Automatic compensation avoiding device of construction hoist

Abstract

The automatic compensating and avoiding device of the construction hoist comprises a mounting bracket, a hoist body is arranged on the mounting bracket, and the hoist body is connected with the mounting bracket in a matched mode; a traction device is arranged on the mounting bracket and is fixedly connected with the elevator body; a plurality of wind direction and wind speed detection sensors are arranged outside the mounting bracket; the elevator body is provided with a data analysis processor which is electrically connected with the wind direction detection sensor and is used for analyzing and processing detection data; a driving controller is arranged on one side of the data analysis processor, the driving controller is electrically connected with the data analysis processor, the driving controller is electrically connected with the traction device and the anti-falling safety device, and whether an output result analyzed by the data analysis processor belongs to a non-Gaussian turbulence excitation state or not is judged through the controller. And if yes, the operation state of the construction hoist is adjusted through the traction device and the anti-falling safety device. The invention aims to solve the problem of safe operation of the construction hoist during buffeting under the excitation of canyon wind non-Gaussian turbulent flow.

Description

Technical Field

[0001] The invention belongs to the technical field of construction engineering and relates to an automatic compensation and avoidance device for a construction hoist. Background Art

[0002] As an indispensable vertical transportation equipment in construction, construction elevators play an important role in improving construction efficiency and ensuring the transportation of personnel and materials. However, in the construction of suspension bridges in canyon areas, construction elevators are the access to the structure above the main tower. Since the main tower is usually the only tall structure in the area and there are no other buildings around to block it, the wind speed is fast. The canyon wind has a great impact on the construction elevators, and the construction elevators often encounter non-Gaussian turbulence excitation caused by canyon wind. This complex airflow condition has strong randomness and instability, which poses a severe challenge to the safe operation of construction elevators.

[0003] Under the non-Gaussian turbulence excitation state, the pulsation characteristics of the airflow are extremely complex and irregular, and the instantaneous impact force generated is far greater than that of ordinary Gaussian turbulence, which will cause the stress condition of the construction elevator to deteriorate sharply, and the structural components will be subjected to alternating stresses that far exceed the normal design standards. For example, the connection parts may become loose due to frequent huge impact forces, and the guide rails may deform under uneven stress, which seriously threaten the overall structural stability of the elevator. Moreover, the vibration amplitude caused by non-Gaussian turbulence excitation is often large and difficult to predict, making it difficult to accurately control the running trajectory of the construction elevator, greatly increasing the risk of collision with surrounding facilities.

[0004] In this complex airflow environment, construction elevators are prone to buffeting, which not only makes passengers feel extremely uncomfortable, but also may cause fatigue damage to the elevator structure components. In severe cases, it may even cause safety accidents and increase the risk of equipment failure. For construction elevators that work in such harsh environments for a long time, their service life will be greatly shortened, increasing construction costs and maintenance workload.

[0005] The main solutions to the vibration problem of construction elevators in complex airflow environments such as canyon winds include optimizing the structural design of the elevators, adding wind barrier facilities, and installing advanced monitoring and control systems. For example, the wind resistance of the elevators can be improved by using stronger materials and optimizing the structural shape; wind barriers can be set up around the construction site to reduce the direct impact of canyon winds; sensors can be used to monitor wind speed and direction in real time, and the operating status of the elevator can be adjusted through the control system.

[0006] However, these methods have certain advantages and disadvantages in practical applications. Although optimizing the structural design can improve wind resistance to a certain extent, it often increases the manufacturing cost and weight of the equipment. The installation and maintenance of wind barriers are relatively complicated, and their effectiveness is limited by factors such as terrain and wind direction. At present, there is no monitoring system and method based on wind for the safe operation of elevators. When dealing with complex airflow conditions such as non-Gaussian turbulence, its response speed and accuracy may be insufficient, and it is difficult to completely eliminate the impact of buffeting.

[0007] In summary, in order to more effectively solve the buffeting problem of construction elevators under the excitation of non-Gaussian turbulence in canyon wind, it is necessary to develop a more intelligent, efficient, economical and practical automatic compensation and avoidance device to ensure the safe and stable operation of the construction elevators. Summary of the invention

[0008] The invention provides an automatic compensation and avoidance device for a construction elevator to solve the problem of the flutter operation of the construction elevator under the excitation of non-Gaussian turbulence of canyon wind.

[0009] In order to solve the above problems, the technical solution adopted by the invention is:

[0010] The automatic compensation and avoidance device of a construction elevator comprises a mounting bracket, on which a lift body is arranged, and the lift body is cooperatively connected with the mounting bracket; a traction device is arranged on the mounting bracket and is fixedly connected with the lift body; a plurality of wind direction and wind speed detection sensors are arranged on the outer side of the mounting bracket; a data analysis processor is arranged on the lift body and is electrically connected with the wind direction detection sensor to analyze and process the detection data; a drive controller is arranged on one side of the data analysis processor, and the drive controller is electrically connected with the data analysis processor, and the drive controller is electrically connected with the traction device and the anti-fall safety device. The output result is analyzed by the data analysis processor, and the controller is used to determine whether it belongs to a non-Gaussian turbulence excitation state. If so, the traction device and the anti-fall safety device adjust the operation state of the construction elevator; the specific processing steps of the data analysis by the data processor are as follows:

[0011] S1 collects wind speed and direction data in real time every 0.1 seconds through wind direction and speed detection sensors;

[0012] S2 performs denoising on the collected data, using moving average method or wavelet transform to remove noise interference and obtain smooth wind speed and wind direction time series data;

[0013] S3 calculates the skewness and kurtosis of wind speed data. Skewness reflects the asymmetry of data distribution, and kurtosis reflects the peak degree of data distribution. For normal distribution, skewness is 0 and kurtosis is 3. Under non-Gaussian turbulence excitation, skewness and kurtosis will deviate from 0 and 3. The skewness calculation formula is as follows:

[0014]

[0015] Where n is the number of data points, x i is the ith wind speed data point, is the mean of wind speed data, s is the standard deviation;

[0016] The kurtosis calculation formula is as follows:

[0017]

[0018] After S4 calculates the skewness and kurtosis, it combines them with the mean and standard deviation of the wind speed into a feature vector, and collects measured wind data under different canyon environments, including known Gaussian turbulence and non-Gaussian turbulence conditions. It calculates the feature vector for the collected data and performs cluster analysis. The clustering results are used to determine the range of the feature vector, which is converted into a dynamic threshold model. When the feature vector of the real-time data exceeds the threshold range of the corresponding category in the area, the following analysis is performed;

[0019] The wind direction is divided into intervals of 0-45 degrees and 45-90 degrees, and the mean and variance of the skewness and kurtosis of wind speed in each wind direction interval are calculated;

[0020] If the skewness and kurtosis of wind speed deviate from the Gaussian distribution characteristics in any wind direction interval, the following analysis is performed;

[0021] The wavelet transform method is used to decompose the wind speed data to obtain wind speed components at different scales. The skewness and kurtosis of the wind speed components at each scale are calculated. If the skewness and kurtosis deviate from 0 and 3 at any two scales and the duration exceeds the threshold, it is determined to be in a canyon wind non-Gaussian turbulence excitation state.

[0022] The principle of this solution is that multiple wind direction and wind speed detection sensors installed on the outside of the mounting bracket collect wind speed and wind direction data every 0.1 seconds to ensure that real-time change information of the wind field can be obtained in time. These raw data will then undergo a preprocessing step and denoising to remove noise that may interfere with the analysis results, thereby obtaining smoother and more accurate wind speed and wind direction time series data.

[0023] Later, in the data analysis stage, the calculated skewness, kurtosis, mean and standard deviation of wind speed are combined into a feature vector, and cluster analysis is performed on a large number of measured wind data in different canyon environments to establish a dynamic threshold model. When the feature vector of real-time data exceeds the normal range, it is preliminarily judged that it may be in a non-Gaussian turbulence excitation state.

[0024] By dividing the wind direction into intervals and calculating the mean and variance of the skewness and kurtosis of the wind speed in each interval, if these parameters continue to deviate from the Gaussian distribution characteristics in a certain wind direction interval, the possibility of being in a non-Gaussian turbulence excitation state increases.

[0025] Finally, the wavelet transform method is used to decompose the wind speed data and analyze the skewness and kurtosis of wind speed components at different scales. If the skewness and kurtosis deviate from 0 and 3 at any two scales, it can be accurately determined that the canyon wind is in a non-Gaussian turbulence excitation state.

[0026] Once it is determined to be in this abnormal state, the drive controller will respond immediately, controlling the traction device and the anti-fall safety device to adjust the operating state of the construction elevator to achieve automatic compensation and avoidance, and ensure the safe and stable operation of the construction elevator.

[0027] The beneficial effects of this program:

[0028] Through the electrical connection of the wind direction and speed sensors and the data processor, the device can detect and judge the non-Gaussian turbulence excitation state of the canyon wind in real time and accurately, and adjust the operating state of the construction elevator in time, reducing the risk of vibration, loss of control and even falling of the construction elevator caused by complex wind conditions, and providing strong protection for the life safety of construction workers.

[0029] Since it can respond to non-Gaussian turbulence excitation in a timely and effective manner, the construction elevator does not have to shut down frequently or reduce its operating speed due to overly conservative safety strategies, allowing the construction elevator to operate more stably and efficiently, reducing construction delays caused by wind conditions, and helping to improve the progress and efficiency of the entire construction project.

[0030] By judging wind conditions and adjusting operating status in a timely manner, the structural damage and component wear caused by strong vibration and abnormal stress on construction elevators can be reduced. This not only extends the service life of the equipment and reduces the maintenance cost of the equipment, but also reduces the indirect economic losses caused by equipment failure and maintenance.

[0031] Furthermore, in the S2 step, a weighted moving average algorithm is adopted, in which data points closer to the current collection time are given higher weights, and the weight coefficient is set according to the exponential decay law. Data closer to the current time are more representative and real-time in describing the current wind conditions. Giving them higher weights can make the processed data more accurately reflect the latest wind dynamics, which helps to discover sudden changes in wind conditions more promptly. Moreover, as time goes by, the weight of old data decreases rapidly, avoiding excessive interference of outdated data on current analysis results, so that the data processing results are more focused on the characteristics of recent wind conditions.

[0032] Furthermore, the wind direction and speed detection sensor adopts a laser Doppler anemometer, which can accurately measure small changes in wind speed and wind direction, providing an accurate and reliable data basis for subsequent data analysis and processing, and helping to more accurately judge the non-Gaussian turbulence excitation state of canyon wind. It can capture rapid changes in wind conditions in an instant and provide timely feedback to the system, so that the system can quickly make corresponding adjustments and effectively respond to sudden abnormal wind conditions, thereby enhancing the real-time and effectiveness of the automatic compensation and avoidance device of the construction elevator.

[0033] Furthermore, in S4, when calculating the feature vector for clustering analysis to construct the dynamic threshold model, a density-based clustering algorithm is used for calculation. In the wind field data, the distribution of wind speed and wind direction may show complex and irregular characteristics. Traditional clustering algorithms may not be able to accurately identify the categories of these irregular shapes. The density-based algorithm is not limited by the shape of the data and can more accurately cluster wind field data points with similar characteristics into one category. In actual wind speed and direction detection, there may be some abnormal or noisy data points. The density-based clustering algorithm can identify these noise points as low-density areas, thereby avoiding their interference with normal clustering results, thereby ensuring the accuracy and reliability of the constructed dynamic threshold model.

[0034] Furthermore, in S4, when the wind direction is divided into intervals of 0-45 degrees and 45-90 degrees to statistically analyze the mean and variance of wind speed skewness and kurtosis, for the data in each wind direction interval, the piecewise linear fitting method is used to perform trend analysis on the wind speed skewness and kurtosis. In different wind direction intervals, the changes in wind speed skewness and kurtosis may not be a single linear relationship, but there are multiple different trend stages. Through piecewise fitting, these local change characteristics can be more accurately described, and the accuracy of data trend analysis can be improved. Moreover, when the wind speed skewness and kurtosis deviate significantly from the normal trend in a certain wind direction interval, the piecewise fitting can more clearly display this anomaly, thereby better discovering possible non-Gaussian turbulence excitation states.

[0035] Furthermore, in S4, when determining that the duration exceeds the threshold, a sliding time window method is adopted, and the length of the time window is dynamically adjusted according to the average turbulence scale of the canyon wind field and the data acquisition frequency. Since the average turbulence scales of wind fields in different canyons are different, a time window of fixed length may not be able to accurately capture the continuous abnormal state in a specific wind field. By making dynamic adjustments according to the actual wind field conditions, it can be ensured that whether the duration exceeds the threshold can be accurately judged under various wind field conditions, thereby improving the accuracy and reliability of the judgment.

[0036] Furthermore, a plurality of dynamic compensation mechanisms are arranged on the elevator body, and the dynamic compensation mechanism includes a telescopic arm, a drive controller and a reset spring. The telescopic arm is a hydraulic cylinder telescopic arm, one end of which is fixedly connected to the drive controller, and the other end is cooperatively connected to the mounting bracket; the telescopic wall is electrically connected to the drive controller, so that when responding to non-Gaussian turbulence excitation of canyon wind, the telescopic arm can quickly and forcefully adjust the position and posture of the construction elevator, effectively reduce the vibration and instability phenomenon, and ensure the smooth operation of the construction elevator. The presence of the reset spring can help the telescopic arm to quickly return to the initial position after the wind condition returns to normal or the telescopic arm is adjusted, so as to prepare for the next wind condition change. This not only improves the response speed of the dynamic compensation mechanism, but also reduces the adjustment time of the telescopic arm, and enhances the adaptability of the construction elevator to wind condition changes. The drive controller can also control the telescopic length and speed of the telescopic arm in real time and accurately according to the results of the wind direction and wind speed detection sensor and the data analysis processor, thereby realizing precise adjustment of the operating state of the construction elevator and improving the effect and accuracy of compensation and avoidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of the structure of the present invention;

[0038] Figure 2 for Figure 1 A partial enlarged view of the

[0039] Figure 3 Flowchart for data analysis and processing. DETAILED DESCRIPTION

[0040] The figure marks in the drawings of the specification include: mounting bracket 1, wind direction and wind speed detection sensor 2, traction device 3, elevator body 4, anti-fall safety device 5, data analysis processor 6, drive controller 7, telescopic arm 8, dynamic compensation mechanism 9, and reset spring 10.

[0041] The embodiment is basically as shown in the attached Figure 1-2 As shown, the automatic compensation and avoidance device of the construction elevator includes a mounting bracket 1, on which the elevator body 4 is cooperatively connected with the mounting bracket 1 and can move up and down along the mounting bracket 1. A traction device 3 is also provided on the mounting bracket 1. The traction device 3 is a traction machine, which is fixedly connected with the elevator body 4 to provide power for the lifting and lowering action of the elevator.

[0042] On the outside of the mounting bracket 1, a plurality of wind direction and wind speed detection sensors 2 are evenly distributed, which can collect wind speed and wind direction data in real time at a frequency of every 0.1 seconds. These wind direction and wind speed detection sensors 2 use laser Doppler anemometers, which can accurately measure small changes in wind speed and wind direction, instantly capture rapid changes in wind conditions, and promptly transmit data to the data analysis processor 6 on the elevator body.

[0043] After receiving the data, the data analysis processor 6 first performs denoising on the collected data and uses a weighted moving average algorithm for processing. Since the data points closer to the current collection time are assigned higher weights, the weight coefficients are set according to the exponential decay law, so that the data can more accurately reflect the latest wind conditions and avoid interference from outdated data.

[0044] Next, the skewness and kurtosis of the wind speed data were calculated and combined with the mean and standard deviation of the wind speed into a feature vector. Then, a large amount of measured wind data in different canyon environments were collected for cluster analysis and a dynamic threshold model was constructed. In this process, a density-based clustering algorithm was used to more accurately cluster wind field data points with similar characteristics into one category, while effectively eliminating the interference of abnormal or noise data points.

[0045] The wind direction is divided into intervals such as 0-45 degrees and 45-90 degrees. For the data in each interval, the piecewise linear fitting method is used to perform trend analysis on the skewness and kurtosis of wind speed. When the skewness and kurtosis are abnormal at any two scales, the energy transfer is abnormal, and the duration exceeds the threshold, it is determined that the canyon wind is in a non-Gaussian turbulence excitation state.

[0046] At this time, the drive controller 7 responds quickly. It is electrically connected to the traction device 3 and the anti-fall safety device 5. On the one hand, it adjusts the output power of the traction device 3. When the drive controller 7 determines that it is in the canyon wind non-Gaussian turbulence excitation state, it is assumed that the elevator originally rises at a uniform speed of 1.5 meters per second. If the wind direction is horizontal from left to right and the wind force is strong, according to the data collected by the wind direction and wind speed detection sensor 2, after being analyzed by the data analysis processor 6, the drive controller 7 will reduce the output power of the traction device 3 by 40%, so that the rising speed of the elevator is slowed down to 0.9 meters per second, so as to reduce the wind's positive resistance to the elevator when it moves upward.

[0047] In actual construction, installation and debugging are carried out according to the above plan to ensure the normal operation of each component, which can enable the construction elevator to work safely, stably and efficiently in harsh environments such as canyon winds.

[0048] The specific processing steps of data processor data analysis are:

[0049] S1 Data collection: The wind direction and wind speed detection sensor 2 collects wind speed and wind direction data every 0.1 seconds in real time.

[0050] S2 performs denoising on the collected data and uses the moving average method to remove noise interference and obtain smooth wind speed and wind direction time series data.

[0051] S3 calculates the skewness and kurtosis of wind speed data. The skewness reflects the asymmetry of the data distribution, and the kurtosis reflects the peak degree of the data distribution. For a normal distribution, the skewness is 0 and the kurtosis is 3. Under non-Gaussian turbulence excitation, the skewness and kurtosis will deviate from 0 and 3.

[0052] The calculation formula is as follows:

[0053] Skewness

[0054]

[0055] Where n is the number of data points, x i is the ith wind speed data point, is the mean of the wind speed data and s is the standard deviation.

[0056] Kurtosis

[0057]

[0058] After calculating the skewness and kurtosis, S4 combines them with the mean and standard deviation of the wind speed into a feature vector, and collects the measured wind data in the canyon environment, including known Gaussian turbulence and non-Gaussian turbulence conditions. It calculates the feature vector for these data and performs cluster analysis. The clustering results are used to determine the feature vector range and convert it into a dynamic threshold model. When the feature vector of the real-time data exceeds the threshold range of the corresponding category in the area, it proceeds to the next step of analysis.

[0059] The wind direction is divided into different intervals of 0-45 degrees and 45-90 degrees, and the mean and variance of the skewness and kurtosis of wind speed in each wind direction interval are calculated;

[0060] If the skewness and kurtosis of wind speed deviate from the Gaussian distribution characteristics in any wind direction interval, the next step of analysis is performed;

[0061] The wavelet transform method is used to decompose the wind speed data to obtain wind speed components at different scales. The skewness and kurtosis of the wind speed components at each scale are calculated. If there is a deviation in skewness and kurtosis at any two scales and the duration exceeds the threshold, it is determined to be in a canyon wind non-Gaussian turbulence excitation state.

[0062] In the step S2, a weighted moving average algorithm is adopted, in which the data points closer to the current collection time are given higher weights, and the weight coefficient is set according to the exponential decay law. The data closer to the current time are more representative and real-time in describing the current wind conditions. Giving them higher weights can make the processed data more accurately reflect the latest wind conditions, which helps to discover sudden changes in wind conditions more promptly. Moreover, as time goes by, the weight of old data decreases rapidly, avoiding excessive interference of outdated data on the current analysis results, so that the data processing results are more focused on the characteristics of recent wind conditions.

[0063] The wind direction and speed detection sensor 2 adopts a laser Doppler anemometer, which can accurately measure small changes in wind speed and wind direction, providing an accurate and reliable data basis for subsequent data analysis and processing, and helping to more accurately judge the non-Gaussian turbulence excitation state of canyon wind. It can capture the rapid changes in wind conditions in an instant and provide timely feedback to the system, so that the system can quickly make corresponding adjustments, effectively respond to sudden abnormal wind conditions, and enhance the real-time and effectiveness of the automatic compensation and avoidance device of the construction elevator.

[0064] In the S4, when calculating the feature vector for clustering analysis and constructing the dynamic threshold model, a density-based clustering algorithm is used for calculation. In the wind field data, the distribution of wind speed and wind direction may show complex and irregular characteristics. Traditional clustering algorithms may not be able to accurately identify the categories of these irregular shapes. The density-based algorithm is not limited by the data shape and can more accurately cluster wind field data points with similar characteristics into one category. In the actual wind speed and direction detection, there may be some abnormal or noisy data points. The density-based clustering algorithm can identify these noise points as low-density areas, thereby avoiding their interference with the normal clustering results, thereby ensuring the accuracy and reliability of the constructed dynamic threshold model.

[0065] In S4, when the wind direction is divided into intervals of 0-45 degrees and 45-90 degrees to statistically analyze the mean and variance of wind speed skewness and kurtosis, for the data in each wind direction interval, a piecewise linear fitting method is used to perform trend analysis on the wind speed skewness and kurtosis. In different wind direction intervals, the changes in wind speed skewness and kurtosis may not be a single linear relationship, but there are multiple different trend stages. Through piecewise fitting, these local change characteristics can be more accurately described, and the accuracy of data trend analysis can be improved. Moreover, when the wind speed skewness and kurtosis deviate significantly from the normal trend in a certain wind direction interval, the piecewise fitting can more clearly display this anomaly, thereby better discovering the existing non-Gaussian turbulence excitation state.

[0066] When determining that the duration exceeds the threshold in S4, a sliding time window method is adopted, and the length of the time window is dynamically adjusted according to the average turbulence scale of the canyon wind field and the data acquisition frequency. Since the average turbulence scales of wind fields in different canyons are different, a time window of fixed length may not be able to accurately capture the continuous abnormal state in a specific wind field. By making dynamic adjustments according to the actual wind field conditions, it can be ensured that whether the duration exceeds the threshold can be accurately judged under various wind field conditions, thereby improving the accuracy and reliability of the judgment.

[0067] Embodiment 2

[0068] A plurality of dynamic compensation mechanisms 9 are arranged on the elevator body, each of which is composed of a hydraulic cylinder telescopic arm 8, a drive controller 7 and a reset spring 10. One end of the telescopic arm 8 is fixedly connected to the drive controller 7, and the other end cooperates with the mounting bracket 1. When the wind condition is abnormal, the drive controller 7 accurately controls the telescopic action of the telescopic arm 8 according to the data analysis results. For example, when the wind blows from the left side and the wind force is strong, the telescopic arm 8 on the left side will extend a certain length according to the wind force, such as 20 cm, while the telescopic arm 8 on the right side may shrink 10 cm. Through this telescopic combination, the position and posture of the construction elevator body 4 are adjusted, so that the elevator body 4 can better adapt to the force of the wind and reduce the vibration and instability. The function of the reset spring 10 is to help the telescopic arm 8 quickly return to the initial position after the wind condition returns to normal, so as to prepare for the next change in wind conditions.

[0069] When the wind direction and wind speed detection sensor 2 detects abnormal wind conditions, after the data analysis processor 6 analyzes and judges and the drive controller 7 receives the instruction, the drive controller 7 will control the telescopic arm 8 in each dynamic compensation mechanism according to the precise data analysis results. For example, in the actual operation scenario, when encountering canyon wind and the wind blows from the left side and the wind force is strong, the telescopic arm 8 on the left side will extend a certain length, such as 20 cm, under the instruction of the drive controller 7, according to the size of the wind force and the current operation state of the elevator. At the same time, in order to maintain the overall balance and stable posture of the elevator, the telescopic arm 8 on the right side will shrink by 10 cm under the control of the drive controller 7. Through such a combination of extension and contraction of coordinated actions, the position and posture of the construction elevator in three-dimensional space can be effectively adjusted. This adjustment makes the elevator no longer passively bear the force of the wind, but can actively adapt to the complex force distribution of the wind, thereby significantly reducing the flutter phenomenon caused by the non-Gaussian turbulence excitation of the canyon wind, greatly improving the unstable state of the elevator in severe wind conditions, and improving the safety and comfort of operation.

[0070] In the dynamic compensation mechanism 9, the reset spring 10 plays an indispensable auxiliary role. When the wind condition gradually returns to normal from abnormal, the telescopic arm 8 is in a non-initial position due to the previous extension or retraction action. The reset spring 10, by virtue of its own elastic characteristics, will generate a force in the opposite direction of the displacement of the telescopic arm 8. Driven by this force, the telescopic arm 8 is helped to quickly and smoothly return to its initial position. For example, when the telescopic arm 8 on the left side extends 20 cm when the wind condition is abnormal, after the wind condition returns to normal, the contraction force generated by the reset spring 10 will cause the telescopic arm 8 to gradually retract until it returns to the initial installation position. In this way, it can not only ensure that the dynamic compensation mechanism 9 will not cause additional interference or influence on the normal operation of the elevator when the wind condition is normal, but also enable the entire dynamic compensation mechanism 9 to quickly prepare for the next possible change in wind conditions, thereby ensuring the cycle effectiveness and reliability of the dynamic compensation mechanism 9, and further improving the ability of the construction elevator to operate stably for a long time in a complex canyon wind environment.

[0071] At the same time, in terms of direction adjustment, the drive controller 7 controls the dynamic compensation mechanism 9 to make the elevator run at a certain angle to the right (downwind direction). For example, according to factors such as the wind intensity and the current position of the elevator, the running direction is adjusted to deviate from the vertical direction by 15 degrees to the right. In this way, the lateral thrust of the wind on the elevator can be partially converted into the power to push it to move in the downwind direction, thereby reducing the risk of wind damage to the stability of the elevator structure, and enabling the elevator to move in the direction of the wind to a certain extent, reducing the buffeting and instability caused by wind resistance, better adapting to the complex environment of the canyon wind, and ensuring its safe and stable operation.

[0072] The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the relevant field are aware of all the common technical knowledge in the technical field to which the invention belongs before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, several deformations and improvements can be made without departing from the structure of the present invention, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. The automatic compensation and avoidance device for construction hoists is characterized by: The invention comprises a mounting bracket, on which a lift body is arranged, and the lift body is connected with the mounting bracket in cooperation; on which a traction device is arranged on the mounting bracket and is fixedly connected with the lift body; on the outer side of the mounting bracket, a plurality of wind direction and wind speed detection sensors are arranged; on the lift body, a data analysis processor is arranged, which is electrically connected with the wind direction and wind speed detection sensors, and analyzes and processes the detection data; on one side of the data analysis processor, a drive controller is arranged, which is electrically connected with the data analysis processor, and which is electrically connected with the traction device and the anti-fall safety device; the controller judges whether the result of the analysis output by the data analysis processor belongs to the non-Gaussian turbulence excitation state, and if so, the traction device and the anti-fall safety device adjust the operation state of the construction lift; the specific processing steps of the data analysis by the data processor are as follows: S1 data collection: wind direction and wind speed detection sensors collect wind speed and wind direction data every 0.1 seconds in real time; S2 performs denoising on the collected data, and can use moving average method or wavelet transform method to remove noise interference and obtain smooth wind speed and wind direction time series data; S3 calculates the skewness and kurtosis of wind speed data. Skewness reflects the asymmetry of data distribution, and kurtosis reflects the peak degree of data distribution. For normal distribution, skewness is 0 and kurtosis is 3. Under non-Gaussian turbulence excitation, skewness and kurtosis will deviate from 0 and 3. The calculation formula is as follows: Skewness Where n is the number of data points, x i is the ith wind speed data point, is the mean of the wind speed data, is the standard deviation; Kurtosis After calculating the skewness and kurtosis, S4 combines them with the mean and standard deviation of the wind speed into a feature vector, and collects measured wind data in different canyon environments, including known Gaussian turbulence and non-Gaussian turbulence. It calculates feature vectors for these data and performs cluster analysis. The clustering results are used to determine the range of feature vectors of different categories, which are converted into a dynamic threshold model. When the feature vector of the real-time data exceeds the threshold range of the corresponding category in the area, it proceeds to the next step of analysis. The wind direction is divided into different intervals of 0-45 degrees and 45-90 degrees, and the mean and variance of the skewness and kurtosis of wind speed in each wind direction interval are calculated; If the skewness and kurtosis of wind speed deviate from the Gaussian distribution characteristics in any wind direction interval, then we proceed to the next step of analysis; The wavelet transform method is used to decompose the wind speed data to obtain wind speed components at different scales. The skewness and kurtosis of the wind speed components at each scale are calculated. If there are skewness and kurtosis anomalies at any two scales, the energy transfer is abnormal, and the duration exceeds the threshold, it is determined to be in a canyon wind non-Gaussian turbulence excitation state.

2. The automatic compensation and avoidance device for a construction elevator according to claim 1, characterized in that: In the step S2, a weighted moving average algorithm is used, wherein data points closer to the current collection time are assigned higher weights, and the weight coefficient is set according to an exponential decay law.

3. The automatic compensation and avoidance device for a construction elevator according to claim 1, characterized in that: The wind direction and wind speed detection sensor adopts a laser Doppler anemometer.

4. The automatic compensation and avoidance device for a construction elevator according to claim 1, characterized in that: In the above S4, when calculating the feature vector for cluster analysis and constructing the dynamic threshold model, a density-based clustering algorithm is used for calculation.

5. The automatic compensation and avoidance device for a construction elevator according to claim 1, characterized in that: In S4, when the wind direction is divided into intervals of 0-45 degrees and 45-90 degrees to calculate the mean and variance of wind speed skewness and kurtosis, for the data in each wind direction interval, a piecewise linear fitting method is used to perform trend analysis on the wind speed skewness and kurtosis.

6. The automatic compensation and avoidance device for a construction elevator according to claim 1, characterized in that: In S4, when the duration is judged to exceed the threshold, a sliding time window method is used, and the length of the time window is dynamically adjusted according to the average turbulence scale and data acquisition frequency of the canyon wind field.

7. The automatic compensation and avoidance device for a construction elevator according to claim 1, characterized in that: A plurality of dynamic compensation mechanisms are arranged on the lift body, and the dynamic compensation mechanisms include a telescopic arm, a drive controller and a reset spring. The telescopic arm is a hydraulic cylinder telescopic arm, one end of which is fixedly connected to the drive controller, and the other end is cooperatively connected to the mounting bracket; the telescopic wall is electrically connected to the drive controller.

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