Defect detection method and device based on NV color center

Abstract

The invention discloses a defect detection method and equipment based on an NV color center, and relates to the technical field of nondestructive testing. Comprising the following steps: preprocessing detection data obtained by magnetic flux leakage detection based on a diamond NV color center, and filtering out abnormal signals; filtering and denoising processing is carried out on the preprocessed data; and judging a defect signal according to the processed data and calculating the depth and width of the defect. According to the method, the reliability and the accuracy of detection data are ensured by establishing data preprocessing and filtering and denoising processes, the signal quality is improved, and interference is reduced; nV color center detection is carried out based on the magnetized leakage magnetic field, grading processing is carried out on detection data, and a corresponding data processing algorithm is adopted, so that the spatial resolution and sensitivity of detection are improved; an evaluation system is established through defect depth and width parameters, and the detection efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of nondestructive testing, and in particular to a defect detection method and device based on NV color centers. Background Art

[0002] Most of the existing instruments for detecting ferromagnetic plates can only perform qualitative crack detection, that is, they can only detect whether there are cracks; most of the instruments for quantitative detection use visual detection, using industrial cameras and computer software algorithms to perform quantitative detection of cracks. The accuracy of the detection depends not only on the precision of the camera, but also on the algorithm. In addition, during the imaging process, it is easily affected by vibration, dust, temperature, etc. in the environment, resulting in poor image quality and affecting the detection accuracy.

[0003] The precision measurement technology based on diamond NV color centers can achieve non-destructive testing through magnetic measurement, with high spatial resolution and sensitivity. Existing defect detection is divided into qualitative and quantitative detection. Qualitative means whether there are defects, and quantitative means detecting the physical size and type of defects. In actual detection tasks, the physical size of the defect often determines the performance of the object being tested and whether it needs to be replaced. At present, defect detection based on NV color centers can only achieve qualitative detection, and how to achieve quantitative detection has become a problem that needs to be solved. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to achieve quantitative detection of defects by using diamond NV color centers with higher precision instead of expensive devices such as industrial cameras with low detection accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a defect detection method based on NV color centers, which includes preprocessing detection data obtained by magnetic flux leakage detection based on diamond NV color centers to filter out abnormal signals; wherein the magnetic flux leakage detection is to use the diamond NV color center to scan the magnetized test piece, and at the same time irradiate the diamond NV color center with laser and microwave, and detect the fluorescence generated by the diamond NV color center to obtain detection data, and the detection data at least includes the fluorescent electrical signal and its corresponding scanning position;

[0008] Perform filtering and denoising on the preprocessed data;

[0009] The defect signal is determined based on the processed data and the depth and width of the defect are calculated.

[0010] As a preferred solution of the defect detection method based on NV color center of the present invention, the preprocessing includes:

[0011] For all fluorescent electrical signals, calculate their root mean square value RMS;

[0012] Determine whether each fluorescent electrical signal is lower than the RMS and whether the absolute value of the difference between the two exceeds a preset threshold;

[0013] If the current fluorescent electrical signal is lower than the RMS and the absolute value of the difference between the two exceeds the preset threshold, the signal is deemed to be an abnormal signal, and all signals lower than the RMS are set to RMS.

[0014] As a preferred solution of the defect detection method based on NV color center of the present invention, the filtering and denoising process includes:

[0015] The high-pass filtering algorithm is used to perform low-frequency denoising on the pre-processed fluorescent electrical signal by setting the cutoff frequency and the filtering order;

[0016] The wavelet denoising algorithm is used to set the wavelet denoising basis function, threshold and threshold processing function to smooth the mutation signal of the fluorescence electrical signal after low-frequency denoising.

[0017] As a preferred solution of the defect detection method based on NV color center of the present invention, the calculation of the depth and width of the defect includes:

[0018] Calculate the root mean square value (RMS) of the fluorescent electrical signal after filtering and denoising;

[0019] Determine whether the signal-to-noise ratio of each signal point is higher than a specified value, and if higher than the specified value, determine that a surface defect exists at the position corresponding to the signal;

[0020] The positions where all fluorescent electrical signals continuously higher than the RMS are located on both sides of the position are classified as the areas where the same defect is located;

[0021] Count the number of fluorescent electrical signal points and the maximum value of the fluorescent electrical signal of the defect;

[0022] The defect width is calculated according to the formula W=N×Δx, where W is the defect width, N is the number of fluorescent electrical signal points, and Δx is the sampling interval; and

[0023] The defect depth is calculated according to the formula D = (Vmax / RMS) × D0, where D is the defect depth, Vmax is the maximum value of the fluorescent electrical signal, and D0 is the reference value, which is calibrated by the detection data of known defects.

[0024] As a preferred solution of the defect detection method based on NV color center of the present invention, in the filtering and denoising process, the parameters used in the algorithm are obtained by the following method:

[0025] Conduct magnetic flux leakage detection based on diamond NV color centers on known defects to obtain detection data;

[0026] The detection data is filtered and denoised by any algorithm, and each debugged data is obtained by debugging the parameters. For each debugged data, the evaluation value is calculated using the formula 10lg[(fluorescent electrical signal at the known defect - the average value of all fluorescent electrical signals) / (fluorescent electrical signal at the known defect after denoising - the average value of all fluorescent electrical signals)], and the parameters corresponding to the required evaluation value are selected as the parameters used by the corresponding algorithm.

[0027] As a preferred solution of the defect detection method based on NV color center of the present invention, in the magnetic flux leakage detection, the microwave radiated to the diamond NV color center has a fixed frequency.

[0028] In a second aspect, an embodiment of the present invention provides a defect detection device based on NV color centers, comprising a memory and a processor, the memory storing a computer program, wherein: when the processor executes the computer program, the steps of the defect detection method based on NV color centers as in the first aspect of the present invention are implemented.

[0029] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the NV color center-based defect detection method of the first aspect of the present invention are implemented.

[0030] In a fourth aspect, an embodiment of the present invention provides a defect detection system based on NV color centers, which includes a magnetic magnetization module, a detection and acquisition module, and a defect detection device based on NV color centers as described in the second aspect;

[0031] The magnetization module is used to perform magnetization processing on the test piece;

[0032] The detection and acquisition module is used to perform magnetic leakage detection of the test piece based on the diamond NV color center, and collect the detected fluorescent electrical signal and its corresponding scanning position to obtain detection data, and transmit the detection data to the defect detection equipment based on the NV color center.

[0033] As a preferred solution of the defect detection system based on NV color center of the present invention, the detection and acquisition module includes:

[0034] Diamond NV color center probe, displacement control mechanism, laser, dichroic plate, filter, photodiode, acquisition module, microwave module; Among them:

[0035] The laser emitted by the laser is reflected by the two-color plate and then irradiated to the diamond NV color center probe;

[0036] The microwave module is used to generate microwaves and radiate the microwaves to the diamond NV color center probe;

[0037] The feedback fluorescence generated by the diamond NV color center probe is transmitted through the dichroic plate, and then transmitted to the filter plate for filtering, and then received by the photodiode and converted into a fluorescent electrical signal;

[0038] The displacement control mechanism is used to control the diamond NV color center probe and the magnetization module to scan the object to be tested and locate the scanning position;

[0039] The acquisition module is used to collect fluorescent electrical signals and scanning positions to obtain detection data.

[0040] The beneficial effects of the present invention are as follows: the NV color center-based defect detection method and equipment provided by the present invention ensure the reliability and accuracy of the detection data by establishing a data preprocessing and filtering denoising process, improve the signal quality, reduce interference, and at the same time improve the accuracy and credibility of the final defect detection result; by performing NV color center detection based on the leakage magnetic field after magnetization, and performing graded processing on the detection data, and adopting a corresponding data processing algorithm, the spatial resolution and sensitivity of the detection are improved, the detection cost and environmental dependence are reduced, and the technical and application feasibility of defect detection is ensured; an evaluation system is established through defect depth and width parameters, and the detection process is regularly optimized and updated, which improves the detection efficiency, reduces the misjudgment rate, and ensures the advancement and practicality of the detection method. The present invention achieves better results in detection accuracy, data processing and detection reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is a flow chart of the defect detection method based on NV color center;

[0043] Figure 2 ODMR spectrum diagram of defect detection method based on NV color center;

[0044] Figure 3 It is the structural diagram of the defect detection system based on NV color center;

[0045] Figure 4is an exemplary structural diagram of a defect detection system based on NV color centers;

[0046] Figure 5 Schematic diagram of the magnetization module of the defect detection system based on NV color centers;

[0047] Figure 6 It is the original data diagram of the defect detection method based on NV color center;

[0048] Figure 7 This is a data diagram after data preprocessing of the defect detection method based on NV color center;

[0049] Figure 8 This is the data diagram after high-pass filtering of the defect detection method based on NV color center;

[0050] Fig. 9 This is the data graph after wavelet denoising processing of the defect detection method based on NV color center. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0054] Example 1

[0055] Reference Figure 1 , which is the first embodiment of the present invention, provides a defect detection method based on NV color centers, including:

[0056] Preprocessing the detection data obtained by magnetic flux leakage detection based on diamond NV color centers to filter out abnormal signals; wherein the magnetic flux leakage detection is to use the diamond NV color center to scan the magnetized test piece, and at the same time irradiate the diamond NV color center with laser and microwave to detect the fluorescence generated by the diamond NV color center to obtain detection data, and the detection data at least includes the fluorescent electrical signal and its corresponding scanning position;

[0057] Perform filtering and denoising on the preprocessed data;

[0058] The defect signal is determined based on the processed data and the depth and width of the defect are calculated.

[0059] Data preprocessing: The original data is as follows Figure 6 As shown in the figure, the root mean square value RMS of the fluorescent electrical signal of the entire data segment is taken, and then each fluorescent electrical signal in the data is judged. If the current signal is lower than the RMS and the absolute value of the difference between the two exceeds 500mv (the fluorescent electrical signal here is a voltage value), the current signal is considered to be an abnormal signal, and all signals lower than the RMS are set to RMS, and the following is obtained: Figure 7 As shown;

[0060] This step is used to identify abnormal signals, remove them, and replace them with RMS. This is because there will be abnormal signals in the detection. For example, when the test piece is a ferromagnetic plate, the leakage magnetic signal generated by the gap between the plates and the introduction of other noises in the environment will also cause abnormal signals. For crack signals, the signal is higher than RMS. The 500mv in this embodiment is an empirical value, which can be determined based on the detected abnormal signal. There may be differences for different detection equipment. For the same detection equipment, the value is unified. If there is no abnormal signal, filtering and denoising is performed.

[0061] Filtering and denoising: Set different cutoff frequencies and filter orders, and use high-pass filtering algorithm to flatten the data baseline to obtain Figure 8 As shown, flattening is to filter out low-frequency noise; then set the wavelet denoising basis function, threshold and threshold processing function to smooth the mutation signal, and get the following Fig. 9 The wavelet denoising base function used in this embodiment is Dobesi wavelet, the threshold is 500, the threshold processing function is a soft function, and other types of parameters can also be selected, mainly based on the noise reduction effect for debugging.

[0062] Among them, high-pass filtering algorithm and wavelet denoising are common data denoising filtering algorithms in one-dimensional signals. Cutoff frequency and filter order are parameters in the high-pass filtering algorithm. By adjusting these two parameters to filter out low-frequency noise, Butterworth filter can be used to implement high-pass filtering. The wavelet denoising process of one-dimensional signals is divided into wavelet decomposition of signals, threshold processing of high-frequency signals, and wavelet reconstruction of signals. By changing the wavelet basis function, threshold processing function, and threshold size, the sudden change signal can be smoothed.

[0063] The selection of parameters of the two algorithms can be determined by experiments. Specifically, by performing magnetic flux leakage detection on known defects and performing algorithm processing on the detection data, the parameters that are conducive to enhancing the defect signal can be selected by debugging the parameters. The judgment formula can be used: 10lg[(fluorescent electrical signal at the known defect - average value of all fluorescent electrical signals) / (fluorescent electrical signal at the known defect after denoising - average value of all fluorescent electrical signals)]. An evaluation value can be obtained by calculation using this formula. The smaller the value, the better. A positive value represents a weakening of negative growth, and a negative value represents an enhancement of positive growth signals. This value should be less than 3dB. Appropriate parameters are selected based on the evaluation value.

[0064] When conducting magnetic flux leakage detection on a test piece such as a ferromagnetic plate component, there may be low-frequency components caused by instrument drift, environmental changes, and other factors. At the same time, there may be some noise signals in the low-frequency area of ​​the data, and these low-frequency components may mask the real changes in the signal. By applying a high-pass filter, these low-frequency components can be removed, reducing the noise interference of the signal and making the signal clearer. By changing the cutoff frequency and filter order of the high-pass filter for debugging, the data baseline can be flattened and low-frequency noise can be removed.

[0065] Wavelet denoising can significantly improve the signal-to-noise ratio of the signal, increase the ratio of the useful signal components to the noise, and thus improve the clarity and readability of the signal. It can achieve the effect of removing noise by setting the high-frequency components of low amplitude to zero. This method smoothes the mutation signal and will not drastically mutate when the defect signal does not reach the extreme value.

[0066] Surface defect signal determination: Calculate the overall RMS of the fluorescent electrical signal after filtering and denoising, and perform defect signal determination on each signal one by one

[0067]

[0068] Because the output signal of the magnetic flux leakage detection in this embodiment is a voltage signal, the measurement data is as follows Figure 6-Figure 9 As shown, the fluorescent electrical signals are all voltage values.

[0069] When the signal-to-noise ratio is higher than the specified value, it is determined that there is a surface defect. The specified value here is an empirical value. In this embodiment, it is 3, and generally between 3-5. The positions corresponding to all the electrical signals on both sides of the position that are continuously higher than the RMS are identified as part of this defect (by judging whether it is higher than the RMS one by one until it is less than or equal to the RMS), and the number of fluorescent electrical signal points and the maximum value of the fluorescent electrical signal of this section of the defect are counted. The width and depth of this section of the defect can be calculated by the following formula:

[0070]

[0071] The reference value is obtained by calibration.

[0072] Preferably, the pretreatment comprises:

[0073] For all fluorescent electrical signals, calculate their root mean square value RMS;

[0074] Determine whether each fluorescent electrical signal is lower than the RMS and whether the absolute value of the difference between the two exceeds a preset threshold;

[0075] If the current fluorescence voltage is lower than RMS and the absolute value of the difference between the two exceeds the preset threshold, the signal is considered an abnormal signal and all signals lower than RMS are set to RMS.

[0076] Preferably, the filtering and denoising process includes:

[0077] The high-pass filtering algorithm is used to perform low-frequency denoising on the pre-processed fluorescent electrical signal by setting the cutoff frequency and the filtering order;

[0078] The wavelet denoising algorithm is used to set the wavelet denoising basis function, threshold and threshold processing function to smooth the mutation signal of the fluorescence electrical signal after low-frequency denoising.

[0079] Preferably, calculating the depth and width of the defect comprises:

[0080] Calculate the root mean square value (RMS) of the fluorescent electrical signal after filtering and denoising;

[0081] Determine whether the signal-to-noise ratio of each signal point is higher than a specified value. If it is higher than the specified value, it is determined that there is a surface defect at the position corresponding to the signal;

[0082] The positions of all the fluorescent electrical signals on both sides of the position that are continuously higher than the RMS are classified as the area where the same defect is located;

[0083] Count the number of fluorescent electrical signal points and the maximum value of the fluorescent electrical signal of the defect;

[0084] The defect width is calculated according to the formula W=N×Δx, where W is the defect width, N is the number of fluorescent electrical signal points, and Δx is the sampling interval; and

[0085] The defect depth is calculated according to the formula D = (Vmax / RMS) × D0, where D is the defect depth, Vmax is the maximum value of the fluorescent electrical signal, and D0 is the reference value, which is calibrated by the detection data of known defects.

[0086] Preferably, in the filtering and denoising process, the parameters used in the algorithm are obtained by the following method:

[0087] Obtain detection data based on magnetic flux leakage detection of known defects based on diamond NV color centers;

[0088] The detection data is filtered and denoised by any algorithm, and each debugged data is obtained by debugging the parameters. For each debugged data, the evaluation value is calculated using the formula 10lg[(fluorescent electrical signal at the known defect - the average value of all fluorescent electrical signals) / (fluorescent electrical signal at the known defect after denoising - the average value of all fluorescent electrical signals)], and the parameters corresponding to the required evaluation value are selected as the parameters used by the corresponding algorithm.

[0089] Preferably, the evaluation value is less than 3dB.

[0090] The magnetic flux leakage detection in this embodiment adopts the fixed frequency method, that is, the microwave radiated to the diamond NV color center is of fixed frequency. The method for obtaining the fixed frequency is to implement microwave frequency sweeping ODMR measurement on the diamond NV color center, that is, irradiate laser and microwave to the diamond NV color center, and scan the microwave frequency to detect the feedback fluorescence. According to the fluorescence and the corresponding microwave frequency, the following is plotted: Figure 2 The ODMR spectrum shown uses the resonance frequency on the ODMR spectrum or the microwave frequency corresponding to the maximum slope of the spectrum on one side of the resonance peak as the fixed frequency for implementing leakage magnetic field detection.

[0091] Example 2

[0092] This embodiment provides a defect detection device based on NV color center, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the defect detection method based on NV color center proposed in Example 1.

[0093] The defect detection device based on NV color center can be a computer device, which can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless method can be achieved through WIFI, an operator network, NFC near-field communication or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0094] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the defect detection method based on NV color centers as proposed in the above embodiment is implemented.

[0095] In summary, beneficial effects.

[0096] Example 3

[0097] This embodiment provides a defect detection system based on NV color centers, such as Figure 3 As shown, it includes: a magnetization module, a detection and acquisition module, and the defect detection device based on NV color center in Example 2 ( Figure 4 Marked as 110);

[0098] The magnetization module is used to magnetize the test piece;

[0099] The detection and acquisition module is used to perform magnetic leakage detection of the test piece based on the diamond NV color center, and to collect the detected fluorescent electrical signal and its corresponding scanning position to obtain detection data, and transmit the detection data to the defect detection equipment based on the NV color center.

[0100] The magnetizing module includes a magnetizer 109, which may be shaped as follows: Figure 5 As shown in FIG. 1 , the armature 109 a and magnets 109 b at both ends of the armature 109 a are placed in a fixed cover 201 together with the diamond NV color center probe 101 , and the displacement control mechanism 102 controls the movement and scanning of the two on the surface of the test piece.

[0101] Preferably, Figure 4 As shown, the detection and acquisition module includes: a diamond NV color center probe 101, a displacement control mechanism 102, a laser 103, a dichroic plate 104, a filter 105, a photodiode 106, an acquisition module 107, and a microwave module 108.

[0102] in:

[0103] The laser emitted by the laser 103 is reflected by the dichroic plate 104 and then irradiated to the diamond NV color center probe 101. The laser generally has a wavelength of 532nm, which excites the NV color center to produce a feedback fluorescence of 637nm.

[0104] The microwave module 108 is used to generate microwaves and radiate the microwaves to the diamond NV color center probe 101. Figure 4 As shown, the microwave source 108a, microwave amplifier 108b, microwave circulator 108c, and microwave antenna 108d are connected in sequence. The microwave signal generated by the microwave source 108a is amplified by the microwave amplifier 108b, and then transmitted to the microwave antenna 108d through the microwave circulator 108c, and radiated to the diamond NV color center probe 101 by the microwave antenna 108d;

[0105] The feedback fluorescence generated by the diamond NV color center 101 probe is transmitted through the dichroic plate 104, and then transmitted to the filter 105 for filtering, and then received by the photodiode 106 and converted into a fluorescent electrical signal;

[0106] The displacement control mechanism 102 is used to control the diamond NV color center probe 101 and the magnetization module to scan the test piece and locate the scanning position. The positioning method can adopt sensor positioning, or calculate the scanning distance according to the scanning speed and time.

[0107] The acquisition module 107 is used to acquire the fluorescent electrical signal and the scanning position to obtain detection data. The acquisition module may exemplarily include an analog integrated circuit.

[0108] The mobile diamond NV color center probe 101 and the magnetization module are scanned on the ferromagnetic plate, the ferromagnetic plate is magnetized, and the leakage magnetic field at the defect is detected. The detection principle is: the 532nm laser 103 emits laser light, which is reflected by the two-color plate 104 and irradiated to the diamond NV color center probe 101, the microwave source 108a generates microwave signals, the microwave amplifier 108b can amplify the microwave power, and the microwave circulator 108c can prevent the reverse transmission of the microwave signal. Then the microwave signal is transmitted to the microwave antenna 108d, which is radiated to the diamond NV color center by the microwave antenna 108d. Under the action of laser, microwave and magnetic field, 637nm feedback fluorescence is generated, which returns to the two-color plate 104 through the original path, and after transmission, it is filtered by the filter 105 and received by the photodiode 106, and converted into a fluorescent electrical signal. The acquisition module 107 collects the fluorescent electrical signal and the scanning position, and transmits the detection data to the defect detection device 110 based on the NV color center. The defect detection device 110 can be implemented by a host computer.

[0109] In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0110] In this embodiment, a batch of standardized Q235 steel plates are selected as test samples, with a sample size of 100mm×100mm×5mm, and surface cracks of different depths and widths are artificially manufactured as standard defects. To ensure the reliability of the test data, 5 standard cracks of different specifications are processed on each steel plate, with a crack depth range of 0.1mm-2mm and a width range of 0.05mm-0.5mm. The standard cracks are made using a precision machining center, and the crack size is calibrated using a three-coordinate measuring instrument as the detection reference value.

[0111] The test process was carried out using a self-developed defect detection system based on NV color centers. First, the steel plate was pre-magnetized using a permanent magnet, and the magnetization intensity was set to 1.2 T. The distance between the NV color center probe and the sample surface was maintained at 100 μm, the scanning step was set to 50 μm, and 100 data were collected at each measuring point to take the average value to improve the signal-to-noise ratio. In order to verify the advantages of the data processing method of the present invention, the same batch of samples were tested using the traditional direct sampling method and the data processing method of the present invention.

[0112] In the data preprocessing stage, the original signal is subjected to RMS analysis, and the threshold is set to 500mV for abnormal signal identification. A Butterworth high-pass filter with a cutoff frequency of 10Hz is used for baseline correction, and the filter order is set to 4. In the wavelet denoising stage, a 4th-order Dobesi wavelet is selected for 3-layer decomposition, and a soft threshold function is used for processing. The threshold is determined to be 500 after experimental optimization. In the defect detection judgment stage, the signal-to-noise ratio judgment threshold is set to 3.5 to distinguish effective defect signals.

[0113] The test data are as follows:

[0114] Table 1 Data before processing

[0115]

[0116]

[0117] Table 2 After data processing

[0118]

[0119] It should be noted that the following conclusions can be drawn from the analysis of the test data:

[0120] The detection accuracy of the method of the present invention generally reaches more than 97%, with the highest reaching 99.4%, while the accuracy of the traditional method fluctuates between 86.8% and 92.6%. This shows that the data processing method of the present invention can significantly improve the accuracy of defect detection, with an average increase of about 10 percentage points.

[0121] The number of missed detections of the traditional method varies between 24 and 48, and the missed detection rate reaches 4.8% to 9.6%; while the number of missed detections of the method of the present invention is only 2 to 8, and the missed detection rate is reduced to 0.4% to 1.6%. This improvement is of great significance for practical engineering applications, because missed detection may lead to potential safety hazards.

[0122] The number of false detections in the traditional method is between 10-25, and the false detection rate is 2%-5%; the number of false detections in the method of the present invention is reduced to 1-9, and the false detection rate is reduced to 0.2%-1.8%. The reduction in the false detection rate means that unnecessary rework and inspection can be reduced, and the detection efficiency can be improved.

[0123] From the data fluctuation range, the standard deviation of the accuracy of the traditional method is about 0.026, while the standard deviation of the method of the present invention is only 0.008, which shows that the method of the present invention has better stability and repeatability. This stability is extremely important for practical application in industrial sites.

[0124] The method of the present invention achieves a double reduction in missed detection rate and false detection rate while maintaining a high detection accuracy, a balance that is difficult to achieve with previous technologies. In particular, in large-scale detection with a sample size of 500, such high accuracy can still be maintained, which fully proves the practical value of this method.

[0125] These data fully prove that the data processing method of the present invention has significant advantages in improving detection accuracy, reducing missed detection rate and false detection rate, and improving detection stability. This method effectively overcomes the problem that traditional methods are susceptible to interference in complex environments through innovative data preprocessing and filtering denoising algorithms, providing more reliable technical support for non-destructive testing in industrial sites.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A defect detection method based on NV color center, characterized in that: include, Preprocessing the detection data obtained by magnetic flux leakage detection based on diamond NV color centers to filter out abnormal signals; wherein the magnetic flux leakage detection is to use the diamond NV color center to scan the magnetized test piece, and at the same time irradiate the diamond NV color center with laser and microwave to detect the fluorescence generated by the diamond NV color center to obtain detection data, and the detection data at least includes the fluorescent electrical signal and its corresponding scanning position; Perform filtering and denoising on the preprocessed data; The defect signal is determined based on the processed data, and the depth and width of the defect are calculated.

2. The defect detection method based on NV color centers according to claim 1, characterized in that: The pre-processing comprises: For all fluorescent electrical signals, calculate their root mean square value RMS; Determine whether each fluorescent electrical signal is lower than the RMS and whether the absolute value of the difference between the two exceeds a preset threshold; If the current fluorescent electrical signal is lower than the RMS and the absolute value of the difference between the two exceeds the preset threshold, the signal is deemed to be an abnormal signal, and all signals lower than the RMS are set to RMS.

3. The defect detection method based on NV color centers according to claim 2, characterized in that: The filtering and denoising process comprises: The high-pass filtering algorithm is used to perform low-frequency denoising on the pre-processed fluorescent electrical signal by setting the cutoff frequency and the filtering order; The wavelet denoising algorithm is used to set the wavelet denoising basis function, threshold and threshold processing function to smooth the mutation signal of the fluorescence electrical signal after low-frequency denoising.

4. The defect detection method based on NV color centers according to any one of claims 1 to 3, characterized in that: The calculation of the depth and width of the defect includes: Calculate the root mean square value (RMS) of the fluorescent electrical signal after filtering and denoising; Determine whether the signal-to-noise ratio of each signal point is higher than a specified value, and if higher than the specified value, determine that a surface defect exists at the position corresponding to the signal; The positions where all fluorescent electrical signals continuously higher than the RMS are located on both sides of the position are classified as the areas where the same defect is located; Count the number of fluorescent electrical signal points and the maximum value of the fluorescent electrical signal of the defect; The defect width is calculated according to the formula W=N×Δx, where W is the defect width, N is the number of fluorescent electrical signal points, and Δx is the sampling interval; and The defect depth is calculated according to the formula D = (Vmax / RMS) × D0, where D is the defect depth, Vmax is the maximum value of the fluorescent electrical signal, and D0 is the reference value, which is calibrated by the detection data of known defects.

5. The defect detection method based on NV color centers according to claim 3, characterized in that: In the filtering and denoising process, the parameters used in the algorithm are obtained by the following method: Conduct magnetic flux leakage detection based on diamond NV color centers on known defects to obtain detection data; The detection data is filtered and denoised by any algorithm, and each debugged data is obtained by debugging the parameters. For each debugged data, the evaluation value is calculated using the formula 10lg[(fluorescent electrical signal at the known defect - the average value of all fluorescent electrical signals) / (fluorescent electrical signal at the known defect after denoising - the average value of all fluorescent electrical signals)], and the parameters corresponding to the required evaluation value are selected as the parameters used by the corresponding algorithm.

6. The defect detection method based on NV color centers according to claim 1, characterized in that: In the magnetic flux leakage detection, the microwaves radiated toward the diamond NV color center have a fixed frequency.

7. A defect detection device based on NV color center, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the defect detection method based on NV color centers described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the defect detection method based on NV color centers described in any one of claims 1 to 6 are implemented.

9. A defect detection system based on NV color center, characterized in that: include: A magnetization module, a detection and acquisition module, and a defect detection device based on NV color centers as described in claim 7; The magnetization module is used to perform magnetization processing on the test piece; The detection and acquisition module is used to perform magnetic leakage detection of the test piece based on the diamond NV color center, and collect the detected fluorescent electrical signal and its corresponding scanning position to obtain detection data, and transmit the detection data to the defect detection equipment based on the NV color center.

10. The defect detection system based on NV color centers according to claim 9, characterized in that: The detection and acquisition module comprises: A diamond NV color center probe (101), a displacement control mechanism (102), a laser (103), a dichroic plate (104), a filter (105), a photodiode (106), a collection module (107), and a microwave module (108); wherein: The laser light emitted by the laser (103) is reflected by the two-color plate and then irradiated onto the diamond NV color center probe; The microwave module (108) is used to generate microwaves and radiate the microwaves to the diamond NV color center probe; The feedback fluorescence generated by the diamond NV color center probe (101) is transmitted through the dichroic plate (104), and then transmitted to the filter (105) for filtering, and then received by the photodiode (106) and converted into a fluorescent electrical signal; A displacement control mechanism (102) is used to control the diamond NV color center probe (101) and the magnetization module to scan the object to be tested and locate the scanning position; The acquisition module (107) is used to acquire the fluorescent electrical signal and the scanning position to obtain detection data.

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