A large steel structure damage detection method and system based on intelligent sensors

Abstract

This invention discloses a method and system for detecting damage to large steel structures based on intelligent sensors, relating to the field of steel structure damage detection technology. The method includes the following steps: S1: Using a drone equipped with a high-definition camera, images are captured of the large steel structure; the images are analyzed to pinpoint the detection area; S2: The thickness of each detection area is measured using a dual-crystal probe thickness gauge; the average thickness of each detection area is measured using a grid division method; the average thickness is used to determine whether the detection area is qualified; areas with excessively low remaining corrosion thickness, areas with acceptable remaining thickness but located in important positions and exhibiting abnormalities such as air bubbles inside the steel structure, and areas with acceptable remaining thickness but located in important positions at the edge of the steel structure are marked. This allows management personnel to target repair work in these areas and facilitates continuous monitoring of areas with acceptable remaining thickness but located in important positions.

Description

Technical Field

[0001] This invention relates to the field of steel structure damage detection technology, specifically to a method and system for detecting damage in large steel structures based on intelligent sensors. Background Technology

[0002] Damage detection of large steel structures is a systematic process that comprehensively utilizes non-destructive testing technology, structural health monitoring, and mechanical analysis to assess the performance degradation of steel caused by factors such as corrosion, cracks, deformation, or fatigue, ensuring structural safety. It typically uses intelligent sensing devices such as eddy current sensors, acoustic emission sensors, electromagnetic sensors, and fiber optic grating sensors to accurately locate high-risk areas, providing a scientific basis for maintenance or reinforcement decisions. It is widely used in the regular maintenance and accident prevention of critical infrastructure such as bridges, factories, and ships.

[0003] Due to the large size of large steel structures and the fact that most of their surfaces are coated with paint, it is impossible to apply coupling agent to the entire structure for corrosion testing. Furthermore, some areas may have intact paint on the surface but internal corrosion. The location of corrosion on the steel structure also affects the test results. For example, corrosion in load-bearing and tensile areas needs to be taken seriously, while corrosion in decorative areas may not seriously affect the normal use of the steel structure. Therefore, corrosion testing of large steel structures relies heavily on manual intervention and judgment. To address this, we propose a method and system for detecting damage to large steel structures based on intelligent sensors. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for detecting damage to large steel structures based on intelligent sensors, thereby solving the aforementioned problems in the prior art.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention provides a method for detecting damage to large steel structures based on intelligent sensors, comprising the following steps:

[0008] S1: Take pictures of the large steel structure using a drone equipped with a high-definition camera, and analyze the pictures to identify the detection area;

[0009] S2: Use a dual-crystal probe thickness gauge to measure the thickness of each detection area. Measure the average thickness of each detection area using a grid division method. Determine whether the detection area is qualified based on the average thickness.

[0010] S3: Analyze the qualified inspection area by taking pictures to determine whether it is an important stress area. Locate the weak area of ​​the important stress area by grid division method. Analyze the weak area with an ultrasonic flaw detector. Determine whether the weak area is qualified based on whether there are pores, bubbles or hollows in the weak area.

[0011] S4: Based on the test results, record the areas that failed the test and the weak areas that need to be continuously monitored, and send the record results to the management personnel.

[0012] Preferably, in S1, the captured image is analyzed to lock the detection area, specifically as follows:

[0013] S101: Acquire captured images, capture peeling areas on the surface of large steel structures in the captured images through color recognition, and define the peeling areas as defined areas;

[0014] S102: The captured image is processed to obtain a grayscale image. The raised and recessed areas of the paint are captured in the grayscale image by brightness recognition. The raised and recessed areas are defined as suspicious areas.

[0015] S103: Obtain all confirmed regions and all suspicious regions, and define all confirmed regions and all suspicious regions as detection regions.

[0016] Preferably, in S2, the thickness of each detection area is measured using a dual-crystal probe thickness gauge, specifically as follows:

[0017] S201: Measure the thickness of all defined areas using a dual-crystal probe thickness gauge;

[0018] S202: Measure the thickness of all suspicious areas using a dual-crystal probe thickness gauge to obtain the standard thickness of the large steel structure. Compare the thickness of each suspicious area with the standard thickness of the large steel structure. If the thickness of the suspicious area is different from the standard thickness of the large steel structure, mark the suspicious area as a confirmed area. If the thickness of the suspicious area is the same as the standard thickness of the large steel structure, delete the area from the detection area.

[0019] Preferably, in S2, the average thickness of each detection area is measured using a grid division method, and the average thickness is used to determine whether the detection area is qualified. Specifically:

[0020] S203: Divide each detection area into nine grid areas according to the area area. Use a dual-crystal probe thickness gauge to measure the thickness of each of the nine grid areas in each detection area. Sum the thicknesses of the nine grid areas and take the average value to obtain the average thickness of each detection area.

[0021] S204: Obtain the standard thickness of the large steel structure, multiply the standard thickness of the large steel structure by 0.9 to obtain the safe thickness of the large steel structure, and determine whether the average thickness of each inspection area is greater than the safe thickness of the large steel structure. If the average thickness of the inspection area is less than or equal to the safe thickness of the large steel structure, the inspection area is defined as an unqualified area. If the average thickness of the inspection area is greater than the safe thickness of the large steel structure, the inspection area is defined as a preliminary qualified area.

[0022] Preferably, in S3, the qualified inspection area is analyzed by taking pictures to determine whether it is a critical stress area. Specifically:

[0023] S301: Obtain the image corresponding to the preliminary qualified area, and determine whether there are bolts or welds in the preliminary qualified area based on the image. If there are welds or bolts in the preliminary qualified area, define the area as an important stress area. If there are no welds or bolts in the preliminary qualified area, execute S302.

[0024] S302: Obtain the construction drawings of the large steel structure, and determine whether the preliminary qualified area is the compression main beam or tension flange of the large steel structure based on the construction drawings. If the preliminary qualified area is the compression main beam or tension flange of the large steel structure, the test area is defined as an important stress area. If the preliminary qualified area is not the compression main beam or tension flange of the large steel structure, the area is defined as a qualified area.

[0025] Preferably, in S3, the weak areas of important stress regions are identified using a mesh generation method, specifically;

[0026] S303: Obtain the thickness of the nine grid areas in each important stress region, arrange the thickness of the nine grid areas in ascending order, select the grid with the smallest thickness and define it as the weak grid;

[0027] S304: Obtain the center point location of the weak mesh, obtain the maximum distance from the center point of the weak mesh to the edge of the weak mesh, and define 2.5 times the maximum distance from the center point of the weak mesh to the edge of the weak mesh as the weak distance. Draw a circle with the center point of the weak mesh as the center and the weak distance as the radius to obtain the weak region.

[0028] Preferably, in S3, an ultrasonic flaw detector is used to analyze the weak area, and the presence or absence of pores, bubbles or voids in the weak area is used to determine whether the weak area is qualified.

[0029] S305: Obtain the weak area and take a picture. Determine whether there is a large steel structure edge or corner position within the weak area based on the picture. If there is a large steel structure edge or corner position within the weak area, mark the weak area as a non-compliant area. If there is no large steel structure edge or corner position within the weak area, proceed to S306.

[0030] S306: Weak areas are measured using an ultrasonic flaw detector to determine whether there are air bubbles or pores in the weak areas. If air bubbles or pores are present in the weak areas, the weak areas are marked as unqualified areas. If there are no air bubbles or pores in the weak areas, the weak areas are marked as qualified areas.

[0031] Preferably, in S4, specifically:

[0032] S401: Establish a test result database, obtain the location of non-conforming areas, include the location of non-conforming areas in the test result database, and send it to the management personnel;

[0033] S402: Obtain the current location of the qualified area, add the current location of the qualified area to the test result database, send the current location information of the qualified area to the management personnel and remind the management personnel that the location needs to be continuously monitored.

[0034] A damage detection system for large steel structures based on intelligent sensors includes the following modules:

[0035] The detection area locking module uses a drone equipped with a high-definition camera to capture images of a large steel structure, and then analyzes these images to lock down the detection area.

[0036] The preliminary corrosion status assessment module uses a dual-crystal probe thickness gauge to measure the thickness of each detection area. The average thickness of each detection area is measured by a grid division method, and the average thickness is used to determine whether the detection area is qualified.

[0037] The weak area judgment module analyzes the captured images to determine whether the qualified inspection area is an important stress area. It uses a grid division method to locate the weak areas of the important stress area, and uses an ultrasonic flaw detector to analyze the weak areas. It judges whether the weak area is qualified based on whether there are pores, bubbles or voids in the weak area.

[0038] The test result recording module records non-compliant areas and weak areas requiring continuous monitoring based on the test results, and sends the recorded results to the management personnel.

[0039] (III) Beneficial Effects

[0040] This invention provides a method and system for detecting damage to large steel structures based on intelligent sensors, which has the following advantages:

[0041] This solution identifies the inspection area by taking pictures, then analyzes the pictures to determine if the qualified inspection area is a critical stress area. Combining the location of the initially qualified area with the analysis of its position on the steel structure, it determines whether that location bears significant tensile or compressive loads. Furthermore, each grid within a single initially qualified area is analyzed using a grid division method to identify the weakest point. This weakest point is then inspected using an ultrasonic flaw detector to check for bubbles, pores, or cracks within the steel structure. If bubbles or pores are present in the weakest area, even if the remaining thickness meets standards, it is difficult to meet the requirements for high-strength tensile support, posing a very high risk of tearing. The location of the weakest point is then determined to determine if it is located at the edge of the steel structure. Since the edge area has a relatively low thickness and lacks the support of a thicker steel body, tearing is more likely. Therefore, a comprehensive analysis is conducted, considering the stress conditions at the location of the initially qualified area and its proximity to the steel structure edge, to determine whether the current corrosion status is acceptable.

[0042] This plan marks areas with excessively low remaining corrosion thickness, areas with acceptable remaining thickness but located in important positions and exhibiting abnormalities such as air bubbles within the steel structure, and areas with acceptable remaining thickness but located in important positions at the edges of the steel structure. This information is then relayed to management personnel, enabling them to target repairs in these areas effectively. It also facilitates continuous monitoring of areas with acceptable remaining thickness but located in important positions, preventing corrosion from worsening and causing tearing. Ultimately, this allows management personnel to perform preventative repairs and maintenance before incidents occur. Attached Figure Description

[0043] Figure 1 This is a flowchart of a method for detecting damage to large steel structures based on intelligent sensors according to the present invention.

[0044] Figure 2 This is a schematic diagram of the structure of a large steel structure damage detection system based on intelligent sensors according to the present invention;

[0045] Figure 3 This is a schematic diagram of the logical structure of a large steel structure damage detection method based on intelligent sensors according to the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please see Figures 1-3This invention provides a method for detecting damage to large steel structures based on intelligent sensors, comprising the following steps:

[0048] S1: Take pictures of the large steel structure using a drone equipped with a high-definition camera, and analyze the pictures to identify the detection area;

[0049] S2: Use a dual-crystal probe thickness gauge to measure the thickness of each detection area. Measure the average thickness of each detection area using a grid division method. Determine whether the detection area is qualified based on the average thickness.

[0050] S3: Analyze the qualified inspection area by taking pictures to determine whether it is an important stress area. Locate the weak area of ​​the important stress area by grid division method. Analyze the weak area with an ultrasonic flaw detector. Determine whether the weak area is qualified based on whether there are pores, bubbles or hollows in the weak area.

[0051] S4: Based on the test results, record the areas that failed the test and the weak areas that need to be continuously monitored, and send the record results to the management personnel.

[0052] In this embodiment, the solution uses a drone equipped with a high-definition camera to take pictures of the large steel structure, thereby providing detection data for the large steel structure inspection. This facilitates targeted regional inspection of the large steel structure. By analyzing the captured pictures, the detection area can be identified, which makes it easier to identify the corrosion area exposed by paint peeling on the surface of the large steel structure, as well as abnormal areas on the paint surface of the large steel structure, such as areas of paint bulging or depression. This makes it easier to analyze and investigate the damage and corrosion under the paint.

[0053] This solution utilizes a dual-crystal probe thickness gauge to measure the thickness of each inspection area, thereby facilitating the understanding of the corrosion status of the steel structure. Based on the thickness, the actual effective support thickness of the steel structure is determined, and then based on the actual thickness, it is determined whether the corrosion status still meets the rigid support requirements of the steel structure. The average thickness is measured through a grid division method, which facilitates the accurate understanding of the thickness of each inspection area, and thus facilitates the accurate understanding of the corrosion status of each inspection area.

[0054] This solution analyzes images to determine if qualified inspection areas are critical stress areas, facilitating further damage assessment of initially qualified areas. It analyzes the location of these initially qualified areas to determine if they bear significant tensile or compressive stress on the steel structure. Using a grid-based approach, each grid within a single initially qualified area is analyzed to identify the weakest point – the thinnest region. Such areas, being thinner at the edges and thinner in the middle, experience greater stress during actual stress, making them more prone to tearing. Therefore, the identified weak areas are then inspected using ultrasonic flaw detectors to check for bubbles, pores, or cracks within the steel structure. If bubbles or pores are present in the weak areas, even if the remaining thickness meets standards, it is difficult to meet high-strength tensile requirements, posing a high risk of tearing. Determining the presence of voids in the weak areas involves identifying whether they are located at the edge of the steel structure. Since the edge is relatively thinner and lacks the support of a thicker steel body, tearing is more likely. Therefore, a comprehensive analysis considering the stress conditions at the location of the initially qualified area and its proximity to the steel structure edge is conducted to determine if the current corrosion status is acceptable.

[0055] This plan marks areas with excessively low remaining corrosion thickness, areas with acceptable remaining thickness but located in important positions and exhibiting abnormalities such as air bubbles within the steel structure, and areas with acceptable remaining thickness but located in important positions at the edges of the steel structure. This information is then relayed to management personnel, enabling them to target repairs in these areas effectively. It also facilitates continuous monitoring of areas with acceptable remaining thickness but located in important positions, preventing corrosion from worsening and causing tearing. Ultimately, this allows management personnel to perform preventative repairs and maintenance before incidents occur.

[0056] In S1, the captured image is analyzed to lock the detection area, specifically as follows:

[0057] S101: Acquire captured images, capture peeling areas on the surface of large steel structures in the captured images through color recognition, and define the peeling areas as defined areas;

[0058] S102: The captured image is processed to obtain a grayscale image. The raised and recessed areas of the paint are captured in the grayscale image by brightness recognition. The raised and recessed areas are defined as suspicious areas.

[0059] S103: Obtain all confirmed regions and all suspicious regions, and define all confirmed regions and all suspicious regions as detection regions.

[0060] In this embodiment, the disappearance of the protection on the outer surface of the steel structure can be determined through the area where the surface paint has peeled off. It is more likely to corrode after being eroded by water vapor. Therefore, the peeled area is the area where thickness detection must be carried out. The bulging or sunken areas may be caused by gas generated by the internal metal oxidation reaction. Therefore, oxidation corrosion is very likely to have occurred inside the paint film, so thickness detection should also be carried out on this area.

[0061] In S2, a double-crystal probe thickness gauge is used to detect the thickness of each detection area, specifically as follows:

[0062] S201: Measure the thickness of all determined areas respectively through the double-crystal probe thickness gauge;

[0063] S202: Measure the thickness of all suspicious areas respectively through the double-crystal probe thickness gauge to obtain the standard thickness of the large steel structure. Compare whether the thickness of each suspicious area is the same as the standard thickness of the large steel structure. If the thickness of the suspicious area is not the same as the standard thickness of the large steel structure, mark this suspicious area as a determined area. If the thickness of the suspicious area is the same as the standard thickness of the large steel structure, delete this area from the detection area.

[0064] In this embodiment, the double-crystal probe thickness gauge is used to detect the thickness of the detection area, and then the detected actual thickness is compared with the normal thickness of the steel structure to judge whether the remaining thickness meets the standard requirements. The comparison result is also convenient for judging whether corrosion has occurred in the bulging and sunken areas inside the paint film;

[0065] It is worth mentioning that the double-crystal probe thickness gauge is an ultrasonic or phased array thickness gauge with two independent wafers, one for transmitting signals and the other for receiving signals. It is a device specially designed for thickness measurement under corrosive rough surfaces or paint film coatings. It is prior art, and the specific working principle will not be elaborated here.

[0066] In S2, the average thickness of each detection area is measured by the grid division method, and whether the detection area is qualified is judged by the average thickness, specifically as follows:

[0067] S203: Divide each detection area into nine grid areas according to the area of the area. Measure the thickness of each grid area in the nine grid areas of each detection area respectively through the double-crystal probe thickness gauge. Sum up the nine grid area thicknesses and take the average value to obtain the average thickness of each detection area;

[0068] S204: Obtain the standard thickness of the large steel structure, multiply the standard thickness of the large steel structure by 0.9 to obtain the safe thickness of the large steel structure, and determine whether the average thickness of each inspection area is greater than the safe thickness of the large steel structure. If the average thickness of the inspection area is less than or equal to the safe thickness of the large steel structure, the inspection area is defined as an unqualified area. If the average thickness of the inspection area is greater than the safe thickness of the large steel structure, the inspection area is defined as a preliminary qualified area.

[0069] In this embodiment, the average thickness of the detection area is measured by a grid division method, which facilitates a more accurate understanding of the thickness parameters of the steel structure. The remaining thickness is compared with 90% of the normal thickness of the steel to determine whether the remaining thickness of the steel can meet the safety standard. In actual work, the grid division method can be used for areas with more than nine grids depending on the specific situation. The assessment of the safe thickness can be selected according to actual needs.

[0070] In S3, the system analyzes captured images to determine whether qualified inspection areas are critical stress areas. Specifically:

[0071] S301: Obtain the image corresponding to the preliminary qualified area, and determine whether there are bolts or welds in the preliminary qualified area based on the image. If there are welds or bolts in the preliminary qualified area, define the area as an important stress area. If there are no welds or bolts in the preliminary qualified area, execute S302.

[0072] S302: Obtain the construction drawings of the large steel structure, and determine whether the preliminary qualified area is the compression main beam or tension flange of the large steel structure based on the construction drawings. If the preliminary qualified area is the compression main beam or tension flange of the large steel structure, the test area is defined as an important stress area. If the preliminary qualified area is not the compression main beam or tension flange of the large steel structure, the area is defined as a qualified area.

[0073] In this embodiment, the presence of bolt welds in the preliminarily qualified areas after screening is analyzed in conjunction with the captured images, which helps to determine whether the area is an important area for tensile or compressive stress. Furthermore, the construction drawings are combined to analyze whether the area is located at the position of the compression main beam and the tension flange, which facilitates further analysis of the stress situation in the area and ultimately determines whether the area is an important stress area.

[0074] In S3, weak areas in important stress regions are identified using a mesh generation method, specifically;

[0075] S303: Obtain the thickness of the nine grid areas in each important stress region, arrange the thickness of the nine grid areas in ascending order, select the grid with the smallest thickness and define it as the weak grid;

[0076] S304: Obtain the center point location of the weak mesh, obtain the maximum distance from the center point of the weak mesh to the edge of the weak mesh, and define 2.5 times the maximum distance from the center point of the weak mesh to the edge of the weak mesh as the weak distance. Draw a circle with the center point of the weak mesh as the center and the weak distance as the radius to obtain the weak region.

[0077] In this embodiment, the measurement data of the average thickness is measured using the mesh division method in the early stage. The center position and radius of the circle with the smallest thickness in a single mesh are determined. The weak area is then obtained based on the center position and radius, which makes it easier to determine the range of the weakest area. This makes it easier to determine whether there are potential risks in the weak area and ultimately to determine whether the corrosion damage in the weak area is acceptable.

[0078] In S3, an ultrasonic flaw detector is used to analyze weak areas. The presence of pores, bubbles, or voids within the weak areas determines whether they are qualified.

[0079] S305: Obtain the weak area and take a picture. Determine whether there is a large steel structure edge or corner position within the weak area based on the picture. If there is a large steel structure edge or corner position within the weak area, mark the weak area as a non-compliant area. If there is no large steel structure edge or corner position within the weak area, proceed to S306.

[0080] S306: Weak areas are measured using an ultrasonic flaw detector to determine whether there are air bubbles or pores in the weak areas. If air bubbles or pores are present in the weak areas, the weak areas are marked as unqualified areas. If there are no air bubbles or pores in the weak areas, the weak areas are marked as qualified areas.

[0081] In this embodiment, an ultrasonic flaw detector is used to detect whether there are bubbles, pores, or cracks inside the steel structure. If bubbles or pores are present in the weak area, even if the remaining thickness meets the standard, it is still difficult to meet the requirements for high-strength support and tensile resistance, and there is an extremely high risk of tearing. The determination of whether there are hollow areas in the weak area is based on whether the weak area is located at the edge of the steel structure. Since the thickness at the edge is relatively low, it is easier to tear without the support of the thick steel body at the edge. Therefore, the current corrosion status is comprehensively analyzed by combining the stress situation of the location of the initially qualified area and the situation of being close to the edge of the steel structure.

[0082] In S4, specifically:

[0083] S401: Establish a test result database, obtain the location of non-conforming areas, include the location of non-conforming areas in the test result database, and send it to the management personnel;

[0084] S402: Obtain the current location of the qualified area, add the current location of the qualified area to the test result database, send the current location information of the qualified area to the management personnel and remind the management personnel that the location needs to be continuously monitored.

[0085] In this embodiment, areas with excessively low remaining corrosion thickness, areas with acceptable remaining thickness but located in important positions and exhibiting abnormalities such as air bubbles inside the steel structure, and areas with acceptable remaining thickness but located in important positions at the edges of the steel structure are marked and reported to management personnel. This allows management personnel to carry out targeted repairs in these areas and to continuously monitor areas with acceptable remaining thickness but located in important positions, preventing corrosion from worsening and causing tearing. Consequently, it facilitates management personnel to carry out early repair and maintenance work before accidents occur.

[0086] Please see Figures 1-3 This invention provides a large-scale steel structure damage detection system based on intelligent sensors, comprising the following modules:

[0087] The detection area locking module uses a drone equipped with a high-definition camera to capture images of a large steel structure, and then analyzes these images to lock down the detection area.

[0088] The preliminary corrosion status assessment module uses a dual-crystal probe thickness gauge to measure the thickness of each detection area. The average thickness of each detection area is measured by a grid division method, and the average thickness is used to determine whether the detection area is qualified.

[0089] The weak area judgment module analyzes the captured images to determine whether the qualified inspection area is an important stress area. It uses a grid division method to locate the weak areas of the important stress area, and uses an ultrasonic flaw detector to analyze the weak areas. It judges whether the weak area is qualified based on whether there are pores, bubbles or voids in the weak area.

[0090] The test result recording module records non-compliant areas and weak areas requiring continuous monitoring based on the test results, and sends the recorded results to the management personnel.

[0091] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0092] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0093] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A large steel structure damage detection method based on intelligent sensors, characterized in that, It includes the following steps: S1: Use a drone equipped with a high-definition camera to take pictures of the large steel structure, and analyze the taken pictures to lock the detection areas; S2: Use a dual-crystal probe thickness gauge to detect the thickness of each detection area respectively, measure the average thickness of each detection area by the grid division method, and judge whether the detection area is qualified according to the average thickness; S3: Analyze whether the qualified detection areas in the taken pictures are important stress-bearing areas through the taken pictures, lock the weak areas of the important stress-bearing areas by the grid division method, use an ultrasonic flaw detector to analyze the weak areas, and judge whether the weak areas are qualified according to whether there are pores, bubbles or hollow areas in the weak areas; S4: Record the unqualified detection areas and the weak areas that need continuous attention according to the detection results, and send the recorded results to the management personnel. 2.The large steel structure damage detection method based on intelligent sensor according to claim 1, characterized in that: In S1, analyze the taken pictures to lock the detection areas. Specifically: S101: Obtain the taken pictures, capture the peeling areas on the surface of the large steel structure in the taken pictures through color recognition, and define the peeling areas as the determined areas; S102: Perform gray-scale processing on the taken pictures to obtain gray-scale pictures, capture the raised areas and sunken areas of the paint film in the gray-scale pictures through brightness recognition, and define the raised areas and sunken areas as the suspicious areas; S103: Obtain all the determined areas and all the suspicious areas, and define all the determined areas and all the suspicious areas as the detection areas. 3.The large steel structure damage detection method based on intelligent sensor according to claim 2, characterized in that: In S2, use a dual-crystal probe thickness gauge to detect the thickness of each detection area respectively. Specifically: S201: Measure the thickness of all the determined areas respectively by the dual-crystal probe thickness gauge; S202: Measure the thickness of all the suspicious areas respectively by the dual-crystal probe thickness gauge, obtain the standard thickness of the large steel structure, compare whether the thickness of each suspicious area is the same as the standard thickness of the large steel structure respectively. If the thickness of the suspicious area is not the same as the standard thickness of the large steel structure, mark the suspicious area as a determined area. If the thickness of the suspicious area is the same as the standard thickness of the large steel structure, delete the area from the detection areas. 4.The large steel structure damage detection method based on intelligent sensor according to claim 1, characterized in that: In S2, measure the average thickness of each detection area by the grid division method, and judge whether the detection area is qualified according to the average thickness. Specifically: S203: Divide each detection area into nine grid areas with equal area according to the area of the area, measure the thickness of each grid area in the nine grid areas in each detection area respectively by the dual-crystal probe thickness gauge, sum up the nine grid area thicknesses and take the average value to obtain the average thickness of each detection area; S204: Obtain the standard thickness of the large steel structure, multiply the standard thickness of the large steel structure by 0.9 to obtain the safety thickness of the large steel structure, judge whether the average thickness of each detection area is greater than the safety thickness of the large steel structure respectively. If the average thickness of the detection area is less than or equal to the safety thickness of the large steel structure, define the detection area as an unqualified area. If the average thickness of the detection area is greater than the safety thickness of the large steel structure, define the detection area as a preliminarily qualified area.

5. The method for damage detection of large steel structure based on smart sensor according to claim 4, characterized in that: In S3, analyze whether the qualified detection areas in the taken pictures are important stress-bearing areas. Specifically: S301: Obtain the image corresponding to the preliminary qualified area, and determine whether there are bolts or welds in the preliminary qualified area based on the image. If there are welds or bolts in the preliminary qualified area, define the area as an important stress area. If there are no welds or bolts in the preliminary qualified area, execute S302. S302: Obtain the construction drawings of the large steel structure, and determine whether the preliminary qualified area is the compression main beam or tension flange of the large steel structure based on the construction drawings. If the preliminary qualified area is the compression main beam or tension flange of the large steel structure, the test area is defined as an important stress area. If the preliminary qualified area is not the compression main beam or tension flange of the large steel structure, the area is defined as a qualified area.

6. The method for damage detection of large steel structure based on smart sensor according to claim 4, characterized in that: In S3, weak areas in important stress regions are identified using a mesh generation method, specifically; S303: Obtain the thickness of the nine grid areas in each important stress region, arrange the nine grid area thicknesses in ascending order, select the grid with the smallest thickness and define it as the weak grid; S304: Obtain the center point position of the weak mesh, obtain the maximum distance from the center point of the weak mesh to the edge of the weak mesh, and define 2.5 times the maximum distance from the center point of the weak mesh to the edge of the weak mesh as the weak distance. Draw a circle with the center point of the weak mesh as the center and the weak distance as the radius to obtain the weak region.

7. The method for damage detection of large steel structure based on smart sensor according to claim 1, characterized in that: In S3, an ultrasonic flaw detector is used to analyze weak areas. The presence of pores, bubbles, or voids within the weak areas determines whether they are qualified. S305: Obtain the weak area and take a picture. Determine whether there is a large steel structure edge or corner position within the weak area based on the picture. If there is a large steel structure edge or corner position within the weak area, mark the weak area as a non-conforming area. If there is no large steel structure edge or corner position within the weak area, proceed to S306. S306: Weak areas are measured using an ultrasonic flaw detector to determine whether there are air bubbles or pores in the weak areas. If air bubbles or pores are present in the weak areas, the weak areas are marked as unqualified areas. If there are no air bubbles or pores in the weak areas, the weak areas are marked as qualified areas.

8. The method for detecting damage to large steel structures based on intelligent sensors according to claim 7, characterized in that: In S4, specifically: S401: Establish a test result database, obtain the location of non-conforming areas, include the location of non-conforming areas in the test result database, and send it to the management personnel; S402: Obtain the current location of the qualified area, add the current location of the qualified area to the test result database, send the current location information of the qualified area to the management personnel and remind the management personnel that the location needs to be continuously monitored.

9. A large steel structure damage detection system based on intelligent sensors, applied to the large steel structure damage detection method based on intelligent sensors in any one of claims 1-8, characterized in that, Includes the following modules: The detection area locking module uses a drone equipped with a high-definition camera to capture images of a large steel structure, and then analyzes these images to lock down the detection area. The preliminary corrosion status assessment module uses a dual-crystal probe thickness gauge to measure the thickness of each detection area. The average thickness of each detection area is measured by a grid division method, and the average thickness is used to determine whether the detection area is qualified. The weak area judgment module analyzes the images taken to determine whether the qualified inspection area is an important stress area. It uses a grid division method to locate the weak areas of the important stress area, and uses an ultrasonic flaw detector to analyze the weak areas. It judges whether the weak area is qualified based on whether there are pores, bubbles or voids in the weak area. The test result recording module records non-compliant areas and weak areas requiring continuous monitoring based on the test results, and sends the recorded results to the management personnel.

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