Full-automatic concrete pole mechanical detection method

Abstract

The invention discloses a full-automatic concrete pole mechanical detection method, and particularly relates to the technical field of concrete product detection, and the method comprises the steps: S1, completing the identity recognition and initial positioning of a pole through an automatic recognition device and a visual perception system, S2, automatically hoisting the pole to a detection station according to a determined hoisting point, and carrying out the automatic leveling, s3, mechanical property testing and appearance quality scanning are synchronously executed at a detection station, and S4, correlation analysis is conducted on obtained mechanical response data and surface defect information, and a comprehensive detection conclusion is automatically generated. According to the method, the full-process automation from electric pole feeding to final quality judgment is realized, the detection efficiency, precision and intelligent level are remarkably improved through multi-dimensional detection and data fusion analysis, and the problems that a traditional detection method is large in man-made interference, low in efficiency and single in detection are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of concrete product detection, and more particularly to a full-automatic concrete pole mechanical detection method. BACKGROUND

[0002] As an important load-bearing component widely used in power transmission, railway catenary and communication network infrastructure, the reliability of the mechanical properties of the concrete pole is directly related to the safe and stable operation of the entire line. Therefore, before leaving the factory and during the operation cycle, the mechanical properties must be strictly detected according to national or industry standards.

[0003] At present, most production enterprises and detection institutions still generally use traditional manual or semi-automatic methods for mechanical property detection of concrete poles. The typical operation process is as follows: the pole is hoisted to the detection pedestal by a crane or manually using a hand-operated hoist, and then it is roughly leveled by manual observation and the use of shims, and then the load is manually applied by using a hydraulic jack loading device, and the cracks are observed by naked eye, the deflection is measured by manual holding of the table, and the data is recorded manually.

[0004] This method has the defects of low automation degree, large human interference factors, safety hazards, single detection dimension, and inability to form comprehensive quality evaluation. In view of this, the present application provides a full-automatic concrete pole mechanical detection method. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the present application provides a full-automatic concrete pole mechanical detection method to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution: a full-automatic concrete pole mechanical detection method, comprising the following steps: S1, pole identity recognition and initial positioning, reading the identity information of the pole by an automatic recognition device, and scanning the pole by a visual perception system to obtain its appearance size data, and determining the hoisting point of the pole based on the appearance size data; S2, adaptive hoisting and leveling, moving the pole to the detection station by the automatic hoisting system according to the hoisting point determined in step S1, and then adjusting the center line of the pole to the horizontal state by the leveling system; S3, multi-functional collaborative detection, in the detection station, performing mechanical property test and appearance quality scanning, wherein: S31, mechanical property test, applying load to the pole by the automatic loading system, and collecting mechanical response data in real time; S32, appearance quality scanning: scanning the surface of the pole by the moving image acquisition device to identify surface defects; S4, data fusion and intelligent decision making, correlating the mechanical response data obtained in step S3 with the surface defect information and automatically generating a comprehensive detection conclusion.

[0007] Preferably, in step S1, the automatic recognition device is an RFID reader or a two-dimensional code scanner, and the visual perception system is a binocular stereo vision sensor which obtains the length and tip diameter of the pole by generating a three-dimensional point cloud model of the pole.

[0008] Preferably, the leveling system in step S2 works as follows: the height of the pole from the ground at both ends is measured by a distance measuring module, the height data is fed back to the control terminal, and the control terminal drives the execution module to adjust the height of one end or both ends of the pole until the center line of the pole is horizontal.

[0009] Preferably, the multi-functional collaborative detection in step S3 further includes: S33, internal non-destructive testing, scanning the key parts of the pole by a non-destructive testing device carried by a mechanical arm or a mobile platform to detect internal defects.

[0010] Preferably, the non-destructive testing device in step S33 is an electromagnetic non-destructive testing device or an ultrasonic flaw detector, wherein the electromagnetic non-destructive testing device detects the distribution and defects of the internal steel bars of the pole by measuring the impedance change of the inductor.

[0011] Preferably, in step S31, the automatic loading system is an actuator driven by a servo motor, which automatically executes a step loading program based on the identity information of the pole and the preset load standard, and collects load, displacement and deflection data in real time.

[0012] Preferably, in step S32, the image acquisition device is a high-definition camera and / or an infrared thermal imager integrated on a mechanical arm or a mobile robot, and the process of identifying surface defects is automatically completed using an image recognition algorithm based on a deep learning model.

[0013] Preferably, the correlation analysis in step S4 specifically includes: correlating the position of the surface defect identified in step S32 and / or the position of the internal defect detected in step S33 with the stress state at that position during the mechanical test in step S31, to evaluate the influence of the defect on the mechanical properties of the pole.

[0014] Preferably, before the mechanical property test in step S31, there is also a zero calibration step, in which the initial readings of the loading system and the sensor are collected in the unloaded state, and they are excluded from the subsequent test data to eliminate errors caused by the deformation of the equipment itself.

[0015] Preferably, the comprehensive detection conclusion generated in step S4 at least includes whether the mechanical properties of the pole meet the standards, a surface defect distribution map, an internal health condition assessment, and a final pass or fail determination result.

[0016] Technical effects and advantages of the present application: 1. The present application completely replaces the manual handling, manual loading, naked-eye observation and manual recording in the traditional method through the process operation of identity recognition, visual positioning, automatic hoisting, automatic leveling, collaborative detection and intelligent decision-making, not only greatly improves the detection efficiency, avoids random errors and subjective misjudgment caused by manual operation, ensures the consistency and objectivity of the detection data, but also fundamentally eliminates the safety hazards of personnel in the heavy component detection site, and realizes reliable operation for 24 hours without interruption in harsh environments; 2. The present application integrates mechanical property testing, appearance quality scanning and internal nondestructive testing into one, which can be completed synchronously or sequentially in one clamping and positioning, can obtain the overall information of the mechanical properties, surface state and internal structure of the pole, solves the low detection range of the traditional method, realizes the comprehensive evaluation of the overall quality state of the pole, and can timely find the potential composite defects that cannot be found in single detection dimension; 3. The present application can intelligently diagnose the actual influence degree of the defect on the safety of the structure by accurately correlating and analyzing the position information of the surface and internal defects and the stress state of the position in the mechanical test, thereby generating a comprehensive detection conclusion containing cause analysis and risk rating, which provides unprecedented depth insight and decision support for the quality judgment and subsequent maintenance of the pole. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The overall step diagram of the present application.

[0018] Figure 2 The method refinement flowchart of the present application.

[0019] Figure 3 The system architecture block diagram of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0021] The present application provides a full-automatic concrete pole mechanical detection method, which needs to be based on a full-automatic concrete pole mechanical detection system, such as Figure 3As shown, the fully automated concrete pole mechanical testing system mainly includes: a central control unit, an automatic identification device, a visual perception system, an automated hoisting system, a leveling system, an automated loading system, an image acquisition device, and a non-destructive testing device. The automatic identification device is an RFID reader or a QR code scanner, and the visual perception system is a binocular stereo vision sensor, which obtains the pole's length and tip diameter by generating a three-dimensional point cloud model of the pole.

[0022] Example 1: This embodiment provides a fully automated mechanical testing process for concrete utility poles, such as... Figure 1 As shown, the specific steps are as follows: S1. Pole identification and initial positioning: The pole's identification information is read by an automatic identification device, and the pole is scanned by a visual perception system to obtain its external dimensions. The hoisting point of the pole is determined based on the external dimensions data. In the specific implementation of this step, the pole to be inspected is first transported to the initial station via a conveyor roller. An RFID reader fixed in the station automatically reads the electronic tag embedded in the end of the pole to obtain its unique identification information, such as production batch, model and design bending moment. Subsequently, the binocular stereo vision sensor installed at the workstation performs a panoramic scan of the pole. Through image processing algorithms, this example uses a three-dimensional reconstruction algorithm to generate a high-precision three-dimensional point cloud model of the pole. Based on this three-dimensional point cloud model, the central control unit automatically calculates the key external dimensions of the pole, including total length, tip diameter, root diameter, and taper. Based on mechanical principles and hoisting stability requirements, it analyzes and determines the optimal hoisting point location. S2. Adaptive hoisting and leveling: Based on the hoisting point determined in step S1, the pole is moved to the inspection station through an automated hoisting system, and then the center line of the pole is adjusted to a horizontal state through a leveling system. In this step, the end effector of the six-axis industrial robot of the automated hoisting system automatically adjusts the gripper posture according to the hoisting point determined in step S1, and reliably grabs the pole. Then, the six-axis industrial robot smoothly moves the pole to a dedicated mechanical testing station and places it on two independently adjustable V-shaped support seats. Next, the leveling system is activated. The leveling system includes a servo electric cylinder, a V-shaped support base, and a distance measuring module. The two distance measuring modules (laser rangefinders) measure the ground height of the two ends of the pole at the corresponding positions of the two V-shaped support bases, and feed the data back to the central control unit in real time. The central control unit, as the control terminal of this embodiment, calculates the height difference between the two ends of the pole and drives the servo electric cylinder under the V-shaped support base. The servo electric cylinder, as the execution module of this embodiment, adjusts the height of one or both ends of the pole until the centerline of the pole is precisely adjusted to a horizontal state, for example, the height difference between the two ends is less than ±1mm. This step lays a solid foundation for the accuracy of subsequent mechanical tests. S3, multi-functional collaborative inspection, performs mechanical performance testing and appearance quality scanning at the inspection station; In practice, after the pole is leveled, the system simultaneously or sequentially performs mechanical performance testing and appearance quality scanning at the inspection station. Specifically: S31. Mechanical performance test: Before the mechanical performance test, the zero-point calibration step is performed: The automated loading system runs in an unloaded state. The automated loading system is an actuator driven by a servo motor. It collects the initial readings of the force sensor and displacement sensor. These readings will be recorded and removed from the subsequent test data to eliminate systematic errors caused by the deformation and gaps of the equipment itself. The base of the pole is fixed by an adaptive hydraulic clamping mechanism. In the automated loading system, the loading head of the electric actuator driven by the servo motor is connected to the cantilever (top) of the pole. The central control unit automatically calls the preset load standard and graded loading program based on the pole identification information (such as design bending moment) obtained in step S1. The actuator applies progressively increasing loads to the pole according to the program. At the same time, high-precision force sensors and laser displacement sensors collect load values, actuator displacements, and pole key points (locations where the pole needs to be detected), such as deflection data at mid-span and pure bending sections, in real time and synchronously to form a complete mechanical response dataset. S32. Appearance quality scanning: During or after the mechanical test, a mobile robot travels along a track parallel to the pole as a mobile platform. The mobile robot integrates a high-definition line array camera and an infrared thermal imager as an image acquisition device. The high-definition camera continuously scans the surface of the pole to obtain high-resolution surface images, while the infrared thermal imager can capture abnormal surface temperature fields caused by internal defects or stress concentration. The acquired image data is transmitted to the central control unit in real time via wireless network. The target detection algorithm based on deep learning (such as YOLOv5 or Faster R-CNN model, which has been pre-trained and optimized with a large amount of concrete defect image data) runs in the central control unit to automatically analyze the image and accurately identify and locate the type, size and location coordinates of defects such as surface cracks, honeycomb and pitting. S4. Data fusion and intelligent decision-making: The mechanical response data obtained in step S3 is correlated and analyzed with the surface defect information, and a comprehensive detection conclusion is automatically generated. In the specific implementation of this step, the information processing platform of the central control unit will perform correlation and fusion analysis on the mechanical response data (such as load-deflection curve, maximum deflection value, cracking load) obtained in step S31 and the surface defect information (such as crack length and location) obtained in step S32. For example, the system will combine the identified crack location information with the mechanical simulation model or measured strain data to analyze the stress state of the pole under load at the crack location. If a crack happens to appear in a high-stress area, the system will mark it as a high-risk defect. Finally, based on the preset qualification standards, the system automatically generates a comprehensive test report, which includes at least: whether the mechanical properties meet the standards (such as the maximum deflection test is qualified), the surface defect distribution map, and the final qualification or non-qualification conclusion.

[0023] Example 2 This embodiment is a preferred solution based on Embodiment 1, which adds an internal non-destructive testing function to provide a more comprehensive quality assessment. In this embodiment, the multi-functional collaborative detection in step S3 further includes: S33. Internal non-destructive testing: Using a non-destructive testing device carried by a robotic arm or mobile platform to scan key parts of the pole to detect internal defects. In practice, this step shares the same mobile robot as step S32, with an additional electromagnetic non-destructive testing (NDT) device integrated on it. As a type of NDT device, when the mobile robot scans the appearance of the pole, the electromagnetic NDT device simultaneously scans the key parts of the pole (such as support points and the points of maximum bending moment). The excitation coil inside the device generates an alternating electromagnetic field. When there is rust or fracture in the steel bars inside the pole or voids in the concrete, the impedance of the induction coil will change. By measuring and analyzing the spectrum of this impedance change, the distribution of steel bars inside the pole, the thickness of the protective layer, and the internal defects can be detected non-destructively. Furthermore, in this embodiment, the data fusion and intelligent decision-making in step S4 will be more in-depth. The correlation analysis specifically includes: multidimensionally correlating the surface defect locations identified in S32 and the internal defect locations detected in S33 with the stress state experienced at those locations during the mechanical test in S31.

[0024] For example, if the system finds internal steel reinforcement corrosion and the deflection at that location exceeds the standard during mechanical testing, the system will comprehensively determine that the pole's mechanical performance is unqualified due to internal structural damage. The comprehensive test conclusion will clearly state the diagnosis that the internal steel reinforcement corrosion caused the mechanical performance to fail to meet the standard, as well as the final unqualified judgment. The report also includes an assessment of the internal health status.

[0025] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fully automated method for mechanical testing of concrete utility poles, characterized in that: Comprising the following steps: S1, pole identity recognition and initial positioning, reading the identity information of the pole through an automatic recognition device, and scanning the pole using a visual perception system to obtain its appearance size data, and determining the hoisting point of the pole based on the appearance size data; S2, adaptive hoisting and leveling, moving the pole to the detection station through the automatic hoisting system according to the hoisting point determined in step S1, and then adjusting the center line of the pole to a horizontal state through the leveling system; S3, multi-functional collaborative detection, on the detection station, performing mechanical property testing and appearance quality scanning, including: S31, mechanical property testing, applying load to the pole through an automatic loading system, and collecting mechanical response data in real time; S32, appearance quality scanning, scanning the surface of the pole through a mobile image acquisition device to identify surface defects; S4, data fusion and intelligent decision-making, correlatively analyzing the mechanical response data and surface defect information obtained in step S3, and automatically generating a comprehensive detection conclusion.

2. The full-automatic concrete pole mechanical detection method according to claim 1, characterized in that: In step S1, the automatic recognition device is an RFID reader or a two-dimensional code scanner, and the visual perception system is a binocular stereo vision sensor, which obtains the length and tip diameter of the pole by generating a three-dimensional point cloud model of the pole.

3. The full-automatic concrete pole mechanical detection method according to claim 1, characterized in that: The working mode of the leveling system in step S2 is: measuring the ground clearance of both ends of the pole through a distance measuring module, feeding the height data to the control terminal, and driving the execution module to adjust the height of one end or both ends of the pole until the center line of the pole is horizontal.

4. The full-automatic concrete pole mechanical detection method according to claim 1, characterized in that: The multi-functional collaborative detection in step S3 further includes: S33, internal non-destructive testing, scanning the key parts of the pole through a non-destructive testing device carried by a mechanical arm or a mobile platform to detect internal defects.

5. The fully automatic concrete pole mechanical detection method according to claim 4, characterized in that: The non-destructive testing device in step S33 is an electromagnetic non-destructive testing device or an ultrasonic flaw detector, wherein the electromagnetic non-destructive testing device detects the distribution and defects of the internal steel bars of the pole by measuring the impedance change of the induction coil.

6. The full-automatic concrete pole mechanical detection method according to claim 1, characterized in that: In step S31, the automatic loading system is an actuator driven by a servo motor, which automatically executes a staged loading program based on the identity information of the pole and the preset load standard, and collects load, displacement and deflection data in real time.

7. The full-automatic concrete pole mechanical detection method according to claim 1, characterized in that: In step S32, the image acquisition device is a high-definition camera and / or an infrared thermal imager integrated on a mechanical arm or a mobile robot, and the process of identifying surface defects is automatically completed using an image recognition algorithm based on a deep learning model. 8.The full-automatic concrete pole mechanical detection method according to claim 4, characterized in that: The correlational analysis in step S4 specifically includes: correlating the position of the surface defect identified in step S32 and / or the position of the internal defect detected in step S33 with the stress state of the position during the mechanical testing in step S31, to evaluate the influence of the defects on the mechanical properties of the pole. 9.The full-automatic concrete pole mechanics detection method according to claim 1, characterized in that: Before performing the mechanical property testing in step S31, a zero calibration step is further included, in which the initial readings of the loading system and the sensor are collected in the empty state, and are excluded from the subsequent test data to eliminate errors caused by the deformation of the equipment itself. 10.The full-automatic concrete pole mechanical detection method according to claim 1, characterized in that: The comprehensive detection conclusion generated in step S4 at least includes whether the mechanical properties of the pole meet the standards, a surface defect distribution map, an internal health condition evaluation, and a final pass or fail determination result.

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