Hanging basket motor driving walking method and system for continuous rigid frame bridge construction

Abstract

The embodiment of the invention relates to the technical field of continuous rigid frame bridge construction, and particularly discloses a hanging basket motor driving walking method and system for continuous rigid frame bridge construction. According to the embodiment of the invention, a task path, a walking control parameter and a three-dimensional path are constructed; walking control of motor driving is carried out; periodic binocular positioning shooting is carried out, a plurality of positioning fixed targets are determined, and corresponding positioning target distances are obtained; performing positioning analysis to generate walking positioning data; and carrying out abnormity analysis on motor-driven walking, and carrying out abnormity grading early warning when a walking abnormal condition exists. When motor-driven walking control is carried out, through binocular positioning shooting and identification analysis, walking positioning data is generated, motor-driven walking abnormity analysis is carried out, and abnormity grading early warning is carried out when a walking abnormal condition exists, so that the spatial position of a hanging basket in the operation process is accurately identified; and the abnormal state of the hanging basket is timely and effectively identified and processed.

Description

Technical Field

[0001] The invention belongs to the technical field of continuous rigid frame bridge construction, and in particular relates to a motor-driven walking method and system for a hanging basket in continuous rigid frame bridge construction. Background Art

[0002] Continuous rigid frame bridge is a bridge structure between continuous beam bridge and T-type rigid frame bridge. Its characteristics are continuous main beam, consolidation of piers and beams, and the use of prestressed concrete structure. The construction of continuous rigid frame bridge is the process of building and installing the main beam, piers, foundation and other parts of the continuous rigid frame bridge according to the design drawings and specification requirements using specific construction methods and processes.

[0003] In the cantilever construction of continuous rigid frame bridges, the motor-driven walking method of the hanging basket is an efficient and controllable construction solution.

[0004] In the prior art, the motor-driven movement of the hanging basket, as a key link in the construction of continuous rigid frame bridges, has obvious deficiencies in spatial positioning. Specifically, there is a lack of a high-precision and fast positioning method to accurately identify the spatial position of the hanging basket during the operation process, resulting in the inability to promptly and effectively identify and handle the abnormal state of the hanging basket. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a method and system for a motor-driven walking of a hanging basket for continuous rigid frame bridge construction, aiming to solve the problems raised in the background technology.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A method for driving a hanging basket motor to travel during construction of a continuous rigid frame bridge, the method specifically comprising the following steps: Receive the construction task of the hanging basket, perform initialization detection, build the task path, walking control parameters and three-dimensional path, and perform track detection and obstacle inspection on the task path; Release the hydraulic brake and perform motor-driven walking control according to the three-dimensional path and the walking control parameters; Perform periodic binocular positioning shooting, obtain multiple binocular shooting images, perform positioning recognition, determine multiple positioning fixed targets, and obtain the corresponding positioning target distances; Perform positioning analysis according to the multiple positioning target distances to generate walking positioning data, and perform motor monitoring to obtain motor monitoring data; According to the walking positioning data and the motor monitoring data, an abnormal analysis of the motor-driven walking is performed, and when there is an abnormal walking condition, an abnormal classification warning is issued.

[0007] As a further limitation of the technical solution of the embodiment of the present invention, the steps of receiving the construction task of the hanging basket, performing initialization detection, constructing a task path, walking control parameters, and a three-dimensional path, and performing track detection and obstacle investigation on the task path specifically include the following steps: Receive the construction task of the hanging basket and determine the target construction hanging basket; Perform initialization detection on the drive motor, sensor, track anchor point, and hydraulic brake of the target construction hanging basket, and determine whether there is an initialization abnormality; When there is no initialization abnormality, construct a task path, walking control parameters, and a three-dimensional path according to the construction task of the hanging basket, where the walking control parameters include walking speed, walking distance, acceleration, and deceleration; Plan a flight inspection path according to the task path; Select an inspection UAV and perform track detection and obstacle investigation on the task path according to the flight inspection path.

[0008] As a further limitation of the technical solution of the embodiment of the present invention, the steps of releasing the hydraulic brake and performing motor-driven walking control according to the three-dimensional path and the walking control parameters specifically include the following steps: Release the hydraulic brake of the target construction hanging basket; Generate corresponding walking control signals according to the three-dimensional path and the walking control parameters; Perform motor-driven walking control on the target construction hanging basket according to the walking control signals.

[0009] As a further limitation of the technical solution of the embodiment of the present invention, the steps of performing periodic binocular positioning photography, obtaining multiple binocular photography images, performing positioning recognition, determining multiple positioning fixed targets, and obtaining the corresponding positioning target distances specifically include the following steps: Obtain the walking positioning period; Perform periodic binocular positioning photography according to the walking positioning period to obtain multiple binocular photography images; Perform positioning recognition on multiple binocular photography images based on a preset plurality of fixed target features to determine multiple positioning fixed targets; Perform distance analysis on multiple binocular photography images to obtain the positioning target distances corresponding to multiple positioning fixed targets.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the steps of performing positioning analysis according to multiple positioning target distances, generating walking positioning data, and performing motor monitoring to obtain motor monitoring data specifically include the following steps: Identify the positioning target directions corresponding to multiple positioning fixed targets; Based on multiple said positioning target directions and multiple corresponding positioning target distances, perform positioning analysis on the target construction hanging basket to generate walking positioning data; Monitor the motors of the target construction hanging basket to obtain motor monitoring data.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the performing abnormal analysis of motor-driven walking based on the walking positioning data and the motor monitoring data, and performing abnormal classification warning when there is a walking abnormal condition specifically includes the following steps: Based on preset abnormal classification data, compare and analyze the walking positioning data and the motor monitoring data to determine whether there is a walking abnormal condition; When there is a walking abnormal condition, determine the abnormal level; If the abnormal level is level one, perform speed reduction control; If the abnormal level is level two, perform emergency stop control; If the abnormal level is level three, perform sound and light alarm.

[0012] A hanging basket motor-driven walking system for continuous rigid frame bridge construction, the system includes an initial detection and processing unit, a motor walking control unit, a walking positioning recognition unit, a positioning analysis and motor monitoring unit, and an abnormal classification warning unit, wherein: The initial detection and processing unit is used to receive the hanging basket construction task, perform initialization detection, construct the task path, walking control parameters, and three-dimensional path, and perform track detection and obstacle investigation on the task path; The motor walking control unit is used to release the hydraulic brake and perform walking control driven by the motor according to the three-dimensional path and the walking control parameters; The walking positioning recognition unit is used to perform periodic binocular positioning shooting, obtain multiple binocular shooting images, perform positioning recognition, determine multiple positioning fixed targets, and obtain the corresponding positioning target distances; The positioning analysis and motor monitoring unit is used to perform positioning analysis based on multiple said positioning target distances to generate walking positioning data, and perform motor monitoring to obtain motor monitoring data; The abnormal classification warning unit is used to perform abnormal analysis of motor-driven walking based on the walking positioning data and the motor monitoring data, and perform abnormal classification warning when there is a walking abnormal condition.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the initial detection and processing unit specifically includes: The task receiving module is used to receive the hanging basket construction task and determine the target construction hanging basket; An initialization detection module is used to perform initialization detection on the driving motor, sensors, track anchor points, and hydraulic brakes of the target construction hanging basket, and determine whether there is an initialization abnormality; A task processing module is used to construct a task path, walking control parameters, and a three-dimensional path according to the hanging basket construction task when there is no initialization abnormality. The walking control parameters include walking speed, walking distance, acceleration, and deceleration; An inspection path planning module is used to plan a flight inspection path according to the task path; A track detection and obstacle troubleshooting module is used to select an inspection unmanned aerial vehicle and perform track detection and obstacle troubleshooting on the task path according to the flight inspection path.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the walking positioning and recognition unit specifically includes: A period acquisition module is used to acquire the walking positioning period; A binocular positioning and shooting module is used to perform periodic binocular positioning and shooting according to the walking positioning period to obtain a plurality of binocular shooting images; A positioning and recognition module is used to perform positioning and recognition on the plurality of binocular shooting images based on a plurality of preset fixed target features to determine a plurality of positioning fixed targets; A distance analysis module is used to perform distance analysis on the plurality of binocular shooting images to obtain the positioning target distances corresponding to the plurality of positioning fixed targets.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the abnormality classification and early warning unit specifically includes: A comparison and analysis module is used to perform comparative analysis on the walking positioning data and the motor monitoring data based on preset abnormality classification data to determine whether there is a walking abnormality; An abnormality level determination module is used to determine the abnormality level when there is a walking abnormality; A speed reduction control module is used to perform speed reduction control when the abnormality level is level one; An emergency stop control module is used to perform emergency stop control when the abnormality level is level two; An audible and visual alarm module is used to perform audible and visual alarm when the abnormality level is level three.

[0016] Compared with the prior art, the beneficial effects of the present invention are: In the embodiments of the present invention, a task path, walking control parameters, and a three-dimensional path are constructed; walking control by motor drive is performed; periodic binocular positioning shooting is carried out to determine multiple positioning fixed targets and obtain the corresponding positioning target distances; positioning analysis is performed to generate walking positioning data; abnormal analysis of motor-driven walking is performed, and when there is an abnormal walking condition, abnormal classification and early warning are carried out. When performing walking control by motor drive, through binocular positioning shooting and recognition analysis, walking positioning data can be generated, and abnormal analysis of motor-driven walking can be performed. When there is an abnormal walking condition, abnormal classification and early warning are carried out, so as to accurately identify the spatial position of the hanging basket during operation and timely and effectively identify and process the abnormal state of the hanging basket. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0018] Figure 1 The flowchart of the method provided by the embodiments of the present invention is shown.

[0019] Figure 2 The flowchart of constructing the task path, walking control parameters, and three-dimensional path in the method provided by the embodiments of the present invention is shown.

[0020] Figure 3 The flowchart of performing walking control by motor drive in the method provided by the embodiments of the present invention is shown.

[0021] Figure 4 The flowchart of performing binocular positioning shooting in the method provided by the embodiments of the present invention is shown.

[0022] Figure 5 The flowchart of positioning analysis and motor monitoring in the method provided by the embodiments of the present invention is shown.

[0023] Figure 6 The flowchart of performing abnormal classification and early warning in the method provided by the embodiments of the present invention is shown.

[0024] Figure 7 The application architecture diagram of the system provided by the embodiments of the present invention is shown.

[0025] Figure 8 The structural block diagram of the initial detection processing unit in the system provided by the embodiments of the present invention is shown.

[0026] Figure 9 The structural block diagram of the walking positioning recognition unit in the system provided by the embodiments of the present invention is shown.

[0027] Figure 10 The structural block diagram of the abnormal grading early warning unit in the system provided by the embodiment of the present invention is shown. Detailed implementation manners

[0028] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0029] It can be understood that in the prior art, the hanging basket motor drives the walking. As a key link in the construction of continuous rigid frame bridges, there are obvious deficiencies in spatial positioning. Specifically, there is a lack of a high-precision and fast positioning means to accurately identify the spatial position of the hanging basket during the operation process, resulting in the inability to timely and effectively identify and handle the abnormal state of the hanging basket.

[0030] To solve the above problems, the embodiment of the present invention receives the hanging basket construction task, performs initialization detection, constructs the task path, walking control parameters and three-dimensional path, and performs track detection and obstacle investigation on the task path; releases the hydraulic brake, and performs walking control of the motor drive according to the three-dimensional path and walking control parameters; performs periodic binocular positioning shooting, obtains a plurality of binocular shooting images, performs positioning recognition, determines a plurality of positioning fixed targets, and obtains the corresponding positioning target distances; performs positioning analysis according to the plurality of positioning target distances, generates walking positioning data, and performs motor monitoring to obtain motor monitoring data; performs abnormal analysis of the motor drive walking according to the walking positioning data and the motor monitoring data, and performs abnormal grading early warning when there is an abnormal walking condition. When performing the walking control of the motor drive, it is possible to generate walking positioning data through binocular positioning shooting and recognition analysis, perform abnormal analysis of the motor drive walking, and perform abnormal grading early warning when there is an abnormal walking condition, so as to accurately identify the spatial position of the hanging basket during the operation process and timely and effectively identify and handle the abnormal state of the hanging basket.

[0031] Figure 1 The flowchart of the method provided by the embodiment of the present invention is shown.

[0032] Specifically, a method for driving the walking of a hanging basket motor in the construction of a continuous rigid frame bridge, the method specifically includes the following steps: Step S101, receive the hanging basket construction task, perform initialization detection, construct the task path, walking control parameters and three-dimensional path, and perform track detection and obstacle investigation on the task path.

[0033] In an embodiment of the present invention, by receiving a hanging basket construction task, performing target planning on the hanging basket construction task, selecting a target construction hanging basket, then performing initialization detection on the drive motor, sensor, track anchor point, and hydraulic brake of the target construction hanging basket, obtaining initial detection data, analyzing the initial detection data to determine whether there is an initialization anomaly, and in the case of no initialization anomaly, constructing a task path according to the hanging basket construction task, and planning the walking speed, walking distance, acceleration, and deceleration to generate walking control parameters. According to the task path and a preset inspection distance, a flight inspection path is planned, and then, from multiple unmanned aerial vehicles, an idle inspection unmanned aerial vehicle is selected, and the inspection unmanned aerial vehicle is controlled for inspection according to the flight inspection path, so that the inspection unmanned aerial vehicle performs track detection and obstacle investigation. Among them, track detection is a process of detecting the flatness of the track of the task path; obstacle investigation is a process of investigating potential obstacles such as steel bar heads and concrete protrusions on the task path.

[0034] Specifically, Figure 2 FIG. shows a flowchart of constructing a task path, walking control parameters, and a three-dimensional path in the method provided by an embodiment of the present invention.

[0035] Among them, in a preferred embodiment provided by the present invention, the receiving a hanging basket construction task, performing initialization detection, constructing a task path, walking control parameters, and a three-dimensional path, and performing track detection and obstacle investigation on the task path specifically include the following steps: Step S1011, receiving a hanging basket construction task and determining a target construction hanging basket; Step S1012, performing initialization detection on the drive motor, sensor, track anchor point, and hydraulic brake of the target construction hanging basket, and determining whether there is an initialization anomaly; Step S1013, when there is no initialization anomaly, constructing a task path, walking control parameters, and a three-dimensional path according to the hanging basket construction task, where the walking control parameters include walking speed, walking distance, acceleration, and deceleration; Step S1014, planning a flight inspection path according to the task path; Step S1015, selecting an inspection unmanned aerial vehicle and performing track detection and obstacle investigation on the task path according to the flight inspection path.

[0036] Specifically, when there is no initialization anomaly, constructing a task path, walking control parameters, and a three-dimensional path according to the hanging basket construction task. Among them, the specific steps for obtaining the walking control parameters are as follows: Performing initialization detection on the drive motor of the target construction hanging basket to obtain the vibration modal data of the hanging basket structure, the rated maximum load value, and the real-time load value; The first three vibration frequencies and the corresponding amplitudes of the hanging basket are obtained by extracting the vibration modal data of the hanging basket structure, and the vibration characteristic coefficient is obtained by weighted summation of the first three vibration frequencies and the corresponding amplitudes of the hanging basket; Multiply the square ratio of the real-time load value to the rated maximum load value by the vibration characteristic coefficient to obtain the load sensitivity factor; Construct a task path to obtain the acceleration time threshold, use the acceleration time threshold as the exponential coefficient, and superimpose the quadratic correlation term of the load sensitivity factor and the real-time load value to generate the dynamic exponential coefficient; Perform initial detection on the track anchor points to obtain the strain data of the track anchor points, and collect external meteorological data to obtain the environmental temperature and humidity values; Obtain the equivalent friction coefficient of the contact surface from the strain data of the track anchor points, and superimpose and correct the equivalent friction coefficient of the contact surface with the environmental temperature and humidity values to obtain the friction compensation factor; Obtain the maximum speed value from the hanging basket construction task, use the dynamic exponential coefficient to correct the maximum speed value to obtain the corrected maximum speed value; input the corrected maximum speed value into the exponential function to generate the speed growth curve; when the cumulative time reaches the acceleration time threshold, switch to the input parameters of the next stage to generate the acceleration stage speed sequence; Obtain the cruising speed by performing track detection and obstacle troubleshooting on the task path, and screen out the effective obstacle targets through the real-time detection data of the UAV to obtain the minimum obstacle spacing; Process the cruising speed with the friction compensation factor to obtain the compensated cruising speed; divide the minimum obstacle spacing by the compensated cruising speed to generate the braking response time window; Determine the total task time through the task path, and construct a speed decay curve using the hyperbolic function based on the braking response time window and the cruising speed; when the cumulative time approaches the total task time, switch to the input parameters of the next stage to generate the deceleration stage speed sequence; Stitch together the acceleration stage speed sequence, the cruising speed, and the deceleration stage speed sequence in chronological order to generate the complete walking speed curve, and obtain the walking control parameters through the complete walking speed curve.

[0037] Further, the hanging basket motor-driven walking method for the continuous rigid frame bridge construction further includes the following steps: Step S102, release the hydraulic brake, and perform motor-driven walking control according to the three-dimensional path and the walking control parameters.

[0038] In the embodiment of the present invention, when both the track detection and obstacle troubleshooting are passed, the hydraulic braking of the target construction hanging basket is released, and then according to the three-dimensional path and the walking control parameters, the corresponding walking control signal is generated, and further, according to the walking control signal, the motor-driven walking control of the target construction hanging basket is carried out.

[0039] Specifically, Figure 3 Fig. shows the flow chart of the motor-driven walking control in the method provided by the embodiment of the present invention.

[0040] Among them, in the preferred embodiment provided by the present invention, the steps of releasing the hydraulic braking and performing the motor-driven walking control according to the three-dimensional path and the walking control parameters specifically include the following steps: Step S1021, release the hydraulic braking of the target construction hanging basket; Step S1022, generate the corresponding walking control signal according to the three-dimensional path and the walking control parameters; Step S1023, perform the motor-driven walking control of the target construction hanging basket according to the walking control signal.

[0041] Specifically, the steps of generating the corresponding walking control signal according to the three-dimensional path and the walking control parameters are as follows: Obtain the three-dimensional path coordinate sequence through the three-dimensional path, calculate the direction change rate between adjacent path points according to the three-dimensional path coordinate sequence, perform moving average and quantization on the direction change rates between multiple adjacent path points to obtain the path curvature intensity index value; Obtain the curvature conversion coefficient through the construction specification, multiply the path curvature intensity index value by the curvature conversion coefficient to obtain the product result, and perform the inverse hyperbolic tangent function processing on the product result to obtain the curvature-velocity mapping reference value; Obtain each real-time pose deviation value through the detection data, calculate the second-order time derivative of each real-time pose deviation value, and obtain the pose deviation compensation factor based on the change trend of the second-order time derivative of each real-time pose deviation value; Calculate the spatial position gradients of two points before and after the current path point based on the three-dimensional path coordinate sequence to obtain the spatial position gradient vector, and perform normalization processing on the spatial position gradient vector to obtain the path gradient intensity value; Obtain the instantaneous speed value through the detection data, calculate the difference between the square of the maximum speed value and the square of the instantaneous speed value to obtain the speed safety margin parameter; Perform the square root operation on the speed safety margin parameter to obtain the effective speed difference value, divide the curvature-velocity mapping reference value by the effective speed difference value to obtain the curvature constraint reference torque; Superimpose the pose deviation compensation factor on the curvature constraint reference torque to generate the drive torque control amount. Perform a reciprocal operation on the path curvature intensity index value to obtain the curvature adaptation speed reference value; Obtain the speed decay coefficient through the path gradient intensity value, obtain the real-time pose deviation direction through the detection data, and compensate the real-time pose deviation direction based on the speed decay coefficient to obtain the error correction speed quantity; Algebraically superimpose the curvature adaptation speed reference value and the error correction speed quantity to obtain a superimposed result, perform a time integration operation on the superimposed result to obtain the current reference speed value, and combine the current reference speed value with the drive torque control quantity to generate a corresponding walking control signal.

[0042] Furthermore, the hanging basket motor-driven walking method for the construction of continuous rigid frame bridges further includes the following steps: Step S103, perform periodic binocular positioning shooting, obtain multiple binocular shooting images, perform positioning recognition, determine multiple positioning fixed targets, and obtain the corresponding positioning target distances.

[0043] In the embodiment of the present invention, by obtaining the walking positioning period, and then according to the walking positioning period, perform periodic binocular positioning shooting on multiple shooting directions, obtain multiple binocular shooting images, and then based on the preset multiple fixed target features, perform positioning object recognition on the multiple binocular shooting images to determine multiple positioning fixed targets, and further perform distance analysis on the multiple binocular shooting images to obtain the positioning target distances corresponding to the multiple positioning fixed targets.

[0044] It can be understood that the positioning fixed target is a specific position of an object with a fixed position on the construction site of a continuous rigid frame bridge, such as the top of a foundation pile, the top of a permanent bearing, the side of a bearing platform, etc.

[0045] Specifically, Figure 4 The flowchart of binocular positioning shooting in the method provided by the embodiment of the present invention is shown.

[0046] Among them, in the preferred embodiment provided by the present invention, the steps of performing periodic binocular positioning shooting, obtaining multiple binocular shooting images, performing positioning recognition, determining multiple positioning fixed targets, and obtaining the corresponding positioning target distances specifically include the following steps: Step S1031, obtain the walking positioning period; Step S1032, according to the walking positioning period, perform periodic binocular positioning shooting to obtain multiple binocular shooting images; Step S1033, based on the preset multiple fixed target features, perform positioning recognition on the multiple binocular shooting images to determine multiple positioning fixed targets; Step S1034: Perform distance analysis on multiple binocular captured images to obtain the positioning target distances corresponding to multiple positioning fixed targets.

[0047] Specifically, the steps for performing distance analysis on multiple binocular captured images to obtain the positioning target distances corresponding to multiple positioning fixed targets are as follows: Perform positioning recognition on multiple binocular captured images to obtain the HSV color space histograms of the regions where the left and right eye feature points are located; Calculate the intersection-over-union ratio of the HSV color space histograms of the regions where the left and right eye feature points are located to obtain the intersection-over-union ratio value, and normalize the intersection-over-union ratio value to generate the color dimension phase velocity value; Use the LBP operator to extract the HSV color space histogram to obtain the texture features; Construct texture feature vectors for the corresponding regions of the left and right eye images using the texture features and obtain the texture dimension similarity value; Obtain the curvature distribution histograms within the neighborhoods of the feature points from multiple binocular captured images and further obtain the curvature distribution difference degree; Generate the geometric dimension similarity value using the curvature distribution difference degree, collect external ambient light data to obtain the adaptive weight coefficient, and adjust the adaptive weight coefficient for each dimension according to the current light intensity to obtain the dynamic adaptive weight coefficient; Multiply the color dimension phase velocity value, the texture dimension similarity value, and the geometric dimension similarity value by their corresponding dynamic adaptive weight coefficients respectively and perform a weighted summation operation to obtain the preliminary confidence factor; Extract the gradient vectors of the left and right images at the feature point coordinates through binocular captured images; Calculate the absolute value of the difference between the gradient vectors of the left and right images in the horizontal and vertical directions, obtain the gradient difference metric value from the absolute value of the difference between the gradient vectors of the left and right images in the horizontal and vertical directions, and perform a square root operation on the gradient difference metric value to obtain the gradient difference square root value; Divide the preliminary confidence factor by the gradient difference square root value to obtain the normalized confidence weight value, obtain the original disparity value through distance analysis of multiple binocular captured images, and based on the normalized confidence weight value and the original disparity value, obtain the confidence-disparity product matrix; Obtain the column sum value of the matrix through the confidence-disparity product matrix, calculate the total sum of the normalized confidence weight values to obtain the total confidence weight value; use the total confidence weight value to perform a weighted average on the column sum value to obtain the optimized disparity reference value; obtain the integral value of the vibration energy through real-time feedback data and perform linear interpolation processing on the integral value of the vibration energy to obtain the environmental complexity coefficient; The second-order derivative of the parallax is obtained by optimizing the parallax reference value, and the product of the second-order derivative of the parallax and the environmental complexity coefficient is superimposed on the original parallax value to obtain the final optimized parallax value; Based on the final optimized parallax value, the positioning target distances corresponding to multiple said positioning fixed targets are obtained.

[0048] Further, the hanging basket motor-driven walking method for continuous rigid frame bridge construction further includes the following steps: Step S104, perform positioning analysis based on multiple said positioning target distances to generate walking positioning data, and perform motor monitoring to obtain motor monitoring data.

[0049] In the embodiment of the present invention, by identifying the positioning target directions corresponding to multiple positioning fixed targets, and then based on multiple positioning target directions and multiple corresponding positioning target distances, perform positioning analysis on the target construction hanging basket to generate walking positioning data of the target construction hanging basket in three-dimensional space, and perform temperature monitoring on the motor of the target construction hanging basket to obtain motor monitoring data.

[0050] Specifically, Figure 5 The flowchart of positioning analysis and motor monitoring in the method provided by the embodiment of the present invention is shown.

[0051] Among them, in the preferred embodiment provided by the present invention, the performing positioning analysis based on multiple said positioning target distances to generate walking positioning data and performing motor monitoring to obtain motor monitoring data specifically includes the following steps: Step S1041, identify the positioning target directions corresponding to multiple said positioning fixed targets; Step S1042, perform positioning analysis on the target construction hanging basket based on multiple said positioning target directions and multiple corresponding positioning target distances to generate walking positioning data; Step S1043, perform motor monitoring on the target construction hanging basket to obtain motor monitoring data.

[0052] Specifically, the performing positioning analysis on the target construction hanging basket based on multiple said positioning target directions and multiple corresponding positioning target distances to generate walking positioning data specifically includes the following steps: Obtain the ranging values at adjacent moments according to multiple said positioning target directions and multiple corresponding positioning target distances; based on the ranging values at adjacent moments, obtain the absolute value of the difference in ranging values through the current moment ranging value and the previous moment ranging value; Perform exponential function transformation on the absolute value of the difference in ranging values to generate a ranging mutation factor; Obtain a direction angle measurement sequence by identifying the positioning target directions corresponding to multiple said positioning fixed targets, and based on the direction angle measurement sequence, obtain the standard deviation of the direction angle measurement values; Perform an error function transformation on the reciprocal of the standard deviation of the direction angle measurement value to obtain an angle dispersion compensation value; Obtain the motor current energy integral value from the motor monitoring data, collect the fuselage vibration amplitude value through a sensor, and based on the motor current energy integral value, obtain the vibration interference intensity value by superimposing the fuselage vibration amplitude value; Divide the linear combination result of the ranging mutation factor and the angle dispersion compensation value by the square root of the vibration interference intensity value to obtain the target confidence weight; Obtain the azimuth angle parameter by identifying the positioning target directions corresponding to multiple said positioning fixed targets, and obtain the cosine value and sine value of the azimuth angle based on the azimuth angle parameter; Calculate the product of the cosine value of the azimuth angle and the ranging value at the current moment, and the product of the sine value of the azimuth angle and the ranging value at the current moment, multiply the two products to construct a two-dimensional plane coordinate, obtain the two-dimensional coordinate of the target based on the two-dimensional plane coordinate, and perform weighted summation on the two-dimensional coordinate of the target using the target confidence weight to obtain the weighted sum coordinate; Obtain the total confidence weight through the target confidence weight, and divide the weighted sum coordinate by the total confidence weight to obtain the weighted average coordinate; Obtain the historical positioning coordinate and the stability coefficient from the historical data, calculate the acceleration parameter of the historical positioning coordinates at adjacent moments to obtain the historical acceleration parameter, and extract the second derivative feature of the historical acceleration parameter to obtain the trajectory smoothness index value; Compensate the trajectory smoothness index value using the stability coefficient to obtain the trajectory smooth correction amount, and superimpose the trajectory smooth correction amount on the weighted average coordinate to obtain the coordinate after boundary constraint; Generate the walking positioning data based on the coordinate after boundary constraint.

[0053] Furthermore, the hanging basket motor-driven walking method for the continuous rigid frame bridge construction further includes the following steps: Step S105, perform an abnormal analysis of the motor-driven walking according to the walking positioning data and the motor monitoring data, and perform abnormal classification and early warning when there is an abnormal walking condition.

[0054] In the embodiment of the present invention, based on the preset abnormal classification data, compare and analyze the walking positioning data and the motor monitoring data to determine whether there is an abnormal walking condition, and when it is determined that there is an abnormal walking condition, determine the abnormal level, and perform different abnormal treatments according to different abnormal levels. Specifically: when the abnormal level is level one, perform speed reduction control; when the abnormal level is level two, perform emergency stop control; when the abnormal level is level three, perform sound and light alarm.

[0055] It can be understood that by performing offset analysis on the walking positioning data, when the offset exceeds 1 cm, it is determined that there is an abnormal walking condition; by performing temperature comparison analysis on the motor monitoring data, when the motor temperature is greater than 60 °C, it is determined that there is an abnormal walking condition.

[0056] Specifically, Figure 6 The flowchart of performing abnormal grading and early warning in the method provided by the embodiment of the present invention is shown.

[0057] Among them, in the preferred embodiment provided by the present invention, the abnormal analysis of the motor-driven walking is performed according to the walking positioning data and the motor monitoring data, and when there is an abnormal walking condition, the abnormal grading and early warning specifically include the following steps: Step S1051, based on the preset abnormal grading data, compare and analyze the walking positioning data and the motor monitoring data to determine whether there is an abnormal walking condition; Step S1052, when there is an abnormal walking condition, determine the abnormal level; Step S1053, if the abnormal level is level one, perform speed reduction control; Step S1054, if the abnormal level is level two, perform emergency stop control; Step S1055, if the abnormal level is level three, perform audible and visual alarm.

[0058] Furthermore, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0059] Among them, in another preferred embodiment provided by the present invention, a hanging basket motor-driven walking system for continuous rigid frame bridge construction includes: An initial detection and processing unit 101, configured to receive the hanging basket construction task, perform initialization detection, construct a task path, walking control parameters, and a three-dimensional path, and perform track detection and obstacle investigation on the task path.

[0060] In an embodiment of the present invention, the initial detection processing unit 101 receives a hanging basket construction task, conducts target planning for the hanging basket construction task, selects a target construction hanging basket, then performs initialization detection on the driving motor, sensor, track anchor point, and hydraulic brake of the target construction hanging basket, obtains initial detection data, analyzes the initial detection data, determines whether there is an initialization anomaly. In the case of no initialization anomaly, according to the hanging basket construction task, a task path is constructed, and the walking speed, walking distance, acceleration, and deceleration are planned to generate walking control parameters. According to the task path and a preset inspection distance, a flight inspection path is planned. Then, from multiple unmanned aerial vehicles, an idle inspection unmanned aerial vehicle is selected, and the inspection unmanned aerial vehicle is controlled for inspection according to the flight inspection path, so that the inspection unmanned aerial vehicle performs track detection and obstacle investigation. Among them, track detection is a process of detecting the flatness of the track of the task path; obstacle investigation is a process of investigating potential obstacles such as steel bar heads and concrete protrusions on the task path.

[0061] Specifically, Figure 8 The structural block diagram of the initial detection processing unit 101 in the system provided by the embodiment of the present invention is shown.

[0062] Among them, in the preferred embodiment provided by the present invention, the initial detection processing unit 101 specifically includes: A task receiving module 1011, configured to receive a hanging basket construction task and determine a target construction hanging basket; An initialization detection module 1012, configured to perform initialization detection on the driving motor, sensor, track anchor point, and hydraulic brake of the target construction hanging basket, and determine whether there is an initialization anomaly; A task processing module 1013, configured to construct a task path, walking control parameters, and a three-dimensional path according to the hanging basket construction task when there is no initialization anomaly, where the walking control parameters include walking speed, walking distance, acceleration, and deceleration; An inspection path planning module 1014, configured to plan a flight inspection path according to the task path; A track detection and obstacle investigation module 1015, configured to select an inspection unmanned aerial vehicle and perform track detection and obstacle investigation on the task path according to the flight inspection path.

[0063] Furthermore, the hanging basket motor-driven walking system for continuous rigid frame bridge construction further includes: A motor walking control unit 102, configured to release the hydraulic brake and perform motor-driven walking control according to the three-dimensional path and the walking control parameters.

[0064] In an embodiment of the present invention, when both the track detection and obstacle detection are passed, the motor walking control unit 102 releases the hydraulic brake of the target construction hanging basket, and then generates corresponding walking control signals according to the three-dimensional path and walking control parameters, and further controls the walking of the target construction hanging basket driven by the motor according to the walking control signals.

[0065] The walking positioning and recognition unit 103 is used to perform periodic binocular positioning shooting, obtain multiple binocular shooting images, perform positioning recognition, determine multiple positioning fixed targets, and obtain the corresponding positioning target distances.

[0066] In an embodiment of the present invention, the walking positioning and recognition unit 103 obtains the walking positioning period, and then performs periodic binocular positioning shooting on multiple shooting directions according to the walking positioning period, obtains multiple binocular shooting images, and then performs positioning object recognition on the multiple binocular shooting images based on a plurality of preset fixed target features, determines multiple positioning fixed targets, and further performs distance analysis on the multiple binocular shooting images to obtain the positioning target distances corresponding to the multiple positioning fixed targets.

[0067] Specifically, Figure 9 The structural block diagram of the walking positioning and recognition unit 103 in the system provided by the embodiment of the present invention is shown.

[0068] Among them, in the preferred embodiment provided by the present invention, the walking positioning and recognition unit 103 specifically includes: A period acquisition module 1031, which is used to acquire the walking positioning period; A binocular positioning shooting module 1032, which is used to perform periodic binocular positioning shooting according to the walking positioning period to obtain multiple binocular shooting images; A positioning recognition module 1033, which is used to perform positioning recognition on the multiple binocular shooting images based on a plurality of preset fixed target features to determine multiple positioning fixed targets; A distance analysis module 1034, which is used to perform distance analysis on the multiple binocular shooting images to obtain the positioning target distances corresponding to the multiple positioning fixed targets.

[0069] Furthermore, the hanging basket motor-driven walking system for continuous rigid frame bridge construction further includes: A positioning analysis and motor monitoring unit 104, which is used to perform positioning analysis according to the multiple positioning target distances, generate walking positioning data, and perform motor monitoring to obtain motor monitoring data.

[0070] In an embodiment of the present invention, the positioning and analysis motor monitoring unit 104 performs positioning analysis on the target construction hanging basket by identifying the positioning target directions corresponding to a plurality of positioning fixed targets, and then generates the walking positioning data of the target construction hanging basket in three-dimensional space according to the plurality of positioning target directions and the plurality of corresponding positioning target distances, and monitors the temperature of the motor of the target construction hanging basket to obtain motor monitoring data.

[0071] The abnormal grading and warning unit 105 is configured to perform abnormal analysis of the motor-driven walking according to the walking positioning data and the motor monitoring data, and perform abnormal grading and warning when there is a walking abnormal condition.

[0072] In an embodiment of the present invention, the abnormal grading and warning unit 105 performs comparative analysis on the walking positioning data and the motor monitoring data based on preset abnormal grading data, determines whether there is a walking abnormal condition, and when it is determined that there is a walking abnormal condition, determines the abnormal level, and performs different abnormal processing according to different abnormal levels. Specifically: when the abnormal level is level one, speed reduction control is performed; when the abnormal level is level two, emergency stop control is performed; when the abnormal level is level three, sound and light alarm is performed.

[0073] Specifically, Figure 10 The structural block diagram of the abnormal grading and warning unit 105 in the system provided by the embodiment of the present invention is shown.

[0074] Among them, in a preferred embodiment provided by the present invention, the abnormal grading and warning unit 105 specifically includes: The comparative analysis module 1051 is configured to perform comparative analysis on the walking positioning data and the motor monitoring data based on preset abnormal grading data to determine whether there is a walking abnormal condition; The abnormal level determination module 1052 is configured to determine the abnormal level when there is a walking abnormal condition; The speed reduction control module 1053 is configured to perform speed reduction control when the abnormal level is level one; The emergency stop control module 1054 is configured to perform emergency stop control when the abnormal level is level two; The sound and light alarm module 1055 is configured to perform sound and light alarm when the abnormal level is level three.

[0075] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are sequentially shown according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0076] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0077] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0078] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

[0079] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for the motor-driven walking of a hanging basket in the construction of a continuous rigid frame bridge, characterized in that, The method specifically includes the following steps: Receive the construction task of the hanging basket, conduct initialization detection, construct the task path, walking control parameters, and three-dimensional path, and conduct track detection and obstacle investigation on the task path; Release the hydraulic brake, and perform walking control driven by the motor according to the three-dimensional path and the walking control parameters; Perform periodic binocular positioning shooting, obtain multiple binocular shooting images, perform positioning recognition, determine multiple positioning fixed targets, and obtain the corresponding positioning target distances; According to the multiple positioning target distances, conduct positioning analysis, generate walking positioning data, and conduct motor monitoring to obtain motor monitoring data; According to the walking positioning data and the motor monitoring data, conduct abnormal analysis of motor-driven walking, and conduct abnormal classification early warning when there is a walking abnormal condition; Among them, the steps of receiving the construction task of the hanging basket, conducting initialization detection, constructing the task path, walking control parameters, and three-dimensional path, and conducting track detection and obstacle investigation on the task path specifically include the following steps: Receive the construction task of the hanging basket and determine the target construction hanging basket; Conduct initialization detection on the drive motor, sensor, track anchor point, and hydraulic brake of the target construction hanging basket, and determine whether there is an initialization abnormality; When there is no initialization abnormality, construct the task path, walking control parameters, and three-dimensional path according to the hanging basket construction task. The walking control parameters include walking speed, walking distance, acceleration, and deceleration; Plan the flight inspection path according to the task path; Select an inspection UAV, and conduct track detection and obstacle investigation on the task path according to the flight inspection path.

2. The hanging basket motor-driven walking method for the construction of a continuous rigid frame bridge according to claim 1, characterized in that, When there is no initialization abnormality, construct the task path, walking control parameters, and three-dimensional path according to the hanging basket construction task. Among them, the specific steps to obtain the walking control parameters are as follows: Conduct initialization detection on the drive motor of the target construction hanging basket to obtain the vibration modal data of the hanging basket structure, the rated maximum load value, and the real-time load value; Extract the first three vibration frequencies and the corresponding amplitudes of the hanging basket from the vibration modal data of the hanging basket structure, and use the first three vibration frequencies and the corresponding amplitudes of the hanging basket to obtain the vibration characteristic coefficient in a weighted summation manner; Multiply the square ratio of the real-time load value to the rated maximum load value by the vibration characteristic coefficient to obtain the load sensitivity factor; Obtain the acceleration time threshold by constructing the task path, use the acceleration time threshold as the exponential coefficient, and superimpose the load sensitivity factor and the quadratic term of the real-time load value to generate the dynamic exponential coefficient; Conduct initialization detection on the track anchor point to obtain the strain data of the track anchor point, and collect external meteorological data to obtain the environmental temperature and humidity values; Obtain the equivalent friction coefficient of the contact surface from the strain data of the track anchor point, and use the environmental temperature and humidity values to superimpose and correct the equivalent friction coefficient of the contact surface to obtain the friction compensation factor; Obtain the maximum speed value from the hanging basket construction task, correct the maximum speed value using the dynamic index coefficient to obtain the corrected maximum speed value; input the corrected maximum speed value into the exponential function to generate a speed growth curve; when the cumulative time reaches the acceleration time threshold, switch to the input parameters of the next stage to generate the speed sequence in the acceleration stage; Obtain the cruising speed by detecting the track and checking for obstacles in the task path, and screen out the effective obstacle targets through the real-time detection data of the drone to obtain the minimum obstacle spacing; Process the cruising speed using the friction compensation factor to obtain the compensated cruising speed; divide the minimum obstacle spacing by the compensated cruising speed to generate the braking response time window; Determine the total task time through the task path, and construct a speed decay curve using the hyperbolic function based on the braking response time window and the cruising speed; when the cumulative time approaches the total task time, switch to the input parameters of the next stage to generate the speed sequence in the deceleration stage; Stitch together the speed sequence in the acceleration stage, the cruising speed, and the speed sequence in the deceleration stage in chronological order to generate a complete walking speed curve, and obtain the walking control parameters through the complete walking speed curve.

3. The hanging basket motor-driven walking method for the construction of continuous rigid frame bridges according to claim 2, characterized in that Perform hydraulic braking release, and perform motor-driven walking control according to the three-dimensional path and the walking control parameters, which specifically includes the following steps: Release the hydraulic brake on the target construction hanging basket; Generate corresponding walking control signals according to the three-dimensional path and the walking control parameters; Perform motor-driven walking control on the target construction hanging basket according to the walking control signals.

4. The hanging basket motor-driven walking method for the construction of a continuous rigid frame bridge according to claim 3, wherein Generate corresponding walking control signals according to the three-dimensional path and the walking control parameters, and the specific steps are as follows: Obtain the three-dimensional path coordinate sequence through the three-dimensional path, calculate the direction change rate between adjacent path points according to the three-dimensional path coordinate sequence, perform a moving average and quantization on the direction change rates between multiple adjacent path points to obtain the path curvature intensity index value; Obtain the curvature conversion coefficient through the construction specification, multiply the path curvature intensity index value by the curvature conversion coefficient to obtain the product result, and perform an inverse hyperbolic tangent function process on the product result to obtain the curvature-speed mapping reference value; Obtain the real-time pose deviation values through the detection data, calculate the second-order time derivative of each real-time pose deviation value, and obtain the pose deviation compensation factor based on the change trend of the second-order time derivative of each real-time pose deviation value; Calculate the spatial position gradients of two points before and after the current path point based on the three-dimensional path coordinate sequence to obtain the spatial position gradient vector, and perform a normalization process on the spatial position gradient vector to obtain the path gradient intensity value; Obtain the instantaneous speed value through the detection data, calculate the difference between the square of the maximum speed value and the square of the instantaneous speed value to obtain the speed safety margin parameter; Perform a square root operation on the speed safety margin parameter to obtain the effective speed difference value, divide the curvature-speed mapping reference value by the effective speed difference value to obtain the curvature constraint reference torque; Superimpose the pose deviation compensation factor on the curvature constraint reference torque to generate the drive torque control amount; Perform a reciprocal operation on the path curvature intensity index value to obtain the curvature adaptation speed reference value; Obtain the speed decay coefficient from the path gradient intensity value, obtain the real-time pose deviation direction from the detection data, and compensate the real-time pose deviation direction based on the speed decay coefficient to obtain the error correction speed quantity; Algebraically superimpose the curvature adaptation speed reference value and the error correction speed quantity to obtain a superimposed result, perform a time integration operation on the superimposed result to obtain the current reference speed value, and combine the current reference speed value with the drive torque control quantity to generate a corresponding walking control signal.

5. The hanging basket motor-driven walking method for continuous rigid frame bridge construction according to claim 4, characterized in that, The periodic binocular positioning shooting, obtaining multiple binocular shooting images, performing positioning recognition, determining multiple positioning fixed targets, and obtaining the corresponding positioning target distances specifically include the following steps: Obtain the walking positioning period; According to the walking positioning period, perform periodic binocular positioning shooting to obtain multiple binocular shooting images; Based on a plurality of preset fixed target features, perform positioning recognition on the multiple binocular shooting images to determine multiple positioning fixed targets; Perform distance analysis on the multiple binocular shooting images to obtain the positioning target distances corresponding to the multiple positioning fixed targets.

6. The hanging basket motor-driven walking method for the construction of continuous rigid frame bridges according to claim 5, characterized in that, Perform distance analysis on the multiple binocular shooting images to obtain the positioning target distances corresponding to the multiple positioning fixed targets. The specific steps are as follows: Obtain the HSV color space histogram of the regions where the left and right eye feature points are located by performing positioning recognition on the multiple binocular shooting images; Calculate the intersection-over-union ratio of the HSV color space histograms of the regions where the left and right eye feature points are located to obtain an intersection-over-union ratio value, and normalize the intersection-over-union ratio value to generate a color dimension phase velocity value; Extract the HSV color space histogram using the LBP operator to obtain texture features; Construct a texture feature vector for the corresponding regions of the left and right eye images using the texture features and obtain a texture dimension similarity value; Obtain the curvature distribution histogram within the neighborhood of the feature points from the multiple binocular shooting images, and further obtain the curvature distribution difference degree; Generate a geometric dimension similarity value using the curvature distribution difference degree, obtain an adaptive weight coefficient by collecting external ambient light data, and adjust the adaptive weight coefficient of each dimension according to the current light intensity to obtain a dynamic adaptive weight coefficient; Multiply the color dimension phase velocity value, the texture dimension similarity value, and the geometric dimension similarity value by the corresponding dynamic adaptive weight coefficients respectively and perform a weighted summation operation to obtain a preliminary confidence factor; Extract the gradient vectors of the left and right images at the feature point coordinates through the binocular shooting images; Calculate the absolute value of the difference between the gradient vectors of the left and right images in the horizontal and vertical directions, obtain a gradient difference metric value from the absolute value of the difference between the gradient vectors of the left and right images in the horizontal and vertical directions, and perform a square root operation on the gradient difference metric value to obtain a gradient difference square root value; Divide the preliminary confidence factor by the gradient difference square root value to obtain a normalized confidence weight value, obtain the original disparity value through distance analysis of the multiple binocular shooting images, and obtain a confidence-disparity product matrix based on the normalized confidence weight value and the original disparity value; The column sum value of the matrix is obtained through the confidence-disparity product matrix, and the total confidence weight value is obtained by calculating the sum of the normalized confidence weight values; the column sum value is weighted and averaged using the total confidence weight value to obtain an optimized disparity reference value; the integral value of the vibration energy is obtained through real-time feedback data, and linear interpolation processing is performed on the integral value of the vibration energy to obtain the environmental complexity coefficient; The second-order derivative of the disparity is obtained through the optimized disparity reference value, and the product of the second-order derivative of the disparity and the environmental complexity coefficient is superimposed on the original disparity value to obtain the final optimized disparity value; Based on the final optimized disparity value, the positioning target distances corresponding to multiple said positioning fixed targets are obtained.

7. The hanging basket motor-driven walking method for continuous rigid frame bridge construction according to claim 6, characterized in that, The performing positioning analysis according to multiple said positioning target distances, generating walking positioning data, and performing motor monitoring to obtain motor monitoring data specifically includes the following steps: Identifying the positioning target directions corresponding to multiple said positioning fixed targets; Performing positioning analysis on the target construction hanging basket according to multiple said positioning target directions and multiple corresponding positioning target distances to generate walking positioning data; Performing motor monitoring on the target construction hanging basket to obtain motor monitoring data.

8. The hanging basket motor-driven walking method for the construction of continuous rigid frame bridges according to claim 7, characterized in that, Performing positioning analysis on the target construction hanging basket according to multiple said positioning target directions and multiple corresponding positioning target distances to generate walking positioning data, and the specific steps are as follows: Obtaining the ranging values at adjacent moments according to multiple said positioning target directions and multiple corresponding positioning target distances; based on the ranging values at adjacent moments, obtaining the absolute value of the difference between the ranging value at the current moment and the ranging value at the previous moment through the current moment ranging value and the previous moment ranging value; Performing an exponential function transformation on the absolute value of the ranging value difference to generate a ranging mutation factor; Obtaining a direction angle measurement sequence by identifying the positioning target directions corresponding to multiple said positioning fixed targets, and obtaining the standard deviation of the direction angle measurement values based on the direction angle measurement sequence; Performing an error function transformation on the reciprocal of the standard deviation of the direction angle measurement values to obtain an angle dispersion compensation value; Obtaining the integral value of the motor current energy through the motor monitoring data, collecting the body vibration amplitude value through the sensor, and based on the integral value of the motor current energy, obtaining the vibration interference intensity value by superimposing the body vibration amplitude value; Dividing the linear combination result of the ranging mutation factor and the angle dispersion compensation value by the square root of the vibration interference intensity value to obtain the target confidence weight; Obtaining the azimuth parameter by identifying the positioning target directions corresponding to multiple said positioning fixed targets, and obtaining the cosine value of the azimuth and the sine value of the azimuth based on the azimuth parameter; Calculating the product of the cosine value of the azimuth and the ranging value at the current moment, and the product of the sine value of the azimuth and the ranging value at the current moment, multiplying the two products to construct a two-dimensional plane coordinate, obtaining the two-dimensional coordinate of the target based on the two-dimensional plane coordinate, and performing weighted summation on the two-dimensional coordinate of the target using the target confidence weight to obtain the weighted sum coordinate; Obtaining the total confidence weight value through the target confidence weight, and dividing the weighted sum coordinate by the total confidence weight value to obtain the weighted average coordinate; Obtain historical positioning coordinates and stability coefficients from historical data, calculate the acceleration parameters of adjacent historical positioning coordinates to obtain historical acceleration parameters, and extract the second derivative features of the historical acceleration parameters to obtain the trajectory smoothness index value; Use the stability coefficient to compensate the trajectory smoothness index value to obtain the trajectory smooth correction amount, and superimpose the trajectory smooth correction amount on the weighted average coordinates to obtain the coordinates after boundary constraint; Generate walking positioning data based on the coordinates after boundary constraint.

9. The hanging basket motor-driven walking method for continuous rigid frame bridge construction according to claim 8, characterized in that, According to the walking positioning data and the motor monitoring data, perform abnormal analysis of motor-driven walking, and when there is a walking abnormal condition, perform abnormal classification warning, which specifically includes the following steps: Based on the preset abnormal classification data, compare and analyze the walking positioning data and the motor monitoring data to determine whether there is a walking abnormal condition; When there is a walking abnormal condition, determine the abnormal level; If the abnormal level is level one, perform speed reduction control; If the abnormal level is level two, perform emergency stop control; If the abnormal level is level three, perform sound and light alarm.

10. A hanging basket motor-driven walking system for the construction of a continuous rigid frame bridge, characterized in that, The system applies the hanging basket motor-driven walking method for continuous rigid frame bridge construction according to any one of claims 1 to 9 above. The system includes: An initial detection processing unit, configured to receive the hanging basket construction task, perform initialization detection, construct a task path, walking control parameters, and a three-dimensional path, and perform track detection and obstacle investigation on the task path; A motor walking control unit, configured to release hydraulic braking and perform walking control of motor drive according to the three-dimensional path and the walking control parameters; A walking positioning recognition unit, configured to perform periodic binocular positioning shooting, obtain multiple binocular shooting images, perform positioning recognition, determine multiple positioning fixed targets, and obtain the corresponding positioning target distances; A positioning analysis and motor monitoring unit, configured to perform positioning analysis based on the multiple positioning target distances, generate walking positioning data, and perform motor monitoring to obtain motor monitoring data; An abnormal classification warning unit, configured to perform abnormal analysis of motor-driven walking according to the walking positioning data and the motor monitoring data, and perform abnormal classification warning when there is a walking abnormal condition.

Images