Bridge component hoisting safety control method based on 5G and edge computing

Abstract

The application discloses a bridge component hoisting safety control method based on 5G and edge computing, relates to the technical field of bridge construction safety control, and comprises the following steps: acquiring crane real-time operation parameters and environmental wind speed and direction data through an edge computing node; acquiring real-time three-dimensional coordinates of personnel in a construction area through a UWB positioning system, and calculating corresponding instantaneous speed vectors of the personnel by the edge computing node; predicting the predicted contact time of the personnel and the dangerous domain boundary; and sending a speed limiting instruction to a crane controller by the edge computing node. The application solves the problem of static electronic fence protection boundary mismatch, forms a complete control link of "prediction - hierarchical intervention - closed loop recovery", significantly reduces the false alarm rate and construction interruption frequency, and takes into account the robustness requirements under complex working conditions such as poor illumination, dust shielding and multipath interference in the bridge construction site, and has the comprehensive advantages of strong predictability, smooth intervention, high automation degree and convenient engineering deployment.

Description

Technical Field

[0001] This invention relates to the field of bridge construction safety control technology, and in particular to a method for safety control of bridge component hoisting based on 5G and edge computing. Background Technology

[0002] Bridge component hoisting is one of the most risky and accident-prone operations in bridge construction. With the continuous expansion of elevated highway construction, the hoisting of large components such as precast beams and steel box girders in complex construction environments is becoming increasingly frequent. The high density of mixed human and machine operations at construction sites, along with numerous intersections between hoisting routes and temporary facilities, significantly increases the difficulty of safety management. Safety control measures for bridge component hoisting operations mainly rely on the following three technical approaches: The first is a passive monitoring scheme based on video surveillance. This involves deploying fixed or pan-tilt-zoom (PTZ) cameras at the construction site, allowing safety management personnel to manually observe the personnel positions and the movement status of hoisted objects in the work area via displays in the monitoring room. Upon detecting abnormalities, communication can be established through voice commands. The first type of alert is provided by voice intercom or broadcast. This method relies heavily on the concentration of personnel and has problems such as incomplete coverage of blind spots and fatigue-induced oversights during long-term monitoring. The second type is a regional protection solution based on positioning tags. This method requires construction personnel to wear GPS, RFID, or Bluetooth positioning tags and sets up a circular or fan-shaped static electronic fence in the area projected by the crane's slewing radius. When the tag signal indicates that a person has entered the fenced area, it triggers an audible and visual alarm or a relay to cut off the power circuit. However, in actual hoisting operations, the danger zone is not a static geometric shape. The range of motion envelope of the hoisted object changes constantly with the slewing of the boom, luffing, lifting and lowering of the hook, and the effects of environmental wind loads. The static fence cannot accurately reflect the real dynamic risk boundary. In addition, there is a significant mechanical braking delay and control system response delay from the time personnel enter the area until the crane comes to a complete stop. If personnel run into the danger zone at a relatively high speed, even if the alarm signal is issued immediately, the load may still collide with the personnel during the braking process. The third type is an active early warning scheme based on multi-source sensor fusion. It uses active detection sensors such as millimeter-wave radar and lidar to detect obstacles around the load and identifies potential collision risks through point cloud data processing. This type of scheme has high accuracy in detecting obstacles at close range, but the equipment cost is high. Moreover, in the dense steel structure environment of bridge construction, the multipath reflection and clutter interference of radar signals are severe, making it difficult to effectively control the false alarm rate.

[0003] However, the current common solutions have many drawbacks, including: existing solutions generally adopt passive protection strategies based on static electronic fences or fixed geometric areas. The dangerous boundary cannot evolve synchronously with the movement of the boom and the disturbance of environmental wind loads in real time. Moreover, they only make binary judgments of "location-area" based on the current coordinates of personnel, lacking the ability to predict the trend of personnel movement and the time of future collisions. As a result, the safety response relies heavily on "entry-stop" hard trigger logic. While causing frequent construction interruptions and mechanical impacts, it is also difficult to adapt to the complex working conditions of bridge construction sites, such as variable lighting, dust obstruction, and multi-path interference of steel structures. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the problems existing in the current bridge component hoisting safety control method based on 5G and edge computing, the present invention is proposed.

[0006] Therefore, the purpose of this invention is to provide a safety control method for bridge component hoisting based on 5G and edge computing. This method is applicable to solving the problem that existing solutions generally adopt passive protection strategies based on static electronic fences or fixed geometric areas. The dangerous boundaries cannot evolve synchronously with the movement of the boom and environmental wind load disturbances in real time. Moreover, they only make binary judgments of "location-area" based on the current coordinates of personnel, lacking the ability to predict the movement trend of personnel and the future collision time. As a result, the safety response relies heavily on "entry-stop" hard trigger logic, which causes frequent construction interruptions and mechanical impacts. It is also difficult to adapt to the complex working conditions of bridge construction sites, such as variable lighting, dust obstruction, and multi-path interference of steel structures.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a safety control method for the hoisting of bridge components based on 5G and edge computing. This method includes acquiring real-time operating parameters of the crane and environmental wind speed and direction data through an edge computing node; generating a dynamically changing three-dimensional hazard domain based on extended Kalman filtering; acquiring real-time three-dimensional coordinates of personnel within the construction area through a UWB positioning system; calculating the instantaneous velocity vector corresponding to the personnel using the edge computing node; predicting the expected contact time between the personnel and the boundary of the hazard domain based on the instantaneous velocity vector and the dynamic boundary of the three-dimensional hazard domain; and when the expected contact time is less than a preset safety time threshold, sending a speed limit command to the crane controller to proactively limit the crane's operating speed before the personnel actually enter the hazard domain.

[0008] As a preferred embodiment of the bridge component hoisting safety control method based on 5G and edge computing described in this invention, the real-time operating parameters of the crane include at least the boom slewing angular velocity, luffing speed, hook height, and hoisting mass; the generation of a three-dimensional hazard domain that dynamically changes over time based on extended Kalman filtering specifically includes: converting the hoisting mass and hook height into swing inertial parameters of the hoisted object, converting the environmental wind speed and direction data into wind disturbance lateral force parameters, and inputting them together into the kinematic prediction model of the hoisted object to generate the motion envelope trajectory of the hoisted object and hook within a preset future time window, and constructing an asymmetric ellipsoidal hazard domain with a windward boundary magnification factor greater than that of the leeward boundary based on the motion envelope trajectory.

[0009] As a preferred embodiment of the bridge component hoisting safety control method based on 5G and edge computing described in this invention, the step of calculating the instantaneous velocity vector corresponding to the personnel by the edge computing node specifically includes: the edge computing node acquiring the continuous three-dimensional coordinate sequence of the personnel collected by the UWB positioning system within a preset time window, calculating the instantaneous velocity components of the personnel in each coordinate axis direction through a quadratic difference algorithm, and then synthesizing a three-dimensional velocity vector carrying direction information and velocity information.

[0010] As a preferred embodiment of the bridge component hoisting safety control method based on 5G and edge computing described in this invention, the method for predicting the estimated contact time between personnel and the boundary of the hazard zone specifically includes: the edge computing node taking the personnel's current three-dimensional coordinates as the starting point, extending a spatial ray along the instantaneous velocity vector direction, and calculating the intersection distance between the spatial ray and the dynamic boundary surface of the three-dimensional hazard zone; dividing the intersection distance by the rate magnitude of the instantaneous velocity vector to obtain the estimated contact time.

[0011] As a preferred embodiment of the bridge component hoisting safety control method based on 5G and edge computing described in this invention, the following steps are taken: when the expected contact time is greater than the preset safety time threshold, the edge computing node maintains the current operating speed of the crane and does not trigger a speed limit command; when the expected contact time is less than the preset safety time threshold, the edge computing node dynamically adjusts the preset safety time threshold according to the current construction stage of the hoisting operation; wherein, the safety time threshold when the hoisted object is in the high-level lifting stage is greater than the safety time threshold when the hoisted object is in the low-level translation stage.

[0012] As a preferred embodiment of the bridge component hoisting safety control method based on 5G and edge computing described in this invention, the speed limit command is a graded speed limit command, specifically as follows: when the expected contact time is within a first threshold range, the edge computing node sends a first-level limit command to the crane controller, limiting the crane's slewing speed to a first percentage of the rated speed; when the expected contact time is within a second threshold range, the edge computing node sends a second-level limit command to the crane controller, limiting the crane's slewing speed to a second percentage of the rated speed; wherein the expected contact time corresponding to the second threshold range is less than the expected contact time corresponding to the first threshold range, and the second percentage is less than the first percentage.

[0013] As a preferred embodiment of the bridge component hoisting safety control method based on 5G and edge computing described in this invention, when the edge computing node detects that the direction of the instantaneous velocity vector of the personnel has deflected, causing the expected contact time to be greater than the preset safety time threshold again, the edge computing node sends a speed limit release command to the crane controller to restore the normal operating speed of the crane.

[0014] Secondly, to further address the aforementioned technical problems, the present invention provides a bridge component hoisting safety control system based on 5G and edge computing, comprising: a hazard domain dynamic generation module, used to acquire real-time crane operating parameters and environmental wind speed and direction data through edge computing nodes, and generate a three-dimensional hazard domain that dynamically changes over time based on extended Kalman filtering; a personnel speed calculation module, used to acquire real-time three-dimensional coordinates of personnel within the construction area through a UWB positioning system, and to calculate the instantaneous velocity vector corresponding to the personnel by edge computing nodes; a contact time prediction module, used to predict the expected contact time between personnel and the boundary of the hazard domain based on the instantaneous velocity vector and the dynamic boundary of the three-dimensional hazard domain by edge computing nodes; and a speed limit execution module, used to send a speed limit command to the crane controller by edge computing nodes when the expected contact time is less than a preset safety time threshold, so as to actively limit the crane's operating speed before personnel actually enter the hazard domain.

[0015] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the safety control method for hoisting bridge components based on 5G and edge computing as described in the first aspect of the present invention.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the safety control method for hoisting bridge components based on 5G and edge computing as described in the first aspect of the present invention.

[0017] The beneficial effects of this invention are as follows: This invention solves the problem of mismatch between static electronic fence protection boundaries by fusing crane operating parameters and environmental wind disturbance data through edge computing nodes; at the same time, it introduces UWB positioning and a secondary differential algorithm to calculate the instantaneous velocity vector of personnel, predicts the expected contact time between personnel and the boundary of the danger zone along the velocity direction, and elevates the safety criterion from the instantaneous spatial judgment of "whether they are in the area" to the spatiotemporal prediction and deduction of "when they will contact the boundary"; based on the threshold range of the contact time, it sends graded speed limit commands to the crane controller, implements smooth active intervention before personnel actually enter the danger zone, and automatically resumes operation after the risk is eliminated, forming a complete control link of "prediction - graded intervention - closed-loop recovery". This invention significantly reduces the false alarm rate and the frequency of construction interruption, while taking into account the robustness requirements of complex working conditions such as severe lighting, dust obstruction and multipath interference at bridge construction sites. It has comprehensive advantages of strong predictability, smooth intervention, high degree of automation and convenient engineering deployment. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating the implementation of the present invention in Example 1. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1 Reference Figure 1 This is the first embodiment of the present invention, which provides a method for safe control of bridge component hoisting based on 5G and edge computing, including the following steps: S1: Obtain real-time operating parameters of the crane and environmental wind speed and direction data through edge computing nodes, and generate a three-dimensional hazard domain that dynamically changes over time based on extended Kalman filtering.

[0023] Preferably, the real-time operating parameters of the crane include at least the boom slewing angular velocity, luffing speed, hook height, and lifting mass; Furthermore, a three-dimensional hazard domain that dynamically changes over time is generated based on extended Kalman filtering, specifically including: The hoisting mass and hook height are converted into the swaying inertial parameters of the hoisted object, and the environmental wind speed and direction data are converted into wind disturbance lateral force parameters. These are then input into the kinematic prediction model of the hoisted object to generate the motion envelope trajectory of the hoisted object and hook within a preset future time window. Based on the motion envelope trajectory, an asymmetric ellipsoidal danger zone with a larger amplification factor on the windward side boundary than on the leeward side boundary boundary is constructed.

[0024] Furthermore, the kinematic prediction model for the suspended object is a hook-suspended object double pendulum model established based on the Lagrange dynamics equations.

[0025] The double pendulum model treats the hook as the first-stage pendulum and the suspended load as the second-stage pendulum suspended below the hook by a sling. The extended Kalman filter uses the boom rotational angular velocity, luffing speed, and hook height as control inputs, the suspended load mass and sling length as system parameters, and the ambient wind speed and direction data as external disturbance observations to recursively estimate the swing angle, angular velocity, and angular acceleration state variables of the double pendulum model, and outputs the set of reachable positions of the suspended load's center of mass in three-dimensional space within a future preset time window.

[0026] Specifically, the asymmetric ellipsoidal danger zone is constructed as follows: the current spatial coordinates of the hook are taken as the center of the ellipsoid, and the maximum offset of the motion envelope trajectory in the east-west, north-south, and vertical directions is taken as the reference value of the three-axis radius of the ellipsoid; the reference radius facing the windward side is multiplied by a wind disturbance amplification factor greater than 1, and the wind disturbance amplification factor is positively correlated with the instantaneous wind speed; the reference radius facing the leeward side is multiplied by a wind disturbance reduction factor less than 1.

[0027] The specific values ​​of the wind disturbance amplification factor and the wind disturbance reduction factor can be determined through trial hoisting calibration experiments at the construction site, or obtained by looking up the wind speed-sway mapping table established based on historical hoisting data.

[0028] For example, in the hoisting operation of a precast beam of a viaduct on a trunk highway, the current boom rotational speed of the crane is 0.05 rad / s, the luffing speed is 0.1 m / s, the hook height is 25 m, and the hoisting mass is 120 t. The edge computing node obtains the above PLC operating parameters, and at the same time, the instantaneous wind speed is collected by the anemometer as 8 m / s and the wind direction is southeast. The hoisting mass and hook height are converted into sling swing inertial parameters, and the wind speed and wind direction are converted into wind disturbance force parameters acting on the lateral windward surface of the hoisted object. All of these are input into the hook-load double pendulum dynamic model established based on the Lagrange equation. The extended Kalman filter uses the boom slewing and luffing speeds as control inputs and wind disturbance as the observed disturbance to recursively estimate the motion envelope trajectory of the crane's center of mass within the next 2.5 seconds. Based on this trajectory, the maximum offset of 2.8m in the east-west direction, 3.2m in the north-south direction, and 1.5m in the vertical direction are taken as the three-axis reference radii. The current wind direction is southeast, and the windward side corresponds to the northeast-southwest axis direction. The reference radius in this direction is multiplied by the wind disturbance amplification factor of 1.4, and the leeward side is multiplied by the wind disturbance reduction factor of 0.7 to generate an asymmetric ellipsoidal danger zone with its center located directly below the hook and three-axis radii of 3.9m, 3.2m, and 1.5m, respectively. This zone is updated in real time at a period of 100ms within the edge nodes as the crane moves and the wind conditions change.

[0029] S2: The real-time three-dimensional coordinates of personnel within the construction area are obtained through the UWB positioning system, and the instantaneous velocity vectors of the personnel are calculated by the edge computing nodes.

[0030] Preferably, the instantaneous velocity vector corresponding to the operator calculated by the edge computing node specifically includes: Edge computing nodes acquire continuous three-dimensional coordinate sequences of personnel collected by the UWB positioning system within a preset time window, calculate the instantaneous velocity components of personnel in each coordinate axis direction through a quadratic difference algorithm, and then synthesize a three-dimensional velocity vector carrying direction and velocity information.

[0031] Specifically, the UWB positioning system includes at least four fixed UWB positioning base stations deployed around the construction area, as well as UWB positioning tags worn by construction workers on their safety helmets or belts.

[0032] UWB positioning base stations use the TDOA (Time Difference of Arrival) positioning algorithm to calculate the three-dimensional spatial coordinates of the positioning tag, with a positioning refresh frequency of no less than 10Hz.

[0033] Edge computing nodes obtain continuous three-dimensional coordinate sequences from the UWB positioning engine via 5G networks or industrial Ethernet.

[0034] Furthermore, when processing continuous three-dimensional coordinate sequences, the quadratic difference algorithm first performs median filtering or moving average filtering preprocessing on the original coordinate sequence to suppress coordinate jump noise caused by the multipath effect of UWB signals in the steel structure construction environment; after filtering, the coordinate values ​​at adjacent sampling times are subjected to first-order backward difference to obtain the velocity components of each axis, and the two consecutive velocity components are subjected to second-order difference to obtain the acceleration components. The current instantaneous velocity vector is then corrected based on the acceleration components to improve the smoothness and accuracy of velocity estimation.

[0035] For example, six UWB positioning base stations are deployed at the construction site, located at the four corners of the hoisting operation area and on top of temporary facilities on both sides, forming a three-dimensional positioning network covering the entire construction area. A construction worker wearing a UWB positioning tag is walking on the southeast side of the hoisting area. The tag transmits pulse signals to the base stations at a frequency of 12Hz. The positioning engine calculates the worker's three-dimensional coordinate sequence at six consecutive sampling times using the TDOA algorithm: (12.3, 8.7, 0.5), (12.5, 8.5, 0.5), (12.8, 8.2, 0.5), (13.1, 7.9, 0.5), (13.4, 7.6, 0.5), (13.7, 7.3, 0.5), in meters. After the edge computing node obtains the sequence, it first performs median filtering with a window width of 3 on each axis coordinate to suppress multipath noise. Then, it performs first-order backward difference on the filtered sequence to obtain the X-axis velocity components of 0.4m / s, 0.5m / s, 0.5m / s, 0.5m / s, 0.5m / s, and 0.5m / s in each sampling interval; the Y-axis velocity components of -0.4m / s, -0.5m / s, -0.5m / s, -0.5m / s, and -0.5m / s; and the Z-axis velocity components of 0. After second-order difference calculation, the acceleration components of each axis are close to zero, indicating that the person is in uniform linear motion. The instantaneous velocity vector of the person is synthesized as follows: direction about 45° west of north, speed about 0.71m / s.

[0036] S3: The edge computing node predicts the estimated contact time between personnel and the boundary of the hazard zone based on the instantaneous velocity vector and the dynamic boundary of the three-dimensional hazard zone.

[0037] Preferably, the predicted contact time between personnel and the boundary of the hazardous area includes: The edge computing node takes the current three-dimensional coordinates of the personnel as the starting point, extends a spatial ray along the instantaneous velocity vector direction, and calculates the distance between the intersection point of the spatial ray and the dynamic boundary surface of the three-dimensional danger zone. Divide the distance between the intersection points by the magnitude of the instantaneous velocity vector to obtain the expected contact time.

[0038] Furthermore, when calculating the estimated contact time, if the instantaneous velocity vector magnitude is zero or lower than the preset static threshold, the edge computing node determines that the person is in a static or quasi-static state and directly outputs the estimated contact time as infinite, without entering the subsequent speed limit judgment process, so as to save edge node computing resources and avoid generating false alarms for stationary personnel.

[0039] Specifically, the distance between the intersection point of the spatial ray and the dynamic boundary surface of the three-dimensional hazardous area is calculated using an analytical geometric method that solves the parametric equations of the ray and the ellipsoid simultaneously.

[0040] The parametric equations of an asymmetric ellipsoid in Cartesian coordinates are: ; in, Let be the coordinate variable of any point on the boundary surface of the hazardous domain in three-dimensional space; is the coordinate of the center of the ellipsoid; is the radius of the ellipsoid in the X-axis direction (usually corresponding to the east-west direction or the tangent direction of the boom's rotation); The radius of the ellipsoid in the Y-axis direction (usually corresponding to the north-south direction or the radial direction of the boom); The radius of the ellipsoid in the Z-axis direction (vertical direction).

[0041] Furthermore, when there are two intersections between the spatial ray and the dynamic boundary surface of the three-dimensional danger zone, the smaller positive real root is taken as the contact time corresponding to the intersection distance; when there is no positive real root, it is determined that the current movement direction of the personnel does not intersect with the danger zone, and the expected contact time is set to infinity.

[0042] When the direction of the instantaneous velocity vector of a person changes, the edge computing node repeatedly performs the calculation of the expected contact time at a fixed time period, preferably 100 milliseconds to 500 milliseconds.

[0043] Specifically, the preset time window is a configurable parameter, typically ranging from 1.0 second to 3.0 seconds.

[0044] The selection of this time window needs to balance the accuracy of prediction and the real-time performance of calculation: if the time window is too short, the prediction will not be conservative enough and will not provide sufficient buffer for the crane to decelerate; if the time window is too long, the prediction uncertainty will increase and may easily trigger unnecessary speed limiting intervention.

[0045] For example, based on the current-moment asymmetric ellipsoidal hazard domain parameters generated in step S1—center coordinates (15.0, 10.0, 25.0), triaxial radii a=3.9m, b=3.2m, c=1.5m—and the personnel's current coordinates (13.7, 7.3, 0.5) and instantaneous velocity vector (0.5, -0.5, 0) m / s calculated in step S2, the edge computing nodes perform spatiotemporal collision prediction. Starting from the personnel's coordinates, a spatial ray is extended along the velocity direction. The ray parameter equation is P = (13.7, 7.3, 0.5) + t·(0.5, -0.5, 0) m / s. Substituting the ray equation into the ellipsoid equation and solving the system of equations, we obtain a quadratic equation with respect to the time parameter t. Solving for the two real roots t1 = 2.1s and t2 = -5.6s, we take the smaller positive real root t = 2.1s as the contact time corresponding to the intersection distance. Combining this with the velocity modulus, we verify that the distance is approximately 1.5m, which is consistent with the geometric relationship. At this point, the expected contact time is 2.1s. The edge nodes repeat the above calculation with a fixed period of 200ms to continuously track the latest spatiotemporal relationship between the personnel movement trajectory and the boundary of the danger zone.

[0046] S4: When the expected contact time is less than the preset safe time threshold, the edge computing node sends a speed limit command to the crane controller to proactively limit the crane's operating speed before personnel actually enter the danger zone.

[0047] Preferably, when the expected contact time is greater than a preset safety time threshold, the edge computing node maintains the current operating speed of the crane and does not trigger a speed limit command; When the expected contact time is less than the preset safety time threshold, the edge computing node dynamically adjusts the preset safety time threshold according to the current construction stage of the hoisting operation. Among them, the safe time threshold when the suspended object is in the high-position lifting stage is greater than the safe time threshold when the suspended object is in the low-position translation stage.

[0048] Specifically, the speed limit instruction is a graded speed limit instruction, the details of which are as follows; When the expected contact time is within the first threshold range, the edge computing node sends a first-level limit command to the crane controller, limiting the crane's slewing speed to a first percentage of the rated speed; When the expected contact time is within the second threshold range, the edge computing node sends a second-level limit command to the crane controller, limiting the crane's slewing speed to a second percentage of the rated speed; The expected contact time corresponding to the second threshold interval is less than the expected contact time corresponding to the first threshold interval, and the second percentage is less than the first percentage.

[0049] Furthermore, when the edge computing node sends a speed limit command to the crane controller, it simultaneously sends a warning signal to the audible and visual alarm device at the construction site. The type of warning signal is associated with the currently triggered speed limit level. The first-level limit command corresponds to a yellow flashing light and an intermittent buzzer, while the second-level limit command corresponds to a red flashing light and a continuous buzzer, to remind construction workers and crane operators that they are currently in an active safety intervention state.

[0050] Furthermore, when the edge computing node detects a deflection in the direction of the instantaneous velocity vector of a person, causing the expected contact time to exceed the preset safe time threshold again, the edge computing node sends a speed limit release command to the crane controller to restore the crane's normal operating speed.

[0051] Specifically, when the edge computing node sends the command to lift the speed limit, it uses a ramp recovery strategy to gradually increase the crane's operating speed to the target speed before the intervention, rather than abruptly jumping to recovery.

[0052] The acceleration slope of the slope recovery strategy should not exceed 0.1 m / s². 2 This is to avoid the impact of sudden speed changes on the stability of the suspended load and the crane's transmission system.

[0053] The triggering conditions for lifting the speed limit command also include: the edge computing node determines that the expected contact time is greater than the preset safe time threshold again for N consecutive sampling cycles, where N is an integer greater than or equal to 3, in order to prevent command oscillation caused by instantaneous positioning jitter.

[0054] For example, during the current construction phase of high-level horizontal movement of the hoisted object, the system's preset safety time benchmark threshold is 3.0s. The estimated contact time output in step S3 is 2.1s, which is less than this benchmark threshold. The edge computing node determines that it needs to enter the graded speed limiting process. Since 2.1s falls within the first threshold range (1.5s, 3.0s), the node sends a first-level speed limit command to the crane PLC, limiting the crane's current slewing speed from 80% to 40% of the rated value. At the same time, the yellow warning light at the construction site begins to flash and is accompanied by intermittent beeping, indicating that the relevant personnel are currently in a first-level active intervention state. About 1.8s later, the personnel realize that they are approaching the hoisting danger zone and change their direction of travel. Step S2 detects that their velocity vector deflects by about 60°. Step S3 recalculates the estimated contact time to 4.5s. The edge node determines that the estimated contact time is greater than the 3.0s threshold for 5 consecutive sampling cycles (200ms per cycle), triggering the speed limit release condition. The node sends a ramp recovery command to the PLC, and the slewing speed is reduced to 0.08m / s. 2 The acceleration slope smoothly recovered to 80% of the rated speed before intervention within about 3.5 seconds, the audible and visual alarms were simultaneously deactivated, and the hoisting operation resumed normal operation.

[0055] In summary, this invention solves the problem of mismatch in the protective boundary of static electronic fences by fusing crane operating parameters and environmental wind disturbance data through edge computing nodes. Simultaneously, it introduces UWB positioning and a secondary differential algorithm to calculate the instantaneous velocity vector of personnel, predicting the estimated contact time between personnel and the boundary of the danger zone along the velocity direction. This elevates the safety criterion from an instantaneous spatial judgment of "whether they are in the area" to a spatiotemporal prediction and deduction of "when they will contact the boundary." Based on the threshold range of the contact time, it sends graded speed limit commands to the crane controller, implementing smooth proactive intervention before personnel actually enter the danger zone, and automatically resuming operation after the risk is eliminated. This forms a complete control link of "prediction—graded intervention—closed-loop recovery." This invention significantly reduces the false alarm rate and the frequency of construction interruptions while also considering the robustness requirements of complex working conditions such as severe lighting, dust obstruction, and multipath interference at bridge construction sites. It has comprehensive advantages of strong predictability, smooth intervention, high automation, and convenient engineering deployment.

[0056] Example 2, an embodiment of the present invention, provides a bridge component hoisting safety control system based on 5G and edge computing, comprising: a hazard domain dynamic generation module, used to acquire real-time operating parameters of the crane and environmental wind speed and direction data through edge computing nodes, and generate a three-dimensional hazard domain that dynamically changes over time based on extended Kalman filtering; a personnel speed calculation module, used to acquire real-time three-dimensional coordinates of personnel within the construction area through a UWB positioning system, and to calculate the instantaneous velocity vector corresponding to the personnel by the edge computing nodes; a contact time prediction module, used by the edge computing nodes to predict the expected contact time between the personnel and the boundary of the hazard domain based on the instantaneous velocity vector and the dynamic boundary of the three-dimensional hazard domain; and a speed limit execution module, used by the edge computing nodes to send a speed limit command to the crane controller when the expected contact time is less than a preset safety time threshold, so as to actively limit the crane's operating speed before the personnel actually enter the hazard domain.

[0057] Example 3 is an embodiment of the present invention, which differs from the previous embodiment in that: If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0058] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0059] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0060] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0061] It should be noted that the above 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 with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for safe control of bridge component hoisting based on 5G and edge computing, characterized in that: include: Real-time operating parameters of the crane and environmental wind speed and direction data are obtained through edge computing nodes, and a three-dimensional hazard domain that dynamically changes over time is generated based on extended Kalman filtering. The real-time three-dimensional coordinates of personnel within the construction area are obtained through the UWB positioning system, and the instantaneous velocity vectors corresponding to the personnel are calculated by the edge computing nodes. The edge computing node predicts the estimated contact time between a person and the boundary of the hazard zone based on the instantaneous velocity vector and the dynamic boundary of the three-dimensional hazard zone. When the expected contact time is less than a preset safety time threshold, the edge computing node sends a speed limit command to the crane controller to proactively limit the crane's operating speed before personnel actually enter the danger zone.

2. The bridge component hoisting safety control method based on 5G and edge computing as described in claim 1, characterized in that: The real-time operating parameters of the crane include at least the boom slewing angular velocity, luffing speed, hook height, and lifting mass; The generation of a dynamically changing three-dimensional hazard domain based on extended Kalman filtering specifically includes: The weight and hook height are converted into the swaying inertial parameters of the suspended object, and the environmental wind speed and direction data are converted into wind disturbance lateral force parameters. These are then input into the kinematic prediction model of the suspended object to generate the motion envelope trajectory of the suspended object and hook within a preset future time window. Based on the motion envelope trajectory, an asymmetric ellipsoidal danger zone with a windward boundary magnification factor greater than that of the leeward boundary is constructed.

3. The bridge component hoisting safety control method based on 5G and edge computing as described in claim 1, characterized in that: The calculation of the instantaneous velocity vector corresponding to the person by the edge computing node specifically includes: The edge computing node acquires the continuous three-dimensional coordinate sequence of the personnel collected by the UWB positioning system within a preset time window, calculates the instantaneous velocity components of the personnel in each coordinate axis direction through a quadratic difference algorithm, and then synthesizes a three-dimensional velocity vector carrying direction information and velocity information.

4. The bridge component hoisting safety control method based on 5G and edge computing as described in claim 1, characterized in that: The predicted contact time between the personnel and the boundary of the hazardous area specifically includes: The edge computing node takes the current three-dimensional coordinates of the personnel as the starting point, extends a spatial ray along the instantaneous velocity vector direction, and calculates the distance between the intersection point of the spatial ray and the dynamic boundary surface of the three-dimensional hazard domain; The expected contact time is obtained by dividing the distance between the intersection points by the magnitude of the instantaneous velocity vector.

5. The bridge component hoisting safety control method based on 5G and edge computing as described in claim 1, characterized in that: When the expected contact time is greater than the preset safety time threshold, the edge computing node maintains the current operating speed of the crane and does not trigger the speed limit command; When the expected contact time is less than the preset safety time threshold, the edge computing node dynamically adjusts the preset safety time threshold according to the current construction stage of the hoisting operation. Among them, the safe time threshold when the suspended object is in the high-position lifting stage is greater than the safe time threshold when the suspended object is in the low-position translation stage.

6. The bridge component hoisting safety control method based on 5G and edge computing as described in claim 5, characterized in that: The speed limit instruction is a graded speed limit instruction, the details of which are as follows; When the expected contact time is within the first threshold range, the edge computing node sends a first-level limit command to the crane controller to limit the crane's slewing speed to a first percentage of the rated speed; When the expected contact time is within the second threshold range, the edge computing node sends a second-level limiting command to the crane controller to limit the crane's slewing speed to a second percentage of the rated speed; Wherein, the expected contact time corresponding to the second threshold interval is less than the expected contact time corresponding to the first threshold interval, and the second percentage is less than the first percentage.

7. The bridge component hoisting safety control method based on 5G and edge computing as described in claim 6, characterized in that: When the edge computing node detects that the direction of the instantaneous velocity vector of the person has deflected, causing the expected contact time to exceed the preset safe time threshold again, the edge computing node sends a speed limit release command to the crane controller to restore the normal operating speed of the crane.

8. A safety control system for bridge component hoisting based on 5G and edge computing, based on the safety control method for bridge component hoisting based on 5G and edge computing as described in any one of claims 1 to 7, characterized in that: include, The hazard domain dynamic generation module is used to obtain real-time operating parameters of the crane and environmental wind speed and direction data through edge computing nodes, and generate a three-dimensional hazard domain that dynamically changes over time based on extended Kalman filtering. The personnel velocity calculation module is used to obtain the real-time three-dimensional coordinates of personnel in the construction area through the UWB positioning system, and the edge computing nodes calculate the instantaneous velocity vector of the personnel. The contact time prediction module is used by edge computing nodes to predict the expected contact time between personnel and the boundary of the hazard area based on the instantaneous velocity vector and the dynamic boundary of the three-dimensional hazard area. The speed limit execution module is used to send a speed limit command from the edge computing node to the crane controller when the expected contact time is less than a preset safety time threshold, so as to proactively limit the crane's operating speed before personnel actually enter the danger zone.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the bridge component hoisting safety control method based on 5G and edge computing as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the bridge component hoisting safety control method based on 5G and edge computing as described in any one of claims 1 to 7.

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