A digital construction site twin application-based wearable wireless positioning device

Abstract

The application discloses a kind of based on digital construction twin application's wearable wireless positioning device, comprising: wearable positioning terminal, for using the high-precision positioning device integrated on wearable equipment and the sensor of preset type follow target user and carry out positioning work;Positioning auxiliary device, pre-arranged in multiple positions of construction site, for assisting wearable positioning terminal to carry out positioning;Digital positioning management platform, for according to the positioning data returned by positioning auxiliary device and wearable positioning terminal target user is positioned accurately and projected to the digital twin space of pre-construction;Cooperative management module, for according to the positioning result of target user in digital twin space, the state of construction equipment on construction site is adjusted in linkage, to reduce the probability of construction accident occurrence.The application can not only monitor the position state of staff on construction site in real time, but also significantly improve the efficiency of construction safety management.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of construction safety, and particularly relates to a wearable wireless positioning device based on a digital construction twin application. BACKGROUND

[0002] In modern construction engineering, construction safety management is the core link of guaranteeing personnel life safety and engineering progress. With the expansion of the construction site, the increase of construction equipment and personnel density, the traditional manual inspection or fixed monitoring equipment is difficult to realize real-time and accurate perception of the dynamic position of personnel and the running state of equipment. Especially in complex construction scenes (such as high-altitude operation, heavy machinery coordination area), safety accidents caused by positioning deviation or information lag occur frequently, and personnel-equipment-environment multi-dimensional collaborative monitoring needs to be realized through digital technology to improve the proactive warning capability of construction site safety management.

[0003] The current construction site positioning technology mainly relies on GPS, UWB (ultra-wideband) or Bluetooth beacon schemes. The signal of GPS attenuates seriously in indoor or dense shielding environment, and the positioning error can reach meters, which cannot meet the demand of centimeter-level precision in construction scenes; although UWB can provide high positioning accuracy, its hardware deployment cost is high, the coverage range is limited, and it lacks the ability to interact with the state of construction equipment. In addition, the existing positioning system is operated in isolation, only provides position data display, and is not deeply integrated with the digital twin model, resulting in that safety management still relies on manual experience judgment. For example, when personnel mistakenly enter the tower crane operation area, the traditional system can only trigger an alarm, but cannot real-time interact to adjust the tower crane operation path or emergency brake, and there is a risk of response lag. At the same time, the existing wearable device has single function, and it is difficult to comprehensively evaluate the safety state of personnel. These technical bottlenecks seriously restrict the intelligent level of construction safety management.

[0004] In view of the above problems, a wearable wireless positioning system integrating high-precision positioning, digital twin and equipment collaborative interaction is urgently needed. By constructing a digital twin space to map the construction site scene, and real-time associating personnel position and equipment running state, active prediction and automatic control of safety risks are realized. Combined with lightweight wearable terminal and distributed positioning auxiliary device, the deployment cost is reduced, and the positioning accuracy and response speed in complex environment are broken through, providing full-scene and dynamic technical support for construction safety management. SUMMARY

[0005] Therefore, the application provides a wearable wireless positioning device based on a digital construction twin application. Through the technical scheme, the position state of workers on the construction site can be monitored in real time, and the efficiency of construction safety management can be significantly improved.

[0006] To achieve the above purpose, the application provides the following technical scheme:

[0007] The application provides a wearable wireless positioning device based on a digital construction site twin application, comprising:

[0008] The wearable positioning terminal is used for following a target user for positioning work by using a high-precision positioning device integrated on the wearable equipment and a preset type of sensor;

[0009] The positioning auxiliary device is pre-arranged at multiple positions of the construction site and is used for assisting the wearable positioning terminal to perform positioning;

[0010] The digital positioning management platform is used for accurately positioning the target user according to the positioning data returned by the positioning auxiliary device and the wearable positioning terminal and projecting the target user to a pre-constructed digital twin space;

[0011] The cooperative management module is used for adjusting the state of the construction equipment on the construction site in a linked mode according to the positioning result of the target user in the digital twin space, so as to reduce the probability of construction accidents.

[0012] Preferably, the wearable positioning terminal comprises:

[0013] The high-precision positioning chip is used for emitting positioning signals to multiple positioning auxiliary devices arranged around the construction site;

[0014] The nine-axis inertial measurement unit contains a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, and the sampling frequency is greater than 100 Hz, which is used for calculating the user gait features and the heading angle in real time and generating pose data;

[0015] The edge computing unit is internally provided with a lightweight pedestrian dead reckoning (PDR) algorithm, and the continuous positioning result resistant to occlusion is output by tightly coupling filtering of the pose data and the ultra-wideband ranging result, and the autonomous inertial navigation mode is started when the signal is interrupted.

[0016] Preferably, the execution of the accurate positioning of the target user by the digital positioning management platform according to the positioning data returned by the positioning auxiliary device and the wearable positioning terminal comprises the following steps:

[0017] When the target user passes through any area, the reception accuracy of the positioning signal in the area is judged;

[0018] If the accuracy is greater than a preset accuracy threshold, the accurate positioning of the target user is continued by using the positioning data;

[0019] If the accuracy is less than the preset accuracy threshold, the wearable positioning terminal performs fusion of the position and attitude data from the pose sensor and the distance measurement data obtained by the UWB ranging at the original data level through the tightly coupled filtering of the pose data and the UWB ranging result, estimates more accurate position and attitude information through the Kalman filtering algorithm, and outputs the continuous positioning result resistant to occlusion, and starts the autonomous inertial navigation mode when the signal is interrupted.

[0020] Preferably, the reception accuracy of the positioning signal by the determined area includes:

[0021] The construction site structure semantics are extracted in advance according to the BIM model of the construction site, a three-dimensional grid map is constructed, and the grid is classified according to the fillers existing in any grid, and a signal shielding coefficient is set for each type of grid;

[0022] Based on the grid classification result of each grid in the three-dimensional grid map, the grid where the plurality of positioning auxiliary devices existing around the selected grid is determined as the first type of grid, and the plurality of grids passing through the connection path from the selected grid to any first type of grid are determined as the second type of grid;

[0023] The distance of the signal passing through each second type of grid is determined, and the shielding strength of each second type of grid to the signal is calculated based on the signal shielding coefficient, and the shielding strength of all second type of grids to the signal on the path from the selected grid to any first type of grid is accumulated to obtain the shielding coefficient of the selected grid to the first type of grid;

[0024] A plurality of shielding coefficients of the selected grid corresponding to a plurality of first type of grids are determined, and the smallest shielding coefficient is determined to reflect the reception accuracy of the positioning signal by the selected grid area.

[0025] Preferably, during the calculation of the reception accuracy of the positioning signal by the selected grid area:

[0026] Every preset time period, the state of the fillers existing at each position on the construction site is updated according to the construction progress of the construction site and the scanning settlement results of the objects at each position on the construction site.

[0027] Preferably, the digital twin space constructed in advance includes:

[0028] Based on the BIM model, a high-precision three-dimensional scene is constructed in the digital twin space, and real-time equipment position and state information is superimposed;

[0029] By integrating a multiphysics simulation model into a high-precision 3D scene, real-time simulation and prediction of equipment operation and falling debris at the construction site are performed, resulting in a digital twin space that can predict changes at the construction site. Within this digital twin space:

[0030] Based on real-time equipment location and status information, a dynamic first-class danger zone is defined, where the first-class danger zone is the external collision zone of the equipment in real-time motion state;

[0031] Based on real-time equipment location and status information, a dynamic second-class danger zone is defined. The second-class danger zone is the area where the hoisted object may fall due to inertia while the equipment is in real-time motion.

[0032] Based on the hazardous work areas in the digital twin space, a third type of hazardous area is defined, which varies with the work height.

[0033] Preferably, the collaborative management module performs the following operations:

[0034] Based on the location results of the target user in the digital twin space, the movement trajectory of the target user is predicted, and the prediction result is obtained.

[0035] Based on the prediction results, it can be determined in advance whether the target user will enter the dangerous area of ​​the construction site. Once it is determined that the user will enter the dangerous area of ​​the construction site:

[0036] If the user is about to enter a Category 3 danger zone, an alert will be issued to the user;

[0037] If a user is about to enter a Class I or Class II hazardous area, the status of construction equipment on the construction site will be adjusted in a coordinated manner based on the calculation results from the digital twin space. The specific adjustment measures include:

[0038] For construction equipment, the original driving trajectory of the construction vehicle is adjusted based on the calculation results of the digital twin space so that the first type of danger zone avoids the predicted location of the target user;

[0039] For construction equipment, the movement trajectory and speed of the equipment are adjusted based on the calculation results of the digital twin space so that the second type of danger zone avoids the predicted location of the target user.

[0040] The present invention has achieved at least the following beneficial effects:

[0041] 1. This technical solution can significantly improve the efficiency of construction safety management. For example, by predicting worker movement paths, the system can adjust equipment operating status in advance, reducing waiting time.

[0042] 2. The system supports multi-device collaboration, such as integration with the control systems of tower cranes, hoists, concrete pump trucks, etc., and supports the automated optimization of the construction process.

[0043] 3. In addition, the system supports historical data backtracking and accident simulation analysis, providing a basis for safety training and process improvement.

[0044] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0045] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration:

[0046] Figure 1 This is a schematic diagram of a wearable wireless positioning device based on a digital construction site twin application in an embodiment of the present invention;

[0047] Figure 2 This is a flowchart illustrating the process of determining the accuracy of a region's reception of positioning signals in an embodiment of the present invention. Detailed Implementation

[0048] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0049] This invention provides a wearable wireless positioning device based on digital construction site twin applications, with reference to... Figure 1 ,include:

[0050] Wearable positioning terminals are used to track and locate target users by utilizing high-precision positioning devices integrated on wearable equipment and preset types of sensors.

[0051] Positioning assistance devices are pre-deployed at multiple locations on the construction site to assist wearable positioning terminals in positioning.

[0052] The digital positioning management platform is used to accurately locate target users and project them onto a pre-built digital twin space based on positioning data returned by positioning assistance devices and wearable positioning terminals.

[0053] The collaborative management module is used to adjust the status of construction equipment on the construction site in a coordinated manner based on the location results of the target user in the digital twin space, so as to reduce the probability of construction accidents.

[0054] The working principle and beneficial effects of the above technical solution are as follows: Wearable positioning terminals integrate high-precision positioning devices (such as RTK-GNSS modules) and sensors to collect real-time data on the target user's position, speed, attitude, and altitude, achieving centimeter-level positioning accuracy. For example, in high-rise building construction, the terminal updates location data 10 times per second, ensuring the real-time nature of worker location information. Positioning auxiliary devices (such as base stations or beacons) are pre-deployed at key locations on the construction site, interacting with the wearable terminal via wireless communication (such as UWB or Bluetooth) to provide auxiliary positioning signals and enhance positioning stability. For example, in basements or obstructed areas, the auxiliary devices can reduce positioning errors from 5 meters to 0.3 meters. After receiving the positioning data, the digital positioning management platform uses the SLAM algorithm to project it in real-time onto a pre-constructed digital twin space. This space is fused with the BIM model and real-time on-site data to achieve three-dimensional visualization. For example, in the tower crane operation area, the platform can update the worker's position to the twin model within 500 milliseconds, ensuring data synchronization. The collaborative management module automatically adjusts the status of construction equipment based on the positioning results in the twin space. For example, when a worker is detected entering a hazardous area (such as within the tower crane's rotation radius), the system triggers the tower crane to decelerate within 2 seconds, sends a vibration alarm to the worker, and simultaneously pushes an alarm message to the safety officer. This technical solution can significantly improve the efficiency of construction safety management. Furthermore, the system supports multi-device collaboration, such as integration with the control systems of tower cranes, hoists, and concrete pump trucks, enabling automated optimization of the construction process. For instance, by predicting worker movement paths, the system can adjust equipment operating status in advance, reducing waiting time. In addition, the system supports historical data review and accident simulation analysis, providing a basis for safety training and process improvement.

[0055] In a preferred embodiment, the wearable positioning terminal includes:

[0056] High-precision positioning chips are used to transmit positioning signals to multiple positioning auxiliary devices deployed around the construction site;

[0057] The nine-axis inertial measurement unit includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, with a sampling frequency >100Hz, used to calculate user gait characteristics and heading angles in real time and generate pose data;

[0058] The edge computing unit incorporates a lightweight pedestrian dead reckoning (PDR) algorithm. Through tight coupling filtering of pose data and ultra-wideband ranging results, it outputs continuous positioning results that are resistant to occlusion and initiates autonomous inertial navigation mode when the signal is interrupted.

[0059] The working principle and beneficial effects of the above technical solution are as follows: A high-precision positioning chip transmits positioning signals to multiple positioning auxiliary devices deployed around the construction site. These signals are based on ultra-wideband (UWB) technology, featuring high precision and strong anti-interference capabilities. The positioning chip transmits signals at a frequency of 10kHz to ensure real-time performance. The nine-axis inertial measurement unit (IMU) includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, with a sampling frequency exceeding 100Hz, enabling real-time capture of minute changes in the user's movements. For example, on complex terrain such as stairs or slopes, the IMU captures gait characteristics through a high sampling rate, calculating an accurate heading angle with an error of less than 2 degrees. The pose data generation frequency is 100Hz, ensuring the smoothness of the motion trajectory.

[0060] The edge computing unit incorporates a lightweight pedestrian dead reckoning (PDR) algorithm, which tightly couples the pose data generated by the IMU with the UWB ranging results using Kalman filtering. When encountering signal obstruction (such as large equipment or densely reinforced areas), the system automatically switches to autonomous inertial navigation mode, using IMU data for short-term (maximum 30 seconds) inertial navigation to ensure positioning continuity. For example, when entering the interior of an unfinished building, the system can maintain positioning accuracy within 50 centimeters even when the UWB signal is interrupted, until the signal is restored. This technical solution significantly improves the reliability and continuity of positioning. Simultaneously, the system's low latency (data processing latency less than 50 milliseconds) ensures real-time performance, supporting instant linkage of construction equipment, such as tower crane avoidance and elevator access control. Furthermore, the high-sampling-rate pose data provides detailed information for worker motion analysis and path planning. For instance, in hazardous area detection, the system can provide a 2-second advance warning that a worker is about to enter a high-risk area, effectively reducing the probability of accidents.

[0061] In a preferred embodiment, the digital positioning management platform performs the following steps to accurately locate the target user based on the positioning data returned by the positioning assistance device and the wearable positioning terminal:

[0062] When the target user traverses any area, determine the accuracy of the area's reception of the positioning signal;

[0063] If the accuracy exceeds the preset threshold, the location data will continue to be used to accurately locate the target user.

[0064] If the accuracy is less than the preset accuracy threshold, the wearable positioning terminal uses the built-in lightweight pedestrian dead reckoning (PDR) algorithm to fuse the position and attitude data from the pose sensor with the distance measurement data obtained from UWB ranging at the raw data level through tight coupling filtering of pose data and ultra-wideband ranging results. The Kalman filter algorithm is then used to estimate more accurate position and attitude information and output as a continuous positioning result to resist occlusion. The autonomous inertial navigation mode is activated when the signal is interrupted.

[0065] The working principle and beneficial effects of the above technical solution are as follows: A high-precision positioning chip transmits positioning signals to multiple positioning auxiliary devices deployed around the construction site. These signals are based on ultra-wideband (UWB) technology and feature high precision and strong anti-interference capabilities. The positioning chip transmits signals at a frequency of 10kHz to ensure real-time performance. The nine-axis inertial measurement unit (IMU) includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, with a sampling frequency exceeding 100Hz, enabling real-time capture of minute changes in the user's movement. For example, on complex terrain such as stairs or slopes, the IMU captures gait characteristics through a high sampling rate and calculates an accurate heading angle with an error of less than 2 degrees. The pose data generation frequency is 100Hz, ensuring the smoothness of the movement trajectory. The edge computing unit incorporates a lightweight pedestrian dead reckoning (PDR) algorithm, which uses Kalman filtering to tightly couple the pose data generated by the IMU with the UWB ranging results. Under good signal conditions, the positioning accuracy can reach 10 centimeters. When encountering signal obstruction (such as large equipment or areas with dense reinforced concrete), the system automatically switches to autonomous inertial navigation mode, using IMU data for short-term (maximum 30 seconds) inertial navigation to ensure continuous positioning. For example, when entering the interior of an unfinished building, the system can maintain positioning accuracy within 50 centimeters using IMU data even if the UWB signal is interrupted, until the signal is restored.

[0066] The digital positioning management platform performs accurate positioning of the target user based on positioning data returned by positioning assistance devices and wearable positioning terminals, including the following steps: Signal accuracy judgment: When the target user traverses any area, the platform first judges the accuracy of the positioning signal reception in that area. This judgment is based on parameters such as signal strength, noise level, and multipath effects. For example, in open areas, signal accuracy is usually higher than 90%, while inside complex structures, accuracy may drop below 60%. Accuracy threshold comparison: If the signal accuracy is greater than a preset accuracy threshold (e.g., 80%), the platform continues to use positioning data to accurately locate the target user. At this time, the UWB signal is the primary positioning data source, and IMU data is used for auxiliary correction. Signal occlusion handling: If the signal accuracy is less than the preset accuracy threshold, the platform triggers the lightweight PDR algorithm built into the wearable positioning terminal. This algorithm fuses the position and attitude judgment results at the raw data level through tight-coupled filtering of pose data and UWB ranging results. The Kalman filter algorithm is used to estimate more accurate position and attitude information, outputting continuous positioning results that are resistant to occlusion. For example, in areas with signal obstruction, the system fuses IMU and UWB data using Kalman filtering to control the positioning error within 50 centimeters. Autonomous Inertial Navigation Mode: When the signal is completely interrupted (e.g., entering a metal container or basement), the system automatically activates autonomous inertial navigation mode. In this mode, IMU data becomes the sole data source, and the PDR algorithm calculates the user's relative position change using gait characteristics and heading angle. For example, during a 30-second signal interruption, the system maintains positioning accuracy within 1 meter using IMU data, ensuring the continuity of the user's location information. This technical solution significantly improves the reliability and continuity of positioning.

[0067] In a preferred embodiment, refer to Figure 2 The accuracy of the area's reception of positioning signals includes:

[0068] The structural semantics of the construction site are extracted in advance based on the BIM model of the construction site, a three-dimensional grid map is constructed, and the grid is classified according to the filling material in any grid. A signal shielding coefficient is set for each type of grid.

[0069] Based on the grid classification results of each grid in the 3D grid map, the grids containing multiple positioning auxiliary devices around the selected grid are identified as first-class grids, and the multiple grids passed through on the path connecting the selected grid to any first-class grid are identified as second-class grids.

[0070] Determine the distance the signal travels through each second-class grid and calculate the shielding strength of each second-class grid for the signal based on the signal shielding coefficient. Accumulate the shielding strength of all second-class grids on the path from the selected grid to any first-class grid to obtain the shielding coefficient from the selected grid to the first-class grid.

[0071] Determine multiple shielding coefficients for the selected grid corresponding to multiple first-class grids, and determine the smallest shielding coefficient to reflect the accuracy of the selected grid area in receiving positioning signals.

[0072] The working principle and beneficial effects of the above technical solution are as follows: By pre-extracting structural semantic information from the BIM model of the construction site, a three-dimensional grid map reflecting the actual layout of the site is constructed. The grids are then classified according to the type of infill material (such as reinforcing steel, concrete, metal sheets, etc.), and a signal shielding coefficient is assigned to each type of grid. For example, the shielding coefficient is 0.8 for densely reinforced steel areas, 0.5 for concrete areas, and 0.1 for open areas. Based on the classification results of each grid in the three-dimensional grid map, the grids containing multiple positioning auxiliary devices around a selected grid are identified as first-class grids, and the grids along the path from the selected grid to any first-class grid are identified as second-class grids. For the path from the selected grid to any first-class grid, the distance the signal travels through each second-class grid is calculated, and the shielding strength of each second-class grid is calculated based on its signal shielding coefficient. The shielding strengths of all second-class grids are accumulated to obtain the total shielding coefficient from the selected grid to that first-class grid. Determine multiple total shielding coefficients for multiple first-class grids corresponding to a selected grid, where the smallest shielding coefficient reflects the accuracy of the selected grid area in receiving positioning signals.

[0073] Regional Signal Accuracy Assessment: On construction sites, the accuracy of positioning signal reception varies significantly across different areas. For example, signal reception is poor in areas with dense rebar and inside buildings, while it is better in open areas. By pre-extracting structural semantic information from the site's BIM model, constructing a 3D grid map, and classifying the grids, the signal reception characteristics of different areas can be accurately identified. For instance, grids in areas with dense rebar are classified as high-shield areas and assigned a higher signal shielding coefficient; while grids in open areas are classified as low-shield areas and assigned a lower signal shielding coefficient.

[0074] Signal shielding strength calculation: Based on the classification results of the 3D grid map, the path from the selected grid to the grid where each positioning aid is located is determined. The shielding strength of each grid along the signal path is calculated and accumulated. This process quantifies the degree of signal attenuation along the path, thereby accurately assessing the signal reception accuracy. For example, for a path passing through multiple highly shielded grids, a large accumulated shielding coefficient indicates severe signal attenuation along that path and low reception accuracy.

[0075] Minimum shielding coefficient determination: By comparing the total shielding coefficients of multiple positioning aids located in the selected grid, the minimum shielding coefficient is selected to reflect the signal reception accuracy of the selected grid area. This method ensures the conservatism and reliability of the evaluation results, guaranteeing that the positioning signal reception accuracy meets the requirements even under the most unfavorable path conditions.

[0076] This solution combines the BIM model and 3D grid map technology of the construction site to accurately assess the signal reception in different areas of the construction site, providing a scientific basis for the optimization and adjustment of the positioning system, thereby improving the accuracy and reliability of positioning and reducing the probability of construction accidents.

[0077] In a preferred embodiment, during the process of calculating the accuracy of the positioning signal reception for the selected grid area:

[0078] Every preset time interval, the status of the filling materials in each location on the construction site is updated based on the construction progress and the scanning and clearing results of items in each location on the construction site.

[0079] The working principle and beneficial effects of the above technical solution are as follows: Based on a preset time period (e.g., every 15 minutes or hour), the system updates the status of infill materials at various locations on the construction site according to the construction progress and the results of scanning and clearing items at each location. For example, when the construction site transitions from the foundation construction stage to the main structure construction stage, some areas may have added densely reinforced steel structures, while other areas may have removed temporary metal supports. Through regular scanning and clearing, the system can promptly identify these changes and update the infill material type and signal shielding coefficient of the corresponding grid in the 3D raster map. The system periodically checks the progress changes at the construction site according to the construction plan and actual progress, and clears items at various locations on the construction site using on-site scanning equipment (such as laser scanners, RFID tags, etc.), identifying newly added, removed, or changed infill materials. Based on the scanning and clearing results, the system updates the infill material type of the corresponding grid in the 3D raster map and resets the corresponding signal shielding coefficient. For example, the infill material type of a grid is updated from "open area" to "densely reinforced steel area," and the signal shielding coefficient is updated from 0.1 to 0.8. For each selected grid cell leading to the grid cell containing the positioning aid, the shielding strength of the signal traversing each second-type grid cell is recalculated, and the new total shielding coefficient is accumulated. The minimum shielding coefficient for each selected grid cell corresponding to multiple first-type grid cells is then redefined to reflect the updated signal reception accuracy. By dynamically adapting to changes in the construction site, the system ensures the accuracy and reliability of the positioning signal, thereby improving construction safety management efficiency and reducing the probability of accidents. This dynamic update mechanism reduces the frequency of manual adjustments and maintenance, lowers system maintenance costs, and enhances the system's robustness in complex environments.

[0080] In a preferred embodiment, the pre-constructed digital twin space includes:

[0081] A high-precision 3D scene is constructed in the digital twin space based on the BIM model, and real-time equipment location and status information are overlaid.

[0082] By integrating a multiphysics simulation model into a high-precision 3D scene, real-time simulation and prediction of equipment operation and falling debris at the construction site are performed, resulting in a digital twin space that can predict changes at the construction site. Within this digital twin space:

[0083] Based on real-time equipment location and status information, a dynamic first-class danger zone is defined, where the first-class danger zone is the external collision zone of the equipment in real-time motion state;

[0084] Based on real-time equipment location and status information, a dynamic second-class danger zone is defined. The second-class danger zone is the area where the hoisted object may fall due to inertia while the equipment is in real-time motion.

[0085] Based on the hazardous work areas in the digital twin space, a third type of hazardous area is defined, which varies with the work height.

[0086] The working principle and beneficial effects of the above technical solution are as follows: By constructing a high-precision 3D scene in a digital twin space based on a BIM model and overlaying it with real-time equipment location and status information, the system can intuitively display the layout and equipment dynamics of the construction site. The BIM model provides accurate building structure and equipment layout information, while real-time data is acquired through IoT sensors and positioning terminals to ensure the real-time performance and accuracy of the scene. For example, on a construction site, the location and operating status of equipment such as tower cranes and elevators are updated every 0.1 milliseconds to ensure that the 3D scene is synchronized with the actual construction site. Multiphysics simulation models are integrated into the high-precision 3D scene to perform real-time simulation and prediction of equipment operation and falling objects at the construction site. For example, a dynamic simulation model can predict the swing amplitude of a tower crane under wind load, or a collision detection algorithm can predict the falling trajectory of a hoisted object. These simulation results are integrated with the 3D scene to form a digital twin space that can extrapolate changes in the construction site. For example, during hoisting operations, the system predicts the falling path of objects through simulation and marks potential hazardous areas in the digital twin space. In the digital twin space, based on real-time equipment location and status information, the system dynamically divides three types of hazardous areas. The first type of hazardous area is the area where the equipment is in real-time motion and could potentially collide with other parts of the structure, such as within the rotation radius of a tower crane. The system calculates and marks potential collision areas in real time based on the equipment's current position and trajectory. The second type of hazardous area is the area where objects being hoisted may fall due to inertia while the equipment is in real-time motion. For example, during hoisting, the system predicts the possible fall range based on the object's kinematic parameters. The third type of hazardous area varies with the working height. For example, on an aerial work platform, the system dynamically adjusts the hazardous area's range based on the platform's height and position. This technical solution integrates BIM models, real-time data, and multiphysics simulation to construct a highly realistic and predictive digital twin space. In this space, the dynamic behavior of construction equipment and materials is simulated and predicted in real time, enabling the system to identify potential hazardous areas in advance. For example, when a tower crane is hoisting a heavy object, the system predicts the object's trajectory through simulation, marks the hazardous area in the digital twin space, and simultaneously sends an alarm to on-site personnel. This real-time hazardous area classification and early warning mechanism significantly improves the safety of the construction site and reduces the risk of accidents. Furthermore, the visualization and predictive capabilities offered by the digital twin space help construction managers optimize construction plans, improve resource utilization efficiency, and reduce construction costs. Through real-time updates and dynamic adjustments, the system adapts to the ever-changing construction site, ensuring its long-term effectiveness and reliability.

[0087] In a preferred embodiment, the collaborative management module performs the following operations:

[0088] Based on the location results of the target user in the digital twin space, the movement trajectory of the target user is predicted, and the prediction result is obtained.

[0089] Based on the prediction results, it can be determined in advance whether the target user will enter the dangerous area of ​​the construction site. Once it is determined that the user will enter the dangerous area of ​​the construction site:

[0090] If the user is about to enter a Category 3 danger zone, an alert will be issued to the user;

[0091] If a user is about to enter a Class I or Class II hazardous area, the status of construction equipment on the construction site will be adjusted in a coordinated manner based on the calculation results from the digital twin space. The specific adjustment measures include:

[0092] For construction equipment, the original driving trajectory of the construction vehicle is adjusted based on the calculation results of the digital twin space so that the first type of danger zone avoids the predicted location of the target user;

[0093] For construction equipment, the movement trajectory and speed of the equipment are adjusted based on the calculation results of the digital twin space so that the second type of danger zone avoids the predicted location of the target user.

[0094] The working principle and beneficial effects of the above technical solution are as follows: Using the real-time positioning results of the target user in the digital twin space, the collaborative management module uses prediction algorithms (such as Kalman filtering or LSTM neural networks) to predict the target user's movement trajectory, obtaining prediction results for the next 5-10 seconds. For example, the system updates the predicted trajectory every second to ensure the real-time nature and accuracy of the prediction. The prediction algorithm considers the user's current speed, direction, and acceleration, as well as kinematic parameters, combined with equipment status and obstacle information in the digital twin space. Based on the prediction results, the system determines in advance whether the target user will enter a hazardous area of ​​the construction site. Hazardous areas include Class I hazardous areas (areas of collision due to the real-time movement of equipment), Class II hazardous areas (areas where objects hoisted by equipment may fall off), and Class III hazardous areas (areas that change with the working height). For example, when it is predicted that the user will continue to move forward at the current speed, the system calculates the probability of overlap between the user's future position and the hazardous area. If the user will enter a Class III hazardous area (such as a high-altitude work area), the system issues an alarm to the user through a wearable positioning terminal. The alarm may be in the form of vibration, sound, or light signals to ensure that the user can detect it in time and take evasive action. For example, when a user approaches a Class 3 danger zone, the terminal will issue a vibration alarm once per second until the user changes direction or leaves the danger zone.

[0095] If a user enters a Class I or Class II hazardous area, the system will adjust the status of construction equipment on the site in a coordinated manner based on the calculations from the digital twin space. Specific adjustment measures are as follows:

[0096] Adjustment measures for Category I hazardous areas: For Category I hazardous areas (the collision zone in real-time equipment movement), the system adjusts the trajectory of construction equipment using real-time data from the digital twin space to avoid the user's predicted location. For example, if it is predicted that a user will enter the tower crane's rotation radius, the system calculates the required adjustment of the tower crane's rotation angle and speed to ensure that the tower crane's trajectory avoids the user's predicted location. Specifically, the system replans the equipment's travel path. For mobile equipment (such as concrete pump trucks or transport vehicles), the system replans its travel path using path planning algorithms to avoid the user's predicted location, shifting the equipment's travel path 2 meters towards a safe area to ensure a safe distance between the user and the equipment. Simultaneously, the system adjusts the equipment's operating speed. For high-speed equipment (such as tower cranes), the system reduces its operating speed to decrease the impact of potential collisions; for example, reducing the tower crane's rotation speed from 30 degrees per minute to 10 degrees per minute.

[0097] Adjustment measures for Category II hazardous areas: For Category II hazardous areas (areas where the hoisted object may detach), the system uses real-time data from a digital twin space to adjust the equipment's trajectory and speed to ensure the object's descent avoids the user's predicted location. For example, when it is predicted that a user will enter the descent area, the system uses a dynamic simulation model to predict the object's descent path and adjusts the hoisting equipment's trajectory to ensure the descent avoids the user's predicted location. This includes adjusting the tower crane's hook height and swing amplitude to ensure the descent area deviates at least 3 meters from the user's predicted location. Simultaneously, the system adjusts the equipment's operating speed, reducing the risk of the object detaching due to inertia; for example, reducing the hoisting speed from 5 meters per minute to 2 meters per minute. These technical solutions significantly improve the safety of the construction site. By predicting the user's movement trajectory in real time and dynamically adjusting equipment status, the system provides early warnings and avoids potential hazards. Furthermore, by adjusting equipment operating parameters in real time, the system optimizes the construction process and improves construction efficiency. Furthermore, the system adapts to the ever-changing construction site through real-time updates and dynamic adjustments, ensuring its long-term effectiveness and reliability.

[0098] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A wearable wireless positioning device based on digital construction site twin applications, characterized in that, include: Wearable positioning terminals are used to track and locate target users by utilizing high-precision positioning devices integrated on wearable equipment and preset types of sensors. Positioning assistance devices are pre-deployed at multiple locations on the construction site to assist wearable positioning terminals in positioning. The digital positioning management platform is used to accurately locate target users and project them onto a pre-built digital twin space based on positioning data returned by positioning assistance devices and wearable positioning terminals. The collaborative management module is used to adjust the status of construction equipment on the construction site in a coordinated manner based on the location results of the target user in the digital twin space, so as to reduce the probability of construction accidents. The digital positioning management platform performs the following steps to accurately locate the target user based on the positioning data returned by positioning assistance devices and wearable positioning terminals: When the target user traverses any area, determine the accuracy of the area's reception of the positioning signal; If the accuracy exceeds the preset threshold, the location data will continue to be used to accurately locate the target user. If the accuracy is less than the preset accuracy threshold, the wearable positioning terminal uses the built-in lightweight pedestrian dead reckoning (PDR) algorithm to fuse the position and attitude data from the pose sensor with the distance measurement data obtained from UWB ranging at the raw data level through tight coupling filtering of pose data and ultra-wideband ranging results. The Kalman filter algorithm is then used to estimate more accurate position and attitude information and output as a continuous positioning result to resist occlusion. The autonomous inertial navigation mode is activated when the signal is interrupted. The accuracy of the area's reception of positioning signals is determined by: The structural semantics of the construction site are extracted in advance based on the BIM model of the construction site, a three-dimensional grid map is constructed, and the grid is classified according to the filling material in any grid. A signal shielding coefficient is set for each type of grid. Based on the grid classification results of each grid in the 3D grid map, the grids containing multiple positioning auxiliary devices around the selected grid are identified as first-class grids, and the multiple grids passed through on the path connecting the selected grid to any first-class grid are identified as second-class grids. Determine the distance the signal travels through each second-class grid and calculate the shielding strength of each second-class grid for the signal based on the signal shielding coefficient. Accumulate the shielding strength of all second-class grids on the path from the selected grid to any first-class grid to obtain the shielding coefficient from the selected grid to the first-class grid. Determine multiple shielding coefficients for the selected grid corresponding to multiple first-class grids, and determine the smallest shielding coefficient to reflect the accuracy of the selected grid area in receiving positioning signals.

2. The wearable wireless positioning device based on digital construction site twin application according to claim 1, characterized in that, Wearable positioning terminals include: High-precision positioning chips are used to transmit positioning signals to multiple positioning auxiliary devices deployed around the construction site; The nine-axis inertial measurement unit includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, with a sampling frequency >100Hz, used to calculate user gait characteristics and heading angles in real time and generate pose data; The edge computing unit incorporates a lightweight pedestrian dead reckoning (PDR) algorithm. Through tight coupling filtering of pose data and ultra-wideband ranging results, it outputs continuous positioning results that are resistant to occlusion and initiates autonomous inertial navigation mode when the signal is interrupted.

3. A wearable wireless positioning device based on digital construction site twin application as described in claim 1, characterized in that, In the process of calculating the accuracy of receiving positioning signals for the selected grid area: Every preset time interval, the status of the filling materials in each location on the construction site is updated based on the construction progress and the scanning and clearing results of items in each location on the construction site.

4. A wearable wireless positioning device based on digital construction site twin application according to claim 1, characterized in that, The pre-built digital twin space includes: A high-precision 3D scene is constructed in the digital twin space based on the BIM model, and real-time equipment location and status information are overlaid. By integrating a multiphysics simulation model into a high-precision 3D scene, real-time simulation and prediction of equipment operation and falling debris at the construction site are performed, resulting in a digital twin space that can predict changes at the construction site. Within this digital twin space: Based on real-time equipment location and status information, a dynamic first-class danger zone is defined, where the first-class danger zone is the external collision zone of the equipment in real-time motion state; Based on real-time equipment location and status information, a dynamic second-class danger zone is defined. The second-class danger zone is the area where the hoisted object may fall due to inertia while the equipment is in real-time motion. Based on the hazardous work areas in the digital twin space, a third type of hazardous area is defined, which varies with the work height.

5. A wearable wireless positioning device based on digital construction site twin application according to claim 4, characterized in that, The collaborative management module performs the following operations: Based on the location results of the target user in the digital twin space, the movement trajectory of the target user is predicted, and the prediction result is obtained. Based on the prediction results, it can be determined in advance whether the target user will enter the dangerous area of ​​the construction site. Once it is determined that the user will enter the dangerous area of ​​the construction site: If the user is about to enter a Category 3 danger zone, an alert will be issued to the user; If a user is about to enter a Class I or Class II hazardous area, the status of construction equipment on the construction site will be adjusted in a coordinated manner based on the calculation results from the digital twin space. The specific adjustment measures include: For construction equipment, the original driving trajectory of the construction vehicle is adjusted based on the calculation results of the digital twin space so that the first type of danger zone avoids the predicted location of the target user; For construction equipment, the movement trajectory and speed of the equipment are adjusted based on the calculation results of the digital twin space so that the second type of danger zone avoids the predicted location of the target user.

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