Steel structure trestle hoisting device

Abstract

The invention relates to a steel structure trestle hoisting device, belongs to the technical field of bridge construction machinery, and is used for solving the technical problems that traditional hoisting equipment is low in mounting precision, poor in posture adjusting efficiency and easy to deform in structure. The device comprises a portal crane main body, wherein an electric trolley of which the stroke is matched with the width of a trestle is arranged at the lower part of a fixed cross beam; the dynamic self-adaptive balance assembly adjusts load balance in real time through a rigid frame, a three-axis tilt angle sensor, a balancing weight and an electric push rod. The lifting appliance group adopts a servo motor to drive four lifting hooks to cooperatively work, and a laser positioning assembly is combined for precise positioning; the digital twinning cooperative control system integrates prediction, control logic and a pre-judgment type intervention module through an embedded industrial controller, analyzes data of an encoder and a pressure sensor in real time, predicts deformation and gravity center shift, and dynamically regulates and controls the hoisting speed, the transverse position of a trolley and the balance weight displacement. The device is suitable for high-precision rapid installation of the large-span steel structure trestle, and the construction safety and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge construction machinery, and more specifically, to a steel structure trestle hoisting device. Background Art

[0002] As a key component of modern transportation infrastructure, the quality of hoisting construction of large-span steel trestles directly affects the safety and service life of bridge structures. At present, the traditional hoisting process of gantry cranes combined with manually adjusted hoists is widely used at home and abroad. Although it can meet basic construction needs, it exposes significant technical bottlenecks under complex working conditions: First, during the hoisting process, due to the large span and low stiffness of the trestles, they are easily affected by wind loads and inertia forces and are prone to flexural deformation. Traditional equipment lacks deformation prediction and dynamic compensation mechanisms, which can easily lead to segment docking misalignment or structural damage; second, the posture adjustment of the hoisting device depends on manual experience, the coordination efficiency of multiple hooks is low, and the existing balancing devices are mostly fixed counterweight modes, which cannot respond to the center of gravity shift in real time and have the risk of overturning; third, the positioning accuracy depends on manual measurement, which is greatly affected by environmental interference and is difficult to meet the millimeter-level docking requirements of large-span segments.

[0003] With the advancement of technology, mechanical linkage structures are gradually introduced into the existing technology to optimize the operating efficiency of a single crane. For example, the steel trestle hoisting device disclosed in the comparative document CN222454340U adjusts the pulley spacing through a bidirectional screw rod, and combines the gear linkage assembly to realize the synchronous retraction and release of the steel cable by the reel, thereby controlling the height difference of the hook to adjust the inclination angle of the trestle. This solution solves the problem that a single crane cannot accurately adjust the angle, but it still has significant limitations: First, the mechanical linkage relies on the preset gear meshing and reel synchronization, lacks the real-time perception and adaptive compensation capabilities of dynamic loads (such as wind loads and inertial forces), resulting in the failure of deformation control under sudden working conditions; second, the device does not integrate a high-precision sensor network, and the positioning relies on manual visual inspection or basic distance measurement tools, making it difficult to achieve millimeter-level docking accuracy; third, the adjustment process requires frequent start and stop of the motor and hydraulic device, with low coordination efficiency, and lacks digital modeling and risk prediction of the entire hoisting process, so there are still hidden dangers in construction safety. In addition, the device does not use advanced technology to simulate and predict the entire hoisting process, and there is still room for improvement in construction efficiency and safety.

[0004] Although some solutions in the existing technology have introduced inclination sensors or laser ranging devices, the lack of fusion analysis and closed-loop feedback control of multi-source sensor data has led to problems such as dynamic balance adjustment lag and insufficient counterweight compensation accuracy. How to achieve the deep integration of deformation prediction during the hoisting process, multi-hook coordinated control and high-precision positioning has become a key problem restricting the efficient and safe construction of steel structure trestles. Summary of the invention

[0005] An object of the present invention is to solve at least the above problems and to provide at least the advantages which will be described hereinafter.

[0006] Another object of the present invention is to provide a steel structure trestle hoisting device, which compensates for the center of gravity deviation in real time through a dynamic adaptive balancing component, combines laser positioning with a digital twin system to achieve millimeter-level precise docking, simultaneously previews working conditions and automatically adjusts hoisting parameters, significantly improving the deformation control accuracy and anti-interference ability of large-span steel trestle hoisting, and ensuring safe and efficient construction.

[0007] In order to achieve these purposes and other advantages according to the present invention, a steel structure trestle hoisting device is provided, comprising: The main body of the gantry crane has an electric trolley that can move laterally installed under the fixed crossbeam, and the moving stroke of the electric trolley is equal to the maximum width of the trestle segment; A dynamic adaptive balancing assembly, which includes a rigid frame connected to the electric trolley, a three-axis tilt sensor installed in the middle of the rigid frame, a counterweight integrated inside the rigid frame, and an electric push rod connected to the counterweight; a spreader assembly comprising four hooks integrated on a rigid frame via a wire rope drum driven by a servo motor; A laser positioning assembly, which includes four reflective laser targets arranged at the four corners of the upper surface of the trestle segment, and four laser rangefinders correspondingly installed at the bottom of the fixed crossbeam; A digital twin collaborative control system, comprising an embedded industrial controller, an incremental encoder mounted on an electric trolley, and four pressure sensors respectively distributed on the pseudo-contact surfaces of four hooks and trestles; the embedded industrial controller is communicatively connected to the servo motor driver, the stepper motor driver of the electric trolley, the laser rangefinder, and the electric push rod driver; Among them, the embedded industrial controller has built-in prediction module, control logic module and predictive intervention module, the prediction module is used to predict the deformation of the trestle segment and the center of gravity offset index of the sling group, the control logic module is communicated with the prediction module and the predictive intervention module, the control logic module is used to compare the predicted trestle segment deformation and the center of gravity offset index of the sling group with the deformation threshold and the offset threshold respectively, and trigger the predictive intervention module when the threshold is exceeded, and the predictive intervention module synchronously adjusts the lifting speed of the servo motor, the lateral position of the electric trolley and the displacement of the counterweight block.

[0008] Preferably, the gantry crane body further comprises a pair of columns, the fixed beam is rigidly connected to the top of the columns, a horizontal slide rail is provided at the bottom of the fixed beam, and the electric trolley is laterally movable on the horizontal slide rail.

[0009] Preferably, the dynamic adaptive balancing component comprises: A guide rail is arranged on the inclined beam of the rigid frame, and a slider is adapted to be arranged on the guide rail; A counterweight block, the bottom of which is provided with a guide groove cooperating with the sliding block, and the top of which is provided with a connecting ear plate; An electric push rod is arranged on the rigid frame, and includes an electric push rod driver, a screw rod, a nut seat and a push rod. The electric push rod driver drives the screw rod through a coupling, and the screw rod is threadedly matched with the nut seat. One end of the push rod is hinged to the nut seat, and the other end is connected to the connecting ear plate on the counterweight block through a universal joint.

[0010] Preferably, the prediction module is configured with a digital twin virtual scene of the hoisting process, and the construction steps are as follows: Real-time collection of vertical distance data h1~h4 of the laser rangefinder, lateral displacement x of the electric car, and pitch angle θ of the three-axis tilt angle sensor x and roll angle θ y , and add timestamps to data through a unified clock source; The 3D model of the trestle segment is discretized according to the finite element mesh, the mesh nodes are bound to the laser target installation positions, and h1~h4 are used as the displacement boundary conditions of the nodes; Based on θ x ,θ y Calculate the Euler angle rotation matrix R (θ x ,θ y ), drives the pose of the spreader group in the virtual model to be updated synchronously; at the same time, according to the displacement x of the electric trolley, the horizontal position coordinates of the spreader group are updated.

[0011] Preferably, the specific method for predicting the deformation amount of the trestle segment in the prediction module includes: Based on the vertical distance data h1~h4 of the laser rangefinder, calculate the maximum height difference of the four corners of the trestle segment ; According to the pressure sensor load F1~F4, according to the formula Calculate the local deformation of each hanging point and the total deformation δ. ; The deformation D of the trestle segment is predicted based on the maximum height difference and the total deformation. ; Where L k Refers to the distance from k slings to the end of the trestle segment in the length direction, m; E refers to the elastic modulus of the trestle segment material, Pa; I refers to the section inertia moment of the trestle segment, m 4 , I is the width of the rectangular section multiplied by the cube of the height of the rectangular section divided by 12.

[0012] Preferably, the specific method for predicting the center of gravity deviation index of the spreader group in the prediction module includes: Obtain the pitch angle θ of the spreader group according to the three-axis inclination sensor x and roll angle θ y , calculate the tilt component T, ; Calculate the pressure sensor load standard deviation σ F ; The center of gravity deviation index G of the spreader group is predicted based on the tilt component and the standard deviation of the pressure sensor load, G=0.8T+0.2σ F .

[0013] Preferably, the logic of the predictive intervention module to synchronously adjust the lifting speed of the servo motor, the lateral position of the electric vehicle and the displacement of the counterweight is: Speed ​​compensation logic for servo motor: According to θ x and θ y The fuzzy PID algorithm is used to generate the speed compensation of the four hooks according to the size and shape of the hooks. , control the lifting speed deviation of the servo motor ≤1mm / s; For the electric trolley avoidance control logic: According to the predicted results of the trestle segment deformation, the target displacement x of the electric trolley is calculated. T =x+0.2△h max , and drive the stepper motor to move in micro-step mode until the error is ≤ 2mm; Dynamic adjustment logic for the counterweight block: According to the tilt direction, the electric push rod drives the counterweight block to move in the opposite direction of the tilt direction.

[0014] Preferably, the digital twin virtual scene of the hoisting process is further provided with a virtual interference warning unit, and its construction logic is: Preloading a three-dimensional point cloud model of the gantry crane body; Real-time calculation of the minimum Euclidean distance d between the deformation contour of the trestle segment and the column point cloud min ; If d is detected three times in a row min <100mm, generate light avoidance instruction △x=0.1(d min -100) and trigger the sound and light alarm set on the gantry crane body.

[0015] Preferably, the dynamic adaptive balancing component further comprises: A piezoelectric ceramic sheet array is arranged on the contact surface between the guide groove and the slider to convert the vibration mechanical energy of the counterweight block when it moves into electrical energy; The supercapacitor, which is arranged on the rigid frame, is used to store the recovered electrical energy and to power the laser rangefinder and the pressure sensor.

[0016] Preferably, the dynamic adaptive balancing component further comprises: A grating strain sensor is arranged in the stress concentration area of ​​the rigid frame to monitor the microstrain of the rigid frame in real time; the grating strain sensor is communicatively connected to the embedded industrial controller and predicts the remaining life of the rigid frame based on Miner's linear cumulative damage theory. When the remaining life is ≤1000 cycles, an early warning signal is triggered.

[0017] The present invention has at least the following beneficial effects: First, the steel structure trestle hoisting device provided by the present invention realizes real-time monitoring and automatic control of the whole hoisting process by integrating the gantry crane body, dynamic balancing components, laser positioning and digital twin system, thereby improving the adjustment efficiency and enhancing the wind load resistance. The laser positioning component realizes the vertical distance measurement accuracy of the four corners of the trestle segment through four laser rangefinders and reflective targets, and effectively controls the docking error by combining the digital twin model to compensate for thermal expansion deformation. The predictive intervention module synchronously adjusts the hook speed, electric trolley position and counterweight displacement based on the predicted deformation amount D and center of gravity offset index G, thereby realizing stable hoisting under complex working conditions. Second, the lateral movement stroke of the electric trolley of the steel structure trestle hoisting device provided by the present invention matches the maximum width of the trestle segment, and there is no need to manually adjust the position of the hoisting point, which reduces the auxiliary time and adapts to the efficient hoisting requirements of different span segments; Thirdly, the dynamic adaptive balancing assembly of the steel structure trestle hoisting device provided by the present invention realizes dynamic displacement compensation of the counterweight block through the cooperation of the three-axis inclination sensor and the electric push rod, so that the inclination angle of the hoisting device is controlled within ±0.1°, thereby reducing the risk of overturning; Fourthly, the prediction module in the steel structure trestle hoisting device provided by the present invention constructs a digital twin scene of the hoisting process, drives the virtual model to update synchronously through the Euler angle rotation matrix, predicts the working conditions in advance and automatically adjusts the parameters, which greatly shortens the response time to sudden wind loads; Fifth, the steel structure trestle hoisting device provided by the present invention adopts fuzzy PID algorithm to generate speed compensation, so as to achieve synchronous lifting error of four hooks ≤1mm / s, and the electric trolley moves in micro-step mode to an error ≤2mm, and cooperates with the reverse displacement of the counterweight block to eliminate the accumulated error of segment tilt; Sixth, the steel structure trestle hoisting device provided by the present invention is designed with a virtual interference warning unit to calculate the minimum distance d between the trestle segment and the column in real time. min , triggering the avoidance command in advance reduces the misjudgment rate and further ensures construction safety; Seventhly, the steel structure trestle hoisting device provided by the present invention is further designed with energy recovery components to achieve green construction.

[0018] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a structural schematic diagram of the steel structure trestle hoisting device in one embodiment of the present invention; Figure 2 It is a schematic diagram of the control flow of the steel structure trestle hoisting device in another embodiment of the present invention.

[0020] Among them, 1. Fixed beam; 2. Column; 3. Electric trolley; 4. Rigid frame; 5. Guide rail; 6. Hook; 7. Counterweight; 8. Electric push rod. DETAILED DESCRIPTION

[0021] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0022] It should be understood that the terms such as “having”, “including” and “comprising” used herein do not exclude the existence or addition of one or more other elements or combinations thereof.

[0023] like Figure 1 and Figure 2 As shown, the present invention provides a steel structure trestle hoisting device, comprising: The main body of the gantry crane has an electric trolley 3 which can move laterally installed at the lower part of the fixed crossbeam 1, and the moving stroke of the electric trolley 3 is equal to the maximum width of the trestle segment; A dynamic adaptive balancing assembly, which includes a rigid frame 4 connected to the electric trolley 3, a three-axis tilt sensor installed in the middle of the rigid frame 4, a counterweight 7 integrated inside the rigid frame 4, and an electric push rod 8 connected to the counterweight 7; A lifting device group, which includes four hooks 6 integrated on a rigid frame 4 through a wire rope drum driven by a servo motor; A laser positioning assembly, which includes four reflective laser targets arranged at the four corners of the upper surface of the trestle segment, and four laser rangefinders correspondingly installed at the bottom of the fixed crossbeam 1; A digital twin collaborative control system, which includes an embedded industrial controller, an incremental encoder installed on the electric trolley 3, and four pressure sensors respectively distributed on the quasi-contact surfaces of the four hooks 6 and the trestles; the embedded industrial controller is communicatively connected to the servo motor driver, the stepper motor driver of the electric trolley 3, the laser rangefinder and the electric push rod 8 driver; Among them, the embedded industrial controller has built-in prediction module, control logic module and predictive intervention module, the prediction module is used to predict the deformation of the trestle segment and the center of gravity offset index of the sling group, the control logic module is communicated with the prediction module and the predictive intervention module, the control logic module is used to compare the predicted trestle segment deformation and the center of gravity offset index of the sling group with the deformation threshold and the offset threshold respectively, and trigger the predictive intervention module when the threshold is exceeded, and the predictive intervention module synchronously adjusts the lifting speed of the servo motor, the lateral position of the electric trolley 3 and the displacement of the counterweight block 7.

[0024] In the above embodiment, the main body of the gantry crane adopts a gantry frame structure, and the fixed beam 1 is supported by the column 2 to ensure stability during the lifting process. The electric trolley 3 is installed at the lower part of the fixed beam 1. The electric trolley 3 is driven by a stepper motor and moves laterally along the fixed beam 1. The stroke covers the maximum width of the trestle segment, ensuring that the sling group can be accurately positioned at any position of the trestle segment, adapting to the lifting requirements of trestle segments of different sizes, reducing the movement frequency of the crane body, and improving operating efficiency.

[0025] In the above embodiment, the rigid frame 4 is used as a carrier to connect the electric trolley 3 and the sling group. It is made of high-strength material, such as Q460C high-dispatch low-alloy steel (yield strength ≥ 460MPa). The single load-bearing capacity can reach 372 tons. The three-axis inclination sensor monitors the inclination angle of the sling group and the trestle segment in real time, with an accuracy of ±0.1°. The electric push rod 8 pushes the counterweight block 7 to change the overall center and reduce the inefficient tilting torque. Each hook 6 is controlled by a servo motor driven wire rope drum. The maximum load of a single hook can reach 50 tons and the total load is 200 tons. The servo motor communicates with the embedded industrial controller to achieve synchronous control of the lifting speed of the four hooks 6 to ensure the horizontal lifting of the trestle segment. Prism targets are installed at the four corners of the trestle segment to ensure the stability of the laser rangefinder signal. The laser rangefinder is installed at the bottom of the fixed beam 1. The distance of the target is measured in real time through triangulation positioning with an accuracy of ±1mm to calculate the spatial position and posture of the trestle segment.

[0026] In the above embodiment, an incremental encoder is used to monitor the displacement of the electric trolley 3. The increment is the resolution of the encoder of 1μm. The pressure sensor is installed on the contact surface between the hook 6 and the trestle segment to feedback the contact force in real time with an accuracy of ±0.5%FS to ensure uniform load distribution. The embedded industrial controller adopts a multi-core processor and integrates a real-time operating system to achieve μ-level response. The prediction module predicts the deformation of the trestle segment and the center of gravity offset index of the sling group during the lifting process in real time. The control logic module compares the predicted value with the deformation threshold and the offset threshold to trigger the predictive intervention module. The servo motor dynamically adjusts the lifting speed of the hook 6 according to the predicted deformation to reduce inertial impact. The electric trolley 3 wants to move to compensate for the center of gravity offset, and the counterweight block 7 adjusts the displacement to coordinately offset the torque.

[0027] According to the above implementation, the working process of the steel structure trestle hoisting device provided by the present invention is as follows: In the preparation stage, the geometric parameters, lifting point positions and target installation coordinates of the trestle segment are input through the human-machine interface. The geometric parameters include the length, width and weight of the trestle segment, and the laser rangefinder and the target are completed. Initial positioning calibration and pressure sensor zero point calibration are completed. In the hoisting stage, the stepper motor drives the electric trolley 3 to move to the preset lateral position, and the four hooks 6 are lowered synchronously. When the pressure sensor detects the contact force, it stops descending. The servo motor lifts the hook 6 at a constant speed, and the laser positioning component monitors the position of the trestle segment in real time. The prediction module calculates the deformation of the trestle segment and the center of gravity offset index of the spreader group according to the current lifting height and speed. If the predicted value exceeds the threshold, the predictive intervention module is triggered, and the electric trolley 3 is synchronously moved laterally to compensate for the center of gravity offset. The electric push rod 8 adjusts the position of the counterweight block 7 to balance the torque, and the servo motor fine-tunes the lifting speed to reduce inertia. The laser positioning component guides the trestle segment to slowly descend to the target position, controls the horizontality and verticality, and after confirming that the trestle segment is in place, the hook 6 gradually releases the load, and the pressure sensor monitors the load change to zero. The electric vehicle returns to its initial position, the hoisting assembly rises to a safe height, and the system enters standby mode.

[0028] According to the above implementation mode, it has at least the following technical effects: First, the inclination angle is monitored in real time by a three-axis inclination sensor, and the electric push rod 8 is used to drive the counterweight block 7 to dynamically adjust the center of gravity, so that the inclination angle is controlled within ±1°, the response time is short, and the hoisting stability is significantly improved to avoid the risk of overturning; second, the laser positioning component obtains the three-dimensional coordinates of the trestle segment in real time through four laser rangefinders, and combines the synchronous control of the four hooks 6 to achieve a horizontal error of ≤2mm / m, with high positioning accuracy, meeting the high-precision installation requirements of bridge projects, etc., and greatly reducing the rework cost; third, the prediction module calculates the deformation of the trestle segment in real time, and triggers intervention in advance through the control logic module, which can effectively prevent structural damage; fourth, the digital twin system realizes real-time mapping of physical and virtual space, integrates pressure sensors and incremental encoders, automatically plans the optimal hoisting path, and reduces the number of trial hoists.

[0029] In one embodiment, the gantry crane body also includes a pair of columns 2, the fixed beam 1 is rigidly connected to the top of the column 2, a horizontal slide rail is provided at the bottom of the fixed beam 1, and the electric trolley 3 can be laterally movably arranged on the horizontal slide rail.

[0030] In the above embodiment, the column 2 is made of high-strength steel, such as Q345 or Q460, etc., and has good compression and bending resistance. During the manufacturing process, the column 2 is precisely processed and welded to ensure its dimensional accuracy and structural strength. The fixed beam 1 is rigidly connected to the top of the column 2 by welding or bolting by using high-strength steel, ensuring that a stable integral structure can be formed between the fixed beam 1 and the column 2 to withstand various loads during the lifting process. The horizontal slide rail is installed at the bottom of the fixed beam 1, which is generally made of track steel with high hardness and wear resistance. During installation, it is necessary to ensure the horizontality and straightness of the slide rail to ensure that the electric trolley 3 can move smoothly on it. After the horizontal slide rail is installed, it needs to be rust-proofed and lubricated to reduce the friction when the electric trolley 3 moves. The electric trolley 3 is installed on the horizontal slide rail, and the electric trolley 3 is driven by a stepper motor so that the electric trolley 3 can move laterally along the horizontal slide rail. The incremental encoder is installed on the electric trolley 3 to monitor the position and moving speed of the electric trolley 3 in real time. The embedded industrial controller accurately controls the operation of the stepper motor according to the information fed back by the encoder, thereby realizing the precise movement of the electric trolley 3 on the horizontal slide rail, and its moving stroke is equal to the maximum width of the trestle segment.

[0031] According to the above-mentioned embodiment, the rigid connection between the column 2 and the fixed crossbeam 1 forms a stable gantry structure, which can effectively disperse and bear the vertical and horizontal loads generated during the lifting process, so that the entire gantry crane body is more stable when lifting large trestle segments, reducing the safety risks caused by shaking or tilting, and improving the safety of the lifting operation. The modular design of the horizontal slide rail and the electric trolley 3 makes the maintenance and overhaul of the equipment more convenient and reduces the downtime of the equipment. The travel that the electric trolley 3 can move on the horizontal slide rail is equal to the maximum width of the trestle segment, so that the lifting device can adapt to trestle segments of different sizes, which improves the versatility and practicality of the equipment.

[0032] In one embodiment, the dynamic adaptive balancing component includes: A guide rail 5 is arranged on the inclined beam of the rigid frame 4, and a slider is adapted to be arranged on the guide rail 5; A counterweight block 7, the bottom of which is provided with a guide groove cooperating with the slider, and the top of which is provided with a connecting ear plate; The electric push rod 8 is arranged on the rigid frame 4, and includes an electric push rod 8 driver, a screw, a nut seat and a push rod. The electric push rod 8 driver drives the screw through a coupling, and the screw is threadedly matched with the nut seat. One end of the push rod is hinged to the nut seat, and the other end is connected to the connecting ear plate on the counterweight block 7 through a universal joint.

[0033] In the above embodiment, a guide rail 5 is installed on the inclined beam of the rigid frame 4 as the basic rail for the movement of the counterweight 7. A slider is adapted on the guide rail 5. A guide groove matching the slider is processed at the bottom of the counterweight 7. The slider is embedded in the guide groove so that the counterweight 7 can move along the direction of the guide rail 5. At the same time, a connecting ear plate is provided on the top of the counterweight 7 for connecting with the electric push rod 8 to ensure that the connection between the two is stable and has a certain degree of flexibility. The electric push rod 8 is installed on the rigid frame 4, and the electric push rod 8 driver, screw, nut seat and push rod inside it are assembled in sequence. The electric push rod 8 driver is connected to the screw through a coupling. When the electric push rod 8 driver receives the control signal, it drives the screw to rotate. Since the screw and the nut seat are threaded, the rotation of the screw will cause the nut seat to move along the axial direction of the screw. One end of the push rod is hinged on the nut seat, and the other end is connected to the connecting ear plate on the counterweight block 7 through a universal joint. The movement of the nut seat is transmitted to the counterweight block 7 through the push rod, thereby realizing the movement of the counterweight block 7 on the guide rail 5. The three-axis inclination sensor monitors the inclination of the rigid frame 4 and the spreader group in real time, and transmits the data to the embedded industrial controller. The prediction module in the embedded industrial controller predicts the center of gravity offset index of the spreader group based on the obtained data and other relevant data. The control logic module compares the predicted center of gravity offset index with the preset offset threshold. If it exceeds the threshold, the predictive intervention module will send a control signal to the electric push rod 8 driver to adjust the action of the electric push rod 8, thereby changing the position of the counterweight block 7 to balance the center of gravity offset. At the same time, the prediction module will also predict the deformation of the trestle segment. When the deformation exceeds the deformation threshold, the predictive intervention module will also coordinately adjust the lifting speed of the servo motor, the lateral position of the electric trolley 3, and the displacement of the counterweight block 7.

[0034] According to the above implementation, the dynamic adaptive balancing assembly realizes the dynamic response of the center of gravity compensation through the precise matching of the guide rail 5 and the slider, combined with the screw transmission of the electric push rod 8, and provides a high-precision and highly reliable dynamic balancing solution for the hoisting of large-span steel structures. Among them, the flexible connection of the universal joint eliminates mechanical stress concentration and improves the fatigue life of the equipment; the modular structure can complete the replacement of core components in a short time, and the maintenance efficiency is high; the guide rail 5 is not only on the inclined beam, which expands the center of gravity adjustment range.

[0035] In one implementation, the prediction module is configured with a digital twin virtual scene of the hoisting process, and the construction steps are as follows: Real-time collection of vertical distance data h1~h4 of the laser rangefinder, lateral displacement x of the electric car 3, and pitch angle θ of the three-axis tilt angle sensor x and roll angle θ y , and add timestamps to data through a unified clock source; The 3D model of the trestle segment is discretized according to the finite element mesh, the mesh nodes are bound to the laser target installation positions, and h1~h4 are used as the displacement boundary conditions of the nodes; Based on θ x ,θ y Calculate the Euler angle rotation matrix R (θ x ,θ y ), drives the position and posture of the spreader group in the virtual model to be updated synchronously; at the same time, according to the displacement x of the electric trolley 3, the horizontal position coordinates of the spreader group are updated.

[0036] In the above embodiment, data is collected using a laser rangefinder, an incremental encoder, and a three-axis tilt angle sensor. The laser rangefinder is installed at the bottom of the fixed crossbeam 1 and is responsible for real-time measurement of the vertical distance to the reflective laser targets at the four corners of the upper surface of the trestle segment to obtain data h1~h4; the incremental encoder is installed on the electric trolley 3 to record the lateral displacement x of the electric trolley 3; the three-axis tilt angle sensor is installed in the middle of the rigid frame 4 to collect the pitch angle θ in real time. x and roll angle θ y At the same time, a unified clock source is introduced. At each data collection moment, the clock source is the collected h1~h4, θ x and θ y Accurate timestamps are added to the data to ensure that all data are consistent in the time dimension, which facilitates accurate synchronization and analysis in the digital twin virtual scene later. Use professional 3D modeling software, such as CATIA, to create an accurate 3D model based on the design drawings and actual dimensions of the trestle segment. The model contains detailed geometric information of the trestle segment, such as shape, size, material properties, etc. The constructed 3D model is imported into finite element analysis software, such as ANSYS, and finite element meshing is performed on it. The density and quality of meshing will affect the accuracy of subsequent analysis, and it needs to be reasonably set according to the actual situation. After the division is completed, the 3D model of the trestle segment is discretized into numerous finite element mesh units and nodes. Bind the finite element mesh segment to the installation position of the laser target so that each node is relative to the actual measurement point, and then input the collected h1~h4 data as the displacement boundary conditions of these bound nodes into the finite element model. The finite element model can simulate the deformation of the trestle segment according to the actual measured vertical distance data.

[0037] In the above embodiment, according to the collected pitch angle θ x and roll angle θ y , use the trigonometric formula to calculate the Euler angle rotation matrix R (θ x ,θ y). The Euler angle rotation matrix can describe the rotational posture of the spreader group in three-dimensional space. Through this matrix, the initial posture of the spreader group can be converted into the current actual posture. In the digital twin virtual scene, the calculated Euler angle rotation matrix is ​​used to drive the posture of the virtual spreader group to be updated synchronously. At the same time, according to the lateral displacement of the electric trolley 3 collected by the incremental encoder, the position coordinates of the virtual spreader group in the horizontal direction are updated, so that the posture of the spreader group in the virtual scene can be consistent with the posture of the spreader group in the actual lifting process. The prediction module in the embedded industrial controller uses the updated digital twin virtual scene, combined with finite element analysis and related mechanical models, to predict the deformation of the trestle segment and the center of gravity offset index of the spreader group. The control logic module compares the predicted deformation and center of gravity offset index with the preset deformation threshold and offset threshold respectively. If the predicted value exceeds the threshold, the predictive intervention module will be triggered immediately to synchronously adjust the lifting speed of the servo motor, the lateral position of the electric trolley 3, and the displacement of the counterweight block 7 to ensure the stability and safety of the lifting process.

[0038] According to the above implementation, during the steel structure trestle hoisting process, first, the unified clock source starts working, and the vertical distance data of the four corners of the trestle segment collected in real time by the laser rangefinder, the lateral displacement obtained by the incremental encoder on the electric trolley 3, and the pitch angle and roll angle collected by the three-axis tilt angle sensor are all timestamped to ensure the time consistency of the data. Then, the pre-established 3D model of the trestle segment is discretized by finite element mesh, the mesh nodes are accurately bound to the installation position of the laser target, and then the displacement boundary conditions of these nodes are input into the digital twin virtual scene. Afterwards, based on the collected θ x and θ y The Euler angle rotation matrix of the spreader group is calculated, and this matrix is ​​used to drive the posture of the spreader group in the virtual scene to be updated synchronously. At the same time, the horizontal position coordinates of the spreader group in the virtual scene are updated according to the displacement of the electric trolley 3. The prediction module in the embedded industrial controller predicts the deformation of the trestle segment and the center of gravity offset index of the spreader group based on this real-time updated digital twin virtual scene. The control logic module compares the prediction result with the preset threshold. When the threshold is exceeded, the predictive intervention module adjusts the lifting speed of the servo motor, the lateral position of the electric trolley 3, and the displacement of the counterweight block 7.

[0039] According to the above implementation, by constructing a digital twin virtual scene of the hoisting process, real-time and accurate mapping of the actual hoisting process can be achieved. The virtual scene is updated by using the collected multi-source data, so that the virtual scene is highly synchronized with the actual hoisting situation, which greatly improves the accuracy of the prediction module for the deformation of the trestle segment and the center of gravity offset index of the sling group. This helps to discover potential hoisting risks in advance, such as excessive deformation of the trestle segment and serious center of gravity offset, so that the predictive intervention module can make timely adjustments to avoid accidents and improve the safety of hoisting operations. At the same time, the digital twin virtual scene can provide operators with intuitive visual displays, so that they can fully understand the status of the hoisting process, make more scientific and reasonable decisions, optimize the hoisting process, and improve hoisting efficiency. Moreover, this design based on digital twins is also convenient for post-analysis and evaluation of the hoisting process, providing valuable experience and data support for subsequent similar projects, and helping to continuously improve hoisting technology and equipment performance.

[0040] In one embodiment, the specific method for predicting the deformation amount of the trestle segment in the prediction module includes: Based on the vertical distance data h1~h4 of the laser rangefinder, calculate the maximum height difference of the four corners of the trestle segment ; According to the pressure sensor load F1~F4, according to the formula Calculate the local deformation of each hanging point and the total deformation δ. ; The deformation D of the trestle segment is predicted based on the maximum height difference and the total deformation. ; Where L k Refers to the distance from k slings to the end of the trestle segment in the length direction, m; E refers to the elastic modulus of the trestle segment material, Pa; I refers to the section inertia moment of the trestle segment, m 4 , I is the width of the rectangular section multiplied by the cube of the height of the rectangular section divided by 12.

[0041] In the above embodiment, the laser rangefinder measures the vertical distance data h1~h4 of the four corners of the trestle segment in real time. After the embedded industrial controller obtains this data, it takes the absolute value of the difference between every two distance data to obtain all possible differences, and then finds the maximum value from these differences, which is the maximum height difference △hmax of the four corners of the trestle segment. The pressure sensors on the four hooks 6 and the quasi-contact surface of the trestle segment collect the loads F1~F4 of each lifting point in real time. At the same time, the elastic modulus E of the trestle segment material, the section moment of inertia I (for rectangular sections, calculated according to the formula based on the section width and height) and the distance L from each sling to the end of the length direction of the trestle segment are obtained in advance. kThe embedded industrial controller calculates the local deformation of each hanging point according to the formula, and then adds the local deformations of the four hanging points to obtain the total deformation. The maximum height difference and the total deformation calculated above are used to obtain the deformation D of the trestle segment, which will be used for subsequent comparison with the deformation threshold.

[0042] According to the above implementation, during the hoisting operation of the steel structure trestle, the laser rangefinder continuously measures the vertical distances h1~h4 of the four corners of the trestle segment and transmits them to the embedded industrial controller, while the pressure sensor collects the loads F1~F4 of each lifting point in real time. After the controller obtains these data, it first calculates the maximum height difference, and then calculates the local deformation of each lifting point and accumulates the total deformation according to the pre-input material parameters (elastic modulus E) of the trestle segment, the cross-sectional parameters (used to calculate the cross-sectional inertia moment I) and the distance from each hoist to the end. Finally, based on the maximum height difference and the total deformation, the deformation D of the trestle segment is predicted. The prediction module provides the D value to the control logic module, which compares it with the preset deformation threshold. If it exceeds the threshold, the predictive intervention module will adjust the lifting speed of the servo motor, the lateral position of the electric trolley 3 and the displacement of the counterweight block 7 to ensure the safety and stability of the hoisting.

[0043] According to the above implementation, the design method for predicting the deformation of the trestle segment has many advantages. First, the height difference measured by the laser rangefinder and the load of the hanging point collected by the pressure sensor are comprehensively considered, and the factors affecting the deformation of the trestle segment are obtained from multiple dimensions, making the prediction of the deformation more comprehensive and accurate. For example, relying solely on the height difference cannot accurately reflect the local deformation caused by the force at the hanging point, and the deformation calculated in combination with the hanging point load can make up for this deficiency. Secondly, the deformation is predicted by combining two key data through a specific weighted formula. After practical or theoretical verification, it can be more in line with the actual deformation of the trestle segment, provide a more accurate decision-making basis for the predictive intervention module, effectively reduce the safety risks caused by excessive deformation of the trestle segment, and improve the reliability and safety of the lifting operation. At the same time, this calculation method is based on common mechanical principles and mathematical models, and the data acquisition and calculation process is relatively clear and easy to implement, with high engineering practicality.

[0044] In one embodiment, the specific method for predicting the center of gravity deviation index of the spreader group in the prediction module includes: Obtain the pitch angle θ of the spreader group according to the three-axis inclination sensor x and roll angle θ y , calculate the tilt component T, ; Calculate the pressure sensor load standard deviation σ F ; The center of gravity deviation index G of the spreader group is predicted based on the tilt component and the standard deviation of the pressure sensor load, G=0.8T+0.2σF .

[0045] In the above embodiment, the three-axis inclination sensor is installed in the middle of the rigid frame 4, which can obtain the pitch angle θ of the spreader group in real time. x and roll angle θ y . After the embedded industrial controller receives the two angle data, it takes their absolute values ​​respectively, and then adds the two absolute values ​​to obtain the tilt component T. The tilt component reflects the degree of tilt of the sling group in space, and is an important indicator for measuring the center of gravity offset. The four pressure sensors are respectively distributed on the pseudo-contact surfaces between the four hooks 6 and the trestle segments, and the loads of each hanging point are collected in real time. After the embedded industrial controller obtains these load data, it calculates the standard deviation of these data according to the standard deviation calculation formula. The standard deviation can reflect the degree of discreteness of the data, reflects the unevenness of the loads at the four hanging points, and is also a key factor in judging whether the center of gravity is offset. After obtaining the tilt component T and the pressure sensor load standard deviation σ F Finally, the embedded industrial controller calculates the center of gravity deviation index G of the spreader group according to the formula. By assigning different weights to the tilt component and the standard deviation of the sensor load, the tilt of the spreader group and the unevenness of the load at the lifting point are comprehensively considered, so as to more accurately predict the degree of center of gravity deviation of the spreader group.

[0046] According to the above embodiment, during the hoisting process of the steel structure trestle, the three-axis inclination sensor continuously monitors the pitch angle θ of the hoisting device group. x and roll angle θ y The data is transmitted to the embedded industrial controller in real time. At the same time, the four pressure sensors continuously collect the loads F1~F4 of each hanging point and send them to the controller. After receiving these data, the controller first calculates the tilt component T, that is, the tilt component T of θ x and θ y Take the absolute value and add them together. Next, calculate the standard deviation σ for the load data F1~F4 F Finally, T and σ F Substitute into the formula G=0.8T+0.2σ F , calculate the center of gravity offset index G of the spreader group. The prediction module provides this G value to the control logic module, and the control logic module compares the G value with the preset offset threshold. If the G value exceeds the threshold, the predictive intervention module will immediately and synchronously adjust the lifting speed of the servo motor, the lateral position of the electric trolley 3, and the displacement of the counterweight block 7 to correct the center of gravity offset of the spreader group and ensure the smoothness and safety of the lifting process.

[0047] According to the above implementation, the design method for predicting the center of gravity offset index of the spreader group has significant advantages. First, the inclination of the spreader group and the unevenness of the load at each lifting point are comprehensively considered. The inclination component T reflects the overall inclination of the spreader group, and the pressure sensor load standard deviation σF It reflects the difference in the forces on the lifting points. By combining the two, the center of gravity offset of the sling group can be comprehensively and accurately judged from multiple angles. Secondly, by calculating the center of gravity offset index through a weighted formula, the weights of the tilt component and the standard deviation can be reasonably allocated according to the actual situation, so that the prediction results are more in line with the actual degree of center of gravity offset, providing a reliable basis for the control logic module. When the center of gravity offset index exceeds the threshold, the predictive intervention module can make timely adjustments to avoid hoisting accidents caused by excessive center of gravity offset, greatly improving the safety and stability of hoisting operations. In addition, the prediction method based on sensor data and mathematical models is adopted, which is convenient for data acquisition and relatively simple in calculation process. It has high practicality and operability and can be widely used in actual engineering.

[0048] In one embodiment, the logic of the predictive intervention module to synchronously adjust the lifting speed of the servo motor, the lateral position of the electric vehicle 3 and the displacement of the counterweight 7 is: Speed ​​compensation logic for servo motor: According to θ x and θ y The fuzzy PID algorithm is used to generate the speed compensation of the four hooks 6. , control the lifting speed deviation of the servo motor ≤1mm / s; For the avoidance control logic of electric trolley 3: According to the prediction results of the deformation of the trestle segment, the target displacement x of electric trolley 3 is calculated. T =x+0.2△h max , and drive the stepper motor to move in micro-step mode until the error is ≤ 2mm; Dynamic adjustment logic for the counterweight 7: According to the tilting direction, the counterweight 7 is driven by the electric push rod 8 to move in the opposite direction of the tilting direction.

[0049] In the above implementation, for the servo motor speed compensation logic: the three-axis inclination sensor collects the pitch angle θ of the spreader group in real time x and roll angle θ y And transmit it to the embedded industrial controller. The controller uses the fuzzy PID algorithm according to the positive and negative signs and numerical values ​​of the two angles. This algorithm combines the flexibility of fuzzy control with the accuracy of PID control, and comprehensively considers the angle value θ k (θ x or θ y ) and its rate of change (dθk) / dt, and calculate the speed compensation △Vk of each of the four hooks 6. Then, the controller sends the speed compensation command to the servo motor driver, accurately controls the operation of the servo motor, ensures that the lifting speed deviation of the four hooks 6 is always ≤1mm / s, and maintains the smooth rise or fall of the trestle segment.

[0050] In the above embodiment, the electric trolley 3 avoids the control logic: the prediction module gives the prediction result of the deformation of the trestle segment, specifically the maximum height difference △h obtained by the laser rangefinder data. max Based on this result, the embedded industrial controller combines the current lateral displacement x of the electric car 3 and calculates the value according to the formula x T =x+0.2△h max Calculate the target displacement x of the electric car 3 T Next, the controller sends a command to the stepper motor driver of the electric trolley 3, driving the stepper motor to operate in micro-step mode, so that the electric trolley 3 moves step by step until the error between the actual position and the target position is ≤2mm, thereby achieving precise avoidance and preventing the risk of collision caused by deformation of the trestle segment.

[0051] In the above embodiment, for the dynamic adjustment logic of the counterweight block 7: after the three-axis inclination sensor detects the tilt direction of the sling group, it transmits the information to the embedded industrial controller. After the controller determines the tilt direction, it sends a signal to the electric push rod 8 driver. The electric push rod 8 driver drives the screw to rotate according to the instruction, and drives the push rod to move through the nut seat. Since the other end of the push rod is connected to the counterweight block 7 through a universal joint, the counterweight block 7 moves in the direction opposite to the tilt direction, adjusts the center of gravity in real time, and maintains the balance of the lifting device.

[0052] According to the above implementation, during the steel structure trestle hoisting process, when the prediction module completes the prediction of the trestle segment deformation and the center of gravity offset index of the hoisting device group, if these predicted values ​​exceed the preset deformation threshold and offset threshold, the predictive intervention module will start to work. x or θ y The data prompts the controller to use the fuzzy PID algorithm to calculate the speed compensation of the four hooks 6. The servo motor driver adjusts the servo motor speed accordingly to ensure that the lifting speed deviation of the hook 6 is extremely small. At the same time, the predicted trestle segment deformation data allows the controller to calculate the target displacement of the electric trolley 3. The stepper motor driver drives the motor to operate in micro-step mode to move the electric trolley 3 accurately to the specified position. In addition, the tilt direction information sensed by the three-axis inclination sensor guides the controller to control the electric push rod 8 to push the counterweight block 7 to move in the opposite direction to offset the tilt trend. Throughout the process, all links work closely together to ensure that the lifting operation continues in a safe and stable state.

[0053] According to the above implementation, in terms of servo motor speed compensation, the fuzzy PID algorithm combines the angle and its rate of change to adjust the speed, which can quickly and accurately respond to the posture changes of the hoisting equipment group, ensure that the trestle segment always remains stable during the lifting process, and effectively avoid the risk of shaking or even falling off due to uneven speed. The avoidance control logic of the electric trolley 3 determines the moving target based on the predicted results of the trestle segment deformation, and cooperates with the micro-step mode of the stepper motor to achieve millimeter-level precision movement, which greatly reduces the collision hazards caused by the deformation of the trestle and improves the safety of the lifting operation. The dynamic adjustment logic of the counterweight block 7 adjusts the counterweight position in real time according to the tilt direction, quickly restores the system balance, and enhances the stability of the lifting device under complex working conditions. In general, the comprehensive control logic of the predictive intervention module has significantly improved the safety, stability and accuracy of the steel structure trestle lifting operation through multi-dimensional collaborative adjustment, greatly reducing the probability of potential accidents and improving construction efficiency.

[0054] In one embodiment, the digital twin virtual scene of the lifting process is further provided with a virtual interference warning unit, and its construction logic is: Preloading a three-dimensional point cloud model of the gantry crane body; Real-time calculation of the minimum Euclidean distance d between the deformation contour of the trestle segment and the point cloud of the column 2 min ; If d is detected three times in a row min <100mm, generate light avoidance instruction △x=0.1(d min -100) and trigger the sound and light alarm set on the gantry crane body.

[0055] In the above-mentioned embodiment, before the lifting operation begins, a three-dimensional point cloud model of the gantry crane is constructed based on the precise size and structure of the gantry crane body using professional three-dimensional modeling software. The model records in detail the spatial position information of each part of the gantry crane body, including the column 2, the fixed beam 1, etc. After the modeling is completed, the three-dimensional point cloud model is preloaded into the storage module of the digital twin virtual scene for subsequent real-time call. During the lifting process, the laser rangefinder continuously collects the vertical distance data h1~h4 of the four corners of the trestle segment, and the prediction module calculates the deformation contour of the trestle segment in real time based on these data and related algorithms. At the same time, the system extracts the point cloud data of column 2 from the preloaded three-dimensional point cloud model. Using the spatial distance calculation algorithm, the minimum Euclidean distance d between the deformation contour of the trestle segment and the point cloud of column 2 is calculated in real time. min The Euclidean distance calculation can accurately measure the spatial distance between two point sets, providing key data support for determining whether there is interference risk. The system sets up a monitoring mechanism to monitor the d minIf dmin < 100 mm is detected for three consecutive times, it indicates that the distance between the trestle segment and column 2 is too close, and there is a risk of interference. At this time, the system uses the formula △x = 0.1 (d min -100) generates a light avoidance instruction, which is used to adjust the lateral displacement of the electric trolley 3 to avoid interference. At the same time, the system triggers the sound and light alarm device installed on the main body of the gantry crane, and sends out an alarm signal to remind the operator to pay attention.

[0056] According to the above implementation, during the preparation stage of steel structure trestle hoisting, the 3D point cloud model has been constructed and preloaded into the digital twin virtual scene. As the hoisting operation starts, the laser rangefinder and the prediction module begin to work together to continuously obtain the actual data of the trestle segment and calculate its deformation contour. The system extracts the point cloud data of column 2 in real time and calculates the minimum Euclidean distance with the deformation contour of the trestle segment. During the entire hoisting process, the monitoring mechanism is always running. Once d min If the distance is less than 100 mm for three times in a row, the system will quickly generate a light avoidance command, control the electric trolley 3 to adjust its position, and trigger an audible and visual alarm. After receiving the alarm, the operator can take further measures according to the actual situation to ensure the safety of the lifting operation.

[0057] According to the above-mentioned implementation mode, firstly, by preloading the three-dimensional point cloud model and calculating the distance in real time, the potential interference risk between the trestle segment and the main column 2 of the gantry crane can be discovered in advance and accurately, which greatly improves the safety of the lifting operation. In traditional lifting operations, it is difficult to manually judge the distance between structures in real time and accurately, and the early warning system can realize automatic monitoring, effectively avoiding collision accidents caused by human negligence. Secondly, the generated light avoidance instruction can automatically control the electric trolley 3 to make fine adjustments, and promptly correct the position of the trestle segment, reducing the possibility of interference and ensuring the smooth progress of the lifting operation. In addition, the triggering of the sound and light alarm can quickly attract the attention of the operator, enabling him to take countermeasures in time, further enhancing the controllability and safety of the entire lifting process.

[0058] In one embodiment, the dynamic adaptive balancing component further comprises: The piezoelectric ceramic sheet array is arranged on the contact surface between the guide groove and the slider, and converts the vibration mechanical energy of the counterweight block 7 when it moves into electrical energy; The supercapacitor, which is arranged on the rigid frame 4, is used to store the recovered electric energy and to power the laser rangefinder and the pressure sensor.

[0059] In the above embodiment, in the dynamic adaptive balancing assembly, the piezoelectric ceramic sheet array is arranged on the contact surface between the guide groove and the slider. When the counterweight 7 moves on the guide rail 5, vibration will be generated due to various factors (such as vibration caused by the operation of the equipment, inertia caused by the movement of the counterweight 7, etc.), and this vibration will cause the piezoelectric ceramic sheet to be subjected to mechanical pressure. According to the piezoelectric effect, the piezoelectric ceramic sheet will generate electrical energy when subjected to mechanical pressure, thereby converting the vibration mechanical energy of the counterweight 7 when it moves into electrical energy. A supercapacitor is installed on the rigid frame 4, which is connected to the piezoelectric ceramic sheet array. When the piezoelectric ceramic sheet array generates electrical energy, the electrical energy will be transmitted to the supercapacitor for storage. The supercapacitor has the characteristics of rapid charging and discharging, and can efficiently store the recovered electrical energy. At the same time, the supercapacitor is connected to the laser rangefinder and the pressure sensor, and when these devices need electricity, the supercapacitor can provide them with a stable power supply.

[0060] According to the above-mentioned embodiment, during the hoisting process of the steel structure trestle, as the electric push rod 8 drives the counterweight 7 to move on the guide rail 5, the vibration of the counterweight 7 is transmitted to the contact surface between the guide groove and the slider, so that the piezoelectric ceramic sheet array arranged on the contact surface is subjected to mechanical pressure. The piezoelectric ceramic sheet converts this mechanical pressure into electrical energy according to the piezoelectric effect, and transmits the electrical energy to the supercapacitor for storage. During the entire hoisting operation, as long as the counterweight 7 moves and vibrates, the piezoelectric ceramic sheet will continue to generate electrical energy and store it in the supercapacitor. During the operation, the laser rangefinder and the pressure sensor will obtain the required electrical energy from the supercapacitor to maintain their normal operation, thereby realizing energy recovery and reuse.

[0061] According to the above implementation mode, firstly, energy recycling is achieved, and the vibration mechanical energy that was originally wasted is converted into electrical energy, thereby improving the utilization efficiency of energy and reducing the energy consumption of the entire lifting device. This not only conforms to the concept of energy conservation and environmental protection, but also reduces the operating cost to a certain extent. Secondly, the laser rangefinder and pressure sensor are powered by supercapacitors, which enhances the stability and reliability of the power supply of these key devices. In some complex lifting environments, power supply fluctuations and the like may occur, and the use of supercapacitors can reduce the impact of such external factors on the equipment, ensuring that the laser rangefinder and pressure sensor can accurately collect data, and providing a strong guarantee for the safety and stability of the entire lifting operation. In addition, the structure of this energy recovery system is relatively simple, and it is easy to integrate into the existing dynamic adaptive balancing components, and has good operability and promotion value.

[0062] In one embodiment, the dynamic adaptive balancing component further comprises: A grating strain sensor is arranged in the stress concentration area of ​​the rigid frame 4 to monitor the microstrain of the rigid frame 4 in real time; the grating strain sensor is communicatively connected to the embedded industrial controller, and predicts the remaining life of the rigid frame based on Miner's linear cumulative damage theory. When the remaining life is ≤1000 cycles, an early warning signal is triggered.

[0063] In the above technical solution, grating strain sensors are precisely installed in stress concentration areas of the rigid frame 4, such as the connection parts between the inclined beams of the rigid frame 4 and other main structural parts, and the nodes that bear large loads. The grating strain sensor works based on the principle of light interference. When the rigid frame 4 is subjected to a slight deformation, the grating pitch of the grating inside the sensor will change, thereby causing the wavelength of the reflected light to change. By detecting the change in the wavelength of the reflected light, the sensor can monitor the micro-strain of the rigid frame 4 in real time and accurately, and convert these strain data into electrical signals for output. The grating strain sensor is connected to the embedded industrial controller through a communication line (such as an optical fiber or a shielded cable) to ensure the accuracy and stability of data transmission and reduce external interference. After the electrical signal output by the sensor is transmitted to the embedded industrial controller, the controller uses the internal signal processing algorithm to convert the electrical signal into an actual strain value. At the same time, the controller will record and analyze these strain data in real time. The embedded industrial controller has a built-in calculation model based on Miner's linear cumulative damage theory. The model calculates the degree of damage accumulated by the rigid frame 4 in each loading cycle based on the real-time monitored microstrain data of the rigid frame 4 and the material properties of the rigid frame 4 (such as fatigue limit, stress-life curve and other parameters pre-stored in the controller). Specifically, for each stress cycle, the corresponding damage rate is calculated, and then the damage rates of each cycle are accumulated. When the cumulative damage is close to 1h, it indicates that the rigid frame 4 is close to its fatigue life limit. The controller predicts the remaining life of the rigid frame 4 in real time based on the cumulative damage value. A remaining life threshold is set in the embedded industrial controller. When the predicted remaining life of the rigid frame 4 is ≤1000 cycles, the controller immediately triggers an early warning signal. The early warning signal can be output in a variety of ways, such as sending a text message to notify the equipment management personnel, displaying a striking warning message on the operation interface of the digital twin collaborative control system, triggering the sound and light alarm device installed on site, etc., so as to promptly remind relevant personnel to inspect, maintain or replace the rigid frame 4.

[0064] According to the above implementation, after the steel structure trestle hoisting device is put into use, the grating strain sensor installed in the stress concentration area of ​​the rigid frame 4 continues to work, capturing the micro-strain changes of the rigid frame 4 caused by the weight of the trestle segment, the vibration during the hoisting process, and other external forces in real time. These micro-strain data are quickly transmitted to the embedded industrial controller through the communication line. The controller processes and analyzes the data, and calculates the degree of damage accumulated by the rigid frame 4 in each hoisting operation cycle based on the Miner linear cumulative damage theory and the material parameters of the rigid frame 4, thereby predicting the remaining life of the rigid frame 4 in real time. In the daily hoisting operation process, as time goes by and the number of hoisting increases, the damage of the rigid frame 4 gradually accumulates, and the remaining life continues to decrease. When the controller predicts that the remaining life of the rigid frame 4 drops to 1000 cycles or less, it immediately triggers a warning signal to notify relevant personnel in various ways, reminding them of the safety risks that the rigid frame 4 may face, and prompting them to take corresponding measures.

[0065] According to the above implementation mode, firstly, by real-time monitoring of the micro-strain of the rigid frame 4 and predicting the remaining life, the potential fatigue damage problem of the rigid frame 4 can be discovered in advance, and serious safety accidents caused by the sudden failure of the rigid frame 4 can be effectively avoided, which greatly improves the safety of the steel structure trestle hoisting operation. Secondly, it provides a scientific basis for the maintenance and management of the equipment. According to the predicted remaining life, the management personnel can reasonably arrange the inspection, maintenance and replacement plan of the rigid frame 4, avoid the waste of resources caused by excessive maintenance, and also prevent the potential safety hazards caused by untimely maintenance, thereby improving the reliability and service life of the equipment. In addition, this design based on advanced sensing technology and theoretical models embodies the characteristics of intelligence and automation, reduces the workload and subjectivity of manual inspections, improves the accuracy of monitoring and prediction, enhances the intelligence level of the entire hoisting device, and adapts to the needs of modern engineering construction for efficient and safe construction.

[0066] The number of equipment and processing scale described here are used to simplify the description of the present invention. The application, modification and variation of the steel structure trestle hoisting device of the present invention are obvious to those skilled in the art.

[0067] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. Steel structure trestle hoisting device, characterized in that: include: The main body of the gantry crane has an electric trolley that can move laterally installed under the fixed crossbeam, and the moving stroke of the electric trolley is equal to the maximum width of the trestle segment; A dynamic adaptive balancing assembly, which includes a rigid frame connected to the electric trolley, a three-axis tilt sensor installed in the middle of the rigid frame, a counterweight integrated inside the rigid frame, and an electric push rod connected to the counterweight; a spreader assembly comprising four hooks integrated on a rigid frame via a wire rope drum driven by a servo motor; A laser positioning assembly, which includes four reflective laser targets arranged at the four corners of the upper surface of the trestle segment, and four laser rangefinders correspondingly installed at the bottom of the fixed crossbeam; A digital twin collaborative control system, comprising an embedded industrial controller, an incremental encoder mounted on an electric trolley, and four pressure sensors respectively distributed on the pseudo-contact surfaces of four hooks and trestles; the embedded industrial controller is communicatively connected to the servo motor driver, the stepper motor driver of the electric trolley, the laser rangefinder, and the electric push rod driver; Among them, the embedded industrial controller has built-in prediction module, control logic module and predictive intervention module, the prediction module is used to predict the deformation of the trestle segment and the center of gravity offset index of the sling group, the control logic module is communicated with the prediction module and the predictive intervention module, the control logic module is used to compare the predicted trestle segment deformation and the center of gravity offset index of the sling group with the deformation threshold and the offset threshold respectively, and trigger the predictive intervention module when the threshold is exceeded, and the predictive intervention module synchronously adjusts the lifting speed of the servo motor, the lateral position of the electric trolley and the displacement of the counterweight block.

2. The steel structure trestle hoisting device according to claim 1, characterized in that: The gantry crane body also includes a pair of columns, the fixed beam is rigidly connected to the top of the columns, a horizontal slide rail is provided at the bottom of the fixed beam, and the electric trolley can be laterally movably arranged on the horizontal slide rail.

3. The steel structure trestle hoisting device according to claim 2, characterized in that: The dynamic adaptive balancing component comprises: A guide rail is arranged on the inclined beam of the rigid frame, and a slider is adapted to be arranged on the guide rail; A counterweight block, the bottom of which is provided with a guide groove cooperating with the sliding block, and the top of which is provided with a connecting ear plate; An electric push rod is arranged on the rigid frame, and includes an electric push rod driver, a screw rod, a nut seat and a push rod. The electric push rod driver drives the screw rod through a coupling, and the screw rod is threadedly matched with the nut seat. One end of the push rod is hinged to the nut seat, and the other end is connected to the connecting ear plate on the counterweight block through a universal joint.

4. The steel structure trestle hoisting device according to claim 3, characterized in that: The prediction module is configured with a digital twin virtual scene of the lifting process, and its construction steps are as follows: Real-time collection of vertical distance data h1~h4 of the laser rangefinder, lateral displacement x of the electric car, and pitch angle θ of the three-axis tilt angle sensor x and roll angle θ y , and add timestamps to data through a unified clock source; The 3D model of the trestle segment is discretized according to the finite element mesh, the mesh nodes are bound to the laser target installation positions, and h1~h4 are used as the displacement boundary conditions of the nodes; Based on θ x ,θ y Calculate the Euler angle rotation matrix R (θ x ,θ y ), drives the pose of the spreader group in the virtual model to be updated synchronously; at the same time, according to the displacement x of the electric trolley, the horizontal position coordinates of the spreader group are updated.

5. The steel structure trestle hoisting device according to claim 4, characterized in that: The specific method for predicting the deformation amount of the trestle segment in the prediction module includes: Based on the vertical distance data h1~h4 of the laser rangefinder, the maximum height difference of the four corners of the trestle segment is calculated; According to the pressure sensor load F1~F4, according to the formula Calculate the local deformation of each hanging point and the total deformation δ. ; The deformation D of the trestle segment is predicted based on the maximum height difference and the total deformation, D=0.6 △ + 0.4 δ; Where L k Refers to the distance from k slings to the end of the trestle segment in the length direction, m; E refers to the elastic modulus of the trestle segment material, Pa; I refers to the section inertia moment of the trestle segment, m 4 , I is the width of the rectangular section multiplied by the cube of the height of the rectangular section divided by 12.

6. The steel structure trestle hoisting device according to claim 4, characterized in that: The specific method for predicting the center of gravity deviation index of the spreader group in the prediction module includes: Obtain the pitch angle θ of the spreader group according to the three-axis inclination sensor x and roll angle θ y , calculate the tilt component T, ; Calculate the pressure sensor load standard deviation σ F ; The center of gravity deviation index G of the spreader group is predicted based on the tilt component and the standard deviation of the pressure sensor load, G=0.8T+0.2σ F .

7. The steel structure trestle hoisting device according to claim 4, characterized in that: The logic of the predictive intervention module to synchronously adjust the lifting speed of the servo motor, the lateral position of the electric car and the displacement of the counterweight block is: Speed ​​compensation logic for servo motor: According to θ x and θ y The fuzzy PID algorithm is used to generate the speed compensation of the four hooks according to the size and shape of the hooks. , control the lifting speed deviation of the servo motor ≤1mm / s; For the electric trolley avoidance control logic: According to the predicted results of the trestle segment deformation, the target displacement x of the electric trolley is calculated. T =x+0.2△h max , and drive the stepper motor to move in micro-step mode until the error is ≤ 2mm; Dynamic adjustment logic for the counterweight block: According to the tilt direction, the electric push rod drives the counterweight block to move in the opposite direction of the tilt direction.

8. The steel structure trestle hoisting device according to claim 4, characterized in that: The digital twin virtual scene of the hoisting process is also provided with a virtual interference warning unit, and its construction logic is as follows: Preloading a three-dimensional point cloud model of the gantry crane body; Real-time calculation of the minimum Euclidean distance d between the deformation contour of the trestle segment and the column point cloud min ; If d is detected three times in a row min <100mm, generate light avoidance instruction △x=0.1(d min -100) and trigger the sound and light alarm set on the gantry crane body.

9. The steel structure trestle hoisting device according to claim 3, characterized in that: The dynamic adaptive balancing component also includes: A piezoelectric ceramic sheet array is arranged on the contact surface between the guide groove and the slider to convert the vibration mechanical energy of the counterweight block when it moves into electrical energy; The supercapacitor, which is arranged on the rigid frame, is used to store the recovered electrical energy and to power the laser rangefinder and the pressure sensor.

10. The steel structure trestle hoisting device according to claim 3, characterized in that: The dynamic adaptive balancing component also includes: A grating strain sensor is arranged in the stress concentration area of ​​the rigid frame to monitor the microstrain of the rigid frame in real time; the grating strain sensor is communicatively connected to the embedded industrial controller and predicts the remaining life of the rigid frame based on Miner's linear cumulative damage theory. When the remaining life is ≤1000 cycles, an early warning signal is triggered.

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