S3. Conduct joint collaborative analysis of construction progress, resource allocation, and safety status based on dynamic construction status information to form a collaborative construction scheduling plan; S4. Push the scheduling plan to the construction execution end, and provide real-time feedback on the execution status through IoT devices to achieve closed-loop control.

2. A construction collaborative management method based on BIM and the Internet of Things according to claim 1, characterized in that: The step S1 of constructing the BIM model includes: Input building geometry data, structural node information and construction process parameters according to the building design drawings in the BIM modeling platform; Import construction schedules, personnel allocation tables, and equipment configuration lists through a multi-dimensional data entry interface; Automatically generate a unified spatial and temporal engineering information model based on set rules and store it in the engineering data set central control library.

3. The construction collaborative management method based on BIM and the Internet of Things according to claim 1 is characterized in that: The real-time data collection of the construction site in step S2 includes: Temperature and humidity sensors, positioning sensors, and personnel identification terminals deployed at construction sites collect environmental parameters, personnel location, and identity information; Using wireless gateways to transmit data to on-site edge computing nodes for preliminary filtering and processing; The processed data is bound to the component nodes in the BIM model through a dedicated interface to generate structured construction status data, which is then stored in the dynamic information database.

4. The construction collaborative management method based on BIM and the Internet of Things according to claim 1 is characterized in that: The joint collaborative analysis in step S3 includes: Construct a multi-dimensional construction status matrix, including personnel-task mapping, equipment-construction section association, and material-time period allocation; Compare completed nodes in the BIM model with on-site data to identify areas with resource mismatches, construction interference, and schedule deviations; The following optimization formula is used to calculate the multi-objective scheduling plan: in, and denote the actual and planned completion time of task i, respectively. Indicates the amount of resources used, Indicates the amount of available resources, C i represents the construction conflict penalty factor of the i-th task, and α, β, and γ are weight factors.

5. The construction collaborative management method based on BIM and the Internet of Things according to claim 1 is characterized in that: The closed-loop control in step S4 includes: The scheduling plan is encoded and pushed to construction execution equipment, such as hoisting machinery, concrete mixing equipment, and personnel terminals; Receive execution status information, including task completion, abnormal events, and timing deviations, through the construction terminal feedback module; The system automatically compares the plan with the actual status, performs early warning analysis on deviations, and generates corrective scheduling suggestions to achieve continuous optimization.

6. A construction collaborative management system based on BIM and the Internet of Things, characterized by: Includes the following modules: BIM model building module, used to create and maintain engineering information models that include geometric information, time schedules, and resource allocation; On-site data acquisition module, used to collect and structure real-time dynamic data of the construction site, achieving synchronization between the physical world and the virtual model; The collaborative analysis and scheduling module is used to jointly analyze construction plans, resource status and on-site information and generate optimal scheduling strategies, forming an adaptive collaborative management and control closed loop.

7. A construction collaborative management system based on BIM and the Internet of Things according to claim 6, characterized in that: The BIM model construction module includes: Drawing parsing unit, used to automatically parse CAD drawings and Revit files to generate preliminary geometric models; Data fusion engine, used to inject construction schedule, personnel information and material list data into the model and establish corresponding relationships; The version control component is used to record the evolution and update operations of the BIM model at each stage, ensuring the consistency and traceability of the models at different stages.

8. The construction collaborative management system based on BIM and the Internet of Things according to claim 6, characterized in that: The field data acquisition module includes: A multi-source sensing device cluster, including temperature and humidity sensors, personnel positioning equipment, and equipment status collectors; Edge computing nodes, used for real-time analysis, anomaly screening, and format standardization of raw data; The model associator module is used to bind the collected data with the components and task nodes in the BIM model, so as to visualize, model and structure the on-site status.

9. The construction collaborative management system based on BIM and the Internet of Things according to claim 6, characterized in that: The collaborative analysis and scheduling module includes: Scheduling analysis engine, which receives construction site status data and performs resource matching and time deduction for planned tasks; The conflict identification submodule identifies potential conflict areas in construction based on the task logic diagram and resource dependency relationships; Feedback response mechanism is used to dynamically adjust scheduling results and push them to the construction terminal, while recording on-site response data for subsequent optimization and model training.

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