Bridge pier column crack automatic detection system based on wall-climbing robot

Abstract

The invention discloses a bridge pier stud crack automatic detection system based on a wall-climbing robot, and relates to the technical field of crack detection, and the system realizes closed-loop operation from accurate crack detection to intelligent repair through a crack detection module and a repair material intelligent spraying detection module. The crack detection module uses a multispectral imaging device, a thermal imaging camera and a laser scanning technology, can collect a multispectral difference value G (x, y), surface temperature distribution T (x, y) and a depth matrix D (x, y) of a crack area, extracts a crack boundary B (x, y) through a high-precision algorithm, and calculates a crack volume Vc. And the repairing material intelligent spraying detection module dynamically calculates the volume Vm of the repairing material according to the volume of the crack, and the SLAM technology is utilized to optimize the spraying path and parameters, so that uniform coverage and efficient utilization of the repairing material are ensured. The system greatly improves the precision of crack detection and repair, and avoids material waste and repair omission.

Description

Technical Field

[0001] The invention relates to the technical field of crack detection, and in particular to an automatic crack detection system for bridge piers based on a wall-climbing robot. Background Art

[0002] Bridge engineering is a vital branch of civil engineering, and its core goal is to ensure the long-term safety and stability of bridge structures. In the maintenance process of bridge engineering, bridge piers are key components that support bridge loads, and their structural state directly affects the service life and safety of bridges. However, seaside bridge piers are exposed to complex natural environments (such as high humidity, salt spray erosion, strong winds, etc.) for a long time, and are prone to structural defects such as cracks. With the development of intelligent robot technology, automatic detection and repair of bridge pier cracks based on wall-climbing robots has gradually become a research hotspot in the field of bridge engineering. Among them, the adsorption adjustment mechanism of dynamic environmental adaptation provides an important guarantee for the stable operation of wall-climbing robots in complex environments.

[0003] At present, the traditional methods of crack detection and repair of bridge piers mainly rely on manual operations, including high-altitude operations or diving detection. These methods are inefficient, costly, and have great safety risks. In addition, the application of automated detection equipment in complex environments is also insufficient: many traditional wall-climbing equipment cannot maintain stable adsorption capacity under high wind speed or slippery surface conditions, which can easily cause the detection equipment to fall off the surface of the bridge pier; during the detection and repair process, the operation range and accuracy are greatly limited due to the inability to adjust the adsorption force and working posture according to the real-time environment, which affects the efficiency and reliability of automated detection and repair. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an automatic detection system for cracks in bridge piers based on a wall-climbing robot, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: including an operating environment detection module, a detection posture adjustment module, a crack detection module, a repair material intelligent spraying detection module and a repair analysis module;

[0006] The working environment detection module is used to install a detection equipment group on the wall-climbing robot, collect working environment data on the surface of the seaside bridge pier, and transmit the collected working environment data to the ground control system of the wall-climbing robot, and then perform preprocessing and feature extraction on the working environment data to obtain an environmental feature set;

[0007] The detection posture adjustment module monitors the actual pressure FPac of the wall-climbing robot's adsorption point and the ideal required pressure FPid to perform deviation assessment. When the adsorption state is assessed to be abnormal, the adsorption force coefficient FP is calculated and output based on the environmental feature set, and the number of adsorption points N is calculated and output based on the adsorption force coefficient FP.

[0008] The crack detection module collects crack data of the seaside bridge piers through imaging equipment, extracts crack boundaries B(x, y) through image processing algorithms to identify and locate cracks in the seaside bridge piers, collects depth distribution data of the crack area through integrated scanning technology after locating, and calculates and outputs the total crack volume Vc;

[0009] The repair material intelligent spraying detection module calculates the repair material volume Vm in real time through the total crack volume Vc, monitors the repair effect through imaging, and outputs the repair integrity error Er;

[0010] The repair analysis module performs repair error evaluation through the output result of the repair integrity error Er, analyzes the repair effects before and after repair, and generates warning information based on the repair error evaluation result.

[0011] Preferably, the working environment detection module includes a data acquisition unit, a preprocessing unit and a feature extraction unit;

[0012] The data acquisition unit is used to install a detection equipment group on the wall-climbing robot to collect real-time operating environment data of the wall-climbing robot when it is operating on the surface of the pier of the seaside bridge;

[0013] The detection equipment group includes an ultrasonic wind speed sensor, a laser roughness meter, an infrared humidity sensor, a conductivity sensor, a barometer and a strain gauge pressure sensor;

[0014] The working environment data include wind speed v, wind direction angle θ, surface roughness coefficient Cr, surface humidity H, salt concentration S, pressure change rate EPR and actual pressure FPac at the adsorption point;

[0015] The preprocessing unit is used to integrate the collected working environment data into the central processor of the wall-climbing robot, connect the ground control system with the central processor of the wall-climbing robot through a wireless network, and transmit the real-time collected working environment data to the ground control system;

[0016] The collected operating environment data are preprocessed in the ground control system, and the preprocessing method includes data cleaning and normalization processing to unify the dimensions of the operating environment data.

[0017] Preferably, the feature extraction unit performs feature extraction on the wind speed v, wind direction angle θ, surface roughness coefficient Cr, surface humidity H and salinity concentration S in the working environment data after preprocessing the working environment data to obtain an environmental feature set;

[0018] The environmental feature set includes wind shear force WF, surface attachment factor SAF, pressure change rate EPR and actual pressure FPac at the adsorption point;

[0019] The wind shear force WF is calculated and extracted by extracting the wind speed v and wind direction angle θ from the working environment data. The specific algorithm formula is: Where ρ represents the air density, the initial setting value, and cos represents the cosine function;

[0020] The surface attachment factor SAF is calculated and extracted by extracting the surface roughness coefficient Cr, surface humidity H and salt concentration S. The specific algorithm formula is: Wherein, k represents the humidity sensitivity coefficient, and e represents the exponential function.

[0021] Preferably, the detection posture adjustment module includes an adsorption force analysis unit, an adsorption force adjustment unit and an adsorption point distribution optimization unit;

[0022] The adsorption force analysis unit extracts the actual pressure FPac of the adsorption point in the environmental feature set and the ideal required pressure FPid initially set by the user, performs deviation evaluation, and analyzes the adsorption force of the current wall-climbing robot during operation. The specific evaluation contents are as follows;

[0023] When the actual pressure at the adsorption point FPac minus the ideal required pressure FPid ≥ F1, the adsorption state is determined to be normal and no adjustment is required;

[0024] When the actual pressure at the adsorption point FPac minus the ideal required pressure FPid < F1, the adsorption state is judged to be abnormal and needs to be adjusted;

[0025] Among them, F1 represents the allowable deviation value, which is initially set by the user.

[0026] Preferably, the adsorption force adjustment unit is used to be triggered when it is determined that the adsorption state is abnormal, and calculates the output adsorption force coefficient FP by extracting the wind shear force WF and the surface attachment factor SAF in the environmental feature set combined with the pressure change rate EPR, and dynamically adjusts the rotor motor power to optimize the adsorption pressure of the wall-climbing robot;

[0027] The adsorption coefficient FP is calculated and output by the following algorithm formula:

[0028]

[0029] In the formula, γ represents the adjustment factor, η represents the adhesion efficiency coefficient, δ represents the dynamic environmental impact coefficient, and sin(φ) represents the surface inclination of the seaside bridge pier. represents the inverse term of the surface attachment factor;

[0030] The adsorption point distribution optimization unit dynamically adjusts the adsorption force of each adsorption point of the wall-climbing robot through the adsorption force coefficient FP, and then calculates and outputs the number of adsorption points N in combination with the center of gravity and the working position of the wall-climbing robot, and dynamically distributes the adsorption points;

[0031] The number of adsorption points N is calculated and outputted by the following algorithm formula;

[0032]

[0033] Where Ftotal represents the load force of the wall-climbing robot, m represents the total number of adsorption points, and FP i represents the adjusted adsorption coefficient of the i-th adsorption point, d i Represents the weight correction value generated by the distance of the i-th adsorption point relative to the center of gravity of the robot.

[0034] Preferably, the crack detection module includes a crack identification unit and a crack volume analysis unit;

[0035] The crack identification unit adjusts the adsorption force of the wall-climbing robot, and then starts the wall-climbing robot to move the seaside bridge pier from bottom to top. During the moving process, the crack data on the surface of the seaside bridge pier is collected through the imaging device installed on the wall-climbing robot.

[0036] The imaging devices include visible light cameras, thermal imaging cameras and multi-spectral imaging devices;

[0037] The crack data includes multi-spectral difference value G(x, y), surface temperature distribution T(x, y) and crack area image;

[0038] The pixel intensity gradient ▽I(x, y) of the crack area image is calculated using an edge detection algorithm, and then the characteristic response of the crack area is calculated based on the multi-spectral difference value G(x, y) and the surface temperature distribution T(x, y), forming a crack boundary B(x, y) to identify and locate the crack position of the seaside bridge pier. After the crack position is identified, the wall-climbing robot automatically stops moving.

[0039] The crack boundary B(x, y) is calculated and outputted by the following algorithm formula;

[0040] B(x, y)=argmax[▽I(x, y)+k·G(x, y)·cos(λ·T(x, y))];

[0041] Where argmax represents the upper limit function, (x, y) represents the coordinates of the seaside bridge pier in the two-dimensional image, k represents the multispectral feature enhancement factor, and λ represents the temperature difference sensitivity coefficient.

[0042] Preferably, the crack volume analysis unit is used to identify and locate the cracks in the seaside bridge pier, capture the depth matrix D (x, y) of the crack area through the laser scanning technology of the wall climbing robot, use the multi-time image reconstruction technology to supplement the three-dimensional data of the crack area, and then calculate the total crack volume Vc through integration and discrete summation;

[0043] The total crack volume Vc is calculated and outputted by the following algorithm formula;

[0044]

[0045] In the formula, Represents the crack boundary range, which is provided by the crack boundary B(x, y), dx represents a small change in the x direction, dy represents a small change in the y direction, and dxdy represents the area of ​​the small matrix region.

[0046] Preferably, the repair material intelligent spraying detection module includes a repair material volume analysis unit and a repair integrity analysis unit;

[0047] The repair material volume analysis unit calculates and outputs the repair material volume Vm based on the total crack volume Vc, adjusts the output parameters of the spraying system in real time, and then dynamically updates the spraying trajectory and optimizes the spraying path based on the three-dimensional data of the crack area using SLAM synchronous positioning and map building technology;

[0048] The repair material volume Vm is calculated and outputted by the following algorithm formula;

[0049] Vm=π·Vc·R+α·sin(ξ)·ΔP;

[0050] Wherein, π represents the circumference of a circle and takes the value of 3.14; R represents the coverage ratio of the repair material; α represents the compensation coefficient of the additional material; sin(ξ) represents the spraying angle correction term; ξ represents the angle between the crack surface and the spraying equipment; and △P represents the spraying pressure difference.

[0051] Preferably, the repair integrity analysis unit scans the repair area with a thermal imaging camera after the crack is repaired, records the actual temperature distribution Tr(x, y) after the repair, and calculates the difference with the ideal temperature to obtain the repair integrity error Er;

[0052] The repair integrity error Er is calculated and outputted by the following algorithm formula:

[0053]

[0054] Where A represents the area of ​​the crack, and Ts(x, y) represents the ideal temperature of the repair area.

[0055] Preferably, the specific repair error evaluation content of the repair analysis module is as follows:

[0056] When the repair integrity error Er>0, it means that the repair is incomplete, and a repair adjustment warning is generated, prompting engineers to perform repair operations through the ground control system;

[0057] When the repair integrity error Er≤0, it means that the repair quality is qualified and no warning needs to be generated.

[0058] The present invention provides an automatic detection system for cracks in bridge piers based on a wall-climbing robot. It has the following beneficial effects:

[0059] (1) The system comprehensively improves the adaptability of the wall-climbing robot to the complex seaside bridge pier working environment through the working environment detection module and the detection posture adjustment module. The working environment detection module uses ultrasonic wind speed sensors, laser roughness meters, infrared humidity sensors and other equipment to collect environmental data such as wind speed, humidity, salt concentration, surface roughness, etc. in real time, and extracts feature sets after normalizing these data. The detection posture adjustment module dynamically calculates the adsorption coefficient FP and the number of adsorption points N based on the feature set, and adjusts the adsorption pressure and point distribution in real time. Compared with the traditional fixed adsorption mode, this system can significantly reduce the adsorption instability caused by slippery surface and strong wind interference, and effectively improve the robot's operating reliability and safety under high humidity, high salt fog and complex surface conditions.

[0060] (2) The system realizes a closed-loop operation from precise crack detection to intelligent repair through the crack detection module and the repair material intelligent spray detection module. The crack detection module uses multispectral imaging equipment, thermal imaging cameras and laser scanning technology to collect the multispectral difference value G(x, y), surface temperature distribution T(x, y) and depth matrix D(x, y) of the crack area, extract the crack boundary B(x, y) and calculate the crack volume Vc through a high-precision algorithm. The repair material intelligent spray detection module dynamically calculates the repair material volume Vm based on the crack volume, and uses SLAM technology to optimize the spray path and parameters to ensure uniform coverage and efficient use of the repair material. Compared with the traditional method of manual detection and fixed spray path, this system greatly improves the accuracy of crack detection and repair, avoids material waste and missed repairs, and ensures the long-term stability of the bridge structure.

[0061] (3) The system realizes real-time monitoring and closed-loop optimization of the repair quality through the repair integrity analysis module. The repair integrity analysis module uses a thermal imaging camera to scan the crack area after repair, collects the actual temperature distribution Tr(x, y), and compares it with the ideal temperature Ts(x, y) of the repair area to calculate the repair integrity error Er. When Er>0, the system automatically generates a warning for spraying adjustment and prompts engineers to perform repair operations through the ground control system; when Er≤0, the repair quality is determined to be qualified and no further operation is required. Through this mechanism, the system effectively solves the drawbacks of traditional repair operations that cannot verify quality or repeat repairs, ensures that the crack area is completely repaired, greatly improves the durability and safety of bridge piers, and provides intelligent support for bridge maintenance and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a schematic flow chart of an automatic bridge pier crack detection system based on a wall-climbing robot. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Example 1

[0065] See also Figure 1 The present invention provides an automatic crack detection system for bridge piers based on a wall-climbing robot. To achieve the above purpose, the present invention is implemented through the following technical solutions: including an operating environment detection module, a detection posture adjustment module, a crack detection module, a repair material intelligent spraying detection module and a repair analysis module;

[0066] The working environment detection module is used to install a detection equipment group on the wall-climbing robot, collect working environment data on the surface of the seaside bridge pier, and transmit the collected working environment data to the ground control system of the wall-climbing robot, and then perform preprocessing and feature extraction on the working environment data to obtain an environmental feature set;

[0067] The detection posture adjustment module monitors the deviation between the actual pressure FPac of the wall-climbing robot's adsorption point and the ideal required pressure FPid. When the adsorption state is abnormal, the adsorption force coefficient FP is calculated and output based on the environmental feature set, and the number of adsorption points N is calculated and output based on the adsorption force coefficient FP.

[0068] The crack detection module collects crack data of seaside bridge piers through imaging equipment, and extracts crack boundaries B(x, y) through image processing algorithms to identify and locate cracks in seaside bridge piers. After locating, the depth distribution data of the crack area is collected through integrated scanning technology, and the total crack volume Vc is calculated and output;

[0069] The intelligent spraying detection module of the repair material calculates the repair material volume Vm in real time through the total crack volume Vc, detects the repair effect through imaging, and outputs the repair integrity error Er;

[0070] The patch analysis module evaluates the patch error based on the output of the patch integrity error Er, analyzes the patch effects before and after the patch, and generates warning information based on the patch error evaluation results.

[0071] In this embodiment, the system uses a variety of sensor devices to collect environmental data on the surface of the seaside bridge pier in real time through the operating environment detection module, and forms an environmental feature set through feature extraction, providing an accurate basis for subsequent posture adjustment and operation decision-making. The detection posture adjustment module dynamically adjusts the adsorption coefficient FP and the number of adsorption points N according to the deviation between the actual pressure FPac and the ideal pressure FPid of the adsorption point monitored in real time, ensuring the stable adsorption and safe operation of the robot in a complex environment. The crack detection module uses imaging equipment combined with image processing algorithms to extract the crack boundary B(x, y) with high precision, and calculates the total volume of the crack Vc through deep scanning to achieve accurate identification and three-dimensional modeling of the crack. The intelligent spraying detection module of the repair material adjusts the volume Vm and spraying path of the repair material in real time based on the crack volume Vc to ensure that the material evenly covers the crack area. At the same time, it monitors the repair effect through imaging equipment and outputs the repair integrity error Er. The repair analysis module generates a repair warning based on the evaluation results of Er, prompting the spraying adjustment or confirming that the repair is qualified. Through the organic integration of the above five modules, the system realizes the full process automation of bridge pier crack detection and repair, which has the following significant advantages: first, it significantly improves the adaptability of robots in complex working environments, ensuring the stability and safety of operations; second, it enhances the accuracy of crack detection and repair, avoiding omissions or material waste; third, through closed-loop control of repair quality, it effectively guarantees the reliability and durability of repair. Through the application of this system, the efficiency and quality of bridge maintenance work have been greatly improved, the safety risks of manual operation have been reduced, and intelligent protection has been provided for the long-term safe operation of bridges.

[0072] Example 2

[0073] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the working environment detection module includes a data acquisition unit, a preprocessing unit and a feature extraction unit;

[0074] The data acquisition unit is used to install a detection equipment group on the wall-climbing robot to collect real-time operating environment data of the wall-climbing robot when it is operating on the surface of the seaside bridge pier;

[0075] The testing equipment group includes ultrasonic wind speed sensors, laser roughness meters, infrared humidity sensors, conductivity sensors, barometers, and strain gauge pressure sensors;

[0076] The working environment data include wind speed v, wind direction angle θ, surface roughness coefficient Cr, surface humidity H, salt concentration S, pressure change rate EPR and actual pressure FPac at the adsorption point;

[0077] Wind speed v and wind direction angle θ are measured by installing an ultrasonic wind speed sensor on the top of the wall-climbing robot and using the change in the propagation speed of ultrasonic waves in the air;

[0078] The surface roughness coefficient Cr is obtained by scanning the bridge pier surface with a laser roughness measuring instrument and analyzing the microscopic geometric characteristics of the surface;

[0079] The surface humidity H, salt concentration S, pressure change rate EPR and actual pressure FPac at the adsorption point are directly collected and obtained through infrared humidity sensor, conductivity sensor, barometer and strain gauge pressure sensor respectively;

[0080] The preprocessing unit is used to integrate the collected working environment data into the central processor of the wall-climbing robot, connect the ground control system with the central processor of the wall-climbing robot through a wireless network, and transmit the real-time collected working environment data to the ground control system;

[0081] The collected operating environment data are preprocessed in the ground control system. The preprocessing methods include data cleaning and normalization to unify the dimensions of the operating environment data.

[0082] After preprocessing the working environment data, the feature extraction unit extracts features of the wind speed v, wind direction angle θ, surface roughness coefficient Cr, surface humidity H and salt concentration S in the working environment data to obtain an environmental feature set;

[0083] The environmental feature set includes wind shear force WF, surface attachment factor SAF, pressure change rate EPR and actual pressure FPac at the adsorption point;

[0084] The wind shear force WF is calculated and extracted by extracting the wind speed v and wind direction angle θ from the working environment data. The specific algorithm formula is: Where ρ represents the air density, the initial setting value, and cos represents the cosine function;

[0085] The surface adhesion factor SAF is calculated and output by extracting the surface roughness coefficient Cr, surface humidity H and salt concentration S. The specific algorithm formula is: Wherein, k represents the humidity sensitivity coefficient, and e represents the exponential function.

[0086] In this embodiment, the system is composed of a data acquisition unit, a preprocessing unit and a feature extraction unit through an operating environment detection module, which fully realizes the collection, processing and feature extraction of complex environmental parameters. The data acquisition unit collects the operating environment data on the surface of the seaside bridge pier through the detection equipment group. These data are transmitted to the ground control system through a wireless network. The preprocessing unit cleans and normalizes the data, and after unifying the dimensions, ensures the standardization and accuracy of the data. The feature extraction unit forms a complete set of environmental features based on the environmental data, providing an accurate basis for the adsorption force adjustment and task execution of the wall-climbing robot. Through the design and implementation of this module, the system achieves the following goals and effects: first, it realizes multi-dimensional, real-time and high-precision monitoring of complex environments to ensure the comprehensiveness and reliability of the data; second, the feature extraction unit converts the complex original environmental data into core feature parameters, which improves the efficiency of subsequent analysis and decision-making. Based on the support of this module, the wall-climbing robot can operate stably in a seaside environment with strong wind speed, complex surface roughness, high humidity and salt spray concentration, which significantly improves the environmental adaptability and operation stability of the system. This not only reduces the risk of falling off due to insufficient adsorption force, but also lays a solid data foundation for bridge crack detection and repair, achieving a comprehensive improvement in operation quality and efficiency.

[0087] Example 3

[0088] This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the detection posture adjustment module includes an adsorption force analysis unit, an adsorption force adjustment unit and an adsorption point distribution optimization unit;

[0089] The adsorption force analysis unit extracts the actual pressure FPac of the adsorption point in the environmental feature set and the ideal required pressure FPid initially set by the user, and performs deviation evaluation to analyze the adsorption force of the current wall-climbing robot during operation. The specific evaluation contents are as follows;

[0090] When the actual pressure at the adsorption point FPac minus the ideal required pressure FPid ≥ F1, the adsorption state is determined to be normal and no adjustment is required;

[0091] When the actual pressure at the adsorption point FPac minus the ideal required pressure FPid < F1, the adsorption state is judged to be abnormal and needs to be adjusted;

[0092] Among them, F1 represents the allowable deviation value, which is initially set by the user.

[0093] The adsorption force adjustment unit is used to be triggered when the adsorption state is determined to be abnormal. By extracting the wind shear force WF and the surface attachment factor SAF in the environmental feature set and combining the pressure change rate EPR, the adsorption force coefficient FP is calculated and output, and the rotor motor power is dynamically adjusted to optimize the adsorption pressure of the wall-climbing robot.

[0094] The adsorption coefficient FP is calculated and output by the following algorithm formula;

[0095]

[0096] Where γ is the adjustment factor used to modify the adsorption force to adapt to the task requirements, such as different load weights or environmental changes, η is the adhesion efficiency coefficient, which is set depending on the adhesion surface material, δ is the dynamic environmental impact coefficient, and sin(φ) is the surface inclination of the seaside bridge pier. It represents the inverse term of the surface adhesion factor. When the surface roughness of the seaside bridge pier is high, the humidity is low, and the salt concentration is low, the surface adhesion is stronger and the adsorption force requirement is reduced;

[0097] The adsorption point distribution optimization unit dynamically adjusts the adsorption force of each adsorption point of the wall-climbing robot through the adsorption force coefficient FP, and then calculates and outputs the number of adsorption points N in combination with the center of gravity and working position of the wall-climbing robot, and dynamically distributes the adsorption points;

[0098] The number of adsorption points N is calculated and output by the following algorithm formula;

[0099]

[0100] Where Ftotal represents the load force of the wall-climbing robot, which represents the total weight of the wall-climbing robot and the gravity component applied by the task on the adsorption point, m represents the total number of adsorption points, and FP i represents the adjusted adsorption coefficient of the i-th adsorption point, d i It represents the weight correction value generated by the distance of the i-th adsorption point relative to the center of gravity of the robot. The closer the adsorption point is to the center of gravity, the smaller its weight correction factor is, and the lower the torque provided is. The farther the adsorption point is from the center of gravity, the larger the weight correction factor is, and the more significant its contribution to stability is. The correction factor is used to adjust the distribution of adsorption points to make the torque more uniform and prevent the robot from overturning or sliding due to the shift of the center of gravity.

[0101] In this embodiment, the system detects the posture adjustment module composed of an adsorption force analysis unit, an adsorption force adjustment unit and an adsorption point distribution optimization unit, which together realize the stable attachment and dynamic posture adjustment of the wall-climbing robot in a complex working environment. The adsorption force analysis unit performs deviation assessment by extracting the actual pressure FPac and the ideal required pressure FPid of the adsorption point concentrated in the environmental characteristics. When insufficient adsorption pressure is detected, the adsorption force adjustment unit calculates the adsorption force coefficient FP and dynamically adjusts the rotor motor power to optimize the adsorption pressure. Subsequently, the adsorption point distribution optimization unit dynamically calculates the number of adsorption points N based on the adjusted adsorption force coefficient FP, the center of gravity position of the wall-climbing robot and the working position, and optimizes the distribution of adsorption points to ensure uniform adsorption force and stable robot posture. Through this module, the wall-climbing robot can adapt to multiple environmental interferences such as wind, high humidity, salt spray concentration and surface inclination in real time on the complex surface of the bridge pier. The implementation of this module achieved the following goals and effects: First, through dynamic adsorption force adjustment and optimization, the robot can maintain stable attachment in complex environments, significantly reducing the risk of falling off due to insufficient adsorption force; second, through the optimization of adsorption point distribution, the robot's center of gravity balance and posture stability are improved, enabling it to move safely and efficiently on complex surfaces.

[0102] Example 4

[0103] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the crack detection module includes a crack identification unit and a crack volume analysis unit;

[0104] The crack identification unit adjusts the adsorption force of the wall-climbing robot and then starts the wall-climbing robot to move the seaside bridge pier from bottom to top. During the moving process, the imaging device installed on the wall-climbing robot is used to collect crack data on the surface of the seaside bridge pier.

[0105] Imaging devices include visible light cameras, thermal imaging cameras, and multispectral imaging devices;

[0106] The crack data include multi-spectral difference value G(x, y), surface temperature distribution T(x, y) and crack area image;

[0107] The pixel intensity gradient ▽I(x, y) of the crack area image is calculated using an edge detection algorithm, and then the characteristic response of the crack area is calculated based on the multi-spectral difference value G(x, y) and the surface temperature distribution T(x, y), forming a crack boundary B(x, y) to identify and locate the crack position of the seaside bridge pier. After the crack position is identified, the wall-climbing robot automatically stops moving.

[0108] The crack boundary B(x, y) is calculated and output by the following algorithm formula;

[0109] B(x, y)=argmax[▽I(x, y)+k·G(x, y)·cos(λ·T(x, y))];

[0110] Where argmax represents the upper limit function, (x, y) represents the coordinates in the two-dimensional image of the seaside bridge pier, k represents the multispectral feature enhancement factor, which is used to adjust the intensity of the multispectral difference value and optimize the recognition effect, and λ represents the temperature difference sensitivity coefficient, which reflects the influence weight of temperature distribution on crack recognition and depends on the thermal properties of the material.

[0111] The significance of the formula is to identify the boundary position of the crack with high precision, avoid misjudgment due to surface stains or reflections, supplement the limitations of single-spectrum images through multi-spectral and temperature difference information, and improve recognition accuracy.

[0112] The crack volume analysis unit is used to identify and locate the cracks in the seaside bridge piers, capture the depth matrix D(x, y) of the crack area through the laser scanning technology of the wall-climbing robot, use the multi-time image reconstruction technology to supplement the three-dimensional data of the crack area, and then calculate the total crack volume Vc through integration and discrete summation;

[0113] The total crack volume Vc is calculated and output by the following algorithm formula;

[0114]

[0115] In the formula, Represents the crack boundary range, which is provided by the crack boundary B(x, y), dx represents a small change in the x direction, dy represents a small change in the y direction, and dxdy represents the area of ​​the small matrix region.

[0116] In this embodiment, the system is composed of a crack recognition unit and a crack volume analysis unit through a crack detection module, which fully realizes the accurate recognition and three-dimensional modeling of cracks in bridge piers. The crack recognition unit starts the detection by adjusting the adsorption force of the wall-climbing robot, moves from the bottom to the top of the pier, and uses the imaging equipment carried by the robot (including visible light camera, thermal imaging camera and multi-spectral imaging equipment) to collect crack data in real time. Combined with the edge detection algorithm, the pixel intensity gradient ▽I(x,y) of the crack area image is calculated, and the crack characteristic response is calculated based on the multi-spectral difference value Gx,y) and the surface temperature distribution Tx,y) to generate the crack boundary B(x,y). After the crack identification is completed, the robot movement is automatically stopped to avoid repeated detection or omission. Based on the identification results, the crack volume analysis unit captures the crack depth matrix D(x,y) through laser scanning technology, generates three-dimensional data of the crack area using multi-time image reconstruction technology, and finally calculates the total volume Vc of the crack by integration and discrete summation. Through advanced imaging and data processing technologies, this module achieves the following goals and effects: First, through the fusion of multispectral and thermal imaging data, the accuracy of crack identification is significantly improved, avoiding misjudgment due to surface stains, light reflections or complex textures; second, by combining laser scanning and 3D modeling technology, it can efficiently and comprehensively capture the depth and volume information of cracks, providing accurate crack parameters for subsequent repairs.

[0117] Example 5

[0118] This embodiment is explained in Example 4. Please refer to Figure 1 ,Specifically: the repair material intelligent spraying detection module includes a repair material volume ,analysis unit and a repair integrity analysis unit;

[0119] The repair material volume analysis unit calculates and outputs the repair material volume Vm based on the total crack volume Vc, adjusts the output parameters of the spraying system in real time, and then uses SLAM synchronous positioning and map building technology to dynamically update the spraying trajectory based on the three-dimensional data of the crack area, optimize the spraying path, and ensure uniform coverage of the crack area;

[0120] The volume of the repair material Vm is calculated and output by the following algorithm formula;

[0121] Vm=π·Vc·R+α·sin(ξ)·ΔP;

[0122] In the formula, π represents the circumference of a circle, which is 3.14; R represents the coverage ratio of the repair material, R>1, indicating complete coverage of the material; α represents the additional material compensation coefficient, which is used to compensate for the loss of spraying materials, such as the decrease in adhesion caused by wind speed or surface roughness; sin(ξ) represents the spraying angle correction term; ξ represents the angle between the surface where the crack is located and the spraying equipment, which is directly obtained through the spraying system; △P represents the spraying pressure difference, which is directly obtained through the spraying system.

[0123] After the crack is repaired, the repair integrity analysis unit scans the repair area with a thermal imaging camera, records the actual temperature distribution Tr(x,y) after the repair, and calculates the difference with the ideal temperature to obtain the repair integrity error Er;

[0124] The repair integrity error Er is calculated and output by the following algorithm formula;

[0125]

[0126] Where A represents the area of ​​the crack region, the crack contour boundary is calculated by the crack detection module, and the total area is obtained, and Ts(x, y) represents the ideal temperature of the repair area.

[0127] In this embodiment, the system is composed of a repair material volume analysis unit and a repair integrity analysis unit through a repair material intelligent spraying detection module, which fully realizes the accurate calculation of repair materials, dynamic spraying and real-time feedback of repair effects. The repair material volume analysis unit calculates the required material volume Vm according to the total crack volume Vc output by the crack detection module, and combines the three-dimensional data of the crack area with SLAM synchronous positioning and map construction technology to update the spraying path in real time to ensure that the crack area is evenly covered with materials. In order to cope with the material loss during the spraying process, the unit dynamically adjusts the output parameters of the spraying system to achieve optimal utilization of materials. The repair integrity analysis unit scans the repaired area through a thermal imaging camera, records the actual temperature distribution Tr(x,y), and calculates the difference with the ideal temperature distribution Tsx,y), outputs the repair integrity error Er, and evaluates the repair effect in real time. When Er>0, the system generates a spraying warning to ensure that the crack is completely repaired. The implementation of this module has achieved the following goals and effects: First, through precise calculation and dynamic spray path optimization, the utilization rate of repair materials has been significantly improved, avoiding material waste and missing coverage; second, through repair integrity analysis, closed-loop monitoring of repair effects has been achieved, effectively ensuring the integrity and durability of the repair area; finally, through intelligent spray adjustment and feedback, the system can adapt to crack repair needs in complex environments and ensure the quality of operations.

[0128] Example 6

[0129] This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: The specific repair error evaluation content of the repair ,analysis module is as follows;

[0130] When the repair integrity error Er>0, it means that the repair is incomplete, and a repair adjustment warning is generated, prompting engineers to perform repair operations through the ground control system;

[0131] When the repair integrity error Er≤0, it means that the repair quality is qualified and no warning needs to be generated.

[0132] In this embodiment, the system establishes an intelligent monitoring and feedback mechanism for the repair quality based on the real-time evaluation of the repair integrity error Er through the repair analysis module. The system judges the repair effect through the error Er output by the repair integrity analysis unit: when Er>0, it means that the repair is incomplete, and the system automatically generates a warning for spraying adjustment, prompting the ground control system to notify the engineer to perform repair operations; when Er≤0, it means that the repair quality is qualified and no further processing is required. This module takes the repair integrity error as the core evaluation indicator, combines thermal imaging data, boundary calculation of the crack detection module and crack area A, comprehensively analyzes the repair effect, and provides intelligent closed-loop control for the repair operation. The implementation of this module has achieved the following goals and effects: First, by accurately judging the repair status, incomplete repairs or substandard quality are avoided, ensuring the integrity and durability of crack repairs; second, the automatically generated warning information significantly improves the repair efficiency and reduces the workload of manual maintenance; finally, through real-time monitoring and dynamic adjustment of the repair results, the system's operation intelligence level is improved.

[0133] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. An automatic detection system for cracks in bridge piers based on a wall-climbing robot, characterized in that: It includes working environment detection module, detection posture adjustment module, crack detection module, repair material intelligent spraying detection module and repair analysis module; The working environment detection module is used to install a detection equipment group on the wall-climbing robot, collect working environment data on the surface of the seaside bridge pier, and transmit the collected working environment data to the ground control system of the wall-climbing robot, and then perform preprocessing and feature extraction on the working environment data to obtain an environmental feature set; The detection posture adjustment module monitors the actual pressure FPac of the wall-climbing robot's adsorption point and the ideal required pressure FPid to perform deviation assessment. When the adsorption state is assessed to be abnormal, the adsorption force coefficient FP is calculated and output based on the environmental feature set, and the number of adsorption points N is calculated and output based on the adsorption force coefficient FP. The crack detection module collects crack data of the seaside bridge piers through imaging equipment, extracts crack boundaries B(x, y) through image processing algorithms to identify and locate cracks in the seaside bridge piers, collects depth distribution data of the crack area through integrated scanning technology after locating, and calculates and outputs the total crack volume Vc; The repair material intelligent spraying detection module calculates the repair material volume Vm in real time through the total crack volume Vc, monitors the repair effect through imaging, and outputs the repair integrity error Er; The repair analysis module performs repair error evaluation through the output result of the repair integrity error Er, analyzes the repair effects before and after repair, and generates warning information based on the repair error evaluation result.

2. The automatic detection system for cracks in bridge piers based on a wall-climbing robot according to claim 1 is characterized in that: The working environment detection module includes a data acquisition unit, a preprocessing unit and a feature extraction unit; The data acquisition unit is used to install a detection equipment group on the wall-climbing robot to collect real-time operating environment data of the wall-climbing robot when it is operating on the surface of the seaside bridge pier; The detection equipment group includes an ultrasonic wind speed sensor, a laser roughness meter, an infrared humidity sensor, a conductivity sensor, a barometer and a strain gauge pressure sensor; The working environment data include wind speed v, wind direction angle θ, surface roughness coefficient Cr, surface humidity H, salt concentration S, pressure change rate EPR and actual pressure FPac at the adsorption point; The preprocessing unit is used to integrate the collected working environment data into the central processor of the wall-climbing robot, connect the ground control system with the central processor of the wall-climbing robot through a wireless network, and transmit the real-time collected working environment data to the ground control system; The collected operating environment data are preprocessed in the ground control system, and the preprocessing method includes data cleaning and normalization processing to unify the dimensions of the operating environment data.

3. The automatic detection system for cracks in bridge piers based on a wall-climbing robot according to claim 2 is characterized in that: The feature extraction unit performs feature extraction on the wind speed v, wind direction angle θ, surface roughness coefficient Cr, surface humidity H and salinity concentration S in the working environment data after preprocessing the working environment data to obtain an environmental feature set; The environmental feature set includes wind shear force WF, surface attachment factor SAF, pressure change rate EPR and actual pressure FPac at the adsorption point; The wind shear force WF is calculated and extracted by extracting the wind speed v and wind direction angle θ from the working environment data. The specific algorithm formula is: Where ρ represents the air density, the initial setting value, and cos represents the cosine function; The surface attachment factor SAF is calculated and extracted by extracting the surface roughness coefficient Cr, surface humidity H and salt concentration S. The specific algorithm formula is: Wherein, k represents the humidity sensitivity coefficient, and e represents the exponential function.

4. The automatic detection system for cracks in bridge piers based on a wall-climbing robot according to claim 1 is characterized in that: The detection posture adjustment module includes an adsorption force analysis unit, an adsorption force adjustment unit and an adsorption point distribution optimization unit; The adsorption force analysis unit extracts the actual pressure FPac of the adsorption point in the environmental feature set and the ideal required pressure FPid initially set by the user, performs deviation evaluation, and analyzes the adsorption force of the current wall-climbing robot during operation. The specific evaluation contents are as follows; When the actual pressure at the adsorption point FPac minus the ideal required pressure FPid ≥ F1, the adsorption state is determined to be normal and no adjustment is required; When the actual pressure at the adsorption point FPac minus the ideal required pressure FPid < F1, the adsorption state is judged to be abnormal and needs to be adjusted; Among them, F1 represents the allowable deviation value, which is initially set by the user.

5. The automatic detection system for cracks in bridge piers based on a wall-climbing robot according to claim 4 is characterized in that: The adsorption force adjustment unit is used to be triggered when it is determined that the adsorption state is abnormal, and calculates the output adsorption force coefficient FP by extracting the wind shear force WF and the surface attachment factor SAF in the environmental feature set combined with the pressure change rate EPR, and dynamically adjusts the rotor motor power to optimize the adsorption pressure of the wall-climbing robot; The adsorption coefficient FP is calculated and output by the following algorithm formula: In the formula, γ represents the adjustment factor, η represents the adhesion efficiency coefficient, δ represents the dynamic environmental impact coefficient, and sin(φ) represents the surface inclination of the seaside bridge pier. represents the inverse term of the surface attachment factor; The adsorption point distribution optimization unit dynamically adjusts the adsorption force of each adsorption point of the wall-climbing robot through the adsorption force coefficient FP, and then calculates and outputs the number of adsorption points N in combination with the center of gravity and the working position of the wall-climbing robot, and dynamically distributes the adsorption points; The number of adsorption points N is calculated and outputted by the following algorithm formula; Where Ftotal represents the load force of the wall-climbing robot, m represents the total number of adsorption points, and FP i represents the adjusted adsorption coefficient of the i-th adsorption point, d i Represents the weight correction value generated by the distance of the i-th adsorption point relative to the center of gravity of the robot.

6. The automatic detection system for cracks in bridge piers based on a wall-climbing robot according to claim 5 is characterized in that: The crack detection module includes a crack identification unit and a crack volume analysis unit; The crack identification unit adjusts the adsorption force of the wall-climbing robot, and then starts the wall-climbing robot to move the seaside bridge pier from bottom to top. During the moving process, the crack data on the surface of the seaside bridge pier is collected through the imaging device installed on the wall-climbing robot. The imaging devices include visible light cameras, thermal imaging cameras and multi-spectral imaging devices; The crack data includes multi-spectral difference value G(x, y), surface temperature distribution T(x, y) and crack area image; The pixel intensity gradient ▽I(x, y) of the crack area image is calculated using an edge detection algorithm, and then the characteristic response of the crack area is calculated based on the multi-spectral difference value G(x, y) and the surface temperature distribution T(x, y), forming a crack boundary B(x, y) to identify and locate the crack position of the seaside bridge pier. After the crack position is identified, the wall-climbing robot automatically stops moving. The crack boundary B(x, y) is calculated and outputted by the following algorithm formula; B(x, y)=argmax[▽I(x, y)+k·G(x, y)·cos(λ·T(x, y))]; Where argmax represents the upper limit function, (x, y) represents the coordinates of the seaside bridge pier in the two-dimensional image, k represents the multispectral feature enhancement factor, and λ represents the temperature difference sensitivity coefficient.

7. The automatic detection system for cracks in bridge piers based on a wall-climbing robot according to claim 6 is characterized by: The crack volume analysis unit is used to capture the depth matrix D (x, y) of the crack area through the laser scanning technology of the wall-climbing robot after identifying and locating the cracks in the seaside bridge pier, use the multi-time image reconstruction technology to supplement the three-dimensional data of the crack area, and then calculate the total crack volume Vc through integration and discrete summation; The total crack volume Vc is calculated and outputted by the following algorithm formula; In the formula, Represents the crack boundary range, which is provided by the crack boundary B(x, y), dx represents a small change in the x direction, dy represents a small change in the y direction, and dxdy represents the area of ​​the small matrix region.

8. The automatic detection system for cracks in bridge piers based on a wall-climbing robot according to claim 7 is characterized in that: The repair material intelligent spraying detection module includes a repair material volume analysis unit and a repair integrity analysis unit; The repair material volume analysis unit calculates and outputs the repair material volume Vm based on the total crack volume Vc, adjusts the output parameters of the spraying system in real time, and then dynamically updates the spraying trajectory and optimizes the spraying path based on the three-dimensional data of the crack area using SLAM synchronous positioning and map building technology; The repair material volume Vm is calculated and outputted by the following algorithm formula; Vm=π·Vc·R+α·sin(ξ)·ΔP; Wherein, π represents the circumference of a circle and takes the value of 3.14; R represents the coverage ratio of the repair material; α represents the compensation coefficient of the additional material; sin(ξ) represents the spraying angle correction term; ξ represents the angle between the crack surface and the spraying equipment; and △P represents the spraying pressure difference.

9. The automatic detection system for cracks in bridge piers based on a wall-climbing robot according to claim 8 is characterized in that: The repair integrity analysis unit scans the repair area with a thermal imaging camera after the crack is repaired, records the actual temperature distribution Tr(x, y) after the repair, and calculates the difference with the ideal temperature to obtain the repair integrity error Er; The repair integrity error Er is calculated and outputted by the following algorithm formula: Where A represents the area of ​​the crack, and Ts(x, y) represents the ideal temperature of the repair area.

10. The automatic detection system for cracks in bridge piers based on a wall-climbing robot according to claim 9, characterized in that: The specific repair error evaluation content of the repair analysis module is as follows: When the repair integrity error Er>0, it means that the repair is incomplete, and a repair adjustment warning is generated, prompting engineers to perform repair operations through the ground control system; When the repair integrity error Er≤0, it means that the repair quality is qualified and no warning needs to be generated.

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