A motor-driven walking method and system for a hanging basket in continuous rigid frame bridge construction

Abstract

The embodiment of the present invention relates to the technical field of continuous rigid frame bridge construction, and specifically discloses a motor-driven walking method and system for a hanging basket in continuous rigid frame bridge construction. The embodiment of the present invention constructs a task path, walking control parameters, and a three-dimensional path; performs motor-driven walking control; performs periodic binocular positioning shooting to determine multiple positioning fixed targets and obtain the corresponding positioning target distances; performs positioning analysis to generate walking positioning data; performs abnormal analysis of motor-driven walking, and performs abnormal classification warnings when there are abnormal walking conditions. When performing motor-driven walking control, it is possible to generate walking positioning data through binocular positioning shooting and recognition analysis, perform abnormal analysis of motor-driven walking, and perform abnormal classification warnings when there are abnormal walking conditions, thereby accurately identifying the spatial position of the hanging basket during the operation process and timely and effectively identifying and processing the abnormal state of the hanging basket.

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] A continuous rigid frame bridge is a bridge structure between continuous beam bridges and T-shaped rigid frame bridges. It features a continuous main beam, consolidated piers and beams, and uses prestressed concrete. Continuous rigid frame bridge construction involves the construction and installation of the main beam, piers, foundation, and other components of a continuous rigid frame bridge, using specific construction methods and processes in accordance with design drawings and specifications.

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

[0004] In the existing technology, 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 operation, resulting in the inability to timely and effectively identify and handle the abnormal status of the hanging basket. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a motor-driven traveling method and system for a hanging basket in the construction of a continuous rigid frame bridge, 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:

[0007] A motor-driven walking method for a hanging basket in continuous rigid frame bridge construction, the method specifically comprising the following steps:

[0008] Receive the construction task of the hanging basket, perform initialization detection, build the task path, travel control parameters and three-dimensional path, and perform track detection and obstacle removal on the task path;

[0009] releasing the hydraulic brake and performing motor-driven walking control according to the three-dimensional path and the walking control parameters;

[0010] 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;

[0011] Perform positioning analysis based on the multiple positioning target distances to generate walking positioning data, and perform motor monitoring to obtain motor monitoring data;

[0012] 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.

[0013] As a further limitation of the technical solution of the embodiment of the present invention, the receiving of the hanging basket construction task, performing initialization detection, constructing the task path, travel control parameters and three-dimensional path, and performing track detection and obstacle removal on the task path specifically include the following steps:

[0014] Receive construction tasks for hanging baskets and determine target construction hanging baskets;

[0015] Performing initialization detection on the target construction hanging basket for the drive motor, sensor, track anchor point and hydraulic brake, and determining whether there is any initialization abnormality;

[0016] When there is no initialization exception, constructing a task path, walking control parameters and a three-dimensional path according to the hanging basket construction task, wherein the walking control parameters include walking speed, walking distance, acceleration and deceleration;

[0017] Plan a flight inspection route based on the mission path;

[0018] Select an inspection drone and perform track detection and obstacle inspection on the mission path according to the flight inspection path.

[0019] As a further limitation of the technical solution of the embodiment of the present invention, the releasing of the hydraulic brake and the performing of motor-driven walking control according to the three-dimensional path and the walking control parameters specifically include the following steps:

[0020] releasing the hydraulic brake on the target construction basket;

[0021] generating a corresponding walking control signal according to the three-dimensional path and the walking control parameters;

[0022] According to the walking control signal, the target construction hanging basket is controlled to walk by a motor.

[0023] As a further limitation of the technical solution of the embodiment of the present invention, the periodic binocular positioning shooting, obtaining multiple binocular shooting images, performing positioning recognition, determining multiple positioning fixed targets, and obtaining corresponding positioning target distances specifically include the following steps:

[0024] Get walking positioning cycle;

[0025] Performing periodic binocular positioning shooting according to the walking positioning cycle to obtain multiple binocular shooting images;

[0026] Based on a plurality of preset fixed target features, positioning and identifying the plurality of binocular images are performed to determine a plurality of positioned fixed targets;

[0027] Perform distance analysis on the multiple binocular images to obtain positioning target distances corresponding to the multiple positioning fixed targets.

[0028] As a further limitation of the technical solution of the embodiment of the present invention, performing positioning analysis based on the multiple positioning target distances, generating walking positioning data, and performing motor monitoring, and obtaining motor monitoring data specifically include the following steps:

[0029] Identifying positioning target directions corresponding to a plurality of positioning fixed targets;

[0030] Performing positioning analysis on the target construction hanging basket according to the plurality of positioning target directions and the plurality of corresponding positioning target distances to generate walking positioning data;

[0031] Perform motor monitoring on the target construction hanging basket to obtain motor monitoring data.

[0032] As a further limitation of the technical solution of the embodiment of the present invention, the abnormal analysis of motor-driven walking based on the walking positioning data and the motor monitoring data, and the abnormal classification warning when there is an abnormal walking condition specifically include the following steps:

[0033] Based on the preset abnormality classification data, the walking positioning data and the motor monitoring data are compared and analyzed to determine whether there is an abnormal walking condition;

[0034] When there is a walking abnormality, determining the abnormality level;

[0035] If the abnormal level is level one, speed reduction control is performed;

[0036] If the abnormal level is level 2, emergency stop control is performed;

[0037] If the abnormality level is level three, an audible and visual alarm is triggered.

[0038] A motor-driven traveling system for a hanging basket for continuous rigid frame bridge construction, comprising an initial detection processing unit, a motor traveling control unit, a traveling positioning identification unit, a positioning analysis motor monitoring unit, and an abnormality classification warning unit, wherein:

[0039] An initial detection processing unit is used to receive a construction task of a hanging basket, perform initialization detection, construct a task path, travel control parameters and a three-dimensional path, and perform track detection and obstacle removal on the task path;

[0040] A motor travel control unit, configured to release the hydraulic brake and perform motor-driven travel control according to the three-dimensional path and the travel control parameters;

[0041] The walking positioning and recognition unit is used to perform periodic binocular positioning shooting, obtain multiple binocular shooting images, perform positioning and recognition, determine multiple positioning fixed targets, and obtain the corresponding positioning target distances;

[0042] A positioning analysis motor monitoring unit is used to perform positioning analysis based on the multiple positioning target distances to generate walking positioning data, and to perform motor monitoring to obtain motor monitoring data;

[0043] The abnormality classification warning unit is used to perform abnormality analysis of motor-driven walking according to the walking positioning data and the motor monitoring data, and to issue an abnormality classification warning when there is an abnormal walking condition.

[0044] As a further limitation of the technical solution of the embodiment of the present invention, the initial detection processing unit specifically includes:

[0045] The task receiving module is used to receive the construction task of the hanging basket and determine the target construction hanging basket;

[0046] An initialization detection module is used to 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 any initialization abnormality;

[0047] 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, and the walking control parameters include walking speed, walking distance, acceleration and deceleration;

[0048] An inspection path planning module is used to plan a flight inspection path according to the task path;

[0049] The track detection and obstacle removal module is used to select an inspection drone and perform track detection and obstacle removal on the mission path according to the flight inspection path.

[0050] As a further limitation of the technical solution of the embodiment of the present invention, the walking positioning and recognition unit specifically includes:

[0051] A cycle acquisition module is used to obtain the walking positioning cycle;

[0052] A binocular positioning shooting module is used to perform periodic binocular positioning shooting according to the walking positioning cycle to obtain multiple binocular shooting images;

[0053] A positioning and recognition module is used to perform positioning and recognition on the plurality of binocular images based on a plurality of preset fixed target features, and determine a plurality of positioned fixed targets;

[0054] The distance analysis module is used to perform distance analysis on the multiple binocular images to obtain the positioning target distances corresponding to the multiple positioning fixed targets.

[0055] As a further limitation of the technical solution of the embodiment of the present invention, the abnormality classification warning unit specifically includes:

[0056] A comparison and analysis module, configured to compare and analyze the walking positioning data and the motor monitoring data based on preset abnormality classification data to determine whether there is an abnormal walking condition;

[0057] An abnormality level determination module, used to determine the abnormality level when there is an abnormal walking condition;

[0058] a speed reduction control module, configured to perform speed reduction control when the abnormality level is level one;

[0059] An emergency stop control module, configured to perform emergency stop control when the abnormality level is level 2;

[0060] The sound and light alarm module is used to make sound and light alarms when the abnormality level is level three.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The embodiment of the present invention constructs a task path, walking control parameters, and a three-dimensional path; performs motor-driven walking control; performs periodic binocular positioning shooting to determine multiple positioning fixed targets and obtain the corresponding positioning target distances; performs positioning analysis to generate walking positioning data; performs abnormal analysis of motor-driven walking, and performs abnormal classification warnings when there are abnormal walking conditions. When performing motor-driven walking control, binocular positioning shooting and recognition analysis can be used to generate walking positioning data, perform abnormal analysis of motor-driven walking, and perform abnormal classification warnings when there are abnormal walking conditions, thereby accurately identifying the spatial position of the hanging basket during the operation process and promptly and effectively identifying and processing the abnormal state of the hanging basket. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0064] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0065] Figure 2 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 is shown.

[0066] Figure 3 A flow chart of motor-driven walking control in the method provided by an embodiment of the present invention is shown.

[0067] Figure 4 A flow chart of binocular positioning shooting in the method provided by an embodiment of the present invention is shown.

[0068] Figure 5 A flow chart of positioning analysis and motor monitoring in the method provided by an embodiment of the present invention is shown.

[0069] Figure 6 A flowchart of abnormality classification warning in the method provided by an embodiment of the present invention is shown.

[0070] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0071] Figure 8 The figure shows a structural block diagram of an initial detection processing unit in a system provided by an embodiment of the present invention.

[0072] Figure 9 The structure block diagram of the walking positioning and recognition unit in the system provided by the embodiment of the present invention is shown.

[0073] Figure 10 The following is a structural block diagram of an abnormality classification warning unit in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, 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 intended to limit the present invention.

[0075] It is understandable that in the existing technology, 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 timely and effectively identify and handle the abnormal state of the hanging basket.

[0076] To solve the above problems, the embodiment of the present invention receives the construction task of the hanging basket, performs initialization detection, constructs the task path, travel control parameters and three-dimensional path, and performs track detection and obstacle inspection on the task path; releases the hydraulic brake, and performs motor-driven travel control according to the three-dimensional path and travel control parameters; performs periodic binocular positioning shooting, obtains multiple binocular shooting images, performs positioning recognition, determines multiple positioning fixed targets, and obtains corresponding positioning target distances; performs positioning analysis based on the multiple positioning target distances, generates travel positioning data, and performs motor monitoring to obtain motor monitoring data; performs motor-driven travel abnormality analysis based on the travel positioning data and motor monitoring data, and performs abnormality classification warning when there is an abnormal walking condition. When performing motor-driven travel control, binocular positioning shooting and recognition analysis can generate travel positioning data, perform motor-driven travel abnormality analysis, and perform abnormality classification warning when there is an abnormal walking condition, thereby accurately identifying the spatial position of the hanging basket during the operation process and timely and effectively identifying and processing the abnormal state of the hanging basket.

[0077] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0078] Specifically, a motor-driven walking method for a hanging basket in the construction of a continuous rigid frame bridge comprises the following steps:

[0079] Step S101: receive a construction task of a hanging basket, perform initialization detection, construct a task path, walking control parameters and a three-dimensional path, and perform track detection and obstacle elimination on the task path.

[0080] In an embodiment of the present invention, by receiving a hanging basket construction task, target planning is performed on the hanging basket construction task, a target construction hanging basket is selected, and then the target construction hanging basket is initialized and tested for the drive motor, sensor, track anchor point and hydraulic brake, and initial detection data is obtained. The initial detection data is analyzed to determine whether there is an initialization abnormality. If there is no initialization abnormality, a task path is constructed according to the hanging basket construction task, and the walking speed, walking distance, acceleration and deceleration are planned to generate walking control parameters. According to the task path and the preset inspection distance, a flight inspection path is planned, and then an idle inspection drone is selected from multiple drones. According to the flight inspection path, the inspection drone is controlled to perform track detection and obstacle inspection, wherein track detection is the process of detecting the flatness of the track of the task path; obstacle inspection is the process of checking potential obstacles such as steel bar heads and concrete protrusions on the task path.

[0081] Specifically, Figure 2A 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 is shown.

[0082] Among them, in the preferred embodiment provided by the present invention, the receiving of the hanging basket construction task, performing initialization detection, constructing the task path, walking control parameters and three-dimensional path, and performing track detection and obstacle troubleshooting on the task path specifically include the following steps:

[0083] Step S1011, receiving a construction task of a hanging basket and determining a target construction hanging basket;

[0084] Step S1012: performing initialization detection on the target construction basket for the drive motor, sensor, track anchor point, and hydraulic brake, and determining whether there is any initialization abnormality;

[0085] Step S1013: When there is no initialization exception, construct a task path, walking control parameters, and a three-dimensional path according to the hanging basket construction task, wherein the walking control parameters include walking speed, walking distance, acceleration, and deceleration;

[0086] Step S1014: planning a flight inspection route according to the task route;

[0087] Step S1015: Select an inspection drone and perform track detection and obstacle inspection on the mission path according to the flight inspection path.

[0088] Specifically, when there is no initialization exception, the task path, walking control parameters and three-dimensional path are constructed according to the hanging basket construction task, wherein the specific steps of obtaining the walking control parameters are as follows:

[0089] Initialize and test the driving motor of the target construction basket to obtain the vibration modal data of the basket structure, the rated maximum load value and the real-time load value;

[0090] The first three-order vibration evaluation rates and 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 using the first three-order vibration evaluation rates and corresponding amplitudes of the hanging basket;

[0091] 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;

[0092] The acceleration time threshold is obtained by constructing the task path, and the acceleration time threshold is used as the exponential coefficient. The quadratic correlation term of the load sensitivity factor and the real-time load value is superimposed to generate a dynamic exponential coefficient.

[0093] Initialize the track anchor point to obtain the track anchor point strain data, and collect external meteorological data to obtain the ambient temperature and humidity values;

[0094] The equivalent friction coefficient of the contact surface is obtained from the track anchor point strain data, and the equivalent friction coefficient of the contact surface is corrected by superimposing it with the ambient temperature and humidity values to obtain the friction compensation factor.

[0095] The maximum speed value is obtained from the hanging basket construction task, and the maximum speed value is corrected using the dynamic exponential coefficient to obtain the corrected maximum speed value; the corrected maximum speed value is input into the exponential function to generate a speed growth curve; when the accumulated time reaches the acceleration time threshold, it is switched to the input parameter of the next stage to generate the acceleration stage speed sequence;

[0096] The cruising speed is obtained by performing track detection and obstacle screening on the mission path, and effective obstacle targets are screened out through real-time detection data of the drone to obtain the minimum obstacle spacing;

[0097] The cruising speed is processed using the friction compensation factor to obtain the compensated cruising speed; the minimum obstacle distance is divided by the compensated cruising speed to generate the braking response time window;

[0098] The total mission time is determined by the mission path. Based on the braking response time window and the cruising speed, a hyperbolic function is used to construct a speed decay curve. When the accumulated time approaches the total mission time, it is switched to the input parameters of the next stage to generate the deceleration phase speed sequence.

[0099] The acceleration phase speed sequence, the cruising speed sequence, and the deceleration phase speed sequence are spliced in chronological order to generate a complete walking speed curve, and the walking control parameters are obtained through the complete walking speed curve.

[0100] Furthermore, the motor-driven walking method for the continuous rigid frame bridge construction further includes the following steps:

[0101] Step S102 : releasing the hydraulic brake and performing motor-driven walking control according to the three-dimensional path and the walking control parameters.

[0102] In an embodiment of the present invention, when both the track detection and obstacle inspection are passed, the hydraulic brake of the target construction basket is released, and then a corresponding walking control signal is generated based on the three-dimensional path and walking control parameters, and then the target construction basket is motor-driven for walking control according to the walking control signal.

[0103] Specifically, Figure 3 A flow chart of motor-driven walking control in the method provided by an embodiment of the present invention is shown.

[0104] In a preferred embodiment of the present invention, the releasing of the hydraulic brake and the motor-driven walking control according to the three-dimensional path and the walking control parameters specifically include the following steps:

[0105] Step S1021, releasing the hydraulic brake on the target construction basket;

[0106] Step S1022: generating a corresponding walking control signal according to the three-dimensional path and the walking control parameters;

[0107] Step S1023: performing motor-driven walking control on the target construction hanging basket according to the walking control signal.

[0108] Specifically, according to the three-dimensional path and the walking control parameters, a corresponding walking control signal is generated, and the specific steps are as follows:

[0109] A three-dimensional path coordinate sequence is obtained through the three-dimensional path. The direction change rate between adjacent path points is calculated based on the three-dimensional path coordinate sequence. The direction change rates between multiple adjacent path points are sliding averaged and quantified to obtain the path curvature intensity index value;

[0110] The curvature conversion coefficient is obtained through construction specifications, the path curvature intensity index value is multiplied by the curvature conversion coefficient to obtain the product result, and the product result is processed by the inverse hyperbolic tangent function to obtain the curvature-velocity mapping benchmark value;

[0111] Obtaining each real-time posture deviation value through detection data, calculating the second-order time derivative of each real-time posture deviation value, and obtaining a posture deviation compensation factor based on the change trend of the second-order time derivative of each real-time posture deviation value;

[0112] Based on the three-dimensional path coordinate sequence, the spatial position gradient of the two points before and after the current path point is calculated to obtain a spatial position gradient vector, and the spatial position gradient vector is normalized to obtain a path gradient intensity value;

[0113] The instantaneous speed value is obtained by detecting the data, and the difference between the square of the maximum speed value and the square of the instantaneous speed value is calculated to obtain the speed safety margin parameter;

[0114] Perform a square root operation on the speed safety margin parameter to obtain the effective value of the speed difference, and divide the curvature-speed mapping reference value by the effective value of the speed difference to obtain the curvature constraint reference torque;

[0115] The curvature constraint reference torque is superimposed with the posture deviation compensation factor to generate the driving torque control amount;

[0116] Perform reciprocal operation on the path curvature intensity index value to obtain the curvature adaptation speed reference value;

[0117] The velocity attenuation coefficient is obtained through the path gradient strength value, the real-time posture deviation direction is obtained through the detection data, and the real-time posture deviation direction is compensated based on the velocity attenuation coefficient to obtain the error correction velocity;

[0118] The curvature adaptation speed reference value and the error correction speed value are algebraically superimposed to obtain the superposition result, and the superposition result is time-integrated to obtain the current reference speed value. The current reference speed value is combined with the drive torque control value to generate the corresponding walking control signal.

[0119] Furthermore, the motor-driven walking method for the continuous rigid frame bridge construction further includes the following steps:

[0120] Step S103 , performing periodic binocular positioning shooting, obtaining multiple binocular shooting images, performing positioning recognition, determining multiple positioning fixed targets, and obtaining corresponding positioning target distances.

[0121] In an embodiment of the present invention, by obtaining a walking positioning cycle, and then performing periodic binocular positioning shooting at multiple shooting directions according to the walking positioning cycle, a plurality of binocular shooting images are obtained, and then based on a plurality of preset fixed target features, positioning object recognition is performed on the plurality of binocular shooting images to determine a plurality of positioning fixed targets, and then distance analysis is performed on the plurality of binocular shooting images to obtain the positioning target distances corresponding to the plurality of positioning fixed targets.

[0122] 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 the continuous rigid frame bridge, such as the top of the foundation pile, the top of the permanent support, the side of the pedestal, etc.

[0123] Specifically, Figure 4 A flow chart of binocular positioning shooting in the method provided by an embodiment of the present invention is shown.

[0124] Among them, in the preferred embodiment provided by the present invention, 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:

[0125] Step S1031, obtaining the walking positioning cycle;

[0126] Step S1032: performing periodic binocular positioning shooting according to the walking positioning cycle to obtain a plurality of binocular shooting images;

[0127] Step S1033, based on a plurality of preset fixed target features, positioning and identifying the plurality of binocular images to determine a plurality of positioned fixed targets;

[0128] Step S1034: performing distance analysis on the plurality of binocular captured images to obtain positioning target distances corresponding to the plurality of positioning fixed targets.

[0129] Specifically, performing distance analysis on the plurality of binocular images to obtain the positioning target distances corresponding to the plurality of positioning fixed targets is performed in the following steps:

[0130] Positioning and identifying the plurality of binocular images to obtain an HSV color space histogram of the area where the left and right eye feature points are located;

[0131] Calculate the intersection-and-union ratio of the HSV color space histograms in the areas where the left and right eye feature points are located to obtain the intersection-and-union ratio value, and normalize the intersection-and-union ratio value to generate the color dimension phase velocity value;

[0132] The LBP operator is used to extract the HSV color space histogram to obtain texture features;

[0133] Use texture features to construct texture feature vectors of corresponding areas of the left and right images, and obtain texture dimension similarity values;

[0134] Obtaining a curvature distribution histogram within a neighborhood of a feature point by analyzing a plurality of binocularly captured images, and further obtaining a curvature distribution difference;

[0135] The geometric dimension similarity value is generated by using the curvature distribution difference, and the adaptive weight coefficient is obtained by collecting external ambient light data. The adaptive weight coefficient of each dimension is adjusted according to the current light intensity to obtain a dynamic adaptive weight coefficient;

[0136] Multiply the color dimension phase velocity value, texture dimension similarity value, and geometric dimension similarity value by the corresponding dynamic adaptive weight coefficients and perform a weighted sum operation to obtain a preliminary confidence factor;

[0137] By taking binocular images, the gradient vectors of the left and right images are extracted at the coordinates of the feature points;

[0138] 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 through the absolute value of the difference between the gradient vectors of the left and right images in the horizontal and vertical directions, perform a square root operation on the gradient difference metric value, and obtain the square root value of the gradient difference;

[0139] Dividing the preliminary confidence factor by the square root of the gradient difference to obtain a normalized confidence weight value, performing distance analysis on the plurality of binocular images to obtain an original disparity value, and obtaining a confidence-disparity product matrix based on the normalized confidence weight value and the original disparity value;

[0140] The column sum of the matrix is obtained by multiplying the confidence-disparity product matrix, and the total confidence weight is obtained by calculating the sum of the normalized confidence weight values. The column sum is weighted averaged using the total confidence weight value to obtain the optimized disparity reference value. The integral value of the vibration energy is obtained through real-time feedback data, and the integral value of the vibration energy is linearly interpolated to obtain the environmental complexity coefficient.

[0141] The second-order derivative of disparity is obtained by optimizing the disparity reference value, and the product of the second-order derivative of disparity and the environmental complexity coefficient is superimposed on the original disparity value to obtain the final optimized disparity value;

[0142] Based on the final optimized disparity value, positioning target distances corresponding to the plurality of positioning fixed targets are obtained.

[0143] Furthermore, the motor-driven walking method for the continuous rigid frame bridge construction further includes the following steps:

[0144] Step S104 , performing positioning analysis based on the multiple positioning target distances to generate walking positioning data, and performing motor monitoring to obtain motor monitoring data.

[0145] In an embodiment of the present invention, by identifying the positioning target directions corresponding to multiple positioning fixed targets, and then performing positioning analysis on the target construction basket based on the multiple positioning target directions and the multiple corresponding positioning target distances, the walking positioning data of the target construction basket in three-dimensional space is generated, and the temperature of the motor of the target construction basket is monitored to obtain motor monitoring data.

[0146] Specifically, Figure 5 A flow chart of positioning analysis and motor monitoring in the method provided by an embodiment of the present invention is shown.

[0147] Among them, in the preferred embodiment provided by the present invention, the positioning analysis is performed based on the multiple positioning target distances to generate walking positioning data, and motor monitoring is performed. The acquisition of motor monitoring data specifically includes the following steps:

[0148] Step S1041, identifying positioning target directions corresponding to a plurality of positioning fixed targets;

[0149] Step S1042, performing positioning analysis on the target construction hanging basket according to the plurality of positioning target directions and the plurality of corresponding positioning target distances, and generating walking positioning data;

[0150] Step S1043: Perform motor monitoring on the target construction basket to obtain motor monitoring data.

[0151] Specifically, according to the multiple positioning target directions and the multiple corresponding positioning target distances, the target construction hanging basket is positioned and analyzed to generate walking positioning data. The specific steps are as follows:

[0152] Obtaining ranging values at adjacent moments according to the plurality of positioning target directions and the plurality of corresponding positioning target distances; obtaining an absolute value of a difference between the ranging values by comparing the ranging value at the current moment with the ranging value at the previous moment based on the ranging values at the adjacent moments;

[0153] Perform exponential function transformation on the absolute value of the distance measurement difference to generate the distance measurement mutation factor;

[0154] Obtaining a direction angle measurement sequence by identifying positioning target directions corresponding to a plurality of positioning fixed targets, and obtaining a standard deviation of the direction angle measurement values based on the direction angle measurement sequence;

[0155] Perform error function transformation on the inverse of the standard deviation of the direction angle measurement value to obtain the angle dispersion compensation value;

[0156] The motor current energy integral value is obtained through the motor monitoring data, and the fuselage vibration amplitude value is collected through the sensor. Based on the motor current energy integral value, the fuselage vibration amplitude value is superimposed to obtain the vibration interference intensity value;

[0157] The linear combination of the ranging mutation factor and the angle dispersion compensation value is divided by the square root of the vibration interference intensity value to obtain the target confidence weight;

[0158] Obtaining an azimuth parameter by identifying the positioning target directions corresponding to the plurality of positioning fixed targets, and obtaining an azimuth cosine value and an azimuth sine value based on the azimuth parameter;

[0159] Calculate the product of the azimuth cosine value and the current distance measurement value, and the product of the azimuth sine value and the current distance measurement value, multiply the two products to construct a two-dimensional plane coordinate, obtain the two-dimensional coordinates of the target based on the two-dimensional plane coordinates, and use the target confidence weight to perform a weighted summation on the two-dimensional coordinates of the target to obtain the weighted summed coordinates;

[0160] The confidence weight sum is obtained by the target confidence weight, and the weighted sum coordinate is divided by the confidence weight sum to obtain the weighted average coordinate;

[0161] The historical positioning coordinates and stability coefficients are obtained through historical data. The acceleration parameters of the historical positioning coordinates at adjacent moments are calculated to obtain the historical acceleration parameters. The second-order derivative characteristics of the historical acceleration parameters are extracted to obtain the trajectory smoothness index value.

[0162] The trajectory smoothness index value is compensated by using the stability coefficient to obtain the trajectory smoothness correction value, and the trajectory smoothness correction value is superimposed on the weighted average coordinate to obtain the coordinate after boundary constraint;

[0163] The walking positioning data is generated based on the coordinates after boundary constraints.

[0164] Furthermore, the motor-driven walking method for the continuous rigid frame bridge construction further includes the following steps:

[0165] Step S105 , performing abnormal analysis of motor-driven walking based on the walking positioning data and the motor monitoring data, and issuing an abnormality classification warning when there is an abnormal walking condition.

[0166] In an embodiment of the present invention, based on preset abnormality classification data, the walking positioning data and the motor monitoring data are compared and analyzed to determine whether there is an abnormal walking condition. When it is determined that there is an abnormal walking condition, the abnormality level is determined, and different abnormality processing is performed according to different abnormality levels. Specifically: when the abnormality level is level one, speed reduction control is performed; when the abnormality level is level two, emergency stop control is performed; when the abnormality level is level three, an audible and visual alarm is performed.

[0167] It can be understood that by performing offset analysis on 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 motor monitoring data, when the motor temperature is greater than 60°C, it is determined that there is an abnormal walking condition.

[0168] Specifically, Figure 6 A flowchart of abnormality classification warning in the method provided by an embodiment of the present invention is shown.

[0169] Among them, in the preferred embodiment provided by the present invention, the abnormal analysis of motor-driven walking based on the walking positioning data and the motor monitoring data, and the abnormal classification warning when there is an abnormal walking condition specifically include the following steps:

[0170] Step S1051: Based on the preset abnormality classification data, the walking positioning data and the motor monitoring data are compared and analyzed to determine whether there is an abnormal walking condition;

[0171] Step S1052, when there is an abnormal walking condition, determining the abnormality level;

[0172] Step S1053: If the abnormal level is level 1, speed reduction control is performed;

[0173] Step S1054: If the abnormality level is level 2, perform emergency stop control;

[0174] Step S1055: If the abnormality level is level three, an audible and visual alarm is performed.

[0175] Further, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0176] Among them, in another preferred embodiment provided by the present invention, a motor-driven walking system for a hanging basket for continuous rigid frame bridge construction includes:

[0177] The initial detection processing unit 101 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 elimination on the task path.

[0178] In an embodiment of the present invention, the initial detection processing unit 101 receives a hanging basket construction task, performs target planning for the hanging basket construction task, selects a target construction hanging basket, and then performs initialization detection on the drive motor, sensor, track anchor point and hydraulic brake of the target construction hanging basket, obtains initial detection data, analyzes the initial detection data, and determines whether there is an initialization abnormality. If there is no initialization abnormality, a task path is constructed according to the hanging basket construction task, and the walking speed, walking distance, acceleration and deceleration are planned to generate walking control parameters. According to the task path and the preset inspection distance, a flight inspection path is planned, and then an idle inspection drone is selected from multiple drones. According to the flight inspection path, the inspection drone is controlled to perform track detection and obstacle inspection, wherein track detection is the process of detecting the flatness of the track of the task path; obstacle inspection is the process of checking potential obstacles such as steel bar heads and concrete protrusions on the task path.

[0179] Specifically, Figure 8 FIG. 1 is a structural block diagram of the initial detection processing unit 101 in the system provided by an embodiment of the present invention.

[0180] In a preferred embodiment of the present invention, the initial detection processing unit 101 specifically includes:

[0181] The task receiving module 1011 is used to receive the construction task of the hanging basket and determine the target construction hanging basket;

[0182] An initialization detection module 1012 is used to perform initialization detection on the target construction basket on the drive motor, sensor, track anchor point and hydraulic brake, and determine whether there is any initialization abnormality;

[0183] The task processing module 1013 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 exception. The walking control parameters include walking speed, walking distance, acceleration and deceleration;

[0184] Inspection path planning module 1014, used to plan a flight inspection path according to the task path;

[0185] The track detection and obstacle removal module 1015 is used to select an inspection drone and perform track detection and obstacle removal on the mission path according to the flight inspection path.

[0186] Furthermore, the motor-driven walking system for the continuous rigid frame bridge construction further includes:

[0187] The motor travel control unit 102 is used to release the hydraulic brake and perform motor-driven travel control according to the three-dimensional path and the travel control parameters.

[0188] In an embodiment of the present invention, when both the track detection and obstacle inspection are passed, the motor travel control unit 102 releases the hydraulic brake of the target construction basket, and then generates a corresponding travel control signal based on the three-dimensional path and travel control parameters, and then performs motor-driven travel control on the target construction basket according to the travel control signal.

[0189] The walking positioning and recognition unit 103 is used to perform periodic binocular positioning shooting, obtain multiple binocular shooting images, perform positioning and recognition, determine multiple positioning fixed targets, and obtain corresponding positioning target distances.

[0190] In an embodiment of the present invention, the walking positioning identification unit 103 obtains a walking positioning cycle, and then performs periodic binocular positioning shooting at multiple shooting directions according to the walking positioning cycle to obtain multiple binocular shooting images. Then, based on multiple preset fixed target features, the multiple binocular shooting images are used to identify positioning objects, determine multiple positioning fixed targets, and then perform distance analysis on the multiple binocular shooting images to obtain the positioning target distances corresponding to the multiple positioning fixed targets.

[0191] Specifically, Figure 9 FIG. 1 shows a structural block diagram of the walking positioning recognition unit 103 in the system provided by an embodiment of the present invention.

[0192] In a preferred embodiment of the present invention, the walking positioning identification unit 103 specifically includes:

[0193] The cycle acquisition module 1031 is used to obtain the walking positioning cycle;

[0194] The binocular positioning and shooting module 1032 is used to perform periodic binocular positioning and shooting according to the walking positioning cycle to obtain multiple binocular shooting images;

[0195] A positioning and recognition module 1033 is configured to perform positioning and recognition on the plurality of binocular images based on a plurality of preset fixed target features, and determine a plurality of positioned fixed targets;

[0196] The distance analysis module 1034 is configured to perform distance analysis on the plurality of binocular images to obtain positioning target distances corresponding to the plurality of positioning fixed targets.

[0197] Furthermore, the motor-driven walking system for the continuous rigid frame bridge construction further includes:

[0198] The positioning analysis and motor monitoring unit 104 is used to perform positioning analysis based on the multiple positioning target distances to generate walking positioning data, and perform motor monitoring to obtain motor monitoring data.

[0199] In an embodiment of the present invention, the positioning analysis motor monitoring unit 104 identifies the positioning target directions corresponding to multiple positioning fixed targets, and then performs positioning analysis on the target construction basket based on the multiple positioning target directions and the multiple corresponding positioning target distances, generates the walking positioning data of the target construction basket in three-dimensional space, and monitors the temperature of the motor of the target construction basket to obtain motor monitoring data.

[0200] The abnormality classification warning unit 105 is used to perform abnormality analysis of the motor-driven walking according to the walking positioning data and the motor monitoring data, and to issue an abnormality classification warning when there is an abnormal walking condition.

[0201] In an embodiment of the present invention, the abnormality grading warning unit 105 compares and analyzes the walking positioning data and the motor monitoring data based on the preset abnormality grading data to determine whether there is an abnormal walking condition, and when it is determined that there is an abnormal walking condition, determines the abnormality level, and performs different abnormality processing according to different abnormality levels. Specifically: when the abnormality level is level one, speed reduction control is performed; when the abnormality level is level two, emergency stop control is performed; when the abnormality level is level three, an audible and visual alarm is performed.

[0202] Specifically, Figure 10 It shows a structural block diagram of the abnormality classification warning unit 105 in the system provided by an embodiment of the present invention.

[0203] In a preferred embodiment of the present invention, the abnormality classification warning unit 105 specifically includes:

[0204] The comparison and analysis module 1051 is used to compare and analyze the walking positioning data and the motor monitoring data based on the preset abnormality classification data to determine whether there is an abnormal walking condition;

[0205] The abnormality level determination module 1052 is used to determine the abnormality level when there is an abnormal walking condition;

[0206] A speed reduction control module 1053, configured to perform speed reduction control when the abnormality level is level one;

[0207] An emergency stop control module 1054 is configured to perform emergency stop control when the abnormality level is level 2;

[0208] The sound and light alarm module 1055 is used to make a sound and light alarm when the abnormality level is level three.

[0209] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0210] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may 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 (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0211] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0212] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0213] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A motor-driven walking method for a hanging basket in the construction of a continuous rigid frame bridge, characterized in that: The method specifically comprises the following steps: Receive the construction task of the hanging basket, perform initialization detection, build the task path, travel control parameters and three-dimensional path, and perform track detection and obstacle removal on the task path; releasing the hydraulic brake and performing 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 based on the multiple positioning target distances to generate walking positioning data, and perform motor monitoring to obtain motor monitoring data; Perform abnormal analysis of motor-driven walking based on the walking positioning data and the motor monitoring data, and issue an abnormality classification warning when there is an abnormal walking condition; The steps of receiving the construction task of the hanging basket, performing initialization detection, constructing the task path, walking control parameters and three-dimensional path, and performing track detection and obstacle troubleshooting on the task path specifically include the following steps: Receive construction tasks for hanging baskets and determine target construction hanging baskets; Performing initialization detection on the target construction hanging basket for the drive motor, sensor, track anchor point and hydraulic brake, and determining whether there is any initialization abnormality; When there is no initialization exception, construct a task path, walking control parameters and a three-dimensional path according to the hanging basket construction task, wherein the walking control parameters include walking speed, walking distance, acceleration and deceleration; Plan a flight inspection route based on the mission path; Select an inspection drone and perform track detection and obstacle inspection on the mission path according to the flight inspection path.

2. The motor-driven walking method for the hanging basket in the construction of a continuous rigid frame bridge according to claim 1 is characterized in that: When there is no initialization exception, the task path, walking control parameters and three-dimensional path are constructed according to the hanging basket construction task. The specific steps for obtaining the walking control parameters are as follows: Initialize and test the driving motor of the target construction basket to obtain the vibration modal data of the basket structure, the rated maximum load value and the real-time load value; The first three-order vibration evaluation rates and 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 using the first three-order vibration evaluation rates and 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; The acceleration time threshold is obtained by constructing the task path, and the acceleration time threshold is used as the exponential coefficient. The quadratic correlation term of the load sensitivity factor and the real-time load value is superimposed to generate a dynamic exponential coefficient. Initialize the track anchor point to obtain the track anchor point strain data, and collect external meteorological data to obtain the ambient temperature and humidity values; The equivalent friction coefficient of the contact surface is obtained from the track anchor point strain data, and the equivalent friction coefficient of the contact surface is corrected by superimposing it with the ambient temperature and humidity values to obtain the friction compensation factor. The maximum speed value is obtained from the hanging basket construction task, and the maximum speed value is corrected using the dynamic exponential coefficient to obtain the corrected maximum speed value; the corrected maximum speed value is input into the exponential function to generate a speed growth curve; when the accumulated time reaches the acceleration time threshold, it is switched to the input parameter of the next stage to generate the acceleration stage speed sequence; The cruising speed is obtained by performing track detection and obstacle screening on the mission path, and effective obstacle targets are screened out through real-time detection data of the drone to obtain the minimum obstacle spacing; The cruising speed is processed using the friction compensation factor to obtain the compensated cruising speed; the minimum obstacle distance is divided by the compensated cruising speed to generate the braking response time window; The total mission time is determined by the mission path. Based on the braking response time window and the cruising speed, a hyperbolic function is used to construct a speed decay curve. When the accumulated time approaches the total mission time, it is switched to the input parameters of the next stage to generate the deceleration phase speed sequence. The acceleration phase speed sequence, the cruising speed sequence, and the deceleration phase speed sequence are spliced in chronological order to generate a complete walking speed curve, and the walking control parameters are obtained through the complete walking speed curve.

3. The motor-driven walking method for the hanging basket in the construction of a continuous rigid frame bridge according to claim 2 is characterized in that: The releasing of 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: releasing the hydraulic brake on the target construction basket; generating a corresponding walking control signal according to the three-dimensional path and the walking control parameters; According to the walking control signal, the target construction hanging basket is controlled to walk by a motor.

4. The motor-driven walking method for the hanging basket in the construction of a continuous rigid frame bridge according to claim 3 is characterized in that: Generate a corresponding walking control signal according to the three-dimensional path and the walking control parameters. The specific steps are as follows: A three-dimensional path coordinate sequence is obtained through the three-dimensional path. The direction change rate between adjacent path points is calculated based on the three-dimensional path coordinate sequence. The direction change rates between multiple adjacent path points are sliding averaged and quantified to obtain the path curvature intensity index value; The curvature conversion coefficient is obtained through construction specifications, the path curvature intensity index value is multiplied by the curvature conversion coefficient to obtain the product result, and the product result is processed by the inverse hyperbolic tangent function to obtain the curvature-velocity mapping benchmark value; Obtaining each real-time posture deviation value through detection data, calculating the second-order time derivative of each real-time posture deviation value, and obtaining a posture deviation compensation factor based on the change trend of the second-order time derivative of each real-time posture deviation value; Based on the three-dimensional path coordinate sequence, the spatial position gradient of the two points before and after the current path point is calculated to obtain a spatial position gradient vector, and the spatial position gradient vector is normalized to obtain a path gradient intensity value; The instantaneous speed value is obtained by detecting the data, and the difference between the square of the maximum speed value and the square of the instantaneous speed value is calculated to obtain the speed safety margin parameter; Perform a square root operation on the speed safety margin parameter to obtain the effective value of the speed difference, and divide the curvature-speed mapping reference value by the effective value of the speed difference to obtain the curvature constraint reference torque; The curvature constraint reference torque is superimposed with the posture deviation compensation factor to generate the driving torque control amount; Perform a reciprocal operation on the path curvature intensity index value to obtain the curvature adaptation speed reference value; The velocity attenuation coefficient is obtained through the path gradient strength value, the real-time posture deviation direction is obtained through the detection data, and the real-time posture deviation direction is compensated based on the velocity attenuation coefficient to obtain the error correction velocity; The curvature adaptation speed reference value and the error correction speed value are algebraically superimposed to obtain the superposition result, and the superposition result is time-integrated to obtain the current reference speed value. The current reference speed value is combined with the drive torque control value to generate the corresponding walking control signal.

5. The motor-driven traveling method for the hanging basket in the construction of a continuous rigid frame bridge according to claim 4 is characterized in that: The process of performing periodic binocular positioning shooting, acquiring multiple binocular shooting images, performing positioning recognition, determining multiple positioning fixed targets, and obtaining corresponding positioning target distances specifically includes the following steps: Get walking positioning cycle; Performing periodic binocular positioning shooting according to the walking positioning cycle to obtain multiple binocular shooting images; Based on a plurality of preset fixed target features, positioning and identifying the plurality of binocular images are performed to determine a plurality of positioned fixed targets; Perform distance analysis on the multiple binocular images to obtain positioning target distances corresponding to the multiple positioning fixed targets.

6. The motor-driven traveling method for the hanging basket in the construction of a continuous rigid frame bridge according to claim 5 is characterized in that: Performing distance analysis on the multiple binocular images to obtain the positioning target distances corresponding to the multiple positioning fixed targets, the specific steps are as follows: Positioning and identifying the plurality of binocular images to obtain an HSV color space histogram of the area where the left and right eye feature points are located; Calculate the intersection-and-union ratio of the HSV color space histograms in the areas where the left and right eye feature points are located to obtain the intersection-and-union ratio value, and normalize the intersection-and-union ratio value to generate the color dimension phase velocity value; The LBP operator is used to extract the HSV color space histogram to obtain texture features; Use texture features to construct texture feature vectors of corresponding areas of the left and right images, and obtain texture dimension similarity values; Obtaining a curvature distribution histogram within a neighborhood of a feature point by analyzing a plurality of binocularly captured images, and further obtaining a curvature distribution difference; The geometric dimension similarity value is generated by using the curvature distribution difference, and the adaptive weight coefficient is obtained by collecting external ambient light data. The adaptive weight coefficient of each dimension is adjusted according to the current light intensity to obtain a dynamic adaptive weight coefficient; Multiply the color dimension phase velocity value, texture dimension similarity value, and geometric dimension similarity value by the corresponding dynamic adaptive weight coefficients and perform a weighted sum operation to obtain a preliminary confidence factor; By taking binocular images, the gradient vectors of the left and right images are extracted at the coordinates of the feature points; 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 through the absolute value of the difference between the gradient vectors of the left and right images in the horizontal and vertical directions, perform a square root operation on the gradient difference metric value, and obtain the square root value of the gradient difference; Dividing the preliminary confidence factor by the square root of the gradient difference to obtain a normalized confidence weight value, performing distance analysis on the plurality of binocular images to obtain an original disparity value, and obtaining a confidence-disparity product matrix based on the normalized confidence weight value and the original disparity value; The column sum of the matrix is obtained by multiplying the confidence-disparity product matrix, and the total confidence weight is obtained by calculating the sum of the normalized confidence weight values. The column sum is weighted averaged using the total confidence weight value to obtain the optimized disparity reference value. The integral value of the vibration energy is obtained through real-time feedback data, and the integral value of the vibration energy is linearly interpolated to obtain the environmental complexity coefficient. The second-order derivative of disparity is obtained by optimizing the disparity reference value, and the product of the second-order derivative of 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, positioning target distances corresponding to the plurality of positioning fixed targets are obtained.

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

8. The motor-driven traveling method for the hanging basket in the construction of a continuous rigid frame bridge according to claim 7 is characterized in that: According to the plurality of positioning target directions and the plurality of corresponding positioning target distances, positioning analysis is performed on the target construction hanging basket to generate walking positioning data. The specific steps are as follows: Obtaining ranging values at adjacent moments according to the plurality of positioning target directions and the plurality of corresponding positioning target distances; obtaining an absolute value of a difference between the ranging values by comparing the ranging value at the current moment with the ranging value at the previous moment based on the ranging values at the adjacent moments; Perform exponential function transformation on the absolute value of the distance measurement difference to generate the distance measurement mutation factor; Obtaining a direction angle measurement sequence by identifying positioning target directions corresponding to a plurality of positioning fixed targets, and obtaining a standard deviation of the direction angle measurement values based on the direction angle measurement sequence; Perform error function transformation on the inverse of the standard deviation of the direction angle measurement value to obtain the angle dispersion compensation value; The motor current energy integral value is obtained through the motor monitoring data, and the fuselage vibration amplitude value is collected through the sensor. Based on the motor current energy integral value, the fuselage vibration amplitude value is superimposed to obtain the vibration interference intensity value; The linear combination of the ranging mutation factor and the angle dispersion compensation value is divided by the square root of the vibration interference intensity value to obtain the target confidence weight; Obtaining an azimuth parameter by identifying the positioning target directions corresponding to the plurality of positioning fixed targets, and obtaining an azimuth cosine value and an azimuth sine value based on the azimuth parameter; Calculate the product of the azimuth cosine value and the current distance measurement value, and the product of the azimuth sine value and the current distance measurement value, multiply the two products to construct a two-dimensional plane coordinate, obtain the two-dimensional coordinates of the target based on the two-dimensional plane coordinates, and use the target confidence weight to perform a weighted summation on the two-dimensional coordinates of the target to obtain the weighted summed coordinates; The confidence weight sum is obtained by the target confidence weight, and the weighted sum coordinate is divided by the confidence weight sum to obtain the weighted average coordinate; The historical positioning coordinates and stability coefficients are obtained through historical data. The acceleration parameters of the historical positioning coordinates at adjacent moments are calculated to obtain the historical acceleration parameters. The second-order derivative characteristics of the historical acceleration parameters are extracted to obtain the trajectory smoothness index value. The trajectory smoothness index value is compensated by using the stability coefficient to obtain the trajectory smoothness correction value, and the trajectory smoothness correction value is superimposed on the weighted average coordinate to obtain the coordinate after boundary constraint; The walking positioning data is generated based on the coordinates after boundary constraints.

9. The motor-driven traveling method for the hanging basket in the construction of a continuous rigid frame bridge according to claim 8, characterized in that: The method of performing abnormal analysis of motor-driven walking according to the walking positioning data and the motor monitoring data and performing abnormal classification warning when there is an abnormal walking condition specifically includes the following steps: Based on the preset abnormality classification data, the walking positioning data and the motor monitoring data are compared and analyzed to determine whether there is an abnormal walking condition; When there is a walking abnormality, determining the abnormality level; If the abnormal level is level one, speed reduction control is performed; If the abnormal level is level 2, emergency stop control is performed; If the abnormality level is level three, an audible and visual alarm is triggered.

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

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