Intelligent monitoring method for construction progress based on BIM and digital twinning

Abstract

This invention relates to the field of construction monitoring technology, specifically to an intelligent monitoring method for construction progress based on BIM and digital twins, comprising the following steps: acquiring the BIM model and construction plan data of the project; performing lightweight processing on the BIM model to establish a digital twin; and bidirectionally mapping the physical construction site and the virtual model; collecting image and video data, environmental and equipment status data, and construction management data from the construction site, and outputting multi-source data after preprocessing by edge computing nodes; mapping the multi-source data into the digital twin in real time to generate actual progress data points; comparing the actual progress data points with the construction plan progress curve to calculate the progress deviation value, and determining the progress status according to a preset threshold; generating a graded response strategy based on the progress status and pushing it out; through the above method, accurate and real-time matching of virtual and physical progress is achieved, improving the level of intelligence in construction progress management.

Description

Technical Field

[0001] This invention relates to the field of building construction monitoring technology, and in particular to an intelligent monitoring method for building construction progress based on BIM and digital twins. Background Technology

[0002] Construction progress monitoring is a core component in ensuring the timely, high-quality, and safe progress of engineering projects. Traditional methods of construction progress monitoring mainly rely on manual inspections, paper records, and phased acceptance, which suffer from prominent problems such as low monitoring efficiency, data lag, large errors, and poor coordination. As the scale of construction projects continues to expand and the structures become increasingly complex, with more and more frequent cross-disciplinary operations, traditional monitoring methods are no longer sufficient to meet the refined and real-time requirements of modern construction management.

[0003] Currently, BIM (Building Information Modeling) technology has been widely applied throughout the entire lifecycle of building engineering, from design and construction to operation and maintenance. Its visualization, parametric, and information-based characteristics provide technical support for construction progress management. However, existing BIM-based progress monitoring methods mostly remain at the static matching level of "model + plan," failing to achieve real-time linkage between the actual construction progress and the BIM model, and struggling to dynamically reflect the dynamic changes in deviations during construction. Digital twin technology can construct a one-to-one mapping relationship between physical entities and virtual models, enabling real-time simulation, status feedback, and dynamic control of physical entities by the virtual model. However, methods that deeply integrate digital twin technology with BIM technology for intelligent monitoring of building construction progress still have the following shortcomings: when comparing physical progress data with the virtual progress model in real time and with precision, it cannot promptly identify progress deviations, failing to fully leverage the advantages of intelligent monitoring, and unable to achieve accurate and real-time matching between virtual and physical progress. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring method for building construction progress based on BIM and digital twins, which aims to solve the technical problems in the existing technology that cannot identify progress deviations in a timely manner, fail to give full play to the advantages of intelligent monitoring, and cannot achieve accurate and real-time matching between virtual and physical progress.

[0005] To achieve the above objectives, this invention employs a method for intelligent monitoring of building construction progress based on BIM and digital twins, comprising the following steps:

[0006] Acquire BIM model and construction plan data for engineering projects, perform lightweight processing on the BIM model, establish a digital twin containing geometric information, attribute information and time dimension, and bidirectionally map the physical construction site and the virtual model.

[0007] Collect image and video data, environmental and equipment status data, and construction management data from the construction site, and output multi-source data after preprocessing by edge computing nodes;

[0008] Multi-source data is mapped into a digital twin in real time to generate actual progress data points. The actual progress data points are compared with the construction plan progress curve to calculate the progress deviation value and determine the progress status according to the preset threshold.

[0009] Based on the progress status, a tiered response strategy is generated, which includes the warning level, explanation of the cause, corrective action suggestions, and resource scheduling plan, and then pushed out.

[0010] Among the steps involved are: acquiring the BIM model and construction plan data of the engineering project, performing lightweight processing on the BIM model, establishing a digital twin containing geometric information, attribute information, and time dimension, and bidirectionally mapping the physical construction site and the virtual model;

[0011] Collect BIM model data and construction plan data for the project; the BIM model data includes architectural, structural and mechanical and electrical information, and the construction plan data includes the overall schedule, the schedule of each sub-item project and the time arrangement of each process.

[0012] Lightweight processing technology is used to optimize BIM models, simplify redundant details, and reduce the pressure of model rendering and data transmission.

[0013] Based on the lightweight BIM model, the geometric and attribute information of component dimensions, material properties, and construction technology is supplemented, and the time node data in the construction plan is integrated to construct a digital twin including three dimensions of geometry, attributes, and time.

[0014] Establish a two-way mapping relationship between the digital twin and the physical construction site, so that the digital twin can reflect the construction status of the physical site in real time.

[0015] The process of collecting image and video data, environmental and equipment status data, and construction management data from the construction site, and then preprocessing this data through edge computing nodes to output multi-source data includes:

[0016] High-definition cameras, IoT sensors, and data acquisition terminals are deployed at the construction site to collect raw data;

[0017] Establish connections between various data acquisition devices and edge computing nodes, and transmit raw data to the edge computing nodes;

[0018] The system preprocesses the raw data, filters out redundant, drifting, and isolated noise data, performs unified conversion on data of different formats, completes data cleaning and standardization, and outputs multi-source data.

[0019] Among the steps involved is deploying high-definition cameras, IoT sensors, and data acquisition terminals at the construction site, and collecting raw data:

[0020] High-definition cameras were used to collect image and video data from various areas of the construction site.

[0021] The system collects environmental data such as temperature, humidity, and wind speed on site through IoT sensors, as well as equipment status data such as the operating status of construction machinery and equipment operating parameters.

[0022] Construction management data such as the arrival of construction personnel, the arrival of materials, and the acceptance of work processes are recorded through data acquisition terminals.

[0023] The process includes preprocessing the raw data, filtering out redundant, drifting, and isolated noise data, uniformly converting data of different formats, completing data cleaning and standardization, and outputting multi-source data.

[0024] The collected raw data is classified and filtered to distinguish between image and video data, environmental data, equipment status data, and construction management data. Corresponding noise filtering methods are adopted according to the characteristics of different types of data. Specifically, redundant data is filtered out by frame deduplication and blur frame removal for image and video data. Drift and isolated noise points are filtered out by statistical outlier filtering and moving average filtering for environmental and equipment status sensor data. Invalid data is filtered out by duplicate record deletion and outlier identification and removal for construction management data.

[0025] Perform unified conversion processing on raw data of different formats; convert image and video data into standardized encoding formats, convert analog data collected by sensors into digital data, and convert construction management data into a unified structured data format;

[0026] Standardize the various types of data after noise filtering and format conversion, and unify the data precision, units and data naming rules;

[0027] All preprocessed data undergoes integrity verification, missing data is appropriately filled in, abnormal data is corrected a second time, and standardized multi-source data is integrated and output.

[0028] Among the steps, the process of generating actual progress data points by mapping multi-source data to a digital twin in real time, comparing these actual progress data points with the construction schedule curve, calculating the progress deviation value, and determining the progress status based on a preset threshold is as follows:

[0029] Establish mapping rules between multi-source data and digital twins, import multi-source data into the digital twin in real time according to the corresponding relationship, and realize the association between data and virtual model;

[0030] Based on multi-source data, the corresponding process and component progress measurement data points are generated in the digital twin, and the actual completion time and completion progress corresponding to each measurement data point are marked.

[0031] Extract the progress data from the construction plan, generate the construction plan progress curve, and obtain the planned progress target corresponding to each time node;

[0032] The actual progress data points are compared with the construction plan progress curve point by point, and the difference between the actual progress and the planned progress at each node is calculated, i.e., the progress deviation value. Combined with the preset progress deviation threshold, the current progress status is determined. The current progress status includes synchronous, ahead, and lagging.

[0033] In the steps of extracting progress data from the construction plan, generating a construction plan progress curve, and obtaining the planned progress target corresponding to each time node:

[0034] Extract progress information from construction plan data; the progress information includes the overall progress plan, the progress plans for sub-projects and individual work items, and the planned start time, planned completion time, duration, and planned progress percentage for each work process;

[0035] The extracted progress data is integrated, sorted by time dimension, and the planned progress targets for each process and component corresponding to each time node are determined.

[0036] A visual curve generation method is used, with time as the horizontal axis and progress completion percentage as the vertical axis, to fit the planned progress targets at each time node and generate a continuous construction plan progress curve. The planned progress values ​​of key nodes are marked on the progress curve. Key nodes include process start nodes, process completion nodes, and sub-item project acceptance nodes.

[0037] The generated construction schedule curve is synchronously imported into the digital twin visualization platform and linked with the digital twin model and actual progress measurement data points.

[0038] The process involves comparing the actual progress data points with the construction schedule curve point by point, calculating the difference between the actual progress and the planned progress at each node (i.e., the progress deviation value), and combining this with a preset progress deviation threshold to determine the current progress status. The current progress status includes the steps of being synchronized, ahead of schedule, or lagging behind.

[0039] Match the actual progress data points generated in the digital twin with the construction plan progress curve according to the time nodes;

[0040] The progress deviation value is calculated point by point. The progress deviation value of each node is obtained by subtracting the planned progress completion ratio from the actual progress completion ratio. A positive deviation value indicates that the progress is ahead, a negative deviation value indicates that the progress is behind, and a zero deviation value indicates that the progress is in sync.

[0041] A preset progress deviation threshold is set, and the calculated progress deviation values ​​of each node are compared with the preset threshold to determine the current progress status. If the deviation value is within the synchronization threshold range, the progress is determined to be synchronized; if the deviation value exceeds the lead threshold, the progress is determined to be ahead; if the deviation value is lower than the lag threshold, the progress is determined to be lagging. At the same time, the deviation of each node is recorded to form a progress deviation detail, which is displayed synchronously on the digital twin visualization platform.

[0042] Among them, the step of generating a graded response strategy based on the progress status, including the warning level, reason explanation, corrective action suggestions, and resource scheduling plan, and then pushing it out:

[0043] Based on the progress status and progress deviation value, and in conjunction with the preset early warning classification standards, the corresponding early warning level is determined;

[0044] By combining multi-source data and the construction status reflected by digital twins, the specific reasons for the schedule deviations are analyzed, and a detailed explanation of the reasons is formed.

[0045] Based on the causes of deviations and the progress status, develop targeted corrective measures.

[0046] After formulating targeted corrective action recommendations based on the causes of deviations and progress status:

[0047] Based on the current status of personnel, machinery, and materials at the construction site, a resource allocation plan is set up according to the corrective action recommendations.

[0048] This invention discloses an intelligent monitoring method for building construction progress based on BIM and digital twins. The method acquires the BIM model and construction plan data of the project, performs lightweight processing on the BIM model, and establishes a digital twin containing geometric information, attribute information, and a time dimension, bidirectionally mapping the physical construction site and the virtual model. It collects image and video data, environmental and equipment status data, and construction management data from the construction site, and preprocesses this data through edge computing nodes to output multi-source data. The multi-source data is mapped into the digital twin in real time to generate actual progress data points. These data points are compared with the construction plan progress curve to calculate the progress deviation value, and the progress status is determined based on a preset threshold. A hierarchical response strategy, including warning level, cause explanation, corrective suggestions, and resource scheduling plan, is generated and pushed out based on the progress status. Through this method, accurate and real-time matching of virtual and physical progress is achieved, improving the intelligent level of construction progress management. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of the steps of the intelligent monitoring method for building construction progress based on BIM and digital twins of the present invention.

[0051] Figure 2 This is a flowchart of steps S100 of the present invention.

[0052] Figure 3 This is a flowchart of steps S200 of the present invention.

[0053] Figure 4 This is a flowchart of steps S300 of the present invention.

[0054] Figure 5 This is a flowchart of steps S400 of the present invention.

[0055] Figure 6 This is a structural principle diagram of the intelligent monitoring system for building construction progress based on BIM and digital twins, which is based on the present invention.

[0056] Figure 7 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0057] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0058] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms "a," "the," and "the" as used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0059] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0060] Please see Figures 1-5 This invention provides a method for intelligent monitoring of building construction progress based on BIM and digital twins, comprising the following steps:

[0061] S100: Acquire the BIM model and construction plan data of the engineering project, perform lightweight processing on the BIM model, establish a digital twin containing geometric information, attribute information, and time dimension, and bidirectionally map the physical construction site and the virtual model; the specific process is as follows:

[0062] S101: Collect BIM model data and construction plan data for the project; the BIM model data includes architectural, structural and mechanical and electrical information, and the construction plan data includes the overall schedule, the schedule of sub-projects and the time arrangement of each process.

[0063] S102: Optimizes BIM models using lightweight processing technology, simplifies redundant details in the model, and reduces the pressure of model rendering and data transmission;

[0064] S103: Based on the lightweight BIM model, supplement the geometric dimensions, material properties, and geometric and attribute information of the components, and integrate the time node data in the construction plan to build a digital twin with three dimensions of geometry, attributes, and time.

[0065] S104: Establish a two-way mapping relationship between the digital twin and the physical construction site, so that the digital twin can reflect the construction status of the physical site in real time.

[0066] In the above process, BIM model data and construction plan data of the project are collected. The BIM model data includes architectural, structural, and MEP information, while the construction plan data includes the overall schedule, the schedule of each sub-item project, and the time arrangement of each process. Specifically, the complete BIM model source files and construction plan files are obtained from the project design unit and construction unit. The BIM model data must cover the information of architectural components such as walls, floors, doors, and windows; the dimensions and reinforcement information of structural components such as beams, slabs, columns, and foundations; and the layout and parameter information of MEP components such as water supply and drainage pipes, electrical lines, and HVAC equipment. The construction plan data must clearly specify the start and completion dates of the overall schedule, the schedule of each sub-item project (such as foundation engineering, main structure engineering, and secondary structure engineering), and the planned start time, planned completion time, and duration of each process (such as formwork, rebar tying, concrete pouring, and component installation). After collection, the data is initially sorted to remove invalid and duplicate data, ensuring the completeness and accuracy of the data.

[0067] Lightweight processing techniques are employed to optimize the BIM model, simplifying redundant details and reducing rendering and data transmission pressure. Specifically, LOD (Level of Detail) lightweight processing technology is used to optimize the BIM model layer by layer. Model details unnecessary for progress monitoring (such as component surface textures and minor details of non-critical nodes) are simplified and removed, while retaining core information required for progress monitoring, such as component geometry, location, material, and relationships. Simultaneously, a model compression algorithm is used to compress the BIM model file, reducing file size and minimizing hardware resource consumption during rendering and bandwidth pressure during data transmission. This ensures the optimized BIM model can load quickly and respond in real-time to subsequent data updates and interactive operations. After lightweight processing, the model is validated to ensure no core data is lost and component relationships are correct.

[0068] Based on the lightweight BIM model, geometric and attribute information of component dimensions, material properties, and construction processes is supplemented, and time node data from the construction plan is integrated to construct a digital twin encompassing geometry, attributes, and time. Specifically, using the lightweight BIM model as the base, the geometric information (such as component length, width, height, and cross-sectional dimensions) and attribute information (such as component material, strength grade, construction process requirements, and installation sequence) of each component are supplemented and improved to ensure the completeness and traceability of information for each component. Subsequently, the construction plan time node data collected in S101 is associated and bound with each component and each process in the BIM model, clarifying each... The planned start time, planned completion time, and duration of each component and process are used to form a three-dimensional data association: geometry (spatial shape of the component), attributes (parameters of the component itself), and time (construction progress nodes). Digital twin modeling software (such as Unity3D and Unreal Engine) is used to import the above three-dimensional data into the modeling environment to build a digital twin that corresponds one-to-one with the physical construction site. During the model generation process, it is necessary to ensure that the spatial position and size of the virtual component are completely consistent with the physical component, and that the time nodes correspond precisely with the construction plan. Finally, the construction and debugging of the digital twin are completed to ensure that the model can accurately map the basic state of the physical site.

[0069] A two-way mapping relationship is established between the digital twin and the physical construction site, enabling the digital twin to reflect the construction status of the physical site in real time. The specific implementation method is as follows: First, a comprehensive review of the core elements of the physical construction site is conducted, including the actual installation locations of building components, the deployment areas and operating trajectories of construction machinery, the work areas of construction personnel, the storage areas and inventory status of materials, and the progress of construction procedures. The one-to-one correspondence between each physical element and its corresponding virtual components and virtual scenes in the digital twin is clarified, and a detailed mapping table is established to ensure that each physical element can find its corresponding mapping object in the virtual model. Second, multiple types of data receiving interfaces are preset in the digital twin to connect to subsequent image and video acquisition equipment, IoT sensors, data acquisition terminals, etc., at the construction site, ensuring that various dynamic change data from the physical construction site can be transmitted to the digital twin in real time and stably. The system first establishes a data synchronization update rule. Based on the progress of physical construction (e.g., component installation, process completion), changes in component status (e.g., not installed, in installation, completed), and adjustments to equipment operating status (e.g., on, off, fault), the system synchronously drives the status updates of corresponding virtual components and virtual scenes in the digital twin. For example, after a beam component is installed on the physical site, the corresponding beam component in the digital twin synchronously switches to the "installed" status and displays relevant information such as installation time, construction team, and acceptance status. Finally, the system completes the debugging of the two-way mapping relationship. By comparing the actual measured data on site with the virtual model data, the system verifies the timeliness and accuracy of the digital twin's response to changes in the physical construction site. This ensures that the digital twin can realistically and in real-time replicate the entire construction scene of the physical site, providing reliable virtual mapping support for subsequent progress monitoring and data matching.

[0070] S200: Collects image and video data, environmental and equipment status data, and construction management data from the construction site. After preprocessing by edge computing nodes, it outputs multi-source data. The specific process is as follows:

[0071] S201: Deploy high-definition cameras, IoT sensors, and data acquisition terminals at the construction site and collect raw data; collect image and video data of various areas of the construction site through high-definition cameras, collect environmental data such as temperature, humidity, and wind speed through IoT sensors, collect equipment status data such as the operating status of construction machinery and equipment working parameters, and record construction management data such as the arrival of construction personnel, the arrival of materials, and the acceptance of work processes through data acquisition terminals;

[0072] S202: Establishes connections between various acquisition devices and edge computing nodes, and transmits raw data to the edge computing nodes;

[0073] S203: Preprocess the raw data, filter out redundant, drifting, and isolated noise data, perform unified conversion on data of different formats, complete data cleaning and standardization, and output multi-source data.

[0074] The process includes preprocessing the raw data, filtering out redundant, drifting, and isolated noise data, uniformly converting data of different formats, completing data cleaning and standardization, and outputting multi-source data.

[0075] The collected raw data is classified and filtered to distinguish between image and video data, environmental data, equipment status data, and construction management data. Corresponding noise filtering methods are adopted according to the characteristics of different types of data. Specifically, redundant data is filtered out by frame deduplication and blur frame removal for image and video data. Drift and isolated noise points are filtered out by statistical outlier filtering and moving average filtering for environmental and equipment status sensor data. Invalid data is filtered out by duplicate record deletion and outlier identification and removal for construction management data.

[0076] Perform unified conversion processing on raw data of different formats; convert image and video data into standardized encoding formats, convert analog data collected by sensors into digital data, and convert construction management data into a unified structured data format;

[0077] Standardize the various types of data after noise filtering and format conversion, and unify the data precision, units and data naming rules;

[0078] All preprocessed data undergoes integrity verification, missing data is appropriately filled in, abnormal data is corrected a second time, and standardized multi-source data is integrated and output.

[0079] In the above process, high-definition cameras, IoT sensors, and data acquisition terminals are deployed at the construction site to collect raw data. High-definition cameras collect image and video data from various areas of the construction site; IoT sensors collect environmental data such as temperature, humidity, and wind speed; and IoT sensors collect equipment status data such as the operating status and parameters of construction machinery. Data acquisition terminals record construction management data including personnel attendance, material arrival, and process acceptance. Specifically, based on the layout characteristics of the construction site and the needs for monitoring construction progress, various acquisition devices are rationally deployed. High-definition cameras are mainly deployed at the construction site entrance and key construction areas (such as the main structure construction layer, material storage area, and machinery operation area). A combination of 360° panoramic cameras and fixed-point cameras is used to ensure no blind spots. The acquisition frequency is set to one frame every 10-30 seconds to capture image and video data from various areas of the construction site in real time, clearly recording the construction process. The system monitors the actual progress of construction processes, component installation, and personnel operations. IoT sensors are deployed according to their monitoring type: environmental sensors (temperature, humidity, wind speed) are placed in different areas of the construction site (e.g., outdoor work areas, indoor construction floors), collecting data every 5 minutes to ensure coverage of the entire construction area's environmental conditions; equipment status sensors are installed on key parts of construction machinery (e.g., tower cranes, construction elevators, concrete pumps) to collect real-time data on machinery operating speed, load, working hours, fault codes, and other equipment operating parameters; data acquisition terminals are provided to construction management personnel and team leaders, using a combination of handheld and fixed terminals to record real-time data on personnel arrival and departure times, work content, material arrival time, specifications, quantity, and acceptance status, as well as the start time, completion time, and acceptance results of each process; after all equipment is deployed, it undergoes debugging and calibration to ensure that the collected raw data is clear, accurate, and continuous.

[0080] To establish connections between various data acquisition devices and edge computing nodes, raw data is transmitted to the edge computing nodes. Specifically, a combination of 5G wireless and wired communication is used to establish stable connections between various data acquisition devices, such as high-definition cameras, IoT sensors, and data acquisition terminals, and edge computing nodes deployed at the construction site. Data with high real-time requirements (such as device status data and image / video data) is transmitted via 5G wireless communication, ensuring data transmission latency is controlled within one second. Construction management data can be transmitted via wired communication to ensure data transmission stability. A one-to-one correspondence is established between devices and edge computing nodes, and a data transmission encryption mechanism is set up to prevent loss, leakage, or tampering during data transmission. Simultaneously, a data transmission anomaly alarm mechanism is set up. When data transmission from a device is interrupted or abnormal, the edge computing node automatically sends an alarm signal to remind staff to promptly investigate the device malfunction, ensuring that raw data can be transmitted to the edge computing nodes in real time and completely.

[0081] The raw data is preprocessed to filter out redundant, drifting, and isolated noise data. Data of different formats is uniformly converted to complete data cleaning and standardization, and multi-source data is output. The specific implementation method is as follows:

[0082] Data Classification and Noise Removal: The collected raw data is classified and filtered, clearly distinguishing four categories: image and video data, environmental data, equipment status data, and construction management data. Corresponding noise removal methods are adopted for the characteristics of different data types to ensure data accuracy. For image and video data, a frame deduplication algorithm is used to remove duplicate frames, and a blurred frame detection algorithm is used to remove blurred and distorted frames, retaining clear and valid image and video frames. Simultaneously, grayscale preprocessing is performed on video frames to reduce the burden of subsequent data processing. For environmental and equipment status sensor data, a statistical outlier filtering algorithm is used to identify and filter drift data and isolated noise points that exceed the normal range. A moving average filtering algorithm is combined to smooth the data, eliminating data fluctuations and ensuring the stability of sensor data. For construction management data, a duplicate record deletion algorithm is used to remove duplicate data, and an outlier detection algorithm (such as based on the 3σ principle) is used to identify and remove obviously unreasonable data (such as construction personnel arriving earlier than the start time, negative material arrival quantities, etc.), filtering out invalid data.

[0083] Unified Data Format Conversion: Raw data in different formats undergoes unified conversion to ensure interoperability and fusion of various data types; image and video data are converted to the H.264 standardized encoding format, unifying video resolution and frame rate for easy synchronous display in the digital twin; analog data (such as voltage and current signals) collected by IoT sensors are converted to digital data via an A / D conversion module, ensuring a unified data precision of two decimal places; construction management data (such as handwritten records and spreadsheet records) are converted to the unified JSON structured data format, clearly defining data fields (such as personnel name, arrival time, material specifications, acceptance results, etc.) to ensure standardized and unified data formats.

[0084] Data standardization processing: After noise filtering and format conversion, all types of data are standardized to unify data precision, units, and naming rules. Specifically, for environmental data, temperature is standardized to degrees Celsius (°C), humidity to percentage (%), and wind speed to meters per second (m / s), with a precision of one decimal place. For equipment status data, rotational speed is standardized to revolutions per minute (r / min), load to kilonewtons (kN), and precision to one decimal place. For construction management data, time records are standardized to the standard time format of "year-month-day hour:minute:second," and material specifications and process names adopt a unified naming convention to avoid data confusion caused by inconsistent naming.

[0085] Data integrity verification and output: All preprocessed data undergoes integrity verification, employing missing value detection algorithms to check for missing values, format abnormalities, and other issues. For missing data, appropriate methods are used to complete it based on the data type. For example, if environmental data is missing, interpolation between adjacent time nodes is used; if construction management data is missing, staff are reminded to enter it. Abnormal data undergoes secondary correction. After confirming the data is correct, all preprocessed standardized data are integrated to output multi-source data that can be directly used for subsequent digital twin mapping and progress analysis. Simultaneously, the preprocessed data is stored in the local database of the edge computing node for easy traceability and retrieval.

[0086] S300: Multi-source data is mapped into a digital twin in real time to generate actual progress data points. These data points are then compared with the construction schedule curve to calculate the progress deviation and determine the progress status based on a preset threshold. The specific process is as follows:

[0087] S301: Establish mapping rules between multi-source data and digital twins, import multi-source data into the digital twin in real time according to the corresponding relationship, and realize the association between data and virtual model;

[0088] S302: Generate progress measurement data points for corresponding processes and components in the digital twin based on multi-source data, and mark the actual completion time and progress corresponding to each measurement data point;

[0089] S303: Extract the progress data from the construction plan, generate the construction plan progress curve, and obtain the planned progress target corresponding to each time node;

[0090] S304: Compare the actual progress data points with the construction plan progress curve point by point, calculate the difference between the actual progress and the planned progress at each node, i.e., the progress deviation value, and determine the current progress status by combining the preset progress deviation threshold; the current progress status includes synchronous, ahead, and lagging.

[0091] In the steps of extracting progress data from the construction plan, generating a construction plan progress curve, and obtaining the planned progress target corresponding to each time node:

[0092] Extract progress information from construction plan data; the progress information includes the overall progress plan, the progress plans for sub-projects and individual work items, and the planned start time, planned completion time, duration, and planned progress percentage for each work process;

[0093] The extracted progress data is integrated, sorted by time dimension, and the planned progress targets for each process and component corresponding to each time node are determined.

[0094] A visual curve generation method is used, with time as the horizontal axis and progress completion percentage as the vertical axis, to fit the planned progress targets at each time node and generate a continuous construction plan progress curve. The planned progress values ​​of key nodes are marked on the progress curve. Key nodes include process start nodes, process completion nodes, and sub-item project acceptance nodes.

[0095] The generated construction schedule curve is synchronously imported into the digital twin visualization platform and linked with the digital twin model and actual progress measurement data points.

[0096] The process involves comparing the actual progress data points with the construction schedule curve point by point, calculating the difference between the actual progress and the planned progress at each node (i.e., the progress deviation value), and combining this with a preset progress deviation threshold to determine the current progress status. The current progress status includes the steps of being synchronized, ahead of schedule, or lagging behind.

[0097] Match the actual progress data points generated in the digital twin with the construction plan progress curve according to the time nodes;

[0098] The progress deviation value is calculated point by point. The progress deviation value of each node is obtained by subtracting the planned progress completion ratio from the actual progress completion ratio. A positive deviation value indicates that the progress is ahead, a negative deviation value indicates that the progress is behind, and a zero deviation value indicates that the progress is in sync.

[0099] A preset progress deviation threshold is set, and the calculated progress deviation values ​​of each node are compared with the preset threshold to determine the current progress status. If the deviation value is within the synchronization threshold range, the progress is determined to be synchronized; if the deviation value exceeds the lead threshold, the progress is determined to be ahead; if the deviation value is lower than the lag threshold, the progress is determined to be lagging. At the same time, the deviation of each node is recorded to form a progress deviation detail, which is displayed synchronously on the digital twin visualization platform.

[0100] In the above process, mapping rules between multi-source data and digital twins are established, and multi-source data is imported into the digital twin in real time according to the corresponding relationship to realize the association between data and virtual models. Specifically, based on the types of multi-source data and the virtual elements of the digital twin, clear mapping rules are established. Image and video data are mapped to the corresponding virtual scene area in the digital twin, achieving real-time synchronous display of the virtual scene and the physical site. Environmental data and equipment status data are mapped to the corresponding environmental modules and equipment models in the digital twin, updating virtual environment parameters and virtual equipment operating status in real time. Construction management data (such as component installation and acceptance, and process completion status) are mapped... The data is mapped to the corresponding components and processes in the digital twin, and the component status and process progress are updated accordingly. A data mapping verification mechanism is established, which includes: (1) Object consistency verification: verifying whether the physical site component / process ID and the digital twin virtual object ID correspond one-to-one to prevent mapping misalignment; (2) Data real-time verification: setting a data transmission delay threshold (≤1 second), and triggering retransmission and alarm if the timeout occurs; (3) Data integrity verification: verifying that the data fields are complete and without missing data, and that the format meets the standardization requirements; (4) Mapping accuracy verification: comparing the measured data of the physical site with the updated data of the virtual model, and confirming that the mapping is effective if the error is within the allowable range. Ensure that each type of multi-source data can be accurately mapped to the corresponding object in the digital twin, and that the data import frequency is consistent with the multi-source data acquisition frequency, so as to realize the real-time association between multi-source data and the virtual model and ensure that the virtual model can reflect the construction status of the physical site in real time.

[0101] Based on multi-source data, corresponding progress measurement data points for each process and component are generated in the digital twin, and the actual completion time and progress of each measurement data point are marked. The specific implementation method is as follows: Based on the multi-source data mapped to the digital twin in step S301, core information that can reflect the construction progress is extracted, such as the completion status of component installation, the progress of process, and the work results of construction personnel. For each process and each component, progress measurement data points are generated according to the actual construction situation. The progress measurement data points are marked with time as the horizontal axis and the completion progress percentage as the vertical axis. The completion progress percentage is determined by the comprehensive data from multiple sources (e.g., the progress percentage is 100% when the component installation is completed, 50% when half is installed, and 0% when not installed). At the same time, the corresponding actual completion time (e.g., component installation completion time, process completion time), construction team, acceptance status, and other auxiliary information are marked for each progress measurement data point to ensure the completeness of the information of each measurement data point. The generated progress measurement data points are synchronously stored in the digital twin database and linked one-to-one with virtual components and processes to facilitate subsequent comparison with the construction plan progress.

[0102] Extract progress data from the construction plan, generate a construction plan progress curve, and obtain the planned progress target corresponding to each time node; the specific implementation method is as follows:

[0103] Progress information extraction: Extract core progress information from the construction plan data collected in step S101. The progress information includes the overall progress plan, the progress plans of sub-projects, and key data such as the planned start time, planned completion time, duration, and planned completion percentage of each process. During the extraction process, invalid and duplicate progress information must be removed to ensure that the extracted progress data is consistent with the actual construction needs and can accurately reflect the planned progress requirements of each stage and process.

[0104] Progress data integration and sorting: The extracted progress data is sorted and integrated, and sorted by time dimension (such as daily, weekly, monthly). The planned progress targets of each process and component corresponding to each time node (such as the 5th of each month, Monday of each week) are clearly defined to ensure that the progress data corresponds one-to-one with the components and processes in the digital twin and to avoid the situation where the progress data is out of sync with the virtual object. At the same time, the integrated progress data is verified to ensure that the planned time of each process is reasonably connected, without conflicts or omissions.

[0105] Construction schedule curve generation: A visual curve generation method is adopted, with time as the horizontal axis and progress completion percentage as the vertical axis. The planned progress targets at each time node are fitted to generate a continuous construction schedule curve. During the curve generation process, a linear fitting algorithm is used to ensure that the curve can smoothly reflect the progress trend of the schedule plan. At the same time, the planned progress values ​​of key nodes are marked on the progress curve. Key nodes include process start nodes, process completion nodes, and sub-item project acceptance nodes. Each key node is marked with the corresponding planned time and planned progress percentage, which facilitates intuitive comparison of the difference between the actual progress and the planned progress.

[0106] Curve Import and Association: The generated construction schedule curve is synchronously imported into the digital twin visualization platform and associated with the digital twin model and actual progress data points. This ensures that the schedule curve can be linked with the virtual model and actual data points in real time. Staff can intuitively view the correspondence between the construction schedule curve and the actual progress data points through the visualization platform, providing a clear reference standard for subsequent progress comparison and deviation calculation.

[0107] The actual progress data points are compared point by point with the construction schedule curve, and the difference between the actual progress and the planned progress at each node is calculated, i.e., the progress deviation value. Combined with a preset progress deviation threshold, the current progress status is determined; the current progress status includes synchronous, ahead of schedule, and lagging. The specific implementation method is as follows:

[0108] Data point and curve matching: The actual progress data points generated in the digital twin are matched one-to-one with the construction plan progress curve according to the time nodes. The matching rules are clearly defined to ensure that the actual data points of the same time node, the same process or component are accurately compared with the corresponding points in the planned progress curve, so as to avoid problems such as misalignment of time nodes and incorrect correspondence of processes, and ensure the accuracy of the comparison results.

[0109] Schedule Deviation Calculation: The schedule deviation is calculated point by point. The calculation rule is to subtract the planned progress completion rate of each time node from the actual progress completion rate of that node to obtain the schedule deviation value for that node. A positive deviation value indicates that the actual progress is ahead of the planned progress, a negative deviation value indicates that the actual progress is behind the planned progress, and a deviation value of zero indicates that the actual progress is in sync with the planned progress. After the calculation is completed, the schedule deviation value of each time node, each process, and each component is recorded to form a detailed deviation ledger for subsequent analysis of the causes of deviation.

[0110] Progress Status Determination: A preset progress deviation threshold is established, which is differentiated based on the importance and construction difficulty of each sub-item. For example, the synchronous threshold range for core processes (such as main structure pouring) is ±2%, the advanced threshold is >2%, and the lagging threshold is <-2%; the synchronous threshold range for general processes (such as secondary structure masonry) is ±5%, the advanced threshold is >5%, and the lagging threshold is <-5%. The calculated progress deviation value of each node is compared with the preset threshold one by one to determine the current progress status: if the deviation value is within the synchronous threshold range, it is determined to be synchronous; if the deviation value exceeds the advanced threshold, it is determined to be ahead of schedule; if the deviation value is lower than the lagging threshold, it is determined to be behind schedule. At the same time, the deviation status and duration of each node are recorded to form a progress deviation detail, which is displayed synchronously on the digital twin visualization platform. Different colors are used to mark different progress statuses (green for synchronous, blue for advanced, and red for lagging) to facilitate staff to quickly identify progress anomalies.

[0111] S400: Generates a tiered response strategy based on the progress status, including warning level, cause explanation, corrective action suggestions, and resource scheduling plan, and pushes it out; the specific process is as follows:

[0112] S401: Based on the progress status and progress deviation value, and in conjunction with the preset early warning classification standards, determine the corresponding early warning level;

[0113] S402: Combining multi-source data and the construction status reflected by digital twins, analyze the specific reasons for the schedule deviations and form a detailed explanation of the reasons;

[0114] S403: Based on the cause of the deviation and the progress status, formulate targeted corrective action recommendations;

[0115] S404: Based on the current status of personnel, machinery, and materials at the construction site, set up a resource scheduling plan according to the corrective action recommendations.

[0116] In the above process, based on the progress status and progress deviation value, and combined with the preset early warning classification standards, the corresponding early warning level is determined. Specifically, a three-level early warning classification standard is preset: general early warning, severe early warning, and emergency early warning. The early warning level is directly related to the progress deviation value and progress status. A general early warning corresponds to a progress lag or advance within the threshold range but close to the threshold boundary (e.g., a core process deviation value of ±1.5% to ±2%), or a short duration of deviation (1 to 2 days). A severe early warning corresponds to a progress lag or advance exceeding the threshold (e.g., a core process deviation value >2% or <-2%), or a longer duration of deviation (3 to 5 days). An emergency early warning corresponds to a progress lag or advance significantly exceeding the threshold (e.g., a core process deviation value >5% or <-5%), or a duration of deviation exceeding 5 days, and may affect the overall progress plan. Based on the progress status determined by S304 and the calculated progress deviation value, combined with the deviation duration, the corresponding early warning level is determined, and the response priority for different early warning levels is clarified: emergency early warning > severe early warning > general early warning.

[0117] By combining multi-source data and the construction status reflected in the digital twin, the specific causes of schedule deviations are analyzed, and a detailed explanation of the causes is formed. The specific implementation method is as follows: The multi-source data output in step S203 and the schedule deviation details generated in step S304 are retrieved, and combined with the virtual construction status displayed on the digital twin visualization platform, the specific causes of schedule deviations are comprehensively analyzed and categorized into five major categories: personnel, machinery, materials, environment, and processes. Personnel factors include a shortage of construction personnel, insufficient skills, and low attendance rates, which can be verified through construction management data and image / video data. Machinery factors include construction machinery malfunctions, insufficient machinery quantity, and low operating efficiency. Factors such as equipment status data and image / video data can be verified; material factors, including material supply delays, substandard material quality, and insufficient material inventory, can be verified through construction management data and material arrival records; environmental factors, including severe weather such as high temperatures, heavy rain, and strong winds, can be verified through environmental data; and process factors, including unreasonable construction processes and poor workflow coordination, can be verified through image / video data and construction management data. After the analysis is completed, a detailed explanation of the causes should be prepared, clarifying the main and secondary causes of the deviations, as well as the degree of impact of each cause on the schedule deviation, to ensure that the cause analysis is comprehensive and accurate, providing a basis for subsequent corrective action recommendations.

[0118] Based on the causes of deviations and the progress status, targeted corrective measures are formulated. Specifically, the implementation method is as follows: combining the causes of deviations analyzed in step S402 and the progress status determined in step S304, actionable and targeted corrective measures are formulated for different causes of deviations and different progress statuses. If the deviation is due to personnel shortages, it is recommended to increase construction personnel, adjust work group divisions, and strengthen personnel training. If the deviation is due to mechanical failures, it is recommended to promptly repair faulty machinery, allocate alternative machinery, and optimize machinery operation scheduling. If the deviation is due to material supply delays, it is recommended to urge suppliers to expedite material supply, adjust material delivery plans, and stockpile emergency materials. If the deviation is due to severe weather, it is recommended to adjust construction procedures, erect protective facilities, and rationally arrange work hours. If the deviation is due to unreasonable processes, it is recommended to optimize construction processes and adjust the sequence of procedures. Corrective measures must clearly define the key points of correction, implementation steps, responsible parties, completion time, and expected corrective effects to ensure that the corrective measures can be implemented effectively and reduce progress deviations.

[0119] Based on the current status of personnel, machinery, and materials at the construction site, a resource scheduling plan is formulated based on corrective action recommendations. The specific implementation method is as follows: First, through multi-source data and a digital twin visualization platform, a comprehensive understanding of the current resource status at the construction site is obtained, including the number, skill level, and on-duty status of construction personnel; the number, operating status, and availability of construction machinery; and the inventory quantity, specifications, and delivery schedule of materials. Second, based on the corrective action recommendations formulated in step S403, and considering the current resource status, a reasonable resource scheduling plan is developed to optimize resource allocation and ensure the smooth implementation of the corrective action recommendations. For example, if the corrective action recommendations are... If additional construction personnel are required, the scheduling plan should specify the number of personnel to be added, their skill requirements, arrival time, and the division of labor. If the corrective measure is to allocate spare machinery, the scheduling plan should specify the allocation route, arrival time, and operator arrangements for the spare machinery. If the corrective measure is to expedite material supply, the scheduling plan should specify the quantity of materials to be supplemented, the supply time, the transportation method, and the stacking arrangement after the materials arrive on site. The resource scheduling plan should balance rationality and economy to avoid resource waste, while clearly defining the responsible party for resource scheduling and the execution time to ensure that resources are in place in a timely manner and to provide support for the corrective measure work.

[0120] Corresponding to the aforementioned embodiments of the intelligent monitoring method for building construction progress based on BIM and digital twins, this application also provides embodiments of an intelligent monitoring system for building construction progress based on BIM and digital twins.

[0121] Figure 6 This is a structural schematic diagram illustrating an intelligent monitoring system for building construction progress based on BIM and digital twins, according to an exemplary embodiment. (Refer to...) Figure 6The system may include: a BIM and digital twin construction module, a multi-source data real-time acquisition module, a real-time mapping and deviation analysis module, and a target response generation module; wherein:

[0122] The BIM and digital twin building module is used to acquire the BIM model and construction plan data of the project, perform lightweight processing on the BIM model, establish a digital twin containing geometric information, attribute information and time dimension, and bidirectionally map the physical construction site and the virtual model.

[0123] The multi-source data real-time acquisition module is used to collect image and video data, environmental and equipment status data, and construction management data from the construction site, and output multi-source data after preprocessing by edge computing nodes;

[0124] The real-time mapping and deviation analysis module is used to map multi-source data into a digital twin in real time, generate actual progress data points, compare the actual progress data points with the construction plan progress curve, calculate the progress deviation value, and determine the progress status according to a preset threshold.

[0125] The target response generation module is used to generate a hierarchical response strategy based on the progress status, including the warning level, reason explanation, corrective suggestions and resource scheduling scheme, and then push it out.

[0126] In this embodiment, the BIM and digital twin construction module acquires the BIM model and construction plan data of the project, performs lightweight processing on the BIM model, and establishes a digital twin containing geometric information, attribute information, and time dimension, bidirectionally mapping the physical construction site and the virtual model. The multi-source data real-time acquisition module collects image and video data, environmental and equipment status data, and construction management data from the construction site, and outputs multi-source data after preprocessing by edge computing nodes. The real-time mapping and deviation analysis module maps the multi-source data into the digital twin in real time, generates actual progress data points, compares the actual progress data points with the construction plan progress curve, calculates the progress deviation value, and determines the progress status according to a preset threshold. The target response generation module generates a hierarchical response strategy based on the progress status, including warning level, cause explanation, correction suggestions, and resource scheduling scheme, and pushes it out. Through the above methods, accurate and real-time matching of virtual and physical progress is achieved, improving the intelligent level of construction progress management.

[0127] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0128] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0129] Accordingly, this application also provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-described intelligent monitoring method for building construction progress based on BIM and digital twins. Figure 7 The diagram shown is a hardware structure diagram of any device with data processing capabilities, which is part of an intelligent monitoring system for building construction progress based on BIM and digital twins provided in an embodiment of the present invention. (Except for...) Figure 7 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0130] Accordingly, this application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the aforementioned intelligent monitoring method for building construction progress based on BIM and digital twins. The computer-readable storage medium can be an internal storage unit of any data processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0131] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0132] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for intelligent monitoring of building construction progress based on BIM and digital twins, characterized in that, Includes the following steps: The process involves acquiring the BIM model and construction plan data for the engineering project, performing lightweight processing on the BIM model, creating a digital twin containing geometric information, attribute information, and a time dimension, and bidirectionally mapping the physical construction site to the virtual model. The specific steps are as follows: Collect BIM model data and construction plan data for the project; the BIM model data includes architectural, structural and mechanical and electrical information, and the construction plan data includes the overall schedule, the schedule of each sub-item project and the time arrangement of each process. Lightweight processing technology is used to optimize BIM models, simplify redundant details, and reduce the pressure of model rendering and data transmission. Based on the lightweight BIM model, the geometric and attribute information of component dimensions, material properties, and construction technology is supplemented, and the time node data in the construction plan is integrated to construct a digital twin including three dimensions of geometry, attributes, and time. Establish a two-way mapping relationship between the digital twin and the physical construction site, so that the digital twin can reflect the construction status of the physical site in real time; Collect image and video data, environmental and equipment status data, and construction management data from the construction site, and output multi-source data after preprocessing by edge computing nodes; Multi-source data is mapped into a digital twin in real time to generate actual progress data points. The actual progress data points are compared with the construction plan progress curve to calculate the progress deviation value and determine the progress status according to the preset threshold. Based on the progress status, a tiered response strategy is generated, which includes the warning level, explanation of the cause, corrective action suggestions, and resource scheduling plan, and then pushed out.

2. The intelligent monitoring method for building construction progress based on BIM and digital twins as described in claim 1, characterized in that, In the process of collecting image and video data, environmental and equipment status data, and construction management data from the construction site, and then preprocessing them through edge computing nodes to output multi-source data: High-definition cameras, IoT sensors, and data acquisition terminals are deployed at the construction site to collect raw data; Establish connections between various data acquisition devices and edge computing nodes, and transmit raw data to the edge computing nodes; The system preprocesses the raw data, filters out redundant, drifting, and isolated noise data, performs unified conversion on data of different formats, completes data cleaning and standardization, and outputs multi-source data.

3. The intelligent monitoring method for building construction progress based on BIM and digital twins as described in claim 2, characterized in that, In the steps of deploying high-definition cameras, IoT sensors, and data acquisition terminals at the construction site and collecting raw data: High-definition cameras were used to collect image and video data from various areas of the construction site. The system collects environmental data such as temperature, humidity, and wind speed on site through IoT sensors, as well as equipment status data such as the operating status of construction machinery and equipment operating parameters. Construction management data such as the arrival of construction personnel, the arrival of materials, and the acceptance of work processes are recorded through data acquisition terminals.

4. The intelligent monitoring method for building construction progress based on BIM and digital twins as described in claim 3, characterized in that, In the steps of preprocessing raw data, filtering out redundant, drifting, and isolated noise data, uniformly converting data of different formats, completing data cleaning and standardization, and outputting multi-source data: The collected raw data is classified and filtered to distinguish between image and video data, environmental data, equipment status data, and construction management data. Corresponding noise filtering methods are adopted according to the characteristics of different types of data. Specifically, redundant data is filtered out by frame deduplication and blur frame removal for image and video data. Drift and isolated noise points are filtered out by statistical outlier filtering and moving average filtering for environmental and equipment status sensor data. Invalid data is filtered out by duplicate record deletion and outlier identification and removal for construction management data. Perform unified conversion processing on raw data of different formats; convert image and video data into standardized encoding formats, convert analog data collected by sensors into digital data, and convert construction management data into a unified structured data format; Standardize the various types of data after noise filtering and format conversion, and unify the data precision, units and data naming rules; All preprocessed data undergoes integrity verification, missing data is appropriately filled in, abnormal data is corrected a second time, and standardized multi-source data is integrated and output.

5. The intelligent monitoring method for building construction progress based on BIM and digital twins as described in claim 1, characterized in that, In the steps of real-time mapping of multi-source data into a digital twin, generating actual progress data points, comparing these data points with the construction schedule curve, calculating the progress deviation, and determining the progress status based on a preset threshold: Establish mapping rules between multi-source data and digital twins, import multi-source data into the digital twin in real time according to the corresponding relationship, and realize the association between data and virtual model; Based on multi-source data, the corresponding process and component progress measurement data points are generated in the digital twin, and the actual completion time and completion progress corresponding to each measurement data point are marked. Extract the progress data from the construction plan, generate the construction plan progress curve, and obtain the planned progress target corresponding to each time node; The actual progress data points are compared with the construction plan progress curve point by point, and the difference between the actual progress and the planned progress at each node is calculated, i.e., the progress deviation value. Combined with the preset progress deviation threshold, the current progress status is determined. The current progress status includes synchronous, ahead, and lagging.

6. The intelligent monitoring method for building construction progress based on BIM and digital twins as described in claim 5, characterized in that, In the steps of extracting progress data from the construction plan, generating a construction plan progress curve, and obtaining the planned progress target corresponding to each time node: Extract progress information from construction plan data; the progress information includes the overall progress plan, the progress plans for sub-projects and individual work items, and the planned start time, planned completion time, duration, and planned progress percentage for each work process; The extracted progress data is integrated, sorted by time dimension, and the planned progress targets for each process and component corresponding to each time node are determined. A visual curve generation method is used, with time as the horizontal axis and progress completion percentage as the vertical axis, to fit the planned progress targets at each time node and generate a continuous construction plan progress curve. The planned progress values ​​of key nodes are marked on the progress curve. Key nodes include process start nodes, process completion nodes, and sub-item project acceptance nodes. The generated construction schedule curve is synchronously imported into the digital twin visualization platform and linked with the digital twin model and actual progress measurement data points.

7. The intelligent monitoring method for building construction progress based on BIM and digital twins as described in claim 6, characterized in that, The process involves comparing the actual progress data points with the construction schedule curve point by point, calculating the difference between the actual progress and the planned progress at each node (i.e., the progress deviation value), and combining this with a preset progress deviation threshold to determine the current progress status. The current progress status includes the steps of being synchronized, ahead of schedule, or lagging behind. Match the actual progress data points generated in the digital twin with the construction plan progress curve according to the time nodes; The progress deviation value is calculated point by point. The progress deviation value of each node is obtained by subtracting the planned progress completion ratio from the actual progress completion ratio. A positive deviation value indicates that the progress is ahead, a negative deviation value indicates that the progress is behind, and a zero deviation value indicates that the progress is in sync. A preset progress deviation threshold is set, and the calculated progress deviation values ​​of each node are compared with the preset threshold to determine the current progress status. If the deviation value is within the synchronization threshold range, the progress is determined to be synchronized; if the deviation value exceeds the lead threshold, the progress is determined to be ahead; if the deviation value is lower than the lag threshold, the progress is determined to be lagging. At the same time, the deviation of each node is recorded to form a progress deviation detail, which is displayed synchronously on the digital twin visualization platform.

8. The intelligent monitoring method for building construction progress based on BIM and digital twins as described in claim 1, characterized in that, In the step of generating a tiered response strategy based on the progress status, including the warning level, cause explanation, corrective action suggestions, and resource scheduling plan, and then pushing it out: Based on the progress status and progress deviation value, and in conjunction with the preset early warning classification standards, the corresponding early warning level is determined; By combining multi-source data and the construction status reflected by digital twins, the specific reasons for the schedule deviations are analyzed, and a detailed explanation of the reasons is formed. Based on the causes of deviations and the progress status, develop targeted corrective measures.

9. The intelligent monitoring method for building construction progress based on BIM and digital twins as described in claim 8, characterized in that, After formulating targeted corrective action recommendations based on the causes of deviations and progress status: Based on the current status of personnel, machinery, and materials at the construction site, a resource allocation plan is set up according to the corrective action recommendations.

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