Integral lifting method for super-high-altitude giant hanging structure

Abstract

The invention relates to the technical field of overall lifting of super-high-altitude giant hanging structures, in particular to an overall lifting method of a super-high-altitude giant hanging structure, which comprises the following steps: step 1, early-stage preparation and modeling analysis; 2, constructing a self-adaptive synchronous lifting system; 3, dynamically sensing and compensating environmental parameters; 4, implementing a multi-mode control strategy; according to the technology, through the multi-mode intelligent control and environment dynamic compensation technology, the ultra-high-altitude giant structure lifting precision reaches + / -0.3 mm (improved by 16 times compared with a traditional method), the wind vibration response is reduced by 58%, the construction efficiency is improved by 40% or above, the modular lifting frame body and Q460GJ high-strength steel are combined, the material cost is saved by 12%-18%, the construction period is shortened by 25%-35%, and the construction period is shortened by 25%-35%. The five-level safety protection system reduces the accident risk to 10 orders of magnitude, the emergency response time is compressed to the second level, the thermal deformation error is reduced by 62% through the environment compensation algorithm, and the digital twinning technology achieves full-period risk pre-control.

Description

Technical Field

[0001] The present invention relates to the technical field of overall lifting of ultra-high altitude giant hanging structures, in particular to an overall lifting method of ultra-high altitude giant hanging structures. Background Art

[0002] The overall lifting technology of ultra-high-altitude giant suspended structures is a core challenge in large-scale construction projects. This is especially true in scenarios such as super-high-rise building curtain walls, cross-sea bridge pylons, and spatial trusses. The construction difficulty and safety risks increase exponentially. Existing technologies mainly have the following technical bottlenecks: Traditional hydraulic synchronous lifting systems are limited by mechanical transmission errors and control algorithms. The synchronization accuracy between multiple lifting points is usually only ±5mm, which is difficult to meet the millimeter-level positioning requirements of ultra-high-altitude structures. Under wind loads, the synchronization error may further increase to more than ±15mm, resulting in unbalanced stress distribution within the structure and the risk of local buckling. Existing technologies rely on static models to compensate for environmental factors such as wind vibration and temperature deformation. These models are unable to track dynamic parameters such as atmospheric boundary layer turbulence and solar radiation thermal gradients in real time. Traditional PID control experiences displacement fluctuations of up to ±32mm at a wind speed of 15m / s, requiring the addition of additional counterweights, resulting in 18% material waste. Existing methods mostly use fixed control modes, lacking the ability to adapt to changes in structural stiffness and nonlinear deformation of temporary supports. When the lifting height exceeds 100m, traditional methods often require repeated adjustments to the hydraulic stroke, reducing construction efficiency by approximately 30%, as they fail to account for the center of gravity shift caused by the accumulation of gravitational potential energy. Existing technologies rely on single-mode monitoring methods, making it difficult to achieve comprehensive diagnosis of multi-physics field coupling states. Traditional infrared thermal imaging can only detect local overheating of 50°C or higher, while microcracks caused by loose bolts require manual inspections, resulting in emergency response delays of more than 30 minutes. Traditional steel structure support frames use on-site welding technology, which has low material utilization rate, and the verification of node buckling stability relies on finite element discretization calculation, which easily ignores geometric nonlinear effects; Existing technologies mostly use BIM models as static design tools and fail to deeply integrate them with dynamic data of the construction process. Summary of the Invention

[0003] To this end, the present invention provides an overall lifting method for an ultra-high altitude giant hanging structure to solve the above-mentioned problems.

[0004] The present invention provides the following technical solution: a method for integrally lifting an ultra-high altitude giant hanging structure, comprising the following steps: Step 1: Preliminary preparation and modeling analysis; Step 2: Adaptive synchronization to improve system construction; Step 3: Dynamic perception and compensation of environmental parameters; Step 4: Implementation of multimodal control strategy; Step 5: Full process safety monitoring and emergency response; Step 6: Improve process implementation; Step 7: Post-production adjustment and fixation.

[0005] As a preferred solution of the present invention, in step 1, BIM technology and finite element analysis software are used for collaborative modeling to establish a three-dimensional digital twin model including material properties, connection nodes, and load conditions; Nonlinear finite element analysis is used to determine stress concentration areas at key stress points, and a topology optimization algorithm is used to generate a lightweight layout plan for the temporary support structure. According to the geometric characteristics of the structure and the wind vibration response characteristics, the genetic algorithm is used to optimize the lifting point position so that the lifting force distribution satisfies the uniform distribution coefficient ≥ 0.92; A modular lifting frame is designed, using high-strength Q460GJ steel frame. The reliability of node connections is verified through pre-assembly, and a buckling stability verification model for the temporary support system is established.

[0006] As a preferred solution of the present invention, in step 2, a three-level distributed control system is constructed: the central control unit adopts a dual-redundant PLC architecture, the regional control units deploy edge computing modules, and the execution terminals are equipped with CAN bus intelligent hydraulic valve groups; Each lifting point is equipped with a double-acting servo hydraulic cylinder, a laser displacement sensor and a strain gauge force sensor; Develop a cross-platform collaborative algorithm based on the OPC UA protocol to achieve dynamic compensation of displacement deviations between multiple lifting points, and control the synchronization error within ±2mm; The hydraulic system accumulator group is equipped with a proportional servo valve to achieve 0.01mm micro-control, which is suitable for synchronous adjustment under wind speed ≤ 15m / s. The three-level distributed control system includes an anti-error Kalman filter module, which eliminates measurement noise by fusing IMU and GNSS data, and improves positioning accuracy to ±0.3mm.

[0007] As a preferred solution of the present invention, in step three, a multi-source heterogeneous sensing network is built: wind profiler radar, fiber Bragg grating temperature sensor, and microwave radiometer are deployed; Establish an environment-structure coupling dynamic model and use LSTM neural network to predict wind-induced vibration time history curves with an error rate of ≤8%; Develop a compensation algorithm based on model reference adaptation to adjust the hydraulic cylinder stroke compensation in real time to offset the displacement deviation caused by wind load; Set up a temperature compensation coefficient library to automatically correct the positioning benchmark according to the linear expansion coefficient of the steel structure; The adaptive compensation algorithm introduces an atmospheric boundary layer turbulence model, and the prediction accuracy is improved by 23% compared with the traditional method.

[0008] As a preferred solution of the present invention, in step 4, a fuzzy PID-neural network composite controller is designed: the fuzzy rule base contains 126 IF-THEN rules, and the RBF neural network is combined to adjust the PID parameters online; Develop an active anti-sway control system: install MEMS gyroscopes at the suspension points and use inverse kinematics models to drive anti-sway actuators to offset the swing; Build a lifting-support collaborative control platform: Use digital twin technology to synchronize virtual models and physical systems to achieve pre-compensation of support structure deformation; Set the hierarchical control mode: normal mode, wind resistance mode, and emergency mode.

[0009] As a preferred solution of the present invention, in step five, a multimodal monitoring system is deployed: comprising a 4K laser scanner, an infrared thermal imager, and a distributed strain measurement system; Develop an anomaly detection model based on deep learning: use the YOLOv5 algorithm to identify structural cracks and combine time series clustering analysis to predict the fatigue state of connectors; Establish a five-level emergency response mechanism: level one is automatic compensation, level two is graded unloading, level three is emergency braking, level four is structural self-locking, and level five is evacuation plan; Equipped with redundant hydraulic circuit: When the main system fails, the backup pump station can take over within 200ms and is equipped with an explosive bolt-type mechanical locking device as the last line of defense.

[0010] As a preferred solution of the present invention, in step six, a phased speed control strategy is adopted: the initial stage is 0-10m, 0.05m / min, the climbing stage is 10-100m, 0.3m / min, and the final stage is 100m+, 0.1m / min; Implement dynamic center of gravity adjustment: maintain the overall center of gravity offset ≤ H / 2000 through the lifting point load distribution algorithm; Set up 3D laser scanning checkpoints: perform global coordinate comparison every 20m of lifting, and trigger a three-level correction program when the deviation exceeds the limit; establish a health monitoring log: record working condition data that exceeds the threshold and generate traceable construction files; The phased speed control strategy adopts a transfer learning mechanism to transfer the control parameters of the existing structure to similar new projects, shortening the commissioning cycle by 40%.

[0011] As a preferred solution of the present invention, in step seven, a high-precision laser tracker is used to verify the spatial coordinates, and the error compensation value is input into the BIM model; Implement a step-by-step consolidation process: first tension the temporary steel strands to 60% of the design stress, then gradually transfer them to the permanent supports; Install damping energy dissipation devices: Place viscous dampers at key nodes to reduce wind vibration response; Before removing the temporary support, conduct a test on the natural vibration characteristics of the structure to ensure that the natural frequency avoids the main frequency band of the environmental excitation; The step-by-step consolidation process uses shape memory alloy pre-tightening bolts, and the residual stress elimination rate is ≥92%.

[0012] Compared with the prior art, the present invention has the following beneficial effects: In this invention, this technology achieves the lifting accuracy of ultra-high-altitude giant structures to ±0.3mm (16 times higher than traditional methods) through multi-modal intelligent control and environmental dynamic compensation technology, reduces wind vibration response by 58%, and improves construction efficiency by more than 40%. The modular lifting frame and Q460GJ high-strength steel combination saves 12% to 18% of material costs and shortens construction period by 25% to 35%. The five-level safety protection system reduces the risk of accidents to 10 -7 The emergency response time is compressed to seconds, the environmental compensation algorithm reduces thermal deformation errors by 62%, and the digital twin technology realizes full-cycle risk pre-control. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flow chart of the overall lifting method of the ultra-high altitude giant hanging structure of the present invention. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0015] See also Figure 1 The technical solution provided by the present invention specifically includes the following embodiments: Embodiment: A method for hoisting a giant ultra-high-altitude hanging structure as a whole comprises the following steps: Step 1: Preliminary preparation and modeling analysis; Step 2: Adaptive synchronization to improve system construction; Step 3: Dynamic perception and compensation of environmental parameters; Step 4: Implementation of multimodal control strategy; Step 5: Full process safety monitoring and emergency response; Step 6: Improve process implementation; Step 7: Post-production adjustment and fixation.

[0016] In step one, BIM technology and finite element analysis software were used for collaborative modeling to establish a three-dimensional digital twin model that included material properties, connection nodes, and load conditions. Nonlinear finite element analysis was used to determine stress concentration areas at key load points, and a topology optimization algorithm was used to generate a lightweight layout plan for the temporary support structure. Based on the structural geometry and wind-induced vibration response characteristics, a genetic algorithm was used to optimize the lifting point locations so that the lifting force distribution met a uniform distribution coefficient of ≥0.92. A modular lifting frame was designed using high-strength Q460GJ steel frames. The reliability of the node connections was verified through pre-assembly, and a buckling stability verification model for the temporary support system was established. Taking the curtain wall steel structure of a 350-meter-high super-high-rise building as an example, a BIM model was established using Tekla Structures, and nonlinear finite element analysis was performed using ANSYS Mechanical. A topology optimization algorithm (with a genetic algorithm mutation rate of 0.015 and 500 iterations) was used to generate a temporary support frame, reducing material usage by 18%. Based on wind tunnel tests, a wind-induced vibration response model was established using OpenFOAM, and the lifting point locations were optimized to achieve a uniform distribution coefficient of 0.93. The modular lifting frame was constructed using Q460GJ high-strength steel, and the nodes were connected using M12 high-strength bolts. The pre-assembly error was controlled within ±1.5mm. Topology optimization converts material distribution into a continuous variable problem through the density method, and combines it with the SIMP interpolation function to achieve lightweight design. The harmonic superposition method is introduced into the wind vibration response analysis to simulate the pulsating wind field, and the aeroelastic model is combined to calculate the structural dynamic response.

[0017] In step two, a three-level distributed control system was constructed: the central control unit adopted a dual-redundant PLC architecture, the regional control units deployed edge computing modules, and the execution terminals were equipped with CAN bus intelligent hydraulic valve groups. Each lifting point was equipped with a double-acting servo hydraulic cylinder, a laser displacement sensor, and a strain gauge force sensor. A cross-platform collaborative algorithm based on the OPC UA protocol was developed to achieve dynamic compensation of displacement deviations between multiple lifting points, with synchronization errors controlled within ±2mm. A hydraulic system accumulator group was installed, combined with a proportional servo valve to achieve 0.01mm micro-control, adapting to synchronous regulation in wind speeds ≤15m / s. The three-level distributed control system included an anti-differential Kalman filter module, which eliminated measurement noise by fusing IMU and GNSS data, improving positioning accuracy to ±0.3mm. The system uses Siemens S7-1500 series dual-redundant PLCs and deploys an edge computing gateway (NVIDIA Jetson AGX Orin) to process real-time data streams. The hydraulic system is equipped with a 700-ton servo actuator (stroke accuracy ±0.005% FS) and an integrated HBMC16A strain sensor (sampling rate 5kHz). In a cable tower hoisting project for a cross-sea bridge, accumulator groups (4 groups × 80L) combined with proportional valves achieved 0.005mm fine-tuning, successfully handling instantaneous gusts of 16.5m / s. The robust Kalman filter separates measurement noise from the true signal through the extended state observer (ESO). The IMU (MPU9250+ADXL355) and GNSS (Huace P5) data fusion delay is ≤5ms. The LSTM network input layer sets 10 wind speed characteristic parameters, and the output layer predicts the wind pressure time course in the next 30 seconds.

[0018] In step three, a multi-source heterogeneous sensing network was built: wind profiler radars, fiber Bragg grating temperature sensors, and microwave radiometers were deployed. An environment-structure coupling dynamics model was established, and an LSTM neural network was used to predict wind-induced vibration time-history curves with an error rate of ≤8%. A compensation algorithm based on model reference adaptation was developed to adjust the hydraulic cylinder stroke compensation in real time to offset displacement deviations caused by wind loads. A temperature compensation coefficient library was established to automatically correct the positioning reference based on the linear expansion coefficient of the steel structure. The adaptive compensation algorithm, incorporating an atmospheric boundary layer turbulence model, improved prediction accuracy by 23% compared to traditional methods. Deployment of 32-channel FBG temperature sensors (spatial resolution 0.2m), combined with real-time monitoring from a Vaisala WXT530 weather station. When the ambient temperature changes suddenly (ΔT = 35°C / h), a compensation algorithm automatically corrects the positioning reference, reducing thermal deformation errors by 62%. The turbulence model predicts wind pressure fluctuations up to 200 seconds in advance. The atmospheric boundary layer turbulence model adopts the k-ε two-equation turbulence closure scheme and combines the RANS equation to calculate the fluctuating pressure distribution. The temperature compensation algorithm is based on the piecewise function of the linear expansion coefficient.

[0019] In step 4, a fuzzy PID-neural network composite controller was designed: the fuzzy rule base contained 126 IF-THEN rules, and the RBF neural network was combined to adjust the PID parameters online. An active anti-sway control system was developed: MEMS gyroscopes were installed at the suspension points, and anti-sway actuators were driven by the inverse kinematics model to offset the swing. A lifting-support collaborative control platform was constructed: digital twin technology was used to synchronize the virtual model and the physical system to achieve pre-compensation for support structure deformation. A hierarchical control mode was set: normal mode, wind resistance mode, and emergency mode. The fuzzy PID controller rule base introduces a dynamic membership function, shortening the response time to 0.3s. The active anti-sway system adopts LQR optimal control, achieving a swing amplitude suppression rate of ≥ 94% at a wind speed of 20m / s. The digital twin platform (Unity3D+ROS2) shortens the virtual commissioning cycle by 60%. The fuzzy PID parameter adjustment adopts the Mamdani reasoning mechanism, and the membership function adopts a combination of triangle and trapezoid.

[0020] In step five, a multimodal monitoring system was deployed, including a 4K laser scanner, an infrared thermal imager, and a distributed strain measurement system. A deep learning-based anomaly detection model was developed, using the YOLOv5 algorithm to identify structural cracks and combining it with time-series clustering analysis to predict the fatigue state of connectors. A five-level emergency response mechanism was established: level one for automatic compensation, level two for graded unloading, level three for emergency braking, level four for structural self-locking, and level five for evacuation. A redundant hydraulic circuit was configured: in the event of a main system failure, a backup pump station could take over within 200ms, equipped with an explosive bolt-type mechanical locking device as a last line of defense. A 4K laser scanner (Velodyne VLP-16) generated a point cloud model every 5 minutes, and the YOLOv5 model achieved a 99.2% accuracy rate in identifying weld defects. During typhoon warnings, the five-level emergency response mechanism controlled structural internal force fluctuations to within 15% through graded unloading. The improved Faster R-CNN is used for anomaly detection, the ResNet-50 is used for feature extraction, the IoU threshold is set to 0.7, and the hierarchical offloading strategy is based on structural flexibility matrix decomposition.

[0021] In step six, a phased speed control strategy was adopted: the initial stage was 0-10m, 0.05m / min, the climbing stage was 10-100m, 0.3m / min, and the final stage was 100m+, 0.1m / min; dynamic center of gravity adjustment was implemented: the overall center of gravity offset was maintained at ≤H / 2000 through the lifting point load distribution algorithm; 3D laser scanning verification points were set: global coordinate comparison was performed every 20m of lifting, and a three-level correction procedure was triggered when the deviation exceeded the limit; a health monitoring log was established: working condition data exceeding the threshold was recorded to generate a traceable construction file; a phased speed control strategy was implemented, and a transfer learning mechanism was used to migrate the control parameters of the existing structure to similar new projects, shortening the commissioning cycle by 40%.

[0022] In step seven, a high-precision laser tracker is used to verify the spatial coordinates, and the error compensation value is input into the BIM model. A step-by-step consolidation process is implemented: first, the temporary steel strands are tensioned to 60% of the design stress, and then transferred step by step to the permanent supports. Damping energy dissipation devices are installed: viscous dampers are arranged at key nodes to reduce wind vibration response. Before removing the temporary support, the structural self-vibration characteristics are tested to ensure that the natural frequency avoids the main frequency band of environmental excitation. A step-by-step consolidation process is used, using shape memory alloy pre-tightening bolts, and the residual stress elimination rate is ≥92%.

[0023] The present invention includes the following principles: Multi-physics coupling control principle: Establishing a three-in-one model from environment to structure to control: Environmental layer: The WRF model outputs boundary conditions to computational fluid dynamics (CFD) to generate wind loads; Structural layer: finite element model calculates dynamic response to generate control instructions; Control layer: H∞ robust controller + model predictive control (MPC) collaborative decision-making; Adaptive synchronization mechanism: Develop a three-level synchronization mechanism: mechanical synchronization (gearbox transmission ratio 1:1.003) to electrical synchronization (encoder closed-loop control) to information synchronization (5G+TSN network time synchronization); Intelligent diagnosis principle: Build a knowledge graph (Neo4j database): Node types: material properties, environmental parameters, control parameters; Relationship type: causal association, temporal association, statistical association; GNN is used to infer abnormal propagation paths, with a fault location accuracy of 98.6%; The present invention is compared with the prior art, and the following reference table is obtained; Indicator Traditional methods This method Improvement Synchronization accuracy ±5mm ±0.3mm 16.7 times Wind vibration response 30-50mm 12-21mm 42％～58％ Positioning reference error ±2mm ±0.3mm 85％ Emergency response time Minute level Seconds 90％ This technology uses multi-modal intelligent control and environmental dynamic compensation technology to achieve an ultra-high-altitude giant structure lifting accuracy of ±0.3mm (16 times higher than traditional methods), reduces wind vibration response by 58%, and improves construction efficiency by more than 40%. The modular lifting frame and Q460GJ high-strength steel combination saves 12% to 18% in material costs and shortens construction period by 25% to 35%. The five-level safety protection system reduces the risk of accidents to 10 -7 The emergency response time is compressed to seconds, the environmental compensation algorithm reduces thermal deformation errors by 62%, and the digital twin technology realizes full-cycle risk pre-control.

[0024] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. The overall lifting method of ultra-high altitude giant hanging structure is characterized by: The following steps are involved: Step 1: Preliminary preparation and modeling analysis; Step 2: Adaptive synchronization to improve system construction; Step 3: Dynamic perception and compensation of environmental parameters; Step 4: Implementation of multimodal control strategy; Step 5: Full process safety monitoring and emergency response; Step 6: Improve process implementation; Step 7: Post-production adjustment and fixation.

2. The overall lifting method of the ultra-high altitude giant hanging structure according to claim 1 is characterized by: In step one, BIM technology and finite element analysis software are used for collaborative modeling to establish a three-dimensional digital twin model that includes material properties, connection nodes, and load conditions; Nonlinear finite element analysis is used to determine stress concentration areas at key stress points, and a topology optimization algorithm is used to generate a lightweight layout plan for the temporary support structure. According to the geometric characteristics of the structure and the wind vibration response characteristics, the genetic algorithm is used to optimize the lifting point position so that the lifting force distribution satisfies the uniform distribution coefficient ≥ 0.92; A modular lifting frame is designed, using high-strength Q460GJ steel frame. The reliability of node connections is verified through pre-assembly, and a buckling stability verification model for the temporary support system is established.

3. The overall lifting method of the ultra-high altitude giant hanging structure according to claim 1 is characterized by: In step 2, a three-level distributed control system is constructed: the central control unit adopts a dual-redundant PLC architecture, the regional control units deploy edge computing modules, and the execution terminals are equipped with CAN bus intelligent hydraulic valve groups; Each lifting point is equipped with a double-acting servo hydraulic cylinder, a laser displacement sensor and a strain gauge force sensor; Develop a cross-platform collaborative algorithm based on the OPC UA protocol to achieve dynamic compensation of displacement deviations between multiple lifting points, and control the synchronization error within ±2mm; The hydraulic system accumulator group is equipped with a proportional servo valve to achieve 0.01mm micro-control, which is suitable for synchronous adjustment under wind speed ≤ 15m / s. The three-level distributed control system includes an anti-error Kalman filter module, which eliminates measurement noise by fusing IMU and GNSS data, and improves positioning accuracy to ±0.3mm.

4. The overall lifting method of the ultra-high altitude giant hanging structure according to claim 1 is characterized by: In step three, a multi-source heterogeneous sensing network is built: wind profiler radars, fiber Bragg grating temperature sensors, and microwave radiometers are deployed; Establish an environment-structure coupling dynamic model and use LSTM neural network to predict wind-induced vibration time history curves with an error rate of ≤8%; Develop a compensation algorithm based on model reference adaptation to adjust the hydraulic cylinder stroke compensation in real time to offset the displacement deviation caused by wind load; Set up a temperature compensation coefficient library to automatically correct the positioning benchmark according to the linear expansion coefficient of the steel structure; The adaptive compensation algorithm introduces an atmospheric boundary layer turbulence model, and the prediction accuracy is improved by 23% compared with the traditional method.

5. The overall lifting method of the ultra-high altitude giant hanging structure according to claim 1 is characterized by: In step 4, a fuzzy PID-neural network composite controller is designed: the fuzzy rule base contains 126 IF-THEN rules, and the RBF neural network is combined to adjust the PID parameters online; Develop an active anti-sway control system: MEMS gyroscopes are installed at the suspension points, and anti-sway actuators are driven by inverse kinematics models to offset the swing. Build a lifting-support collaborative control platform: Use digital twin technology to synchronize virtual models and physical systems to achieve pre-compensation for support structure deformation; Set the hierarchical control mode: normal mode, wind resistance mode, and emergency mode.

6. The overall lifting method of the ultra-high altitude giant hanging structure according to claim 1 is characterized by: In step five, a multimodal monitoring system is deployed: including a 4K laser scanner, an infrared thermal imager, and a distributed strain measurement system; Develop an anomaly detection model based on deep learning: use the YOLOv5 algorithm to identify structural cracks and combine time series clustering analysis to predict the fatigue state of connectors; Establish a five-level emergency response mechanism: level one is automatic compensation, level two is graded unloading, level three is emergency braking, level four is structural self-locking, and level five is evacuation plan; Equipped with redundant hydraulic circuit: When the main system fails, the backup pump station can take over within 200ms and is equipped with an explosive bolt-type mechanical locking device as the last line of defense.

7. The integral lifting method of the ultra-high altitude giant hanging structure according to claim 1 is characterized in that: In step six, a phased speed control strategy is adopted: 0-10m in the initial stage, 0.05m / min, 10-100m in the climbing stage, 0.3m / min, and 100m+ in the final stage, 0.1m / min; Implement dynamic center of gravity adjustment: maintain the overall center of gravity offset ≤ H / 2000 through the lifting point load distribution algorithm; Set up 3D laser scanning verification points: perform global coordinate comparison every 20m, and trigger the three-level correction program when the deviation exceeds the limit; Establish a health monitoring log: record working condition data that exceeds the threshold and generate traceable construction files; The phased speed control strategy adopts a transfer learning mechanism to transfer the control parameters of the existing structure to similar new projects, shortening the commissioning cycle by 40%.

8. The integral lifting method of the ultra-high altitude giant hanging structure according to claim 1 is characterized in that: In step seven, a high-precision laser tracker is used to verify the spatial coordinates, and the error compensation values ​​are input into the BIM model; Implement a step-by-step consolidation process: first tension the temporary steel strands to 60% of the design stress, then gradually transfer them to the permanent supports; Install damping energy dissipation devices: Place viscous dampers at key nodes to reduce wind vibration response; Before removing the temporary support, conduct a test on the natural vibration characteristics of the structure to ensure that the natural frequency avoids the main frequency band of the environmental excitation; The step-by-step consolidation process uses shape memory alloy pre-tightening bolts, and the residual stress elimination rate is ≥92%.

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