A CT defect detection evaluation method and device, electronic equipment and storage medium

Abstract

The application relates to a CT detection defect evaluation method and device, electronic equipment and a storage medium, wherein the CT detection defect evaluation method comprises the following steps: performing CT detection on a composite material blade without defects to generate a two-dimensional image of the composite material blade without defects; performing CT detection on a to-be-detected composite material blade in the same way as the composite material blade without defects to generate a two-dimensional image of the to-be-detected composite material blade; and determining a CT detection defect evaluation result of the to-be-detected composite material blade according to the two-dimensional image of the to-be-detected composite material blade, the two-dimensional image of the composite material blade without defects and a preset image defect identification threshold. The application can significantly improve the CT detection efficiency and the accuracy of defect identification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of nondestructive testing, in particular to a CT defect evaluation method and device, electronic equipment and storage medium. BACKGROUND

[0002] Composite materials have been widely used in important fields such as aerospace, and the quality and safety of composite parts are generally concerned and valued. Nondestructive testing is currently a key technical means for composite part quality testing and internal defect characterization, evaluation and nondestructive testing. Currently, important composite parts need to be 100% nondestructively tested. X-ray CT is an important detection method for key structures of complex composite materials. Currently, the CT method for composite material detection mainly evaluates the CT detection result based on the CT image and its gray scale change. One method is to observe the gray scale change of the corresponding two-dimensional (2D) image of the detected part cross section from the obtained CT three-dimensional (3D) image, and the detection result is judged and the defect is distinguished by the detection technician. In order to effectively judge and distinguish the defects of the detection result, the 2D image of each cross section of the detected part needs to be extracted from the 3D image. The image data is large, the manual discrimination intensity is large, and the evaluation efficiency is very low. Long-time image discrimination and viewing by manual work is easy to cause fatigue and missed detection. The judgment of the detection result is easily affected by the subjective factors of the detection personnel, thereby affecting the reliability and accuracy of the detection result. As an improvement, an intelligent algorithm is introduced to realize the automatic judgment of the CT detection result. At present, the reverse thinking mode is mainly adopted, that is, a large number of defect cases are collected first, and then a defect discrimination model is constructed to automatically distinguish the CT detection result. For this purpose, a large amount of known defect case data is required, but there is still a lack of sufficient composite material CT detection case data with defects, thereby affecting the accuracy and universality of intelligent discrimination. SUMMARY

[0003] (1) Technical problem to be solved

[0004] The present application provides a CT defect evaluation method, device, electronic equipment and storage medium, and the technical problem to be solved is: for batch composite blade CT detection, in order to improve the detection efficiency and the accuracy and reliability of defect discrimination, it is urgent to create a CT detection result automatic evaluation method to realize the rapid and accurate detection of composite blade CT and improve the detection efficiency and the accuracy and reliability of defect discrimination of composite blade CT.

[0005] (2) Technical scheme

[0006] In a first aspect, the present application provides a CT defect evaluation method, comprising:

[0007] The CT detection is performed on the defect-free composite blade to generate a two-dimensional image of the defect-free composite blade;

[0008] The CT detection is performed on the to-be-detected composite blade in the same manner as the defect-free composite blade to generate a two-dimensional image of the to-be-detected composite blade;

[0009] According to the two-dimensional image of the to-be-detected composite blade, the two-dimensional image of the defect-free composite blade, and a preset image defect recognition threshold, a CT detection defect evaluation result of the to-be-detected composite blade is determined.

[0010] Further, the CT detection is performed on the defect-free composite blade to generate a two-dimensional image of the defect-free composite blade, comprising:

[0011] The CT detection is performed on the defect-free composite blade to obtain a three-dimensional image of the defect-free composite blade;

[0012] According to the three-dimensional image of the defect-free composite blade, a plurality of two-dimensional images of the defect-free composite blade are generated.

[0013] Further, the CT detection is performed on the defect-free composite blade to obtain a three-dimensional image of the defect-free composite blade, comprising:

[0014] The X-ray radiography is performed on the defect-free composite blade to obtain a plurality of X-ray digital images of the defect-free composite blade at different X-ray irradiation angles;

[0015] According to the plurality of X-ray digital images of the defect-free composite blade, a three-dimensional image of the defect-free composite blade is generated.

[0016] Further, according to the three-dimensional image of the defect-free composite blade, a plurality of two-dimensional images of the defect-free composite blade are generated, comprising:

[0017] According to a preset cross-sectional thickness of adjacent two-dimensional images, a plurality of two-dimensional images are extracted from the three-dimensional image of the defect-free composite blade.

[0018] Further, according to the two-dimensional image of the to-be-detected composite blade, the two-dimensional image of the defect-free composite blade, and a preset image defect recognition threshold, a CT detection defect evaluation result of the to-be-detected composite blade is determined, comprising:

[0019] A gray scale difference between the two-dimensional image of the to-be-detected composite blade and the two-dimensional image of the defect-free composite blade is determined.

[0020] According to the gray scale difference and a preset image defect recognition threshold, a CT detection defect evaluation result of the to-be-detected composite blade is determined.

[0021] Further, the gray scale difference between the two-dimensional image of the composite material blade to be detected and the two-dimensional image of the defect-free composite material blade is determined, comprising:

[0022] According to the gray scale of the corresponding position of the two-dimensional image and the pre-determined defect positive evaluation threshold deviation, the gray scale difference between the two-dimensional image of the composite material blade to be detected and the two-dimensional image of the defect-free composite material blade is determined.

[0023] Further, according to the gray scale difference and the preset image defect identification threshold, the CT detection defect evaluation result of the composite material blade to be detected is determined, comprising:

[0024] If the gray scale difference is greater than the preset image defect identification threshold, it is determined that the CT detection defect evaluation result of the composite material blade to be detected is that there is a defect.

[0025] If the gray scale difference is less than or equal to the preset image defect identification threshold, it is determined that the CT detection defect evaluation result of the composite material blade to be detected is that there is no defect.

[0026] In a second aspect, the present application provides a CT detection defect evaluation device, comprising:

[0027] A first detection module is configured to perform CT detection on a defect-free composite material blade to generate a two-dimensional image of the defect-free composite material blade.

[0028] A second detection module is configured to perform CT detection on a composite material blade to be detected in the same way as the defect-free composite material blade to generate a two-dimensional image of the composite material blade to be detected.

[0029] A defect evaluation module is configured to determine a CT detection defect evaluation result of the composite material blade to be detected according to the two-dimensional image of the composite material blade to be detected, the two-dimensional image of the defect-free composite material blade, and a preset image defect identification threshold.

[0030] In a third aspect, the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the CT detection defect evaluation method as described above.

[0031] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the CT detection defect evaluation method as described above.

[0032] (3) Advantageous effects

[0033] The above technical solutions of the present application have the following advantages:

[0034] The CT defect evaluation method provided in the first aspect of the present application is based on X-ray imaging and intelligent feature intelligent recognition principle, and through being oriented to the composite material blade to be detected, an active forward recognition method is adopted, a small sample CT standard legend of the composite material blade without defects is acquired first, as a limited small sample comparison reference data; then, a forward discrimination model is created, and 3D display / 2D automatic discrimination of the composite material blade detection result is realized. The significant advantage is that the discrimination of the CT detection result and the automatic evaluation of the defect can be realized without a large number of defect image cases, the CT detection efficiency and the accuracy of the defect discrimination are significantly improved, it is more universal, and the rapid recognition of the CT detection result is easier to realize.

[0035] It can be understood that the beneficial effects of the second aspect, the third aspect and the fourth aspect described above can be referred to the related description in the first aspect described above, and will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0037] Figure 1 The flowchart of the CT defect evaluation method provided by the present application is shown in the figure;

[0038] Figure 2 The schematic diagram of the CT detection system provided by the present application is shown in the figure;

[0039] Figure 3 The schematic diagram of 2D image demodulation provided by the present application is shown in the figure;

[0040] Figure 4 The structural schematic diagram of the CT defect evaluation device provided by the present application is shown in the figure;

[0041] Figure 5 The structural schematic diagram of the electronic device provided by the present application is shown in the figure. DETAILED DESCRIPTION

[0042] In the following description, specific details such as specific system structures, techniques, etc. are presented in order to thoroughly understand the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits and methods are omitted to avoid unnecessary details that hinder the description of the present application.

[0043] It should be understood that the word "comprising" when used in the specification and claims of this application indicates the existence of the stated features, integers, steps, operations, elements, and / or components but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0044] In addition, in the description of the application and the appended claims, the terms "first", "second", "third", etc. are used only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0045] In the present application, the reference "one embodiment" or "some embodiments" means that the specific features, structures or characteristics described in connection with the embodiment are included in one or more embodiments of the application. Therefore, the statements "in one embodiment", "in some embodiments", "in other some embodiments", "in further some embodiments" and the like appearing in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "including", "containing", "having" and their variants mean "including but not limited to", unless otherwise specifically emphasized. "Multiple" means "two or more".

[0046] The current CT method for composite material detection is mainly based on CT image and its gray scale change, and the CT detection result is evaluated. One method is based on the obtained CT three-dimensional (3D) image, and the gray scale change of the corresponding two-dimensional (2D) image of the corresponding section of the detected part is observed to judge and identify the defects by the detection technician. The main disadvantages are: 1) the 2D image of each section of the detected part needs to be extracted from the 3D image to effectively judge and identify the defects, the image data is large, the manual identification intensity is large, and the judgment efficiency is very low; 2) long-time image identification and viewing by manual is easy to cause fatigue and missed detection; 3) the detection identification is easily affected by the subjective factors of the detection personnel, and then the reliability and accuracy of the detection result are affected. As an improvement, intelligent algorithm is introduced to realize automatic identification of CT detection result, and the main disadvantages are: 1) the current mainly adopts reverse recognition method, collects enough defect cases, then constructs defect identification model to automatically identify CT detection result, for this, a large amount of known defect case data is needed, and at present, there is still a lack of enough representative composite material CT detection case data with defects, thereby affecting the accuracy and universality of intelligent identification. It is difficult to meet the requirements of composite material blade CT detection in batch production process.

[0047] In view of the above problems, the purpose of the present application is to provide a CT detection defect evaluation method for composite material blades, which can improve the CT detection efficiency, defect evaluation accuracy and detection result reliability.

[0048] The specific embodiments of the present application will be further described in detail below in combination with the drawings and examples. The following examples are used to illustrate the present application, but not to limit the scope of the present application.

[0049] As shown in the CT detection defect evaluation method provided by the present embodiment, the method comprises the following steps: Figure 1

[0050] S100, CT detection is performed on a defect-free composite material blade to generate a two-dimensional image of the defect-free composite material blade.

[0051] In some embodiments, the CT detection performed on the defect-free composite material blade to generate a two-dimensional image of the defect-free composite material blade comprises:

[0052] CT detection is performed on the defect-free composite material blade to obtain a three-dimensional image of the defect-free composite material blade.

[0053] According to the three-dimensional image of the defect-free composite material blade, a plurality of two-dimensional images of the defect-free composite material blade are generated.

[0054] In some embodiments, the CT detection performed on the defect-free composite material blade to obtain a three-dimensional image of the defect-free composite material blade comprises:

[0055] X-ray radiography is performed on the defect-free composite material blade to obtain a plurality of X-ray digital images of the defect-free composite material blade at different X-ray irradiation angles.

[0056] According to the plurality of X-ray digital images of the defect-free composite material blade, a three-dimensional image of the defect-free composite material blade is generated.

[0057] In some embodiments, according to the three-dimensional image of the defect-free composite material blade, a plurality of two-dimensional images of the defect-free composite material blade are generated, which comprises:

[0058] According to the preset cross-sectional thickness of adjacent two-dimensional images, a plurality of two-dimensional images are extracted from the three-dimensional image of the defect-free composite material blade.

[0059] In application, the system of CT detection mainly consists of an X-ray machine 1, a detector 2, a CT turntable 3, a CT data acquisition 4, a 3D image reconstruction 5, a 2D image demodulation 6, a forward modeling 7, a forward defect discrimination 8, a result display 9, etc., as shown in Figure 2 ​The X-ray machine 1 generates an X-ray irradiation beam required for CT detection, and transilluminates the composite material blade 10 to be detected; the detector 2 receives the X-ray after penetrating the composite material blade, and forms an X-ray digital image E, and a plurality of X-ray digital images E θ are composed, and are represented by formula (1):

[0060]

[0061] In the formula, θ is the rotation angle of the CT turntable; n° is the maximum rotation angle of the CT turntable, generally n° is between 180°-360°; E θ is the X-ray digital image corresponding to each θ.

[0062] The X-ray digital images E θ of different X-ray irradiation angles θ are obtained through the CT turntable 3; the CT data acquisition 4 records all the X-ray digital images E θ from the detector 2; the 3D image reconstruction 5 reconstructs all the X-ray digital images E 3D obtained from the CT data acquisition 4, and generates a 3D image G 3D of the composite material blade to be detected; the 2D image demodulation 6 demodulates the 3D image G 3D in a determined composite material blade observation axis direction, and generates a 2D image G 2D , which is represented by formula (2).

[0063]

[0064] In the formula, m is the 2D image G 3D demodulated from the 3D image G 2D , which is determined by formula (3).

[0065] The 2D image demodulation method is described in detail in the following. Figure 3 As shown in the figure: first, the observation direction is determined in the 3D image, such as the radial direction (z direction) of the composite material blade, and the radial length L of the detected blade is obtained; then, according to the detection requirements and the defect acceptance level, the 2D image demodulation coefficient m is selected, and m is determined according to formula (3):

[0066]

[0067] In the formula, ΔL is the cross-sectional thickness of the composite material blade in the z direction corresponding to the selected adjacent two 2D images, ΔL=kd, where d is the minimum defect size required to be detected, and k is a robustness coefficient, generally selected between 0.1-0.5. Finally, all the 2D images G 3D of the detected composite material blade corresponding to the z direction are extracted from the 3D image G 2Dm 2D images are generated, as shown in Equation (2). The m 2D images are saved sequentially to complete the forward modeling of the CT detection results.

[0068] use Figure 2 The CT inspection system shown performs CT inspection on a known defect-free composite material blade, and generates a 2D image of the defect-free composite material blade according to equation (2).

[0069] S200. Perform CT inspection on the composite material blade to be inspected using the same method as for defect-free composite material blades to generate a two-dimensional image of the composite material blade to be inspected.

[0070] In applications, it can be utilized Figure 1 The CT inspection system shown performs CT inspection on the composite blade to be inspected using the same method as for a defect-free composite blade, and generates a 2D image of the composite blade to be inspected according to equation (2).

[0071] S300. Based on the two-dimensional image of the composite material blade to be inspected, the two-dimensional image of the defect-free composite material blade, and the preset image defect recognition threshold, determine the CT inspection defect assessment result of the composite material blade to be inspected.

[0072] In some embodiments, the CT defect assessment result of the composite material blade under test is determined based on a two-dimensional image of the blade to be tested, a two-dimensional image of the defect-free composite material blade, and a preset image defect recognition threshold, including:

[0073] Determine the grayscale difference between the two-dimensional image of the composite blade to be inspected and the two-dimensional image of the defect-free composite blade;

[0074] Based on the grayscale difference and the preset image defect recognition threshold, the CT inspection defect assessment result of the composite material blade to be inspected is determined.

[0075] In some embodiments, determining the grayscale difference between a two-dimensional image of the composite blade to be inspected and a two-dimensional image of a defect-free composite blade includes:

[0076] Based on the grayscale values ​​at corresponding positions in the two-dimensional image and the pre-determined defect positive evaluation threshold deviation, the grayscale difference between the two-dimensional image of the composite blade to be inspected and the two-dimensional image of the defect-free composite blade is determined.

[0077] In some embodiments, the CT defect assessment result of the composite material blade to be inspected is determined based on the grayscale difference and a preset image defect recognition threshold, including:

[0078] If the gray scale difference is greater than the preset image defect recognition threshold, it is determined that the CT detection defect evaluation result of the composite blade to be detected is defective.

[0079] If the gray scale difference is less than or equal to the preset image defect recognition threshold, it is determined that the CT detection defect evaluation result of the composite blade to be detected is non-defective.

[0080] In application, G 2DS and G 2D gray scale difference Here, δ is the positive evaluation threshold deviation of the defect, which is determined by the CT test. When the defect is evaluated, here is the CT image defect recognition threshold, when the non-defective indication is evaluated. The corresponding and m values are recorded and saved to form a discrimination result image for result display.

[0081] The overall steps of the CT result evaluation method are: selecting a non-defective composite blade; selecting a CH system suitable for the composite blade (see Figure 2 ); selecting tube voltage, tube current, exposure, CT scanning mode and other parameters; setting CT detection standard threshold Composite blade clamping and protection setting; CT detection; save CT data to form 3D image data G 3DS ; generate 2D image data G 3DS from G 2DS ; turn off the X-ray source and remove the non-defective composite blade; clamp the composite blade to be detected, and repeat the generation of 3D image G 3D and 2D image data G 2D of the composite blade to be detected for positive defect discrimination until all the composite blades are detected, and the CT detection is ended and the machine is turned off.

[0082] The CT detection defect evaluation method provided by the embodiment of the application is based on X-ray imaging and image intelligent feature intelligent recognition principle, through facing the composite blade to be detected, using the active positive recognition method, first acquiring a small sample CT standard image of the known non-defective composite blade as a limited small sample comparison reference data; then, by creating a positive discrimination model, realizing 3D display / 2D automatic discrimination of the composite blade detection result. Its significant advantage is that a large number of defect image cases are not needed, and the maximum discrimination of the CT detection result and the automatic evaluation of the defect can be realized, which is more conducive to improving the CT detection efficiency and the accuracy of defect discrimination, has more universality, is easier to realize the rapid recognition of the CT detection result, and is more suitable for rapid CT detection and defect evaluation of the composite blade in batch production process.

[0083] Corresponding to the CT detection defect evaluation method described in the above embodiment, as shown in Figure 4 The embodiment provides a CT detection defect evaluation device 400, which comprises:

[0084] A first detection module 401 is configured to perform CT detection on a composite material blade without defects to generate a two-dimensional image of the composite material blade without defects.

[0085] A second detection module 402 is configured to perform CT detection on a composite material blade to be detected in the same way as the composite material blade without defects to generate a two-dimensional image of the composite material blade to be detected.

[0086] A defect evaluation module 403 is configured to determine a CT detection defect evaluation result of the composite material blade to be detected according to the two-dimensional image of the composite material blade to be detected, the two-dimensional image of the composite material blade without defects, and a preset image defect identification threshold.

[0087] It should be noted that the information interaction and execution process between the above modules / units are based on the same concept as the method embodiments of the present application, and the specific functions and technical effects brought by them can be referred to the method embodiments part, which will not be repeated here.

[0088] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of functional units and modules is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or software. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the method embodiments, which will not be repeated here.

[0089] The embodiment of the present application also provides an electronic device 500, as shown in Figure 5 The electronic device 500 comprises a memory 501, a processor 502, and a computer program 503 stored in the memory 501 and executable on the processor 502, and the processor 502 implements the steps of the CT detection defect evaluation method provided by the first aspect when executing the computer program 503.

[0090] In application, the electronic device can include, but is not limited to, a processor and a memory,Figure 5 The electronic device is merely an example and does not limit the electronic device, which can include more or fewer components than illustrated, or combine some components, or have different components, such as input / output devices, network access devices, and the like. The input / output devices can include a camera, an audio acquisition / play device, a display screen, and the like. The network access devices can include a network module for wireless network with external devices.

[0091] In applications, the processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0092] In applications, the memory can be an internal storage unit of the electronic device in some embodiments, such as a hard disk or a memory of the electronic device. The memory can also be an external storage device of the electronic device in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like equipped on the electronic device. The memory can include both the internal storage unit and the external storage device of the electronic device. The memory is used to store an operating system, application programs, a boot loader, data, and other programs, such as program codes of computer programs, and the like. The memory can also be used to temporarily store data that has been output or will be output.

[0093] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps in each method embodiment.

[0094] The computer program can be stored in a computer readable storage medium. The computer readable storage medium can be a floppy disk, a USB (Universal Serial Bus) flash disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic tape, a hard disk, an optical disc, a computer database, or the like.

[0095] Those skilled in the art can understand that the devices and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0096] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, and another point is that the coupling or direct coupling or communication connection between the shown or discussed mutual elements can be through some interface, indirect coupling or communication connection between devices can be electrical, mechanical or other forms.

[0097] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method of CT detection defect evaluation, characterized by, The method comprises the following steps: CT detection is performed on a defect-free composite blade to generate a two-dimensional image of the defect-free composite blade; CT detection is performed on a to-be-detected composite blade in the same manner as the defect-free composite blade to generate a two-dimensional image of the to-be-detected composite blade; A CT detection defect evaluation result of the to-be-detected composite blade is determined according to the two-dimensional image of the to-be-detected composite blade, the two-dimensional image of the defect-free composite blade, and a preset image defect identification threshold value; The CT detection of the defect-free composite blade to generate the two-dimensional image of the defect-free composite blade comprises the following steps: CT detection is performed on a defect-free composite blade to obtain a three-dimensional image of the defect-free composite blade; A plurality of two-dimensional images of the defect-free composite blade are generated according to the three-dimensional image of the defect-free composite blade; The generation of the plurality of two-dimensional images of the defect-free composite blade according to the three-dimensional image of the defect-free composite blade comprises the following steps: According to the preset cross-sectional thickness of the composite blade in the radial direction corresponding to the two adjacent two-dimensional images , the three-dimensional image of the defect-free composite blade is extracted in the radial direction m two-dimensional image, ; wherein L is the radial length of the composite blade, = 0.5 , is the minimum defect size to be detected, is the robustness factor.

2. The CT detection defect evaluation method of claim 1, wherein, The CT detection of the defect-free composite blade to obtain the three-dimensional image of the defect-free composite blade comprises the following steps: X-ray radiography is performed on a defect-free composite blade to obtain a plurality of X-ray digital images of the defect-free composite blade at different X-ray irradiation angles; A three-dimensional image of the defect-free composite blade is generated according to the plurality of X-ray digital images of the defect-free composite blade.

3. The CT detection defect evaluation method of claim 1, wherein, The determination of the CT detection defect evaluation result of the to-be-detected composite blade according to the two-dimensional image of the to-be-detected composite blade, the two-dimensional image of the defect-free composite blade, and the preset image defect identification threshold value comprises the following steps: A gray scale difference between the two-dimensional image of the to-be-detected composite blade and the two-dimensional image of the defect-free composite blade is determined; The CT detection defect evaluation result of the to-be-detected composite blade is determined according to the gray scale difference and the preset image defect identification threshold value.

4. The CT detection defect evaluation method of claim 3, wherein, The determination of the gray scale difference between the two-dimensional image of the to-be-detected composite blade and the two-dimensional image of the defect-free composite blade comprises the following steps: The gray scale difference between the two-dimensional image of the to-be-detected composite blade and the two-dimensional image of the defect-free composite blade is determined according to the gray scale of the corresponding positions of the two-dimensional images and a pre-determined defect positive evaluation threshold value deviation.

5. The CT detection defect evaluation method of claim 3, wherein, The determination of the CT detection defect evaluation result of the to-be-detected composite blade according to the gray scale difference and the preset image defect identification threshold value comprises the following steps: If the gray scale difference is greater than the preset image defect identification threshold value, it is determined that the CT detection defect evaluation result of the to-be-detected composite blade is that there is a defect; If the gray scale difference is less than or equal to the preset image defect identification threshold value, it is determined that the CT detection defect evaluation result of the to-be-detected composite blade is that there is no defect.

6. A CT detection defect evaluation device characterized by comprising: The method comprises the following steps: A first detection module is configured to perform CT detection on a defect-free composite blade to generate a two-dimensional image of the defect-free composite blade; The second detection module is configured to perform CT detection on the to-be-detected composite blade in the same manner as the defect-free composite blade, and generate a two-dimensional image of the to-be-detected composite blade. The defect evaluation module is configured to determine a CT detection defect evaluation result of the to-be-detected composite blade according to the two-dimensional image of the to-be-detected composite blade, the two-dimensional image of the defect-free composite blade, and a preset image defect identification threshold. The first detection module is specifically configured to: perform CT detection on the defect-free composite blade to obtain a three-dimensional image of the defect-free composite blade; generate a plurality of two-dimensional images of the defect-free composite blade according to the three-dimensional image of the defect-free composite blade; The generation of the plurality of two-dimensional images of the defect-free composite blade according to the three-dimensional image of the defect-free composite blade comprises: According to the preset cross-sectional thickness of the composite blade in the radial direction corresponding to the two adjacent two-dimensional images , the three-dimensional image of the defect-free composite blade is extracted in the radial direction m two-dimensional image, ; wherein L is the radial length of the composite blade, = 0.5 , is the minimum defect size to be detected, is the robustness factor.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the CT detection defect evaluation method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 7. The computer program is executed by the processor to implement the CT detection defect evaluation method according to any one of claims 1 to 5.

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