Safety management method and device for construction process of building engineering

Abstract

The present invention relates to a method for safety management of a construction process of a building project, comprising the following steps: obtaining construction site data of the building project, and constructing a three-dimensional model of the construction site based on the construction site data; classifying objects at the construction site within the three-dimensional model to obtain dynamic objects and static objects, and obtaining safety management measures based on the analysis results; utilizing digital twin technology to perform visual simulation of the safety management measures to obtain simulation results; obtaining safety management criteria, and evaluating the simulation results based on the safety management criteria to obtain simulation evaluation results; if the simulation evaluation results do not meet the safety management criteria, iteratively optimizing the safety management measures until the simulation evaluation results meet the safety management criteria; solving the problem of being unable to fully grasp the real-time status of the construction site and being unable to accurately predict and control the safety hazards caused by dynamic changes.

Description

Technical Field

[0001] The present invention relates to the technical field of construction engineering, and in particular to a construction engineering construction process safety management method, device, equipment and storage medium. Background Art

[0002] In the current construction industry, with the rapid development and increasing sophistication of construction technology, safety issues during construction are becoming increasingly prominent. Traditional construction site safety management relies primarily on manual inspections and record-keeping, which has many shortcomings in terms of real-time monitoring of the construction site, the timely detection and early warning of potential risks, and the effective formulation and implementation of safety management measures. For example, it is impossible to fully grasp the real-time status of the construction site, it is difficult to accurately predict and control the safety hazards caused by dynamic changes, and there is a lack of systematic and detailed risk assessment and control methods for the entire construction process. In addition, with the trend of intelligent construction and digital transformation, how to use advanced information technology to improve the efficiency and accuracy of construction safety management has become a key issue that needs to be addressed urgently. Summary of the Invention

[0003] The main purpose of the present invention is to provide a construction process safety management method, device, equipment and storage medium, which solves the safety hazards caused by the inability to fully grasp the real-time status of the construction site and the difficulty in accurately predicting and controlling dynamic changes.

[0004] To achieve the above objectives, the present invention provides a construction process safety management method, comprising the following steps:

[0005] Acquiring construction site data of a building project, and constructing a three-dimensional model of the construction site based on the construction site data;

[0006] Classifying objects at the construction site within the three-dimensional model to obtain dynamic objects and static objects, and tracking the dynamic objects using GPS technology to obtain trajectories of the dynamic objects;

[0007] Dynamically updating the three-dimensional model based on the trajectory of the dynamic object to obtain an updated three-dimensional model;

[0008] Conducting a risk analysis of the construction process based on the updated three-dimensional model to obtain risk analysis results, and obtaining safety management measures based on the risk analysis results; wherein the safety management measures include safety protection measures for on-site personnel, safety management of machinery and equipment, and dynamic risk control during the construction process;

[0009] Use digital twin technology to conduct visual simulation of safety management measures and obtain simulation results;

[0010] Obtain safety management criteria, evaluate the simulation results based on the safety management criteria to obtain simulation evaluation results, and if the simulation evaluation results do not meet the safety management criteria, iteratively optimize the safety management measures until the simulation evaluation results meet the safety management criteria.

[0011] As a further aspect of the present invention, the construction site data includes construction terrain data, building layout data, dynamic object data, historical accident data, and public facilities and infrastructure data.

[0012] As a further solution of the present invention, a three-dimensional model of the construction site is constructed based on the construction site data, including:

[0013] Taking aerial photos of the construction site using a drone to obtain construction site images, extracting data from the construction site images, and obtaining construction site data of the construction project;

[0014] Converting the construction site data into a standard data set for a three-dimensional modeling program;

[0015] Using 3D modeling software, constructing a 3D model of the construction site based on the standard data set to obtain a preliminary 3D model of the construction site;

[0016] Performing component identification and division of the building object in the preliminary three-dimensional model using a preset component identification algorithm to obtain building element subdivisions; wherein the building elements include building envelope components, electromechanical pipeline components, and site facility components;

[0017] Obtaining building attributes by consulting on-site construction logs, and assigning attributes to the building element segments based on the building attributes using an attribute annotation algorithm to obtain building element segments with attributes; wherein the attributes include safety attributes, environmental attributes, life cycle attributes, construction attributes, and structural attributes;

[0018] Based on the subdivision of the building elements with attributes, a three-dimensional model of the construction site is obtained.

[0019] As a further solution of the present invention, objects at the construction site within the three-dimensional model are classified to obtain dynamic objects and static objects, and the dynamic objects are tracked using GPS technology to obtain trajectories of the dynamic objects, including:

[0020] Identifying entity objects within the three-dimensional model to obtain identified objects, and classifying the identified objects to obtain dynamic objects and static objects;

[0021] The real-time dynamic location data of the dynamic object is collected through a preset GPS positioning system to obtain a continuous geographical location information stream;

[0022] The Kalman filter algorithm is used to calculate the trajectory of the continuous geographic location information stream to obtain the precise motion trajectory of the dynamic object in the three-dimensional model; wherein the precise motion trajectory serves as the trajectory of the dynamic object.

[0023] As a further solution of the present invention, a risk analysis of the construction process is performed based on the updated three-dimensional model to obtain risk analysis results, and safety management measures are obtained based on the risk analysis results, including:

[0024] Conduct in-depth analysis of the updated 3D model using the Fault Tree Analysis method to identify potential risk sources;

[0025] Performing a risk analysis on the construction process based on the potential risk sources to obtain a risk analysis result; wherein the risk analysis result is a list of risks that may be faced during the construction process; the risk list includes multiple risk events and risk coefficients corresponding to the risk events;

[0026] Based on the risk coefficient, perform qualitative and quantitative risk assessment on each risk event to obtain a corresponding risk level;

[0027] Determine targeted safety management measures based on the corresponding risk level.

[0028] As a further solution of the present invention, obtaining a safety management criterion, evaluating the simulation result based on the safety management criterion to obtain a simulation evaluation result, and if the simulation evaluation result does not meet the safety management criterion, iteratively optimizing the safety management measures until the simulation evaluation result meets the safety management criterion, including:

[0029] Obtaining construction safety regulations, and extracting information from the construction safety regulations to obtain safety management guidelines;

[0030] Using a fuzzy comprehensive evaluation algorithm, based on the safety management criteria, the simulation results are qualitatively and quantitatively evaluated to obtain a simulation evaluation result;

[0031] Comparing the simulation assessment results with the safety management criteria to obtain comparative results, wherein the comparative results include a safety performance index;

[0032] Using the particle swarm optimization algorithm, if the comparison results do not meet the safety management criteria, the existing safety management measures are optimized and adjusted until the simulation evaluation results meet the safety management criteria.

[0033] As a further solution of the present invention, digital twin technology is used to perform visual simulation of safety management measures to obtain simulation results, including:

[0034] Use digital twin technology to digitize safety management measures and obtain digital safety management measures;

[0035] The digital safety management measures are virtually embedded in the three-dimensional model to obtain a simulated deployment of the safety measures; wherein the simulated deployment of the safety measures is a simulated environment configuration for implementing the safety management measures during the construction process;

[0036] Continuous dynamic simulation is performed on the simulation deployment of the safety measures to obtain dynamic simulation results.

[0037] The present invention also provides a construction process safety management device for a building project, comprising:

[0038] An acquisition module, configured to acquire construction site data of a building project and construct a three-dimensional model of the construction site based on the construction site data;

[0039] A classification module is used to classify objects at the construction site within the three-dimensional model into dynamic objects and static objects, and to track the dynamic objects using GPS technology to obtain trajectories of the dynamic objects;

[0040] an updating module, configured to dynamically update the three-dimensional model based on the trajectory of the dynamic object to obtain an updated three-dimensional model;

[0041] An analysis module, configured to perform risk analysis on the construction process based on the updated three-dimensional model, obtain risk analysis results, and obtain safety management measures based on the risk analysis results;

[0042] The simulation module is used to use digital twin technology to perform visual simulation of safety management measures and obtain simulation results;

[0043] The evaluation module is used to obtain safety management criteria, evaluate the simulation results based on the safety management criteria, and obtain simulation evaluation results. If the simulation evaluation results do not meet the safety management criteria, the safety management measures are iteratively optimized until the simulation evaluation results meet the safety management criteria.

[0044] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0045] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0046] The method, device, equipment and storage medium for safety management of the construction process of a construction project provided by the present invention include the following steps: obtaining construction site data of the construction project, and constructing a three-dimensional model of the construction site based on the construction site data; classifying objects at the construction site within the three-dimensional model to obtain dynamic objects and static objects, and obtaining safety management measures based on the analysis results; using digital twin technology to perform visual simulation of the safety management measures to obtain simulation results; obtaining safety management criteria, and evaluating the simulation results based on the safety management criteria to obtain simulation evaluation results. If the simulation evaluation results do not meet the safety management criteria, the safety management measures are iteratively optimized until the simulation evaluation results meet the safety management criteria; previewing and verifying the proposed safety management measures through visual simulation solves the problems of being unable to fully grasp the real-time status of the construction site and the difficulty in accurately predicting and controlling the safety hazards caused by dynamic changes, thereby greatly enhancing the foresight and effectiveness of safety management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the steps of a construction engineering construction process safety management method according to one embodiment of the present invention;

[0048] Figure 2 This is a structural block diagram of a construction engineering construction process safety management device according to one embodiment of the present invention;

[0049] Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0050] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0052] like Figure 1 As shown, Figure 1 This is a schematic diagram of the steps of a construction engineering construction process safety management method according to one embodiment of the present invention;

[0053] In one embodiment of the present invention, a construction process safety management method is provided, comprising the following steps:

[0054] Step S1: Acquire construction site data of a construction project, and construct a three-dimensional model of the construction site based on the construction site data.

[0055] Specifically, drones equipped with high-definition cameras or lidar equipment are used to collect comprehensive, high-precision data from the construction site, generating point cloud data or high-definition images. Various sensors, such as GPS receivers, motion sensors, and environmental monitoring sensors, are deployed at the construction site to collect real-time information such as geographic location, object movement, and environmental parameters. Architectural engineering drawings from the design phase, including structural, architectural, utilities, heating, and ventilation systems, are integrated to extract necessary 2D plan and cross-sectional information. Detailed records and photographs are taken of human activity, material storage, temporary facilities, and mechanical equipment at the construction site. Point cloud data collected by drones or lidar is denoised, classified, and segmented to extract point cloud models of the topography and building structure. Computer vision technology is used to generate detailed texture maps and geometric models through multi-angle image stitching and 3D reconstruction algorithms. Based on CAD drawings, 2D design information is converted into 3D model elements, including basic components such as beams, columns, slabs, and walls. The point cloud model, image model, and CAD model are integrated to form a comprehensive 3D model that includes spatial position, shape, size, and material properties. Based on actual site conditions, the initially constructed 3D model is supplemented and corrected for details to ensure consistency with the construction site. Relevant information, such as material properties, construction progress, and safety warning areas, is overlaid on the 3D model to achieve visual correlation. As construction progresses, on-site data is continuously updated to keep the 3D model synchronized with the construction site, facilitating tracking and management of the construction process.

[0056] The above steps can achieve the following technical benefits: Achieve 3D visualization of the construction site, enabling managers to intuitively understand and control the overall situation. Improve decision-making efficiency in construction planning, material scheduling, and construction organization. Perform collision detection and spatial conflict analysis to proactively prevent design flaws and technical errors during construction. Effectively manage and monitor construction safety, including tracking dynamic objects and providing risk warnings. Support digital twin applications, providing a reliable basis for construction process simulation and emergency plan development. Promote collaborative work on construction projects and strengthen information sharing and communication among all parties involved.

[0057] Step S2: Classify the objects at the construction site within the three-dimensional model to obtain dynamic objects and static objects, and track the dynamic objects using GPS technology to obtain trajectories of the dynamic objects.

[0058] Specifically, all physical objects in the 3D model are first identified and labeled, distinguishing between static objects (e.g., building structures, fixed positions of mechanical equipment) and dynamic objects (e.g., construction vehicles, cranes, worker paths) whose positions change over time. Based on the characteristics of each object in the model, labels are assigned, such as "static" or "dynamic," and may be further refined to specific types (e.g., tower cranes, excavators, transport vehicles, etc.). Ensure that all dynamic objects to be tracked are equipped with GPS positioning devices that transmit location information in real time or periodically. Establish a data interface to receive real-time location data from GPS devices and convert it into location information in a unified geographic coordinate system. This received GPS location data is matched with the corresponding dynamic objects in the 3D model, mapping the real-world location information to the virtual model. Based on the continuously received GPS coordinates of the dynamic objects, algorithms such as interpolation are used to calculate their precise movement trajectories in 3D space. This calculated trajectory data is loaded into the 3D model in real time, and the models of the dynamic objects are dynamically updated and displayed along their actual trajectories.

[0059] The above steps achieve the following technical benefits: By classifying and tracking dynamic and static objects on the construction site, managers can clearly understand the real-time distribution and usage of site resources, improving resource allocation efficiency. Real-time monitoring of the position and movement of dynamic objects helps promptly detect abnormal behavior or potential safety hazards, preventing accidents. Accurate dynamic trajectory data provides a scientific basis for decision-making in construction scheduling, equipment dispatching, and staffing. Visually displaying dynamic trajectories on a 3D model helps all participants quickly understand construction progress and dynamic changes, enhancing collaborative work efficiency.

[0060] Step S3: dynamically updating the three-dimensional model based on the trajectory of the dynamic object to obtain an updated three-dimensional model.

[0061] Specifically, the acquired GPS trajectory data of dynamic objects is integrated and preprocessed to ensure that the data is complete and continuous, suitable for real-time updating of the three-dimensional model. The model components corresponding to the dynamic objects are found in the three-dimensional model, and an association relationship with the actual object is established so that the model components can be dynamically adjusted according to the actual trajectory data. The integrated trajectory data is used to drive the dynamic objects in the three-dimensional model to perform motion simulation and create corresponding time series animations. Keyframe animation technology or a physics engine can be used to simulate the real motion state, so that the dynamic objects move smoothly along the actual trajectory. The latest trajectory data is read in real time, the position and posture of the dynamic objects in the model are instantly updated, and real-time rendering is performed through the three-dimensional rendering engine. Ensure that the display of the three-dimensional model is highly consistent with the actual on-site situation, including speed, direction, and position information. Design an easy-to-use user interface that allows users to choose to view the trajectory of dynamic objects and the corresponding three-dimensional model status in different time periods, or fast-forward and replay trajectory animations.

[0062] The above steps achieve the following technical benefits: Dynamically updating the 3D model accurately reflects the real-time construction site scene, helping managers effectively understand the real-time status and range of dynamic objects. It provides near-real-time feedback on safety, scheduling, and other issues, facilitating timely decision-making and response. By analyzing dynamic trajectories, it is possible to predict the likely future locations of dynamic objects, enabling advance planning and optimization of the construction process. Communication efficiency among project parties is improved, as the 3D dynamic model provides a more intuitive and detailed visual representation, facilitating the understanding and exchange of complex information.

[0063] Step S4: Conduct a risk analysis on the construction process based on the updated three-dimensional model to obtain risk analysis results, and obtain safety management measures based on the risk analysis results; wherein, the safety management measures include safety protection measures for on-site personnel, safety management of mechanical equipment, and dynamic risk control during the construction process.

[0064] Specifically, using updated 3D models, they systematically identify potential risk points during construction, such as elevated work areas, intersecting work zones, and heavy machinery operating spaces. By integrating the dynamic object trajectories within the model, they analyze potential safety hazards. Based on the construction scenario depicted in the 3D model, they utilize relevant algorithms or evaluation systems to quantitatively assess identified risk points, taking into account factors such as, but not limited to, personnel density, machinery movement paths, and structural stability. Dynamic simulations of the construction process are conducted using the updated 3D model to predict risk trends and potential consequences under various construction conditions, further refining the risk level and impact scope. Based on the risk analysis results, appropriate personnel safety protection plans are designed and implemented for specific risk points, such as adding safety isolation zones, arranging dedicated supervisors, and strengthening the use of personal protective equipment. Based on the operational trajectories and working environments of the machinery and equipment in the model, equipment safety operating procedures, maintenance plans, and emergency shutdown protocols are developed. The 3D model is used to monitor construction progress and site status changes in real time, allowing for dynamic adjustments to the construction plan to mitigate risk, such as adjusting the work sequence and restricting high-risk operations during specific periods.

[0065] The above steps achieve the following technical benefits: Through in-depth analysis of the 3D model, potential risk sources can be more accurately identified, risk early warning capabilities can be improved, and the occurrence of accidents can be reduced. This enables three-dimensional and refined management of the construction site, enhancing control over multi-dimensional risk factors such as people, machines, materials, and the environment. The real-time updated 3D model makes risk management more timely and targeted, facilitating rapid response and flexible adjustment of management measures to ensure a safe and controllable construction process. This improves the scientific nature and transparency of construction safety management, promotes coordination and collaboration among all aspects, and effectively improves construction efficiency and project quality.

[0066] Step S5: Use digital twin technology to perform visual simulation of safety management measures to obtain simulation results.

[0067] Specifically, a digital twin model consistent with the actual construction site is created, with the updated 3D model as the core component of the twin. This model is connected to a real-time data interface to import dynamic data, including personnel, equipment, and the environment. Developed safety management measures (such as on-site personnel safety protection measures, machinery and equipment safety management regulations, and dynamic risk control strategies during construction) are digitally encoded and integrated into the digital twin model. Different simulation scenarios are set based on the construction progress and potential situations, such as sudden safety incidents and construction under unusual weather conditions, to ensure that the simulation covers a wide range of possible scenarios. The digital twin system's simulation function is activated, and the encoded safety management measures are run in the simulated environment to observe their effectiveness and execution in different scenarios. The simulation process and results are displayed through a 3D visualization interface, including but not limited to evacuation routes, equipment operation demonstrations, and the process by which risk control measures take effect. The simulation results are analyzed to extract key indicators and data, such as response time, risk mitigation efficiency, and resource scheduling rationality.

[0068] The above steps achieve the following technical benefits: By simulating the effectiveness of safety management measures in different scenarios, the effectiveness of measures and potential issues can be evaluated before actual implementation, leading to optimization and improvement. Visual simulation provides management with an intuitive basis for decision-making, helping them understand the impact of various measures and develop more scientific and reasonable safety management systems. Testing and adjusting safety management measures in a simulated environment reduces costs caused by practical errors and improves overall operational efficiency at the construction site. Simulation results can also be used as material for employee safety education and training, helping workers familiarize themselves with and master the correct implementation of safety management measures.

[0069] Step S6, obtain safety management criteria, evaluate the simulation results based on the safety management criteria, and obtain simulation evaluation results. If the simulation evaluation results do not meet the safety management criteria, iteratively optimize the safety management measures until the simulation evaluation results meet the safety management criteria.

[0070] Specifically, relevant national standards, industry specifications, and internal company safety regulations and systems are collected to clarify specific safety management requirements and objectives, which are then converted into a quantitative or qualitative evaluation indicator system. Simulation results generated using digital twin technology are compared and analyzed with pre-determined safety management guidelines. The simulation results are individually verified to ensure compliance with the guidelines using the established evaluation indicators. Data analysis and model deduction are used to determine the degree to which the actual implementation of safety management measures during the simulation matches the intended objectives. If the simulation evaluation results indicate that certain safety management measures fail to meet the guidelines, in-depth research is conducted to identify the causes and bottlenecks. Based on the feedback from the simulation evaluation results, existing safety management measures are revised and improved. This may include adding new safety precautions, adjusting the enforcement of existing measures, and optimizing construction processes to reduce risks. The updated and optimized measures are then embedded in the digital twin model for a new round of simulations. This iterative process of simulation, evaluation, and optimization is repeated until the simulation evaluation results demonstrate that all safety management measures meet the established safety management guidelines.

[0071] The above steps achieve the following technical benefits: Through repeated simulation and optimization, safety management measures are closely integrated with the actual construction process, ensuring precise adaptation and effective implementation. A closed-loop optimization mechanism for safety management measures has been established, contributing to the continuous improvement of safety management during construction. Through simulation experiments, problems can be identified and corrected before actual construction begins, significantly reducing the risk of accidents caused by improper safety management. Through continuous iterative optimization, human and material resources are rationally allocated and utilized, improving construction efficiency and reducing costs.

[0072] In a specific embodiment, the construction site data includes construction terrain data, building layout data, dynamic object data, historical accident data, and public facilities and infrastructure data.

[0073] In a specific embodiment, constructing a three-dimensional model of the construction site based on the construction site data includes:

[0074] Taking aerial photos of the construction site using a drone to obtain construction site images, extracting data from the construction site images, and obtaining construction site data of the construction project;

[0075] Converting the construction site data into a standard data set for a three-dimensional modeling program;

[0076] Using 3D modeling software, constructing a 3D model of the construction site based on the standard data set to obtain a preliminary 3D model of the construction site;

[0077] Performing component identification and division of the building object in the preliminary three-dimensional model using a preset component identification algorithm to obtain building element subdivisions; wherein the building elements include building envelope components, electromechanical pipeline components, and site facility components;

[0078] Obtaining building attributes by consulting on-site construction logs, and assigning attributes to the building element segments based on the building attributes using an attribute annotation algorithm to obtain building element segments with attributes; wherein the attributes include safety attributes, environmental attributes, life cycle attributes, construction attributes, and structural attributes;

[0079] Based on the subdivision of the building elements with attributes, a three-dimensional model of the construction site is obtained.

[0080] Specifically, first, drones equipped with high-definition cameras are used to conduct comprehensive aerial photography of the construction site, acquiring high-resolution images. The drone images are then processed, using image recognition and machine learning techniques to extract key construction site features, such as building outlines, roads, and facility layouts. This information is then converted into construction site data for the construction project. Data format conversion: The extracted raw construction site data is converted to a format suitable for 3D modeling programs, forming a standard dataset that includes but is not limited to coordinate information, shape descriptions, and dimensional parameters. 3D modeling: Professional 3D modeling software, such as Revit, SketchUp, or BIM-related software, is used to import the standard dataset and automatically or manually construct a preliminary 3D model of the construction site. Component identification and segmentation: A pre-set component recognition algorithm is used to automatically decompose the building objects in the preliminary 3D model, identifying building envelope components (such as walls, roofs, doors, and windows), electromechanical pipeline components (such as electrical wiring and water supply and drainage pipes), and site facility components (such as scaffolding, temporary roads, and safety fencing). Review construction logs: Review on-site logs from the construction process to obtain specific attribute information for building elements, including but not limited to safety requirements, environmental impacts, service life, construction techniques, structural bearing capacity, etc. Using attribute labeling algorithms, the acquired building attribute information is directly attached to the corresponding building element segments. This means that each segmented building element is assigned attributes, making it a 3D model element with rich information. Integrate attribute information: Integrate the attributed building element segments into the entire 3D model to form a 3D model of the construction site that includes detailed attribute information. This model not only reflects the spatial layout, but also reflects the attribute characteristics of each part, providing rich data support for construction management.

[0081] The above steps achieve the following technical benefits: By constructing a 3D model with attributes, refined construction site management can be achieved, improving construction quality and safety management. This provides detailed data reference for decision-making in construction planning, material procurement, schedule scheduling, risk warnings, and environmental monitoring. This facilitates efficient information transfer and collaboration between different disciplines and departments, reducing misunderstandings and conflicts. 3D visualization technology allows non-professionals to quickly understand complex construction situations, improving communication efficiency among all project parties. The attribute information contained in the model can be used to simulate and predict the construction process, identifying potential problems in advance and optimizing construction plans.

[0082] In a specific embodiment, objects at the construction site within the three-dimensional model are classified to obtain dynamic objects and static objects, and the dynamic objects are tracked using GPS technology to obtain trajectories of the dynamic objects, including:

[0083] Identifying entity objects within the three-dimensional model to obtain identified objects, and classifying the identified objects to obtain dynamic objects and static objects;

[0084] The real-time dynamic location data of the dynamic object is collected through a preset GPS positioning system to obtain a continuous geographical location information stream;

[0085] The Kalman filter algorithm is used to calculate the trajectory of the continuous geographic location information stream to obtain the precise motion trajectory of the dynamic object in the three-dimensional model; wherein the precise motion trajectory serves as the trajectory of the dynamic object.

[0086] Specifically, all physical objects within the 3D model are identified and marked using the intelligent recognition capabilities built into or integrated into the 3D modeling software. Identified physical objects are then classified, based on whether they are moving or changing state, into dynamic objects (such as construction vehicles, machinery, and personnel) and static objects (such as completed building structures and fixed facilities). Dynamic objects are equipped with GPS positioning devices to acquire and record their location data in real time. Using a pre-configured GPS positioning system, the latitude and longitude coordinates of dynamic objects, along with their timestamps, are continuously collected, forming a continuous stream of geolocation information. This continuous stream of geolocation information is processed using a Kalman filter algorithm, which combines historical location information with current measurement errors to optimally estimate the true location of dynamic objects. Through iterative calculation and optimization of this position information, the precise motion trajectory of the dynamic objects is ultimately determined within the 3D model. This trajectory reflects the spatial distribution and movement path of the dynamic objects over time at the actual construction site.

[0087] The above steps achieve the following technical benefits: Through GPS tracking and the Kalman filter algorithm, the behavior patterns and operational status of dynamic objects on the construction site can be understood in real time, enabling managers to adjust plans and dispatch resources in a timely manner. Accurate calculation of dynamic trajectories not only facilitates post-event analysis but also visually displays the history and current status of dynamic object activity within a 3D model, enhancing understanding and control of complex construction sites. For potential safety hazards, such as when a dynamic object approaches a dangerous area, early warnings can be issued based on its trajectory, allowing preventive measures to avert accidents.

[0088] In a specific embodiment, identifying a physical object in the three-dimensional model to obtain an identified object includes:

[0089] Inputting the entity object in the three-dimensional model into a preset recognition algorithm for object recognition; wherein the recognition algorithm includes a C2f module, an SPDConv module, a GsConv module, a neck network and a YOLOv5 module;

[0090] Extracting object features from the entity objects in the three-dimensional model using the C2f module to obtain extracted object features;

[0091] Performing spatial depth conversion on the extracted object features through the SPDConv module to obtain spatial depth enhanced features;

[0092] Performing lightweight convolution processing on the spatial depth enhancement feature through the GsConv module to obtain optimized features;

[0093] Performing multi-level feature fusion on the optimized features through the neck network to obtain fused features of different scales;

[0094] The YOLOv5 module is used to identify the fused features to obtain an identified object; wherein the identified object includes dynamic objects and static objects, the dynamic objects include construction vehicles, machinery and personnel, and the static objects include completed building structures and fixed facilities.

[0095] Specifically, YOLOv5 serves as the basic framework for the recognition algorithm. The C2f module is embedded in the feature extraction phase of the YOLOv5 backbone network. This module extracts object features from physical objects within the 3D model to avoid feature information loss. The SPDConv module is introduced after the C2f phase of the backbone network. Composed of a spatial-to-depth layer and a non-strided convolutional layer, SPDConv converts spatial features to channel dimensions while preserving global spatial feature information. The GsConv module is combined with some standard convolutions in the neck network to reduce the model's parameter count and computational complexity while maintaining the accuracy and robustness of disease recognition. GsConv is a lightweight convolutional layer design that combines dense and sparse convolutions. By introducing global sparsity, it maintains connections between channels while performing convolution calculations only on sparse locations. The neck network further fuses these features, and the fused result is then fed into the YOLOv5 module for recognition. In a specific embodiment, the process of identifying physical objects within a three-dimensional model can be broken down into the following key steps, using YOLOv5 as the basic framework combined with the C2f, SPDConv, GsConv modules, and the neck network for identification: Object feature extraction: First, the C2f module is used to extract features of the physical objects within the three-dimensional model. The C2f module uses feature branching processing and a fusion strategy to enhance the model's perception of detailed features and semantic features at different scales, thereby avoiding the loss of feature information. The output of this step is the extracted object features. Spatial-depth conversion: Next, the SPDConv module performs spatial-depth conversion on the object features extracted by the C2f module. SPDConv consists of a spatial-to-depth layer and a non-strided convolutional layer, which converts features in the spatial dimension to the channel dimension while preserving global spatial feature information. This step enhances the expressive power of the features, and the output is spatial-depth-enhanced features. Lightweight convolution processing: The GsConv module then performs lightweight convolution processing on the spatial-depth-enhanced features output by SPDConv. As a lightweight convolution design, GsConv introduces global sparsity. While maintaining connections between channels, it only performs convolution calculations on sparse locations, reducing the amount of calculation and parameters while maintaining recognition accuracy and robustness. The output of this step is the optimized feature. Multi-level feature fusion: The neck network further performs multi-level feature fusion on the optimized features of the GsConv module. This process integrates feature maps at different levels through feature fusion techniques such as upsampling and splicing, captures feature information from coarse-grained to fine-grained, and provides comprehensive multi-scale feature support for the final target recognition. Target recognition: Finally, the YOLOv5 module receives the multi-scale features fused by the neck network and performs target recognition. As an efficient target detection framework, YOLOv5 can identify and locate physical objects in three-dimensional models.Recognized objects are categorized into two types: dynamic objects, including construction vehicles, machinery, and personnel; these objects move or change within the 3D model. Static objects, including completed building structures and fixed facilities, remain stationary within the 3D model. Through this series of steps, the recognition algorithm effectively identifies different types of physical objects within the 3D model, providing accurate object information for further application processing.

[0096] In a specific embodiment, a risk analysis is performed on the construction process based on the updated three-dimensional model to obtain risk analysis results, and safety management measures are obtained based on the risk analysis results, including:

[0097] Conduct in-depth analysis of the updated 3D model using the Fault Tree Analysis method to identify potential risk sources;

[0098] Performing a risk analysis on the construction process based on the potential risk sources to obtain a risk analysis result; wherein the risk analysis result is a list of risks that may be faced during the construction process; the risk list includes multiple risk events and risk coefficients corresponding to the risk events;

[0099] Based on the risk coefficient, perform qualitative and quantitative risk assessment on each risk event to obtain a corresponding risk level;

[0100] Determine targeted safety management measures based on the corresponding risk level.

[0101] Specifically, this solution involves a risk analysis of the construction process, based on an updated 3D model, to ultimately develop safety management measures. This process involves multiple interconnected and logically cohesive steps. These steps are sequentially implemented, ultimately generating a safety management strategy that effectively addresses potential risks during construction. First, based on the updated 3D model, the solution utilizes an in-depth Fault Tree Analysis (FTTA) approach. FTA is a systematic analytical tool that identifies and analyzes potential risk sources by constructing a logical tree structure. The updated 3D model provides detailed spatial and structural information about the construction site, enabling the FTA to more accurately capture potential risk sources during construction. These risk sources may include structural weaknesses, improper equipment operation, material degradation, and changes in environmental conditions. Through in-depth analysis, the FTA effectively identifies and categorizes these risk sources, laying the foundation for subsequent risk analysis. After identifying potential risk sources, a risk analysis of the construction process is conducted based on these risk sources. The goal of this risk analysis is to generate comprehensive risk analysis results that not only list the various risks that may be encountered during construction but also, through detailed analysis, derive the corresponding risk coefficients for each risk event. The risk analysis results are essentially a list of multiple risk events and their corresponding risk factors. Each risk event describes a potential risk scenario, such as equipment failure, worker injury, or environmental pollution. The risk factor quantifies the likelihood and severity of these risk events. These factors help us understand which risk events require the most attention during construction. After obtaining the risk analysis results, the next step is to conduct qualitative and quantitative risk assessments for each risk event based on these risk factors. Qualitative assessments primarily analyze the nature of the risk event through experience and expert judgment, such as determining whether a risk event is sudden or cumulative. Quantitative assessments utilize mathematical models and statistical methods to quantify the probability of a risk event and the potential losses it could cause. By combining qualitative and quantitative assessments, each risk event is ultimately assigned a risk level. This risk level not only reflects the potential harm of the risk event but also provides clear guidance for the development of subsequent safety management measures. Finally, targeted safety management measures are determined based on these risk levels. This process is a key component of the overall plan and the core step in the final implementation of risk management. Based on different risk levels, safety management measures may include strengthening safety training, optimizing construction processes, adding monitoring equipment, and conducting regular inspections. These measures aim to reduce the probability of various risks occurring during the construction process, or minimize their impact if risks do occur.For high-risk events, safety management measures are often more stringent and complex, requiring, for example, multiple layers of protection, real-time monitoring, and the preparation of emergency plans. For low-risk events, management measures may be more flexible and simple, primarily focusing on prevention. The entire plan systematizes and structures the complex risk management process through multiple, interconnected steps. Each step is closely centered around the updated three-dimensional model, from preliminary risk source identification to detailed risk event analysis and ultimately the formulation of safety management measures, ensuring that all risks that may be encountered during the construction process are fully assessed and managed. This systematic risk management approach not only improves the reliability of construction safety but also provides clear guidance for actual operations, ultimately ensuring the smooth progress of the construction project and the safety of construction workers.

[0102] The above steps achieve the following technical benefits: Using 3D models and fault tree analysis, potential risk points can be more accurately identified, enabling more targeted risk management. Anticipating potential risk events helps mitigate unexpected incidents, reducing project costs and delays. Safety management measures based on risk levels help rationally allocate safety resources, prioritizing the most pressing and significant safety hazards. Through scientific risk assessment and control measures, the overall safety of the construction process can be effectively improved, protecting workers' lives and project assets.

[0103] In a specific embodiment, obtaining a safety management criterion, evaluating the simulation result based on the safety management criterion to obtain a simulation evaluation result, and if the simulation evaluation result does not meet the safety management criterion, iteratively optimizing the safety management measures until the simulation evaluation result meets the safety management criterion, includes:

[0104] Obtaining construction safety regulations, and extracting information from the construction safety regulations to obtain safety management guidelines;

[0105] Using a fuzzy comprehensive evaluation algorithm, based on the safety management criteria, the simulation results are qualitatively and quantitatively evaluated to obtain a simulation evaluation result;

[0106] Comparing the simulation assessment results with the safety management criteria to obtain comparative results, wherein the comparative results include a safety performance index;

[0107] Using the particle swarm optimization algorithm, if the comparison results do not meet the safety management criteria, the existing safety management measures are optimized and adjusted until the simulation evaluation results meet the safety management criteria.

[0108] Specifically, first, a comprehensive collection of relevant national and local construction safety regulations and documents, including but not limited to safety production laws and regulations, industry standards, and guidelines, is conducted. The collected construction safety regulations are thoroughly reviewed and information extracted, resulting in a summary of specific safety management guidelines applicable to the current construction project. These guidelines will serve as the standard for subsequent simulation evaluations. A fuzzy comprehensive evaluation algorithm is used to transform the extracted safety management guidelines into an operational evaluation index system. The construction process simulation results based on the updated 3D model are input into the fuzzy comprehensive evaluation model. A qualitative and quantitative assessment of each safety factor in the simulation is performed, resulting in the calculated simulation evaluation results, including a safety performance index (SPI) that reflects the overall safety level. The simulation evaluation results are then compared with the safety management guidelines to determine their degree of compliance, resulting in a comparison result. If the SPI does not meet the pre-defined safety management guidelines, the current construction plan presents a safety risk. For cases where the comparison results indicate non-compliance with the safety management guidelines, a particle swarm optimization algorithm is used to iteratively optimize the safety management measures. The optimization goal is to improve the SPI so that the simulation evaluation results meet the safety management guidelines. The particle swarm optimization algorithm simulates the intelligent behavior of the group, searches for the optimal solution through continuous iteration, and gradually adjusts the existing safety management measures, including but not limited to improving construction technology, strengthening the configuration of safety facilities, and improving emergency plans.

[0109] The above steps achieve the following technical benefits: They ensure that construction safety management is directly aligned with national and industry regulations, enhancing compliance and authority. A fuzzy comprehensive evaluation algorithm enables a multi-dimensional, multi-level qualitative and quantitative assessment of the safety status of complex construction processes. The particle swarm optimization algorithm quickly and effectively identifies the optimal combination of safety management measures, enabling real-time optimization and adjustment of areas that fail to meet safety requirements. The entire process forms a feedback loop based on simulation and evaluation results, effectively promoting the continuous improvement and enhancement of construction safety management.

[0110] In a specific embodiment, a fuzzy comprehensive evaluation algorithm is used to perform qualitative and quantitative evaluation on the simulation results based on the safety management criteria to obtain simulation evaluation results, including:

[0111] Performing information entropy increase processing on the safety management criteria to obtain preliminary safety management criteria;

[0112] Performing a multi-dimensional trade-off spatial analysis on the preliminary safety management criteria to obtain a spatial analysis result; wherein the spatial analysis result is used to ensure whether the preliminary safety management criteria are applicable to the current construction environment;

[0113] If applicable, a preset fuzzy comprehensive evaluation algorithm is used to perform multi-scale fusion processing on the preliminary safety management criteria to generate a comprehensive safety management criteria set;

[0114] Performing fuzzy interpolation evaluation on the simulation results based on the comprehensive safety management criteria set to obtain preliminary qualitative evaluation results;

[0115] Conduct nonlinear fuzzy deep learning framework analysis on the preliminary qualitative assessment results to generate quantitative safety performance indicators;

[0116] Based on the fuzzy Gaussian Bayesian network, dual mapping is performed on the quantitative safety performance indicators to obtain simulation evaluation results;

[0117] Check whether there are any simulation assessment results that do not meet the preliminary safety management criteria in the simulation assessment results. If so, generalize and decompose the simulation assessment results that do not meet the preliminary safety management criteria and make optimization adjustments to gradually revise the safety management measures until the simulation assessment results that do not meet the preliminary safety management criteria meet the safety management criteria.

[0118] Specifically, the core of this step is to utilize a fuzzy comprehensive evaluation algorithm to qualitatively and quantitatively evaluate simulation results, ensuring that the application of safety management guidelines meets the needs of the current construction environment. The implementation of this approach involves multiple key steps, which are sequentially interconnected and logically progressed, ultimately generating a comprehensive and accurate safety assessment. First, at the outset, we perform information entropy increase on the safety management guidelines. The purpose of this information entropy increase is to increase the complexity and adaptability of the preliminary safety management guidelines by introducing and considering more uncertainties. This process allows us to capture more subtle fluctuations in variables and potential risks when addressing safety management issues, thereby generating more comprehensive preliminary safety management guidelines. These preliminary guidelines can be considered the cornerstone of the safety management system and will serve as the primary basis for evaluating simulation results in subsequent steps. Next, these preliminary safety management guidelines undergo a multidimensional trade-off space analysis. This multidimensional trade-off space analysis examines the trade-offs between different safety factors to ensure that these guidelines are applicable to the current construction environment. This process not only considers the relative importance of different factors but also identifies the applicability of the preliminary safety management guidelines through a multidimensional assessment. The results of the spatial analysis will determine whether these preliminary guidelines accurately reflect the real-world needs of the current construction environment. If the analysis indicates that the guidelines are applicable, we proceed to the next step; if not, the guidelines will need to be adjusted or redesigned to ensure their applicability. Once the preliminary safety management guidelines are confirmed to be applicable to the current environment, we perform a multi-scale fusion of these guidelines using a pre-defined fuzzy comprehensive evaluation algorithm. Multi-scale fusion allows us to integrate information from various sources at different scales, expanding the preliminary safety management guidelines into a more comprehensive set of safety management guidelines. The key to this step lies in the fusion process, which integrates information from multiple levels and scales into a holistic safety management framework, ensuring that the comprehensive set of safety management guidelines covers all potential safety risks and countermeasures. Based on this comprehensive set of safety management guidelines, we perform a fuzzy interpolation evaluation on the simulation results. Fuzzy interpolation evaluation is a crucial qualitative analysis process. By mapping the simulation results onto the comprehensive set of safety management guidelines, we generate preliminary qualitative assessment results. This process not only helps identify potential issues in the simulation results but also preliminarily screens risk factors that do or do not meet the safety management guidelines. The preliminary qualitative assessment results are then analyzed using a nonlinear fuzzy deep learning framework. This step introduces deep learning technology, processing complex safety performance data in a nonlinear manner to ultimately generate quantitative safety performance indicators. This nonlinear fuzzy deep learning framework is capable of processing large-scale, complex safety data, extracting the key indicators most important for safety management and quantifying these indicators into specific data for further analysis. These quantitative indicators provide a solid foundation for subsequent analysis and decision-making.Based on a fuzzy Gaussian Bayesian network, we perform a dual mapping on these quantitative safety performance indicators. This dual mapping process matches the quantitative indicators with safety management guidelines to generate simulation evaluation results. This process ensures that the final simulation evaluation results not only reflect the findings from the qualitative analysis but also accurately quantify these findings into specific performance indicators. This allows the simulation evaluation results to more intuitively and accurately reflect the actual safety conditions during construction. After generating the simulation evaluation results, we examine them, paying particular attention to any deviations from the preliminary safety management guidelines. If any non-compliant evaluation results are found, they are corrected through generalized decomposition and optimization adjustments. The generalized decomposition process breaks down complex problems into smaller, controllable units and then gradually optimizes these units to ensure that all evaluation results ultimately meet the requirements of the preliminary safety management guidelines. This gradual adjustment process ensures the effectiveness and adaptability of safety management measures, thereby achieving the ultimate safety goal. In summary, this solution uses a multi-step analysis and processing approach, utilizing a fuzzy comprehensive evaluation algorithm to develop from preliminary safety management guidelines to the final comprehensive safety management strategy, ensuring that each step undergoes rigorous analysis and optimization. The resulting simulation evaluation results are not only comprehensive and accurate, but can also effectively guide safety management work during the construction process.

[0119] In a specific embodiment, digital twin technology is used to perform visual simulation of safety management measures to obtain simulation results, including:

[0120] Use digital twin technology to digitize safety management measures and obtain digital safety management measures;

[0121] The digital safety management measures are virtually embedded in the three-dimensional model to obtain a simulated deployment of the safety measures; wherein the simulated deployment of the safety measures is a simulated environment configuration for implementing the safety management measures during the construction process;

[0122] Continuous dynamic simulation is performed on the simulation deployment of the safety measures to obtain dynamic simulation results.

[0123] Specifically, each established safety management measure is first digitally described in detail, including its content, implementation process, scope of impact, and timelines. This information is then converted into rules and instructions that can be implemented in a digital environment, forming a digital safety management plan. The digitally processed safety management measures are then deeply integrated with a 3D construction site model. This involves deploying these measures in a virtual environment, configuring their functionality, and demonstrating their effectiveness, thereby creating a simulated safety measure deployment. For example, the specific location and scope of safety fences, warning signs, emergency passages, and emergency equipment can be virtually represented. Leveraging the continuous dynamic simulation capabilities of digital twin technology, the simulated safety measure deployment is dynamically simulated in real time or over a predetermined period. During the simulation, various construction activities, equipment operation, and personnel flow are simulated to verify the safety measures' effectiveness and ability to respond to emergencies at different construction stages and under various conditions.

[0124] The above steps can achieve the following technical benefits: Digital twin technology enables visual simulation of safety management measures, enabling refined management of the construction process and helping to identify and address security vulnerabilities that might otherwise be overlooked. The simulation provides real-time feedback on the effectiveness of safety measures, helping managers anticipate potential risks and quickly adjust and optimize measures to ensure construction safety. Virtual simulation eliminates the need for repeated trial and error adjustments during actual construction, significantly reducing trial and error costs and potential safety risks. Visual simulation of safety measures can also be used for training and education, helping construction workers more intuitively understand and adhere to safety regulations, thereby enhancing safety awareness and execution across the workforce.

[0125] The above describes the construction process safety management method of the construction project in the embodiment of the present invention. The following describes the construction process safety management device of the construction project in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a construction engineering construction process safety management device includes:

[0126] An acquisition module 21 is configured to acquire construction site data of a construction project and construct a three-dimensional model of the construction site based on the construction site data;

[0127] a classification module 22 for classifying objects at the construction site within the three-dimensional model into dynamic objects and static objects, and tracking the dynamic objects using GPS technology to obtain trajectories of the dynamic objects;

[0128] An updating module 23, configured to dynamically update the three-dimensional model based on the trajectory of the dynamic object to obtain an updated three-dimensional model;

[0129] An analysis module 24 is configured to perform a risk analysis on the construction process based on the updated three-dimensional model, obtain risk analysis results, and obtain safety management measures based on the risk analysis results;

[0130] The simulation module 25 is used to use digital twin technology to perform visual simulation of safety management measures and obtain simulation results;

[0131] The evaluation module 26 is used to obtain safety management criteria, evaluate the simulation results based on the safety management criteria, and obtain simulation evaluation results. If the simulation evaluation results do not meet the safety management criteria, the safety management measures are iteratively optimized until the simulation evaluation results meet the safety management criteria.

[0132] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0133] Reference Figure 3 The embodiment of the present invention further provides a computer device, the internal structure of which can be as follows Figure 3 As shown. The computer device includes a processor, memory, display screen, input device system, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0134] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0135] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0136] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0137] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0138] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A construction process safety management method, characterized by: The following steps are involved: Acquiring construction site data of a building project, and constructing a three-dimensional model of the construction site based on the construction site data; Classifying objects at the construction site within the three-dimensional model to obtain dynamic objects and static objects, and tracking the dynamic objects using GPS technology to obtain trajectories of the dynamic objects; Dynamically updating the three-dimensional model based on the trajectory of the dynamic object to obtain an updated three-dimensional model; Conducting a risk analysis of the construction process based on the updated three-dimensional model to obtain risk analysis results, and obtaining safety management measures based on the risk analysis results; wherein the safety management measures include safety protection measures for on-site personnel, safety management of machinery and equipment, and dynamic risk control during the construction process; Use digital twin technology to conduct visual simulation of safety management measures and obtain simulation results; Obtaining a safety management criterion, evaluating the simulation result based on the safety management criterion to obtain a simulation evaluation result, and if the simulation evaluation result does not meet the safety management criterion, iteratively optimizing the safety management measures until the simulation evaluation result meets the safety management criterion; Identifying the entity object in the three-dimensional model to obtain the identified object includes: Inputting the entity object in the three-dimensional model into a preset recognition algorithm for object recognition; wherein the recognition algorithm includes a C2f module, an SPDConv module, a GsConv module, a neck network and a YOLOv5 module; Extracting object features from the entity objects in the three-dimensional model using the C2f module to obtain extracted object features; Performing spatial depth conversion on the extracted object features through the SPDConv module to obtain spatial depth enhanced features; Performing lightweight convolution processing on the spatial depth enhancement feature through the GsConv module to obtain optimized features; Performing multi-level feature fusion on the optimized features through the neck network to obtain fused features of different scales; The YOLOv5 module is used to identify the integrated features to obtain an identified object; wherein the identified object includes a dynamic object and a static object, the dynamic object includes construction vehicles, machinery and personnel, and the static object includes a completed building structure and fixed facilities; Obtaining a safety management criterion, evaluating the simulation result based on the safety management criterion to obtain a simulation evaluation result, and if the simulation evaluation result does not meet the safety management criterion, iteratively optimizing the safety management measures until the simulation evaluation result meets the safety management criterion, including: Obtaining construction safety regulations, and extracting information from the construction safety regulations to obtain safety management guidelines; Using a fuzzy comprehensive evaluation algorithm, based on the safety management criteria, the simulation results are qualitatively and quantitatively evaluated to obtain a simulation evaluation result; Comparing the simulation assessment results with the safety management criteria to obtain comparative results, wherein the comparative results include a safety performance index; Using the particle swarm optimization algorithm, if the comparison results do not meet the safety management criteria, the existing safety management measures are optimized and adjusted until the simulation evaluation results meet the safety management criteria; The simulation results are qualitatively and quantitatively evaluated based on the safety management criteria using a fuzzy comprehensive evaluation algorithm to obtain simulation evaluation results, including: Performing information entropy increase processing on the safety management criteria to obtain preliminary safety management criteria; Performing a multi-dimensional trade-off spatial analysis on the preliminary safety management criteria to obtain a spatial analysis result; wherein the spatial analysis result is used to ensure whether the preliminary safety management criteria are applicable to the current construction environment; If applicable, a preset fuzzy comprehensive evaluation algorithm is used to perform multi-scale fusion processing on the preliminary safety management criteria to generate a comprehensive safety management criteria set; Performing fuzzy interpolation evaluation on the simulation results based on the comprehensive safety management criteria set to obtain preliminary qualitative evaluation results; Conduct nonlinear fuzzy deep learning framework analysis on the preliminary qualitative assessment results to generate quantitative safety performance indicators; Based on the fuzzy Gaussian Bayesian network, dual mapping is performed on the quantitative safety performance indicators to obtain simulation evaluation results; Check whether there are any simulation assessment results that do not meet the preliminary safety management criteria in the simulation assessment results. If so, generalize and decompose the simulation assessment results that do not meet the preliminary safety management criteria and make optimization adjustments to gradually revise the safety management measures until the simulation assessment results that do not meet the preliminary safety management criteria meet the safety management criteria.

2. The construction process safety management method of a construction project according to claim 1 is characterized by: The construction site data includes construction terrain data, building layout data, dynamic object data, historical accident data, and public facilities and infrastructure data.

3. The construction process safety management method of a construction project according to claim 1 is characterized by: Constructing a three-dimensional model of the construction site based on the construction site data, including: Taking aerial photos of the construction site using a drone to obtain construction site images, extracting data from the construction site images, and obtaining construction site data of the construction project; Converting the construction site data into a standard data set for a three-dimensional modeling program; Using 3D modeling software, constructing a 3D model of the construction site based on the standard data set to obtain a preliminary 3D model of the construction site; Performing component identification and division of the building object in the preliminary three-dimensional model using a preset component identification algorithm to obtain building element subdivisions; wherein the building elements include building envelope components, electromechanical pipeline components, and site facility components; Obtaining building attributes by consulting on-site construction logs, and assigning attributes to the building element segments based on the building attributes using an attribute annotation algorithm to obtain building element segments with attributes; wherein the attributes include safety attributes, environmental attributes, life cycle attributes, construction attributes, and structural attributes; Based on the subdivision of the building elements with attributes, a three-dimensional model of the construction site is obtained.

4. The construction process safety management method of a construction project according to claim 1 is characterized by: Classifying objects at the construction site within the three-dimensional model to obtain dynamic objects and static objects, and tracking the dynamic objects using GPS technology to obtain trajectories of the dynamic objects, including: Identifying entity objects within the three-dimensional model to obtain identified objects, and classifying the identified objects to obtain dynamic objects and static objects; The real-time dynamic location data of the dynamic object is collected through a preset GPS positioning system to obtain a continuous geographical location information stream; The Kalman filter algorithm is used to calculate the trajectory of the continuous geographic location information stream to obtain the precise motion trajectory of the dynamic object in the three-dimensional model; wherein the precise motion trajectory serves as the trajectory of the dynamic object.

5. The construction process safety management method of a construction project according to claim 1 is characterized by: Conduct a risk analysis of the construction process based on the updated three-dimensional model to obtain risk analysis results, and obtain safety management measures based on the risk analysis results, including: Conduct in-depth analysis of the updated 3D model using the Fault Tree Analysis method to identify potential risk sources; Performing a risk analysis on the construction process based on the potential risk sources to obtain a risk analysis result; wherein the risk analysis result is a list of risks that may be faced during the construction process; the risk list includes multiple risk events and risk coefficients corresponding to the risk events; Based on the risk coefficient, perform qualitative and quantitative risk assessment on each risk event to obtain a corresponding risk level; Determine targeted safety management measures based on the corresponding risk level.

6. The construction process safety management method of a construction project according to claim 1 is characterized by: Using digital twin technology, we can conduct visual simulation of safety management measures and obtain simulation results, including: Use digital twin technology to digitize safety management measures and obtain digital safety management measures; The digital safety management measures are virtually embedded in the three-dimensional model to obtain a simulated deployment of the safety measures; wherein the simulated deployment of the safety measures is a simulated environment configuration for implementing the safety management measures during the construction process; Continuous dynamic simulation is performed on the simulation deployment of the safety measures to obtain dynamic simulation results.

7. A construction engineering construction process safety management device, characterized in that: include: An acquisition module, configured to acquire construction site data of a building project and construct a three-dimensional model of the construction site based on the construction site data; A classification module is used to classify objects at the construction site within the three-dimensional model into dynamic objects and static objects, and to track the dynamic objects using GPS technology to obtain trajectories of the dynamic objects; an updating module, configured to dynamically update the three-dimensional model based on the trajectory of the dynamic object to obtain an updated three-dimensional model; An analysis module, configured to perform risk analysis on the construction process based on the updated three-dimensional model, obtain risk analysis results, and obtain safety management measures based on the risk analysis results; The simulation module is used to use digital twin technology to perform visual simulation of safety management measures and obtain simulation results; an evaluation module, configured to obtain a safety management criterion, evaluate the simulation result based on the safety management criterion, and obtain a simulation evaluation result; if the simulation evaluation result does not meet the safety management criterion, iteratively optimize the safety management measures until the simulation evaluation result meets the safety management criterion; Identifying the entity object in the three-dimensional model to obtain the identified object includes: Inputting the entity object in the three-dimensional model into a preset recognition algorithm for object recognition; wherein the recognition algorithm includes a C2f module, an SPDConv module, a GsConv module, a neck network and a YOLOv5 module; Extracting object features from the entity objects in the three-dimensional model using the C2f module to obtain extracted object features; Performing spatial depth conversion on the extracted object features through the SPDConv module to obtain spatial depth enhanced features; Performing lightweight convolution processing on the spatial depth enhancement feature through the GsConv module to obtain optimized features; Performing multi-level feature fusion on the optimized features through the neck network to obtain fused features of different scales; The YOLOv5 module is used to identify the integrated features to obtain an identified object; wherein the identified object includes a dynamic object and a static object, the dynamic object includes construction vehicles, machinery and personnel, and the static object includes a completed building structure and fixed facilities; Obtaining a safety management criterion, evaluating the simulation result based on the safety management criterion to obtain a simulation evaluation result, and if the simulation evaluation result does not meet the safety management criterion, iteratively optimizing the safety management measures until the simulation evaluation result meets the safety management criterion, including: Obtaining construction safety regulations, and extracting information from the construction safety regulations to obtain safety management guidelines; Using a fuzzy comprehensive evaluation algorithm, based on the safety management criteria, the simulation results are qualitatively and quantitatively evaluated to obtain a simulation evaluation result; Comparing the simulation assessment results with the safety management criteria to obtain comparative results, wherein the comparative results include a safety performance index; Using the particle swarm optimization algorithm, if the comparison results do not meet the safety management criteria, the existing safety management measures are optimized and adjusted until the simulation evaluation results meet the safety management criteria; The simulation results are qualitatively and quantitatively evaluated based on the safety management criteria using a fuzzy comprehensive evaluation algorithm to obtain simulation evaluation results, including: Performing information entropy increase processing on the safety management criteria to obtain preliminary safety management criteria; Performing a multi-dimensional trade-off spatial analysis on the preliminary safety management criteria to obtain a spatial analysis result; wherein the spatial analysis result is used to ensure whether the preliminary safety management criteria are applicable to the current construction environment; If applicable, a preset fuzzy comprehensive evaluation algorithm is used to perform multi-scale fusion processing on the preliminary safety management criteria to generate a comprehensive safety management criteria set; Performing fuzzy interpolation evaluation on the simulation results based on the comprehensive safety management criteria set to obtain preliminary qualitative evaluation results; Conduct nonlinear fuzzy deep learning framework analysis on the preliminary qualitative assessment results to generate quantitative safety performance indicators; Based on the fuzzy Gaussian Bayesian network, dual mapping is performed on the quantitative safety performance indicators to obtain simulation evaluation results; Check whether there are any simulation assessment results that do not meet the preliminary safety management criteria in the simulation assessment results. If so, generalize and decompose the simulation assessment results that do not meet the preliminary safety management criteria and make optimization adjustments to gradually revise the safety management measures until the simulation assessment results that do not meet the preliminary safety management criteria meet the safety management criteria.

8. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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