Dynamic stress self-balancing system for high-altitude steel platform foundation of tower crane
By real-time monitoring and dynamic adjustment of the axial pressure of the steel pipe column, the high-altitude steel platform of the tower crane achieves stress self-balance, solves the problem of uneven force on the steel pipe column, and ensures the safety and stability of the tower crane.
Patent Information
- Application Number
- CN202510995571.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-18
- Publication Date
- 2025-10-17
AI Technical Summary
During the lifting and translation process of the tower crane's high-altitude steel platform, uneven force on the steel pipe columns leads to local overload, which may cause buckling or fracture, affecting the stability and safety of the steel platform.
The detection module collects the axial pressure of the steel pipe column in real time. The data processing module dynamically analyzes and prompts the operator to adjust the boom angle through the early warning module. The support height between the steel pipe column and the steel platform is dynamically adjusted in combination with the cylinder and execution module to achieve stress self-balancing.
Through real-time monitoring and dynamic adjustment, local overload of the steel pipe column is avoided, the stability and safety of the steel platform under complex working conditions are ensured, and the buckling or breakage of the steel pipe column is prevented.
Smart Images
Figure CN120793735A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of crane balance system, in particular to a tower crane high-altitude steel platform foundation dynamic stress self-balancing system. BACKGROUND
[0002] Tower crane, full name tower crane, is a common hoisting equipment on construction sites, mainly used for vertical and horizontal transportation of materials in high-rise building construction process. Tower crane is characterized by its high tower body and flexible rotating boom, which can work efficiently in a small space. It is usually installed at the center of the construction site, and the precise hoisting of building materials such as reinforcement, formwork and concrete is realized by adjusting the angle and length of the boom, lifting height and rotating the tower body.
[0003] However, due to the limitation of some construction sites, such as the installation position of the tower crane being on a slope or on the arch of a large bridge, it is necessary to set up multiple vertical steel pipe columns to horizontally install the steel platform to fix the tower crane. When on the arch of a large bridge, since the arch is arc-shaped, in order to ensure that the steel platform is in a horizontal state, six steel pipe columns of appropriate length need to be cut according to the curvature of the arch, and all the steel pipe columns are welded on two different arches with their top ends located on the same horizontal plane. In this way, when the steel platform is installed on the top of the six steel pipe columns, the steel platform can be ensured to be in a horizontal state, and finally the standard section of the tower crane can be fixed and installed on the horizontal steel platform. However, the steel platform installed in this way will change its position in space as the tower crane lifts and moves, and the load bearing capacity of the six steel pipe columns under the steel platform will also change as the tower crane lifts and moves. The steel pipe column closest to the horizontal distance of the load will bear more pressure, while the steel pipe column farthest from the horizontal distance of the load will bear relatively less pressure. This will result in uneven stress on the six steel pipe columns under the steel platform, leading to uneven stress and possible local overload, causing the steel pipe column to buckle or break. The buckling or breaking of the steel pipe column will cause the steel platform to tilt or twist, breaking the initial horizontal state of the steel platform. SUMMARY
[0004] The present application aims to provide a tower crane high-altitude steel platform foundation dynamic stress self-balancing system to solve the problem of possible local overload during the use of the tower crane.
[0005] To solve the above technical problems, the present application provides the following technical solution: a tower crane high-altitude steel platform foundation dynamic stress self-balancing system, comprising a detection module, a data processing module and a warning module. The detection module is used to collect the axial pressure received by each steel pipe column. The data processing module is used for judging and comparing the axial pressure value of each steel pipe column collected with the preset steel pipe column bearing axial pressure threshold value; the axial pressure value of the steel pipe column is in the range of 80% to 90%, 90% to 95%, 95% to 100% or exceeds the threshold value; The early warning module is used for issuing early warning according to the judgment output by the data processing module, and outputting a three-level early warning to the tower crane when the axial pressure value of the steel pipe column is in the range of 80% to 90% of the threshold value, outputting a two-level early warning to the tower crane when the axial pressure value of the steel pipe column is in the range of 90% to 95% of the threshold value, outputting a one-level early warning to the tower crane when the axial pressure value of the steel pipe column is in the range of 95% to 100% of the threshold value, and outputting an overload to the tower crane when the axial pressure value of the steel pipe column exceeds the threshold value.
[0006] The working principle of the present application is as follows: when in use, the detection module serves as the perception layer of the system and continuously collects the axial pressure of each steel pipe column. The detection module transmits the monitoring data to the data processing module in real time, the data processing module compares the axial pressure value of each steel pipe column with the preset bearing axial pressure threshold value, and accurately judges the threshold interval of the axial pressure value of the steel pipe column, i.e. in the range of 80% to 90%, 90% to 95%, 95% to 100% of the threshold value or exceeds the threshold value. The early warning module issues corresponding early warning according to the judgment output by the data processing module. When the axial pressure value of the steel pipe column is in the range of 80% to 90% of the threshold value, the early warning module outputs a three-level early warning to the tower crane, prompting the operator to pay attention to the pressure change; when the pressure value is in the range of 90% to 95% of the threshold value, a two-level early warning is output, reminding the operator to be vigilant; when the pressure value is in the range of 95% to 100% of the threshold value, a one-level early warning is output, warning that the situation is relatively critical; once the axial pressure value of the steel pipe column exceeds the threshold value, the early warning module immediately outputs an overload signal to the tower crane to remind the relevant personnel to take timely measures to avoid local overload of the steel pipe column, thereby ensuring the safety and stability of the steel platform and the tower crane.
[0007] The present application has the following advantages: Compared with the existing steel platform which relies on the static bearing mode of steel pipe columns of fixed length, the present application realizes closed-loop control of perception, judgment and early warning by collecting the axial pressure data of the steel pipe column in real time through the detection module and dynamically analyzing the stress distribution through the data processing module, so that the operator can adjust the boom angle, speed and the like to optimize the position of the heavy object according to the early warning prompt, disperse the pressure, relieve the local stress and avoid local overload.
[0008] Further, six oil cylinders and an execution module are further included; the data processing module is further used for calculating an average pressure value that each steel pipe column should bear according to an axial pressure value that each steel pipe column bears, comparing the axial pressure of each steel pipe column with the average pressure value, judging whether the axial pressure of a certain steel pipe column or several steel pipe columns is greater than the average pressure value, and calculating the extension amount of the oil cylinder according to the difference between the axial pressure and the average pressure value when the axial pressure value is greater than the average pressure value; The six oil cylinders are respectively fixedly installed between the six steel pipe columns and the steel platform. The execution module is used for controlling the oil cylinder to start according to the extension amount.
[0009] When the tower crane hoists heavy objects, the detection module monitors the axial pressure that each steel pipe column bears in real time. In the process of hoisting, translating and rotating of the tower crane, the spatial position of the heavy object changes constantly, and the pressure that each steel pipe column bears also dynamically changes. The detection module transmits the acquired pressure data to the data processing module. The data processing module calculates the average pressure value that all the steel pipe columns should bear according to the received axial pressure value of each steel pipe column. Then, the data processing module compares the axial pressure of each steel pipe column with the average pressure value one by one, and judges whether the axial pressure of a certain steel pipe column or several steel pipe columns is greater than the average pressure value. If it is detected that there is a steel pipe column whose axial pressure value is greater than the average pressure value, the data processing module further accurately calculates the extension amount of the oil cylinder on the corresponding steel pipe column according to the difference between the axial pressure and the average pressure value of the steel pipe column. Since the six oil cylinders are respectively fixedly installed between the steel pipe columns and the steel platform, they are the key execution components for realizing stress self-balancing. The execution module accurately controls each oil cylinder according to the extension amount of the oil cylinder calculated by the data processing module. When a certain steel pipe column bears too much pressure, the corresponding oil cylinder extends according to the calculated extension amount, the support height between the steel pipe column and the steel platform is changed, the stress distribution of the steel platform is adjusted, and part of the pressure borne by the steel pipe column with too much pressure is transferred to other steel pipe columns, so that the stress of each steel pipe column gradually tends to be balanced. Similarly, when a certain steel pipe column bears too little pressure, the corresponding oil cylinder retracts according to the calculated retraction amount.
[0010] 1. Compared with the current solution to the uneven stress of the steel platform, the current solution is to enhance the strength of the steel pipe column or optimize the fixing structure of the steel pipe column and the steel platform, and essentially to improve the strength of the steel pipe column and the fixing component. The current solution realizes dynamic regulation and control of the pressure of the steel pipe column by real-time monitoring of the detection module, accurate calculation of the data processing module and dynamic adjustment of the extension amount of the oil cylinder by the early warning module, so that the steel platform can remain stable under complex working conditions such as frequent hoisting, translation and rotation of the tower crane, and the stress of the steel platform and the steel pipe column is uniform.
[0011] 2. The scheme forms a prevention mechanism for potential faults of the steel pipe column by real-time monitoring and dynamic adjustment of the pressure of the steel pipe column. When the pressure of a certain steel pipe column exceeds the average pressure value, the system adjusts the pressure distribution through the oil cylinder to prevent the steel pipe column from buckling or breaking.
[0012] Further, the data processing module further comprises a learning module. The learning module forms a data set by the axial pressure detected by the detection module and the historical data of the extension amount of the oil cylinder calculated by the data processing module according to the axial pressure, and imports the data set into a deep learning model for training to obtain a trained deep learning model. The extension amount of each oil cylinder is predicted in advance through the trained deep learning model. In the construction process of the deck type concrete arch bridge, the building materials are usually placed in a fixed area, and the construction area does not change within a certain period of time (usually several days). Therefore, the rotation angle of the tower crane when lifting the building materials to the construction area does not have a large difference. Therefore, after the deep learning model in the learning module is trained, the learning module predicts the extension amount of each oil cylinder in advance according to the lifting weight and the rotation angle of the tower crane while the tower crane is lifting the building materials. Then, the extension amount of the oil cylinder is adjusted 0.5 seconds in advance through the execution module during the rotation of the tower crane, reducing the hysteresis of the oil cylinder when starting to work during the rotation of the tower crane, and further increasing the stability of the steel platform.
[0013] Further, the deep learning model in the learning module adopts a space-time graph convolution network model (ST-GCN), and the training method comprises: S1, the axial pressure, lifting weight and rotation amplitude of the six steel pipe columns are acquired in real time, and are sampled at a time window of 300 seconds; S2, the six steel pipe columns are abstracted as six graph nodes of a graph, the weight between the nodes is defined by the physical connection relationship of the rectangular array, the weight of the adjacent steel pipe columns is 0.8, and the weight of the diagonal steel pipe columns is 0.5; S3, each node contains the current pressure, historical pressure change rate and steel pipe column stiffness, and forms a space-time graph data after normalization; S4, the stress transmission rule between the steel pipe columns and the time sequence dynamic change are captured by space graph convolution and time convolution; S5, the pressure prediction error, the stress consistency of adjacent steel pipe columns and the total pressure conservation physical constraint are fused; S6, the model is trained by using the Adam optimizer, and the optimal parameters are saved after convergence; S7, the real-time collected space-time graph data is input into the trained ST-GCN model, the ST-GCN model generates the pressure change curve of each steel pipe column in the future 30 seconds, and the extension amount of the corresponding oil cylinder is calculated according to the pressure change curve of the steel pipe column.
[0014] Further, the oil cylinder adopts a hydraulic servo oil cylinder, and a calculation formula of an extension amount (AL) of the hydraulic servo oil cylinder is as follows: AL=(axial pressure-average pressure)×0.01 mm / kN.
[0015] Further, in the data processing module, a calculation formula of the average pressure value of the six steel pipe columns is as follows:
[0016] Further, the system further comprises four hydraulic pull rods, two of which are arranged at two ends of the steel platform respectively, and the two hydraulic pull rods in the same group are located at two sides of the same end of the steel platform, the top end of the hydraulic pull rod is fixedly connected with the steel platform, and the bottom end of the hydraulic pull rod is fixedly connected with the steel pipe column below the other end of the steel platform. The purpose is that when the steel platform is tilted by supporting the steel platform through the extension of the oil cylinder, the hydraulic pull rod connected with the higher end of the steel platform is started through the execution module, and the downward traction is formed on the steel platform through the contraction of the hydraulic pull rod, so as to avoid the tilting of the steel platform and keep the horizontal state. At the same time, after the traction is completed, the hydraulic pull rod can transfer part of the pressure borne by the steel platform to the steel pipe column below the other end of the steel platform, so that the pressure is shared. BRIEF DESCRIPTION OF DRAWINGS
[0017] Figure 1 FIG. 1 is a logic block diagram of a tower crane high-altitude steel platform foundation dynamic stress self-balancing system embodiment 1 of the present application; Figure 2 FIG. 2 is a logic block diagram of a tower crane high-altitude steel platform foundation dynamic stress self-balancing system embodiment 2 of the present application; Figure 3 FIG. 3 is a logic block diagram of a tower crane high-altitude steel platform foundation dynamic stress self-balancing system embodiment 3 of the present application. DETAILED DESCRIPTION
[0018] The following will be further described in detail through specific embodiments: Embodiment 1 is basically as shown in FIG. 1: Figure 1 The tower crane high-altitude steel platform foundation dynamic stress self-balancing system comprises a detection module, a data processing module, and a warning module. The data processing module is used for comparing the axial pressure value borne by each steel pipe column with a preset threshold value of the steel pipe column bearing axial pressure; and judging whether the axial pressure value borne by the steel pipe column is within 80%~90%, 90%~95%, 95%~100% or exceeds the threshold value. The early warning module is used for issuing early warning according to the judgment output by the data processing module, and outputs a third-level early warning to the tower crane when the axial pressure value borne by the steel pipe column is within 80%-90% of the threshold value, outputs a second-level early warning to the tower crane when the axial pressure value borne by the steel pipe column is within 90%-95% of the threshold value, outputs a first-level early warning to the tower crane when the axial pressure value borne by the steel pipe column is within 95%-100% of the threshold value, and outputs an overload to the tower crane when the axial pressure value borne by the steel pipe column exceeds the threshold value.
[0019] The specific implementation process is as follows: In use, the detection module serves as the perception layer of the system and continuously collects the axial pressure borne by each steel pipe column. The detection module transmits the monitoring data to the data processing module in real time, and the data processing module compares the axial pressure value of each steel pipe column with the preset bearing axial pressure threshold value, and carefully judges the threshold interval in which the axial pressure value borne by the steel pipe column is located, i.e. 80%-90%, 90%-95%, 95%-100% of the threshold value, or exceeds the threshold value. The early warning module issues corresponding early warning according to the judgment output by the data processing module. When the axial pressure value borne by the steel pipe column is within 80%-90% of the threshold value, the early warning module outputs a third-level early warning to the tower crane, prompting the operator to pay attention to the pressure change; when the pressure value is within 90%-95% of the threshold value, a second-level early warning is output, reminding the operator to be vigilant; when the pressure value is within 95%-100% of the threshold value, a first-level early warning is output, warning that the situation is relatively critical; once the axial pressure value borne by the steel pipe column exceeds the threshold value, the early warning module immediately outputs an overload signal to the tower crane to remind relevant personnel to take timely measures to avoid buckling or rupture caused by local overload of the steel pipe column, and to ensure the safety and stability of the steel platform and the tower crane.
[0020] Embodiment 2 is basically as shown in the accompanying Figure 2 The tower crane high-altitude steel platform foundation dynamic stress self-balancing system comprises a detection module, a data processing module, six oil cylinders (hydraulic servo oil cylinders), an early warning module and an execution module. The detection module is used for collecting the axial pressure borne by each steel pipe column, the hoisting weight of the tower crane and the rotation amplitude. The data processing module calculates the average pressure value that should be borne by each steel pipe column according to the axial pressure value borne by each steel pipe column, and the calculation formula of the average pressure value of the six steel pipe columns is: = .
[0021] The axial pressure of each steel pipe column is compared with the average pressure value to determine whether the axial pressure of one or several steel pipe columns is greater than the average pressure value, and when the axial pressure value is greater than the average pressure value, the data processing module calculates the extension amount of the oil cylinder according to the difference between the axial pressure and the average pressure value; the extension amount (ΔL) of the oil cylinder is calculated according to the formula: ΔL = (axial pressure-average pressure) x 0.01 mm / kN.
[0022] The oil cylinder is fixedly installed between the steel pipe column and the steel platform, respectively; The early warning module is used to control the oil cylinder to start according to the extension amount.
[0023] The data processing module further includes a learning module, the learning module forms a data set through the data detected by the detection module and the historical data of the extension amount of the oil cylinder calculated by the data processing module according to the axial pressure, and imports the data set into a deep learning model for training to obtain a trained deep learning model, and the trained deep learning model is used to predict the extension amount of each oil cylinder in advance.
[0024] The deep learning model in the learning module adopts a space-time graph convolution network model (ST-GCN), and the training method includes: S1, the axial pressure, the lifting weight and the rotation amplitude of the six steel pipe columns are acquired in real time, and are sampled according to a 300-second time window; S2, the six steel pipe columns are abstracted as six graph nodes of a graph, the weight between the nodes is defined through a rectangular array physical connection relationship, the weight of adjacent steel pipe columns is 0.8, and the weight of diagonal steel pipe columns is 0.5; S3, each node contains the current pressure, the historical pressure change rate and the steel pipe column stiffness, and forms space-time graph data after normalization; S4, the stress transmission law between the steel pipe columns and the time sequence dynamic change are captured through space graph convolution and time convolution; S5, the pressure prediction error, the stress consistency of adjacent steel pipe columns and the total pressure conservation physical constraint are fused; S6, the ST-GCN model is trained through historical data using an Adam optimizer, and the optimal parameters are saved after convergence; S7, the real-time collected space-time graph data are input into the trained ST-GCN model, the ST-GCN model generates the pressure change curve of each steel pipe column in the future 30 seconds, and the extension amount of the corresponding oil cylinder is calculated according to the pressure change curve of the steel pipe column.
[0025] The specific implementation process is as follows: In the case of real-time feedback, the detection module monitors the axial pressure on each steel pipe column in real time when the tower crane is lifting heavy objects. During the lifting and translation of the tower crane, the spatial position of the heavy object changes constantly, and the pressure on each steel pipe column also changes dynamically. The detection module transmits the pressure data obtained to the data processing module, which calculates the average pressure value that all steel pipe columns should bear according to the received axial pressure value of each steel pipe column. Subsequently, the data processing module compares the axial pressure of each steel pipe column with the average pressure value one by one to determine whether the axial pressure of one or more steel pipe columns is greater than the average pressure value. If a steel pipe column with an axial pressure greater than the average pressure value is detected, the data processing module will further calculate the extension amount of the corresponding oil cylinder on the steel pipe column according to the difference between the axial pressure and the average pressure value. Since the six oil cylinders are fixedly installed between the steel pipe columns and the steel platform, they are the key executive components for achieving stress self-balancing. The warning module precisely controls each oil cylinder according to the extension amount calculated by the data processing module. When a steel pipe column bears excessive pressure, the corresponding oil cylinder extends according to the calculated extension amount, changes the support height between the steel pipe column and the steel platform, adjusts the stress distribution of the steel platform, and transfers part of the pressure borne by the steel pipe column with excessive pressure to other steel pipe columns, so that the stress of each steel pipe column gradually tends to balance. Similarly, when a steel pipe column bears insufficient pressure, the corresponding oil cylinder retracts according to the calculated retraction amount.
[0026] In the prediction adjustment mode, first, the axial pressure, lifting weight, rotation amplitude and other data of the six steel pipe columns are collected in real time with a time window of 300 seconds; then the six steel pipe columns are abstracted as six nodes of a graph, and according to the physical connection relationship of the rectangular array, a weight of 0.8 is assigned between adjacent steel pipe column nodes, a weight of 0.5 is assigned between diagonal steel pipe column nodes, and each node is integrated into the current pressure, historical pressure change rate, steel pipe column stiffness and other characteristics and normalized to construct a space-time graph data; then the space-time graph convolution is used to capture the stress transfer law between the steel pipe columns, and the time convolution is used to mine the time series dynamic characteristics of the pressure change; then during training, the pressure prediction error, the stress consistency of adjacent steel pipe columns, and the total pressure conservation are fused as physical constraints, and the ST-GCN model is trained based on historical data using the Adam optimizer, and the optimal parameters are saved after convergence; finally, the real-time collected space-time graph data is input into the trained model, the model generates the pressure change curve of each steel pipe column in the next 30 seconds, and the extension amount required by the corresponding oil cylinder to balance the stress is calculated according to the curve, so as to realize the prediction and adjustment of the stress change of the tower crane steel platform.
[0027] Embodiment 3 The difference between Embodiment 3 and Embodiment 2 is as shown in Figure 3 The four hydraulic pull rods are arranged in two groups at two ends of the steel platform respectively, two hydraulic pull rods in the same group are located at two sides of the same end of the steel platform respectively, the top end of the hydraulic pull rod is fixedly connected with the steel platform, and the bottom end of the hydraulic pull rod is fixedly connected with the steel pipe column below the other end of the steel platform.
[0028] In use, when the oil cylinder extends to support the steel platform so that the steel platform is inclined, the hydraulic pull rod connected with the higher end of the steel platform is started by the execution module, and the downward traction is formed on the steel platform by the contraction of the hydraulic pull rod, so that the inclination of the steel platform is avoided, and the horizontal state is maintained. Meanwhile, after the traction is completed, the hydraulic pull rod can transfer part of the pressure borne by the steel platform to the steel pipe column below the other end of the steel platform, so that the pressure is shared.
Claims
1. The dynamic stress self-balancing system of the tower crane high-altitude steel platform foundation is characterized by: Including detection module, data processing module and early warning module; The detection module is used to collect the axial pressure on each steel pipe string; The data processing module is used to judge and compare the collected axial pressure value of each steel pipe string with a preset threshold value of the axial pressure of the steel pipe string; judge whether the axial pressure value of the steel pipe string is between 80% and 90%, 90% and 95%, 95% and 100% of the threshold value or exceeds the threshold value; The early warning module is used to issue an early warning based on the judgment output by the data processing module. When the axial pressure value of the steel pipe string is within the range of 80% to 90% of the threshold, a third-level early warning is output to the tower crane; when the axial pressure value of the steel pipe string is within the range of 90% to 95% of the threshold, a second-level early warning is output to the tower crane; when the axial pressure value of the steel pipe string is within the range of 95% to 100% of the threshold, a first-level early warning is output to the tower crane; when the axial pressure value of the steel pipe string exceeds the threshold, an overload warning is output to the tower crane.
2. The dynamic stress self-balancing system for the high-altitude steel platform foundation of a tower crane according to claim 1 is characterized by: It also includes six oil cylinders and an execution module; the data processing module is further used to calculate the average pressure value that each steel pipe string should be subjected to based on the axial pressure value subjected to each steel pipe string, compare the axial pressure of each steel pipe string with the average pressure value, and determine whether the axial pressure of one or several steel pipe strings is greater than the average pressure value. When the axial pressure value is greater than the average pressure value, the data processing module calculates the extension amount of the oil cylinder based on the difference between the axial pressure and the average pressure value; The six oil cylinders are fixedly installed between the six steel pipe columns and the steel platform; The execution module is used to control the start of the oil cylinder according to the extension amount.
3. The dynamic stress self-balancing system for the high-altitude steel platform foundation of a tower crane according to claim 2 is characterized by: The data processing module also includes a learning module, which forms a data set through the historical data of the axial pressure detected by the detection module and the extension amount of the cylinder calculated by the data processing module based on the axial pressure, and imports the data set into a deep learning model for training to obtain a trained deep learning model. The trained deep learning model is used to predict the extension amount of each cylinder in advance.
4. The dynamic stress self-balancing system for the high-altitude steel platform foundation of a tower crane according to claim 3 is characterized by: The deep learning model in the learning module adopts a spatiotemporal graph convolutional network model, and the training method includes: S1, real-time acquisition of the axial pressure, lifting weight, and rotation amplitude of six steel pipe columns, sampling in a 300-second time window; S2, abstract the six steel pipe columns into six graph nodes, and define the weights between nodes through the physical connection relationship of the rectangular array. The weight of adjacent steel pipe columns is 0.8, and the weight of diagonal steel pipe columns is 0.5; S3, each node contains the current pressure, historical pressure change rate, and steel column stiffness, which are normalized to form a spatiotemporal graph data; S4, captures the stress transfer law and time series dynamic changes between steel pipe columns through spatial graph convolution plus temporal convolution; S5, fusion pressure prediction error, stress consistency of adjacent steel strings, and total pressure conservation physical constraints; S6, uses the Adam optimizer to train the model using historical data and saves the optimal parameters after convergence; In step S7, the real-time spatiotemporal graph data is input into the trained ST-GCN model. The ST-GCN model generates the pressure change curve of each steel pipe string in the next 30 seconds and calculates the extension of the corresponding oil cylinder based on the pressure change curve of the steel pipe string.
5. The dynamic stress self-balancing system for the high-altitude steel platform foundation of a tower crane according to claim 4 is characterized in that: The oil cylinder adopts a hydraulic servo oil cylinder, and the calculation formula of the extension amount ΔL of the hydraulic servo oil cylinder is: ΔL=(axial pressure-average pressure)×0.01mm / kN.
6. The dynamic stress self-balancing system for the high-altitude steel platform foundation of a tower crane according to claim 5 is characterized by: In the data processing module, the calculation formula for the average pressure value of the six steel pipe strings is: = 。 7. The dynamic stress self-balancing system for the high-altitude steel platform foundation of a tower crane according to claim 6 is characterized by: It also includes four hydraulic pull rods, which are arranged in groups of two at both ends of the steel platform. The two hydraulic pull rods in the same group are located on both sides of the same end of the steel platform. The top end of the hydraulic pull rod is fixedly connected to the steel platform, and the bottom end of the hydraulic pull rod is fixedly connected to the steel pipe column below the other end of the steel platform.