Industrial ultrasonic nondestructive testing image processing and analyzing method and system

Abstract

The invention discloses a method and a system for processing and analyzing an industrial ultrasonic nondestructive testing image, and relates to the technical field of image analysis. The method for processing and analyzing the industrial ultrasonic nondestructive testing image comprises the following steps of: evaluating noise influence; carrying out distortion influence evaluation; and evaluating image processing quality. According to the method, the image noise influence value is obtained through noise influence evaluation, then the image distortion influence value is obtained through distortion influence evaluation, and whether image detection quality adjustment is carried out or not is judged based on the image noise influence value and the image distortion influence value. Finally, whether image processing quality adjustment is carried out or not is judged in combination with the obtained image processing quality evaluation value, the effect of improving the industrial ultrasonic nondestructive testing image processing quality is achieved, and the problem that in the prior art, the quality of the processed and analyzed industrial ultrasonic nondestructive testing image is unstable is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of image analysis, and in particular to a method and system for processing and analyzing industrial ultrasonic nondestructive testing images. Background Art

[0002] Industrial ultrasonic nondestructive testing is a nondestructive testing method that uses ultrasonic technology to detect and analyze materials and structures. By using the interaction between ultrasonic waves and workpieces, the reflected, transmitted and scattered echoes are analyzed to detect and characterize the macroscopic defects, geometric characteristics, organizational structure and mechanical properties of the workpiece. It is widely used in defect detection, quality control and monitoring of metals, composite materials, welded joints, pipelines, etc. With the rapid development of computer technology and image processing technology, people have begun to introduce image processing technology into ultrasonic nondestructive testing. By analyzing the detection image and applying image processing techniques such as image enhancement, image segmentation and mathematical statistics methods, the implementation algorithm of image processing can be determined, so as to obtain the actual position of the detection target, realize the tracking of the detection target, and ensure the real-time performance of ultrasonic nondestructive testing. The processing and analysis of ultrasonic nondestructive testing images is an important research direction in this field. The purpose is to extract useful information from ultrasonic signals, identify defects, evaluate quality and provide decision support.

[0003] Existing methods mainly identify defect edges by detecting sudden changes in brightness or intensity in images, highlight defective parts by enhancing the contrast and clarity of images, and divide images into multiple regions for further analysis.

[0004] For example, the invention patent application with publication number: CN118297915A discloses a non-destructive testing image processing method, device and storage medium, including: performing mask generation processing on a weld space sequence image and outputting a mask image; calculating a bounding box of the mask image based on the searched mask boundary; sampling at intervals along the bounding box of the mask image to generate multiple quadrilateral grids; taking the position of the pixels of the upper boundary of the mask image and the average position of the pixels of the lower boundary corresponding to the pixels of the upper boundary as the initial point, taking the average point of the sampling points of the upper boundary of each quadrilateral grid and the corresponding sampling points of the lower boundary as the target point, performing column reverse mapping and resampling on the pixel positions of the upper and lower boundaries of the mask image to form a mask image with the weld straightened, that is, a cuttable area; cutting the cuttable area to obtain an image of interest containing only the weld area.

[0005] For example, the invention patent application with publication number CN117372380A discloses an industrial ultrasonic non-destructive testing image data enhancement method based on a generative adversarial network, which includes: 1. Using ultrasonic non-destructive testing equipment to test a variety of different objects to be tested to obtain a data set. 2. Constructing a generative adversarial network model. 3. Training the generative adversarial network model. 4. Inputting the ultrasonic test image that needs data enhancement into the generative network in the generative adversarial network model to generate an amplified ultrasonic test image.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, in the petrochemical industry, since equipment such as pipelines often operate under harsh conditions such as high temperature and high pressure, the outline and characteristics of defects may be masked by noise, making it difficult to accurately identify the defects, affecting the image quality analysis results, and leading to the problem of unstable quality of industrial ultrasonic non-destructive testing images processed and analyzed. Summary of the invention

[0008] The embodiment of the present application solves the problem of unstable quality of industrial ultrasonic non-destructive testing images processed and analyzed in the prior art by providing a method for processing and analyzing industrial ultrasonic non-destructive testing images, thereby improving the processing quality of industrial ultrasonic non-destructive testing images.

[0009] The embodiment of the present application provides a method for processing and analyzing industrial ultrasonic nondestructive testing images, comprising the following steps: S1, performing noise impact assessment based on acquired ultrasonic nondestructive monitoring data to obtain an image noise impact value, wherein the image noise impact value is used to assess the degree of influence of noise on the detection image when industrial ultrasonic nondestructive testing is performed on industrial pipelines; S2, performing distortion impact assessment based on acquired industrial pipeline ultrasonic probe data to obtain an image distortion impact value, and judging whether to perform image detection quality adjustment based on the image noise impact value and the image distortion impact value, wherein the image distortion impact value is used to assess the degree of influence of ultrasonic probe sensitivity on detection image distortion when industrial ultrasonic nondestructive testing is performed on industrial pipelines; S3, obtaining an image processing quality assessment value by combining the image noise impact value and the image distortion impact value for image quality adjustment and the acquired image processing related data, and judging whether to perform image processing quality adjustment based on the image processing quality assessment value, wherein the image processing quality assessment value is used to assess the processing quality of the detection image during image processing of the detection image.

[0010] Furthermore, the ultrasonic nondestructive monitoring data includes pipeline temperature difference, pipeline pressure difference, detection thermal noise, detection output signal signal-to-noise ratio and detection input signal signal-to-noise ratio; the industrial pipeline ultrasonic probe data includes sound wave propagation loss, probe transmission power and probe input signal strength; the image processing related data includes maximum brightness, minimum brightness, and detection image peak signal-to-noise ratio; the pipeline temperature difference represents the absolute value of the difference between the temperature of a preset point inside the pipeline and the temperature of the corresponding preset point outside the pipeline; the pipeline pressure difference represents the absolute value of the difference between the pressure of a preset point inside the pipeline and the pressure of the corresponding preset point outside the pipeline.

[0011] Furthermore, the specific process of performing noise impact assessment based on the acquired ultrasonic nondestructive monitoring data to obtain the image noise impact value is as follows: the image noise impact value is obtained according to the thermal noise impact value, the input signal impact value, the output signal impact value and the preset weight obtained from the database; the thermal noise impact value is obtained by processing and analyzing the pressure-temperature impact factor, the detected thermal noise and the preset thermal noise average threshold obtained from the database; the pressure-temperature impact factor is obtained by processing and analyzing the pipeline temperature difference, the pipeline pressure difference and the preset temperature difference maximum threshold and the preset pressure difference maximum threshold obtained from the database; the input signal impact value is obtained by processing and analyzing the pressure-temperature impact factor, the detected input signal signal-to-noise ratio and the preset input signal-to-noise ratio maximum threshold obtained from the database; the output signal impact value is obtained by processing and analyzing the pressure-temperature impact factor, the detected output signal signal-to-noise ratio and the preset output signal-to-noise ratio maximum threshold obtained from the database; the preset weights include a first noise weight, a second noise weight and a third noise weight.

[0012] Furthermore, the limiting expression of the image noise impact value is as follows:

[0013]

[0014]

[0015] In the formula, ZSYX a RZS represents the image noise impact value corresponding to the a-th detection image, a=1,2,...,z, a represents the number of the detection image, z represents the total number of detection images, a Represents the thermal noise impact value corresponding to the a-th detection image, SR a Indicates the input signal impact value corresponding to the a-th detection image, SC a represents the output signal influence value corresponding to the a-th detection image, e represents a natural constant, ω1 represents the first noise weight, ω2 represents the second noise weight, and ω3 represents the third noise weight.

[0016] Furthermore, the specific process of performing distortion impact assessment based on the acquired industrial pipeline ultrasonic probe data to obtain the image distortion impact value is as follows: the sound wave propagation impact value is obtained by processing and analyzing the pressure-temperature impact factor, the sound wave propagation loss, and the preset sound wave propagation loss maximum threshold obtained from the database; the detection sensitivity impact value is obtained by processing and analyzing the pressure-temperature impact factor, the probe transmission power, the probe input signal strength, and the preset detection sensitivity maximum threshold obtained from the database; the image distortion impact value is obtained based on the relative relationship between the sound wave propagation impact value and the detection sensitivity impact value.

[0017] Furthermore, the specific process of judging whether to adjust the image detection quality based on the image noise impact value and the image distortion impact value is as follows: when the image noise impact value and the image distortion impact value meet the image detection condition, the image detection quality adjustment is not performed, otherwise the image detection quality adjustment is performed; the image detection quality adjustment includes image noise impact adjustment and image distortion impact adjustment; the image noise impact adjustment includes sending a prompt to a preset personnel to adjust the frequency of ultrasonic detection and perform joint noise reduction; the image detection condition indicates that the image noise impact value is not higher than the reference noise impact average value obtained from the database, and the image distortion impact value is not higher than the reference distortion impact average value obtained from the database.

[0018] Furthermore, the specific process of adjusting the image distortion effect is as follows: sending a prompt to the preset personnel to adjust the pulse width of the ultrasonic detection, and when the monitored image distortion effect value meets the image detection condition, stopping the image distortion effect adjustment, otherwise performing high-temperature coupling; if high-temperature coupling is performed, then when the image distortion effect value monitored after high-temperature coupling meets the image detection condition, stopping the image distortion effect adjustment, otherwise sending a detection image abnormality prompt to the preset personnel.

[0019] Furthermore, the image processing quality evaluation value is obtained by processing the peak signal-to-noise ratio influence value and the contrast influence value; the peak signal-to-noise ratio influence value is obtained by processing and analyzing the noise-distortion influence factor, the peak signal-to-noise ratio of the detected image, and a preset peak signal-to-noise ratio maximum threshold value obtained from a database; the noise-distortion influence factor is obtained by processing and analyzing the image distortion influence value and the peak signal-to-noise ratio of the detected image after image detection quality adjustment; the contrast influence value is obtained by processing and analyzing the noise-distortion influence factor, the maximum brightness value, the minimum brightness value, and the preset contrast maximum threshold value obtained from a database.

[0020] Furthermore, the specific process of judging whether to adjust the image processing quality based on the image processing quality assessment value is as follows: judging whether the image processing quality assessment value meets the image processing quality condition; the image processing quality condition indicates that the image processing quality assessment value is not lower than the reference image processing quality average value obtained from the database; when the image processing quality assessment value meets the image processing quality condition, no image processing quality adjustment is performed and the detection image that meets the image processing quality condition is analyzed, otherwise, image processing quality adjustment is performed, and the image processing quality adjustment includes modulus maximum denoising and image edge enhancement.

[0021] The embodiment of the present application provides a system for processing and analyzing industrial ultrasonic nondestructive testing images, including a noise impact assessment module, a distortion impact assessment module and an image processing quality assessment module: wherein the noise impact assessment module is used to perform noise impact assessment according to the acquired ultrasonic nondestructive monitoring data to obtain an image noise impact value, and the image noise impact value is used to assess the degree of influence of noise on the detection image when the industrial ultrasonic nondestructive testing is performed on the industrial pipeline; the distortion impact assessment module is used to perform distortion impact assessment according to the acquired industrial pipeline ultrasonic probe data to obtain an image distortion impact value, and judge whether to perform image detection quality adjustment based on the image noise impact value and the image distortion impact value, and the image distortion impact value is used to assess the degree of influence of ultrasonic probe sensitivity on detection image distortion when the industrial ultrasonic nondestructive testing is performed on the industrial pipeline; the image processing quality assessment module is used to combine the image noise impact value and the image distortion impact value for image quality adjustment and the acquired image processing related data to obtain an image processing quality assessment value, and judge whether to perform image processing quality adjustment based on the image processing quality assessment value, and the image processing quality assessment value is used to assess the processing quality of the detection image during the image processing of the detection image.

[0022] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0023] 1. The image noise impact value is obtained by performing noise impact assessment, and then the image distortion impact value is obtained by performing distortion impact assessment. Then, based on the image noise impact value and the image distortion impact value, it is judged whether to adjust the image detection quality. Finally, it is judged whether to adjust the image processing quality in combination with the obtained image processing quality assessment value, thereby achieving the improvement of image processing quality, and then achieving the improvement of industrial ultrasonic non-destructive testing image processing quality, and effectively solving the problem of unstable quality of industrial ultrasonic non-destructive testing images processed and analyzed in the prior art.

[0024] 2. The image noise influence value is obtained by using the thermal noise influence value, the input signal influence value, the output signal influence value and the preset weight, thereby achieving accurate quantification of the degree of influence of noise on the detection image during industrial ultrasonic non-destructive testing, and thus achieving improved accuracy in evaluating the degree of influence of noise on the detection image during industrial ultrasonic non-destructive testing of industrial pipelines.

[0025] 3. By judging whether the image noise influence value and the image distortion influence value meet the image detection conditions and adjusting the image detection quality, and then judging whether the image processing quality assessment value meets the image processing quality conditions and adjusting the image processing quality, the reliability of image detection and image processing is improved, thereby achieving the improvement of the effectiveness of detection images in the process of industrial ultrasonic nondestructive testing image processing and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flowchart of a method for processing and analyzing industrial ultrasonic nondestructive testing images provided in an embodiment of the present application;

[0027] Figure 2 An overall flow chart provided for the embodiments of the present application;

[0028] Figure 3 A statistical diagram of changes in the sound wave propagation impact value-image distortion impact value provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram of the structure of an industrial ultrasonic nondestructive testing image processing and analysis system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiment of the present application solves the problem of unstable quality of industrial ultrasonic nondestructive testing images processed and analyzed in the prior art by providing a method for processing and analyzing industrial ultrasonic nondestructive testing images. The noise impact is evaluated by ultrasonic nondestructive monitoring data to obtain an image noise impact value. Then, the distortion impact is evaluated according to industrial pipeline ultrasonic probe data to obtain an image distortion impact value. Based on the image noise impact value and the image distortion impact value, it is determined whether to adjust the image detection quality. Then, the image noise impact value and the image distortion impact value for image quality adjustment and the acquired image processing related data are combined to obtain an image processing quality evaluation value. Finally, based on the image processing quality evaluation value, it is determined whether to adjust the image processing quality, thereby improving the image processing quality of industrial ultrasonic nondestructive testing.

[0031] The technical solution in the embodiment of the present application is to solve the problem of unstable quality of the industrial ultrasonic non-destructive testing images processed and analyzed above. The overall idea is as follows:

[0032] The image noise impact value is obtained by performing noise impact assessment, and then the image distortion impact value is obtained by performing distortion impact assessment. Then, based on the image noise impact value and the image distortion impact value, it is determined whether to adjust the image detection quality. Finally, the image processing quality assessment value obtained is combined to determine whether to adjust the image processing quality, thereby achieving the effect of improving the image processing quality of industrial ultrasonic nondestructive testing.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] like Figure 1 As shown, it is a flow chart of a method for processing and analyzing industrial ultrasonic nondestructive testing images provided by an embodiment of the present application, the method comprising the following steps: S1, noise impact assessment: performing noise impact assessment according to the acquired ultrasonic nondestructive monitoring data to obtain an image noise impact value, the image noise impact value is used to assess the degree of influence of noise on the detection image when performing industrial ultrasonic nondestructive testing on industrial pipelines; S2, distortion impact assessment: performing distortion impact assessment according to the acquired industrial pipeline ultrasonic probe data to obtain an image distortion impact value, judging whether to adjust the image detection quality based on the image noise impact value and the image distortion impact value, the image distortion impact value is used to assess the ... S3, image processing quality evaluation: combining the image noise influence value and the image distortion influence value for image quality adjustment and the acquired image processing related data to obtain the image processing quality evaluation value, and judging whether to perform image processing quality adjustment based on the image processing quality evaluation value, the image processing quality evaluation value is used to evaluate the processing quality of the detection image during the image processing process of the detection image, and the image processing quality adjustment is used to improve the processing quality of the detection image.

[0035] It should be added that the ultrasonic non-destructive monitoring data include pipeline temperature difference, pipeline pressure difference, detection thermal noise, detection output signal signal-to-noise ratio and detection input signal signal-to-noise ratio; the industrial pipeline ultrasonic probe data include sound wave propagation loss, probe transmission power and probe input signal strength; the image processing related data include maximum brightness, minimum brightness and detection image peak signal-to-noise ratio; the pipeline temperature difference represents the absolute value of the difference between the temperature of a preset point inside the pipeline and the temperature of the corresponding preset point outside the pipeline; the pipeline pressure difference represents the absolute value of the difference between the pressure of a preset point inside the pipeline and the pressure of a corresponding preset point outside the pipeline; the detection output signal signal-to-noise ratio represents the signal-to-noise ratio of the ultrasonic signal output by the ultrasonic probe; the detection input signal signal-to-noise ratio represents the signal-to-noise ratio of the industrial pipeline reflection signal received by the ultrasonic probe.

[0036] In this embodiment, the image noise impact value, the image distortion impact value and the image processing quality assessment value jointly determine the quality of the detection image. The image noise impact value and the image distortion impact value are the basis for image processing quality assessment. When the image noise impact value and the image distortion impact value do not meet the image detection conditions, it is necessary to adjust the image detection quality to obtain a detection image that meets the image detection conditions. If the image processing quality assessment value does not meet the image processing quality conditions, the image processing quality adjustment is performed to ensure the quality and reliability of the obtained detection image. For example, when performing defect detection on industrial pipelines, the higher the image processing quality assessment value, the more helpful it is to discover defects such as cracks and corrosion inside the pipeline, improve the detection efficiency, and thus achieve the improvement of the image processing quality of industrial ultrasonic non-destructive testing.

[0037] It needs to be explained that, at a preset time point, the temperature of a preset point inside the pipeline and a preset point outside the corresponding pipeline are measured respectively by a temperature sensor, and the absolute value of the difference between the two is obtained to obtain the pipeline temperature difference. The pressure of a preset point inside the pipeline and a preset point outside the corresponding pipeline are measured respectively by a pressure sensor, and the absolute value of the difference between the two is obtained to obtain the pipeline pressure difference. The internal preset point and the corresponding external preset point of the pipeline are set by the preset personnel. The detection thermal noise in the ultrasonic non-destructive testing process is measured by a noise analyzer. The ultrasonic signal output by the ultrasonic probe and the received industrial pipeline reflection signal are measured by an ultrasonic probe (ultrasonic flaw detector) and a signal generator, and analyzed to obtain the detection output signal signal-to-noise ratio and the detection input signal signal-to-noise ratio. The ultrasonic probe monitors and analyzes the change in energy loss before and after the ultrasonic signal propagates in the pipeline to obtain the sound wave propagation loss. The probe transmission power is measured by an ultrasonic power meter. The probe input signal strength is measured by an ultrasonic probe (ultrasonic flaw detector) and a signal generator. The maximum brightness, minimum brightness, and peak signal-to-noise ratio of the detection image are obtained by image processing software (such as Photoshop, MATLAB, etc.). The ultrasonic non-destructive monitoring data, industrial pipeline ultrasonic probe data, and image processing related data are all the results obtained by a single measurement.

[0038] Furthermore, the specific process of performing noise impact assessment on the acquired ultrasonic nondestructive monitoring data to obtain the image noise impact value is as follows: According to the thermal noise impact value (i.e., RZS in the restricted expression of the image noise impact value), a ), the input signal impact value (that is, the SR in the limiting expression of the image noise impact value a ) and the output signal influence value (i.e., the SC in the limiting expression of the image noise influence value a) and the preset weight obtained from the database to obtain the image noise influence value; the thermal noise influence value is obtained by processing and analyzing the pressure-temperature influence factor, the detected thermal noise and the preset thermal noise average threshold obtained from the database, and the thermal noise influence value is used to reflect the influence of the pipeline temperature difference and the pipeline pressure difference on the detected thermal noise; the pressure-temperature influence factor is obtained by processing and analyzing the pipeline temperature difference, the pipeline pressure difference, and the preset temperature difference maximum threshold and the preset pressure difference maximum threshold obtained from the database; the input signal influence value is obtained by processing and analyzing the pressure-temperature influence factor, the detected input signal signal-to-noise ratio and the preset input signal-to-noise ratio maximum threshold obtained from the database, and the input signal signal-to-noise ratio influence value is used to reflect the influence of the pipeline The output signal influence value is obtained by processing and analyzing the pressure-temperature influence factor, the detection output signal signal-noise ratio and the preset output signal-noise ratio maximum threshold obtained from the database, and the output signal signal-noise ratio influence value is used to reflect the influence of the pipeline temperature difference and the pipeline pressure difference on the detection output signal signal-noise ratio; the preset weights include the first noise weight, the second noise weight and the third noise weight; the first noise weight is used to reflect the influence of the thermal noise influence value on the image noise influence value; the second noise weight is used to reflect the influence of the input signal influence value on the image noise influence value; the third noise weight is used to reflect the influence of the output signal influence value on the image noise influence value.

[0039] Among them, the limiting expression of the image noise impact value is as follows:

[0040]

[0041] In the formula, ZSYX a RZS represents the image noise impact value corresponding to the a-th detection image, a=1,2,...,z, a represents the number of the detection image, z represents the total number of detection images, a Represents the thermal noise impact value corresponding to the a-th detection image, SR a Indicates the input signal impact value corresponding to the a-th detection image, SC a represents the output signal impact value corresponding to the a-th detection image, T a represents the pipeline temperature difference corresponding to the a-th detection image, P a represents the pipeline pressure difference corresponding to the a-th detection image, represents the detection thermal noise corresponding to the a-th detection image, represents the signal-to-noise ratio of the detection input signal corresponding to the a-th detection image, represents the signal-to-noise ratio of the detection output signal corresponding to the a-th detection image, T 0 Indicates the preset maximum temperature difference threshold, P 0 Indicates the preset maximum pressure difference threshold, RZS0 Represents the preset thermal noise average threshold, SR 0 Indicates the preset maximum input signal-to-noise ratio threshold, SC 0 represents the preset maximum output signal-to-noise ratio threshold, e represents a natural constant, ω1 represents the first noise weight, ω2 represents the second noise weight, ω3 represents the third noise weight, is the pressure-temperature influence factor.

[0042] In this embodiment, the aforementioned database is a database for storing various types of setting data established before the design of a processing and analysis method for industrial ultrasonic nondestructive testing images provided in an embodiment of the present application. The database includes but is not limited to thermal noise, output signal signal-to-noise ratio, input signal signal-to-noise ratio, etc., and the various numerical values ​​therein are directly set by technical personnel. For example, the preset maximum temperature difference threshold is represented by the maximum value of the pipeline temperature difference in the historical time period in the database, the preset maximum pressure difference threshold is represented by the maximum value of the pipeline pressure difference in the historical time period in the database, the preset thermal noise average threshold is represented by the average value of the thermal noise in the historical time period in the database, the preset input signal-to-noise ratio maximum threshold is represented by the maximum value of the input signal-to-noise ratio in the historical time period in the database, and the preset output signal-to-noise ratio maximum threshold is represented by the maximum value of the output signal-to-noise ratio in the historical time period in the database.

[0043] It should be added that a mapping set of thermal noise influence values, input signal influence values, output signal influence values ​​and corresponding preset weights is pre-set in the database. The mapping set is used to describe the mapping relationship between the thermal noise influence values, input signal influence values ​​and output signal influence values ​​and the corresponding preset weights. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, the value range is 0-1. By inputting the real-time thermal noise influence value, input signal influence value and output signal influence value into the mapping set, the corresponding first noise weight, second noise weight and third noise weight can be obtained.

[0044] Specifically, assuming that the first noise weight, the second noise weight, and the third noise weight are fixed to 0.4, 0.4, and 0.2 respectively, the thermal noise impact value RZS a The range is 0.5-1.5, and the input signal affects the value SR a The range is 0.5-1.5, and the output signal affects the value SC a The range is 0.5-1.5, as shown in Table 1, which is a statistical table of changes in image noise impact values:

[0045] Table 1 Statistics of changes in image noise impact values

[0046]

[0047]

[0048] As can be seen from the table above, as the thermal noise impact value RZS a 、Input signal impact value SR a and output signal influence value SC a As the value of image noise increases gradually, the image noise influence value also increases gradually, which means that when performing industrial ultrasonic nondestructive testing on industrial pipelines, the influence of noise on the detection image gradually increases.

[0049] It should be understood that the algorithm of this embodiment combines ultrasonic nondestructive monitoring data analysis to obtain the image noise impact value. The greater the pipeline temperature difference and pipeline pressure difference, the greater the influence of the pipeline temperature difference and pipeline pressure difference on the thermal noise impact value, the input signal impact value and the output signal impact value, resulting in an increase in the image noise impact value. The greater the detection thermal noise, the greater the influence of thermal noise when performing industrial ultrasonic nondestructive testing on industrial pipelines, resulting in an increase in the image noise impact value. The greater the detection input signal signal-to-noise ratio and the detection output signal signal-to-noise ratio, the better the quality of the input signal and the output signal when performing industrial ultrasonic nondestructive testing on industrial pipelines, resulting in a decrease in the image noise impact value.

[0050] It should be understood that the ultrasonic nondestructive monitoring data in the algorithm of this embodiment does not exist independently, but has mutual correlation, and needs comprehensive analysis. The greater the pipeline temperature difference, the more the pipeline material expands, which in turn changes the pressure distribution inside the pipeline. With the increase of pipeline temperature difference and pipeline pressure difference, the thermal motion of microscopic particles intensifies, resulting in an increase in thermal noise, which also affects the signal-to-noise ratio of the output signal. In the process of signal propagation and reception, thermal noise may mask the ultrasonic signal, resulting in a decrease in the signal-to-noise ratio of the output signal. Changes in the pipeline temperature difference may affect the propagation speed and attenuation characteristics of the ultrasonic signal, thereby affecting the reception quality of the input signal and the output signal. An increase in the pipeline temperature difference may cause increased signal attenuation, thereby reducing the signal-to-noise ratio, and thus increasing the impact on the detection image, which may cause an increase in the image noise impact value. The parameters of the algorithm of this embodiment need to jointly consider the impact on the results. By analyzing the comprehensive impact between the parameters, the accurate quantification of the impact of noise on the detection image during industrial ultrasonic nondestructive testing of industrial pipelines is achieved, thereby achieving the improvement of the image processing quality of industrial ultrasonic nondestructive testing.

[0051] Furthermore, the specific process of performing distortion impact assessment on the acquired industrial pipeline ultrasonic probe data to obtain the image distortion impact value is as follows: the sound wave propagation impact value (i.e., the CBSS in the restricted expression of the image distortion impact value) is obtained by processing and analyzing the pressure-temperature impact factor, the sound wave propagation loss, and the preset maximum threshold of the sound wave propagation loss obtained from the database. a), the acoustic wave propagation influence value is used to describe the influence of pipeline temperature difference and pipeline pressure difference on acoustic wave propagation loss; the detection sensitivity influence value (i.e., the LMD in the limiting expression of the image distortion influence value) is obtained by processing and analyzing the pressure-temperature influence factor, the probe transmission power, the probe input signal strength, and the preset detection sensitivity maximum threshold obtained from the database a ), the detection sensitivity influence value is used to describe the influence of pipeline temperature difference and pipeline pressure difference on the detection sensitivity of the ultrasonic probe; the image distortion influence value is obtained according to the relative relationship between the sound wave propagation influence value and the detection sensitivity influence value.

[0052] Among them, the image distortion impact value is obtained by the following method:

[0053]

[0054] In the formula, SZYX a It represents the image distortion impact value corresponding to the a-th detection image, a=1,2,...,z, a represents the number of the detection image, z represents the total number of detection images, CBSS a Indicates the sound wave propagation influence value corresponding to the a-th detection image, LMD a represents the detection sensitivity impact value corresponding to the a-th detection image, T a represents the pipeline temperature difference corresponding to the a-th detection image, P a represents the pipeline pressure difference corresponding to the a-th detection image, represents the sound wave propagation loss corresponding to the a-th detection image, represents the probe transmission power corresponding to the a-th detection image, represents the probe input signal strength corresponding to the a-th detection image, T 0 Indicates the preset maximum temperature difference threshold, P 0 Indicates the preset maximum pressure difference threshold, CBSS 0 Indicates the preset maximum threshold of sound wave propagation loss, LMD 0 represents the preset maximum detection sensitivity threshold, e represents the natural constant, is the pressure-temperature influence factor.

[0055] In this embodiment, the preset maximum threshold of sound wave propagation loss is represented by the maximum value of sound wave propagation loss in the historical time period in the database, and the preset maximum threshold of detection sensitivity is represented by the maximum value of detection sensitivity in the historical time period in the database.

[0056] Specifically, assuming that the sound wave propagation impact value CBSS a The range is 0.55-1, and the detection sensitivity influence value LMD a Fixed to 0.75, such as Figure 3As shown, it is a statistical diagram of the change of the sound wave propagation impact value-image distortion impact value provided by the embodiment of the present application, Figure 3 It can be seen that as the sound wave propagation influence value gradually increases, the image distortion influence value gradually increases, which means that the sound wave propagation loss gradually increases the image distortion, which in turn leads to a gradual increase in the distortion degree of the detection image during industrial ultrasonic nondestructive testing of industrial pipelines.

[0057] It should be understood that the algorithm of this embodiment combines the industrial pipeline ultrasonic probe data analysis to obtain the image distortion impact value. The greater the pipeline temperature difference and pipeline pressure difference, the greater the influence of the pipeline temperature difference and pipeline pressure difference on the sound wave propagation impact value and the detection sensitivity impact value, resulting in an increase in the image distortion impact value. The greater the sound wave propagation loss, the greater the influence of the sound wave propagation loss when performing industrial ultrasonic non-destructive testing on industrial pipelines, resulting in an increase in the image distortion impact value. The greater the probe input signal strength and the smaller the probe transmission power, the higher the sensitivity of the ultrasonic probe, resulting in a decrease in the image distortion impact value.

[0058] It should be understood that the industrial pipeline ultrasonic probe data in the algorithm of this embodiment does not exist independently, but is interrelated and needs to be comprehensively analyzed. The temperature difference will cause thermal expansion of the pipeline material, which will affect the propagation speed of the sound wave in the pipeline and the sound wave propagation loss. The greater the sound wave propagation loss, the smaller the received sound wave signal strength. The greater the pipeline temperature difference, the more significant the change in the sound wave propagation speed, which may lead to an increase in the image distortion impact value. The greater the pressure difference, the more obvious the impact on the sound wave propagation, which may lead to image distortion and reduced detection sensitivity. The greater the input signal strength, the more likely it is that the ultrasonic probe will be saturated or the image will be distorted, which will lead to an increase in the image distortion impact value. The parameters of the algorithm of this embodiment need to consider the impact on the results together. By analyzing the comprehensive impact of the parameters, the accurate quantification of the impact of the ultrasonic probe sensitivity on the detection image distortion during industrial ultrasonic nondestructive testing of industrial pipelines is achieved, thereby achieving the improvement of the image processing quality of industrial ultrasonic nondestructive testing.

[0059] Furthermore, the specific process of judging whether to adjust the image detection quality based on the image noise influence value and the image distortion influence value is as follows: judging whether the image noise influence value and the image distortion influence value meet the image detection conditions; when the image noise influence value and the image distortion influence value meet the image detection conditions, it indicates that the detection quality of the detection image is qualified and no image detection quality adjustment is performed; otherwise, it indicates that the detection quality of the detection image is unqualified and image detection quality adjustment is performed; image detection quality adjustment includes image noise influence adjustment and image distortion influence adjustment; image noise influence adjustment includes sending prompts to preset personnel to adjust the frequency of ultrasonic detection and performing joint noise reduction; adjusting the frequency of ultrasonic detection is used to improve the detection accuracy of the detection image; joint noise reduction means jointly reducing the influence of noise on the detection image through wavelet threshold noise reduction and set empirical mode decomposition; image detection condition indicates that the image noise influence value is not higher than the reference noise influence average value obtained from the database, and the image distortion influence value is not higher than the reference distortion influence average value obtained from the database.

[0060] It should be added that the specific process of adjusting the image noise impact is as follows: a prompt is sent to the preset personnel to adjust the frequency of ultrasonic detection. When the monitored image noise impact value meets the image detection conditions, the image noise impact adjustment is stopped, otherwise joint noise reduction is performed; if joint noise reduction is performed (when the monitored image noise impact value does not meet the image detection conditions), then when the monitored image noise impact value meets the image detection conditions after adjusting the frequency of ultrasonic detection, the image noise impact adjustment is stopped, otherwise a prompt of abnormal detection image is sent to the preset personnel.

[0061] The specific process of adjusting the image distortion effect is as follows: sending a prompt to the preset personnel to adjust the pulse width of the ultrasonic detection. When the monitored image distortion effect value meets the image detection conditions, the image distortion effect adjustment is stopped, otherwise high-temperature coupling is performed to adjust the pulse width of the ultrasonic detection to improve the flexibility of the ultrasonic detection. If high-temperature coupling is performed (when the monitored image distortion effect value does not meet the image detection conditions), then when the image distortion effect value monitored after high-temperature coupling meets the image detection conditions, the image distortion effect adjustment is stopped, otherwise a prompt of abnormal detection image is sent to the preset personnel. High-temperature coupling means that the accuracy of the transmission data of the ultrasonic probe is improved by using a high-temperature coupling agent.

[0062] In this embodiment, the reference noise influence average value is represented by the average value of the image noise influence value of the historical time period in the database, and the reference distortion influence average value is represented by the average value of the image distortion influence value of the historical time period in the database; sending a prompt to the preset personnel to adjust the frequency of the ultrasonic probe detection step by step by a preset multiple, which is helpful to improve the reflection and scattering characteristics of the ultrasonic wave at the defect, making the defect easier to identify; using wavelet transform to decompose the detection image into sub-bands of different frequencies and scales, and then removing the high-frequency components in the noise sub-band by a threshold set by the preset personnel, decomposing the image into a series of intrinsic mode functions, and removing noise by screening and reconstructing IMF (Intrinsic Mode Function, intrinsic mode function), which is helpful to remove random noise and interference in the detection image; sending a prompt to the preset personnel to adjust the pulse width of the ultrasonic probe detection step by step by a preset multiple, which is helpful to improve the reflection and scattering intensity of the ultrasonic wave at the defect; high-temperature coupling agents, such as silicone high-temperature coupling agents, aluminum oxide high-temperature coupling agents, etc., are helpful to improve the contact and transmission efficiency between the ultrasonic probe and the detection material, thereby achieving the improvement of the image processing quality of industrial ultrasonic non-destructive testing.

[0063] Furthermore, the image processing quality evaluation value is affected by the peak signal-to-noise ratio (i.e., FZ in the constraint expression of the image processing quality evaluation value). b ) and contrast impact value (i.e., DBD in the limiting expression of image processing quality evaluation value b ) is processed; the peak signal-to-noise ratio influence value is obtained by processing and analyzing the noise-distortion influence factor, the peak signal-to-noise ratio of the detection image and the preset peak signal-to-noise ratio maximum threshold obtained from the database; the noise-distortion influence factor is obtained by processing and analyzing the image distortion influence value and the peak signal-to-noise ratio of the detection image after the image detection quality adjustment; the contrast influence value is obtained by processing and analyzing the noise-distortion influence factor, the maximum brightness, the minimum brightness and the preset contrast maximum threshold obtained from the database; the peak signal-to-noise ratio influence value is used to reflect the influence of the image noise influence value and the image distortion influence value after the image detection quality adjustment on the peak signal-to-noise ratio of the qualified detection image; the contrast influence value is used to reflect the influence of the image noise influence value and the image distortion influence value after the image detection quality adjustment on the contrast of the qualified detection image; the qualified detection image represents the detection image after the image detection quality adjustment.

[0064] Among them, the image processing quality evaluation value is obtained by the following method:

[0065]

[0066] In the formula, ZSYX brepresents the image processing quality evaluation value corresponding to the bth qualified detection image, b=1,2,...,g, b represents the number of qualified detection images, g represents the total number of qualified detection images, FZ b It represents the peak signal-to-noise ratio impact value corresponding to the b-th qualified detection image, DBD b represents the contrast impact value corresponding to the bth qualified detection image, represents the peak signal-to-noise ratio of the detection image corresponding to the b-th qualified detection image, represents the maximum brightness value corresponding to the bth qualified detection image, Indicates the minimum brightness value corresponding to the bth qualified detection image, ZSYX b ' represents the image noise impact value after image detection quality adjustment corresponding to the bth qualified detection image, SZYX b ' represents the image distortion impact value after image detection quality adjustment corresponding to the bth qualified detection image, FZ 0 Indicates the preset peak signal-to-noise ratio maximum threshold, DBD 0 represents the preset maximum contrast threshold, e represents a natural constant, is the noise-distortion impact factor.

[0067] In this embodiment, the preset peak signal-to-noise ratio maximum threshold is represented by the maximum value of the peak signal-to-noise ratio in the historical time period in the database, and the preset contrast maximum threshold is represented by the maximum value of the contrast in the historical time period in the database.

[0068] It should be understood that the algorithm of this embodiment combines the image noise impact value and the image distortion impact value with the image processing related data analysis to obtain the image processing quality evaluation value. The larger the image noise impact value and the image distortion impact value, the greater the impact of image noise and image distortion on the image processing quality, resulting in a decrease in the image processing quality evaluation value. The higher the peak signal-to-noise ratio of the detected image, the better the image processing quality, resulting in an increase in the image processing quality evaluation value. The larger the difference between the maximum brightness and the minimum brightness, the higher the image contrast, resulting in an increase in the image processing quality evaluation value.

[0069] It should be understood that the image noise impact value and the image distortion impact value and the image processing related data in the algorithm of this embodiment do not exist independently, but are interrelated and need to be comprehensively analyzed. The larger the image noise impact value, the more noise there is in the detection image, which will reduce the clarity, contrast and overall quality of the detection image, resulting in a decrease in the difference between the maximum and minimum brightness values, thereby causing the image processing quality evaluation value to decrease. The larger the image distortion impact value, the more serious the distortion of the detection image, which will destroy the original structure and information of the detection image. Therefore, the increase in the image distortion impact value may also lead to a decrease in the image processing quality evaluation value. The noise and distortion of the detection image may increase the noise level in the image and destroy the original signal of the image, resulting in a decrease in the peak signal-to-noise ratio of the detection image, thereby resulting in a decrease in the image processing quality evaluation value. The parameters of the algorithm of this embodiment need to jointly consider the impact on the results. By analyzing the comprehensive impact between the parameters, the accurate quantification of the processing quality of the detection image during the image processing of the detection image is achieved, thereby achieving the improvement of the image processing quality of industrial ultrasonic non-destructive testing.

[0070] Furthermore, the specific process of judging whether to adjust the image processing quality based on the image processing quality evaluation value is as follows: judging whether the image processing quality evaluation value meets the image processing quality condition; the image processing quality condition indicates that the image processing quality evaluation value is not lower than the reference image processing quality average value obtained from the database; when the image processing quality evaluation value meets the image processing quality condition, it indicates that the image processing quality is qualified, and no image processing quality adjustment is performed and the detection image that meets the image processing quality condition is analyzed; otherwise, it indicates that the image processing quality is unqualified and image processing quality adjustment is performed, and the image processing quality adjustment includes modulus maximum denoising and image edge enhancement; modulus maximum denoising means improving the image processing quality by a preset number of wavelet transforms; image edge enhancement means enhancing the edge information of the detection image by differential edge detection.

[0071] In this embodiment, the average value of the reference image processing quality is represented by the average value of the image processing quality evaluation value of the historical time period in the database. The modulus maximum denoising aims to utilize the characteristics of the wavelet transform to remove the noise components in the detection image while retaining the important details and features of the detection image, thereby improving the processing quality of the detection image. The preset number of times is determined by the preset personnel based on the noise level of the detection image. The differential edge detection utilizes the differential method (such as gradient operator, Sobel operator, Prewitt operator, etc.) to perform edge detection on the image, thereby identifying the edge information in the detection image, which helps to improve the processing quality of the detection image, thereby achieving the improvement of the image processing quality of industrial ultrasonic non-destructive testing.

[0072] like Figure 3As shown, it is a structural schematic diagram of a processing and analysis system for industrial ultrasonic nondestructive testing images provided in an embodiment of the present application. A processing and analysis system for industrial ultrasonic nondestructive testing images provided in an embodiment of the present application includes a noise impact assessment module, a distortion impact assessment module and an image processing quality assessment module: wherein the noise impact assessment module is used to perform noise impact assessment according to the acquired ultrasonic nondestructive monitoring data to obtain an image noise impact value, and the image noise impact value is used to assess the degree of influence of noise on the detection image when performing industrial ultrasonic nondestructive testing on industrial pipelines; the distortion impact assessment module is used to perform distortion impact assessment according to the acquired industrial pipeline ultrasonic probe data to obtain an image distortion impact value, based on the image noise impact value and the image The distortion influence value determines whether to adjust the image detection quality. The image distortion influence value is used to evaluate the influence of the ultrasonic probe sensitivity on the distortion of the detection image when performing industrial ultrasonic non-destructive testing on industrial pipelines. The image detection quality adjustment is used to improve the detection quality of the detection image; the image processing quality assessment module is used to combine the image noise influence value and the image distortion influence value for image quality adjustment and the acquired image processing related data to obtain the image processing quality assessment value, and determine whether to adjust the image processing quality based on the image processing quality assessment value. The image processing quality assessment value is used to evaluate the processing quality of the detection image during the image processing process of the detection image. The image processing quality adjustment is used to improve the processing quality of the detection image.

[0073] In this embodiment, the noise impact assessment module, the distortion impact assessment module, and the image processing quality assessment module work together to ensure the quality and accuracy of the ultrasonic nondestructive testing images of industrial pipelines. When the image noise impact value monitored by the noise impact assessment module and the image distortion impact value monitored by the distortion impact assessment module meet the image detection conditions, the image noise impact value and the image distortion impact value that meet the image detection conditions are transmitted to the image processing quality assessment module. When the image processing quality assessment value monitored by the image processing quality assessment module meets the image processing quality conditions, a detection image that meets the image processing quality conditions is obtained, thereby ensuring the reliability and effectiveness of the ultrasonic nondestructive testing of industrial pipelines, thereby achieving the improvement of the image processing quality of industrial ultrasonic nondestructive testing.

[0074] In summary, the embodiment of the present application obtains an image noise impact value by performing a noise impact assessment, then obtains an image distortion impact value by performing a distortion impact assessment, and then determines whether to adjust the image detection quality based on the image noise impact value and the image distortion impact value. Finally, it determines whether to adjust the image processing quality based on the acquired image processing quality assessment value, thereby improving the image processing quality, and further improving the image processing quality of industrial ultrasonic nondestructive testing, effectively solving the problem of unstable quality of industrial ultrasonic nondestructive testing images processed and analyzed in the prior art.

[0075] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0079] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0080] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for processing and analyzing industrial ultrasonic nondestructive testing images, characterized in that: The following steps are involved: S1, performing noise impact assessment based on the acquired ultrasonic nondestructive monitoring data to obtain an image noise impact value, wherein the image noise impact value is used to assess the degree of influence of noise on the detection image when performing industrial ultrasonic nondestructive testing on industrial pipelines; S2, performing distortion impact assessment according to the acquired industrial pipeline ultrasonic probe data to obtain an image distortion impact value, and judging whether to perform image detection quality adjustment based on the image noise impact value and the image distortion impact value, wherein the image distortion impact value is used to assess the influence of ultrasonic probe sensitivity on detection image distortion when performing industrial ultrasonic nondestructive testing on industrial pipelines; S3, combining the image noise impact value and the image distortion impact value for image quality adjustment and the acquired image processing related data to obtain an image processing quality evaluation value, and judging whether to perform image processing quality adjustment based on the image processing quality evaluation value, wherein the image processing quality evaluation value is used to evaluate the processing quality of the detection image during the image processing of the detection image.

2. The method for processing and analyzing industrial ultrasonic nondestructive testing images according to claim 1, characterized in that: The ultrasonic nondestructive monitoring data includes pipeline temperature difference, pipeline pressure difference, detection thermal noise, detection output signal signal-to-noise ratio and detection input signal signal-to-noise ratio; The industrial pipeline ultrasonic probe data includes sound wave propagation loss, probe transmission power and probe input signal strength; The image processing related data includes the maximum brightness, the minimum brightness, and the peak signal-to-noise ratio of the detected image; The pipeline temperature difference represents the absolute value of the difference between the temperature of a preset point inside the pipeline and the temperature of a corresponding preset point outside the pipeline; The pipeline pressure difference represents the absolute value of the difference between the pressure at a preset point inside the pipeline and the pressure at a corresponding preset point outside the pipeline.

3. The method for processing and analyzing industrial ultrasonic nondestructive testing images according to claim 2, characterized in that: The specific process of performing noise impact assessment based on the acquired ultrasonic nondestructive monitoring data to obtain the image noise impact value is as follows: Obtaining an image noise impact value according to the thermal noise impact value, the input signal impact value, the output signal impact value, and a preset weight obtained from a database; The thermal noise impact value is obtained by processing and analyzing the pressure-temperature impact factor, the detected thermal noise, and a preset thermal noise average threshold obtained from a database; The pressure-temperature influencing factor is obtained by processing and analyzing the pipeline temperature difference, the pipeline pressure difference, and the preset temperature difference maximum threshold and the preset pressure difference maximum threshold obtained from the database; The input signal impact value is obtained by processing and analyzing the pressure-temperature impact factor, the detected input signal signal-to-noise ratio, and the preset input signal-to-noise ratio maximum threshold obtained from the database; The output signal impact value is obtained by processing and analyzing the pressure-temperature impact factor, the detection output signal signal-to-noise ratio, and the preset output signal-to-noise ratio maximum threshold obtained from the database; The preset weights include a first noise weight, a second noise weight and a third noise weight.

4. The method for processing and analyzing industrial ultrasonic nondestructive testing images as claimed in claim 3, characterized in that: The limiting expression of the image noise impact value is as follows: In the formula, ZSYX a RZS represents the image noise impact value corresponding to the a-th detection image, a=1,2,...,z, a represents the number of the detection image, z represents the total number of detection images, a Represents the thermal noise impact value corresponding to the a-th detection image, SR a Indicates the input signal impact value corresponding to the a-th detection image, SC a represents the output signal influence value corresponding to the a-th detection image, e represents a natural constant, ω1 represents the first noise weight, ω2 represents the second noise weight, and ω3 represents the third noise weight.

5. The method for processing and analyzing industrial ultrasonic nondestructive testing images according to claim 2, characterized in that: The specific process of performing distortion impact assessment based on the acquired industrial pipeline ultrasonic probe data to obtain the image distortion impact value is as follows: The sound wave propagation influence value is obtained by processing and analyzing the pressure-temperature influence factor, the sound wave propagation loss, and the preset maximum threshold of the sound wave propagation loss obtained from the database; The detection sensitivity influence value is obtained by processing and analyzing the pressure-temperature influence factor, the probe transmission power, the probe input signal strength and the preset detection sensitivity maximum threshold value obtained from the database; The image distortion impact value is obtained according to the relative relationship between the sound wave propagation impact value and the detection sensitivity impact value.

6. The method for processing and analyzing industrial ultrasonic nondestructive testing images according to claim 5, characterized in that: The specific process of judging whether to adjust the image detection quality based on the image noise impact value and the image distortion impact value is as follows: When the image noise impact value and the image distortion impact value meet the image detection conditions, the image detection quality adjustment is not performed, otherwise the image detection quality adjustment is performed; The image detection quality adjustment includes image noise impact adjustment and image distortion impact adjustment; The image noise impact adjustment includes sending a prompt to a preset person to adjust the frequency of ultrasonic testing and perform joint noise reduction; The image detection condition indicates that the image noise impact value is not higher than the reference noise impact average value obtained from the database, and the image distortion impact value is not higher than the reference distortion impact average value obtained from the database.

7. The method for processing and analyzing industrial ultrasonic nondestructive testing images according to claim 6, characterized in that: The specific process of adjusting the image distortion effect is as follows: Send reminders to preset personnel to adjust the pulse width of ultrasonic testing. When the monitored image distortion impact value meets the image testing conditions, stop adjusting the image distortion impact, otherwise perform high temperature coupling; If high temperature coupling is performed, when the image distortion impact value monitored after high temperature coupling meets the image detection condition, the image distortion impact adjustment is stopped, otherwise a detection image abnormality prompt is sent to the preset personnel.

8. The method for processing and analyzing industrial ultrasonic nondestructive testing images as claimed in claim 2, characterized in that: The image processing quality evaluation value is obtained by processing the peak signal-to-noise ratio impact value and the contrast impact value; The peak signal-to-noise ratio impact value is obtained by processing and analyzing the noise-distortion impact factor, the peak signal-to-noise ratio of the detected image, and a preset peak signal-to-noise ratio maximum threshold obtained from a database; The noise-distortion impact factor is obtained by processing and analyzing the image distortion impact value after image detection quality adjustment and the peak signal-to-noise ratio of the detection image; The contrast impact value is obtained by processing and analyzing the noise-distortion impact factor, the maximum brightness value, the minimum brightness value, and a preset maximum contrast threshold value obtained from a database.

9. The method for processing and analyzing industrial ultrasonic nondestructive testing images as claimed in claim 8, characterized in that: The specific process of judging whether to adjust the image processing quality based on the image processing quality evaluation value is as follows: Determining whether the image processing quality assessment value meets the image processing quality condition; The image processing quality condition indicates that the image processing quality evaluation value is not lower than the reference image processing quality average value obtained from the database; When the image processing quality evaluation value meets the image processing quality condition, the image processing quality adjustment is not performed and the detection image that meets the image processing quality condition is analyzed. Otherwise, the image processing quality adjustment is performed, and the image processing quality adjustment includes modulus maximum denoising and image edge enhancement.

10. An industrial ultrasonic nondestructive testing image processing and analysis system, characterized in that: Including noise impact assessment module, distortion impact assessment module and image processing quality assessment module: The noise impact assessment module is used to perform noise impact assessment based on the acquired ultrasonic nondestructive monitoring data to obtain an image noise impact value, and the image noise impact value is used to assess the degree of influence of noise on the detection image when performing industrial ultrasonic nondestructive testing on industrial pipelines; The distortion impact assessment module is used to perform distortion impact assessment based on the acquired industrial pipeline ultrasonic probe data to obtain an image distortion impact value, and determine whether to perform image detection quality adjustment based on the image noise impact value and the image distortion impact value. The image distortion impact value is used to assess the influence of ultrasonic probe sensitivity on detection image distortion when performing industrial ultrasonic nondestructive testing on industrial pipelines; The image processing quality assessment module is used to obtain an image processing quality assessment value by combining the image noise impact value and the image distortion impact value for image quality adjustment and the acquired image processing related data, and to determine whether to perform image processing quality adjustment based on the image processing quality assessment value. The image processing quality assessment value is used to assess the processing quality of the detection image during the image processing of the detection image.

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