A Method and System for Intelligent Identification and Early Warning of Construction Risks Based on BIM Model

Abstract

This application provides a method and system for intelligent identification and early warning of construction risks based on BIM models, belonging to the field of construction risk early warning technology. The method includes: acquiring a building information model and historical meteorological data of a target scene; constructing a numerical wind tunnel model to perform transient wind field simulation, and combining the transient wind field simulation results to identify regions and obtain a priori sets of regions of interest; defining a real-time set of regions of interest for the target scene based on the construction progress information and the priori sets of regions of interest; collecting real-time wind data, and combining the real-time wind data and the numerical wind tunnel model to traverse the real-time sets of regions of interest for dynamic risk assessment and obtain a real-time risk coefficient set; visually marking the regions in the building information model's visualization interface, and combining the visual marking results with the real-time risk coefficient set for risk early warning. This solves the technical problem of insufficient real-time warning in traditional construction risk early warning methods in the prior art.

Description

Technical Field

[0001] This invention relates to the field of construction risk early warning, and in particular to a method and system for intelligent identification and early warning of construction risks based on BIM models. Background Technology

[0002] Construction risk early warning is a core element in ensuring construction safety. A timely and effective early warning mechanism can identify potential safety hazards in advance and reduce the incidence of safety accidents.

[0003] However, traditional construction risk warning usually relies on manual inspection and experience judgment. It lacks quantitative basis for assessing environmental risks such as wind and is difficult to systematically integrate multi-source information, resulting in insufficient real-time risk warning and easy to miss the opportunity to deal with the situation.

[0004] With the maturity of BIM technology, its advantages in 3D modeling, full-element information integration and visualization have gradually become prominent, providing technical support for breaking through the limitations of traditional construction risk identification and early warning.

[0005] Therefore, there is an urgent need for a BIM model-based intelligent identification and early warning method for construction risks, which can achieve accurate identification and timely early warning of construction risks by integrating BIM technology, real-time monitoring data and other multi-source information. Summary of the Invention

[0006] This invention addresses the technical problem of insufficient real-time warning in traditional construction risk warning methods in the prior art by providing a construction risk intelligent identification and warning method and system based on BIM model.

[0007] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0008] In a first aspect, the present invention provides a method for intelligent identification and early warning of construction risks based on BIM models, including:

[0009] Acquire building information models and historical meteorological data for the target scene;

[0010] A numerical wind tunnel model is constructed based on the building information model and historical meteorological data to simulate transient wind fields. The results of the transient wind field simulation are then used to identify regions and obtain a set of prior interest regions.

[0011] Based on the construction progress information of the target scenario and the prior interest area set, define the real-time interest area set of the target scenario;

[0012] Collect real-time wind data, and combine the real-time wind data with the numerical wind tunnel model to traverse the real-time area of ​​interest set for dynamic risk assessment and obtain a real-time risk coefficient set;

[0013] In the visualization interface of the building information model, multiple real-time areas of interest are visually marked according to the real-time risk coefficient set, and risk warnings are issued by combining the visualization marking results with the real-time risk coefficient set.

[0014] Secondly, this invention provides a building construction risk intelligent identification and early warning system based on BIM models, including:

[0015] The data acquisition module is used to acquire the building information model and historical meteorological data of the target scene;

[0016] The region identification module is used to construct a numerical wind tunnel model based on the building information model and historical meteorological data, perform transient wind field simulation, and identify regions by combining the transient wind field simulation results to obtain a priori set of regions of interest.

[0017] The attention area definition module is used to define the real-time attention area set of the target scene based on the construction progress information of the target scene and the prior attention area set;

[0018] The risk assessment module is used to collect real-time wind data and combine the real-time wind data with the numerical wind tunnel model to traverse the real-time focus area set for dynamic risk assessment and obtain a real-time risk coefficient set.

[0019] The risk warning module is used to visually mark multiple real-time areas of concern in the visualization interface of the building information model according to the real-time risk coefficient set, and to issue risk warnings by combining the visualization marking results with the real-time risk coefficient set.

[0020] The beneficial effects of this invention are:

[0021] Compared to existing technologies, this application first acquires the building information model (BIM) and historical meteorological data of the target scenario, overcoming the dual limitations of fuzzy structural information and missing environmental parameters in traditional risk assessments, thus providing a data foundation for subsequent dynamic risk simulation and early warning. Secondly, a numerical wind tunnel model is constructed based on the BIM and historical meteorological data to simulate transient wind fields. The simulation results are then used to identify regions and obtain a priori set of areas of interest. Utilizing historical data and simulation techniques, a list of potentially high-risk areas at each construction stage is determined, identifying potential risk points in advance and providing reliable support for subsequent risk assessment and early warning. Thirdly, based on the construction progress information and the priori set of areas of interest, a real-time set of areas of interest for the target scenario is defined, ensuring that all high-risk points at the current stage are included in the monitoring scope, providing precise targeting for dynamic risk assessment. Furthermore, real-time wind data is collected, and combined with the numerical wind tunnel model, dynamic risk assessment is performed by traversing the real-time set of areas of interest to obtain a real-time risk coefficient set. This achieves dynamic quantification of current construction risks, solving the problem of insufficient real-time performance in traditional methods and providing a reliable quantitative basis for subsequent risk early warning. Finally, in the building information model's visualization interface, multiple real-time areas of concern are visually marked according to the real-time risk coefficient set. The visualization marking results are combined with the real-time risk coefficient set to provide risk warnings. Through color visualization and multiple risk warning methods, risk information is ensured to reach the entire chain from the management end to the operation end, significantly shortening the risk response time.

[0022] Through the above technical solutions, this application provides a structured three-dimensional structural benchmark and quantified environmental parameter samples for risk assessment by acquiring the BIM model and historical meteorological data of the target scenario, solving the problems of fuzzy structural information and missing environmental data in traditional methods. It constructs a numerical wind tunnel model and performs transient wind field simulation to identify a priori areas of interest, achieving precise location of historically high-risk areas and overcoming the limitations of traditional assessments that rely on experience and lack systematicity. By dynamically defining the real-time area of ​​interest set through construction progress information, the risk monitoring scope is dynamically adjusted with the construction stage, avoiding misalignment of monitoring dynamic scenarios. Combined with real-time wind data and the numerical wind tunnel model, dynamic risk assessment is performed, outputting a real-time risk coefficient set, achieving real-time quantitative updates of risks and solving the problem of delayed early warning. Finally, risk marking and multi-channel early warning are completed in the BIM visualization interface, realizing closed-loop management from risk identification to handling. This improves the efficiency of construction risk identification and the timeliness of early warning, providing intelligent technical support for construction safety management. Attached Figure Description

[0023] Figure 1 A flowchart illustrating the intelligent identification and early warning method for building construction risks based on BIM models provided by this invention;

[0024] Figure 2 This is a structural diagram of the intelligent identification and early warning system for building construction risks based on BIM models provided by the present invention.

[0025] In the attached diagram, the components represented by each number are as follows:

[0026] Data acquisition module 11, area identification module 12, area of ​​interest definition module 13, risk assessment module 14, risk warning module 15. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0029] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0030] Example 1, as Figure 1 As shown, this embodiment of the invention provides a method for intelligent identification and early warning of construction risks based on BIM models, including:

[0031] S10: Obtain the building information model and historical meteorological data of the target scene.

[0032] Traditional construction risk management relies heavily on two-dimensional drawings and experience-based judgment, making it difficult to accurately depict the dynamic structural morphology of the construction area, such as changes in the spatial layout of scaffolding erection and component hoisting. Furthermore, it lacks quantitative basis for predicting risks related to environmental factors like wind. In contrast, BIM models provide three-dimensional structured data containing multi-dimensional information such as geometric dimensions, material properties, and construction stage markers. This data can provide a precise structural morphology benchmark for risk assessment; historical meteorological data can provide a data foundation for identifying extreme wind conditions and is the core basis for determining the most unfavorable convective information.

[0033] To address the aforementioned issues, this application obtains the building information model and historical meteorological data of the target scene.

[0034] Building Information Modeling (BIM) refers to the BIM model of the target construction scenario, containing geometric information (such as structural dimensions, spatial layout, and component locations) throughout the building's entire lifecycle, physical information (such as material strength and fire resistance rating), and functional information (such as component connection methods and construction process requirements). For example, a native BIM model containing data from all disciplines, including architecture, structure, and MEP (mechanical, electrical, and mechanical systems), can be obtained from the design institute as the BIM model for the target scenario, providing a spatial benchmark for risk assessment. Similarly, historical meteorological data, including wind speed data (such as average wind speed and maximum wind speed), wind direction data (such as wind direction frequency distribution and prevailing wind direction), and extreme weather records (such as typhoons and severe convective weather), can be obtained from meteorological departments, meteorological databases, or third-party meteorological service platforms. This historical meteorological data provides an environmental benchmark for wind field simulation. By analyzing historical wind environment characteristics, especially extreme wind conditions, historical references can be provided for subsequent identification of high-risk areas, ensuring that risk assessments cover potential adverse meteorological conditions.

[0035] Further, step S10 of the method, after acquiring the building information model and historical meteorological data of the target scene, includes:

[0036] Obtain construction implementation planning information for the target scenario and extract multiple construction stages;

[0037] Based on the multiple construction stages, the building information model is marked in stages to obtain model marking results, wherein the model marking results include multiple sets of stage marks and associated stage model boundaries;

[0038] The building information model and the model labeling results are associated and stored.

[0039] In this embodiment, the construction implementation planning information of the target scenario is first obtained, and multiple construction stages are extracted. The construction implementation planning information includes documents such as the overall construction schedule, the division of sub-projects, and the process connection scheme. For example, the construction implementation planning information of the target scenario is obtained, and multiple construction stages are extracted from the overall construction schedule and the division of sub-projects. For instance, the foundation stage takes 1-30 days, the main structure stage takes 31-180 days, and the decoration stage takes 181-270 days. By extracting multiple construction stages, the dynamically changing construction process is broken down into static stage units, enabling risk assessment to adapt to the construction content of different stages.

[0040] Secondly, based on multiple construction stages, the Building Information Model (BIM) is marked in stages to obtain model marking results. These results include multiple sets of stage markers and associated stage model boundaries. For example, based on the extracted construction stages, stage marking is performed in the BIM model to obtain model marking results including multiple sets of stage markers and associated stage model boundaries. Stage markers assign a unique identifier to each construction stage, such as ST-01 for the foundation and ST-02 for the main structure's third floor, associating it with information such as the construction content, schedule, and responsible personnel for that stage. Stage model boundaries delineate the construction area of ​​that stage in the BIM model using 3D boundary lines or color blocks. For example, the boundary of the main structure's third floor construction stage includes the third-floor slabs, wall components, corresponding work surfaces, and material storage areas. The model marking results reflect the specific construction information of that stage.

[0041] Finally, the building information model (BIM) and model labeling results are linked and stored. For example, through database technology, such as the linked storage function of a BIM collaboration platform, the three-dimensional geometric data of the BIM model is bound with the model labeling results. When performing wind field simulation or risk assessment later, the BIM model of the current construction stage can be directly retrieved, rather than the complete building model, which reduces the amount of calculation and avoids interference from irrelevant areas in risk analysis.

[0042] In summary, compared to existing technologies, this application obtains a building information model and historical meteorological data for the target scenario. This overcomes the dual limitations of ambiguous structural information and missing environmental parameters in traditional risk assessments, providing a data foundation for subsequent dynamic risk simulation and early warning.

[0043] S20: Construct a numerical wind tunnel model based on the building information model and historical meteorological data, perform transient wind field simulation, and combine the transient wind field simulation results to identify regions and obtain a priori interest region set.

[0044] Building construction is a dynamic process, and the structural forms at different construction stages differ significantly, causing the effects of wind loads to change dynamically with each construction stage. Moreover, natural wind fields are transient, such as gusts and turbulence. It is impossible to accurately capture the impact of wind on the construction area by relying solely on experience or simplified calculations, which may lead to delayed or distorted risk assessments.

[0045] To address the aforementioned issues, this application constructs a numerical wind tunnel model based on the building information model and historical meteorological data, performs transient wind field simulation, and combines the transient wind field simulation results to identify regions and obtain a priori sets of regions of interest.

[0046] Specifically, step S20 in the method includes:

[0047] Based on the model labeling results, the building information model is randomly selected to obtain the regional building information model;

[0048] Combining the preset time window constraints, the historical meteorological data is analyzed to obtain the most unfavorable environmental convection information, wherein the most unfavorable environmental convection information includes at least wind speed and wind direction indicators.

[0049] Based on the regional building information model and the most unfavorable environmental convection information, the numerical wind tunnel model is constructed, and transient wind field simulation is performed accordingly to obtain the transient wind field simulation results, wherein the transient wind field simulation results include at least wind pressure distribution and turbulence intensity distribution.

[0050] Traverse the model labeling results and iteratively select the regional building information model to perform transient wind field simulation;

[0051] Based on multiple transient wind field simulation results, a dual-objective threshold screening is performed to determine multiple prior interest regions, obtain the prior interest region set, and establish a region-stage correlation between the prior interest region set and the model labeling results.

[0052] In this embodiment, the building information model is first randomly selected based on the model labeling results to obtain a regional building information model. For example, a local area model of a certain construction stage can be randomly selected from the building information model based on multiple sets of stage labels from the model labeling results. For example, the rebar binding work area on the second floor of the main structure can be used as the regional building information model. The regional building information model only includes the components under construction, temporary facilities, and work spaces of this construction stage. Its data scale is much smaller than that of the complete building model, which can significantly reduce the computational load of subsequent transient wind field simulation, improve analysis efficiency, and accurately focus on the risk characteristics unique to this construction stage.

[0053] Secondly, by combining a preset time window constraint, historical meteorological data is analyzed to obtain the most unfavorable environmental convective information. The preset time window constraint is a pre-defined statistical range of historical meteorological data that matches the current construction phase. For example, if the current regional building information model corresponds to July as the construction phase, the preset time window constraint can be determined as meteorological data from July of the past five years. Those skilled in the art can dynamically adjust this constraint based on the climate characteristics of the construction area and the project cycle to ensure that the selected meteorological data is relevant to the wind environment characteristics of the actual construction period. The most unfavorable environmental convective information includes at least wind speed and wind direction indicators. Wind speed indicators include the historical maximum instantaneous wind speed, short-term gust peak value, and wind speed values ​​exceeding construction safety limits within the preset time window constraint. Wind direction indicators include the wind direction directly facing the temporary enclosure or high-altitude work platform facade, and historically dangerous wind directions that have led to safety accidents within the preset time window constraint. In this way, by clearly defining the extreme wind environment parameters of the same historical period, extreme working conditions are provided for constructing the numerical wind tunnel model, ensuring that the simulation scenario fully covers the wind environment that may be encountered during the construction phase.

[0054] Furthermore, based on the regional building information model and the convection information of the most unfavorable environment, a numerical wind tunnel model is constructed, and transient wind field simulation is performed accordingly to obtain the transient wind field simulation results. Among them, the numerical wind tunnel model is a virtual wind tunnel system built based on computational fluid dynamics (CFD) technology, which can accurately simulate the fluid motion state when air flows through the construction area. The input features of the numerical wind tunnel model include the regional building information model (reflecting the structural morphology details of the current construction stage) and the convection information of the most unfavorable environment (including wind speed and direction under extreme wind conditions). Transient wind field simulation refers to simulating the dynamic evolution of unsteady wind fields, i.e., simulating the irregular changes in wind speed and direction over time, such as sudden intensification of gusts and short-term changes in wind direction. The results of transient wind field simulation include at least wind pressure distribution and turbulence intensity distribution. Wind pressure distribution refers to the spatial distribution of pressure exerted by wind on various components (such as scaffolding poles, formwork, and temporarily stacked materials) within the regional building information model and the force per unit area. High-pressure areas may cause component deformation or even collapse. Turbulence intensity distribution refers to the random pulsation intensity of airflow velocity vectors, such as the irregular fluctuation amplitude of wind speed and direction. High-turbulence areas may cause scaffolding flutter, swaying of hoisted components, and interference with the balance and stability of high-altitude workers. By conducting transient wind field simulation under the most unfavorable environmental convection information, the dynamic interaction process between wind and construction area can be reconstructed, providing quantifiable physical parameters for risk assessment.

[0055] For example, transient wind field simulation can be carried out through the following technical path: Based on computational fluid dynamics (CFD) technology, the regional building information model is divided into structured grids. The grid size can be dynamically set according to actual needs. The inlet boundary is set as a velocity inlet (matching wind speed and wind direction indices), and the outlet boundary is set as a free flow outlet. The Reynolds time-averaged equation (RANS) combined with the k-ε turbulence model can be used for solving. Finally, the transient wind field simulation is completed through iterative calculation, and the results such as wind pressure cloud map and turbulence intensity vector map of the construction area are output.

[0056] Furthermore, the model labeling results are traversed, and regional building information models are iteratively selected from the building information models. Transient wind field simulation is performed according to the aforementioned method to ensure that wind-induced risk simulation is covered for all construction stages.

[0057] Finally, based on multiple transient wind field simulation results, dual-objective threshold screening was performed to determine multiple prior interest regions, obtain a prior interest region set, and establish a region-stage correlation between the prior interest region set and the model labeling results. The dual-objective threshold screening is based on multiple transient wind field simulation results, combined with the characteristics of the construction stage. Risk thresholds are set for wind pressure distribution and turbulence intensity distribution respectively. For example, during the main structure construction stage, the thresholds are set to wind pressure ≥ 0.5 kPa and turbulence intensity ≥ 0.2, while during the roof operation stage, due to the special characteristics of the high-altitude environment, the thresholds can be adjusted to wind pressure ≥ 0.3 kPa and turbulence intensity ≥ 0.15. Areas that simultaneously meet these two risk thresholds are selected as prior interest areas. These prior interest areas have high wind pressure and turbulence intensity under extreme wind conditions and are high-risk points that have been quantitatively verified in historical simulations. All the selected prior interest areas are integrated to form a prior interest area set, such as the corner of the 3rd floor scaffolding of the main structure and the edge of the roof material stacking area. The prior interest area set is a list of high-risk areas based on historical data and transient wind field simulations, providing key monitoring targets for risk monitoring in the subsequent real-time construction stage. For example, based on the model labeling results, the prior interest areas are associated with the construction stages in the model labeling results. For instance, the corner of the 3rd floor scaffolding of the main structure is associated with the 3rd floor construction stage of the main structure, and the edge of the roof material stacking area is associated with the roof construction stage, forming a region-stage association relationship between the prior interest area set and the model labeling results. Subsequently, the prior interest area corresponding to the real-time construction stage can be quickly retrieved, avoiding indiscriminate monitoring that leads to low efficiency.

[0058] In summary, compared to existing technologies, this application constructs a numerical wind tunnel model based on the aforementioned building information model and historical meteorological data to simulate transient wind fields. The simulation results are then used to identify areas of interest and obtain a priori set of regions of interest. Thus, by utilizing historical data and simulation technology, a list of potentially high-risk areas can be identified at each construction stage, allowing for early detection of potential risks and providing reliable support for subsequent risk assessment and early warning.

[0059] S30: Define the real-time interest set of the target scene based on the construction progress information of the target scene and the prior interest set.

[0060] Traditional risk management methods often rely on fixed risk lists or manual inspections, which cannot dynamically adapt to real-time changes during the construction phase, nor can they couple and analyze real-world risks with historical risk points, potentially leading to delayed risk warnings and missed detections of key locations.

[0061] To address the aforementioned issues, this application defines a real-time region of interest set for the target scenario based on the construction progress information of the target scenario and the prior region of interest set.

[0062] Specifically, step S30 in the method includes:

[0063] Based on the construction progress information, determine the real-time construction stage and extract the corresponding stage's workload information;

[0064] The process involves analyzing the phased engineering quantity information to determine multiple phased areas of interest in the target scenario. These phased areas of interest include at least material storage areas and personnel gathering areas.

[0065] Based on the real-time construction phase, the prior interest region set is called in stages in combination with the region-phase association relationship to obtain the phase-prior interest region set;

[0066] Merge multiple phase-focused regions with the phase-prior focus set to obtain the real-time focus set.

[0067] In this embodiment, the real-time construction stage is first determined based on the construction progress information, and the corresponding stage quantity information is extracted. For example, the real-time construction stage can be determined from the progress report, supervision log, and construction status update of the BIM model. For instance, the real-time construction stage corresponding to the rebar tying of the second floor of the main structure is the construction of the second floor of the main structure. Then, specific quantity information, such as the construction scope, work content, and resource distribution of the real-time construction stage, is extracted from the real-time construction stage as the stage quantity information.

[0068] Secondly, by analyzing the phased project quantity information, multiple phase-specific areas of concern in the target scenario are identified. These phase-specific areas of concern include at least material storage areas and personnel gathering areas, which are inherently high-risk areas. For example, the three-dimensional coordinates of the material storage areas and personnel gathering areas in the target scenario can be determined from the resource distribution in the phased project quantity information. For instance, the area at the second floor elevation of 12.5m, with X=10-15m and Y=20-25m, is a rebar storage area; the area at the second floor elevation of 12.5m, with X=25-28m and Y=30-35m, is a worker rest area. In this way, multiple phase-specific areas of concern inherently possessing high-risk attributes in the current construction phase are identified, and these areas of concern must be included in the monitoring scope.

[0069] Furthermore, based on the real-time construction phase, and combined with the region-phase association, the prior interest area set is invoked in stages to obtain the phase-prior interest area set. For example, when the real-time construction phase is the construction of the second floor of the main structure, based on the region-phase association, all prior interest areas associated with the real-time construction phase are retrieved from the prior interest area set. For instance, the construction of the second floor of the main structure is associated with the corner of the western external scaffolding of the second floor of the main structure, and this is included in the phase-prior interest area set. The phase-prior interest area set consists of high-risk areas that are susceptible to extreme wind conditions at this stage, as verified by historical meteorological data and transient wind field simulation. Its risk characteristics are highly matched with the real-time construction phase, providing a precise reference based on historical experience for risk monitoring at the current stage.

[0070] Finally, multiple stages of concern areas are merged with the stage-priority concern area set to obtain the real-time concern area set. For example, multiple stages of concern areas with high-risk attributes during the real-time construction phase are spatially merged with the stage-priority concern area set determined based on historical meteorological data and transient wind field simulations. This forms a real-time concern area set that includes both current and historical risk areas. For instance, if multiple stages of concern areas are rebar storage areas and worker rest areas, and the stage-priority concern area set is the western exterior scaffolding on the second floor of the main structure, the merged set would be: {rebar storage area, worker rest area, western exterior scaffolding on the second floor of the main structure}. If multiple stages of concern areas and the stage-priority concern area set spatially overlap, they are merged into the same monitoring unit to avoid duplicate monitoring. This covers the risk points of the real-time construction phase and the risk areas prone to problems in this phase based on historical experience, forming an accurate and comprehensive list of real-time monitoring objects.

[0071] In summary, compared to existing technologies, this application defines a real-time set of areas of interest for the target scenario based on the construction progress information of the target scenario and the aforementioned prior set of areas of interest. This ensures that all high-risk points at the current stage are included in the monitoring scope, providing precise targeting for dynamic risk assessment.

[0072] S40: Collect real-time wind data, and combine the real-time wind data with the numerical wind tunnel model to traverse the real-time area of ​​interest set for dynamic risk assessment and obtain a real-time risk coefficient set.

[0073] Construction risk assessment needs to address real-time scenarios such as changes in construction phases and dynamic changes in wind fields. However, traditional methods often rely on fixed risk lists or offline numerical simulations, resulting in a lag between risk assessment results and actual working conditions, making it impossible to capture real-time risks in a timely manner.

[0074] To address the aforementioned issues, this application collects real-time wind data and combines the real-time wind data with the numerical wind tunnel model to perform dynamic risk assessment by traversing the real-time area of ​​interest set, thereby obtaining a real-time risk coefficient set.

[0075] Specifically, step S40 in the method includes:

[0076] Based on the aforementioned real-time construction phase, the corresponding regional building information model is extracted.

[0077] Analyze the real-time wind data, extract the real-time wind speed and real-time wind direction, and update the regional building information model based on the real-time wind speed and real-time wind direction;

[0078] Transient wind field simulation is performed based on the updated regional building information model to obtain real-time transient wind field simulation results, wherein the real-time transient wind field simulation results include at least wind pressure distribution and turbulence distribution;

[0079] The real-time region of interest set is matched with the real-time transient wind field simulation results to extract risk assessment indicators for multiple real-time regions of interest. The risk assessment indicators include at least turbulence intensity, turbulence kinetic energy, pressure fluctuation rate and dynamic pressure.

[0080] Based on the risk assessment indicators, perform dynamic risk assessments on multiple real-time monitoring areas, obtain multiple real-time risk coefficients, and output them as the real-time risk coefficient set.

[0081] In this embodiment, the regional building information model is first extracted based on the real-time construction stage. For example, if the real-time construction stage is the construction of the second floor of the main structure, the regional building information model of the second floor of the main structure is extracted from the BIM model. The regional building information model includes parameters such as the components under construction and temporary facilities of the second floor of the main structure.

[0082] Secondly, real-time wind data is analyzed to extract real-time wind speed and direction, and the regional building information model is updated based on these data. For example, real-time wind data can be collected using anemometers, wind direction sensors, and other equipment deployed at the construction site. Real-time wind speed (e.g., 8 m / s) and real-time wind direction (e.g., southeast wind, 135°) can then be extracted from this data and used as new boundary conditions. These real-time wind speed and direction are then input into the regional building information model, replacing the fixed parameters of the most unfavorable environmental convection information. The updated regional building information model is linked to the on-site wind conditions in real time, ensuring that subsequent simulation results more closely reflect real-time wind environment parameters.

[0083] Secondly, based on the updated regional building information model, transient wind field simulation is performed using the same method as step S20 to obtain real-time transient wind field simulation results. These results include at least wind pressure distribution and turbulence distribution. Wind pressure distribution refers to the pressure distribution of real-time wind data on the surface of components within the region, such as a wind pressure of 0.4 kPa in a certain area. Turbulence distribution refers to the distribution of the degree of turbulence in real-time airflow, such as a turbulence intensity of 0.18 in a certain personnel work area. The real-time transient wind field simulation results are obtained based on real-time wind data and are closer to the real-time risk state.

[0084] Furthermore, the locations of the real-time focus area set and the real-time transient wind field simulation results are matched, and multiple risk assessment indicators for the real-time focus areas are extracted from the matched real-time transient wind field simulation results. These risk assessment indicators include at least turbulence intensity, turbulent kinetic energy, pressure fluctuation rate, and dynamic pressure: Turbulence intensity refers to the degree of fluctuation in airflow velocity; higher turbulence intensity indicates more turbulent airflow and a greater likelihood of exacerbating safety risks; turbulent kinetic energy refers to the amount of energy generated by turbulent motion; greater turbulent kinetic energy indicates higher impact energy on the building structure and a greater likelihood of exacerbating safety risks; pressure fluctuation rate refers to the amplitude of wind pressure fluctuation over time; greater pressure fluctuation rate indicates a greater likelihood of fatigue damage to components; and dynamic pressure refers to the amount of pressure converted from the kinetic energy of airflow, directly reflecting the thrust of wind on objects. Thus, the risk assessment indicators characterize the impact mechanism of the wind environment on the construction area from four dimensions: airflow stability (turbulence intensity), energy destructiveness (turbulent kinetic energy), load fluctuation (pressure fluctuation rate), and direct force (dynamic pressure), providing reliable physical parameter basis for subsequent quantitative risk assessment.

[0085] Finally, dynamic risk assessments are performed on multiple real-time concern areas based on risk assessment indicators, resulting in multiple real-time risk coefficients, which are then output as a set of real-time risk coefficients. For example, real-time risk coefficients can be calculated by weighted summation, and differentiated weights (with a total weight sum of 1) can be assigned to risk assessment indicators based on the risk characteristics of different real-time concern areas. For instance, for personnel work areas, since personnel safety is directly related to airflow stability, the weights assigned to turbulence intensity, turbulence kinetic energy, pressure fluctuation rate, and dynamic pressure are 0.4, 0.1, 0.2, and 0.3, respectively. For material storage areas, since storage stability depends on wind thrust and load fluctuations, the weights assigned to turbulence intensity, turbulence kinetic energy, pressure fluctuation rate, and dynamic pressure are 0.1, 0.2, 0.3, and 0.4, respectively. Secondly, safety thresholds for risk assessment indicators can be pre-set based on prior data such as construction safety specifications and historical data. By calculating the ratio of the risk assessment indicator to the safety threshold, the risk assessment indicator is converted into a risk contribution value between 0 and 1. The higher the risk contribution value, the closer the risk is to the threshold, i.e., the greater the risk. For example, the safety thresholds for risk assessment indicators can be set based on prior data such as construction safety specifications and historical data as turbulence intensity ≤ 0.2 and turbulent kinetic energy ≤ 2.0m. 2 / s 2 Pressure fluctuation rate ≤ 0.15 kPa / s, dynamic pressure ≤ 0.6 kPa, when the turbulence intensity of a certain real-time area of ​​interest is 0.15 and the turbulent kinetic energy is 1.8 m... 2 / s 2 When the pressure fluctuation rate is 0.12 kPa / s and the dynamic pressure is 0.3 kPa, the risk contribution values ​​of the risk assessment indicators can be calculated as follows: 0.15 / 0.2 = 0.75, 1.8 / 2.0 = 0.9, 0.12 / 0.15 = 0.8, and 0.3 / 0.6 = 0.5. If the weights of the turbulence intensity, turbulence kinetic energy, pressure fluctuation rate, and dynamic pressure in the real-time monitoring area are 0.4, 0.1, 0.2, and 0.3 respectively, the real-time risk coefficient of the real-time monitoring area can be calculated by weighted summation as follows: 0.4 × 0.75 + 0.1 × 0.9 + 0.2 × 0.8 + 0.3 × 0.5 = 0.7. In this way, multiple real-time risk coefficients can be calculated using the same method and output as a set of real-time risk coefficients.

[0086] Furthermore, the step of "collecting real-time wind data, and combining the real-time wind data with the numerical wind tunnel model to traverse the real-time area of ​​interest set for dynamic risk assessment and obtain a real-time risk coefficient set" further includes:

[0087] Obtain the most unfavorable environmental convection information and the transient wind field simulation results corresponding to the real-time construction stage, and conduct a priori risk assessment accordingly;

[0088] Obtain multiple sets of real-time risk coefficients and multiple sets of real-time wind data corresponding to the real-time construction stage;

[0089] Iteratively calculate the difference wind data between multiple real-time wind data and the most unfavorable environmental convection information to obtain a difference wind data set;

[0090] Iteratively calculate the risk coefficient difference data between multiple real-time risk coefficient sets and prior risk assessment results to obtain multiple risk difference datasets;

[0091] Using the differential wind force dataset as input and multiple risk difference datasets as supervision, a migration assessment model is constructed and trained, and dynamic risk assessment is performed based on the migration assessment model.

[0092] In this embodiment, the most unfavorable environmental convection information and transient wind field simulation results corresponding to the real-time construction stage are first obtained, and a priori risk assessment is performed accordingly. For example, based on step S20, the most unfavorable environmental convection information and transient wind field simulation results corresponding to the real-time construction stage are obtained. Following the same method as the aforementioned steps, turbulence intensity, turbulent kinetic energy, pressure fluctuation rate, and dynamic pressure are obtained from the transient wind field simulation results, and a priori risk assessment is performed to obtain the priori risk assessment result. The priori risk assessment result is a risk coefficient assuming the real-time construction stage is under the most unfavorable environmental convection information, which can be used as a benchmark reference to compare the difference between real-time risk and historical extreme risks.

[0093] Secondly, based on the aforementioned steps, multiple real-time wind force data are obtained during the real-time construction phase, and corresponding sets of real-time risk coefficients are obtained.

[0094] Next, the difference between multiple real-time wind data and the worst-case environmental convection information is iteratively calculated to obtain a difference wind data set. For example, if the real-time wind data shows a wind speed of 8 m / s and a wind direction of 135°, while the worst-case environmental convection information shows a historical maximum wind speed of 12 m / s and a historical dangerous wind direction of 90°, then the wind speed difference is -4 m / s and the wind direction difference is 45°, forming quantified difference wind data. In this way, the difference between multiple real-time wind data and the worst-case environmental convection information is iteratively calculated to obtain a difference wind data set, which can reflect the deviation between real-time wind data and historical worst-case environmental convection information.

[0095] Furthermore, the risk coefficient difference data between multiple real-time risk coefficient sets and prior risk assessment results is iteratively calculated to obtain multiple risk difference datasets. For example, the difference between the real-time risk coefficient sets and prior risk assessment results is calculated. For instance, if the real-time risk coefficient is 0.7 and the prior risk assessment result is 0.9, then the difference between the real-time risk coefficient set and the prior risk assessment result is 0.7 - 0.9 = -0.2, which serves as risk coefficient difference data, indicating that the real-time risk is 20% lower than the historical extreme risk. Thus, by iteratively calculating the risk coefficient difference data between multiple real-time risk coefficient sets and prior risk assessment results, multiple risk difference datasets are obtained, quantifying the deviation between the actual risk and the historical extreme risk.

[0096] Finally, using the differential wind force dataset as input and multiple risk difference datasets as supervision, a transfer assessment model is constructed and trained, and dynamic risk assessment is performed based on the transfer assessment model. For example, the transfer assessment model can be trained using the following technical path: 1. Data preparation: Divide the differential wind force dataset and multiple risk difference datasets into training, validation, and test sets according to a ratio of 7:1.5:1.5. 2. Model construction: The model can be built based on a transfer learning architecture, mainly consisting of a pre-trained feature extractor, a domain adaptation layer, and a task prediction layer. The pre-trained feature extractor uses a CNN-LSTM hybrid network pre-trained on a large-scale public wind field database (such as the open-source dataset for building wind engineering). The CNN module (containing three convolutional layers with kernel sizes of 3×3, 5×5, and 3×3) is used to extract spatial features of wind speed and direction differences. The LSTM layer (two bidirectional LSTM layers) is used to capture the temporal correlation of wind field differences, with 60% of the weights frozen. It retains the ability to extract general wind field features; the domain adaptation layer consists of two fully connected layers (128 and 64 nodes respectively) and a domain adversarial loss module. By minimizing the distribution difference between the source domain (public dataset) and the target domain (current engineering dataset), it transforms the pre-trained features into a feature space suitable for the current construction scenario. The domain adversarial loss is implemented using a gradient inversion layer (GRL), which forces the feature extractor to learn domain-invariant features; the task prediction layer consists of one fully connected layer (32 nodes) and an output layer. The output layer uses a linear activation function to directly predict the output risk system difference data. 3. Model Training: Using differential wind data from the training set as input and differential data from multiple risk systems as supervision, the Adam optimizer was selected. The initial learning rate was set to 0.001, dynamically adjusted using a cosine annealing strategy (decreasing by 10% every 5 rounds). The loss function was a weighted combination of mean squared error (MSE) and domain adversarial loss (weight ratio 7:3). MSE ensures the closeness of the predicted value to the actual risk deviation, while domain adversarial loss improves the model's cross-scenario adaptability. Training was iterated for 50 rounds. After each round, the loss value was calculated using the validation set. When the validation set loss decreased by less than 1e-4 for 5 consecutive rounds, an early stopping mechanism was triggered, indicating model convergence. Finally, the model performance was evaluated using the test set. If the mean absolute error (MAE) ≤ 0.05, training was considered complete, and the transfer evaluation model was output. Thus, by reusing general wind field knowledge through transfer learning and combining it with a domain adaptation mechanism to adapt to specific engineering scenarios, the model's prediction accuracy for wind conditions specific to construction sites was improved while maintaining training efficiency.

[0097] In summary, compared to existing technologies, this application collects real-time wind data and combines this real-time wind data with the numerical wind tunnel model to perform dynamic risk assessment by traversing the real-time area of ​​interest set, thereby obtaining a real-time risk coefficient set. This achieves dynamic quantification of current construction risks, solves the problem of insufficient real-time performance in traditional methods, and provides a reliable quantitative basis for subsequent risk warnings.

[0098] S50: In the visualization interface of the building information model, multiple real-time areas of concern are visually marked according to the real-time risk coefficient set, and risk warnings are given by combining the visualization marking results with the real-time risk coefficient set.

[0099] The aforementioned steps obtain a real-time risk coefficient set, providing a quantitative basis for risk warning. Based on this, risk warnings can be issued, forming a closed-loop warning mechanism from data quantification to multi-dimensional response, ensuring the speed of risk response.

[0100] To address the aforementioned issues, this application uses the visualization interface of the building information model to visually mark multiple real-time areas of interest based on the real-time risk coefficient set, and combines the visualization marking results with the real-time risk coefficient set to provide risk warnings.

[0101] Specifically, step S50 in the method includes:

[0102] By traversing multiple real-time attention areas and combining the real-time risk coefficient with preset color mapping rules, color marking information is obtained and rendered onto the visualization interface to obtain the visualization marking result.

[0103] The system continuously monitors the set of real-time risk coefficients, and when any of the real-time risk coefficients exceeds a preset risk threshold, it generates and sends a risk warning signal.

[0104] The risk warning signal is used to trigger at least one of the following operations:

[0105] The corresponding real-time attention area is highlighted and flashed in the visualization interface;

[0106] Push a warning message containing the visual marking results and the real-time risk coefficient to the designated terminal devices;

[0107] Activate the audible and visual alarm devices and electronic fences deployed in the target scene.

[0108] In this embodiment, multiple real-time areas of interest are first traversed. Color-coded information is obtained by combining real-time risk coefficients with preset color mapping rules, and then rendered onto the visualization interface to obtain the visualization marking results. For example, multiple real-time areas of interest are traversed, and the real-time risk coefficients are associated with preset color mapping rules. For instance, low risk (e.g., real-time risk coefficient ≤ 0.6) is pre-defined as green, medium risk (e.g., 0.6 < real-time risk coefficient ≤ 0.8) as yellow, and high risk (e.g., real-time risk coefficient > 0.8) as red. Color-coded information for multiple real-time areas of interest is generated according to the color mapping rules and accurately rendered onto the BIM model's visualization interface to form the visualization marking results. In this way, risk levels are intuitively distinguished by color, improving the speed of locating high-risk areas.

[0109] Secondly, the system continuously monitors the real-time risk coefficient set. When any real-time risk coefficient exceeds a preset risk threshold, a risk warning signal is generated and sent. The preset risk threshold can be set differently based on construction specifications and regional characteristics; for example, it can be set to 0.7 (more stringent) for areas with high population density, and 0.8 for material storage areas, ensuring the warning is targeted. The risk warning signal triggers at least one of the following actions: highlighting and flashing the corresponding real-time focus area in the visual interface; pushing a warning message containing the visual marker results and real-time risk coefficient to predetermined terminal devices; or activating the audible and visual alarm devices and electronic fences deployed in the target scene.

[0110] For example, the dynamic changes of the real-time risk coefficient set are continuously tracked, such as updating the data every 10 seconds. When the real-time risk coefficient of any area exceeds the preset risk threshold (e.g., 0.8), a risk warning signal is generated and sent. The warning signal is responded to through multiple channels to ensure that the risk information reaches relevant personnel efficiently. Specifically, this includes: flashing the area that triggers the warning in red and enlarging the boundary in the BIM visualization interface; pushing warning messages to the mobile APP, walkie-talkies and other terminal devices of construction managers, safety officers, etc.; and triggering the sound and light alarm devices around the high-risk area to sound an alarm.

[0111] In summary, compared to existing technologies, this application, in the visualization interface of the building information model, visually marks multiple real-time areas of concern according to the real-time risk coefficient set, and combines the visualization marking results with the real-time risk coefficient set to provide risk warnings. Thus, through color visualization and multiple risk warning methods, risk information is ensured to reach the entire chain from the management end to the operational end, significantly shortening risk response time.

[0112] In summary, the embodiments of this application have at least the following technical effects:

[0113] Compared to existing technologies, this application first acquires the building information model and historical meteorological data of the target scenario. This overcomes the dual limitations of ambiguous structural information and missing environmental parameters in traditional risk assessments, providing a data foundation for subsequent dynamic risk simulation and early warning.

[0114] Secondly, this application constructs a numerical wind tunnel model based on the aforementioned building information model and historical meteorological data to simulate transient wind fields. The simulation results are then used to identify areas of interest and obtain a priori set of regions of interest. In this way, by utilizing historical data and simulation technology, a list of potentially high-risk areas at each construction stage is determined, identifying potential risk points in advance and providing reliable support for subsequent risk assessment and early warning.

[0115] Furthermore, this application defines a real-time set of areas of interest for the target scenario based on the construction progress information of the target scenario and the aforementioned set of areas of interest. This ensures that all high-risk points at the current stage are included in the monitoring scope, providing precise targeting for dynamic risk assessment.

[0116] Furthermore, this application collects real-time wind data and combines this real-time wind data with the numerical wind tunnel model to perform dynamic risk assessment by traversing the real-time area of ​​interest set, thereby obtaining a real-time risk coefficient set. In this way, dynamic quantification of current construction risks is achieved, solving the problem of insufficient real-time performance of traditional methods and providing a reliable quantitative basis for subsequent risk warnings.

[0117] Finally, in the visualization interface of the building information model, this application visually marks multiple real-time areas of concern according to the real-time risk coefficient set, and combines the visualization marks with the real-time risk coefficient set to provide risk warnings. In this way, color visualization and multiple risk warning methods ensure that risk information reaches the entire chain from the management end to the operational end, significantly shortening risk response time.

[0118] Through the above technical solutions, this application provides a structured three-dimensional structural benchmark and quantified environmental parameter samples for risk assessment by acquiring the BIM model and historical meteorological data of the target scenario, solving the problems of fuzzy structural information and missing environmental data in traditional methods. It constructs a numerical wind tunnel model and performs transient wind field simulation to identify a priori areas of interest, achieving precise location of historically high-risk areas and overcoming the limitations of traditional assessments that rely on experience and lack systematicity. By dynamically defining the real-time area of ​​interest set through construction progress information, the risk monitoring scope is dynamically adjusted with the construction stage, avoiding misalignment of monitoring dynamic scenarios. Combined with real-time wind data and the numerical wind tunnel model, dynamic risk assessment is performed, outputting a real-time risk coefficient set, achieving real-time quantitative updates of risks and solving the problem of delayed early warning. Finally, risk marking and multi-channel early warning are completed in the BIM visualization interface, realizing closed-loop management from risk identification to handling. This improves the efficiency of construction risk identification and the timeliness of early warning, providing intelligent technical support for construction safety management.

[0119] Example 2, as Figure 2 As shown, based on the same inventive concept as the intelligent identification and early warning method for construction risks based on BIM models provided in Embodiment 1, this embodiment of the invention also provides an intelligent identification and early warning system for construction risks based on BIM models, including:

[0120] Data acquisition module 11 is used to acquire building information model and historical meteorological data of the target scene;

[0121] The region identification module 12 is used to construct a numerical wind tunnel model based on the building information model and historical meteorological data, perform transient wind field simulation, and identify regions by combining the transient wind field simulation results to obtain a priori interest region set.

[0122] The attention area definition module 13 is used to define the real-time attention area set of the target scene based on the construction progress information of the target scene and the prior attention area set.

[0123] Risk assessment module 14 is used to collect real-time wind data and combine the real-time wind data with the numerical wind tunnel model to traverse the real-time focus area set for dynamic risk assessment and obtain a real-time risk coefficient set.

[0124] The risk warning module 15 is used to visually mark multiple real-time areas of concern in the visualization interface of the building information model according to the real-time risk coefficient set, and to give a risk warning by combining the visualization marking results with the real-time risk coefficient set.

[0125] Specifically, the data acquisition module 11 is used for:

[0126] Obtain construction implementation planning information for the target scenario and extract multiple construction stages;

[0127] Based on the multiple construction stages, the building information model is marked in stages to obtain model marking results, wherein the model marking results include multiple sets of stage marks and associated stage model boundaries;

[0128] The building information model and the model labeling results are associated and stored.

[0129] Specifically, the region identification module 12 is used for:

[0130] Based on the model labeling results, the building information model is randomly selected to obtain the regional building information model;

[0131] Combining the preset time window constraints, the historical meteorological data is analyzed to obtain the most unfavorable environmental convection information, wherein the most unfavorable environmental convection information includes at least wind speed and wind direction indicators.

[0132] Based on the regional building information model and the most unfavorable environmental convection information, the numerical wind tunnel model is constructed, and transient wind field simulation is performed accordingly to obtain the transient wind field simulation results, wherein the transient wind field simulation results include at least wind pressure distribution and turbulence intensity distribution.

[0133] Traverse the model labeling results and iteratively select the regional building information model to perform transient wind field simulation;

[0134] Based on multiple transient wind field simulation results, a dual-objective threshold screening is performed to determine multiple prior interest regions, obtain the prior interest region set, and establish a region-stage correlation between the prior interest region set and the model labeling results.

[0135] Specifically, the region of interest definition module 13 is used for:

[0136] Based on the construction progress information, determine the real-time construction stage and extract the corresponding stage's workload information;

[0137] The process involves analyzing the phased engineering quantity information to determine multiple phased areas of interest in the target scenario. These phased areas of interest include at least material storage areas and personnel gathering areas.

[0138] Based on the real-time construction phase, the prior interest region set is called in stages in combination with the region-phase association relationship to obtain the phase-prior interest region set;

[0139] Merge multiple phase-focused regions with the phase-prior focus set to obtain the real-time focus set.

[0140] Specifically, the risk assessment module 14 is used for:

[0141] Based on the aforementioned real-time construction phase, the corresponding regional building information model is extracted.

[0142] Analyze the real-time wind data, extract real-time wind speed and real-time wind direction, and update the regional building information model based on the real-time wind speed and real-time wind direction;

[0143] Transient wind field simulation is performed based on the updated regional building information model to obtain real-time transient wind field simulation results, wherein the real-time transient wind field simulation results include at least wind pressure distribution and turbulence distribution;

[0144] The real-time region of interest set is matched with the real-time transient wind field simulation results to extract risk assessment indicators for multiple real-time regions of interest. The risk assessment indicators include at least turbulence intensity, turbulence kinetic energy, pressure fluctuation rate and dynamic pressure.

[0145] Based on the risk assessment indicators, perform dynamic risk assessments on multiple real-time monitoring areas, obtain multiple real-time risk coefficients, and output them as the real-time risk coefficient set.

[0146] Furthermore, after obtaining the real-time risk coefficient set, the process also includes:

[0147] Obtain the most unfavorable environmental convection information and the transient wind field simulation results corresponding to the real-time construction stage, and conduct a priori risk assessment accordingly;

[0148] Obtain multiple sets of real-time risk coefficients and multiple sets of real-time wind data corresponding to the real-time construction stage;

[0149] Iteratively calculate the difference wind data between multiple real-time wind data and the most unfavorable environmental convection information to obtain a difference wind data set;

[0150] Iteratively calculate the risk coefficient difference data between multiple real-time risk coefficient sets and prior risk assessment results to obtain multiple risk difference datasets;

[0151] Using the differential wind force dataset as input and multiple risk difference datasets as supervision, a migration assessment model is constructed and trained, and dynamic risk assessment is performed based on the migration assessment model.

[0152] The risk warning module 15 is specifically used for:

[0153] By traversing multiple real-time attention areas and combining the real-time risk coefficient with preset color mapping rules, color marking information is obtained and rendered onto the visualization interface to obtain the visualization marking result.

[0154] The system continuously monitors the set of real-time risk coefficients, and when any of the real-time risk coefficients exceeds a preset risk threshold, it generates and sends a risk warning signal.

[0155] The risk warning signal is used to trigger at least one of the following operations:

[0156] The corresponding real-time attention area is highlighted and flashed in the visualization interface;

[0157] Push a warning message containing the visual marking results and the real-time risk coefficient to the designated terminal devices;

[0158] Activate the audible and visual alarm devices and electronic fences deployed in the target scene.

[0159] In summary, the embodiments of this application have at least the following technical effects:

[0160] Compared to existing technologies, this application firstly acquires the building information model (BIM) and historical meteorological data of the target scenario through a data acquisition module. This overcomes the dual limitations of fuzzy structural information and missing environmental parameters in traditional risk assessments, providing a data foundation for subsequent dynamic risk simulation and early warning. Secondly, through a region identification module, a numerical wind tunnel model is constructed based on the BIM and historical meteorological data to simulate transient wind fields. The simulation results are then combined to identify regions and obtain a priori set of areas of interest. Using historical data and simulation technology, a list of potentially high-risk areas in each construction stage is determined, identifying potential risk points in advance and providing reliable support for subsequent risk assessment and early warning. Thirdly, through a region of interest definition module, a real-time set of areas of interest for the target scenario is defined based on the construction progress information and the priori set of areas of interest. This ensures that all high-risk points in the current stage are included in the monitoring scope, providing precise targeting for dynamic risk assessment. Furthermore, through the risk assessment module, real-time wind data is collected and combined with a numerical wind tunnel model to dynamically assess the risk across a set of real-time areas of interest, obtaining a set of real-time risk coefficients. This enables dynamic quantification of current construction risks, solving the problem of insufficient real-time performance in traditional methods and providing a reliable quantitative basis for subsequent risk warnings. Finally, through the risk warning module, multiple real-time areas of interest are visually marked according to the real-time risk coefficient set in the building information model's visualization interface. Risk warnings are then issued based on the visual marking results and the real-time risk coefficient set. Color visualization and multiple risk warning methods ensure that risk information reaches the entire chain from management to operation, significantly shortening risk response time. This improves the efficiency of construction risk identification and the timeliness of warnings, providing intelligent technical support for construction safety management.

[0161] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0162] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0163] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0164] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0165] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0166] Although preferred embodiments of the invention have been described, those skilled in the art, once they have learned the basic inventive concept, can make other changes and modifications to these embodiments.

[0167] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for intelligent identification and early warning of construction risks based on BIM models, characterized in that, include: Acquire building information models and historical meteorological data for the target scene; A numerical wind tunnel model is constructed based on the building information model and historical meteorological data to simulate transient wind fields. The results of the transient wind field simulation are then used to identify regions and obtain a set of prior interest regions. Based on the construction progress information of the target scenario and the prior interest area set, define the real-time interest area set of the target scenario; Collect real-time wind data, and combine the real-time wind data with the numerical wind tunnel model to traverse the real-time area of ​​interest set for dynamic risk assessment and obtain a real-time risk coefficient set; In the visualization interface of the building information model, multiple real-time areas of interest are visually marked according to the real-time risk coefficient set, and risk warnings are issued by combining the visualization marking results with the real-time risk coefficient set.

2. The intelligent identification and early warning method for construction risks based on BIM models as described in claim 1, characterized in that, After acquiring the building information model and historical meteorological data of the target scene, the following steps are taken: Obtain construction implementation planning information for the target scenario and extract multiple construction stages; Based on the multiple construction stages, the building information model is marked in stages to obtain model marking results, wherein the model marking results include multiple sets of stage marks and associated stage model boundaries; The building information model and the model labeling results are associated and stored.

3. The intelligent identification and early warning method for construction risks based on BIM models as described in claim 2, characterized in that, A numerical wind tunnel model is constructed based on the building information model and historical meteorological data to simulate transient wind fields. The simulation results are then used to identify regions of interest and obtain a priori set of regions of interest, including: Based on the model labeling results, the building information model is randomly selected to obtain the regional building information model; Combining the preset time window constraints, the historical meteorological data is analyzed to obtain the most unfavorable environmental convection information, wherein the most unfavorable environmental convection information includes at least wind speed and wind direction indicators. Based on the regional building information model and the most unfavorable environmental convection information, the numerical wind tunnel model is constructed, and transient wind field simulation is performed accordingly to obtain the transient wind field simulation results, wherein the transient wind field simulation results include at least wind pressure distribution and turbulence intensity distribution. Traverse the model labeling results and iteratively select the regional building information model to perform transient wind field simulation; Based on multiple transient wind field simulation results, a dual-objective threshold screening is performed to determine multiple prior interest regions, obtain the prior interest region set, and establish a region-stage correlation between the prior interest region set and the model labeling results.

4. The intelligent identification and early warning method for construction risks based on BIM models as described in claim 3, characterized in that, Based on the construction progress information of the target scenario and the prior interest region set, a real-time interest region set for the target scenario is defined, including: Based on the construction progress information, determine the real-time construction stage and extract the corresponding stage's workload information; The process involves analyzing the phased engineering quantity information to determine multiple phased areas of interest in the target scenario. These phased areas of interest include at least material storage areas and personnel gathering areas. Based on the real-time construction phase, the prior interest region set is called in stages in combination with the region-phase association relationship to obtain the phase-prior interest region set; Merge multiple phase-focused regions with the phase-prior focus set to obtain the real-time focus set.

5. The intelligent identification and early warning method for construction risks based on BIM models as described in claim 4, characterized in that, Collecting real-time wind data and combining the real-time wind data with the numerical wind tunnel model, performing dynamic risk assessment by traversing the real-time area of ​​interest set, and obtaining a real-time risk coefficient set, also includes: Based on the aforementioned real-time construction phase, the corresponding regional building information model is extracted. Analyze the real-time wind data, extract the real-time wind speed and real-time wind direction, and update the regional building information model based on the real-time wind speed and real-time wind direction; Transient wind field simulation is performed based on the updated regional building information model to obtain real-time transient wind field simulation results, wherein the real-time transient wind field simulation results include at least wind pressure distribution and turbulence distribution; The real-time region of interest set is matched with the real-time transient wind field simulation results to extract risk assessment indicators for multiple real-time regions of interest. The risk assessment indicators include at least turbulence intensity, turbulence kinetic energy, pressure fluctuation rate and dynamic pressure. Based on the risk assessment indicators, perform dynamic risk assessments on multiple real-time monitoring areas, obtain multiple real-time risk coefficients, and output them as the real-time risk coefficient set.

6. The intelligent identification and early warning method for construction risks based on BIM models as described in claim 5, characterized in that, Real-time wind data is collected, and combined with the real-time wind data and the numerical wind tunnel model, dynamic risk assessment is performed by traversing the real-time area of ​​interest set to obtain a real-time risk coefficient set. The process then includes: Obtain the most unfavorable environmental convection information and the transient wind field simulation results corresponding to the real-time construction stage, and conduct a priori risk assessment accordingly; Obtain multiple sets of real-time risk coefficients and multiple sets of real-time wind data corresponding to the real-time construction stage; Iteratively calculate the difference wind data between multiple real-time wind data and the most unfavorable environmental convection information to obtain a difference wind data set; Iteratively calculate the risk coefficient difference data between multiple real-time risk coefficient sets and prior risk assessment results to obtain multiple risk difference datasets; Using the differential wind force dataset as input and multiple risk difference datasets as supervision, a migration assessment model is constructed and trained, and dynamic risk assessment is performed based on the migration assessment model.

7. The intelligent identification and early warning method for construction risks based on BIM models as described in claim 5, characterized in that, In the visualization interface of the building information model, multiple real-time areas of interest are visually marked according to the real-time risk coefficient set, and risk warnings are issued by combining the visualization marking results with the real-time risk coefficient set, including: By traversing multiple real-time attention areas and combining the real-time risk coefficient with preset color mapping rules, color marking information is obtained and rendered onto the visualization interface to obtain the visualization marking result. The system continuously monitors the set of real-time risk coefficients, and when any of the real-time risk coefficients exceeds a preset risk threshold, it generates and sends a risk warning signal. The risk warning signal is used to trigger at least one of the following operations: The corresponding real-time attention area is highlighted and flashed in the visualization interface; Push a warning message containing the visual marking results and the real-time risk coefficient to the designated terminal devices; Activate the audible and visual alarm devices and electronic fences deployed in the target scene.

8. A building construction risk intelligent identification and early warning system based on BIM model, characterized in that, For performing the method according to any one of claims 1-7, comprising: The data acquisition module is used to acquire the building information model and historical meteorological data of the target scene; The region identification module is used to construct a numerical wind tunnel model based on the building information model and historical meteorological data, perform transient wind field simulation, and identify regions by combining the transient wind field simulation results to obtain a priori set of regions of interest. The attention area definition module is used to define the real-time attention area set of the target scene based on the construction progress information of the target scene and the prior attention area set; The risk assessment module is used to collect real-time wind data and combine the real-time wind data with the numerical wind tunnel model to traverse the real-time focus area set for dynamic risk assessment and obtain a real-time risk coefficient set. The risk warning module is used to visually mark multiple real-time areas of concern in the visualization interface of the building information model according to the real-time risk coefficient set, and to issue risk warnings by combining the visualization marking results with the real-time risk coefficient set.

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