Method, device, electronic equipment and storage medium for repeated X-ray weld film detection

Abstract

The present invention relates to the technical field of weld negative film detection, and is a detection method, device, electronic device and storage medium applicable to repeated X-ray weld negative films, including: respectively determining the weld region, base metal region and full film region in the first X-ray weld negative film and the second X-ray weld negative film; selecting at least two region types, and respectively determining the hash value and Hamming distance of the corresponding regions in the first X-ray weld negative film and the second X-ray weld negative film; and determining the similarity of the first X-ray weld negative film and the second X-ray weld negative film in each selected region according to the hash value and Hamming distance. The present invention divides the polar regions of the X-ray weld negative film, determines the similarity of the corresponding regions by calculating the hash values and Hamming distances of at least two regions, and thereby determines whether two X-ray weld negative films are repeated. This process effectively realizes the automation of repeated X-ray weld negative film detection and improves the efficiency of repeated X-ray weld negative film detection.

Description

Technical Field

[0001] The present invention relates to the technical field of weld negative film detection, and is a detection method, device, electronic device and storage medium applicable to repeated X-ray weld negative films. Background Art

[0002] When using an X-ray flaw detection instrument to image internal defects in a weld, in the scenario of taking multiple shots of the same object, when the angles, positions or parameters are the same or when the X-ray flaw detection instrument malfunctions, it may lead to the generation of repeated X-ray weld negative films. In addition, it may also be caused by manual cheating methods such as copying X-ray weld negative films, covering the weld area or base metal area of one X-ray weld negative film to the weld area or base metal area of another X-ray weld negative film, resulting in the generation of repeated X-ray weld negative films.

[0003] Therefore, before weld defect detection, it is generally necessary to evaluate the quality of X-ray weld negative films. This evaluation mainly detects the imaging quality, repeatability, etc. of X-ray weld negative films, so as to ensure the quality of X-ray weld negative film data and prevent cheating.

[0004] Currently, most of the detection of the repeatability of X-ray weld negative films is completed manually, and a small part is completed by machines. Completing it manually depends on the staff to conduct repeated detection of X-ray weld negative films according to the image shooting parameters and image identifiers. This method has a high accuracy rate, but the efficiency is very low. Completing it by machine is to use computer algorithms or neural network models to conduct repeated detection of X-ray weld negative films. However, using neural network models to conduct repeated detection of X-ray weld negative films has problems such as occupying a large amount of computing resources and storage resources, being easily limited to local convergence, and the detection accuracy of neural network models being greatly affected by the quality and quantity of samples, which is likely to cause inaccurate detection.

[0005] Existing public patent document 1, with the publication number CN118015305A, discloses a method for identifying the same welding ray negative films, which includes: establishing a historical negative film image library; training a first preset model with historical negative film images to obtain a first model for identifying defect classification features, and collecting dynamic information used to characterize the change state of the neural network during the training process of the first model. Based on this, training a second preset model to obtain a second model for identifying defect index features indicating the defect features on the negative film image; using each model to predict the defect classification features and defect index features of the negative film to be identified respectively; screening out the images with the same type of defects as the negative film to be identified from the image library and recording them as the first images, and comparing and analyzing the first images with the predicted defect index features to diagnose whether there are images with the same welding features as the negative film to be identified in the image library.

[0006] The existing publicly disclosed patent document II, with the publication number CN110084807B, discloses a method for detecting forgery of weld flaw detection negative images. First, the gelatin weld flaw detection negative image is scanned into a digital weld flaw detection negative image, and the digital weld flaw detection negative images are sorted according to the negative number, and the pictures are submitted in the order of the negative number. Then, the feature abstracts represented by Hash values in the weld flaw detection negative images are extracted and compared with the feature abstracts stored in the database to detect duplicate submitted weld flaw detection negative images. Further, the lap zone matching detection of the newly submitted weld flaw detection negative image and the last saved weld flaw detection negative image in the database is performed based on the SIFT algorithm and the SSIM similarity, and the weld flaw detection negative images that are not duplicate submitted but have mismatched lap zones can be detected. Summary of the Invention

[0007] The present invention provides a detection method, device, electronic device and storage medium applicable to repeated X-ray weld negatives, which overcomes the deficiencies of the above-mentioned prior art and can effectively solve the problems that the repeated detection efficiency of the existing manual X-ray weld negative repeated detection method is low, and the neural network model X-ray weld negative repeated detection method requires a large amount of computing resources and storage resources.

[0008] One of the technical solutions of the present invention is achieved by the following measures: A detection method applicable to repeated X-ray weld negatives includes:

[0009] Respectively determine the weld zone, base metal zone and full negative zone in the first X-ray weld negative and the second X-ray weld negative;

[0010] Select at least two zone types, and respectively determine the Hash value and Hamming distance of the corresponding zones in the first X-ray weld negative and the second X-ray weld negative;

[0011] According to the Hash value and Hamming distance, determine the similarity of the first X-ray weld negative and the second X-ray weld negative in each selected zone;

[0012]

[0013] Wherein, is the similarity of zone x; is the Hamming distance of zone x; is the length of the Hash value of zone x in the original X-ray weld negative, and the original X-ray weld negative is the first X-ray weld negative or the second X-ray weld negative;

[0014] Judge whether all similarities are less than or equal to the set threshold, and in response to no, there are duplicate negatives.

[0015] The following is a further optimization and / or improvement of the above-mentioned invention technical solution:

[0016] The above-mentioned determination of the weld region, base metal region, and full film region in the first X-ray weld film and the second X-ray weld film respectively includes:

[0017] On the first X-ray weld film, extract multiple sets of column pixel points horizontally based on a set width, and use the column pixel point with the largest peak width in each set of column pixel points as the center point of the weld region;

[0018] Obtain the true curve and the fitting curve of each set of column pixel points, and use the column pixel point with the largest difference in gray value between the true curve and the fitting curve as the boundary point of the weld region, where the fitting curve is obtained by fitting using the least squares method;

[0019] Use the median absolute deviation method to remove the abnormal boundary points among all the weld region boundary points, and extract the effective weld region boundary points;

[0020] Based on the effective weld region boundary points, determine the upper boundary and the lower boundary of the weld region, and divide the weld region according to the upper boundary and the lower boundary of the weld region;

[0021] In the first X-ray weld film, the remaining region after removing the weld region is the base metal region;

[0022] Repeat the above process to determine the weld region, base metal region, and full film region in the second X-ray weld film.

[0023] The above-mentioned determination of the upper boundary and the lower boundary of the weld region based on the effective weld region boundary points, and the division of the weld region according to the upper boundary and the lower boundary of the weld region includes:

[0024] Based on the effective weld region boundary points, determine the upper boundary point set and the lower boundary point set;

[0025] Use the value corresponding to the minimum ordinate in the upper boundary point set as the upper boundary of the divided region, and use the value corresponding to the maximum ordinate in the lower boundary point set as the lower boundary of the divided region to obtain the vertical range of the weld region;

[0026] Divide the weld region based on the vertical range.

[0027] The above-mentioned selection of at least two region types, and the determination of the hash value and Hamming distance of the corresponding regions in the first X-ray weld film and the second X-ray weld film respectively includes:

[0028] Select at least two region types from the weld region, base metal region, and full film region;

[0029] For each region type, use the pHash method to calculate the hash values of the corresponding regions in the first X-ray weld film and the second X-ray weld film respectively;

[0030] For each region type, determine the Hamming distance between the hash value of the corresponding region in the first X-ray weld film and the hash value of the corresponding region in the second X-ray weld film.

[0031] Judge whether all similarities are less than or equal to the set threshold. In response to no, determine the original X-ray weld film selected when calculating the similarity of the region. After determination, the other X-ray weld film is the duplicate film of the original X-ray weld film.

[0032] The second technical solution of the present invention is achieved by the following measures: A detection device applicable to duplicate X-ray weld films, including:

[0033] A region segmentation unit that respectively determines the weld region, the base metal region, and the full film region in the first X-ray weld film and the second X-ray weld film;

[0034] A first detection unit that selects at least two region types and respectively determines the hash value and the Hamming distance of the corresponding regions in the first X-ray weld film and the second X-ray weld film;

[0035] A second detection unit that determines the similarity of the first X-ray weld film and the second X-ray weld film in each selected region according to the hash value and the Hamming distance;

[0036]

[0037] Among them, is the similarity of region x; is the Hamming distance of region x; is the length of the hash value of region x in the original X-ray weld film, and the original X-ray weld film is the first X-ray weld film or the second X-ray weld film;

[0038] A judgment unit that judges whether all similarities are less than the set threshold. In response to no, there is a duplicate film.

[0039] The following is a further optimization and / or improvement of the above-mentioned technical solution of the invention:

[0040] The above-mentioned region segmentation unit includes:

[0041] A weld region segmentation module, including:

[0042] Extract a plurality of column pixel point sets horizontally on the first X-ray weld film based on the set width, and use the column pixel point with the largest peak width in each column pixel point set as the center point of the weld region;

[0043] Obtain the true curve and the fitted curve of each set of column pixels, and use the column pixel with the largest difference in gray value between the true curve and the fitted curve as the boundary point of the weld area, where the fitted curve is obtained by using the least squares method;

[0044] Use the median absolute deviation method to remove the abnormal boundary points among all the boundary points of the weld area, and extract the effective boundary points of the weld area;

[0045] Based on the effective boundary points of the weld area, determine the upper and lower boundaries of the weld area, and segment the weld area according to the upper and lower boundaries of the weld area;

[0046] The base material area segmentation module removes the remaining area of the weld area in the first X-ray weld negative to obtain the base material area.

[0047] The above-mentioned first detection unit includes:

[0048] The area selection module selects at least two area types from the weld area, the base material area, and the full area of the negative;

[0049] The hash value calculation module uses the pHash method to calculate the hash values of the corresponding areas in the first X-ray weld negative and the second X-ray weld negative for each area type;

[0050] The Hamming distance calculation module determines the Hamming distance between the hash value of the corresponding area in the first X-ray weld negative and the hash value of the corresponding area in the second X-ray weld negative.

[0051] The third technical solution of the present invention is achieved by the following measures: An electronic device includes a processor and a memory, and a computer program is stored in the memory. The computer program is loaded and executed by the processor to implement the steps in the detection method applicable to repeated X-ray weld negatives.

[0052] The fourth technical solution of the present invention is achieved by the following measures: A storage medium stores a computer program readable by a computer, and the computer program is set to execute the steps in the detection method applicable to repeated X-ray weld negatives when running.

[0053] The present invention divides the polar regions of X-ray weld negatives. By calculating the hash values and Hamming distances of at least two regions, the similarity of the corresponding regions is determined, thereby judging whether two X-ray weld negatives are repeated. This process effectively realizes the automation of repeated X-ray weld negative detection, improves the efficiency of repeated X-ray weld negative detection, and has good robustness against some artificial forgeries and tampering. Compared with manual detection, it is accurate and efficient. Compared with neural network model detection, it does not require a large amount of computing resources and storage resources of the computer, nor does it need to be updated regularly, and is not affected by the quality of the training samples. Brief Description of the Drawings

[0054] FIG Figure 1 is a schematic flowchart of the detection method provided by an embodiment of the present invention.

[0055] FIG Figure 2 is a schematic flowchart of the weld area extraction method provided by an embodiment of the present invention.

[0056] FIG Figure 3 is a schematic flowchart of the weld area segmentation method provided by an embodiment of the present invention.

[0057] FIG Figure 4 is a schematic flowchart of the method for obtaining the hash value and Hamming distance provided by an embodiment of the present invention.

[0058] FIG Figure 5 is a schematic diagram of an X-ray weld negative to be detected provided by an embodiment of the present invention, where Figure 5 (a) is the first X-ray weld negative,[[]] Figure 5 (b) is the second X-ray weld negative.

[0059] FIG Figure 6 is a schematic diagram of the curve fitting result provided by an embodiment of the present invention, where Figure 6 (a) is the curve fitting result schematic diagram of the first X-ray weld negative Figure 5 (a), Figure 6 (b) is the curve fitting result schematic diagram of the second X-ray weld negative Figure 5 (b).

[0060] FIG Figure 7 is the schematic diagram of the extraction result of the weld area boundary points provided by an embodiment of the present invention for FIG Figure 5 , where Figure 7 (a) is the extraction result schematic diagram of the weld area boundary points of the first X-ray weld negative Figure 5 (a), Figure 7 (b) is the extraction result schematic diagram of the weld area boundary points of the second X-ray weld negative Figure 5 (b).

[0061] FIG Figure 8 is the schematic diagram of the weld area segmentation result provided by an embodiment of the present invention for FIG Figure 5 , where Figure 8 (a) is the weld area segmentation result schematic diagram of the first X-ray weld negative Figure 5 (a), Figure 8 (b) is the weld area segmentation result schematic diagram of the second X-ray weld negative Figure 5 (b).

[0062] FIG Figure 9Another schematic diagram of an X-ray weld negative to be detected provided by an embodiment of the present invention, where Figure 9 (a) is the original X-ray weld negative, Figure 9 and (b) is the tampered X-ray weld negative.

[0063] Appendix Figure 10 Another schematic diagram of the weld area segmentation provided by an embodiment of the present invention, where Figure 9 Figure 10 (a1), Figure 10 (b1) are respectively the schematic diagrams of the extraction results of the weld area boundary points of the original X-ray weld negative Figure 9 (a) and the tampered X-ray weld negative Figure 9 (b), Figure 10 (a2), Figure 10 (b2) are respectively the schematic diagrams of the weld area segmentation of the original X-ray weld negative Figure 9 (a) and the tampered X-ray weld negative Figure 9 (b).

[0064] Appendix Figure 11 Another schematic diagram of an X-ray weld negative to be detected provided by an embodiment of the present invention, where Figure 11 (a) is the original X-ray weld negative, Figure 11 (b) is the tampered X-ray weld negative.

[0065] Appendix Figure 12 Another schematic diagram of the weld area segmentation provided by an embodiment of the present invention, where Figure 11 Figure 12 (a1), Figure 12 (b1) are respectively the schematic diagrams of the extraction results of the weld area boundary points of the original X-ray weld negative Figure 11 (a) and the tampered X-ray weld negative Figure 11 (b), Figure 12 (a2), Figure 12 (b2) are respectively the schematic diagrams of the weld area segmentation of the original X-ray weld negative Figure 11 (a) and the tampered X-ray weld negative Figure 11 (b).

[0066] Appendix Figure 13 Another schematic diagram of an X-ray weld negative to be detected provided by an embodiment of the present invention, where Figure 13 (a) is the first original X-ray weld negative, Figure 13 (b) is the second original X-ray weld negative, Figure 13 (c) is the tampered X-ray weld negative.

[0067] Appendix Figure 14 Another schematic diagram of the weld area segmentation provided by an embodiment of the present invention, where Figure 13 ​​Schematic diagram of weld region segmentation, where Figure 14 (a1), Figure 14 and (b1) are respectively the schematic diagrams of the extraction results of the weld region boundary points of the first original X-ray weld negative Figure 13 (a) and the tampered X-ray weld negative Figure 13 (c), Figure 14 (a2), Figure 14 and (b2) are respectively the schematic diagrams of the weld region segmentation of the first original X-ray weld negative Figure 13 (a) and the tampered X-ray weld negative Figure 13 (c).

[0068] Appendix Figure 15 is also a schematic diagram of an X-ray weld negative to be detected provided by an embodiment of the present invention, where Figure 15 (a) is the first original X-ray weld negative, Figure 15 (b) is the second original X-ray weld negative, Figure 15 (c) is the tampered X-ray weld negative.

[0069] Appendix Figure 16 is the schematic diagram of the weld region segmentation provided by an embodiment of the present invention Figure 15 , where Figure 16 (a1), Figure 16 and (b1) are respectively the schematic diagrams of the extraction results of the weld region boundary points of the first original X-ray weld negative Figure 15 (a) and the tampered X-ray weld negative Figure 15 (c), Figure 16 (a2), Figure 16 and (b2) are respectively the schematic diagrams of the weld region segmentation of the first original X-ray weld negative Figure 15 (a) and the tampered X-ray weld negative Figure 15 (c).

[0070] Appendix Figure 17 is the schematic diagram of the structure of the detection device provided by an embodiment of the present invention. Specific embodiments

[0071] The present invention is not limited by the following embodiments, and the specific implementation manners can be determined according to the technical solutions of the present invention and the actual situation.

[0072] Those skilled in the art can understand that, unless specifically stated, in the embodiments of the present invention, a "module" or "unit" refers to a computer program with a predetermined function or a part of a computer program, which works together with other related parts to achieve a predetermined goal, and can be fully or partially implemented by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an overall module or unit that includes the function of the module or unit.

[0073] In addition, "a plurality of" in the embodiments of the present invention means two or more, and "first", "second", etc. are used for distinguishing descriptions and should not be understood as implying relative importance.

[0074] The embodiments of the present invention provide a detection method, device, electronic device, and storage medium applicable to repeated X-ray weld film negatives. The detection device applicable to repeated X-ray weld film negatives can be integrated in a computer device, which can be a server or a terminal device, etc.; it can also be jointly executed by a terminal and a server. The above examples should not be construed as a limitation to the present invention.

[0075] The above terminal can include a mobile phone, a wearable intelligent device, a tablet computer, a laptop computer, a personal computer (PC), a vehicle-mounted computer, etc., and the present invention does not limit this. The present invention does not limit the number of terminal devices.

[0076] The above server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The present invention does not limit this.

[0077] For example, the computer device respectively determines the weld area, the base metal area, and the full film area in the first X-ray weld film negative and the second X-ray weld film negative, selects at least two area types, respectively determines the hash value and the Hamming distance of the corresponding areas in the first X-ray weld film negative and the second X-ray weld film negative, determines the similarity of the first X-ray weld film negative and the second X-ray weld film negative in each selected area according to the hash value and the Hamming distance, and determines whether all similarities are less than a set threshold. In response to a negative result, there are duplicate film negatives. This not only realizes the automation of detecting repeated X-ray weld film negatives, ensures the accuracy of detection, but also does not require a large amount of computer computing resources and storage resources like the machine detection method using a neural network model.

[0078] Based on this, the technical solution of the present invention will be introduced and illustrated below in combination with several examples.

[0079] Example 1: As shown in the appendix Figure 1 The embodiment of the present invention discloses a detection method applicable to repeated X-ray weld film negatives, including:

[0080] Step S110: respectively determine the weld region, the base metal region, and the full film region in the first X-ray weld film negative and the second X-ray weld film negative.

[0081] The above-mentioned region segmentation can be completed by means of image recognition and the like.

[0082] Step S120: select at least two region types, and respectively determine the hash value and the Hamming distance of the corresponding regions in the first X-ray weld film negative and the second X-ray weld film negative.

[0083] The above-mentioned Hamming distance is used to determine the similarity between two hash values. The smaller the Hamming distance, the higher the similarity between the two hash values. In this embodiment, the Hamming distance is introduced to determine the similarity between the hash values of the same type of regions in the first X-ray weld film negative and the second X-ray weld film negative.

[0084] Specifically, for example, if the weld region and the base metal region are selected, it is necessary to determine the hash value of the weld region and the hash value of the base metal region in the first X-ray weld film negative, the hash value of the weld region and the hash value of the base metal region in the second X-ray weld film negative, and determine the similarity (i.e., the Hamming distance) between the hash values of the weld regions in the first X-ray weld film negative and the second X-ray weld film negative, and the similarity (i.e., the Hamming distance) between the hash values of the base metal regions in the first X-ray weld film negative and the second X-ray weld film negative.

[0085] Step S130: determine the similarity of the first X-ray weld film negative and the second X-ray weld film negative in each selected region according to the hash value and the Hamming distance;

[0086]

[0087] Wherein, is the similarity of region x; is the Hamming distance of region x; is the length of the hash value of region x in the original X-ray weld film negative, and the original X-ray weld film negative is the first X-ray weld film negative or the second X-ray weld film negative.

[0088] The selection of the above-mentioned original X-ray weld film negative can be determined as needed.

[0089] It should also be noted that the Hamming distance is the number of different elements in two images or image regions calculated based on the hash value. Therefore, the difference between two images or image regions is the ratio of the number of different elements in the two images or image regions to the length of the hash value, and the similarity between the two images or image regions is the ratio of the number of identical elements in the two images or image regions to the hash length. And the sum of the difference and the similarity is 1. Thus, the similarity calculation formula in step S130 of the present invention is obtained, and the similarity between each region of two or more X-ray weld film negatives is calculated through this formula.

[0090] Furthermore, the similarity calculation formula established by the present invention based on the Hamming distance and the hash value, due to the high efficiency of calculating based on the Hamming distance and the high robustness to slight changes, can be effectively applied to the repetitive detection of a large number of X-ray weld film negatives.

[0091] Step S140, determine whether all similarities are less than or equal to a set threshold. In response to no, there are duplicate film negatives.

[0092] It should be noted that to determine whether all similarities are less than or equal to a set threshold, in response to no, determine the original X-ray weld film negative selected when obtaining the similarity of the region. After determination, another X-ray weld film negative is the duplicate film negative of the original X-ray weld film negative. If is the length of the hash value of the first X-ray weld film negative region x, then if it is determined that not all similarities are less than the set threshold, it indicates that the second X-ray weld film negative is the duplicate film negative of the first X-ray weld film negative, and determine the region x where the similarity is greater than the set threshold. Region x is the region where the duplication occurs.

[0093] In this step, weights can also be introduced, that is, set the weights of each region according to different proportions of the region area, perform weighted fusion on the similarities of each selected region to obtain a comprehensive similarity, and compare it with the set threshold to determine whether there are duplicate film negatives, thereby preventing the situation where the similarity is low due to a small offset of the image, and improving the accuracy of detection.

[0094] The present invention discloses a detection method applicable to repetitive X-ray weld film negatives. It divides the polar regions of the X-ray weld film negatives, determines the similarity of the corresponding regions by calculating the hash values and Hamming distances of at least two regions, and thereby determines whether two X-ray weld film negatives are duplicates. This process effectively realizes the automation of repetitive X-ray weld film negative detection, improves the efficiency of repetitive X-ray weld film negative detection, and has good robustness to some artificial forgery and tampering. It is more accurate and efficient compared to manual detection, and compared to neural network model detection, it does not require a large amount of computing resources and storage resources of the computer, nor does it need to be updated and trained regularly, and is not affected by the quality of the training samples.

[0095] Embodiment 2: The embodiment of the present invention is a further optimization of the above embodiment, wherein the weld area, the base material area and the whole film area in the first X-ray weld film and the second X-ray weld film are respectively determined, including:

[0096] (1) The entire area of ​​the first X-ray weld film is regarded as the entire film area.

[0097] (2) As attached Figure 2 As shown, extracting the weld area in the first X-ray weld film includes:

[0098] Step S210, extracting multiple column pixel point sets along the horizontal direction based on the set width on the first X-ray weld film, and taking the column pixel point with the largest peak width in each column pixel point set as the center point of the weld area.

[0099] The set width may be 10 to 200 pixels, which is specifically determined by the length of the X-ray weld film.

[0100] After obtaining multiple column pixel point sets as above, mean filtering can be performed on each column pixel point set to remove noise, but a balance must be struck between distortion and denoising effects, and a convolution kernel of appropriate size must be selected.

[0101] After mean filtering, the peak position is found in each column pixel set. If there is only one peak, the corresponding column pixel point is used as the weld center point of the column pixel set. If there are multiple peaks, the column pixel point with the largest peak width is selected as the weld area center point of the column pixel set. If the peak position is not found in the column pixel set, the column pixel set is skipped.

[0102] Since there will be interference information such as digital marks on both sides of the weld area, welding head name, date information and label text on the X-ray weld film, multiple peaks may appear. For multiple peaks, considering that the width of the weld area is usually larger than the width of the characters, the sum of the width of the brightness increasing interval and the width of the decreasing interval within a certain brightness range on the left and right sides of the peak is taken as the width interval of the peak, and the peak with the largest value is selected as the center point of the weld.

[0103] Step S220, obtaining a true curve and a fitting curve for each set of column pixel points, and taking the column pixel point with the largest grayscale value difference between the true curve and the fitting curve as the boundary point of the weld area, wherein the fitting curve is obtained by least squares fitting.

[0104] Use the least squares method to fit the column pixel grayscale curve to the column pixel point set, that is, fit the data to a polynomial of a given order (which can be but is not limited to 2). Specifically, for a set of data , fitting is performed using a k-th order polynomial, and the target polynomial for fitting is as follows:

[0105]

[0106] After fitting, the coefficients of the k-th order polynomial are obtained, and then the fitting error is used to evaluate the fitting effect. The fitting error is as follows:

[0107]

[0108] Among them, is the fitting error; k is the order of the polynomial; are the coefficients of the k-th order polynomial; is the true gray value of the true curve of the column pixel point set at the i-th column pixel point; is the position of the i-th column pixel point of the column pixel point set.

[0109] According to the fitted k-th order polynomial, the following formula is used to calculate successively on both sides of the peak point to find the boundary points on both sides (that is, find the column pixel point with the largest difference between the real value curve and the fitting curve);

[0110]

[0111] Among them, is the difference; is the fitted gray value of the fitting curve of the column pixel point set at the i-th column pixel point; is the true gray value of the true curve of the column pixel point set at the i-th column pixel point.

[0112] Step S230, use the median absolute deviation method to remove the abnormal boundary points among all the weld region boundary points and extract the effective weld region boundary points.

[0113] The median absolute deviation method uses the distance between each value in the dataset and the median to measure the dispersion of the data and is widely used in anomaly detection. Specifically, as follows:

[0114]

[0115] Among them, is the pixel position of the i-th boundary point; is the set of positions of all boundary points, is the median of the calculated data; in this example, the absolute deviation between each weld region boundary point and the median is calculated respectively, and then the median of all absolute deviations is calculated to obtain the MAD threshold.

[0116] Based on the MAD threshold, the following abnormal boundary point judgment conditions are constructed, and points that meet the conditions are abnormal boundary points;

[0117]

[0118] Among them, is the pixel position of the i-th weld area boundary point, is the median of the pixel positions of all weld area boundary points.

[0119] Step S240: Based on the valid weld area boundary points, determine the upper and lower boundaries of the weld area, and segment the weld area according to the upper and lower boundaries of the weld area.

[0120] Specifically, as shown in the appendix Figure 3 shown, it includes:

[0121] Step S241: Based on the valid weld area boundary points, determine the upper boundary point set and the lower boundary point set.

[0122] According to whether the weld area boundary points in the true curve of the column pixel point set are on the left or right side of the peak, the upper boundary point set and the lower boundary point set are divided.

[0123] Step S242: Take the value corresponding to the minimum ordinate in the upper boundary point set as the upper boundary of the segmented area, and take the value corresponding to the maximum ordinate in the lower boundary point set as the lower boundary of the segmented area to obtain the vertical range of the weld area.

[0124] Step S243: Segment the weld area based on the vertical range.

[0125] (3) Remove the remaining area of the weld area in the first X-ray weld film to obtain the base material area.

[0126] (4) Repeat the above process to determine the weld area, base material area and full film area in the second X-ray weld film.

[0127] Example 3: As shown in the appendix Figure 4 shown, the embodiment of the present invention further optimizes the above embodiments, in which at least two region types are selected, and the hash values and Hamming distances of the corresponding regions are respectively determined in the first X-ray weld film and the second X-ray weld film, including:

[0128] Step S310: Select at least two region types from the weld area, base material area and full film area.

[0129] Step S320: For each region type, use the pHash method to calculate the hash values of the corresponding regions in the first X-ray weld film and the second X-ray weld film respectively.

[0130] Specifically, the process of using the pHash method to calculate the hash value of a certain area in the X-ray weld film includes:

[0131] (1) Read the X-ray weld film and perform a two-dimensional discrete cosine transform (DCT transform) on it to obtain the low-frequency part of the X-ray weld film, that is, the film contour information;

[0132]

[0133] in, , are all orthogonal normalization coefficients; when , All are 0, ,otherwise, ; Pixel points in the X-ray weld film The grayscale value of , N is the dimension of the X-ray weld film.

[0134] (2) Cut off the 8x8 area (low-frequency coefficients) in the upper left corner of the DCT transform result and call this area , then Perform mean calculation to obtain the average value A;

[0135]

[0136] (3) Each value in is compared with the average value A to generate a binary matrix, in which the comparison of each element is performed using the following formula;

[0137]

[0138] (4) Finally, the binary matrix Converted into a one-dimensional array and merged into a string form, which is the pHash value of the X-ray weld film.

[0139] Step S330: for each region type, determine the Hamming distance between the hash value of the corresponding region in the first X-ray weld film and the hash value of the corresponding region in the second X-ray weld film.

[0140] Specifically, taking the selected area types of weld area and base material area as an example, the hash value, Hamming distance and similarity of the corresponding areas in the first X-ray weld film and the second X-ray weld film are calculated respectively.

[0141] The hash values ​​of the first X-ray weld film in the corresponding weld area and parent material area are as follows:

[0142]

[0143] in, is the weld area of the first X-ray weld film; is the base metal area of the first X-ray weld film; is the hash value of the weld area in the first X-ray weld film; is the hash value of the base metal area in the first X-ray weld film;

[0144] The hash values of the second X-ray weld film in the corresponding weld area and base metal area are as follows:

[0145]

[0146] Among them, is the weld area of the second X-ray weld film; is the base metal area of the second X-ray weld film; is the hash value of the weld area in the second X-ray weld film; is the hash value of the base metal area in the second X-ray weld film;

[0147] The Hamming distance between the first X-ray weld film and the second X-ray weld film in the weld area is as follows:

[0148]

[0149] Among them, is the Hamming distance of the weld area;

[0150] The Hamming distance between the first X-ray weld film and the second X-ray weld film in the base metal area is as follows:

[0151]

[0152] Among them, is the Hamming distance of the base metal area;

[0153] The above represents counting the number of elements with value 1 in the statistical binary string. The positions where the two hash values are different after XOR are obtained, and then using for statistics to obtain the difference positions between the two.

[0154] Based on the hash values and Hamming distances, determine the similarity between the first X-ray weld film and the second X-ray weld film in each selected area;

[0155]

[0156] Among them, is the similarity between the first X-ray weld film and the second X-ray weld film in the weld area; is the similarity between the first X-ray weld film and the second X-ray weld film in the base metal area.

[0157] In Embodiment 1, it is described that the Hamming distance is the number of different elements in two images or image regions calculated based on the hash value. Therefore, the difference between two images or image regions is the ratio of the number of different elements in the two images or image regions to the length of the hash value, and the similarity between the two images or image regions is the ratio of the number of identical elements in the two images or image regions to the hash length. Moreover, the sum of the difference and the similarity is 1. Thus, the present invention obtains the similarity calculation formula in step S130 and calculates the similarity between each region of two or more X-ray weld film negatives through this formula.

[0158] It should also be noted that in the above formula, represents the number of elements with a value of 1 in the binary string. The positions where the two hash values are different are obtained by XOR operation, and then is used for statistics to obtain the difference positions between the two, that is, the number of different elements between the two.

[0159] Embodiment 4: In the embodiment of the present invention, specific X-ray weld film negatives are introduced to verify the effectiveness of the detection method applicable to repeated X-ray weld film negatives disclosed in the present invention.

[0160] (1) Obtain two original X-ray weld film negatives as shown in the appendix Figure 5 and set them as the first X-ray weld film negative Figure 5 (a) and the second X-ray weld film negative Figure 5 (b);

[0161] (2) Perform Gaussian filtering and smoothing processing on the first X-ray weld film negative Figure 5 (a) and the second X-ray weld film negative Figure 5 (b) to remove noise;

[0162] (3) Extract multiple sets of column pixel points along the horizontal direction on the first X-ray weld film negative based on a set width, and use the column pixel point with the largest peak width in each set of column pixel points as the center point of the weld region. The blue curve in the appendix Figure 6 is a set of column pixel points, and the column pixel point with the largest peak width is used as the center point of the weld region of this column (i.e., the set of column pixel points);

[0163] (4) Obtain the true curve and the fitting curve of each set of column pixel points, and use the column pixel point with the largest gray value difference between the true curve and the fitting curve as the boundary point of the weld region. The yellow curve in the appendix Figure 6 is the fitting curve obtained by using the least squares method for a set of column pixel points, and the green curve is the gray value difference curve between the true curve and the fitting curve corresponding to this set of column pixel points. In the appendix Figure 6The column pixel points with the largest difference in gray scale values among the red points are used as the boundary points of the weld area;

[0164] It should be noted that Figure 6 (a) and Figure 6 (b) are respectively Figure 5 (a) and Figure 5 (b) schematic diagrams of the curve fitting results.

[0165] Based on steps (3) and (4), the extraction results of the boundary points of the weld area are as shown in the appendix Figure 7 shown, in the appendix Figure 7 the red points are the center points of the weld area, and the blue points are the boundary points of the weld area; Figure 7 (a) and Figure 7 (b) are respectively Figure 5 (a) and Figure 5 (b) schematic diagrams of the extraction results of the boundary points of the weld area;

[0166] (5) Determine the upper and lower boundaries of the weld area based on the effective boundary points of the weld area, and divide the weld area according to the upper and lower boundaries of the weld area. As shown in the appendix Figure 8 the green rectangular frame in it is the weld area obtained by segmentation, Figure 8 (a) and Figure 8 (b) are respectively Figure 5 (a) and Figure 5 (b) schematic diagrams of the weld area segmentation results;

[0167] (6) Select the weld area, the base metal area, and the entire film area, determine the hash values and Hamming distances of the corresponding areas in the first X-ray weld film and the second X-ray weld film respectively, and determine the similarity of the first X-ray weld film and the second X-ray weld film in each selected area according to the hash values and Hamming distances, as shown in Table 1;

[0168] Table 1 Similarity calculation results

[0169] .

[0170] If the weld area, the base metal area, and the entire film area in the table are all less than or equal to the set threshold (the set threshold is set to 90%), then there is no duplicate film between the first X-ray weld film and the second X-ray weld film.

[0171] Example 5: In this embodiment of the present invention, duplicate X-ray weld films are introduced from three aspects: adding noise, increasing brightness, and replacing the weld area to verify the effectiveness of the detection method for duplicate X-ray weld films disclosed in the present invention.

[0172] (1) Add noise to the original X-ray weld film to obtain the corresponding tampered X-ray weld film, as shown in the appendixFigure 9 As shown, the original X-ray weld film is Figure 9 (a), and the tampered X-ray weld film is Figure 9 (b), where the specific parameters of the noise include: the type is Gaussian noise, the mean is 0, and the variance sigma is 6. The extraction results of the weld area are as shown in the appendix Figure 10 ( Figure 10 (a1), Figure 10 (b1) are respectively Figure 9 (a) and Figure 9 (b) of the extraction results of the boundary points of the weld area. Figure 10 (a2), Figure 10 (b2) are respectively Figure 9 (a) and Figure 9 (b) of the segmentation results of the weld area). The similarity degrees of the weld area, the base metal area, and the entire film area obtained by using the method disclosed in the present invention are shown in Table 2:

[0173] Table 2 Similarity calculation results

[0174] .

[0175] It can be seen from Table 2 that the original X-ray weld film and the tampered X-ray weld film are both greater than the set threshold (the set threshold is set to 90%) in the weld area, the base metal area, and the entire film area, then the two X-ray weld films are duplicates.

[0176] (2) After increasing the brightness of the original X-ray weld film, the corresponding tampered X-ray weld film is obtained, as shown in the appendix Figure 11 As shown, the original X-ray weld film is Figure 11 (a), and the tampered X-ray weld film is Figure 11 (b), where the specific parameters for increasing the brightness include: the brightness adjustment factor factor is 1.2. The extraction results of the weld area are as shown in the appendix Figure 12 ( Figure 12 (a1), Figure 12 (b1) are respectively Figure 11 (a) and Figure 11 (b) of the extraction results of the boundary points of the weld area. Figure 12 (a2), Figure 12 (b2) are respectively Figure 11 (a) and Figure 11 (b) of the segmentation results of the weld area). The similarity degrees of the weld area, the base metal area, and the entire film area obtained by using the method disclosed in the present invention are shown in Table 3:

[0177] Table 3 Similarity calculation results

[0178] .

[0179] As can be seen from Table 3, if the original X-ray weld film and the tampered X-ray weld film are both greater than the set threshold (the set threshold is set to 90%) in the weld area, the base metal area, and the entire film area, then the two X-ray weld films are duplicates.

[0180] (III) As shown in the appendix Figure 13 Replace the weld area of the first original X-ray weld film Figure 13 (a) with the weld area of the first original X-ray weld film Figure 13 (b) to form a tampered X-ray weld film Figure 13 (c). The weld area extraction results are as shown in the appendix Figure 14 ( Figure 14 (a1), Figure 14 (b1) are respectively Figure 13 (a), Figure 13 the weld area boundary point extraction results of (c). Figure 14 (a2), Figure 14 (b2) are respectively Figure 13 (a), Figure 13 the weld area segmentation results of (c). The similarity of the weld area, the base metal area, and the entire film area obtained by using the method disclosed in the present invention is shown in Table 4:

[0181] Table 4. Similarity calculation results

[0182] .

[0183] As can be seen from Table 4, if the original X-ray weld film and the tampered X-ray weld film are both greater than the set threshold (the set threshold is set to 90%) in the base metal area and the entire film area, then the two X-ray weld films are duplicates in the base metal area and the entire film area.

[0184] (IV) As shown in the appendix Figure 15 Replace the base metal area of the first original X-ray weld film Figure 15 (a) with the base metal area of the second original X-ray weld film Figure 15 (b) to form a tampered X-ray weld film Figure 15 (c). The weld area extraction results are as shown in the appendix Figure 16 ( Figure 16 (a1), Figure 16 (b1) are Figure 15 (a), Figure 15 the weld area boundary point extraction results of (c). Figure 16 (a2), Figure 16 (b2) are as shown in the appendix Figure 15 in Figure 15 (a), Figure 15(Segmentation results of the weld area in (c)), the similarities of the weld area, base metal area, and full negative area obtained by using the method disclosed in the present invention are shown in Table 5:

[0185] Table 5. Similarity calculation results

[0186] 。

[0187] As can be seen from Table 5, if the weld areas of the original X-ray weld negative and the tampered X-ray weld negative are both greater than the set threshold (the set threshold is set to 90%), then the two X-ray weld negatives are repeated in the weld area.

[0188] Through the description of the above four examples, the method disclosed in the present invention can accurately detect the repetition of X-ray weld negatives.

[0189] Example 6: As shown in the appendix Figure 17 The embodiment of the present invention discloses a detection device applicable to repeated X-ray weld negatives, including:

[0190] An area segmentation unit that respectively determines the weld area, base metal area, and full negative area in the first X-ray weld negative and the second X-ray weld negative;

[0191] A first detection unit that selects at least two area types and respectively determines the hash value and Hamming distance of the corresponding areas in the first X-ray weld negative and the second X-ray weld negative;

[0192] A second detection unit that determines the similarity of the first X-ray weld negative and the second X-ray weld negative in each selected area according to the hash value and Hamming distance;

[0193]

[0194] Among them, is the similarity of area x; is the Hamming distance of area x; is the length of the hash value of area x in the original X-ray weld negative, and the original X-ray weld negative is the first X-ray weld negative or the second X-ray weld negative;

[0195] A judgment unit that judges whether all similarities are less than the set threshold, and in response to a negative result, there are duplicate negatives.

[0196] Among them, the area segmentation unit includes:

[0197] A weld area segmentation module, including:

[0198] Extract multiple sets of column pixel points horizontally on the first X-ray weld film negative based on a set width, and use the column pixel point with the largest peak width in each set of column pixel points as the center point of the weld area;

[0199] Obtain the real curve and the fitting curve of each set of column pixel points, and use the column pixel point with the largest gray value difference between the real curve and the fitting curve as the boundary point of the weld area, where the fitting curve is obtained by using the least squares method;

[0200] Use the median absolute deviation method to remove the abnormal boundary points among all the boundary points of the weld area, and extract the effective boundary points of the weld area;

[0201] Determine the upper boundary and the lower boundary of the weld area based on the effective boundary points of the weld area, and segment the weld area according to the upper boundary and the lower boundary of the weld area;

[0202] The base material area segmentation module removes the remaining area of the weld area in the first X-ray weld film negative as the base material area.

[0203] Among them, the first detection unit includes:

[0204] The area selection module selects at least two area types among the weld area, the base material area, and the entire film area;

[0205] The hash value calculation module, for each area type, uses the pHash method to calculate the hash values of the corresponding areas in the first X-ray weld film negative and the X-ray weld film negative respectively;

[0206] The Hamming distance calculation module determines the Hamming distance between the hash value of the corresponding area in the first X-ray weld film negative and the hash value of the corresponding area in the X-ray weld film negative.

[0207] Embodiment 7: An embodiment of the present invention discloses a storage medium, on which a computer program readable by a computer is stored, and the computer program is set to execute a detection method applicable to repeated X-ray weld film negatives when running.

[0208] The above storage medium may include but is not limited to: various media such as USB flash drives, read-only memories, mobile hard disks, magnetic disks, or optical discs that can store computer programs.

[0209] Embodiment 8: An embodiment of the present invention discloses an electronic device, including a processor and a memory, and a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement a detection method applicable to repeated X-ray weld film negatives.

[0210] The above-mentioned processor may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the disclosure of the present invention. It can also be a combination that realizes computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, and so on. The memory may include, but is not limited to, various media that can store computer programs, such as USB flash drives, read-only memories, mobile hard disks, magnetic disks, or optical discs.

[0211] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0212] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one or more of the flows Figure 1 or a combination of multiple flows and / or blocks

[0213] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that realizes the functions specified in Figure 1 one or more of the flows Figure 1 or a combination of multiple flows and / or blocks

[0214] The above content is only a specific implementation manner of the present invention, which has strong adaptability and implementation effects. However, the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A method for detecting repeated X-ray weld films, characterized in that: include: Determine the weld area, the base material area and the whole film area in the first X-ray weld film and the second X-ray weld film respectively; Select at least two region types, and determine hash values ​​and Hamming distances of corresponding regions in the first X-ray weld film and the second X-ray weld film, respectively; Determine the similarity between the first X-ray weld film and the second X-ray weld film in each selected area according to the hash value and the Hamming distance; in, For Region x similarity; For Region x The Hamming distance of is the hash value length of region x in the original X-ray weld film, where the original X-ray weld film is the first X-ray weld film or the second X-ray weld film; Determine whether all similarities are less than or equal to a set threshold, and if no, duplicate negatives exist; Wherein, the weld area, the base material area and the whole film area in the first X-ray weld film and the second X-ray weld film are determined respectively, including: Extracting multiple column pixel point sets in the horizontal direction based on the set width on the first X-ray weld film, and taking the column pixel point with the largest peak width in each column pixel point set as the center point of the weld area; Obtain the true curve and the fitting curve of each column pixel set, and take the column pixel point with the largest gray value difference between the true curve and the fitting curve as the boundary point of the weld area, wherein the fitting curve is obtained by fitting using the least square method; The median absolute deviation method is used to remove abnormal boundary points in all weld area boundary points and extract effective weld area boundary points; Determine the upper boundary and the lower boundary of the weld area based on the boundary points of the effective weld area, and divide the weld area according to the upper boundary and the lower boundary of the weld area; The remaining area after removing the weld area in the first X-ray weld film is the base material area; Repeat the above process to determine the weld area, base material area and the entire film area in the second X-ray weld film.

2. The detection method for repeated X-ray weld films according to claim 1, characterized in that: The method of determining the upper boundary and the lower boundary of the weld area based on the boundary points of the effective weld area, and segmenting the weld area according to the upper boundary and the lower boundary of the weld area, comprises: Determine an upper boundary point set and a lower boundary point set based on the boundary points of the effective weld area; The value corresponding to the minimum ordinate in the upper boundary point set is used as the upper boundary of the segmentation area, and the value corresponding to the maximum ordinate in the lower boundary point set is used as the lower boundary of the segmentation area to obtain the vertical range of the weld area; Splits the weld area based on its vertical extent.

3. The detection method for repeated X-ray weld films according to claim 1 or 2, characterized in that: The selecting at least two region types and determining the hash values ​​and Hamming distances of the corresponding regions in the first X-ray weld film and the second X-ray weld film respectively include: Select at least two area types from the weld area, parent material area, and film full area; For each region type, the pHash method is used to calculate the hash values ​​of the corresponding regions in the first X-ray weld film and the second X-ray weld film respectively; For each region type, a Hamming distance between a hash value of a corresponding region in the first X-ray weld film and a hash value of a corresponding region in the second X-ray weld film is determined.

4. The detection method for repeated X-ray weld films according to claim 1 or 2, characterized in that: The determination is as to whether all similarities are less than or equal to a set threshold. If no, the original X-ray weld film selected when obtaining the similarity of the region is determined, and after determination, the other X-ray weld film is a duplicate of the original X-ray weld film.

5. A detection device for repeated X-ray weld films using the method as claimed in any one of claims 1 to 4, characterized in that: include: A region segmentation unit is used to determine the weld region, the base material region and the entire region of the film in the first X-ray weld film and the second X-ray weld film respectively; A first detection unit selects at least two region types and determines hash values ​​and Hamming distances of corresponding regions in the first X-ray weld film and the second X-ray weld film, respectively; A second detection unit determines the similarity between the first X-ray weld film and the second X-ray weld film in each selected area according to the hash value and the Hamming distance; in, For Region x similarity; For Region x The Hamming distance of is the hash value length of region x in the original X-ray weld film, where the original X-ray weld film is the first X-ray weld film or the second X-ray weld film; A judging unit, judging whether all similarities are less than a set threshold, and in response to a negative being negative, there are duplicate negatives; The region segmentation unit includes: Weld area segmentation module, including: Extracting multiple column pixel point sets in the horizontal direction based on the set width on the first X-ray weld film, and taking the column pixel point with the largest peak width in each column pixel point set as the center point of the weld area; Obtain the true curve and the fitting curve of each column pixel set, and take the column pixel point with the largest gray value difference between the true curve and the fitting curve as the boundary point of the weld area, wherein the fitting curve is obtained by fitting using the least square method; The median absolute deviation method is used to remove abnormal boundary points in all weld area boundary points and extract effective weld area boundary points; Determine the upper boundary and the lower boundary of the weld area based on the boundary points of the effective weld area, and divide the weld area according to the upper boundary and the lower boundary of the weld area; A base metal region segmentation module is used to segment the base metal region by removing the weld region from the first X-ray weld film; The weld region segmentation module and the parent material region segmentation module repeat the above process to determine the weld region, the parent material region and the entire film region in the second X-ray weld film.

6. The detection device for repeated X-ray weld films according to claim 5, characterized in that: The first detection unit comprises: A region selection module selects at least two region types from the weld region, the base material region, and the entire film region; The hash value obtaining module calculates the hash values ​​of the corresponding areas in the first X-ray weld film and the second X-ray weld film respectively for each area type by using the pHash method; The Hamming distance obtaining module determines the Hamming distance between the hash value of the corresponding area in the first X-ray weld film and the hash value of the corresponding area in the second X-ray weld film.

7. An electronic device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the steps in the method according to any one of claims 1 to 4.

8. A storage medium, characterized in that: The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute the steps of the method according to any one of claims 1 to 4 when running.

Images