Panoramic visual detection method and system for corrosion defect of steel structure metal tool

Abstract

The invention relates to the technical field of defect detection, in particular to a steel structure metal tool corrosion defect panoramic visual detection method and system. A plurality of sound wave vibrations on the steel structure metal surface of a bridge are collected, a vibration response signal of each sound wave vibration is obtained, a frequency difference is obtained through calculation, and a preliminary spatial distribution diagram of a corrosion area is obtained. And calculating the propagation path difference of the micro vibration sensor by using the initial spatial distribution diagram. And acquiring an attenuation distribution diagram by using a sound wave attenuation formula, and determining the position coordinates of the corrosion area. And based on the position coordinates, a three-dimensional corrosion model of the steel structure metal surface is constructed and optimized. And outputting a multi-level panoramic view based on the three-dimensional corrosion model. According to the invention, corrosion conditions inside and outside steel structure metal of a bridge can be detected, and the defect detection accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of defect detection, and in particular to a panoramic visual detection method and system for rust defects in metal tools of steel structures. Background Art

[0002] In bridge engineering, a large number of steel structural metal fixtures are widely used in key structures such as main beams, piers, cables, supports, connectors, and guardrails. These metal fixtures assume the supporting, connecting, and load-bearing functions of the bridge, and are particularly important in bridge types such as steel bridges, cable-stayed bridges, and suspension bridges. Steel structural metal fixtures have a significant impact on the overall performance of bridge engineering. Their structural strength, load-bearing capacity, fatigue resistance, stability, and durability directly determine the safety and service life of the bridge. Once steel structural metal fixtures rust, they may lead to a decrease in structural strength and load-bearing capacity, thereby weakening the bridge's fatigue resistance and increasing the risk of fracture.

[0003] Currently, commonly used technologies for detecting corrosion of metal fixtures on steel structures include ultrasonic testing, magnetic particle testing, X-ray testing, eddy current testing, and visual testing. Ultrasonic and X-ray testing can effectively detect internal defects in metals, but they are relatively expensive. Magnetic particle and visual testing are suitable for detecting surface corrosion, but are significantly affected by factors such as light and surface contamination, resulting in limited accuracy. Therefore, existing detection methods still have limitations, especially since internal metal corrosion is difficult to detect, and conventional technologies tend to focus on surface defects. Furthermore, environmental conditions such as insufficient light, surface contamination, or a rust coating can significantly reduce detection accuracy, posing certain challenges to bridge safety monitoring.

[0004] To this end, a panoramic visual detection method and system for rust defects in steel structure metal appliances are proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a panoramic visual detection method and system for rust defects of steel structure metal appliances, which is used to detect the rust conditions inside and outside the steel structure metal of the bridge and improve the accuracy of defect detection. Several acoustic wave vibrations on the surface of the steel structure metal of the bridge are collected, and the vibration response signal of each acoustic wave vibration is obtained. The frequency difference is calculated to obtain a preliminary spatial distribution map of the rusted area. Using the preliminary spatial distribution map, the propagation path difference of the micro vibration sensor is calculated. The attenuation distribution map is obtained using the acoustic wave attenuation formula to determine the position coordinates of the rusted area. Based on the position coordinates, a three-dimensional corrosion model of the steel structure metal surface is constructed and optimized. A multi-level panoramic view is output based on the three-dimensional corrosion model.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method and system for panoramic visual detection of rust defects in steel structure metal tools, comprising:

[0008] Arrange a plurality of acoustic wave excitation devices on the metal surface of the steel structure of the bridge, emit acoustic wave pulses of different frequencies to stimulate the metal surface of the steel structure, and obtain a plurality of acoustic wave vibrations;

[0009] A panoramic detection array consisting of a plurality of micro vibration sensors is arranged on the metal surface of the steel structure to collect the vibration response signal of each acoustic wave vibration in real time;

[0010] Calculating a frequency difference using the acoustic wave excitation device and the vibration response signal; smoothing the frequency difference using an interpolation algorithm to obtain a preliminary spatial distribution map of the corrosion area;

[0011] Utilizing the preliminary spatial distribution map, the propagation path differences of the micro-vibration sensors are calculated based on the time synchronization data of each micro-vibration sensor and the initial time of emission of the acoustic wave pulse by each acoustic wave excitation device; obtaining an attenuation distribution map using an acoustic wave attenuation formula; and determining the position coordinates of the corroded area based on the attenuation distribution map;

[0012] Based on the position coordinates, a three-dimensional corrosion model of the metal surface of the steel structure is constructed; the size and depth of the corrosion area are further calculated and the three-dimensional corrosion model is optimized; based on the three-dimensional corrosion model, a multi-level panoramic view is output, including the overall distribution of the corrosion area, the depth change gradient, and the surface texture details of the corrosion.

[0013] 2. A panoramic visual inspection method for rust defects of steel structure metal tools according to claim 1, characterized in that the acoustic wave excitation devices are arranged in a ring array to ensure that the area covered by each acoustic wave excitation device has no blind spots; the operating frequency range of the acoustic wave excitation device is set to cover each frequency band;

[0014] Several of the micro-vibration sensors are tightly fitted to the metal surface of the steel structure; the sampling rate of the micro-vibration sensor is set according to the excitation frequency setting of the acoustic wave excitation device and the Nyquist sampling theorem; the several micro-vibration sensors collect the vibration response signal of each acoustic wave vibration in real time, including the vibration amplitude, frequency and phase.

[0015] Furthermore, the collected vibration response signal is preprocessed, including denoising, signal enhancement and signal synchronization;

[0016] Obtaining the sound wave frequency of each of the sound wave excitation devices; performing a fast Fourier transform on each of the vibration response signals to convert them into a frequency domain signal, and extracting the frequency components of the frequency domain signal; comparing each of the sound wave frequencies with each of the frequency components to calculate the frequency difference;

[0017] The spatial position coordinates of each of the micro-vibration sensors are recorded, the frequency difference is associated with the spatial position coordinates, and the frequency difference is smoothed using an interpolation algorithm to obtain a preliminary spatial distribution map of the rusted area.

[0018] Furthermore, time synchronization data of each micro-vibration sensor is obtained, and the initial time of emission of the acoustic wave pulse by each acoustic wave excitation device is obtained; propagation time is calculated based on the time synchronization data and the initial time; propagation distance of each micro-vibration sensor is calculated based on the propagation time, and the propagation distances of all the micro-vibration sensors are compared to deduce propagation path differences;

[0019] Based on the propagation time and the frequency attenuation characteristics of the corroded area, an energy loss pattern is modeled using an acoustic wave attenuation formula, and an attenuation distribution diagram is obtained;

[0020] The position coordinates of the corroded area are determined according to the propagation time, the propagation path difference, the energy loss pattern, and the attenuation distribution map.

[0021] Furthermore, a three-dimensional corrosion model of the metal surface of the steel structure is constructed using a triangulation method;

[0022] The depth of the corroded area is calculated according to the attenuation distribution diagram, the propagation time and the frequency difference; for the corroded area, the surface area and the measured length are calculated to estimate the size of the corroded area.

[0023] A panoramic visual inspection system for rust defects in steel structures and metal tools, comprising:

[0024] The sound wave collection unit is equipped with a plurality of sound wave excitation devices on the metal surface of the steel structure of the bridge, which emits sound wave pulses of different frequencies to stimulate the metal surface of the steel structure and obtain a plurality of sound wave vibrations;

[0025] A vibration sensing array deployment unit is configured to configure a panoramic detection array composed of a plurality of micro vibration sensors on the metal surface of the steel structure to collect the vibration response signal of each acoustic vibration in real time;

[0026] The corrosion area distribution acquisition unit calculates the frequency difference using the acoustic wave excitation device and the vibration response signal; smoothes the frequency difference using an interpolation algorithm to obtain a preliminary spatial distribution map of the corrosion area;

[0027] The corrosion area positioning unit calculates the propagation path difference of the micro-vibration sensors using the preliminary spatial distribution map, based on the time synchronization data of each micro-vibration sensor and the initial time of emission of the acoustic wave pulse by each acoustic wave excitation device, obtains an attenuation distribution map using an acoustic wave attenuation formula, and determines the position coordinates of the corrosion area based on the attenuation distribution map.

[0028] A three-dimensional corrosion model and panoramic view construction unit constructs a three-dimensional corrosion model of the metal surface of the steel structure based on the position coordinates; further calculates the size and depth of the corrosion area and optimizes the three-dimensional corrosion model; and outputs a multi-level panoramic view based on the three-dimensional corrosion model, including the overall distribution of the corrosion area, the depth change gradient, and the surface texture details of the corrosion.

[0029] Furthermore, the acoustic wave excitation devices are arranged in a ring array to ensure that there are no blind spots in the area covered by each acoustic wave excitation device; and the operating frequency range of the acoustic wave excitation device is set to cover each frequency band;

[0030] Several of the micro-vibration sensors are tightly fitted to the metal surface of the steel structure; the sampling rate of the micro-vibration sensor is set according to the excitation frequency setting of the acoustic wave excitation device and the Nyquist sampling theorem; the several micro-vibration sensors collect the vibration response signal of each acoustic wave vibration in real time, including the vibration amplitude, frequency and phase.

[0031] Furthermore, the collected vibration response signal is preprocessed, including denoising, signal enhancement and signal synchronization;

[0032] Obtaining the sound wave frequency of each of the sound wave excitation devices; performing a fast Fourier transform on each of the vibration response signals to convert them into a frequency domain signal, and extracting the frequency components of the frequency domain signal; comparing each of the sound wave frequencies with each of the frequency components to calculate the frequency difference;

[0033] The spatial position coordinates of each of the micro-vibration sensors are recorded, the frequency difference is associated with the spatial position coordinates, and the frequency difference is smoothed using an interpolation algorithm to obtain a preliminary spatial distribution map of the rusted area.

[0034] Furthermore, time synchronization data of each micro-vibration sensor is obtained, and the initial time of emission of the acoustic wave pulse by each acoustic wave excitation device is obtained; propagation time is calculated based on the time synchronization data and the initial time; propagation distance of each micro-vibration sensor is calculated based on the propagation time, and the propagation distances of all the micro-vibration sensors are compared to deduce propagation path differences;

[0035] Based on the propagation time and the frequency attenuation characteristics of the corroded area, an energy loss pattern is modeled using an acoustic wave attenuation formula, and an attenuation distribution map is obtained in combination with the preliminary spatial distribution map;

[0036] The position coordinates of the corroded area are determined according to the propagation time, the propagation path difference, the energy loss pattern, and the attenuation distribution map.

[0037] Furthermore, a three-dimensional corrosion model of the metal surface of the steel structure is constructed using a triangulation method;

[0038] The depth of the corroded area is calculated according to the attenuation distribution diagram, the propagation time and the frequency difference; for the corroded area, the surface area and the measured length are calculated to estimate the size of the corroded area.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. By analyzing the frequency differences in the vibration response signal and performing preprocessing such as denoising and synchronization, this method can effectively eliminate the interference of external factors such as environmental noise and illumination changes on detection accuracy. This makes the extraction of frequency features more accurate and stable. Using an interpolation algorithm to smooth the frequency differences, a continuous and clear preliminary spatial distribution map of the rusted area can be generated. This step significantly improves the spatial resolution and reliability of the detection results, while providing basic data support for the subsequent precise positioning of the rusted area and the construction of a three-dimensional model, reducing error accumulation.

[0041] 2. This invention uses the time-synchronized data from the vibration sensor and the initial time of the acoustic excitation device to accurately calculate the differences in propagation paths. Combined with the acoustic attenuation formula, this method generates an attenuation distribution map, which can refine the spatial location of the corroded area. This analysis, based on the propagation characteristics of acoustic waves, overcomes the shortcomings of frequency difference analysis, resulting in more comprehensive and accurate detection results. The attenuation distribution map clearly reflects the depth and extent of corrosion, providing a reliable basis for the location coordinates of the corroded area. This process further enhances the physical accuracy of the detection technology, ensuring the authenticity and consistency of the test results with the actual situation.

[0042] 3. This invention constructs a three-dimensional corrosion model based on precise location coordinates. This model not only comprehensively displays the distribution of corroded areas but also quantitatively assesses their size and depth, providing a comprehensive and intuitive analysis of the extent of corrosion in steel structures. By optimizing the three-dimensional model, the detailed characteristics of corrosion defects can be more accurately reflected, providing a scientific basis for engineering maintenance and repair. This digital three-dimensional visualization method also enables long-term monitoring and trend analysis, reducing human error, providing an efficient and intelligent technical means for bridge structure health management, saving maintenance costs and improving detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of a method for panoramic visual detection of rust defects in steel structure metal tools according to the present invention;

[0044] Figure 2 This is a data flow diagram of a panoramic visual detection method for rust defects in steel structure metal tools according to the present invention;

[0045] Figure 3 This is a system structure diagram of a panoramic visual detection system for rust defects in steel structure metal tools according to the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0047] To detect corrosion on the interior and exterior of bridge steel structures and improve defect detection accuracy, the present invention provides a panoramic visual inspection method and system for corrosion defects in steel structure metalware. To illustrate the effectiveness of the present invention, the following examples will be used to illustrate the effectiveness of the present invention.

[0048] Example 1

[0049] A bridge engineering inspection and maintenance company A ensures the long-term safety and service life of bridges through various services such as inspection, monitoring, maintenance and reinforcement. As the global bridge aging problem intensifies, the demand for inspection and maintenance continues to increase, especially in coastal, humid and industrially polluted areas. The corrosion of steel structures will gradually reduce the bearing capacity and safety of bridges. In order to detect the corrosion of the internal and external metal structures of bridges, a bridge engineering inspection and maintenance company A uses a panoramic visual inspection method for rust defects of steel structure metal tools. Figure 1 , shows a method flow chart of a panoramic visual inspection method for rust defects of steel structure metal tools. Figure 2 , showing a data flow diagram of a panoramic visual detection method for rust defects in steel structure metal appliances.

[0050] Reference Figure 1 In step S01, a plurality of acoustic wave excitation devices are arranged on the metal surface of the steel structure of the bridge, and acoustic wave pulses of different frequencies are emitted to stimulate the metal surface of the steel structure to obtain a plurality of acoustic wave vibrations.

[0051] Specifically, the acoustic wave excitation devices are arranged in a circular array to cover different directions and angles of the steel structure surface, ensuring that there are no blind spots in the area covered by each of the acoustic wave excitation devices. The circular array can be adjusted according to the geometric shape of the bridge steel structure, such as circular, elliptical or polygonal. According to the propagation characteristics of the acoustic wave signal and the size of the detection area, the distance between the excitation devices is set to avoid signal interference while ensuring that the coverage area is not missed. For example, for smaller areas, a small spacing (such as 10-20 cm) can be used; and for large-area detection, the spacing can be appropriately increased (such as 50-100 cm).

[0052] The operating frequency range of the acoustic excitation device is set to cover each frequency band. The low frequency is 20Hz-1kHz, which is used to detect large-scale corrosion and cracks in steel structures; the medium frequency is 1kHz-10kHz, which is used to detect medium-depth defects such as surface cracks and localized voids; and the high frequency is 10kHz-200kHz, which is used to detect small defects and subtle changes within the material.

[0053] The design and placement of acoustic wave excitation devices in a circular array ensures comprehensive detection of blind spots on and within bridge steel structures. By properly selecting the type and frequency range of acoustic wave excitation devices, combined with equipment mounting and anti-interference protection measures, corrosion defects on metal surfaces can be captured efficiently and accurately.

[0054] Further, refer to Figure 1 In step S02, a panoramic detection array consisting of a plurality of micro vibration sensors is configured on the metal surface of the steel structure to collect the vibration response signal of each sound wave vibration in real time.

[0055] Specifically, several micro-vibration sensors are placed tightly against the steel structure's metal surface, distributed in a grid or array to ensure full coverage of the detection area. Sensor density is increased in high-risk areas for corrosion, such as welds, corners, and joints. For thicker steel structures, sensors are deployed at varying depths to capture a more comprehensive vibration response signal.

[0056] According to the excitation frequency setting of the acoustic excitation device, use the Nyquist sampling theorem to set the sampling rate of the micro-vibration sensor. Set the sampling rate of the sensor to at least twice the excitation frequency to ensure that there is no aliasing during the signal acquisition process. For example, for low-frequency excitation (20Hz to 1kHz), set the sampling rate to 2kHz or higher; for high-frequency excitation (1kHz to 200kHz), set the sampling rate to 500kHz or higher.

[0057] Several micro-vibration sensors collect the vibration response signals of each acoustic vibration in real time, including amplitude, frequency, and phase. Tables 1 and 2 show the vibration response signals obtained for low-frequency excitation and high-frequency excitation, respectively. For low-frequency excitation, the sampling rate was set to 2 kHz; for high-frequency excitation, the sampling rate was set to 500 kHz.

[0058] Table 1 Examples of vibration response signals obtained by low-frequency excitation

[0059] Time (ms) Vibration amplitude (μm) Frequency (Hz) Phase (°) 0.000 0.0 500 0 0.500 1.2 500 90 1.000 2.5 500 180 1.500 1.2 500 270 2.000 0.0 500 360

[0060] Table 2 Examples of vibration response signals obtained by high-frequency excitation

[0061] Time (ms) Vibration amplitude (μm) Frequency (Hz) Phase (°) 0.000 0.0 50000 0 0.020 2.0 50000 90 0.040 3.5 50000 180 0.060 2.0 50000 270 0.080 0.0 50000 360

[0062] By deploying micro-vibration sensors on the metal surfaces of steel structures and collecting vibration response signals in real time, we provide high-quality data for detecting and modeling corroded areas. By rationally selecting sensor types, optimizing sensor placement, adjusting sampling rates, and implementing real-time, synchronized data acquisition, we achieve comprehensive coverage of the steel structure's inspection area while ensuring signal accuracy and consistency. Incorporating noise suppression, dynamic gain adjustment, and abnormal data verification further enhances the reliability of the collected signals.

[0063] Further, refer to Figure 1 In step S03, the frequency difference in the vibration response signal is analyzed and preprocessed; the frequency difference is calculated using the acoustic wave excitation device and the vibration response signal; the frequency difference is smoothed using an interpolation algorithm to obtain a preliminary spatial distribution map of the rusted area.

[0064] Specifically, the collected vibration response signal is preprocessed, including denoising, signal enhancement and signal synchronization;

[0065] Get the sound wave frequency f of the jth sound wave excitation device j ; Perform fast Fourier transform on each of the vibration response signals, convert it into a frequency domain signal, and extract the frequency component f of the i-th frequency domain signal i ; Each of the sound wave frequencies f j and each of the frequency components f i Compare and calculate the frequency difference Δf, the calculation formula is Δf=f0-f j .

[0066] Record the spatial position coordinates (x k ,y k ,z k ), the frequency difference Δf and the spatial position coordinate (x k ,yk ,z k ) association, use the interpolation algorithm to smooth the frequency difference, the calculation formula is:

[0067]

[0068] Among them, Δf(x,y,z) represents the preliminary spatial distribution map; ω k Represents the weight, which depends on the target point (x, y, z) and the spatial position coordinates (x k ,y k ,z k ) between the Euclidean distance d k ; K represents the total number of micro vibration sensors.

[0069] By calculating frequency differences and correlating them with the spatial positions of the sensors, this method can quickly locate areas of corrosion on steel structures, avoiding the limitations of traditional manual inspection. An interpolation algorithm smoothes frequency differences, filling in gaps between sensor arrays and generating a continuous spatial distribution map, effectively reducing blind spots and improving corrosion detection accuracy. Combined with the three-dimensional coordinate data from the vibration sensors, this method is applicable to the surface geometry of complex bridge steel structures, providing a preliminary global view of corrosion distribution.

[0070] Further, refer to Figure 1 In step S04, the propagation path difference of the micro-vibration sensor is calculated based on the time synchronization data of each micro-vibration sensor and the initial time of the acoustic wave pulse emitted by each acoustic wave excitation device; an attenuation distribution map is obtained using the acoustic wave attenuation formula; and the position coordinates of the rusted area are determined based on the attenuation distribution map.

[0071] Specifically, the time synchronization data t of the kth micro vibration sensor is obtained. k , and obtain the initial time t of the jth acoustic wave excitation device emitting the acoustic wave pulse 0j According to the time synchronization data t k and the initial time t 0j Calculate the propagation time Δt jk , the calculation formula is: Δt jk =t k -t 0j .

[0072] According to the propagation time Δt jk Calculate the propagation distance d of each micro vibration sensor jk , the calculation formula is:

[0073] d jk =v·Δt jk ;

[0074] Where v represents the speed of sound wave propagation in the steel structure, which is determined by the material properties.

[0075] Compare the propagation distances of all the micro vibration sensors to derive the propagation path difference Δd kl , the calculation formula is Δd kl =|d k -d l |, where Δd kl represents the propagation path difference between the kth micro-vibration sensor and the lth micro-vibration sensor.

[0076] According to the propagation time Δt jk The energy loss pattern is modeled using the sound wave attenuation formula and the frequency attenuation characteristics of the rusted area, and the attenuation distribution diagram is obtained. Specifically, based on the sound wave energy A received by the kth micro vibration sensor k and propagation distance d jk , modeling energy losses:

[0077]

[0078] Among them, A k A represents the sound wave energy received by the k-th vibration sensor; 0j represents the initial acoustic wave energy emitted by the jth acoustic wave excitation device; α represents the energy attenuation coefficient; and e represents the natural base.

[0079] The acoustic wave energy of all micro-vibration sensors is combined to draw an attenuation distribution map.

[0080] Based on the propagation time, the propagation path difference and the energy loss pattern, the position coordinates of the corroded area are determined using a multilateral positioning algorithm, and further corrected based on the attenuation distribution map and path calibration.

[0081] Propagation time provides preliminary positioning, path differences optimize geometric coordinates, and attenuation patterns reveal depth information. Ultimately, a high-precision 3D model is constructed through a combination of attenuation distribution maps and path calibration. This method accurately demonstrates the distribution and depth variations of corroded areas, providing more comprehensive and detailed inspection results, and providing strong support for the precise maintenance and safety of bridge steel structures.

[0082] Further, refer to Figure 1 In step S05, a three-dimensional corrosion model of the metal surface of the steel structure is constructed based on the position coordinates; the size and depth of the corrosion area are further calculated and the three-dimensional corrosion model is optimized; and a multi-level panoramic view is output based on the three-dimensional corrosion model, including the distribution of the overall corrosion area, the depth gradient, and the surface texture details of the corrosion.

[0083] Specifically, a three-dimensional corrosion model of the metal surface of the steel structure is constructed using a triangulation method, and all corrosion points are connected into a triangular mesh to represent the surface.

[0084] Calculate the depth of the rusted area based on the attenuation distribution diagram, the propagation time, and the frequency difference

[0085]

[0086] Among them, A k A represents the sound wave energy received by the kth micro vibration sensor; 0j represents the initial acoustic wave energy emitted by the jth acoustic wave excitation device; α represents the energy attenuation coefficient.

[0087] For the rusted area, triangulation is used to calculate the areas of all triangles and sum them to obtain the surface area of ​​the rusted area. The Canny edge detection algorithm is used to extract the edges of the rusted area. The extracted edge contour points form a polygon, and the side length can be calculated by calculating the Euclidean distance between each two consecutive points. The sum of all edge segments is calculated to obtain the total length of the rusted area.

[0088] Based on the calculated depth, surface area, and total length, the 3D model is further adjusted and optimized using the least squares method. This method is well-known and will not be described in detail here. Based on the 3D corrosion model, a multi-layered panoramic view is generated, including the overall distribution of the corrosion area, the depth gradient, and the surface texture details of the corrosion.

[0089] By combining acoustic wave detection, triangulation, and attenuation models, we can accurately construct a three-dimensional corrosion model. We can then further optimize the size and depth of the corrosion area using depth, surface area, and boundary information. This refined analysis method effectively eliminates external environmental interference and provides more accurate corrosion detection results.

[0090] By combining acoustic excitation and vibration response signals, the system effectively detects corrosion defects on the metal surfaces of bridge steel structures, eliminating interference from ambient light and surface contamination to ensure detection accuracy. Multi-frequency excitation and a panoramic detection array capture precise vibration data, and interpolation algorithms and acoustic attenuation models are used to accurately map the spatial distribution and depth of corroded areas. This process constructs a highly accurate three-dimensional corrosion model, providing a multi-layered, panoramic view, comprehensively assessing the health of the steel structure, and assisting in developing informed maintenance plans to extend the life of the bridge.

[0091] Example 2

[0092] This embodiment proposes a panoramic visual inspection system for corrosion defects of steel structure metal tools, which at least includes all the features of the above-mentioned embodiment method and further improves it. Figure 3 As shown, a system structure diagram of a panoramic visual inspection system for rust defects of steel structure metal appliances is presented.

[0093] The system's vibration sensing array deployment unit can be improved by introducing a multi-frequency collaborative excitation scheme. This selects representative low-, medium-, and high-frequency pulse signals, and uses an automatic frequency scanning mechanism to conduct detailed analysis of the response of corroded areas at different frequencies. Furthermore, an adaptive excitation power and frequency adjustment mechanism dynamically adjusts the frequency and power of the acoustic excitation based on factors such as the surface material of the bridge steel structure and the degree of corrosion, enabling comprehensive perception of different types of corrosion defects.

[0094] In the rust area distribution acquisition unit of the system, time-frequency analysis methods such as wavelet transform or short-time Fourier transform can be introduced to perform more refined signal processing, especially when the changes in the rust area are subtle, which helps to extract more detailed features and reduce the impact of noise.

[0095] The system's corrosion area location unit calculates frequency differences, propagation path differences, and attenuation distribution maps, using machine learning algorithms such as neural networks and support vector machines for automatic signal classification and pattern recognition, leading to optimization. These algorithms can learn from multiple signal features in complex environments, further improving the accuracy of corrosion defect identification.

[0096] In the system's 3D corrosion model and panoramic view construction unit, in addition to spatial coordinates and depth, surface morphology analysis can be incorporated into the 3D corrosion model. Using 3D scanning or LiDAR technology, the steel structure's surface topography can be further refined to more accurately analyze corrosion details. To improve the model's real-time performance and accuracy, a dynamic update function can be designed. Through real-time monitoring and regular inspection data updates, an adaptive corrosion development model can be constructed to predict future trends in corrosion areas and display changes at different time points in a panoramic view.

[0097] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A panoramic visual detection method for corrosion defects of steel structure metal tools, characterized in that: include: Arrange a plurality of acoustic wave excitation devices on the metal surface of the steel structure of the bridge, emit acoustic wave pulses of different frequencies to stimulate the metal surface of the steel structure, and obtain a plurality of acoustic wave vibrations; A panoramic detection array consisting of a plurality of micro vibration sensors is arranged on the metal surface of the steel structure to collect the vibration response signal of each acoustic wave vibration in real time; Calculating a frequency difference using the acoustic wave excitation device and the vibration response signal; smoothing the frequency difference using an interpolation algorithm to obtain a preliminary spatial distribution map of the corrosion area; Utilizing the preliminary spatial distribution map, the propagation path differences of the micro-vibration sensors are calculated based on the time synchronization data of each micro-vibration sensor and the initial time of emission of the acoustic wave pulse by each acoustic wave excitation device; obtaining an attenuation distribution map using an acoustic wave attenuation formula; and determining the position coordinates of the corroded area based on the attenuation distribution map; Based on the position coordinates, a three-dimensional corrosion model of the metal surface of the steel structure is constructed; the size and depth of the corrosion area are further calculated, and the three-dimensional corrosion model is optimized; A multi-level panoramic view is output based on the three-dimensional corrosion model, including the distribution of the entire corrosion area, the depth gradient, and the surface texture details of the corrosion.

2. A panoramic visual detection method for corrosion defects of steel structure metal tools according to claim 1, characterized in that: Arrange the acoustic wave excitation devices in a ring array to ensure that there are no blind spots in the area covered by each acoustic wave excitation device; set the operating frequency range of the acoustic wave excitation device to cover each frequency band; Several of the micro-vibration sensors are tightly fitted to the metal surface of the steel structure; the sampling rate of the micro-vibration sensor is set according to the excitation frequency setting of the acoustic wave excitation device and the Nyquist sampling theorem; the several micro-vibration sensors collect the vibration response signal of each acoustic wave vibration in real time, including the vibration amplitude, frequency and phase.

3. A panoramic visual inspection method for corrosion defects of steel structure metal tools according to claim 1, characterized in that: Preprocessing the collected vibration response signal, including denoising, signal enhancement and signal synchronization; Obtaining the sound wave frequency of each of the sound wave excitation devices; performing a fast Fourier transform on each of the vibration response signals to convert them into a frequency domain signal, and extracting the frequency components of the frequency domain signal; comparing each of the sound wave frequencies with each of the frequency components to calculate the frequency difference; The spatial position coordinates of each of the micro-vibration sensors are recorded, the frequency difference is associated with the spatial position coordinates, and the frequency difference is smoothed using an interpolation algorithm to obtain a preliminary spatial distribution map of the rusted area.

4. A panoramic visual inspection method for corrosion defects of steel structure metal tools according to claim 3, characterized in that: Obtaining time synchronization data for each of the micro-vibration sensors and obtaining the initial time of emission of the acoustic wave pulse by each of the acoustic wave excitation devices; calculating a propagation time based on the time synchronization data and the initial time; calculating a propagation distance for each of the micro-vibration sensors based on the propagation time, comparing the propagation distances of all the micro-vibration sensors, and deriving a propagation path difference; Based on the propagation time and the frequency attenuation characteristics of the corroded area, an energy loss pattern is modeled using an acoustic wave attenuation formula, and an attenuation distribution diagram is obtained; The position coordinates of the corroded area are determined according to the propagation time, the propagation path difference, the energy loss pattern, and the attenuation distribution map.

5. The method for panoramic visual inspection of rust defects of steel structure metal tools according to claim 1, characterized in that: constructing a three-dimensional corrosion model of the metal surface of the steel structure using a triangulation method; The depth of the corroded area is calculated according to the attenuation distribution diagram, the propagation time and the frequency difference; for the corroded area, the surface area and the measured length are calculated to estimate the size of the corroded area.

6. A panoramic visual inspection system for corrosion defects of steel structure metal tools, characterized by: include: The sound wave collection unit is equipped with a plurality of sound wave excitation devices on the metal surface of the steel structure of the bridge, which emits sound wave pulses of different frequencies to stimulate the metal surface of the steel structure and obtain a plurality of sound wave vibrations; A vibration sensing array deployment unit is configured to configure a panoramic detection array composed of a plurality of micro vibration sensors on the metal surface of the steel structure to collect the vibration response signal of each acoustic vibration in real time; The corrosion area distribution acquisition unit calculates the frequency difference using the acoustic wave excitation device and the vibration response signal; smoothes the frequency difference using an interpolation algorithm to obtain a preliminary spatial distribution map of the corrosion area; The corrosion area positioning unit calculates the propagation path difference of the micro-vibration sensors using the preliminary spatial distribution map, based on the time synchronization data of each micro-vibration sensor and the initial time of emission of the acoustic wave pulse by each acoustic wave excitation device, obtains an attenuation distribution map using an acoustic wave attenuation formula, and determines the position coordinates of the corrosion area based on the attenuation distribution map. A three-dimensional corrosion model and panoramic view construction unit constructs a three-dimensional corrosion model of the metal surface of the steel structure based on the position coordinates; further calculates the size and depth of the corrosion area and optimizes the three-dimensional corrosion model; and outputs a multi-level panoramic view based on the three-dimensional corrosion model, including the overall distribution of the corrosion area, the depth change gradient, and the surface texture details of the corrosion.

7. A panoramic visual inspection system for corrosion defects of steel structure metal tools according to claim 6, characterized in that: Arrange the acoustic wave excitation devices in a ring array to ensure that there are no blind spots in the area covered by each acoustic wave excitation device; set the operating frequency range of the acoustic wave excitation device to cover each frequency band; Several of the micro-vibration sensors are tightly fitted to the metal surface of the steel structure; the sampling rate of the micro-vibration sensor is set according to the excitation frequency setting of the acoustic wave excitation device and the Nyquist sampling theorem; the several micro-vibration sensors collect the vibration response signal of each acoustic wave vibration in real time, including the vibration amplitude, frequency and phase.

8. A panoramic visual inspection system for corrosion defects of steel structure metal tools according to claim 6, characterized in that: Preprocessing the collected vibration response signal, including denoising, signal enhancement and signal synchronization; Obtaining the sound wave frequency of each of the sound wave excitation devices; performing a fast Fourier transform on each of the vibration response signals to convert them into a frequency domain signal, and extracting the frequency components of the frequency domain signal; comparing each of the sound wave frequencies with each of the frequency components to calculate the frequency difference; The spatial position coordinates of each of the micro-vibration sensors are recorded, the frequency difference is associated with the spatial position coordinates, and the frequency difference is smoothed using an interpolation algorithm to obtain a preliminary spatial distribution map of the rusted area.

9. A panoramic visual inspection system for corrosion defects of steel structure metal tools according to claim 8, characterized in that: Obtaining time synchronization data for each of the micro-vibration sensors and obtaining the initial time of emission of the acoustic wave pulse by each of the acoustic wave excitation devices; calculating a propagation time based on the time synchronization data and the initial time; calculating a propagation distance for each of the micro-vibration sensors based on the propagation time, comparing the propagation distances of all the micro-vibration sensors, and deriving a propagation path difference; Based on the propagation time and the frequency attenuation characteristics of the corroded area, an energy loss pattern is modeled using an acoustic wave attenuation formula, and an attenuation distribution map is obtained in combination with the preliminary spatial distribution map; The position coordinates of the corroded area are determined according to the propagation time, the propagation path difference, the energy loss pattern, and the attenuation distribution map.

10. A panoramic visual inspection system for corrosion defects of steel structure metal tools according to claim 6, characterized in that: constructing a three-dimensional corrosion model of the metal surface of the steel structure using a triangulation method; The depth of the corroded area is calculated according to the attenuation distribution diagram, the propagation time and the frequency difference; for the corroded area, the surface area and the measured length are calculated to estimate the size of the corroded area.

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