Control system and method for remotely operating anchor protection robot

Abstract

The invention discloses a control system and method for remotely operating an anchor protection robot, and relates to the technical field of anchor protection robots. According to the method, a closed-loop control process from environmental parameter acquisition, wind load modeling and dynamic compensation control to optimization evaluation is realized. And when the compensation efficiency is lower than 90%, further analysis of the vibration suppression efficiency is automatically triggered, and a joint torque compensation coefficient alpha and a posture adjustment sensitivity coefficient delta are dynamically adjusted based on a feedback result, so that a wind load compensation and posture adjustment strategy is optimized. And under the condition that the compensation efficiency is normal, the system continues to execute the current operation so as to ensure that the anchor protection robot is always in an efficient operation state. According to the mechanism, the adaptive capacity of the robot in a dynamic environment is improved, action errors and operation energy consumption caused by wind load interference are effectively reduced, and stability and safety in a complex operation scene are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of anchor guarding robots, and in particular to a control system and method for remotely operating an anchor guarding robot. Background Art

[0002] In the construction of large-scale infrastructure such as highways, bridges and geological engineering, anchor robots are important equipment in this field, dedicated to ensuring the stability and safety of structures by fixing anchor points. In practical applications, such robots need to fix support parts, adjust tension distribution and accurately position in complex terrain environments at the construction site. With the diversification of construction environments, such as the challenges of high wind speed, high humidity and complex vibration environments, higher operational stability and remote control accuracy requirements are put forward for anchor robots.

[0003] At present, in harsh environments such as highway bridge construction, anchor robots often face difficulties such as strong wind load interference, complex terrain vibration, and signal delay. Existing robot control methods mainly rely on fixed parameter control or simple manual compensation strategies, and have poor adaptability to dynamic environments. For example, under high wind speed conditions, wind loads will significantly affect the robot's motion accuracy and stability, which may cause anchor point positioning deviation, loosening of fixed supports, and even overturning or damage to equipment. In addition, traditional vibration suppression methods are mostly based on passive damping, and there is no effective response mechanism for complex real-time dynamic wind load effects. These shortcomings significantly limit the application efficiency and reliability of anchor robots in complex environments. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a control system and method for remotely operating an anchor guarding robot, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: comprising the following steps:

[0006] S1. During the construction of highway bridges, a sensor group is installed on the anchor robot to collect wind load impact data in real time, and then the collected wind load impact data is transmitted to the remote control platform through the wireless communication network;

[0007] S2. receiving wind load impact data in real time in the remote control platform, preprocessing the wind load impact data to obtain a standard data set, performing feature extraction on the standard data set to obtain a wind load impact data set, and then constructing a time series database to store the wind load impact data set;

[0008] S3, extracting the wind load influence data set, calculating the wind load working force Fw, and then calculating and outputting the predicted tilt angle Qp of the robot based on the wind load working force Fw, analyzing the posture angle deviation prediction of the anchor robot under the wind load, and calculating and predicting the wind direction WDt+1 based on the wind load influence data set;

[0009] S4, based on the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1, combined with the wind load impact data set, respectively calculate and output the compensation joint torque value JTc, the posture adjustment amount △PA and the actual action execution delay Tc, and dynamically adjust the robot action parameters to offset the interference of the wind load on the operation;

[0010] S5. After dynamic adjustment, the adjusted real-time feedback data is recalculated, the robot operation status is monitored, and an evaluation algorithm model is constructed to calculate and output the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the adjusted feedback data. Wind load evaluation is performed based on the output results of the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF, and vibration effect evaluation is triggered based on the wind load evaluation results.

[0011] Preferably, said S1 includes S11 and S12;

[0012] S11, using the sensor group installed on the anchor protection robot, set the sensor group collection frequency to an interval of 30 minutes to collect wind impact data in real time;

[0013] The sensor group includes an ultrasonic anemometer, a laser Doppler wind direction sensor, a humidity sensor and a laser tilt sensor

[0014] The wind impact data include wind speed WS, wind direction fluctuation WD, air humidity RH and robot attitude angle PA;

[0015] S12. By equipping the installed sensor group with a 5G network communication module and building a remote control platform to connect the 5G network communication modules of the sensor group with each other through wireless communication, the wind impact data collected in real time is transmitted to the remote control platform through the wireless communication network.

[0016] Preferably, S2 includes S21, S22 and S23;

[0017] S21, receiving wind impact data in real time in the remote control platform, and preprocessing the wind impact data, wherein the preprocessing method includes data cleaning and data normalization, and marking timestamps for all parameters in the preprocessed wind impact data to obtain a standard data set;

[0018] S22, based on the standard data set, feature extraction is performed to obtain the wind speed change rate △WS, wind direction fluctuation range WDvar and tilt vibration frequency VF, and then integrated with the air humidity RH to obtain the wind load influence data set;

[0019] S23. By constructing a time series database and setting a write port, a write port, a real-time storage table and a historical storage table, the wind load impact data set is stored in the real-time storage table through the write port, and the original wind load impact data set in the real-time storage table is automatically transferred to the historical storage table and sorted in timestamp order.

[0020] Preferably, said S3 includes S31, S32 and S33;

[0021] S31, extracting the load impact data set in real time through the write port, and calculating the wind load working force Fw in the remote control platform;

[0022] The wind load working force Fw is calculated and outputted by the following algorithm formula:

[0023] Fw=0.5·ρ·Cd·(A·cos(WD))·(ΔWS·RH);

[0024] In the formula, ρ represents air density, Cd represents drag coefficient, A represents the total area of ​​the robot facing the airflow, and cos represents cosine function;

[0025] S32, predicting the tilt angle offset using a dynamics formula based on the wind load working force Fw, calculating and outputting the predicted tilt angle Qp, and analyzing the offset of the anchor robot's attitude angle under the action of the wind load;

[0026] The predicted tilt angle Qp is calculated and outputted by the following dynamic formula:

[0027]

[0028] Wherein, sin(WD) represents the wind force component coefficient, arctan represents the inverse tangent function, sin represents the sine function, Mr represents the anti-overturning moment, and kd represents the influence coefficient of frequency on the moment.

[0029] Preferably, S33, combining a time series analysis algorithm based on historical wind direction fluctuation WD data with real-time calculation, outputting a predicted wind direction WDt+1, and analyzing short-term analysis and prediction;

[0030] The predicted wind direction WDt+1 is calculated and outputted by the following algorithm formula:

[0031] WDt+1=WDt+β·WDvar+γ;

[0032] Where WDt represents the current wind direction, β represents the adjustment coefficient, which is used to quantify the impact of the instability of the current wind direction on the future wind direction, and γ represents the environmental factor correction term.

[0033] Preferably, S4 includes S41, S42 and S43;

[0034] S41, the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1 are combined with the current wind direction fluctuation WD in the wind load impact data set to comprehensively calculate the compensation joint torque value JTc, and the compensation joint torque value JTc adjustment command is sent to the anchor robot processor through the remote control platform to control the servo joint motor to dynamically adjust the movement accuracy under the wind load interference;

[0035] The compensation joint torque value JTc is calculated and outputted by the following algorithm formula;

[0036] JTc=JT+α·Fw·sin(WD)+δ·Qp;

[0037] In the formula, JT represents the original joint torque, α represents the joint torque compensation coefficient, and δ represents the posture compensation sensitivity coefficient;

[0038] S42, calculating and outputting the attitude adjustment amount △PA through the current tilt vibration frequency VF, sending the attitude adjustment amount △PA command to the anchor robot processor through the remote control platform, and controlling the servo joint motor to dynamically adjust and suppress vibration;

[0039] The posture adjustment amount ΔPA is calculated and outputted by the following algorithm formula:

[0040] △PA=-kp·VF;

[0041] Where -kp represents the vibration suppression coefficient.

[0042] Preferably, S43, after performing joint torque compensation and vibration suppression on the anchor protection robot, the wind speed change rate △WS in the wind load impact data set is extracted to calculate and output the actual action execution delay Tc, dynamically adjust the action delay time, and optimize the execution error caused by wind speed changes;

[0043] The actual action execution delay Tc is calculated and output by the following algorithm formula;

[0044]

[0045] Where T0 represents the basic action delay, WS threshold Indicates the wind speed change threshold, which is determined according to the dynamic response capability of the robot's actions and environmental requirements. It is used to control the delay adjustment amplitude to prevent over-compensation or under-compensation.

[0046] Preferably, said S5 includes S51 and S52;

[0047] S51, recalculate the adjusted real-time feedback data after dynamic adjustment, the feedback data includes the compensated wind load working force Fw after and the compensated vibration frequency VF after ;

[0048] Based on the compensated wind load working force Fw after The wind load compensation efficiency Bc is calculated and output, and the wind load efficiency after the servo motor compensation of the anchor robot joint is analyzed. The specific algorithm is as follows;

[0049] Based on the output result of the wind load compensation efficiency Bc, a wind load evaluation is performed to analyze the weakening effect of the wind load compensation on the actual wind force. The specific evaluation contents are as follows;

[0050] When the wind load compensation efficiency Bc is less than 90%, it indicates that the wind load compensation is abnormal, and the vibration suppression compensation analysis is triggered;

[0051] When the wind load compensation efficiency Bc ≥ 90%, it indicates that the compensation effect is normal, and the current operation continues.

[0052] Preferably, in step S52, when the wind load compensation is abnormal, the compensated vibration frequency VF after compensation is calculated based on the compensated vibration frequency VF after The vibration suppression efficiency ZVF is calculated and output, and the vibration suppression efficiency of the servo motor of the anchor robot joint after compensation is analyzed; the vibration suppression efficiency ZVF is calculated and output by the following algorithm formula: ZVF = VF - VF after ;

[0053] Based on the output result of the vibration suppression efficiency ZVF, a vibration effect evaluation is performed to analyze the vibration suppression effect. The specific evaluation contents are as follows;

[0054] When the vibration suppression efficiency ZVF>0, it means that the vibration frequency suppression efficiency is normal. At this time, the attitude adjustment amount △PA command is valid and no adjustment is required;

[0055] When the vibration suppression efficiency ZVF≤0, it indicates that the vibration frequency suppression efficiency is abnormal. At this time, feedback is given to step S4 to adjust the control joint torque compensation coefficient α.

[0056] A control system for remotely operating an anchor robot, comprising an operating environment monitoring module, a wind load extraction module, a wind load analysis and prediction module, a real-time compensation control module and a feedback evaluation module;

[0057] The working environment monitoring module collects wind load impact data in real time by installing a sensor group on the anchor robot during the construction of the highway bridge, and then transmits the collected wind load impact data to the remote control platform through a wireless communication network;

[0058] The wind load extraction module receives wind load impact data in real time in the remote control platform, pre-processes the wind load impact data, obtains a standard data set, extracts features from the standard data set to obtain a wind load impact data set, and then constructs a time series database to store the wind load impact data set;

[0059] The wind load analysis and prediction module calculates the wind load working force Fw by extracting the wind load influence data set, and then calculates the predicted tilt angle Qp of the output robot based on the wind load working force Fw, analyzes the posture angle deviation prediction of the anchor robot under the wind load, and calculates and predicts the wind direction WDt+1 based on the wind load influence data set;

[0060] The real-time compensation control module calculates and outputs the compensation joint torque value JTc, the posture adjustment amount △PA and the actual action execution delay Tc respectively based on the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1, combined with the wind load influence data set, and dynamically adjusts the robot action parameters to offset the interference of the wind load on the operation;

[0061] The feedback evaluation module recalculates the adjusted real-time feedback data after dynamic adjustment, monitors the robot's operating status, and constructs an evaluation algorithm model. It calculates and outputs the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the adjusted feedback data, and performs wind load evaluation based on the output results of the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF, and triggers vibration effect evaluation based on the wind load evaluation results.

[0062] The present invention provides a control system and method for remotely operating an anchor robot. It has the following beneficial effects:

[0063] (1) This method installs a sensor group on the anchor robot to obtain real-time wind impact data, and transmits the data to the remote control platform through the 5G network communication module. This method realizes accurate perception and real-time response to wind dynamics in complex environments. Based on the comprehensive calculation of wind load working force Fw, predicted tilt angle Qp and predicted wind direction WDt+1, this method can dynamically adjust the compensation joint torque JTc, posture adjustment amount ΔPA and actual action execution delay Tc, effectively offset the impact of wind load interference on operation, avoid problems such as anchor point positioning offset and fixed support loosening, and significantly improve the robot's operation stability and accuracy in complex environments such as highway and bridge construction.

[0064] (2) This method relies on the remote control platform to preprocess the wind load impact data, including data cleaning, normalization and timestamp marking, build a standard data set and extract characteristic parameters. By integrating the wind load impact data set and storing it in a time series database, it provides real-time storage and historical data comparison functions. Based on the real-time feedback data, the wind load compensation efficiency Bc and vibration suppression efficiency ZVF are calculated and output. This method realizes the full intelligent monitoring and dynamic optimization adjustment of the anchor robot. Through the high-bandwidth and low-latency characteristics of 5G communication, the remote platform can send adjustment commands to the robot processor in real time to control the dynamic compensation of the servo joint motor, which significantly improves the intelligent operation level of the anchor robot in complex environments and reduces the need for manual intervention.

[0065] (3) This method constructs an evaluation algorithm model for wind load compensation efficiency Bc and vibration suppression efficiency ZVF. This method realizes a closed-loop control process from environmental parameter acquisition, wind load modeling, dynamic compensation control to optimization evaluation. When the compensation efficiency is lower than 90%, further analysis of the vibration suppression efficiency is automatically triggered, and the joint torque compensation coefficient α and the posture adjustment sensitivity coefficient δ are dynamically adjusted based on the feedback results, thereby optimizing the wind load compensation and posture adjustment strategies. When the compensation efficiency is normal, the system continues to perform the current operation to ensure that the anchor robot is always in an efficient operating state. This mechanism not only improves the robot's adaptability in dynamic environments, but also effectively reduces the motion errors and operating energy consumption caused by wind load interference, ensuring stability and safety in complex operating scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic diagram of the steps of a control method for remotely operating an anchor guarding robot according to the present invention;

[0067] Figure 2 The present invention is a flow chart of a control system for remotely operating an anchor guarding robot. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0069] Example 1

[0070] See also Figure 1The present invention provides a control method for remotely operating an anchor guarding robot. To achieve the above purpose, the present invention is implemented by the following technical scheme: comprising the following steps:

[0071] S1. During the construction of highway bridges, a sensor group is installed on the anchor robot to collect wind load impact data in real time, and then the collected wind load impact data is transmitted to the remote control platform through the wireless communication network;

[0072] S2. receiving wind load impact data in real time in the remote control platform, preprocessing the wind load impact data to obtain a standard data set, extracting features from the standard data set to obtain a wind load impact data set, and then constructing a time series database to store the wind load impact data set;

[0073] S3, extract the wind load influence data set, calculate the wind load working force Fw, and then calculate and output the predicted tilt angle Qp of the robot based on the wind load working force Fw, analyze the posture angle deviation prediction of the anchor robot under the wind load, and calculate and predict the wind direction WDt+1 based on the wind load influence data set;

[0074] S4, based on the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1, combined with the wind load impact data set, respectively calculate and output the compensation joint torque value JTc, the posture adjustment amount △PA and the actual action execution delay Tc, and dynamically adjust the robot action parameters to offset the interference of the wind load on the operation;

[0075] S5. After dynamic adjustment, the adjusted real-time feedback data is recalculated, the robot operation status is monitored, and an evaluation algorithm model is constructed to calculate and output the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the adjusted feedback data. Wind load evaluation is performed based on the output results of the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF, and vibration effect evaluation is triggered based on the wind load evaluation results.

[0076] In this embodiment, the method collects key data such as wind speed, wind direction fluctuation, humidity and attitude angle in real time by installing a sensor group on the anchor robot, and transmits it to the remote control platform through the 5G wireless communication network, so as to realize accurate data collection and efficient transmission in complex environments, and preprocesses, extracts features and stores time series of data on the remote control platform to form a wind load impact data set, which provides a reliable data basis for subsequent dynamic modeling. Then, by calculating the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1, the dynamic prediction of the robot's attitude angle offset and wind direction change is realized, providing accurate input data for operation stability control. Combined with the above calculation results, the robot motion parameters are dynamically adjusted, including the joint torque compensation value JTc, the attitude adjustment amount ΔPA and the actual action delay Tc, so as to effectively offset the influence of wind load interference on the operation and ensure the accuracy and stability of the robot's motion. The evaluation algorithm model is constructed through real-time feedback data, and the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF are dynamically evaluated to form a closed-loop optimization mechanism, which further improves the robot's adaptability and intelligent control level in complex working environments. The overall approach enables the anchor robot to perceive the dynamic wind load environment in real time, accurately model, dynamically compensate and intelligently optimize, thus achieving the goal of improving operation stability, operation accuracy and remote control intelligence. Through dynamic evaluation and closed-loop feedback, it not only improves the robot's operating reliability and efficiency in high wind load environments, but also reduces safety risks and energy waste caused by environmental interference, thereby achieving an overall improvement in construction quality, efficiency and safety.

[0077] Example 2

[0078] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: S1 includes S11 and S12;

[0079] S11, using the sensor group installed on the anchor protection robot, set the sensor group collection frequency to an interval of 30 minutes to collect wind impact data in real time;

[0080] The sensor group includes ultrasonic anemometer, laser Doppler wind direction sensor, humidity sensor and laser tilt sensor. The wind impact data include wind speed WS, wind direction fluctuation WD, air humidity RH and robot attitude angle PA;

[0081] S12. By equipping the installed sensor group with a 5G network communication module and building a remote control platform to connect the 5G network communication modules of the sensor group with each other through wireless communication, the wind impact data collected in real time is transmitted to the remote control platform through the wireless communication network.

[0082] In this embodiment, the method collects wind impact data in real time with a collection frequency of 30 minutes through the sensor group installed on the anchor robot, and builds a comprehensive perception capability of the dynamic environment. Then, by carrying a 5G network communication module, the real-time collected wind impact data is efficiently transmitted to the remote control platform, ensuring low latency and high reliability of data transmission, and providing basic support for subsequent data processing and dynamic compensation. This method achieves the purpose of real-time perception of wind load changes and dynamic monitoring of environmental parameters, and significantly improves the adaptability and information interaction efficiency of the anchor robot in complex environments. By integrating high-precision sensors and high-speed wireless communication technology, the robot can accurately capture wind interference information, and the remote control platform can receive and process these data in real time, providing key support for subsequent dynamic compensation and optimization. This real-time monitoring and efficient transmission capability not only improves the stability and accuracy of the anchor robot in harsh environments, but also reduces the need for manual intervention, improves the intelligence level of the system, and ultimately achieves comprehensive optimization of construction quality and safety.

[0083] Example 3

[0084] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S2 includes S21, S22 and S23;

[0085] S21. receiving wind impact data in real time in the remote control platform, and preprocessing the wind impact data, wherein the preprocessing method includes data cleaning and data normalization, and marking timestamps for all parameters in the preprocessed wind impact data to obtain a standard data set;

[0086] S22, based on the standard data set, feature extraction is performed to obtain the wind speed change rate △WS, wind direction fluctuation range WDvar and tilt vibration frequency VF, and then integrated with the air humidity RH to obtain the wind load impact data set;

[0087] The wind speed change rate △WS is obtained by extracting the wind speed WS at the current time t and the wind speed WS at the previous time, and calculating the change rate. The specific algorithm formula is: In the formula, △t represents the time step, which means the time interval between the current time t and the previous time t-1;

[0088] The wind direction fluctuation range WDvar is calculated by extracting the wind direction fluctuation WD. The specific algorithm formula is: WDvar = max(WD)-min(WD); where max(WD) represents the upper limit of wind direction fluctuation, and min(WD) represents the lower limit of risk fluctuation;

[0089] The tilt vibration frequency VF is obtained by extracting the robot posture angle PA at the current time t and the main vibration frequency extracted from the Fourier transform result. The specific algorithm formula is: VF = FFT (PA (t)), FFT represents the Fourier transform result;

[0090] S23. By constructing a time series database and setting a write port, a write port, a real-time storage table and a historical storage table, the wind load impact data set is stored in the real-time storage table through the write port, and the original wind load impact data set in the real-time storage table is automatically transferred to the historical storage table and sorted in timestamp order.

[0091] In this embodiment, the method receives the wind impact data transmitted through the 5G network in real time through the remote control platform, performs pre-processing such as cleaning and normalization on the data, eliminates noise and outliers, and marks timestamps for all parameters to form a standardized data set to ensure the accuracy and consistency of the data. Through the feature extraction algorithm, the wind speed change rate ΔWS, wind direction fluctuation range WDvar and tilt vibration frequency VF are calculated from the standard data set, and integrated with the air humidity RH to generate a comprehensive wind load impact data set, which provides complete input data for subsequent analysis and dynamic compensation. At the same time, an efficient time series database is constructed, write ports and write ports are set, and the real-time storage table is used in combination with the historical storage table. The data is classified, stored and managed in timestamp order, which provides convenience for historical comparison and dynamic analysis. Through this method, the purpose of efficient management and comprehensive analysis of wind impact data is achieved, and the response ability of the anchor robot to changes in complex environmental parameters is significantly improved. Combined with data preprocessing and feature extraction, redundancy and errors in the original data are eliminated, providing accurate and specific input data for subsequent calculations. The introduction of the time series database not only realizes the coordinated storage and call of real-time data and historical data, but also improves the efficiency of data retrieval and analysis. This optimized data management process not only enhances the accuracy and intelligence of the robot control system, but also lays a solid foundation for dynamic compensation and real-time evaluation, thereby significantly improving the stability and accuracy of robot operations.

[0092] Example 4

[0093] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S3 includes S31, S32 and S33;

[0094] S31, extracting the load impact data set in real time through the write port, and calculating the wind load working force Fw in the remote control platform;

[0095] The wind load working force Fw is calculated and output by the following algorithm formula;

[0096] Fw=0.5·ρ·Cd·(A·cos(WD))·(△WS·RH);

[0097] In the formula, ρ represents the air density, and the air density value at standard atmospheric pressure is 1.225 kg / m 3 , Cd represents the drag coefficient, which describes the drag coefficient between the robot surface and the air, and is a dimensionless value. A represents the total area of ​​the robot facing the airflow. Cos represents the cosine function. Cos(WD) is used to analyze the dynamic changes of the windward area.

[0098] S32, predicting the tilt angle offset using a dynamics formula based on the wind load working force Fw, calculating and outputting the predicted tilt angle Qp, and analyzing the offset of the anchor robot's attitude angle under the action of the wind load;

[0099] The predicted tilt angle Qp is calculated and output by the following dynamic formula:

[0100]

[0101] Wherein, sin(WD) represents the wind force component coefficient, arctan represents the inverse tangent function, sin represents the sine function, Mr represents the anti-rollover moment, which is determined by the robot structure design parameters, and kd represents the influence coefficient of frequency on the moment, which is determined by combining the robot structure material and vibration analysis experiments.

[0102] S33, based on the time series analysis algorithm of historical wind direction fluctuation WD data combined with real-time calculation, output the predicted wind direction WDt+1, and analyze the short-term analysis and forecast;

[0103] The predicted wind direction WDt+1 is calculated and output by the following algorithm formula;

[0104] WDt+1=WDt+β·WDvar+γ;

[0105] Where WDt represents the current wind direction, β represents the adjustment coefficient, which is used to quantify the impact of the instability of the current wind direction on the future wind direction, and γ represents the environmental factor correction term.

[0106] In this embodiment, the remote control platform of the method extracts the wind load impact data set in real time through the write port, and uses the wind load calculation formula, combined with the air density ρ, the drag coefficient Cd, the robot's windward area A and the dynamic wind direction change cos (WD), to accurately calculate the wind load working force Fw. Based on the calculated wind load working force Fw, the robot's tilt angle offset under the action of wind load is predicted by the dynamic formula, the predicted tilt angle Qp is output, and the dynamic change of the robot's posture angle is analyzed. The tilt angle calculation combines the wind force component sin (WD), the anti-tilting moment Mr and the frequency influence coefficient kd on the moment, and realizes the accurate prediction of the robot's posture offset in a complex mechanical environment. Combined with the historical wind direction fluctuation data and the time series analysis algorithm, the wind direction change trend WDt+1 in the short term is dynamically predicted by adjusting the coefficient β and the environmental factor correction term γ, providing key input for the optimization of the robot's subsequent actions. Through this method, the purpose of dynamically analyzing the wind load force, real-time prediction of the robot's posture offset and short-term wind direction change is achieved, and the adaptive ability and dynamic response level of the anchor robot to complex environments are significantly enhanced. The precise calculation of wind load operating force provides efficient input for compensating joint torque and posture adjustment. The tilt angle prediction lays a mechanical foundation for the robot's motion stability control, while the wind direction prediction further improves the robot's perception of dynamic wind fields.

[0107] Example 5

[0108] This embodiment is explained in Example 4. Please refer to Figure 1 , specifically: S4 includes S41, S42 and S43;

[0109] S41, the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1 are combined with the current wind direction fluctuation WD in the wind load impact data set to comprehensively calculate the compensation joint torque value JTc, and the compensation joint torque value JTc adjustment command is sent to the anchor robot processor through the remote control platform to control the servo joint motor to dynamically adjust to maintain the action accuracy under the wind load interference;

[0110] The compensation joint torque value JTc is calculated and output by the following algorithm formula;

[0111] JTc=JT+α·Fw·sin(WD)+δ·Qp;

[0112] In the formula, JT represents the original joint torque, α represents the joint torque compensation coefficient, and δ represents the posture compensation sensitivity coefficient;

[0113] S42, calculating and outputting the attitude adjustment amount △PA through the current tilt vibration frequency VF, sending the attitude adjustment amount △PA command to the anchor robot processor through the remote control platform, and controlling the servo joint motor to dynamically adjust and suppress vibration;

[0114] The attitude adjustment amount △PA is calculated and output by the following algorithm formula;

[0115] △PA=-kp·VF;

[0116] In the formula, -kp represents the vibration suppression coefficient, which indicates the response intensity of posture adjustment to vibration frequency and is optimized through experiments.

[0117] S43, after performing joint torque compensation and vibration suppression on the anchor protection robot, the wind speed change rate △WS in the wind load impact data set is extracted to calculate and output the actual action execution delay Tc, dynamically adjust the action delay time, and optimize the execution error caused by wind speed changes;

[0118] The actual action execution delay Tc is calculated and output by the following algorithm formula;

[0119]

[0120] Where T0 represents the basic action delay, WS threshold Indicates the wind speed change threshold, which is determined according to the dynamic response capability of the robot's actions and environmental requirements. It is used to control the delay adjustment amplitude to prevent over-compensation or under-compensation.

[0121] In this embodiment, the method is based on the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1, combined with the current wind direction fluctuation WD, through comprehensive calculation of the compensation joint torque value JTc, and the adjustment command is sent to the robot processor through the remote control platform, the servo joint motor is dynamically controlled to offset the wind load interference, and the high precision and stability of the robot action are ensured. The posture adjustment amount ΔPA is calculated by the current tilt vibration frequency VF, and the adjustment command is sent to the robot in real time to control the servo motor to dynamically suppress vibration and reduce the influence of the posture angle fluctuation on the operation. By extracting the wind speed change rate ΔWS, the actual action execution delay Tc is calculated, the action delay time is dynamically adjusted, the execution error caused by wind speed changes is optimized, and the continuity and accuracy of the robot operation are ensured. Through this method, the purpose of real-time compensation of wind load interference, dynamic suppression of posture vibration and optimization of action delay is achieved, and the operation reliability and stability of the anchor robot in complex environments are significantly improved. The dynamic adjustment of the compensation joint torque effectively offsets the influence of high wind load on the robot's action accuracy; the precise calculation and real-time execution of the posture adjustment amount significantly reduce the operation deviation caused by vibration; the optimization and regulation of action delay further ensure the smoothness and accuracy of the robot's action.

[0122] Example 6

[0123] This embodiment is explained in Example 5. Please refer to Figure 1, specifically: S5 includes S51 and S52;

[0124] S51, recalculate the adjusted real-time feedback data after dynamic adjustment, the feedback data includes the compensated wind load working force Fw after and the compensated vibration frequency VF after ;

[0125] Based on the compensated wind load working force Fw after The wind load compensation efficiency Bc is calculated and output, and the wind load efficiency after the servo motor compensation of the anchor robot joint is analyzed. The specific algorithm is as follows;

[0126] Based on the output result of wind load compensation efficiency Bc, wind load evaluation is performed to analyze the weakening effect of wind load compensation on actual wind force. The specific evaluation contents are as follows;

[0127] When the wind load compensation efficiency Bc is less than 90%, it indicates that the wind load compensation is abnormal. At this time, the vibration suppression compensation analysis is triggered to further analyze the cause of the abnormal wind load compensation.

[0128] When the wind load compensation efficiency Bc ≥ 90%, it indicates that the compensation effect is normal, and the current operation continues.

[0129] S52: If the wind load compensation is abnormal, then the compensated vibration frequency VF after compensation is calculated. after The vibration suppression efficiency ZVF is calculated and output, and the vibration suppression efficiency of the servo motor of the anchor robot joint is analyzed and analyzed; the vibration suppression efficiency ZVF is calculated and output by the following algorithm formula: ZVF = VF-VF after ;

[0130] Then, based on the output results of the vibration suppression efficiency ZVF, the vibration effect is evaluated and the vibration suppression effect is analyzed. The specific evaluation contents are as follows;

[0131] When the vibration suppression efficiency ZVF>0, it means that the vibration frequency suppression efficiency is normal. At this time, the attitude adjustment amount △PA command is valid and no adjustment is required;

[0132] When the vibration suppression efficiency ZVF≤0, it indicates that the vibration frequency suppression efficiency is abnormal. At this time, feedback is given to step S4 to adjust the control joint torque compensation coefficient α.

[0133] In this embodiment, the method provides real-time feedback of compensated data, including the compensated wind load operating force Fw after and the compensated vibration frequency VF after, calculate the wind load compensation efficiency Bc to evaluate the effect of the servo motor on the wind interference after compensation. When the wind load compensation efficiency Bc is less than 90%, S52 is automatically triggered to perform vibration suppression compensation analysis to further investigate the cause of abnormal wind load compensation; if the wind load compensation efficiency Bc is greater than or equal to 90%, it means that the compensation effect is normal and the operation can continue. In S52, by analyzing the vibration frequency VF after compensation after Calculate the vibration suppression efficiency ZVF and evaluate the vibration suppression effect of the posture adjustment amount ΔPA. When ΔVF>0, it means that the vibration suppression strategy is effective; otherwise, it will be fed back to S4 to adjust the joint torque compensation coefficient α to optimize the compensation strategy. Through this method, the purpose of real-time monitoring of wind load compensation effect and dynamic optimization of vibration suppression strategy is achieved, and the intelligent operation capability of the anchor robot in complex wind load environment is significantly improved. The evaluation of wind load compensation efficiency can quickly detect the effectiveness of the compensation strategy and ensure that the robot always operates in an efficient state; further analysis of vibration suppression efficiency provides accurate feedback for the posture adjustment strategy and enhances the dynamic response capability of vibration interference. Combined with the multi-level evaluation and feedback loop of wind load and vibration, this method realizes the continuous and stable operation of the robot in high wind load scenarios, which not only improves the operating efficiency and control accuracy, but also effectively reduces the equipment loss and safety risks caused by interference, providing a solid guarantee for construction in complex environments.

[0134] Example 7

[0135] See also Figure 1 and Figure 2 , a control system for remotely operating an anchor guard robot, comprising an operating environment monitoring module, a wind load extraction module, a wind load analysis and prediction module, a real-time compensation control module and a feedback evaluation module;

[0136] The working environment monitoring module collects wind load impact data in real time by installing a sensor group on the anchor robot during the construction of the highway bridge, and then transmits the collected wind load impact data to the remote control platform through the wireless communication network;

[0137] The wind load extraction module receives wind load impact data in real time in the remote control platform, pre-processes the wind load impact data, obtains a standard data set, extracts features from the standard data set to obtain a wind load impact data set, and then constructs a time series database to store the wind load impact data set;

[0138] The wind load analysis and prediction module extracts the wind load impact data set to calculate the wind load operating force Fw, and then calculates the predicted tilt angle Qp of the output robot based on the wind load operating force Fw, analyzes the posture angle deviation prediction of the anchor robot under the action of wind load, and calculates and predicts the wind direction WDt+1 based on the wind load impact data set;

[0139] The real-time compensation control module calculates and outputs the compensation joint torque value JTc, the posture adjustment amount △PA and the actual action execution delay Tc based on the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1, combined with the wind load impact data set, and dynamically adjusts the robot motion parameters to offset the interference of the wind load on the operation;

[0140] The feedback evaluation module recalculates the adjusted real-time feedback data after dynamic adjustment, monitors the robot's operating status, and builds an evaluation algorithm model. It calculates and outputs the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the adjusted feedback data, and performs wind load evaluation based on the output results of the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF, and triggers vibration effect evaluation based on the wind load evaluation results.

[0141] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A control method for remotely operating an anchor guarding robot, characterized in that: The following steps are involved: S1. During the construction of highway bridges, a sensor group is installed on the anchor robot to collect wind load impact data in real time, and then the collected wind load impact data is transmitted to the remote control platform through the wireless communication network; S2. receiving wind load impact data in real time in the remote control platform, preprocessing the wind load impact data to obtain a standard data set, performing feature extraction on the standard data set to obtain a wind load impact data set, and then constructing a time series database to store the wind load impact data set; S3, extracting the wind load influence data set, calculating the wind load working force Fw, and then calculating and outputting the predicted tilt angle Qp of the robot based on the wind load working force Fw, analyzing the posture angle deviation prediction of the anchor robot under the wind load, and calculating and predicting the wind direction WDt+1 based on the wind load influence data set; S4, based on the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1, combined with the wind load impact data set, respectively calculate and output the compensation joint torque value JTc, the posture adjustment amount △PA and the actual action execution delay Tc, and dynamically adjust the robot action parameters to offset the interference of the wind load on the operation; S5. After dynamic adjustment, the adjusted real-time feedback data is recalculated, the robot operation status is monitored, and an evaluation algorithm model is constructed to calculate and output the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the adjusted feedback data. Wind load evaluation is performed based on the output results of the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF, and vibration effect evaluation is triggered based on the wind load evaluation results.

2. A control method for remotely operating an anchor guarding robot according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, using the sensor group installed on the anchor protection robot, set the sensor group collection frequency to an interval of 30 minutes to collect wind impact data in real time; The sensor group includes an ultrasonic anemometer, a laser Doppler wind direction sensor, a humidity sensor and a laser tilt sensor The wind impact data include wind speed WS, wind direction fluctuation WD, air humidity RH and robot attitude angle PA; S12. By equipping the installed sensor group with a 5G network communication module and building a remote control platform to connect the 5G network communication modules of the sensor group with each other through wireless communication, the wind impact data collected in real time is transmitted to the remote control platform through the wireless communication network.

3. A control method for remotely operating an anchor guarding robot according to claim 2, characterized in that: The S2 includes S21, S22 and S23; S21, receiving wind impact data in real time in the remote control platform, and preprocessing the wind impact data, wherein the preprocessing method includes data cleaning and data normalization, and marking timestamps for all parameters in the preprocessed wind impact data to obtain a standard data set; S22, based on the standard data set, feature extraction is performed to obtain the wind speed change rate △WS, wind direction fluctuation range WDvar and tilt vibration frequency VF, and then integrated with the air humidity RH to obtain the wind load influence data set; S23. By constructing a time series database and setting a write port, a write port, a real-time storage table and a historical storage table, the wind load impact data set is stored in the real-time storage table through the write port, and the original wind load impact data set in the real-time storage table is automatically transferred to the historical storage table and sorted in timestamp order.

4. A control method for remotely operating an anchor guarding robot according to claim 3, characterized in that: The S3 includes S31, S32 and S33; S31, extracting the load impact data set in real time through the write port, and calculating the wind load working force Fw in the remote control platform; The wind load working force Fw is calculated and outputted by the following algorithm formula: Fw=0.5·ρ·Cd·(A·cos(WD))·(ΔWS·RH); In the formula, ρ represents air density, Cd represents drag coefficient, A represents the total area of ​​the robot facing the airflow, and cos represents cosine function; S32, predicting the tilt angle offset using a dynamics formula based on the wind load working force Fw, calculating and outputting the predicted tilt angle Qp, and analyzing the offset of the anchor robot's attitude angle under the action of the wind load; The predicted tilt angle Qp is calculated and outputted by the following dynamic formula: Wherein, sin(WD) represents the wind force component coefficient, arctan represents the inverse tangent function, sin represents the sine function, Mr represents the anti-overturning moment, and kd represents the influence coefficient of frequency on the moment.

5. A control method for remotely operating an anchor guarding robot according to claim 4, characterized in that: S33, based on the time series analysis algorithm of historical wind direction fluctuation WD data combined with real-time calculation, output the predicted wind direction WDt+1, and analyze the short-term sharing forecast; The predicted wind direction WDt+1 is calculated and outputted by the following algorithm formula: WDt+1=WDt+β·WDvar+γ; Where WDt represents the current wind direction, β represents the adjustment coefficient, which is used to quantify the impact of the instability of the current wind direction on the future wind direction, and γ represents the environmental factor correction term.

6. A control method for remotely operating an anchor guarding robot according to claim 1, characterized in that: The S4 includes S41, S42 and S43; S41, the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1 are combined with the current wind direction fluctuation WD in the wind load impact data set to comprehensively calculate the compensation joint torque value JTc, and the compensation joint torque value JTc adjustment command is sent to the anchor robot processor through the remote control platform to control the servo joint motor to dynamically adjust the movement accuracy under the wind load interference; The compensation joint torque value JTc is calculated and outputted by the following algorithm formula; JTc=JT+α·Fw·sin(WD)+δ·Qp; In the formula, JT represents the original joint torque, α represents the joint torque compensation coefficient, and δ represents the posture compensation sensitivity coefficient; S42, calculating and outputting the attitude adjustment amount △PA through the current tilt vibration frequency VF, sending the attitude adjustment amount △PA command to the anchor robot processor through the remote control platform, and controlling the servo joint motor to dynamically adjust and suppress vibration; The posture adjustment amount ΔPA is calculated and outputted by the following algorithm formula: △PA=-kp·VF; Where -kp represents the vibration suppression coefficient.

7. A control method for remotely operating an anchor guarding robot according to claim 6, characterized in that: S43, after performing joint torque compensation and vibration suppression on the anchor robot, the wind speed change rate △WS in the wind load influence data set is extracted to calculate and output the actual action execution delay Tc, dynamically adjust the action delay time, and optimize the execution error caused by wind speed change; The actual action execution delay Tc is calculated and output by the following algorithm formula; Where T0 represents the basic action delay, WS threshold Indicates the wind speed change threshold, which is determined according to the dynamic response capability of the robot's actions and environmental requirements. It is used to control the delay adjustment amplitude to prevent over-compensation or under-compensation.

8. A control method for remotely operating an anchor guarding robot according to claim 7, characterized in that: The S5 includes S51 and S52; S51, recalculate the adjusted real-time feedback data after dynamic adjustment, the feedback data includes the compensated wind load working force Fw after and the compensated vibration frequency VF after ; Based on the compensated wind load working force Fw after The wind load compensation efficiency Bc is calculated and output, and the wind load efficiency after the servo motor compensation of the anchor robot joint is analyzed. The specific algorithm is as follows; Based on the output result of the wind load compensation efficiency Bc, a wind load evaluation is performed to analyze the weakening effect of the wind load compensation on the actual wind force. The specific evaluation contents are as follows; When the wind load compensation efficiency Bc is less than 90%, it indicates that the wind load compensation is abnormal, and the vibration suppression compensation analysis is triggered; When the wind load compensation efficiency Bc ≥ 90%, it indicates that the compensation effect is normal, and the current operation continues.

9. A control method for remotely operating an anchor guarding robot according to claim 8, characterized in that: S52: If the wind load compensation is abnormal, then the compensated vibration frequency VF after compensation is calculated. after The vibration suppression efficiency ZVF is calculated and output, and the vibration suppression efficiency of the servo motor of the anchor robot joint after compensation is analyzed; the vibration suppression efficiency ZVF is calculated and output by the following algorithm formula: ZVF = VF - VF after ; Based on the output result of the vibration suppression efficiency ZVF, a vibration effect evaluation is performed to analyze the vibration suppression effect. The specific evaluation contents are as follows; When the vibration suppression efficiency ZVF>0, it means that the vibration frequency suppression efficiency is normal. At this time, the attitude adjustment amount △PA command is valid and no adjustment is required; When the vibration suppression efficiency ZVF≤0, it indicates that the vibration frequency suppression efficiency is abnormal. At this time, it is fed back to step S4 to adjust the control joint torque compensation coefficient α.

10. A control system for remotely operating an anchor guarding robot, applied to a control method for remotely operating an anchor guarding robot as claimed in any one of claims 1 to 9, characterized in that: It includes working environment monitoring module, wind load extraction module, wind load analysis and prediction module, real-time compensation control module and feedback evaluation module; The working environment monitoring module collects wind load impact data in real time by installing a sensor group on the anchor robot during the construction of the highway bridge, and then transmits the collected wind load impact data to the remote control platform through a wireless communication network; The wind load extraction module receives wind load impact data in real time in the remote control platform, pre-processes the wind load impact data, obtains a standard data set, extracts features from the standard data set to obtain a wind load impact data set, and then constructs a time series database to store the wind load impact data set; The wind load analysis and prediction module calculates the wind load working force Fw by extracting the wind load influence data set, and then calculates the predicted tilt angle Qp of the output robot based on the wind load working force Fw, analyzes the posture angle deviation prediction of the anchor robot under the wind load, and calculates and predicts the wind direction WDt+1 based on the wind load influence data set; The real-time compensation control module calculates and outputs the compensation joint torque value JTc, the posture adjustment amount △PA and the actual action execution delay Tc respectively based on the wind load working force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1, combined with the wind load influence data set, and dynamically adjusts the robot action parameters to offset the interference of the wind load on the operation; The feedback evaluation module recalculates the adjusted real-time feedback data after dynamic adjustment, monitors the robot's operating status, and constructs an evaluation algorithm model. It calculates and outputs the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the adjusted feedback data, and performs wind load evaluation based on the output results of the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF, and triggers vibration effect evaluation based on the wind load evaluation results.

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