Wearable wireless positioning device based on digital construction site twin application

Abstract

The invention discloses a wearable wireless positioning device based on digital construction site twin application, and the device comprises a wearable positioning terminal which is used for carrying out the positioning work along with a target user through a high-precision positioning device integrated on wearable equipment and a sensor of a preset type; the positioning auxiliary devices are arranged at a plurality of positions of the construction site in advance and are used for assisting the wearable positioning terminal in positioning; 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; and the collaborative management module is used for carrying out linkage type adjustment on the state of the construction equipment on the construction site according to the positioning result of the target user in the digital twin space so as to reduce the occurrence probability of construction accidents. According to the invention, not only can the position state of the worker on the construction site be monitored in real time, but also the construction safety management efficiency can be obviously improved.

Description

Technical Field

[0001] The present invention belongs to the field of construction safety technology, and specifically relates to a wearable wireless positioning device based on digital construction site twin applications. Background Art

[0002] In modern construction projects, construction safety management is a core component of ensuring worker safety and project progress. As construction sites expand in size and the density of construction equipment and personnel increases, traditional manual inspections or fixed monitoring equipment struggle to accurately and accurately detect the dynamic location of personnel and the operating status of equipment in real time. Especially in complex construction scenarios (such as high-altitude operations and areas with heavy machinery), safety incidents caused by positioning errors or information lags are frequent. Digital technology is needed to achieve multi-dimensional, coordinated monitoring of personnel, equipment, and the environment, enhancing the proactive early warning capabilities of construction site safety management.

[0003] Current construction site positioning technologies primarily rely on solutions such as GPS, UWB (ultra-wideband), or Bluetooth beacons. GPS suffers from severe signal attenuation indoors or in densely obstructed environments, resulting in positioning errors reaching the meter level, making it unable to meet the centimeter-level accuracy requirements required in construction scenarios. While UWB offers high positioning accuracy, it suffers from high hardware deployment costs, limited coverage, and a lack of integration with construction equipment status. Furthermore, existing positioning systems often operate in isolation, providing only location data without deep integration with digital twin models. This results in safety management still relying on manual judgment. For example, if a person strays into a tower crane operating area, traditional systems can only trigger an alarm but cannot adjust the crane's path or initiate emergency braking in real time, resulting in a risk of delayed response. Furthermore, existing wearable devices have limited functionality, making it difficult to comprehensively assess personnel safety status. These technical bottlenecks severely restrict the level of intelligent construction safety management.

[0004] To address these issues, a wearable wireless positioning system that integrates high-precision positioning, digital twins, and device collaboration is urgently needed. By building a digital twin space that maps the actual construction site and correlates personnel locations with equipment operating status in real time, proactive safety risk prediction and automated management and control can be achieved. Combining lightweight wearable terminals with distributed positioning assistance devices reduces deployment costs while breaking through bottlenecks in positioning accuracy and response speed in complex environments, providing full-scenario, dynamic technical support for construction safety management. Summary of the Invention

[0005] In view of this, the present invention proposes a wearable wireless positioning device based on the digital construction site twin application. Through the technical solution of the present invention, not only can the location status of workers on the construction site be monitored in real time, but also the efficiency of construction safety management can be significantly improved.

[0006] In order to achieve the above object, the present invention provides the following technical solutions: The present invention provides a wearable wireless positioning device based on digital construction site twin applications, comprising: Wearable positioning terminal, which uses a high-precision positioning device integrated in the wearable equipment and a preset type of sensor to follow the target user for positioning; Positioning assistance devices are pre-deployed at multiple locations on the construction site to assist wearable positioning terminals in positioning; A digital positioning management platform, which is used to accurately locate the target user based on the positioning data returned by the positioning auxiliary device and the wearable positioning terminal, and project the positioning data into a pre-built digital twin space; The collaborative management module is used to make linkage adjustments to the status of construction equipment on the construction site based on the positioning results of the target user in the digital twin space, so as to reduce the probability of construction accidents.

[0007] Preferably, the wearable positioning terminal includes: High-precision positioning chip, used to transmit positioning signals to multiple positioning auxiliary devices deployed around the construction site; A nine-axis inertial measurement unit (IMU) includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. The unit has a sampling frequency of >100Hz and is used to calculate user gait characteristics and heading angles in real time and generate posture data. The edge computing unit has a built-in lightweight pedestrian dead reckoning (PDR) algorithm. It outputs anti-occlusion continuous positioning results through tight coupling filtering of posture data and ultra-wideband ranging results, and starts autonomous inertial navigation mode when the signal is interrupted.

[0008] Preferably, the digital positioning management platform accurately locates the target user according to the positioning data returned by the positioning auxiliary device and the wearable positioning terminal, including the following steps: When the target user passes through any area, the accuracy of the area's reception of positioning signals is determined; If it is greater than the preset accuracy threshold, the positioning data will continue to be used to accurately locate the target user; If it is less than the preset accuracy threshold, the wearable positioning terminal uses the built-in lightweight pedestrian dead reckoning (PDR) algorithm to tightly couple the position and attitude data from the posture sensor with the distance measurement data obtained by UWB ranging through filtering, and fuses the position and attitude judgment results at the raw data level. The Kalman filter algorithm is used to estimate more accurate position and attitude information, and outputs the continuous positioning result as anti-occlusion, and starts the autonomous inertial navigation mode when the signal is interrupted.

[0009] Preferably, determining the accuracy of receiving the positioning signal in the area includes: Extract the construction site structure semantics based on the BIM model of the construction site in advance, build a three-dimensional grid map, classify the grids according to the fillers in any grid, and set the signal shielding coefficient for each type of grid; Based on the grid classification results of each grid in the three-dimensional grid map, the grids where the multiple positioning auxiliary devices are located around the selected grid are determined to be first-category grids, and the multiple grids on the path connecting the selected grid to any first-category grid are determined to be second-category grids; Determining the distance that the signal traverses each second-type grid and calculating the shielding strength of each second-type grid against the signal based on the signal shielding coefficient, accumulating the shielding strength of all second-type grids on the path from the selected grid to any first-type grid to obtain the shielding coefficient from the selected grid to the first-type grid; A plurality of shielding coefficients corresponding to a plurality of first-type grids of the selected grid are determined, and the minimum shielding coefficient is determined to reflect the reception accuracy of the selected grid area for the positioning signal.

[0010] Preferably, in the process of calculating the reception accuracy of the positioning signal in the selected grid area: At preset time intervals, the status of the fillers at various locations on the construction site is updated based on the construction progress of the construction site and the results of scanning and clearing items at various locations on the construction site.

[0011] Preferably, the pre-built digital twin space includes: Build high-precision three-dimensional scenes in the digital twin space based on the BIM model, and overlay real-time equipment location and status information; By integrating the multi-physics simulation model into a high-precision 3D scene, real-time simulation predictions can be made on the equipment operation and the status of construction debris at the construction site. This creates a digital twin space that can simulate changes in the construction site. In the digital twin space: Based on the real-time device location and status information, the dynamic first-class danger zone is divided. The first-class danger zone is the shape collision area of ​​the device in real-time motion state. Based on the real-time equipment position and status information, dynamic second-class danger zones are divided. The second-class danger zones are locations where hoisted objects may fall due to inertia when the equipment is in real-time motion. Based on the dangerous operation areas in the digital twin space, a third type of dangerous area that changes with the operation height is divided.

[0012] Preferably, the collaborative management module performs the following operations: Based on the positioning results of the target user in the digital twin space, the target user's movement trajectory is predicted to obtain the prediction result; Based on the prediction results, determine 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 the third category dangerous area, an alarm will be issued to the user; If the user is about to enter a Class I or Class II hazardous area, the status of the construction equipment on the construction site will be adjusted in a coordinated manner based on the calculation results of the digital twin space. The specific adjustment measures include: For construction equipment, the original driving trajectory of the construction vehicle equipment is adjusted based on the calculation results of the digital twin space so that the first type of dangerous area 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 area avoids the predicted location of the target user.

[0013] The present invention has achieved at least the following beneficial effects: 1. This technical solution can significantly improve construction safety management efficiency. For example, by predicting workers' movement paths, the system can adjust equipment operating status in advance, reducing waiting time.

[0014] 2. The system supports multi-device collaboration, such as integration with the control systems of tower cranes, elevators, concrete pump trucks, and other equipment, supporting automated optimization of construction processes.

[0015] 3. In addition, the system supports historical data backtracking and accident simulation analysis, providing a basis for safety training and process improvement.

[0016] Other advantages, objectives and features of the present invention will be described in the following description and will be apparent to those skilled in the art to some extent, or those skilled in the art can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention provides the following drawings for illustration: Figure 1 Schematic diagram of a wearable wireless positioning device based on a digital construction site twin application in an embodiment of the present invention; Figure 2 This is a flow chart of determining the accuracy of receiving positioning signals in an area according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0019] The present invention provides a wearable wireless positioning device based on digital construction site twin application, referring to Figure 1 ,include: Wearable positioning terminal, which uses a high-precision positioning device integrated in the wearable equipment and a preset type of sensor to follow the target user for positioning; Positioning assistance devices are pre-deployed at multiple locations on the construction site to assist wearable positioning terminals in positioning; A digital positioning management platform, which is used to accurately locate the target user based on the positioning data returned by the positioning auxiliary device and the wearable positioning terminal, and project the positioning data into a pre-built digital twin space; The collaborative management module is used to make linkage adjustments to the status of construction equipment on the construction site based on the positioning results of the target user in the digital twin space, so as to reduce the probability of construction accidents.

[0020] The working principle and beneficial effects of the above technical solution are as follows: A wearable positioning terminal integrates a high-precision positioning device (such as an RTK-GNSS module) and sensors to collect real-time location, velocity, attitude, and altitude data of the target user, achieving centimeter-level positioning accuracy. For example, during high-rise building construction, the terminal updates its location data 10 times per second, ensuring real-time worker location information. Positioning assistance devices (such as base stations or beacons) are pre-deployed at key locations on the construction site. They interact with the wearable terminal via wireless communications (such as UWB or Bluetooth) to provide auxiliary positioning signals and enhance positioning stability. For example, in basements or obstructed areas, the assistance devices can reduce positioning error from 5 meters to 0.3 meters. After receiving positioning data, the digital positioning management platform uses a SLAM algorithm to project it in real time into a pre-built digital twin space. This space is integrated with real-time on-site data through the BIM model to achieve three-dimensional visualization. For example, in a tower crane operating area, the platform can update the worker's location 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 dangerous area (such as within the rotation radius of a tower crane), the system triggers the crane to slow down within 2 seconds, sends a vibration alarm to the worker, and simultaneously pushes an alert message to the safety officer. This technical solution can significantly improve the efficiency of construction safety management. At the same time, the system supports multi-device collaboration, such as integration with the control systems of equipment such as tower cranes, elevators, and concrete pump trucks, to achieve automated optimization of the construction process. For example, by predicting the movement path of workers, the system can adjust the operating status of the equipment in advance and reduce waiting time. In addition, the system supports historical data backtracking and accident simulation analysis, providing a basis for safety training and process improvement.

[0021] In a preferred embodiment, the wearable positioning terminal includes: High-precision positioning chip, used to transmit positioning signals to multiple positioning auxiliary devices deployed around the construction site; A nine-axis inertial measurement unit (IMU) includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. The unit has a sampling frequency of >100Hz and is used to calculate user gait characteristics and heading angles in real time and generate posture data. The edge computing unit has a built-in lightweight pedestrian dead reckoning (PDR) algorithm. It outputs anti-occlusion continuous positioning results through tight coupling filtering of posture data and ultra-wideband ranging results, and starts autonomous inertial navigation mode when the signal is interrupted.

[0022] The working principle and beneficial effects of the above technical solution are as follows: positioning signals are transmitted through a high-precision positioning chip to multiple positioning auxiliary devices deployed around the construction site. These signals are based on ultra-wideband (UWB) technology and are characterized by 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) contains a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. The sampling frequency exceeds 100Hz and can capture the user's subtle movement changes in real time. For example, on complex terrain such as stairs or slopes, the IMU captures gait characteristics through a high sampling rate and calculates the precise heading angle with an error of less than 2 degrees. The posture data is generated at a frequency of 100Hz to ensure the smoothness of the motion trajectory.

[0023] The edge computing unit incorporates a lightweight pedestrian dead reckoning (PDR) algorithm, tightly coupling the IMU-generated pose data with UWB ranging results through a Kalman filter. When encountering signal obstruction (e.g., around large equipment or areas with dense rebar), the system automatically switches to autonomous inertial navigation mode, using IMU data for a short period (up to 30 seconds) of inertial navigation to ensure positioning continuity. For example, when entering an unfinished building, the system can leverage IMU data to maintain positioning accuracy within 50 centimeters until the UWB signal is restored, even if the UWB signal is interrupted. This technical solution significantly improves positioning reliability and continuity. Furthermore, the system's low latency (data processing delay less than 50 milliseconds) ensures real-time performance, supporting instant linkage of construction equipment, such as crane avoidance and elevator access control. Furthermore, the high-sampling-rate pose data provides detailed information for construction worker motion analysis and path planning. For example, when detecting hazardous areas, the system can provide a two-second advance warning of workers entering high-risk areas, effectively reducing the probability of accidents.

[0024] In a preferred embodiment, the digital positioning management platform accurately locates the target user based on the positioning data returned by the positioning auxiliary device and the wearable positioning terminal, including the following steps: When the target user passes through any area, the accuracy of the area's reception of positioning signals is determined; If it is greater than the preset accuracy threshold, the positioning data will continue to be used to accurately locate the target user; If it is less than the preset accuracy threshold, the wearable positioning terminal uses the built-in lightweight pedestrian dead reckoning (PDR) algorithm to tightly couple the position and attitude data from the posture sensor with the distance measurement data obtained by UWB ranging through filtering, and fuses the position and attitude judgment results at the raw data level. The Kalman filter algorithm is used to estimate more accurate position and attitude information, and outputs the continuous positioning result as anti-occlusion, and starts the autonomous inertial navigation mode when the signal is interrupted.

[0025] 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 aids deployed around the construction site. These signals, based on ultra-wideband (UWB) technology, are highly accurate and highly resistant to interference. The positioning chip transmits signals at a frequency of 10kHz to ensure real-time performance. The nine-axis inertial measurement unit (IMU) comprises a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer, with a sampling frequency exceeding 100Hz, enabling real-time capture of subtle changes in user movement. For example, on complex terrain such as stairs or slopes, the IMU captures gait characteristics at a high sampling rate and calculates precise heading angles with an error of less than 2 degrees. Position data is generated at a frequency of 100Hz to ensure smooth motion trajectories. The edge computing unit incorporates a lightweight pedestrian dead reckoning (PDR) algorithm that tightly couples the IMU-generated position data with UWB ranging data using a Kalman filter. Under good signal conditions, positioning accuracy can reach 10 cm. When encountering signal obstruction (such as large equipment or areas with dense steel reinforcement), the system automatically switches to autonomous inertial navigation mode, using IMU data for a short period (up to 30 seconds) of inertial navigation to ensure positioning continuity. For example, when entering an unfinished building, the system can use IMU data to maintain positioning accuracy within 50 cm even if the UWB signal is interrupted until the signal is restored.

[0026] The digital positioning management platform accurately locates the target user based on positioning data returned by positioning assistance devices and wearable positioning terminals. The following steps are involved: Signal Accuracy Determination: When the target user traverses any area, the platform first determines the accuracy of the positioning signal received in that area. This determination is based on parameters such as signal strength, noise level, and multipath. For example, in open areas, signal accuracy is typically above 90%, while within complex structures, accuracy may drop below 60%. Accuracy Threshold Comparison: If the signal accuracy exceeds a preset accuracy threshold (e.g., 80%), the platform continues to use the positioning data to accurately locate the target user. In this case, the UWB signal serves as the primary positioning data source, with IMU data used for auxiliary corrections. Signal Occlusion Handling: If the signal accuracy falls below the preset accuracy threshold, the platform triggers the lightweight PDR algorithm built into the wearable positioning terminal. This algorithm fuses position and attitude determination results at the raw data level through tightly coupled filtering of pose data and UWB ranging results. A 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 signal-blocked areas, the system uses Kalman filtering to fuse IMU and UWB data to control the positioning error within 50 centimeters. Autonomous inertial navigation mode: When the signal is completely interrupted (such as entering a metal container or basement), the system automatically starts the autonomous inertial navigation mode. At this time, IMU data becomes the only data source, and the PDR algorithm calculates the user's relative position change through gait characteristics and heading angles. For example, within 30 seconds of signal interruption, the system maintains positioning accuracy within 1 meter through IMU data, ensuring the continuity of user location information. This technical solution can significantly improve the reliability and continuity of positioning.

[0027] In a preferred embodiment, referring to Figure 2 , the accuracy of the positioning signal received by the judgment area includes: Extract the construction site structure semantics based on the BIM model of the construction site in advance, build a three-dimensional grid map, classify the grids according to the fillers in any grid, and set the signal shielding coefficient for each type of grid; Based on the grid classification results of each grid in the three-dimensional grid map, the grids where the multiple positioning auxiliary devices are located around the selected grid are determined to be first-category grids, and the multiple grids on the path connecting the selected grid to any first-category grid are determined to be second-category grids; Determining the distance that the signal traverses each second-type grid and calculating the shielding strength of each second-type grid against the signal based on the signal shielding coefficient, accumulating the shielding strength of all second-type grids on the path from the selected grid to any first-type grid to obtain the shielding coefficient from the selected grid to the first-type grid; A plurality of shielding coefficients corresponding to a plurality of first-type grids of the selected grid are determined, and the minimum shielding coefficient is determined to reflect the reception accuracy of the selected grid area for the positioning signal.

[0028] The working principle and beneficial effects of the above technical solution are as follows: By pre-extracting structural semantic information from the construction site's BIM model, a three-dimensional grid map reflecting the actual construction site layout is constructed. The grids are then classified according to the type of infill (e.g., rebar, concrete, sheet metal, etc.), and a signal shielding coefficient is assigned to each grid type. For example, the shielding coefficient for areas with dense rebar is 0.8, for areas with concrete 0.5, and for open areas 0.1. Based on the classification results for each grid in the three-dimensional grid map, the grids containing multiple positioning aids surrounding the 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 traverses through each second-class grid is calculated. The shielding strength of each second-class grid is then calculated based on the signal shielding coefficient of each second-class grid. The shielding strengths of all second-class grids are accumulated to obtain the total shielding coefficient from the selected grid to the first-class grid. A plurality of total shielding coefficients corresponding to a plurality of first-type grids of the selected grid are determined, wherein the smallest shielding coefficient reflects the reception accuracy of the selected grid area for the positioning signal.

[0029] Regional signal accuracy assessment: On a construction site, 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 signal reception is better in open areas. By pre-extracting structural semantic information from the site's BIM model, constructing a three-dimensional grid map, and performing grid classification, the signal reception characteristics of different areas can be accurately identified. For example, grids in areas with dense rebar are classified as high-shielding areas and assigned a higher signal shielding coefficient; whereas grids in open areas are classified as low-shielding areas and assigned a lower signal shielding coefficient.

[0030] Signal shielding strength calculation: Based on the classification results of the 3D grid map, the path from the selected grid to the grids containing each positioning aid 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, accurately assessing signal reception accuracy. For example, a path passing through multiple high-shielding grids will have a large cumulative shielding coefficient, indicating severe signal attenuation and low reception accuracy.

[0031] Minimum shielding coefficient determination: By comparing the total shielding coefficients of multiple positioning aids in a selected grid, the minimum shielding coefficient is selected to reflect the signal reception accuracy of the selected grid area. This method ensures a conservative and reliable evaluation result, ensuring that positioning signal reception accuracy meets requirements even under the most unfavorable path conditions.

[0032] By combining the construction site's BIM model with 3D grid mapping technology, this solution can accurately assess the signal reception conditions in different areas of the construction site, providing a scientific basis for optimizing and adjusting the positioning system, thereby improving positioning accuracy and reliability and reducing the probability of construction accidents.

[0033] In a preferred embodiment, in the process of calculating the reception accuracy of the positioning signal in the selected grid area: At preset time intervals, the status of the fillers at various locations on the construction site is updated based on the construction progress of the construction site and the results of scanning and clearing items at various locations on the construction site.

[0034] The working principle and beneficial effects of the above technical solution are as follows: Based on the construction progress and the results of scanning and clearing items at various locations on the construction site, the status of filler materials at various locations on the construction site is updated at preset time intervals (e.g., every 15 minutes or every hour). For example, when a construction site moves from the foundation construction phase to the main construction phase, some areas may have newly added structures with dense rebar, while others may have removed temporary metal supports. Through regular scanning and clearing, the system can promptly identify these changes and update the filler type and signal shielding coefficient for the corresponding grids in the three-dimensional grid map. Based on the construction plan and actual progress, the system regularly checks progress changes at the construction site and uses on-site scanning equipment (such as laser scanners and RFID tags) to clear items at various locations on the construction site, identifying any added, removed, or changed filler materials. Based on the scan and clearing results, the filler type for the corresponding grid in the three-dimensional grid map is updated and the corresponding signal shielding coefficient is reset. For example, the filler type for a grid can be updated from "open area" to "dense rebar area," and the signal shielding coefficient can be updated from 0.1 to 0.8. For each selected grid cell along the path to the grid containing the positioning aid, the shielding strength of the signal traversing each second-class grid cell is recalculated and accumulated to obtain a new total shielding coefficient. The minimum shielding coefficient for multiple first-class grid cells corresponding to the selected grid cell is then re-determined to reflect the updated signal reception accuracy. By dynamically adapting to changes in the construction site, the system ensures the accuracy and reliability of positioning signals, thereby improving construction safety management efficiency and reducing the probability of accidents. This dynamic update mechanism reduces the frequency of manual adjustments and maintenance, lowering system maintenance costs while enhancing the system's robustness in complex environments.

[0035] In a preferred embodiment, the pre-built digital twin space includes: Build high-precision three-dimensional scenes in the digital twin space based on the BIM model, and overlay real-time equipment location and status information; By integrating the multi-physics simulation model into a high-precision 3D scene, real-time simulation predictions can be made on the equipment operation and the status of construction debris at the construction site. This creates a digital twin space that can simulate changes in the construction site. In the digital twin space: Based on the real-time device location and status information, the dynamic first-class danger zone is divided. The first-class danger zone is the shape collision area of ​​the device in real-time motion state. Based on the real-time equipment position and status information, dynamic second-class danger zones are divided. The second-class danger zones are locations where hoisted objects may fall due to inertia when the equipment is in real-time motion. Based on the dangerous operation areas in the digital twin space, a third type of dangerous area that changes with the operation height is divided.

[0036] 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 the BIM model and overlaying real-time equipment location and status information, the system can intuitively display the layout and dynamics of the construction site. The BIM model provides precise information on the building structure and equipment layout, while real-time data is obtained through IoT sensors and positioning terminals, ensuring the real-time and accuracy of the scene. For example, on a construction site, the location and operating status of equipment such as cranes and elevators are updated every 0.1 millisecond to ensure that the 3D scene is synchronized with the actual construction site. Multi-physics simulation models are integrated into the high-precision 3D scene to simulate and predict equipment operation and the status of dropped objects at the construction site in real time. For example, a dynamic simulation model can be used to predict the swing amplitude of a crane under wind load, or a collision detection algorithm can be used to 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 simulate changes in the construction site. For example, during a hoisting operation, the system uses simulation to predict the falling path of an object and marks potential danger zones in the digital twin space. In the digital twin space, the system dynamically divides three danger zones based on real-time equipment location and status information. The first type of danger zone is the physical collision zone of the equipment in real-time motion, such as within the rotation radius of a tower crane. The system uses the equipment's current position and trajectory to calculate and mark potential collision zones in real time. The second type of danger zone is the location where a hoisted object could fall due to inertia while the equipment is in real-time motion. For example, during the hoisting process, the system uses the object's kinematic parameters to predict the potential fall range. The third type of danger zone varies with the working height. For example, on an aerial work platform, the system dynamically adjusts the danger zone based on the platform's height and position. This technical solution integrates BIM models, real-time data, and multi-physics simulation to construct a highly realistic and predictive digital twin space. Within this space, the dynamic behavior of construction equipment and materials is simulated and predicted in real time, enabling the system to proactively identify potential danger zones. For example, when a tower crane is lifting a heavy object, the system uses simulation to predict the object's fall trajectory, marks the danger zone in the digital twin space, and simultaneously sends an alert to on-site personnel. This real-time danger zone demarcation and early warning mechanism significantly improves construction site safety and reduces the risk of accidents. Furthermore, the visualization and predictive capabilities provided by the digital twin space help construction managers optimize construction plans, improve resource utilization, and reduce construction costs. The system adapts to the ever-changing construction site through real-time updates and dynamic adjustments, ensuring its long-term effectiveness and reliability.

[0037] In a preferred embodiment, the collaborative management module performs the following operations: Based on the positioning results of the target user in the digital twin space, the target user's movement trajectory is predicted to obtain the prediction result; Based on the prediction results, determine 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 the third category dangerous area, an alarm will be issued to the user; If the user is about to enter a Class I or Class II hazardous area, the status of the construction equipment on the construction site will be adjusted in a coordinated manner based on the calculation results of the digital twin space. The specific adjustment measures include: For construction equipment, the original driving trajectory of the construction vehicle equipment is adjusted based on the calculation results of the digital twin space so that the first type of dangerous area 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 area avoids the predicted location of the target user.

[0038] The working principle and beneficial effects of the above technical solution are as follows: Based on the real-time positioning results of the target user in the digital twin space, the collaborative management module uses a prediction algorithm (such as a Kalman filter or LSTM neural network) to predict the target user's movement trajectory, generating predictions for the next 5 to 10 seconds. For example, the system updates the predicted trajectory once per second to ensure real-time and accurate predictions. The prediction algorithm considers the user's current kinematic parameters such as speed, direction, and acceleration, as well as the 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 dangerous area on the construction site. Dangerous areas include Category 1 (areas with external collisions in the real-time motion of the equipment), Category 2 (areas where hoisted objects may fall off the equipment), and Category 3 (areas that vary with the working height). For example, if the user is predicted to continue moving forward at their current speed, the system calculates the probability of their future position overlapping with a dangerous area. If the user enters a Category 3 dangerous area (such as an overhead working area), the system issues an alert to the user via a wearable positioning terminal. The alert can be in the form of vibration, sound, or light signals, ensuring that the user is aware and takes evasive action in a timely manner. For example, when a user approaches a Category 3 danger zone, the terminal sends a vibration alert every second until the user changes direction or leaves the danger zone.

[0039] If the user is about to enter the first or second dangerous area, the system will make linkage adjustments to the status of the construction equipment on the construction site based on the calculation results of the digital twin space. The specific adjustment measures are as follows: Adjustment measures for Category 1 hazardous areas: For Category 1 hazardous areas (collision zones caused by the equipment's real-time motion), the system uses real-time data from the digital twin space to adjust the construction equipment's trajectory to avoid the user's predicted position. For example, if it predicts that a user will enter the crane's rotation radius, the system calculates the crane's required rotation angle and speed to ensure the crane's trajectory avoids the user's predicted position. Specifically, the system replans the equipment's path. For mobile equipment (such as concrete pumps or transport vehicles), the system uses a path planning algorithm to reroute the equipment to avoid the user's predicted position, offsetting the equipment's path by 2 meters toward a safe zone to ensure a safe distance between the user and the equipment. The system also adjusts the equipment's operating speed. For high-speed equipment (such as tower cranes), the system reduces its speed to reduce the impact of a potential collision. For example, the crane's rotation speed can be reduced from 30 degrees per minute to 10 degrees per minute.

[0040] Adjustment measures for Category II hazardous areas: For Category II hazardous areas (areas where hoisted objects may fall off), the system uses real-time data from the digital twin space to adjust the equipment's trajectory and speed to ensure the object's drop zone avoids the user's predicted location. For example, if a user is predicted to enter the drop zone, the system uses a dynamic simulation model to predict the object's drop path and adjusts the hoisting equipment's trajectory to avoid the user's predicted location. The system also adjusts the crane's hook height and swing amplitude to keep the object's drop zone at least 3 meters away from the user's predicted location. Simultaneously, the system adjusts the equipment's operating speed to reduce the risk of the object falling due to inertia, for example, by reducing the hoist's lifting speed from 5 meters per minute to 2 meters per minute. This technical solution significantly improves construction site safety. By predicting the user's movement trajectory in real time and dynamically adjusting equipment status, the system provides early warning and prevents potential hazardous situations. Furthermore, by adjusting equipment operating parameters in real time, the system optimizes construction processes and improves efficiency. In addition, the system adapts to the constant changes in the construction site through real-time updates and dynamic adjustments, ensuring its long-term effectiveness and reliability.

[0041] 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 limiting. 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 in form and details 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 application, characterized in that: include: Wearable positioning terminal, which uses a high-precision positioning device integrated in the wearable equipment and a preset type of sensor to follow the target user for positioning; Positioning assistance devices are pre-deployed at multiple locations on the construction site to assist wearable positioning terminals in positioning; A digital positioning management platform, which is used to accurately locate the target user based on the positioning data returned by the positioning auxiliary device and the wearable positioning terminal, and project the positioning data into a pre-built digital twin space; The collaborative management module is used to make linkage adjustments to the status of construction equipment on the construction site based on the positioning results of the target user in the digital twin space, so as to reduce the probability of construction accidents.

2. A 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 chip, used to transmit positioning signals to multiple positioning auxiliary devices deployed around the construction site; A nine-axis inertial measurement unit (IMU) includes a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. The unit has a sampling frequency of >100Hz and is used to calculate user gait characteristics and heading angles in real time and generate posture data. The edge computing unit has a built-in lightweight pedestrian dead reckoning (PDR) algorithm. It outputs anti-occlusion continuous positioning results through tight coupling filtering of posture data and ultra-wideband ranging results, and starts autonomous inertial navigation mode when the signal is interrupted.

3. A wearable wireless positioning device based on digital construction site twin application according to claim 1, characterized in that: The digital positioning management platform accurately locates the target user based on the positioning data returned by the positioning auxiliary device and the wearable positioning terminal, including the following steps: When the target user passes through any area, the accuracy of the area's reception of positioning signals is determined; If it is greater than the preset accuracy threshold, the positioning data will continue to be used to accurately locate the target user; If it is less than the preset accuracy threshold, the wearable positioning terminal uses the built-in lightweight pedestrian dead reckoning (PDR) algorithm to tightly couple the position and attitude data from the posture sensor with the distance measurement data obtained by UWB ranging through filtering, and fuses the position and attitude judgment results at the raw data level. The Kalman filter algorithm is used to estimate more accurate position and attitude information, and outputs the continuous positioning result as anti-occlusion, and starts the autonomous inertial navigation mode when the signal is interrupted.

4. A wearable wireless positioning device based on digital construction site twin application according to claim 3, characterized in that: The accuracy of the positioning signal received by the judgment area includes: Extract the construction site structure semantics based on the BIM model of the construction site in advance, build a three-dimensional grid map, classify the grids according to the fillers in any grid, and set the signal shielding coefficient for each type of grid; Based on the grid classification results of each grid in the three-dimensional grid map, the grids where the multiple positioning auxiliary devices are located around the selected grid are determined to be first-category grids, and the multiple grids on the path connecting the selected grid to any first-category grid are determined to be second-category grids; Determining the distance that the signal traverses each second-type grid and calculating the shielding strength of each second-type grid against the signal based on the signal shielding coefficient, accumulating the shielding strength of all second-type grids on the path from the selected grid to any first-type grid to obtain the shielding coefficient from the selected grid to the first-type grid; A plurality of shielding coefficients corresponding to a plurality of first-type grids of the selected grid are determined, and the minimum shielding coefficient is determined to reflect the reception accuracy of the selected grid area for the positioning signal.

5. A wearable wireless positioning device based on digital construction site twin application according to claim 4, characterized in that: In the process of calculating the reception accuracy of the positioning signal in the selected grid area: At preset time intervals, the status of the fillers at various locations on the construction site is updated based on the construction progress of the construction site and the results of scanning and clearing items at various locations on the construction site.

6. A wearable wireless positioning device based on digital construction site twin application according to claim 1, characterized in that: Pre-built digital twin spaces include: Build high-precision three-dimensional scenes in the digital twin space based on the BIM model, and overlay real-time equipment location and status information; By integrating the multi-physics simulation model into a high-precision 3D scene, real-time simulation predictions can be made on the equipment operation and the status of construction debris at the construction site. This creates a digital twin space that can simulate changes in the construction site. In the digital twin space: Based on the real-time device location and status information, the dynamic first-class danger zone is divided. The first-class danger zone is the shape collision area of ​​the device in real-time motion state. Based on the real-time equipment position and status information, dynamic second-class danger zones are divided. The second-class danger zones are locations where hoisted objects may fall due to inertia when the equipment is in real-time motion. Based on the dangerous operation areas in the digital twin space, a third type of dangerous area that changes with the operation height is divided.

7. A wearable wireless positioning device based on digital construction site twin application according to claim 6, characterized in that: The collaborative management module performs the following operations: Based on the positioning results of the target user in the digital twin space, the target user's movement trajectory is predicted to obtain the prediction result; Based on the prediction results, determine 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 the third category dangerous area, an alarm will be issued to the user; If the user is about to enter a Class I or Class II hazardous area, the status of the construction equipment on the construction site will be adjusted in a coordinated manner based on the calculation results of the digital twin space. The specific adjustment measures include: For construction equipment, the original driving trajectory of the construction vehicle equipment is adjusted based on the calculation results of the digital twin space so that the first type of dangerous area 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 area avoids the predicted location of the target user.

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