A control system and method for remotely operating an anchor protection robot

Abstract

The present invention discloses a control system and method for remotely operating an anchor protection robot, which relates to the technical field of anchor protection robots. The method realizes a closed-loop control process from environmental parameter acquisition, wind load modeling, dynamic compensation control to optimization evaluation by constructing an evaluation algorithm model for wind load compensation efficiency Bc and vibration suppression efficiency ZVF. 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 attitude adjustment sensitivity coefficient δ are dynamically adjusted based on the feedback results, so as to optimize the wind load compensation and attitude adjustment strategies. Under the condition of normal compensation efficiency, the system continues to execute the current operation to ensure that the anchor protection robot is always in a high-efficiency operation state. This mechanism not only improves the adaptability of the robot in a dynamic environment, but also effectively reduces the action error and operation energy consumption caused by wind load interference, and ensures the stability and safety in complex operation scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of anchor protection robots, and specifically to a control system and method for remotely operating an anchor protection robot. Background Technique

[0002] In the construction of large-scale infrastructure such as highways, bridges, and geological engineering, anchor protection robots, as important equipment in this field, are dedicated to ensuring the stability and safety of structures through fixed anchor points. In practical applications, such robots need to complete the fixation of support members, the adjustment of tensile force distribution, and precise positioning in complex terrain environments at the construction site. With the diversification of construction environments, such as challenges in high wind speed, high humidity, and complex vibration environments, higher requirements for the operation stability and remote control accuracy of anchor protection robots are put forward.

[0003] At the present stage, in harsh environments such as highway bridge construction, anchor protection robots often face problems such as strong wind load interference, complex terrain vibration, and signal delay. The 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, the wind load will significantly affect the action accuracy and stability of the robot, which may lead to anchor point positioning deviation, loosening of fixed supports, and even overturning or damage of the equipment. In addition, traditional vibration suppression means are mostly based on passive damping, and there is no effective response mechanism for the influence of complex real-time dynamic wind loads. These deficiencies significantly limit the application efficiency and reliability of anchor protection robots in complex environments. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a control system and method for remotely operating an anchor protection robot, which solves the problems mentioned in the background technique.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: including the following steps:

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

[0007] S2. The wind load influence data is received in real time in the remote control platform, and the wind load influence data is preprocessed to obtain a standard data set, and then feature extraction is performed on the standard data set to obtain a wind load influence data set, and then a time series database is constructed to store the wind load influence data set;

[0008] S3. Extract the wind load impact data set, calculate the wind load operation force Fw, then calculate and output the predicted tilt angle Qp of the robot based on the wind load operation force Fw, analyze the prediction of the attitude angle deviation of the bolting robot under the action of the wind load, and at the same time calculate and predict the wind direction WDt+1 based on the wind load impact data set;

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

[0010] S5. After dynamic adjustment, recalculate the adjusted real-time feedback data, monitor the running state of the robot, and construct an evaluation algorithm model. Calculate and output the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the obtained feedback data after adjustment, and conduct a wind load evaluation based on the output results of the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF, and trigger a vibration effect evaluation based on the wind load evaluation result.

[0011] Preferably, S1 includes S11 and S12;

[0012] S11. Through the sensor group installed on the bolting robot, set the acquisition frequency of the sensor group to be every 30 minutes, and collect wind force 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 inclinometer

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

[0015] S12. By installing the sensor group on the 5G network communication module, and constructing a wireless communication connection between the remote control platform and the 5G network communication module of the sensor group respectively, transmit the real-time collected wind force impact data to the remote control platform through the wireless communication network.

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

[0017] S21. Receive the wind force impact data in real time in the remote control platform, and preprocess the wind force impact data. The preprocessing methods include data cleaning and data normalization, and mark time stamps for all parameters in the preprocessed wind force impact data to obtain a standard data set;

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

[0019] S23. By constructing a time series database, setting the write port, the write-out port, the real-time storage table, and the historical storage table, store the wind load influence data set into the real-time storage table through the write port, and automatically transfer the original wind load influence data set in the real-time storage table to the historical storage table, and sort it in the order of time stamps.

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

[0021] S31. Real-time extract the load influence data set through the write-out port, and in the remote control platform, calculate the wind load working force Fw;

[0022] The wind load working force Fw is calculated and output through the following algorithm formula;

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

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

[0025] S32. Then, based on the wind load working force Fw, use the dynamic formula to predict the tilt angle deviation, calculate and output the predicted tilt angle Qp, and analyze the offset of the attitude angle of the anchor protection robot under the action of the wind load;

[0026] The predicted tilt angle Qp is calculated and output through the following dynamic formula;

[0027]

[0028] In the formula, sin(WD) represents the wind force component coefficient, arctan represents the arctangent 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. Combine the time series analysis algorithm of the historical wind direction fluctuation WD data with the real-time calculation to output the predicted wind direction WDt+1, and analyze the short-term analysis and prediction;

[0030] The predicted wind direction WDt+1 is calculated and output through the following algorithm formula;

[0031] WDt+1 = WDt + β·WDvar + γ;

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

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

[0034] S41: Combine the wind load operation force Fw, the predicted tilt angle Qp and the predicted wind direction WDt+1, and comprehensively calculate and compensate the joint torque value JTc by combining the current wind direction fluctuation WD in the wind load influence dataset, and send the compensation joint torque value JTc adjustment command to the anchor protection robot processor through the remote control platform to control the servo joint motor to dynamically adjust and maintain the action accuracy under the wind load interference;

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

[0036] JTc = JT + α·Fw·sin(WD) + δ·Qp;

[0037] Wherein, JT represents the original joint torque, α represents the joint torque compensation coefficient, and δ represents the attitude compensation sensitivity coefficient;

[0038] S42: Calculate and output the attitude adjustment amount △PA through the current tilt vibration frequency VF, and send the attitude adjustment amount △PA command to the anchor protection robot processor through the remote control platform to control the servo joint motor to dynamically adjust and suppress vibration;

[0039] The attitude adjustment amount △PA is calculated and output through the following algorithm formula;

[0040] △PA = -kp·VF;

[0041] Wherein, -kp represents the vibration suppression coefficient.

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

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

[0044]

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

[0046] Preferably, 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 operating force Fw after and the compensated vibration frequency VF after ;

[0048] Based on the compensated wind load operating force Fw after calculate and output the wind load compensation efficiency Bc, and analyze the wind load efficiency of the servo motor of the re-anchoring robot joint after compensation. The specific algorithm is as follows;

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

[0050] When the wind load compensation efficiency Bc < 90%, it indicates that the wind load compensation is abnormal, and at this time, trigger the vibration suppression compensation analysis;

[0051] When the wind load compensation efficiency Bc ≥ 90%, it indicates that the compensation effect is normal, and at this time, continue to execute the current operation.

[0052] Preferably, S52, when it is analyzed that the wind load compensation is abnormal, then based on the compensated vibration frequency VF after compensation after calculate and output the vibration suppression efficiency ZVF, and analyze the vibration suppression efficiency of the servo motor of the re-anchoring robot joint after compensation; the vibration suppression efficiency ZVF is calculated and output through the following algorithm formula: ZVF = VF - VF after ;

[0053] Based on the output result of the vibration suppression efficiency ZVF, conduct a vibration effect assessment to analyze the vibration suppression effect. The specific assessment content is as follows;

[0054] When the vibration suppression efficiency ZVF > 0, it indicates that the vibration frequency suppression efficiency is normal, and 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, and at this time, feedback to step S4 to adjust the control joint torque compensation coefficient α.

[0056] A control system for remotely operating a re-anchoring robot, including a working 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 described working environment monitoring module, during the construction of highway bridges, installs a sensor group on the anchor protection robot to collect wind force impact data in real time, 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 the wind load impact data in real time in the remote control platform, preprocesses the wind load impact data to obtain a standard data set, then 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 extracts the wind load impact data set, calculates the wind load operation force Fw, then calculates and outputs the predicted tilt angle Qp of the robot based on the wind load operation force Fw, analyzes the attitude angle offset prediction of the anchor protection robot under the action of the wind load, and at the same time calculates and predicts the wind direction WDt+1 based on the wind load impact data set;

[0060] The real-time compensation control module, based on the wind load operation force Fw, the predicted tilt angle Qp, and the predicted wind direction WDt+1, combines the wind load impact data set, calculates and outputs the compensation joint torque value JTc, the attitude adjustment amount △PA, and the actual action execution delay Tc respectively, 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 running state of the robot, constructs an evaluation algorithm model, calculates and outputs the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the adjusted obtained feedback data, and conducts a wind load evaluation based on the output results of the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF, and triggers a vibration effect evaluation based on the wind load evaluation result.

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

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

[0064] (2) This method relies on a remote control platform to preprocess the data on the impact of wind load, including data cleaning, normalization, and timestamp marking, constructs a standard data set and extracts characteristic parameters, stores the integrated data set on the impact of wind load in a time series database, and provides a function for real-time storage and comparison with historical data. Based on the real-time feedback data, the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF are calculated and output. This method realizes the full-process intelligent monitoring and dynamic optimization adjustment of the bolt support 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, significantly improving the intelligent operation level of the bolt support robot in complex environments and reducing the need for manual intervention.

[0065] (3) By constructing an evaluation algorithm model for the wind load compensation efficiency Bc and the 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 attitude adjustment sensitivity coefficient δ are dynamically adjusted based on the feedback results, so as to optimize the wind load compensation and attitude adjustment strategies. When the compensation efficiency is normal, the system continues to execute the current operation to ensure that the bolt support robot is always in an efficient operating state. This mechanism not only improves the adaptability of the robot in a dynamic environment, but also effectively reduces the action errors and operating energy consumption caused by wind load interference, ensuring stability and safety in complex operation scenarios. Description of the Drawings

[0066] Figure 1 Schematic diagram of the steps of a control method for remotely operating a bolt support robot according to the present invention;

[0067] Figure 2 Schematic diagram of the control system process of a control method for remotely operating a bolt support robot according to the present invention. Detailed Embodiments

[0068] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0069] Embodiment 1

[0070] Please refer to Figure 1, the present invention provides a control method for remotely operating an anchor protection robot. To achieve the above objectives, the present invention is realized through the following technical solutions: including the following steps:

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

[0072] S2. In the remote control platform, the wind load influence data is received in real time, and the wind load influence data is preprocessed to obtain a standard data set. Then, feature extraction is performed on the standard data set to obtain a wind load influence data set, and a time series database is constructed to store the wind load influence data set;

[0073] S3. Extract the wind load influence data set, calculate the wind load operating force Fw, then calculate and output the predicted tilt angle Qp of the robot based on the wind load operating force Fw, analyze the predicted attitude angle deviation of the anchor protection robot under the action of the wind load, and at the same time calculate and predict the wind direction WDt+1 based on the wind load influence data set;

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

[0075] S5. After dynamic adjustment, recalculate the adjusted real-time feedback data, monitor the running state of the robot, and construct an evaluation algorithm model. Calculate and output the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the adjusted feedback data obtained, and conduct a wind load evaluation based on the output results of the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF, and trigger a vibration effect evaluation based on the wind load evaluation result.

[0076] In this embodiment, the method installs a sensor group on the bolting robot to collect key data such as wind speed, wind direction fluctuation, humidity, and attitude angle in real time, and transmits the data to the remote control platform through the 5G wireless communication network, achieving accurate data collection and efficient transmission in complex environments. The data is preprocessed, feature extracted, and stored in time series on the remote control platform to form a wind load impact data set, providing a reliable data basis for subsequent dynamic modeling. By calculating the wind load operation force Fw, predicting the tilt angle Qp, and predicting the 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. Combining the above calculation results, the robot's action parameters are dynamically adjusted, including the joint torque compensation value JTc, the attitude adjustment amount ΔPA, and the actual action delay Tc, effectively offsetting the impact of wind load interference on the operation and ensuring the accuracy and stability of the robot's actions. By constructing an evaluation algorithm model through real-time feedback data, the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF are dynamically evaluated to form a closed-loop optimization mechanism, further improving the robot's adaptability and intelligent control level in complex operation environments. The overall method realizes the real-time perception, accurate modeling, dynamic compensation, and intelligent optimization of the bolting robot for the dynamic wind load environment, achieving the purpose of improving operation stability, operation accuracy, and remote control intelligence. Through dynamic evaluation and closed-loop feedback, not only the operation reliability and efficiency of the robot in high wind load environments are improved, but also the safety risks and energy waste caused by environmental interference are reduced, thus realizing the comprehensive improvement of construction quality, efficiency, and safety.

[0077] Embodiment 2

[0078] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: S1 includes S11 and S12;

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

[0080] The sensor group includes an ultrasonic anemometer, a laser Doppler wind direction sensor, a humidity sensor, and a laser inclination sensor. The wind force impact data includes wind speed WS, wind direction fluctuation WD, air humidity RH, and the robot's attitude angle PA;

[0081] S12. By equipping the installed sensor group with a 5G network communication module and establishing wireless communication connections between the remote control platform and the 5G network communication module of the sensor group respectively, transmit the real-time collected wind force impact data to the remote control platform through the wireless communication network.

[0082] In this embodiment, the method uses the sensor group installed on the bolt support robot to collect wind influence data at a frequency of 30 minutes, and constructs a comprehensive perception ability for the dynamic environment. Then, by carrying a 5G network communication module, the wind influence data collected in real time is efficiently transmitted to the remote control platform, ensuring low latency and high reliability of data transmission, and providing a 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, significantly improving the adaptability and information interaction efficiency of the bolt support robot in complex environments. By integrating high-precision sensors and high-speed wireless communication technologies, 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 ability of real-time monitoring and efficient transmission not only improves the stability and accuracy of the bolt support robot in harsh environments, but also reduces the need for manual intervention, improves the intelligent level of the system, and finally realizes the overall optimization of construction quality and safety.

[0083] Embodiment 3

[0084] This embodiment is an explanatory description based on Embodiment 2. Please refer to Figure 1 , specifically: S2 includes S21, S22 and S23;

[0085] S21. Receive the wind influence data in real time in the remote control platform, and preprocess the wind influence data. The preprocessing methods include data cleaning and data normalization, and mark the time stamps for all parameters in the preprocessed wind influence data to obtain a standard data set;

[0086] S22. Based on the standard data set, perform feature extraction to obtain the wind speed change rate △WS, the wind direction fluctuation range WDvar, and the tilt vibration frequency VF, and then integrate them with the air humidity RH to obtain a wind load influence 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 performing a change rate calculation. The specific algorithm formula is: In the formula, △t represents the time step, which is the time interval between the current time t and the previous time t-1;

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

[0089] The tilt vibration frequency VF is obtained by extracting the robot's pose angle PA at the current moment t and the main vibration frequency extracted from the Fourier transform result. The specific algorithm formula is: VF = FFT(PA(t)), where FFT represents the Fourier transform result;

[0090] S23. By constructing a time-series database, setting up write ports, read ports, real-time storage tables, and historical storage tables, the wind load impact dataset is stored in the real-time storage table through the write port, and the original wind load impact dataset 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 force impact data transmitted through the 5G network in real time through the remote control platform, performs preprocessing such as data cleaning and normalization on the data, eliminates noise and outliers, and marks timestamps for all parameters to form a standardized dataset to ensure the accuracy and consistency of the data. Then, through the feature extraction algorithm, the wind speed change rate ΔWS, the wind direction fluctuation range WDvar, and the tilt vibration frequency VF are calculated from the standard dataset and integrated with the air humidity RH to generate a comprehensive wind load impact dataset, providing complete input data for subsequent analysis and dynamic compensation. At the same time, an efficient time-series database is constructed, write ports and read ports are set, the real-time storage table and the historical storage table are used in combination, and the data is classified and stored and managed in timestamp order, providing convenience for historical comparison and dynamic analysis. Through this method, the purpose of efficiently managing and comprehensively analyzing wind force impact data is achieved, significantly improving the response ability of the bolt support robot to changes in complex environmental parameters. Combining data preprocessing and feature extraction eliminates redundancy and errors in the original data, providing accurate and specific input data for subsequent calculations. The introduction of the time-series database not only realizes the collaborative storage and call of real-time data and historical data, but also improves the data retrieval and analysis efficiency. This optimized data management process not only enhances the accuracy and intelligence level of the robot control system, but also lays a solid foundation for dynamic compensation and real-time evaluation, thus significantly improving the stability and accuracy of robot operations.

[0092] Embodiment 4

[0093] This embodiment is an explanatory description based on Embodiment 3. Please refer to Figure 1 , specifically: S3 includes S31, S32, and S33;

[0094] S31. The load impact dataset is extracted in real time through the read port, and in the remote control platform, the wind load operation force Fw is calculated;

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

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

[0097] Wherein, ρ represents the air density, and the air density value of the standard atmospheric pressure is taken as 1.225 kg / m 3 , Cd represents the drag coefficient, which describes the drag coefficient between the surface of the robot and the air, and is a dimensionless value. A represents the total area of the robot facing the air flow. cos represents the cosine function, and cos(WD) is used to analyze the dynamic change of the windward area;

[0098] S32. Then, based on the wind load operating force Fw, use the dynamic formula to predict the tilt angle offset, calculate and output the predicted tilt angle Qp, and analyze the offset amount of the attitude angle of the anchor protection robot under the action of the wind load;

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

[0100]

[0101] Wherein, sin(WD) represents the wind force component coefficient, arctan represents the arctangent function, sin represents the sine function, Mr represents the anti-overturning 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 experiment.

[0102] S33. Combine the time series analysis algorithm of the historical wind direction fluctuation WD data with the real-time calculation to output the predicted wind direction WDt+1, and analyze the short-term analysis and prediction;

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

[0104] WDt+1 = WDt + β·WDvar + γ;

[0105] Wherein, WDt represents the current wind direction, β represents the adjustment coefficient, which is used to quantify the influence 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 influence data set in real time through the output port, and uses the wind load calculation formula to accurately calculate the wind load operation force Fw in combination with the air density ρ, the drag coefficient Cd, the windward area A of the robot, and the dynamic wind direction change cos(WD). Based on the calculated wind load operation force Fw, the tilt angle offset of the robot under the action of the wind load is predicted through the dynamic formula, the predicted tilt angle Qp is output, and the dynamic change of the robot's attitude angle is analyzed. The tilt angle calculation comprehensively considers the wind force component sin(WD), the anti-overturning moment Mr, and the influence coefficient kd of the frequency on the moment, realizing the accurate prediction of the robot's attitude offset in a complex mechanical environment. Combining historical wind direction fluctuation data and time series analysis algorithms, the dynamic change trend WDt+1 of the wind direction in the short term is predicted by adjusting the coefficient β and the environmental factor correction term γ, providing key inputs for the subsequent action optimization of the robot. Through this method, the purposes of dynamically analyzing the wind load force, real-time predicting the robot's attitude offset, and short-term wind direction change are achieved, significantly enhancing the adaptive ability and dynamic response level of the bolt support robot to complex environments. The accurate calculation of the wind load operation force provides efficient inputs for compensating the joint moment and the attitude adjustment amount. The tilt angle prediction lays a mechanical foundation for the stability control of the robot's actions, and the wind direction prediction further improves the robot's perception ability of the dynamic wind field.

[0107] Embodiment 5

[0108] This embodiment is an explanatory description based on Embodiment 4. Please refer to Figure 1 , specifically: S4 includes S41, S42, and S43;

[0109] S41. Combine the wind load operation force Fw, the predicted tilt angle Qp, and the predicted wind direction WDt+1 with the current wind direction fluctuation WD in the wind load influence data set for comprehensive calculation to compensate the joint moment value JTc, and send the compensation joint moment value JTc adjustment command to the bolt support robot processor through the remote control platform to control the servo joint motor to dynamically adjust and maintain the action accuracy under the wind load interference;

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

[0111] JTc = JT + α·Fw·sin(WD) + δ·Qp;

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

[0113] S42. Calculate and output the attitude adjustment amount △PA through the current tilt vibration frequency VF, and send the attitude adjustment amount △PA command to the bolt support robot processor through the remote control platform to control the servo joint motor to dynamically adjust for vibration suppression;

[0114] The attitude adjustment amount ΔPA is calculated and output through the following algorithm formula;

[0115] ΔPA = -kp·VF;

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

[0117] S43. After performing joint torque compensation and vibration suppression on the bolt support robot, by extracting the wind speed change rate ΔWS in the wind load influence dataset, calculate and output the actual action execution delay Tc, dynamically adjust the action delay time, and optimize the execution error caused by the wind speed change;

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

[0119]

[0120] In the formula, T0 represents the basic action delay, WS threshold represents the wind speed change threshold, which is determined according to the dynamic response ability of the robot's actions and environmental requirements, and 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 operating force Fw, the predicted tilt angle Qp, and the predicted wind direction WDt+1. Combining the current wind direction fluctuation WD, through comprehensive calculation, compensate the joint torque value JTc, and send the adjustment command to the robot processor through the remote control platform to dynamically control the servo joint motor to offset the wind load interference, ensuring the high precision and stability of the robot's actions. Calculate the attitude adjustment amount ΔPA through the current tilt vibration frequency VF, and send the adjustment command to the robot in real time to control the servo motor to dynamically suppress vibration and reduce the impact of the attitude angle fluctuation on the operation. By extracting the wind speed change rate ΔWS, calculate the actual action execution delay Tc, dynamically adjust the action delay time, and optimize the execution error caused by the wind speed change, ensuring the coherence and accuracy of the robot's operation. Through this method, the purpose of real-time compensating wind load interference, dynamically suppressing attitude vibration, and optimizing action delay is achieved, significantly improving the operation reliability and stability of the bolt support robot in a complex environment. The dynamic adjustment of the compensated 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 attitude adjustment amount significantly reduce the operation deviation caused by vibration; the optimized regulation of the action delay further ensures the smoothness and accuracy of the robot's actions.

[0122] Embodiment 6

[0123] This embodiment is an explanatory description based on Embodiment 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 operating force Fw after and the compensated vibration frequency VF after ;

[0125] Based on the compensated wind load operating force Fw after calculate and output the wind load compensation efficiency Bc, and analyze the wind load efficiency of the servo motor of the re-anchoring robot joint after compensation. The specific algorithm is as follows;

[0126] Then, based on the output result of the wind load compensation efficiency Bc, conduct a wind load assessment to analyze the weakening effect of the wind load compensation on the actual wind force. The specific assessment content is as follows;

[0127] When the wind load compensation efficiency Bc < 90%, it indicates that the wind load compensation is abnormal. At this time, trigger the vibration suppression compensation analysis to further analyze the reason for the abnormal wind load compensation;

[0128] When the wind load compensation efficiency Bc ≥ 90%, it indicates that the compensation effect is normal. At this time, continue to execute the current operation.

[0129] S52, when it is analyzed that the wind load compensation is abnormal, then based on the compensated vibration frequency VF after compensation after calculate and output the vibration suppression efficiency ZVF, and analyze the vibration suppression efficiency of the servo motor of the re-anchoring robot joint after compensation; The vibration suppression efficiency ZVF is calculated and output through the following algorithm formula: ZVF = VF - VF after ;

[0130] Then, based on the output result of the vibration suppression efficiency ZVF, conduct a vibration effect assessment to analyze the vibration suppression effect. The specific assessment content is as follows;

[0131] When the vibration suppression efficiency ZVF > 0, it indicates 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 to step S4 to adjust the control joint torque compensation coefficient α.

[0133] In this embodiment, the method obtains the compensated data in real time through feedback, 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 weakening effect of the servo motor on wind interference after compensation. When the wind load compensation efficiency Bc < 90%, automatically trigger S52 for vibration suppression compensation analysis to further investigate the reasons for abnormal wind load compensation; if the wind load compensation efficiency Bc ≥ 90%, it indicates 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 to evaluate the vibration suppression effect of the attitude adjustment amount ΔPA. When ΔVF > 0, it indicates that the vibration suppression strategy is effective; otherwise, it will feedback 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 the wind load compensation effect and dynamic optimization of the vibration suppression strategy is achieved, significantly improving the intelligent operation ability of the bolt support robot in a complex wind load environment. The evaluation of the wind load compensation efficiency can quickly detect the effectiveness of the compensation strategy and ensure that the robot always operates in an efficient state; the further analysis of the vibration suppression efficiency provides accurate feedback for the attitude adjustment strategy and strengthens the dynamic response ability to vibration interference. Combining the multi-level evaluation and feedback closed-loop of wind load and vibration, this method realizes the continuous and stable operation of the robot in high wind load scenarios, not only improving the operation efficiency and control accuracy, but also effectively reducing equipment losses and safety risks caused by interference, providing a solid guarantee for construction in complex environments.

[0134] Embodiment 7

[0135] Please refer to Figure 1 and Figure 2 , a control system for remotely operating a bolt support robot, including an operation 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] During the construction of highway bridges, the operation environment monitoring module installs a sensor group on the bolt support robot to collect wind force influence data in real time, and then transmits the collected wind load influence data to the remote control platform through a wireless communication network;

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

[0138] The wind load analysis and prediction module extracts the wind load influence data set, calculates the wind load operation force Fw, then calculates and outputs the predicted tilt angle Qp of the robot based on the wind load operation force Fw, analyzes the attitude angle offset prediction of the bolt support robot under the action of wind load, and at the same time calculates and predicts the wind direction WDt+1 based on the wind load influence data set;

[0139] Based on the wind load operation force Fw, the predicted tilt angle Qp, and the predicted wind direction WDt+1, the real-time compensation control module combines the wind load influence data set to calculate and output the compensation joint torque value JTc, the attitude adjustment amount △PA, and the actual action execution delay Tc respectively, and dynamically adjusts the robot action 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 the re-dynamic adjustment, monitors the running state of the robot, constructs an evaluation algorithm model, calculates and outputs the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the obtained feedback data after adjustment, and conducts a wind load evaluation based on the output results of the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF, and triggers a vibration effect evaluation based on the wind load evaluation result.

[0141] Although the embodiments of the present invention have been shown and described, various changes, modifications, substitutions, and variations can be made to these embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present 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, by installing a sensor group on the anchor robot, setting the sensor group collection frequency to an interval of 30 minutes, and collecting 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, recalculating the adjusted real-time feedback data after dynamic adjustment, wherein 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: When the wind load compensation is abnormal, 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 monitors the robot's operating status and constructs an evaluation algorithm model by recalculating the adjusted real-time feedback data after dynamic adjustment, calculates and outputs the wind load compensation efficiency Bc and the vibration suppression efficiency ZVF for the adjusted feedback data, 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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