BIM-based construction progress intelligent management and control method and system

Abstract

This invention relates to the field of BIM technology, specifically to a BIM-based intelligent construction progress management method and system. It constructs a BIM digital twin cloud model, integrates panoramic imagery and LiDAR point cloud data, dynamically selects data sources to update the model through algorithms, extracts the project topology to establish a construction network diagram, generates state evolution trajectories by combining environmental and resource data, predicts and visualizes milestone times, compares on-site data with the BIM model to generate progress deviation information, performs stability analysis, triggers resource allocation when thresholds are exceeded, synchronizes material suppliers and on-site management, dynamically adjusts personnel, equipment, and materials, dynamically adjusts milestone plans based on multiple data sources, and presents and guides allocation through the BIM model to ensure construction progress is consistent with the plan. Through nonlinear dynamic system modeling, combined with high-precision data such as LiDAR point clouds, it captures complex interactive relationships and dynamic characteristics during the construction process.

Description

Technical Field

[0001] This invention relates to the field of building information modeling technology, specifically to a BIM-based intelligent management and control method and system for construction progress. Background Technology

[0002] Building Information Modeling (BIM) technology, as a crucial tool for the digitization of building engineering, has been widely applied in the design, construction, and operation and maintenance phases of engineering projects. Essentially, BIM is an application of digital twin technology, where an unrendered 3D model is first created based on drawings, and actual 3D data is acquired synchronously during construction. The BIM model is then rendered (colored) based on this actual data to reflect the actual construction status. However, current BIM technology still has many shortcomings in construction progress control. Traditional construction progress management relies heavily on manual experience, resulting in strong subjectivity, poor real-time performance, and insufficient foresight. While some projects have introduced BIM technology for construction progress visualization, most remain at the static comparison level, unable to effectively address the complex changes and uncertainties during construction, and lack intuitive rendering methods for visualization.

[0003] In existing technologies, construction progress management systems typically employ linear prediction models or simple statistical methods, making it difficult to accurately capture the nonlinear characteristics and multi-factor interactions during construction. Furthermore, existing systems often adopt a passive response strategy when facing schedule deviations, addressing problems only after they occur, lacking proactive early warning and precise intervention capabilities. Resource allocation is usually based on fixed plans or simple adjustment rules, making it difficult to achieve dynamic optimization of resource allocation. In terms of data acquisition, it often relies solely on image data or manual measurements, lacking effective integration with high-precision measurement technologies such as LiDAR.

[0004] Therefore, there is an urgent need to develop a construction progress management method and system that integrates BIM digital twin technology, multi-source data acquisition technology, nonlinear dynamic system theory and intelligent control strategies, in order to improve the accuracy of construction progress prediction, the timeliness of control and the optimization of resource allocation, while improving management efficiency through intuitive visualization. Summary of the Invention

[0005] The purpose of this invention is to provide a BIM-based intelligent construction progress management method and system. By constructing a nonlinear dynamic prediction model, an adaptive weight learning mechanism, and a stability analysis control strategy, it achieves accurate prediction, proactive early warning, and intelligent regulation of construction progress, solving the problems of inaccurate prediction, delayed response, and imprecise regulation in traditional construction progress management. Simultaneously, by integrating panoramic imagery and LiDAR point cloud data, and employing visualization technologies such as color-grading rendering, it improves data accuracy and management intuitiveness.

[0006] This invention discloses a BIM-based intelligent construction progress management and control method, including:

[0007] Establish a BIM digital twin model of the current project and a cloud model of the on-site construction data;

[0008] Acquire panoramic images and LiDAR point cloud data of the current construction site, and import the panoramic images and LiDAR point cloud data into the cloud model;

[0009] A decision-making algorithm is constructed to dynamically determine whether to update the BIM model with image modeling data or point cloud data for a specific area, thereby reducing manual intervention.

[0010] Based on the aforementioned cloud model, a nonlinear dynamic prediction model for construction progress is constructed, including:

[0011] Extract the engineering topology from the BIM model and establish a construction network diagram;

[0012] The panoramic image data and LiDAR point cloud data are mapped onto the construction network diagram to obtain the current construction status vector.

[0013] By combining the environmental impact matrix and the resource allocation matrix, the construction status evolution trajectory is generated;

[0014] Extract milestone completion time predictions from the construction status evolution trajectory;

[0015] The predicted completion time of the milestone is visualized in the BIM model using color-grading rendering, where different completion areas are represented by different colored pixel blocks, and the size of the pixel block indicates the degree of completion.

[0016] The on-site construction information identified from the panoramic image and LiDAR point cloud data is compared with the BIM model to generate construction progress deviation information, which includes deviation value, deviation type, deviation range and deviation processing time.

[0017] Based on the construction progress deviation information and the construction state evolution trajectory, a construction progress stability analysis is performed to generate stability indicators.

[0018] When the stability index exceeds a preset threshold, resource allocation optimization is triggered, including:

[0019] The construction progress deviation information will be synchronized with the material suppliers and site management.

[0020] The material supplier will allocate the raw materials needed at the construction site in real time based on the aforementioned construction progress deviation information;

[0021] Based on the aforementioned construction progress deviation information, the on-site management team makes real-time adjustments to the deployment of personnel, equipment, and materials at the construction site.

[0022] Based on the panoramic images, LiDAR point cloud data and environmental data, the project milestone completion time plan is dynamically adjusted to generate a project milestone completion time prediction plan.

[0023] The project milestone completion time forecast plan is presented intuitively to the supervisor and relevant parties through the BIM model, and sent to the material supplier and site management to guide the dynamic allocation of materials and personnel until the construction progress is consistent with the construction plan.

[0024] Preferably, the construction of the nonlinear dynamic prediction model for construction progress further includes:

[0025] Establish a weight matrix of influencing factors to represent the degree of influence of each factor on the construction progress;

[0026] Acquire historical construction data and form a historical data tensor;

[0027] The weight matrix of influencing factors is initialized based on the historical data tensor;

[0028] Once the actual construction progress data is obtained, the prediction error between the predicted progress and the actual progress is calculated.

[0029] Based on the prediction error, the weight matrix of the influencing factors is adaptively adjusted to improve the prediction accuracy.

[0030] Preferably, the construction progress stability analysis includes:

[0031] Based on the aforementioned construction state evolution trajectory, the phase space of the construction system is reconstructed;

[0032] Calculate indicators characterizing system stability and assess the stability of construction progress;

[0033] Establish a stability threshold system to divide the construction state space into safe zones, warning zones, and danger zones;

[0034] When the stability index enters the warning zone, an early warning message is generated and visualized in the BIM model through a yellow warning sign;

[0035] When the stability index enters the danger zone, the resource allocation optimization is triggered and visualized in the BIM model through a red warning sign.

[0036] Preferably, the comparison of the on-site construction information identified from the panoramic image and LiDAR point cloud data with the BIM model specifically includes:

[0037] The information on the on-site construction process in the panoramic image and LiDAR point cloud data of the current construction site is identified based on the cloud model and compared with the BIM model.

[0038] The algorithm prioritizes LiDAR point cloud data as the baseline value, and panoramic image data is only used when the LiDAR data quality is insufficient or missing.

[0039] If the current construction progress at the site lags behind the BIM model, the current construction progress at the site and the construction progress of the BIM model are obtained, and the deviation between the current construction progress and the construction progress of the BIM model is calculated. In the BIM model, the lagging area is represented by blue rendering.

[0040] If the current on-site construction progress exceeds the BIM model, the current on-site construction progress and the construction progress of the BIM model are obtained, and the deviation value between the current on-site construction progress and the construction progress of the BIM model is calculated. In the BIM model, the area ahead of schedule is represented by green rendering.

[0041] Preferably, the information during the on-site construction process includes: personnel information, machinery information, material information, progress information, and site environment information.

[0042] Preferably, the environmental data includes weather information, and the dynamic adjustment of the project milestone completion time plan specifically includes:

[0043] Based on changes in weather factors and their impact on project construction, analyze the impact of weather on various professional sub-projects, construction progress, and project milestone completion time plans.

[0044] The construction schedule is dynamically adjusted based on the current weather forecast at the construction site. The dynamic adjustment method includes: making dynamic progress forecasts based on the project duration, sub-project duration, and process duration to obtain an updated project milestone completion time forecast plan.

[0045] The project milestone completion time forecast plan is visualized in the BIM model by combining a timeline with color-coded rendering.

[0046] The project milestone completion time forecast plan is sent to the material supplier and the site management. The material supplier updates the material plan and executes the corresponding delivery plan based on the project milestone completion time forecast plan. The site management updates the material demand plan based on the project milestone completion time forecast plan and dynamically adjusts the on-site human resources and equipment resources according to the changes in the construction progress.

[0047] Preferably, the project milestone completion time prediction plan includes:

[0048] Generate a project milestone completion time plan based on the on-site construction progress;

[0049] The construction plan is divided into units for each professional engineering project, and separate construction plans are developed for main construction, installation, outdoor construction and other project types.

[0050] The construction plan for each specialty includes: the overall project plan, the phased project plan, and the daily project plan. The daily project plan is prepared after the overall project plan and the phased project plan are approved and confirmed, and is consistent with the overall project plan and the phased project plan.

[0051] The project milestone completion time plan is visualized in the BIM model using pixel blocks of different sizes and colors. The size of the pixel block indicates the degree of completion, and the color indicates the progress status.

[0052] As a preferred option, it also includes:

[0053] Based on the BIM model and the on-site construction data, a construction risk knowledge base is established;

[0054] A risk list is generated using the project task sheet's planning information, and the link between the risk list and the construction risk knowledge base is established.

[0055] Based on the analysis of project site photos and LiDAR point cloud data, construction risk control recommendations were issued for key risk projects.

[0056] Risk items are dynamically rated and classified based on the urgency of the task. When the urgency of the risk analysis exceeds the preset threshold, low-risk items are reclassified as medium-risk items.

[0057] In the BIM model, risk levels and distribution are visually displayed using warning icons of different colors: red indicates high-risk areas, yellow indicates medium-risk areas, and green indicates low-risk areas, ensuring that supervisors and managers can intuitively identify risk conditions.

[0058] Preferably, the cloud model for establishing the BIM digital twin model and on-site construction data of the current project includes:

[0059] Collect construction data throughout the entire project lifecycle, including drawings, task orders, and material information;

[0060] The drawing is parsed parametrically to generate the result of the parametric parsing.

[0061] Establish a component database and connect the data interface of the component database with the data interface of the parameterized parsing result;

[0062] The results of the parametric parsing are synchronized to the component attributes of the BIM model components through the component database;

[0063] Extract the project task sheet's planning information and extract the corresponding building information from the BIM model;

[0064] The project construction schedule is automatically scheduled based on the planned information and the corresponding building information in the project task sheet, and a project construction schedule plan is generated.

[0065] The construction schedule is visualized in the BIM model using color-coded rendering, allowing supervisors and managers to intuitively understand the status of the schedule.

[0066] A BIM-based intelligent construction progress management system, used to implement the aforementioned BIM-based intelligent construction progress management method, includes:

[0067] The BIM digital twin model creation unit is used to create a BIM digital twin model of the current project and a cloud model of the on-site construction data.

[0068] The construction site data acquisition unit is used to acquire panoramic images and LiDAR point cloud data of the current construction site, and import the panoramic images and LiDAR point cloud data into the cloud model.

[0069] The data source determination unit is used to construct a determination algorithm to dynamically determine whether to use image modeling data or point cloud data to update the BIM model for a specific area, thereby reducing manual intervention.

[0070] The nonlinear prediction module is used to construct a nonlinear dynamic prediction model for construction progress based on the cloud model, including: extracting the engineering topology from the BIM model and establishing a construction network diagram; mapping the panoramic image data and LiDAR point cloud data onto the construction network diagram to obtain the current construction state vector; combining the environmental impact matrix and resource allocation matrix to generate a construction state evolution trajectory; extracting milestone completion time predictions from the construction state evolution trajectory; and visualizing the milestone completion time predictions in the BIM model using color-grading rendering.

[0071] The construction progress deviation analysis unit is used to compare the on-site construction information identified in the panoramic image and LiDAR point cloud data with the BIM model to generate construction progress deviation information, which includes deviation value, deviation type, deviation range and deviation processing time.

[0072] The stability analysis module is used to perform construction progress stability analysis based on the construction progress deviation information and the construction state evolution trajectory, and generate stability indicators.

[0073] The resource allocation optimization unit is used to trigger resource allocation optimization when the stability index exceeds a preset threshold, including: synchronizing the construction progress deviation information to the material supplier and the site management; the material supplier real-time allocating the raw materials required by the construction site according to the construction progress deviation information; and the site management real-time adjusting the deployment of personnel, equipment, and materials at the construction site according to the construction progress deviation information.

[0074] The construction schedule adjustment unit is used to dynamically adjust the project milestone completion time plan based on the panoramic image, LiDAR point cloud data and environmental data, and generate a project milestone completion time prediction plan.

[0075] The visualization unit is used to visualize the project milestone completion time prediction plan through the BIM model using pixel blocks of different sizes and colors. The size of the pixel block indicates the degree of completion, and the color indicates the progress status.

[0076] The cloud model update unit is used to send the project milestone completion time prediction plan to the material suppliers and site management to guide the dynamic allocation of materials and personnel until the construction progress is consistent with the construction plan.

[0077] 1. Improve the accuracy of schedule prediction: By modeling nonlinear dynamic systems and combining high-precision data such as LiDAR point clouds, the complex interaction relationships and dynamic characteristics in the construction process are captured, and the prediction accuracy is improved by 30% to 50% compared with traditional linear models, especially in complex projects and long-term prediction.

[0078] 2. Enhance early warning capabilities: By using stability analysis technology to assess the status of the construction system and combining it with color-coded warning signs in the BIM model, potential schedule risks can be identified 3 to 7 days in advance, providing managers with sufficient response time;

[0079] 3. Optimize resource allocation: Through accurate progress forecasting and deviation analysis, dynamic optimization of resource allocation is achieved, increasing resource utilization by 15% to 25% and reducing resource waste and emergency dispatch costs;

[0080] 4. Improve management efficiency: Build a closed-loop management system of prediction-analysis-control-feedback, and use intuitive visualization methods such as pixel block size and color to display construction progress and risk status, reduce management costs by 40% to 60%, improve project collaboration efficiency, and reduce information asymmetry and communication costs;

[0081] 5. Enhanced adaptability: Through adaptive learning mechanisms and multi-source data judgment algorithms, the system can continuously optimize model parameters and data source selection, adapt to changes in different construction stages and environmental conditions, and demonstrate the ability to continuously learn and optimize. Attached Figure Description

[0082] Figure 1 This is the overall architecture diagram of the BIM-based intelligent construction progress management and control system of the present invention. Detailed Implementation

[0083] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0084] like Figure 1 As shown, the BIM-based intelligent construction progress management and control system 100 provided by this invention includes: a BIM digital twin model establishment unit 110, a site data acquisition unit 120, a data source determination unit 125, a nonlinear prediction module 130, a construction progress deviation analysis unit 140, a stability analysis module 150, a resource allocation optimization unit 160, a construction progress plan adjustment unit 170, a visualization display unit 175, and a cloud model update unit 180. The units interact with each other via a data bus, forming a complete closed loop of data acquisition, processing, analysis, decision-making, and execution.

[0085] The intelligent construction progress management method of the present invention is implemented with the support of the above-mentioned system architecture, and mainly includes the following steps:

[0086] In one embodiment of the present invention, it is first necessary to establish a BIM digital twin model of the current project and a cloud model of the on-site construction data. The BIM digital twin model establishment unit 110 is responsible for collecting construction data throughout the entire project lifecycle, including drawings, task orders, and material information, and establishing a standardized BIM model. This unit supports multiple BIM model formats, such as Revit, Naviswork, Bentley, Tekla, and MicroStation.

[0087] Preferably, the BIM digital twin model building unit 110 performs parametric analysis on the collected construction drawings, generates analysis results, and synchronizes these results to the attributes of the BIM model components. To achieve this process, the system establishes a component database and connects the interface of this database with the interface of the parametric analysis results, completing data synchronization through the component database. This parametric analysis method significantly improves the consistency and completeness between the BIM model and the actual construction data.

[0088] Furthermore, the system extracts planning information from the project task list and corresponding building information from the BIM model. Based on these two pieces of information, it automatically schedules the project construction progress and generates an initial project construction schedule. This step realizes the transformation from a static BIM model to dynamic construction management, laying the foundation for subsequent intelligent control. The generated construction schedule is displayed in the BIM model using a color-coded rendering method through the visualization unit 175, enabling supervisors and managers to intuitively understand the status of the planned progress.

[0089] The site data acquisition unit 120 is responsible for acquiring panoramic images and LiDAR point cloud data of the current construction site and importing this data into the cloud model. This unit uses high-definition camera equipment and LiDAR scanners deployed at the construction site to acquire panoramic image data and high-precision point cloud data of the construction site at a preset frequency (usually once per hour).

[0090] In practical applications, the construction site data acquisition unit 120 not only acquires static images and point clouds, but also short video clips to capture dynamic information during the construction process. After preliminary processing (including noise reduction, lighting correction, perspective transformation, point cloud registration, etc.), the acquired data is uploaded to the cloud server and imported into the cloud model.

[0091] The panoramic imaging technology used in this invention supports 360° coverage without blind spots, providing complete visual information of the construction site; while LiDAR point cloud data provides high-precision three-dimensional spatial information, which greatly improves the completeness and accuracy of construction information acquisition compared with traditional fixed-angle monitoring methods.

[0092] The data source determination unit 125 is responsible for constructing the determination algorithm to dynamically decide whether to use image modeling data or point cloud data to update the BIM model for a specific area, reducing manual intervention. This unit follows these principles when processing data:

[0093] For critical structural components requiring high-precision measurement (such as columns, beams, load-bearing walls, etc.), LiDAR point cloud data should be used preferentially.

[0094] For components with irregular or complex shapes, LiDAR point cloud data should be used first.

[0095] For information on decorative components or materials, panoramic imagery data should be used preferentially.

[0096] When LiDAR data is of insufficient quality (e.g., low point cloud density, occlusion) or missing, panoramic image data is used as a supplement.

[0097] The decision algorithm evaluates the data source selection for each region based on the following formula:

[0098] ,

[0099] in: Rate the data source for the i-th region. As regional importance weight, To score the quality of point cloud data, To score the quality of image data, , , For the corresponding weighting coefficients. When If the value exceeds a preset threshold, point cloud data is selected; otherwise, image data is selected. For example, for the column-beam connection of a high-rise building, the regional importance weight... Point cloud data quality score: 0.8 (high importance) The image data quality score is 0.7 (good). The value is 0.5 (generally). If the weights are set to... , , Then the data source score for this region 0.28 + 0.1 = 0.7, which exceeds the preset threshold of 0.6. Therefore, point cloud data is selected as the main data source for this area.

[0100] The nonlinear prediction module 130 is one of the core innovations of this invention, responsible for building a nonlinear dynamic prediction model for construction progress based on a cloud model. This model treats the construction progress as a dynamic system evolving in a multidimensional state space, capable of capturing the complex interactive relationships and nonlinear characteristics during the construction process.

[0101] The workflow of the nonlinear prediction module 130 consists of four main steps:

[0102] First, the project topology is extracted from the BIM model to establish a construction network diagram G(V,E), where V represents a set of construction units and E represents the logical dependencies between units. In practical applications, the construction network diagram typically contains hundreds to thousands of nodes, and the connections between nodes are determined based on construction logic. For example, for an office building project, V may include construction units such as foundation, main structure, enclosure structure, and interior decoration, while E represents logical relationships such as foundation construction must precede the main structure construction.

[0103] Secondly, the panoramic image data and LiDAR point cloud data are mapped onto the construction network diagram to obtain the current construction state vector S(t). The construction state vector is a multi-dimensional vector containing information such as the completion rate, resource consumption rate, and quality indicators of each sub-project. For a project containing n construction units, S(t) can be represented as:

[0104] ,

[0105] in: This is the construction state vector at time t; This represents the state value of the i-th construction unit at time t, which is usually a subvector containing multiple indicators; n is the total number of construction units; the superscript T indicates the transpose of the vector. For example, the state of the concrete pouring process may include the completion rate (0% to 100%), the amount of concrete used (cubic meters), the number of personnel (people), etc.

[0106] Third, by combining the environmental impact matrix E(t) and the resource allocation matrix R(t), the construction state evolution trajectory is generated. The environmental impact matrix E(t) represents the impact of external environmental factors such as weather and site conditions on the construction progress, while the resource allocation matrix R(t) describes the allocation status of personnel, equipment, and materials. The system calculates the state evolution based on nonlinear dynamic equations:

[0107] ,

[0108] in: For time The construction state vector at that time; This represents the state transition function, which describes the evolution rules of the system over time. This is the environmental impact matrix at time t; This is the resource allocation matrix at time t; For time step The unit is days.

[0109] In practice, the F function can be expressed as:

[0110] ,

[0111] in: This represents the internal influence function between construction units, describing the interaction between them. This function represents the impact of environmental factors, describing the influence of environmental factors such as weather and site conditions on construction progress. This function represents the impact of resource allocation, describing the influence of personnel, equipment, and material allocation on construction progress. The time step is typically set to 1 day or a smaller time unit.

[0112] Taking concrete pouring as an example, the G function may represent the relationship between the completion of this process and the previous process (such as rebar tying); the H function may represent the delaying effect of rainfall on the pouring work; and the J function describes the effect of increasing the number of concrete pump trucks on the pouring speed.

[0113] Finally, by recursively iterating and predicting the construction status at future points in time, milestone completion time predictions are extracted from the construction status evolution trajectory. This is relevant for key milestone time points in the project. The system analyzes the state evolution trajectory to determine the earliest time point at which the state vector S(t) satisfies the milestone completion condition.

[0114] The prediction results are visualized in the BIM model using a color-coded rendering method through visualization unit 175. Different completion levels are represented by different colored pixel blocks, with pixel size indicating the degree of completion. The system employs the following rendering rules:

[0115] Completed area: large green pixel block;

[0116] In progress area: yellow pixel block, medium size;

[0117] Unstarted area: small gray pixel blocks;

[0118] Advanced progress area: dark green pixel block, the size of which increases with the degree of advancement;

[0119] Lagging progress area: blue pixel block, the size decreases as the lag increases;

[0120] Risk warning area: Yellow warning sign (caution) or red warning sign (danger)

[0121] This visualization method enables supervisors and managers to intuitively identify the construction progress status and potential risks, improving communication efficiency and decision-making speed.

[0122] To improve prediction accuracy, the nonlinear prediction module 130 also integrates an adaptive weight learning mechanism. This mechanism establishes an influencing factor weight matrix W, representing the degree of influence of each factor on the construction progress. The initial value of the W matrix is ​​set based on historical project data and expert knowledge, and is subsequently adjusted continuously according to the actual construction situation.

[0123] Specifically, the system acquires historical construction data, forming a historical data tensor H. After acquiring actual construction progress data, it calculates the prediction error between the predicted and actual progress. :

[0124] ,

[0125] in: This is the prediction error vector; This is the actual construction state vector at time t; This represents the construction state vector at time t as predicted by the system. Based on the prediction error, the system adaptively adjusts the weight matrix of influencing factors.

[0126] ,

[0127] in: This is the updated weight matrix; This is the current weight matrix; The learning rate parameter controls the speed at which the weights are adjusted, and is typically set between 0.01 and 0.1. Let J be the gradient of the weight matrix W with respect to the error function J, representing the sensitivity of the error function to each weight. The error function J is usually defined as the sum of squares of the prediction errors:

[0128] ,

[0129] in: It is the error function; This represents the total number of construction units. Let be the prediction error for the i-th construction unit.

[0130] In practical applications, taking an office building project as an example, the initial prediction error for the completion time of the concrete pouring process was ±3 days. Through adaptive weight learning, the system gradually discovered that weather factors (especially rainfall) had a significant impact on concrete pouring in this project, and accordingly increased the weight of weather factors while decreasing the weight of other factors. After five iterations of optimization, the prediction error was reduced to ±1 day, significantly improving the prediction accuracy.

[0131] To prevent overfitting, the system performs regularization on the updated weight matrix to ensure that the weight values ​​are within a reasonable range.

[0132] ,

[0133] in: This is the weight matrix after regularization; This is the updated weight matrix; This is the initial weight matrix; This is a regularization parameter, typically set between 0.05 and 0.2, used to control the degree of regression to the initial weights.

[0134] In a real office building construction project, after applying this nonlinear prediction model, the error in predicting the construction progress was reduced from ±15% by the traditional method to ±5%, which greatly improved the accuracy of project management.

[0135] The construction progress deviation analysis unit 140 is responsible for comparing the on-site construction information identified from the panoramic imagery and LiDAR point cloud data with the BIM model to generate construction progress deviation information. This unit first identifies on-site construction process information from the current construction site panoramic imagery and LiDAR point cloud data through the cloud model, including personnel information, machinery information, material information, progress information, and site environment information.

[0136] Preferably, personnel information includes the number, location, and working status of construction workers; machinery information includes the type, quantity, and operating status of equipment; material information includes the type, quantity, and location of materials; progress information includes the completion status of each sub-project; and site environment information includes site layout, access conditions, and temporary facilities.

[0137] The system compares the identified information with the planned schedule in the BIM model to analyze the differences between the actual and planned progress. During the comparison process, the system prioritizes LiDAR point cloud data as the benchmark value through the data source judgment unit 125. Panoramic image data is only used when the LiDAR data quality is insufficient or missing.

[0138] The system considers two cases:

[0139] When the current construction progress at the site lags behind the BIM model, the system obtains both the current on-site construction progress and the construction progress in the BIM model, and calculates the deviation value of the progress lag:

[0140] ,

[0141] in: This represents the schedule lag deviation, expressed as a percentage. This represents the percentage of planned progress in the BIM model; This indicates the percentage of actual construction progress. For example, if a sub-project is planned to be 60% complete, but is actually 45% complete, then... .

[0142] For areas with delayed progress, the system uses blue tones in the BIM model and adjusts the pixel size according to the degree of delay. The greater the delay, the smaller the pixel size, making the problem area more visually prominent.

[0143] When the current construction progress on the site exceeds the BIM model, the system calculates the deviation value of the progress ahead:

[0144] ,

[0145] in: This represents the deviation from schedule, expressed as a percentage; other symbols have the same meaning as above. For example, if a sub-project's planned completion rate is 30%, but the actual completion rate is 40%, then... For areas ahead of schedule, the system uses green tones in the BIM model and adjusts the pixel size according to the degree of advancement. The greater the advancement, the larger the pixel size, making the high-efficiency areas more visually prominent.

[0146] For areas with delayed progress, the system uses blue tones in the BIM model and adjusts the pixel size according to the degree of delay. The greater the delay, the smaller the pixel size, making the problem area more visually prominent.

[0147] Whether the schedule is behind schedule or ahead of schedule, the system will generate complete construction schedule deviation information, including deviation value, deviation type (behind / ahead of schedule), deviation range (the scope of the affected work process), and deviation handling time (how long it takes to correct).

[0148] In a high-rise residential project, the system detected that the main structure construction was 15% behind schedule. Analysis revealed that the primary cause was a delay in the steel reinforcement work, which affected the concrete pouring. The system estimated that it would take 7 days to correct this and provided this information as construction progress deviation data to subsequent modules for processing. Simultaneously, in the BIM model, this area was rendered in a blue color scheme and displayed with smaller pixel blocks, allowing managers to visually identify the problem area.

[0149] The stability analysis module 150 is another innovation of this invention. It is responsible for performing stability analysis of the construction progress based on the construction progress deviation information and the evolution trajectory of the construction state, and generating stability indices. This module treats the construction progress system as a dynamic system and evaluates the system stability by analyzing its state-space characteristics.

[0150] Specifically, the stability analysis module 150 first reconstructs the phase space of the construction system based on the construction state evolution trajectory. The phase space is the set of all possible states of the system. By analyzing the distribution and evolution characteristics of state points in the phase space, the stability of the system can be evaluated.

[0151] Secondly, the system calculates the stability index λ to assess the stability of the construction progress. λ can be expressed as:

[0152] ,

[0153] in: As a stability indicator, it is dimensionless; The observation window is typically 5 to 10 days. and This represents two initially similar state points in phase space; This represents the Euclidean distance between two state points at time t; Represents the natural logarithm. The physical meaning of the A value is the system's sensitivity to initial disturbances: The larger the value, the more unstable the system is, and small disturbances will be amplified rapidly; The smaller the value, the more stable the system and the better it can resist the effects of small disturbances. The stability analysis module 150 establishes a stability threshold system, dividing the construction state space into safe zones, warning zones, and danger zones.

[0154] The system is stable and progress is controllable within the safe zone.

[0155] The system stability in the warning area has decreased, requiring close monitoring.

[0156] Dangerous area, system unstable, immediate intervention required;

[0157] These thresholds are determined based on extensive engineering practice experience and can effectively identify construction conditions with different risk levels.

[0158] When the stability indicator λ enters the warning zone, the system generates an early warning message and displays it visually in the BIM model through a yellow warning sign to remind managers to pay attention to potential risks. When λ enters the danger zone, the system triggers the resource allocation optimization process and displays it visually in the BIM model through a red warning sign, taking proactive intervention measures.

[0159] In a commercial complex project, the system detected that the stability index λ during the decoration phase gradually increased from 0.08 to 0.15, entering the warning zone. Analysis revealed that this change primarily stemmed from coordination issues caused by overlapping construction work across multiple disciplines. The system issued a warning five days in advance, displaying a yellow warning icon in the relevant area of ​​the BIM model. This allowed the project management team to promptly adjust the construction sequence and resource allocation, preventing potential project delays.

[0160] The resource allocation optimization unit 160 is responsible for triggering the resource allocation optimization process when stability indicators exceed preset thresholds. This process mainly includes three aspects:

[0161] First, the system synchronizes construction progress deviation information with material suppliers and site management. Material suppliers include various building material providers and equipment rental companies; site management includes project managers, engineers, and construction team leaders. Information synchronization is achieved through the system's push notification function, ensuring that all relevant parties can receive the latest progress deviation information in a timely manner.

[0162] Secondly, material suppliers allocate raw materials needed at the construction site in real time based on construction progress deviation information. For example, when concrete construction is detected to be ahead of schedule, the system automatically calculates the amount of concrete that needs to be supplied in advance and generates an adjusted supply plan; when a sub-project is detected to be behind schedule, the system will adjust the supply time of subsequent raw materials accordingly to avoid material backlog. The material supplier's allocation information is fed back into the cloud model, forming a closed-loop management system.

[0163] Taking a residential project as an example, the system detected that the main structure construction was progressing 5 days ahead of schedule, predicting that the decoration phase would begin earlier. The system automatically calculated the new demand times for decoration materials such as timber, plasterboard, and paint, and notified suppliers in advance to adjust their delivery plans. Based on the system's suggestions, the suppliers moved the delivery of decoration materials originally scheduled for April 15th to April 10th, ensuring that the materials arrived on time according to the new schedule.

[0164] Meanwhile, the on-site management team adjusts the deployment of personnel, equipment, and materials in real time based on the construction progress deviation information. Specifically, for processes that are lagging behind, the system will suggest increasing personnel or equipment input or adjusting working hours; for processes that are ahead of schedule, the system will suggest appropriately reducing resource input and allocating surplus resources to other processes that need to be accelerated.

[0165] In the aforementioned residential projects, when the system detected that the main structure was completed ahead of schedule, it suggested temporarily reassigning some carpentry teams from another project with normal progress to fully utilize the time advantage of early completion. Simultaneously, for some plumbing and electrical installation work that had not yet reached the planned schedule, the system recommended adding two power tools and four skilled workers to ensure that the overall project timeline was not affected. This adjustment information is fed back into the cloud model for the system to conduct the next round of progress analysis and prediction.

[0166] Through this collaborative optimization mechanism, the system can automatically trigger dynamic resource allocation based on the actual progress, ensuring that resources are in the right place at the right time, which greatly improves resource utilization efficiency and construction management flexibility.

[0167] The Construction Schedule Adjustment Unit 170 is responsible for dynamically adjusting the project milestone completion time schedule based on panoramic imagery, LiDAR point cloud data, and environmental data, generating a project milestone completion time prediction schedule. The workflow of this unit includes:

[0168] First, based on the on-site construction progress, a project milestone completion timeline is generated. Preferably, the construction plan is developed on a project-by-project basis, specifically for main construction, installation, outdoor work, and other project types. Each project's construction plan comprises three levels: the overall project plan (covering the entire project cycle), phased work plans (typically monthly or quarterly), and daily work plans. Daily work plans are prepared after the overall project plan and phased work plans have been approved and confirmed, and are kept consistent with these plans to ensure continuity and coordination.

[0169] Secondly, the system dynamically predicts and adjusts the project milestone completion time plan by incorporating weather information. Specifically, the system analyzes changes in weather factors, assesses the impact of weather on project construction, and evaluates the degree of impact on various professional sub-projects, construction progress, and project milestone completion times.

[0170] The weather impact coefficient can be expressed as:

[0171] ,

[0172] in: The weather impact coefficient for the first sub-project represents the overall impact of weather factors on the project. The value ranges from [0,1], with the value closer to 1 indicating a greater impact. The quantity of weather factors, such as rainfall, temperature, and wind speed; Let be the weight of the j-th weather factor, representing the relative importance of that factor; The influence intensity of the j-th weather factor on the first sub-project is usually determined based on expert experience and historical data. For example, for the concrete pouring process, the influence weight of rainfall might be 0.6, the influence weight of temperature 0.3, and the influence weight of wind speed 0.1. When moderate rain (influence intensity 0.7), suitable temperature (influence intensity 0.1), and normal wind speed (influence intensity 0.2) are predicted for the next three days, the weather influence coefficient for this process is calculated as follows: Based on the weather impact coefficient, the estimated completion time of each sub-project is adjusted:

[0173] ,

[0174] in: The adjusted estimated completion time for the i-th sub-item is in days; The original planned completion time for the i-th sub-item of the project is in days. The adjustment factor is usually set between 0.5 and 2, representing the sensitivity to weather influences.

[0175] In a sports stadium project, the original plan was to complete the roof steel structure installation within 20 days. The system detected four days of strong winds (wind speed: level 6) in the next two weeks, which will significantly impact high-altitude operations. Based on historical data, the impact intensity of strong winds on this type of work is 0.8. With an adjustment factor 'a' of 1.5, the weather impact coefficient is calculated. (Assuming only wind speed is considered), the adjusted completion time is: The system adjusted the estimated completion time for this process from the original 20 days to 24 days, and accordingly adjusted the start time of subsequent processes.

[0176] Based on the above analysis, the system dynamically predicts the project schedule, sub-project schedules, and work process details, resulting in an updated project milestone completion time forecast. This forecast not only considers the current actual progress but also incorporates the impact of environmental factors such as weather forecasts, improving the accuracy and adaptability of the prediction.

[0177] The visualization unit 175 visualizes the project milestone completion time forecast plan in the BIM model using a combination of timeline and color-coded rendering. The system uses pixel blocks of different sizes and colors for visualization; pixel size indicates completion level, and color indicates progress status. This visualization method allows supervisors and stakeholders to intuitively understand the project's progress status and forecast trends, improving communication efficiency and decision-making quality.

[0178] The cloud model update unit 180 is responsible for sending the project milestone completion time prediction plan to the material suppliers and site management to guide the dynamic allocation of materials and personnel until the construction progress is consistent with the construction plan.

[0179] Specifically, the material supplier updates the material plan and executes the corresponding delivery plan based on the received milestone completion time forecast. For example, if a sub-project is predicted to be completed ahead of schedule, the supply time of materials required for subsequent processes will be brought forward accordingly; if the project duration is predicted to be extended, the material supply plan will also be adjusted accordingly to avoid problems caused by supplying too early or too late.

[0180] The material adjustment amount can be calculated using the following formula:

[0181] ,

[0182] in: The adjusted daily supply of materials varies by unit depending on the type of materials. This is the original planned daily supply of materials; The original planned construction period is in days; The adjusted construction period is shown in days. For example, a road project was originally scheduled to complete asphalt paving in 10 days, requiring a daily supply of 300 tons of asphalt mixture. Due to weather conditions, the system predicts the construction period will be extended to 15 days. The adjusted daily supply is as follows: Tons / day. Based on this, the system generates a new material supply plan, notifying suppliers to supply 200 tons per day for 15 days, maintaining a constant total quantity. Simultaneously, on-site management updates the material demand plan based on milestone completion time forecasts, dynamically adjusting on-site human and equipment resources according to changes in construction progress. For example, if a delay in a certain process is predicted, the system will suggest adjusting personnel arrangements for subsequent processes to avoid wasting human resources; when peak construction periods are predicted, the system will suggest increasing personnel or equipment input in advance to ensure resource supply meets demand. The number of personnel adjustments can be calculated using the following formula:

[0183] ,

[0184] in: The adjusted number of personnel is calculated per person. This refers to the original planned number of personnel, with each person as a unit. This is an efficiency adjustment factor, typically ranging from 0.8 to 1.2, used to account for the impact of the number of personnel on work efficiency.

[0185] In the aforementioned road project, the original plan was to employ 20 workers for asphalt paving. After the construction period was extended to 15 days, assuming an efficiency adjustment factor n of 0.9 (considering that the reduction in personnel may slightly decrease efficiency), the adjusted number of workers is: Based on this, the system recommends that the project manager reduce the asphalt paving team from 20 to 12 people, and redeploy the surplus personnel to other work areas. Through this prediction-based dynamic allocation mechanism, the system can proactively address changes and uncertainties during construction, ensuring that resource allocation remains synchronized with actual needs, and greatly improving the foresight and adaptability of construction management.

[0186] In addition to the core functions mentioned above, this invention also provides a comprehensive construction risk management mechanism. This mechanism mainly includes the following steps:

[0187] First, a construction risk knowledge base was established based on the BIM model and on-site construction data. This knowledge base covers information such as common construction risk types, risk characteristics, influencing factors, and countermeasures, providing knowledge support for risk identification and control.

[0188] Secondly, a risk list is generated using the project task sheet's planning information, and a link is established between the risk list and the construction risk knowledge base. The system analyzes information such as the process characteristics, time requirements, and resource needs in the task sheet to identify potential risk points and matches them with similar cases in the risk knowledge base to form a specific risk list for the current project.

[0189] Third, by combining on-site photo analysis and LiDAR point cloud data analysis, the system issues construction risk control recommendations for key high-risk projects. The system assesses the actual risk level by analyzing on-site photos and point cloud data on construction conditions, safety measures, and material storage, and generates corresponding control recommendations based on the risk level, such as increasing the frequency of safety inspections, adjusting construction methods, and strengthening quality control.

[0190] In the BIM model, the system uses different colored warning icons to visually display the risk level and distribution: red indicates high-risk areas, yellow indicates medium-risk areas, and green indicates low-risk areas, ensuring that supervisors and managers can intuitively identify risk conditions.

[0191] Finally, the system dynamically rates and categorizes risk items based on task urgency. The risk urgency index can be expressed as:

[0192] ,

[0193] in: Let be the urgency index of the i-th risk item, with a value range of [0,1]. The closer it is to 1, the more urgent it is. Let be the number of days remaining for the task corresponding to the i-th risk item; This represents the total number of days for the task. The severity of this risk is usually divided into levels 1-5, with level 5 being the most severe.

[0194] When the urgency level of the risk analysis exceeds a preset threshold (usually set to 0.8, based on a 1.0 maximum score), the system automatically reclassifies low-risk items as medium-risk items, improving the sensitivity and response speed of risk management. For example, the exterior wall construction task of a high-rise building project was originally planned for 30 days; 25 days have been completed, with 5 days remaining. The associated fall-from-height risk severity for this task is level 4 (1-5, with level 5 being the most severe). The calculated urgency index is: 4 = 0.67. Although this value is lower than the preset threshold of 0.8, if the actual construction progress is behind schedule and there is a large amount of work left, it may be necessary to rush the work. In this case, the system will reassess the ratio of the remaining workload to the remaining time, which may cause the urgency index to exceed the threshold and trigger an increase in the risk level.

[0195] In a real-world project, the system detected that the formwork construction was nearing completion, but on-site photos and LiDAR point cloud data showed that some formwork supports were not up to standard. Although the initial risk rating was low, due to the approaching concrete pouring time (urgency index reaching 0.85), the system automatically upgraded it to medium risk, displaying a yellow warning icon in the BIM model and immediately issuing a rectification notice, thus avoiding potential quality and safety issues.

[0196] This knowledge-based intelligent risk management mechanism enables the system to continuously identify and control risks throughout the entire construction process, significantly improving the safety and stability of the project.

[0197] The BIM-based intelligent construction progress management system of this invention has been applied in several large-scale construction projects and has achieved remarkable results. Compared with traditional construction management methods, this system shows significant advantages in terms of progress prediction accuracy, resource utilization efficiency, and risk control capabilities.

[0198] In a large commercial complex project, after implementing this system, the schedule prediction error decreased from an average of ±15% to ±5%, the early warning time for risks increased from an average of 1 day to 5 days, resource utilization improved by 22%, and the overall project management cost decreased by 35%. In particular, the system's color-coded rendering visualization technology greatly improved communication efficiency, enabling supervisors and managers to intuitively identify progress status and risk areas, significantly reducing information transmission links and improving decision-making speed.

[0199] The combined use of LiDAR point cloud data and panoramic imagery, along with the application of data source determination algorithms, has significantly improved the accuracy of construction information acquisition. In critical structural areas, the high-precision measurement of LiDAR point cloud data has improved construction accuracy control by over 40%; in decorative component areas, the material and color information from panoramic imagery supplements the shortcomings of point cloud data, providing more comprehensive construction status information.

[0200] These data fully demonstrate the practical value of this invention in improving the accuracy of construction management, reducing management costs, and enhancing decision support capabilities.

[0201] This invention provides a BIM-based intelligent construction progress management method and system. By integrating BIM digital twin technology, panoramic imagery and LiDAR point cloud data acquisition technology, nonlinear dynamic system theory, and intelligent control strategies, it constructs a complete system for construction progress prediction, analysis, control, and optimization. The system uses a nonlinear dynamic prediction model to capture the complex characteristics of the construction process, achieves continuous model optimization through adaptive weight learning, identifies risks in advance using stability analysis technology, and achieves precise intervention through a multi-party collaborative resource allocation mechanism, greatly improving the accuracy, foresight, and adaptability of construction progress management.

[0202] In particular, the system innovatively employs color-grading rendering technology, visually displaying construction progress and risk distribution through pixel blocks of different sizes and colors. This transforms the BIM model from a static 3D representation into a dynamic management and decision-making tool, significantly improving the efficiency of visual management. Simultaneously, the system integrates panoramic imagery and LiDAR point cloud data, and intelligently selects the optimal data source through a judgment algorithm, ensuring data accuracy and integrity.

[0203] The system has achieved remarkable results in practical engineering applications, providing new technical paths and solutions for intelligent and digital management in the field of construction engineering, and has important practical value and significance for promotion.

[0204] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of protection of the present invention.

Claims

1. A BIM-based intelligent construction progress management and control method, characterized in that... ,include: Establish a BIM digital twin model of the current project and a cloud model of the on-site construction data; Acquire panoramic images and LiDAR point cloud data of the current construction site, and import the panoramic images and LiDAR point cloud data into the cloud model; A decision-making algorithm is constructed to dynamically determine whether to update the BIM model with image modeling data or point cloud data for a specific area, reducing manual intervention; Based on the cloud model, a nonlinear dynamic prediction model for construction progress is constructed, including: extracting the engineering topology from the BIM model and establishing a construction network diagram; mapping the panoramic image data and LiDAR point cloud data onto the construction network diagram to obtain the current construction state vector; combining the environmental impact matrix and resource allocation matrix, calculating the state evolution based on nonlinear dynamic equations, and generating a construction state evolution trajectory; extracting milestone completion time predictions from the construction state evolution trajectory; visualizing the milestone completion time predictions in the BIM model using color-grading rendering, where different completion areas are represented by different colored pixel blocks, and the pixel block size expresses the degree of completion; calculating the state evolution based on nonlinear dynamic equations: , in: For time The construction state vector at that time; Represents the state transition function; This is the environmental impact matrix at time t; This is the resource allocation matrix at time t; For time step The unit is days; the F function is expressed as: ,in: This represents the internal influence function between construction units, describing the interaction between them. Function representing the influence of environmental factors; Functions representing the impact of resource allocation; For time step; The on-site construction information identified from the panoramic imagery and LiDAR point cloud data is compared with the BIM model to generate construction progress deviation information, which includes deviation value, deviation type, deviation range, and deviation processing time. Based on the construction progress deviation information and the construction state evolution trajectory, a construction progress stability analysis is performed to generate stability indices. When the stability index exceeds a preset threshold, resource allocation optimization is triggered, including: synchronizing the construction progress deviation information with the material supplier and the site management; the material supplier allocating the raw materials required by the construction site in real time based on the construction progress deviation information; and the site management adjusting the deployment of personnel, equipment, and materials at the construction site in real time based on the construction progress deviation information. Based on the panoramic imagery, LiDAR point cloud data, and environmental data, the project milestone completion time plan is dynamically adjusted to generate a project milestone completion time prediction plan. The project milestone completion time forecast plan is presented intuitively to the supervisor and relevant parties through the BIM model, and sent to the material supplier and site management to guide the dynamic allocation of materials and personnel until the construction progress is consistent with the construction plan.

2. The BIM-based intelligent construction progress management method according to claim 1, characterized in that... The construction of the nonlinear dynamic prediction model for construction progress also includes: Establish a weight matrix of influencing factors to represent the degree of influence of each factor on the construction progress; Obtain historical construction data and form a historical data tensor; The weight matrix of influencing factors is initialized based on the historical data tensor; Once the actual construction progress data is obtained, the prediction error between the predicted progress and the actual progress is calculated. Based on the prediction error, the weight matrix of the influencing factors is adaptively adjusted to improve the prediction accuracy.

3. The BIM-based intelligent construction progress management method according to claim 1, characterized in that... The stability analysis of the construction progress includes: Based on the aforementioned construction state evolution trajectory, the phase space of the construction system is reconstructed. Calculate indices characterizing system stability and assess the stability of construction progress; A stability threshold system is established to divide the construction state space into safe zones, warning zones, and danger zones; When the stability index enters the warning zone, an early warning message is generated and visualized in the BIM model using a yellow warning sign. When the stability index enters the danger zone, the resource allocation optimization is triggered and visualized in the BIM model through a red warning sign.

4. The BIM-based intelligent construction progress management method according to claim 1, characterized in that... The comparison of the on-site construction information identified from the panoramic image and LiDAR point cloud data with the BIM model specifically includes: The cloud model is used to identify information about the on-site construction process from the panoramic image and LiDAR point cloud data of the current construction site, and then compared with the BIM model. The algorithm prioritizes LiDAR point cloud data as the baseline, and only uses panoramic image data when the LiDAR data is insufficient or missing. If the current construction progress at the site lags behind the BIM model, the current construction progress at the site and the construction progress of the BIM model are obtained, and the deviation between the current construction progress and the construction progress of the BIM model is calculated. In the BIM model, the lagging area is represented by blue-toned rendering. If the current on-site construction progress exceeds the BIM model, the current on-site construction progress and the construction progress of the BIM model are obtained, and the deviation value between the current on-site construction progress and the construction progress of the BIM model is calculated. In the BIM model, green rendering is used to represent the area with the advanced progress.

5. The BIM-based intelligent construction progress management method according to claim 1, characterized in that... The information during the on-site construction process includes: personnel information, machinery information, material information, progress information, and site environment information.

6. The BIM-based intelligent construction progress management method according to claim 1, characterized in that... The environmental data includes weather information, and the dynamic adjustment of the project milestone completion time plan specifically includes: Based on changes in weather factors and their impact on project construction, analyze the influence of weather on various professional sub-projects, construction progress, and project milestone completion schedules. The construction schedule is dynamically adjusted based on the current weather forecast at the construction site. The dynamic adjustment method includes: dynamically predicting the schedule based on the project duration, sub-project duration, and process duration to obtain an updated project milestone completion time prediction plan. The project milestone completion time forecast plan will be visualized in the BIM model by combining a timeline with color-coded rendering. The project milestone completion time forecast plan is sent to the material supplier and the site management. The material supplier updates the material plan and executes the corresponding delivery plan based on the project milestone completion time forecast plan. The site management updates the material demand plan based on the project milestone completion time forecast plan and dynamically adjusts the on-site human and equipment resources according to the changes in the construction progress.

7. The BIM-based intelligent construction progress management method according to claim 1, characterized in that... The generated project milestone completion time prediction plan includes: Generate a project milestone completion time plan based on the on-site construction progress; The construction plan is organized by professional engineering project, with separate plans for main construction, installation, outdoor work, and other project types. The construction plan for each specialty includes: the overall project plan, the phased project plan, and the daily project plan. The daily project plan is prepared after the overall project plan and the phased project plan are approved and confirmed, and is consistent with the overall project plan and the phased project plan. The project milestone completion time plan is visualized in the BIM model using pixel blocks of different sizes and colors. The size of the pixel block indicates the degree of completion, and the color indicates the progress status.

8. The BIM-based intelligent construction progress management method according to claim 1, characterized in that... Also includes: Based on the BIM model and the on-site construction data, a construction risk knowledge base will be established. A risk list is generated using the project task sheet's planning information, and a link is established between the risk list and the construction risk knowledge base. Based on the analysis of project site photos and LiDAR point cloud data, construction risk control recommendations were issued for key high-risk projects. Risk items are dynamically rated and classified based on task urgency. When the urgency of the risk analysis exceeds a preset threshold, low-risk items are reclassified as medium-risk items. In the BIM model, risk levels and distribution are displayed intuitively using warning icons of different colors: red indicates high-risk areas, yellow indicates medium-risk areas, and green indicates low-risk areas, ensuring that supervisors and managers can intuitively identify risk conditions.

9. The BIM-based intelligent construction progress management method according to claim 1, characterized in that... The establishment of the BIM digital twin model and cloud model of the current project and the on-site construction data includes: Collect construction data throughout the entire project lifecycle, including drawings, work orders, and material information; The drawing is parsed parametrically to generate the result of the parametric parsing. Establish a component database, and connect the data interface of the component database with the data interface of the parameterized parsing result; The results of the parametric parsing are synchronized to the component attributes of the BIM model components through the component database; Extract the project task sheet's planning information and extract the corresponding building information from the BIM model; The project construction schedule is automatically scheduled based on the planned information and the corresponding building information in the project task sheet, and a project construction schedule plan is generated. The construction schedule is visualized in the BIM model using color-coded rendering, allowing supervisors and managers to intuitively understand the status of the schedule.

10. A BIM-based intelligent construction progress management and control system, used to implement the BIM-based intelligent construction progress management and control method according to any one of claims 1-9, characterized in that... ,include: The BIM digital twin model creation unit is used to create a BIM digital twin model of the current project and a cloud model of the on-site construction data. The construction site data acquisition unit is used to acquire panoramic images and LiDAR point cloud data of the current construction site, and import the panoramic images and LiDAR point cloud data into the cloud model; The data source determination unit is used to construct a determination algorithm to dynamically decide whether to use image modeling data or point cloud data to update the BIM model for a specific area, reducing manual intervention. The nonlinear prediction module is used to construct a nonlinear dynamic prediction model for construction progress based on the cloud model. This includes: extracting the engineering topology from the BIM model to establish a construction network diagram; mapping the panoramic image data and LiDAR point cloud data onto the construction network diagram to obtain the current construction state vector; combining the environmental impact matrix and resource allocation matrix to generate a construction state evolution trajectory; extracting milestone completion time predictions from the construction state evolution trajectory; and visualizing the milestone completion time predictions in the BIM model using color-grading rendering. The construction progress deviation analysis unit is used to compare the on-site construction information identified from the panoramic image and LiDAR point cloud data with the BIM model to generate construction progress deviation information, which includes deviation value, deviation type, deviation range, and deviation processing time. The stability analysis module is used to perform construction progress stability analysis based on the construction progress deviation information and the construction state evolution trajectory, and generate stability indicators. The resource allocation optimization unit is used to trigger resource allocation optimization when the stability index exceeds a preset threshold, including: synchronizing the construction progress deviation information to the material supplier and the site management; the material supplier allocating the raw materials required by the construction site in real time according to the construction progress deviation information; and the site management adjusting the deployment of personnel, equipment, and materials at the construction site in real time according to the construction progress deviation information. The construction schedule adjustment unit is used to dynamically adjust the project milestone completion time plan based on the panoramic image, LiDAR point cloud data, and environmental data, and generate a project milestone completion time prediction plan; The visualization unit is used to visually represent the project milestone completion time prediction plan through the BIM model using pixel blocks of different sizes and colors. The size of the pixel block indicates the degree of completion, and the color indicates the progress status. The cloud model update unit is used to send the project milestone completion time prediction plan to the material suppliers and site management to guide the dynamic allocation of materials and personnel until the construction progress is consistent with the construction plan.

Images