A nondestructive testing method and system for high-profile skid landing gear

Abstract

The present disclosure provides a method and system for non-destructive testing of high-profile skid landing gear, which relates to the technical field of landing gear quality testing. The method comprises: obtaining basic parameter information of a target high-profile skid landing gear and determining the landing gear structure information; determining a layout plan of an X-ray flaw detection device based on the landing gear structure information; laying out the X-ray flaw detection device according to the layout plan and acquiring a set of flaw detection image data; inputting the set of flaw detection image data into a defect recognition model and outputting defect feature information, wherein the defect feature information includes defect type information, defect location information, and defect size information; and generating auxiliary maintenance information based on the defect type information, defect location information, and defect size information. The method solves the technical problem in the prior art of insufficient accuracy and efficiency in defect detection of skid landing gear due to poor defect recognition effect on the skid landing gear.

Description

Technical Field

[0001] The present disclosure relates to the technical field of landing gear quality inspection, and in particular to a non-destructive inspection method and system for a high-profile skid landing gear. Background Art

[0002] The high-profile skid landing gear is a helicopter landing device used to absorb helicopter landing energy, reduce landing loads and transfer ground loads, support the helicopter on the ground and provide an interface for ground taxiing equipment.

[0003] Non-destructive testing methods refer to the use of sound, light, electricity, heat, magnetism and radiation to interact with the object being tested. Without destroying or damaging the structure and performance of the object being tested, they can detect internal and external defects in materials, components or equipment, and determine the size, shape and location of the defects.

[0004] Currently, there is a technical problem in the prior art that the defect recognition effect of the skid landing gear is poor, which leads to insufficient accuracy and efficiency of the landing gear defect detection. Summary of the Invention

[0005] The present disclosure provides a high-profile skid landing gear non-destructive testing method and system to solve the technical problem in the prior art of poor defect recognition of the skid landing gear, which leads to insufficient accuracy and efficiency in landing gear defect detection.

[0006] According to a first aspect of the present disclosure, a method for nondestructive testing of a high-profile skid landing gear is provided, comprising: obtaining basic parameter information of a target high-profile skid landing gear, and determining landing gear structural information based on the basic parameter information; determining a layout scheme of an X-ray flaw detection device according to the landing gear structural information; deploying the X-ray flaw detection device according to the layout scheme, and acquiring a flaw detection image data set through the X-ray flaw detection device; inputting the flaw detection image data set into a defect recognition model, and outputting defect feature information, wherein the defect feature information includes defect type information, defect location information, and defect size information; and generating auxiliary maintenance information based on the defect type information, defect location information, and defect size information.

[0007] According to a second aspect of the present disclosure, a high-profile skid landing gear non-destructive testing system is provided, comprising: a basic parameter information acquisition module, the basic parameter information acquisition module is used to acquire basic parameter information of a target high-profile skid landing gear, and determine the landing gear structure information based on the basic parameter information; a flaw detection device layout plan acquisition module, the flaw detection device layout plan acquisition module is used to determine the layout plan of the X-ray flaw detection device according to the landing gear structure information; a flaw detection image data acquisition module, the flaw detection image data acquisition module is used to layout the X-ray flaw detection device according to the layout plan, and obtain a flaw detection image data set through the X-ray flaw detection device; a defect recognition module, the defect recognition module is used to input the flaw detection image data set into a defect recognition model and output defect feature information, wherein the defect feature information includes defect type information, defect location information and defect size information; an auxiliary maintenance information acquisition module, the auxiliary maintenance information acquisition module is used to generate auxiliary maintenance information according to the defect type information, defect location information and defect size information.

[0008] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0009] at least one processor; and

[0010] a memory communicatively connected to the at least one processor; wherein,

[0011] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method according to the first aspect.

[0012] According to a nondestructive testing method for a high-profile skid landing gear adopted by the present disclosure, basic parameter information of a target high-profile skid landing gear is obtained, and landing gear structural information is determined based on the basic parameter information; a layout plan of an X-ray flaw detection device is determined based on the landing gear structural information; the X-ray flaw detection device is deployed according to the layout plan, and a flaw detection image data set is obtained by the X-ray flaw detection device; the flaw detection image data set is input into a defect recognition model to output defect feature information, wherein the defect feature information includes defect type information, defect location information, and defect size information; auxiliary maintenance information is generated based on the defect type information, defect location information, and defect size information. The present disclosure uses an X-ray flaw detection device to perform comprehensive defect detection on the high-profile skid landing gear and obtain flaw detection images. By identifying defects of the landing gear based on the flaw detection images, defect feature information is obtained. Based on this information, a maintenance warning is issued, and personnel are assisted in performing inspection and maintenance of the landing gear, thereby achieving the technical effect of improving the accuracy and efficiency of landing gear defect detection without damaging the landing gear.

[0013] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without any creative work.

[0015] Figure 1 A schematic flow chart of a nondestructive testing method for a high-profile skid landing gear provided in an embodiment of the present disclosure;

[0016] Figure 2 A schematic structural diagram of a high-profile skid landing gear nondestructive testing system provided by an embodiment of the present disclosure;

[0017] Figure 3 A schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.

[0018] Explanation of the reference numerals: basic parameter information acquisition module 11, flaw detection device layout plan acquisition module 12, flaw detection image data acquisition module 13, defect recognition module 14, auxiliary maintenance information acquisition module 15, electronic device 800, processor 801, memory 802, bus 803. DETAILED DESCRIPTION

[0019] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0020] In order to solve the technical problem in the prior art that the defect recognition effect of the skid landing gear is poor, resulting in insufficient accuracy and efficiency in the defect detection of the landing gear, the inventors of the present invention have obtained a high-profile skid landing gear non-destructive detection method and system through creative work.

[0021] Example 1

[0022] Figure 1A diagram of a high-profile skid landing gear nondestructive testing method provided in an embodiment of the present application is provided. The method is applied to a landing gear nondestructive testing system, and the landing gear nondestructive testing system is communicatively connected to an X-ray flaw detection device, such as Figure 1 As shown, the method includes:

[0023] Step S100: acquiring basic parameter information of a target high-profile skid landing gear, and determining landing gear structure information based on the basic parameter information;

[0024] Specifically, an embodiment of the present application provides a nondestructive testing method for high-profile skid landing gear. A nondestructive testing method is a method that utilizes the interaction of sound, light, electricity, heat, magnetism, and radiation with the object being tested to detect internal and external defects in materials, components, or equipment, without destroying or damaging the structure and performance of the object being tested, and can determine the size, shape, and location of the defects. The method is applied to a landing gear nondestructive testing system, which is a system platform for quality testing high-profile skid landing gear. An X-ray flaw detection device is a device that emits X-rays to the surface of the landing gear and obtains flaw detection images. The landing gear nondestructive testing system is connected in communication with the X-ray flaw detection device to enable interactive information transmission. Specifically, basic parameter information of the target high-profile skid landing gear is obtained. The target high-profile skid landing gear refers to the high-profile skid landing gear to be subjected to non-destructive testing. The basic parameter information includes information such as the model, composition, and size of the target high-profile skid landing gear. The basic parameter information is analyzed to extract the landing gear structure information. The landing gear structure information refers to the structural composition and size of the landing gear. For example, the high-profile skid landing gear is mainly composed of front / rear cross tubes, left / right slide tubes, spring plates, left / right boarding pedals, maintenance pedals, etc. Skid damper installation interfaces are left on the left and right sides of the front cross tube.

[0025] Step S200: determining a layout plan of the X-ray flaw detection device according to the landing gear structure information;

[0026] In the step S200 of determining the layout of the X-ray flaw detection device according to the landing gear structure information, the embodiment of the present application includes:

[0027] Step S210: Acquire detection range information of the X-ray flaw detection device;

[0028] Step S220: determining landing gear size information according to the landing gear structure information;

[0029] Step S230: Dividing the target high-profile skid landing gear into detection areas according to the landing gear size information to obtain multiple detection areas;

[0030] Step S240: Determine a layout plan of the X-ray flaw detection device according to the multiple detection areas and the detection range information.

[0031] Wherein, the arrangement scheme of the X-ray flaw detection device is determined according to the multiple detection areas and the detection range information. In this embodiment of the application, step S240 includes:

[0032] Step S241: performing regional plane composition analysis on the multiple detection areas to determine regional plane information of each detection area;

[0033] Step S242: acquiring a set of the number and a set of the layout positions of the X-ray flaw detection devices in the plurality of detection areas according to the regional plane information and the detection range information;

[0034] Step S243: generating the layout plan based on the layout number set and the layout position set.

[0035] Specifically, X-ray flaw detection equipment can be used to obtain flaw detection images of the landing gear for landing gear quality inspection. Therefore, it is necessary to first obtain the layout plan of the X-ray flaw detection equipment. The layout plan includes the layout position and number of X-ray flaw detection devices. Simply put, it is to determine the locations of the landing gear where several X-ray flaw detection devices are to be deployed. After the X-ray flaw detection devices are deployed according to the layout plan, non-destructive testing of the landing gear can be carried out in all directions.

[0036] Specifically, a layout plan for the X-ray flaw detection device is determined based on the landing gear structure information. First, the detection range information of the X-ray flaw detection device is obtained. The detection range information refers to the area of ​​an object that an X-ray flaw detection device can detect. Furthermore, the landing gear size information is determined based on the landing gear structure information. The landing gear size information includes the size of each structural position. Based on the landing gear size information, the target high-profile skid landing gear is divided into detection areas. The specific division method can be customized. For example, each structural position can be used as a detection area based on the landing gear structure. If a structural position is large, the structural position can be divided into two detection areas. Each detection area is marked with a location, thereby obtaining multiple detection areas. The layout plan for the X-ray flaw detection device is determined based on the multiple detection areas and the detection range information. In short, based on the sizes of the multiple detection areas, the number and location of X-ray flaw detection devices required for each detection area are determined. For example, a certain area only requires one X-ray flaw detection device at the center of the area, while another area requires one X-ray flaw detection device at the upper and lower positions. Based on this, a layout plan is obtained. Achieve the technical effect of ensuring detection accuracy and defect detection accuracy.

[0037] Specifically, the penetration depth of X-rays is limited. To ensure that every position on the landing gear can be detected, regional plane composition analysis is required for multiple detection areas. Specifically, the landing gear structure contains some curved and bent areas, which may make these areas difficult to detect due to the curvature. Therefore, these locations require the installation of multiple X-ray flaw detection devices. Plane composition analysis is to determine how many planes need to be detected in each detection area. For example, a straight area is composed of only one plane, while a curved area is composed of multiple planes. Based on this, regional plane information is determined for each detection area. Regional plane information refers to the number of planes that constitute the area. Furthermore, based on the regional plane information and detection range information, a set of layout numbers and layout positions of X-ray flaw detection devices for multiple detection areas is obtained. The data in the layout number set corresponds to the data in the layout position set one-to-one, and a layout plan is generated based on the layout number set and layout position set. This achieves the technical effect of rationally deploying X-ray flaw detection devices, ensuring the accuracy of defect detection, and providing data support for subsequent defect detection.

[0038] Step S300: deploying the X-ray flaw detection device according to the deployment plan, and acquiring a flaw detection image data set through the X-ray flaw detection device;

[0039] Specifically, the deployment plan includes the deployment position and deployment number of X-ray flaw detection devices. Based on this, the X-ray flaw detection devices are deployed, and a flaw detection image data set is obtained through the deployed X-ray flaw detection devices. The flaw detection image data set includes the flaw detection images acquired by each X-ray flaw detection device. Specifically, the process of the X-ray flaw detection device acquiring the flaw detection image is as follows: the X-ray flaw detection device emits X-rays to the surface of the landing gear, converts the invisible X-ray flaw detection information into visible light flaw detection information through signal conversion, and then converts the optical signal into an electrical signal to complete the photoelectric conversion and obtain the flaw detection image.

[0040] Step S400: inputting the flaw detection image data set into a defect recognition model, and outputting defect feature information, wherein the defect feature information includes defect type information, defect location information, and defect size information;

[0041] The step S400 of inputting the flaw detection image data set into the defect recognition model and outputting defect feature information includes:

[0042] Step S410: Acquire a plurality of historical flaw detection image data and pre-process them to obtain a plurality of sample flaw detection image data;

[0043] Step S420: Acquire multiple sample defect feature information corresponding to the multiple sample flaw detection image data;

[0044] Step S430: constructing the defect recognition model using the plurality of sample flaw detection image data and the plurality of sample defect feature information;

[0045] Step S440: inputting the flaw detection image data set into the defect recognition model and outputting defect feature information.

[0046] Wherein, the defect recognition model is constructed by using the plurality of sample flaw detection image data and the plurality of sample defect feature information. In the embodiment of the present application, step S430 includes:

[0047] Step S431: constructing the defect recognition model based on the BP neural network, wherein the input data of the defect recognition model is the flaw detection image data set, and the output data is the defect feature information;

[0048] Step S432: marking the plurality of sample flaw detection image data and the plurality of sample defect feature information as construction data;

[0049] Step S433: The defect recognition model is trained and verified using the constructed data to obtain a defect recognition model that meets preset requirements.

[0050] Specifically, the defect recognition model is a functional model that analyzes a set of flaw detection image data to obtain defect feature information. The input data of the defect recognition model is the flaw detection image data set, and the output data is defect feature information. Defect feature information includes defect type information, defect location information, and defect size information. Defect type information refers to the type of defect present in the landing gear, including defects such as porosity, slag inclusions, and incomplete weld penetration; defect location information refers to the location of the defect on the landing gear; and defect size information refers to the size of the defect. Specifically, the location of the defective landing gear will transmit more X-rays, forming a bright spot or a bright line in the flaw detection image. The defect recognition model uses this characteristic to identify the defect type, defect location, and defect size.

[0051] Specifically, a set of flaw detection image data is input into a defect recognition model, and defect feature information is output. First, based on historical detection data, multiple historical flaw detection image data are obtained and preprocessed to obtain multiple sample flaw detection image data. Historical flaw detection image data refers to flaw detection images acquired by an X-ray flaw detection device over a period of time. These multiple historical flaw detection image data are then preprocessed, which involves noise reduction. This noise reduction can be performed using methods such as mean filtering and median filtering. The preprocessed multiple historical flaw detection image data are then used as multiple sample flaw detection image data. Furthermore, based on the historical detection data, multiple sample defect feature information corresponding to the multiple sample flaw detection image data is obtained. The multiple sample flaw detection image data and the multiple sample defect feature information are used to construct a defect recognition model, resulting in a defect recognition model that meets the requirements. The flaw detection image data set is input into the defect recognition model, and defect feature information is output. This achieves the technical effect of accurately identifying landing gear defects and improving defect recognition accuracy.

[0052] Specifically, the defect recognition model is constructed as follows: based on the BP neural network, a defect recognition model is constructed. The input data of the defect recognition model is a set of flaw detection image data, and the output data is defect feature information. There is a one-to-one correspondence between multiple sample flaw detection image data and multiple sample defect feature information. The multiple sample flaw detection image data and the multiple sample defect feature information are marked so that the correspondence between the two can be clearly seen, which facilitates supervision and adjustment of the output data of the model. The marked multiple sample flaw detection image data and the multiple sample defect feature information are used as construction data. The defect recognition model is trained and verified through the construction data to obtain a defect recognition model that meets the preset requirements. Specifically, the construction data can be randomly divided into two parts, namely a training data set and a test data set. The defect recognition model is supervised and trained through the training data set. Each group of sample flaw detection image data in the training data set is input into the defect recognition model. The output of the defect recognition model is supervised and adjusted through the sample defect feature information corresponding to this group of sample flaw detection image data in the training data set. When the output result of the defect recognition model is consistent with the corresponding sample defect feature information, the training of the current group is completed. When all the training data in the training data set are trained, the training of the defect recognition model is completed. The trained defect recognition model is tested for accuracy using data from the test dataset. If the defect recognition accuracy meets the preset requirements, the defect recognition model is complete. Otherwise, the model needs to be retrained until the test accuracy meets the preset requirements. By building a defect recognition model, the characteristics of landing gear defects can be accurately identified, improving defect detection efficiency.

[0053] Step S500: generating auxiliary repair information according to the defect type information, defect location information and defect size information.

[0054] The auxiliary repair information is generated according to the defect type information, defect location information, and defect size information. In this embodiment of the application, step S500 includes:

[0055] Step S510: generating multi-level maintenance warning information according to the defect type information, defect location information and defect size information;

[0056] Step S520: generating multi-level maintenance strategy information according to the multi-level maintenance warning information;

[0057] Step S530: Based on the multi-level maintenance strategy information, a maintenance control instruction is started to generate auxiliary maintenance information.

[0058] Specifically, the defect type information, defect location information and defect size information are analyzed to determine the severity of the quality defects in the landing gear, and based on historical experience, maintenance strategies are recommended to generate auxiliary maintenance information, which includes defect feature information and its corresponding maintenance strategy.

[0059] Specifically, multi-level maintenance warning information is generated based on defect type information, defect location information, and defect size information. Simply put, it is to analyze the defect type information, defect location information, and defect size information to determine the severity of the quality defects in the landing gear. For example, the larger the defect size, the more serious the quality problem. Multi-level maintenance warning information is multi-level warning information generated according to the severity of the defects in the landing gear. The more serious the defects in the landing gear, the higher the level of the warning information. The maintenance strategies corresponding to maintenance warning information of different levels will also be different. The maintenance strategy information includes maintenance time arrangement, maintenance staff arrangement, maintenance method, etc. Multi-level maintenance strategy information is generated based on multi-level maintenance warning information, which means that each level of maintenance warning information corresponds to a maintenance strategy information of the same level. Multi-level maintenance warning information and multi-level maintenance strategy information correspond to each other. According to the multi-level maintenance strategy information, the maintenance control instruction is started. The maintenance control instruction is used to control the generation of auxiliary maintenance information to assist the staff in performing landing gear maintenance. The auxiliary maintenance information is used to remind the staff to repair the landing gear, so as to achieve the effect of auxiliary maintenance of the high-profile skid landing gear.

[0060] In this embodiment, step S600 includes:

[0061] Step S610: Analyze and identify the flaw detection image data set to obtain detection position information;

[0062] Step S620: analyzing the detected position information to determine whether the detected position information completely covers the target high-profile skid landing gear;

[0063] Step S630: If the detected position information does not completely cover the target high-profile skid landing gear, obtain uncovered position information;

[0064] Step S640: Adjust the deployment plan according to the uncovered location information.

[0065] Specifically, the flaw detection image data set is analyzed and identified to identify the detection positions corresponding to the flaw detection image data set on the landing gear, thereby obtaining the detection position information. The detection position information includes the detection positions of all deployed X-ray flaw detection devices. The detection position information is analyzed to determine whether the detection position information completely covers the target high-profile skid landing gear, that is, to determine whether the detection position information includes the entire target high-profile skid landing gear. If the detection position information does not completely cover the target high-profile skid landing gear, uncovered position information is obtained. The uncovered position information refers to the position on the target high-profile skid landing gear that has not been detected. The layout plan is adjusted according to the uncovered position information. For example, according to the size of the uncovered position, one or more X-ray flaw detection devices are determined to be deployed at the uncovered position, or the position of the X-ray flaw detection device at other positions is adjusted, so as to achieve the technical effect of ensuring that the target high-profile skid landing gear can be completely detected by the X-ray flaw detection device and improving the accuracy of subsequent defect identification and detection.

[0066] Based on the above analysis, it can be seen that the present disclosure provides a non-destructive testing method for high-profile skid landing gear. In this embodiment, based on an X-ray flaw detection device, all-round defect detection is performed on the high-profile skid landing gear and flaw detection images are obtained. By identifying the defects of the landing gear based on the flaw detection images, defect feature information is obtained. Based on this, maintenance warnings are performed, and staff are assisted in the inspection and maintenance of the landing gear, so as to achieve the technical effect of improving the accuracy and efficiency of landing gear defect detection while ensuring that the landing gear is not damaged.

[0067] Example 2

[0068] Based on the same inventive concept as the nondestructive testing method for a high-profile skid landing gear in the above embodiment, Figure 2 As shown, the present application also provides a high-profile skid landing gear non-destructive testing system, the system is communicatively connected to the X-ray flaw detection device, and the system includes:

[0069] A basic parameter information acquisition module 11 is used to acquire basic parameter information of a target high-profile skid landing gear and determine landing gear structure information based on the basic parameter information;

[0070] a flaw detection device layout plan acquisition module 12, the flaw detection device layout plan acquisition module 12 being used to determine a layout plan of the X-ray flaw detection device according to the landing gear structure information;

[0071] a flaw detection image data acquisition module 13, the flaw detection image data acquisition module 13 being used to deploy the X-ray flaw detection device according to the deployment plan and to acquire a flaw detection image data set through the X-ray flaw detection device;

[0072] A defect recognition module 14 is configured to input the flaw detection image data set into a defect recognition model and output defect feature information, wherein the defect feature information includes defect type information, defect location information, and defect size information;

[0073] The auxiliary maintenance information acquisition module 15 is used to generate auxiliary maintenance information according to the defect type information, defect location information and defect size information.

[0074] Furthermore, the system further comprises:

[0075] a detection range information acquisition module, the detection range information acquisition module being used to acquire detection range information of the X-ray flaw detection device;

[0076] a landing gear size information determination module, the landing gear size information determination module being configured to determine landing gear size information based on the landing gear structure information;

[0077] a detection area division module, the detection area division module being configured to divide the target high-profile skid landing gear into detection areas according to the landing gear size information, and obtain a plurality of detection areas;

[0078] A layout scheme determination module is used to determine the layout scheme of the X-ray flaw detection device according to the multiple detection areas and the detection range information.

[0079] Furthermore, the system further comprises:

[0080] a regional plane composition analysis module, configured to perform regional plane composition analysis on the plurality of detection areas to determine regional plane information of each detection area;

[0081] a flaw detection device layout analysis module, the flaw detection device layout analysis module being configured to obtain a layout number set and a layout position set of X-ray flaw detection devices in the plurality of detection areas based on the regional plane information and the detection range information;

[0082] A layout plan generation module is used to generate the layout plan based on the layout number set and the layout position set.

[0083] Furthermore, the system further comprises:

[0084] a flaw detection image analysis and recognition module, the flaw detection image analysis and recognition module being used to analyze and recognize the flaw detection image data set to obtain detection position information;

[0085] a detection position analysis module, configured to analyze the detection position information and determine whether the detection position information completely covers the target high-profile skid landing gear;

[0086] a coverage degree analysis module, configured to obtain uncovered position information if the detected position information does not completely cover the target high-profile ski landing gear;

[0087] A deployment plan adjustment module is used to adjust the deployment plan according to the uncovered location information.

[0088] Furthermore, the system further comprises:

[0089] A sample flaw detection image data acquisition module, which is used to acquire a plurality of historical flaw detection image data and pre-process them to obtain a plurality of sample flaw detection image data;

[0090] a sample defect feature information acquisition module, the sample defect feature information acquisition module being used to acquire a plurality of sample defect feature information corresponding to the plurality of sample flaw detection image data;

[0091] A defect recognition model construction module, wherein the defect recognition model construction module is used to construct the defect recognition model using the plurality of sample flaw detection image data and the plurality of sample defect feature information;

[0092] A model output module is used to input the flaw detection image data set into the defect recognition model and output defect feature information.

[0093] Furthermore, the system further comprises:

[0094] A model building module, wherein the model building module is used to build the defect recognition model based on the BP neural network, wherein the input data of the defect recognition model is the flaw detection image data set, and the output data is the defect feature information;

[0095] A construction data acquisition module, wherein the construction data acquisition module is used to identify the plurality of sample flaw detection image data and the plurality of sample defect feature information as construction data;

[0096] The model training module is used to train and verify the defect recognition model through the construction data to obtain a defect recognition model that meets preset requirements.

[0097] Furthermore, the system further comprises:

[0098] A multi-level maintenance warning information generation module, the multi-level maintenance warning information generation module is used to generate multi-level maintenance warning information according to the defect type information, defect location information and defect size information;

[0099] A multi-level maintenance strategy information generation module, the multi-level maintenance strategy information generation module is used to generate multi-level maintenance strategy information according to the multi-level maintenance warning information;

[0100] An auxiliary maintenance module is used to start maintenance control instructions and generate auxiliary maintenance information according to the multi-level maintenance strategy information.

[0101] The specific example of the high-profile skid landing gear nondestructive testing method in the aforementioned embodiment 1 is also applicable to the high-profile skid landing gear nondestructive testing system in this embodiment. Through the aforementioned detailed description of the high-profile skid landing gear nondestructive testing method, those skilled in the art can clearly understand the high-profile skid landing gear nondestructive testing system in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0102] Example 3

[0103] Figure 3 is a schematic diagram according to the third embodiment of the present disclosure, as shown in Figure 3 As shown, the electronic device 800 in the present disclosure may include: a processor 801 and a memory 802 .

[0104] Memory 802 is used to store programs. Memory 802 may include volatile memory (volatile memory), such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc. Memory may also include non-volatile memory (non-volatile memory), such as flash memory. Memory 802 is used to store computer programs (such as applications, functional modules, etc. that implement the above-mentioned methods), computer instructions, etc. The above-mentioned computer programs, computer instructions, etc. may be partitioned and stored in one or more memories 802. Furthermore, the above-mentioned computer programs, computer instructions, data, etc. can be called by processor 801.

[0105] The aforementioned computer programs, computer instructions, etc. may be partitioned and stored in one or more memories 802 . Furthermore, the aforementioned computer programs, computer instructions, etc. may be called by the processor 801 .

[0106] The processor 801 is configured to execute the computer program stored in the memory 802 to implement the various steps in the method involved in the above embodiment.

[0107] For details, please refer to the relevant description in the previous method embodiment.

[0108] The processor 801 and the memory 802 may be independent structures or integrated structures. When the processor 801 and the memory 802 are independent structures, the memory 802 and the processor 801 may be coupled via a bus 803 .

[0109] The electronic device of this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same and will not be repeated here.

[0110] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0111] According to an embodiment of the present disclosure, the present disclosure also provides a computer program product, which includes: a computer program, the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0112] It should be understood that the various forms of the processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in a different order.

[0113] As long as the expected results of the technical solutions disclosed in this disclosure can be achieved, this document does not impose any limitations here.

[0114] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A non-destructive testing method for a high-profile skid landing gear, characterized in that: The method is applied to a landing gear nondestructive testing system, wherein the landing gear nondestructive testing system is communicatively connected to an X-ray flaw detection device, and the method comprises: Obtaining basic parameter information of a target high-profile skid landing gear, and determining landing gear structure information based on the basic parameter information; determining a layout plan of the X-ray flaw detection device according to the landing gear structure information; deploying the X-ray flaw detection device according to the deployment plan, and acquiring a set of flaw detection image data through the X-ray flaw detection device; Inputting the flaw detection image data set into a defect recognition model to output defect feature information, wherein the defect feature information includes defect type information, defect location information and defect size information; Auxiliary repair information is generated according to the defect type information, defect location information and defect size information.

2. The method according to claim 1, wherein Determining the layout plan of the X-ray flaw detection device according to the landing gear structure information includes: Obtaining detection range information of the X-ray flaw detection device; determining landing gear size information according to the landing gear structure information; Dividing the target high-profile skid landing gear into detection areas according to the landing gear size information to obtain a plurality of detection areas; A layout plan of the X-ray flaw detection device is determined according to the multiple detection areas and the detection range information.

3. The method according to claim 2, wherein The determining of the layout scheme of the X-ray flaw detection device according to the multiple detection areas and the detection range information includes: Performing regional plane composition analysis on the multiple detection areas to determine regional plane information of each detection area; Obtaining, based on the regional plane information and the detection range information, a set of layout numbers and a set of layout positions of X-ray flaw detection devices in the multiple detection areas; The layout plan is generated based on the layout number set and the layout position set.

4. The method according to claim 1, wherein After deploying the X-ray flaw detection device according to the deployment plan and acquiring a flaw detection image data set through the X-ray flaw detection device, and before inputting the flaw detection image data set into a defect recognition model and outputting defect feature information, the method further includes: Analyzing and identifying the flaw detection image data set to obtain detection position information; Analyzing the detected position information to determine whether the detected position information completely covers the target high-profile skid landing gear; If the detected position information does not completely cover the target high-profile skid landing gear, obtaining uncovered position information; The deployment plan is adjusted according to the uncovered location information.

5. The method according to claim 1, wherein The step of inputting the flaw detection image data set into a defect recognition model and outputting defect feature information includes: Acquire multiple historical flaw detection image data, and pre-process them to obtain multiple sample flaw detection image data; Acquire a plurality of sample defect feature information corresponding to the plurality of sample flaw detection image data; constructing the defect recognition model using the plurality of sample flaw detection image data and the plurality of sample defect feature information; The flaw detection image data set is input into the defect recognition model, and defect feature information is output.

6. The method according to claim 5, wherein The method of constructing the defect recognition model by using the plurality of sample flaw detection image data and the plurality of sample defect feature information includes: Based on the BP neural network, the defect recognition model is constructed, wherein the input data of the defect recognition model is the flaw detection image data set, and the output data is the defect feature information; Marking the plurality of sample flaw detection image data and the plurality of sample defect feature information as construction data; The defect recognition model is trained and verified by using the constructed data to obtain a defect recognition model that meets preset requirements.

7. The method according to claim 1, wherein The generating of auxiliary repair information according to the defect type information, defect location information and defect size information includes: Generate multi-level maintenance warning information according to the defect type information, defect location information and defect size information; generating multi-level maintenance strategy information according to the multi-level maintenance warning information; According to the multi-level maintenance strategy information, maintenance control instructions are started to generate auxiliary maintenance information.

8. A high-profile skid landing gear non-destructive testing system, characterized in that: The system is in communication with an X-ray flaw detection device, and the system comprises: A basic parameter information acquisition module, the basic parameter information acquisition module is used to obtain basic parameter information of the target high-profile skid landing gear and determine the landing gear structure information based on the basic parameter information; a flaw detection device layout plan acquisition module, the flaw detection device layout plan acquisition module being used to determine a layout plan of the X-ray flaw detection device according to the landing gear structure information; a flaw detection image data acquisition module, the flaw detection image data acquisition module being used to deploy the X-ray flaw detection device according to the deployment plan and to acquire a flaw detection image data set through the X-ray flaw detection device; a defect recognition module, the defect recognition module being configured to input the flaw detection image data set into a defect recognition model and output defect feature information, wherein the defect feature information includes defect type information, defect location information, and defect size information; An auxiliary maintenance information acquisition module is used to generate auxiliary maintenance information based on the defect type information, defect location information and defect size information.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

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