A chemical pipeline leakage prevention safety detection method

Abstract

The present invention relates to the field of image data processing technology, and specifically to a chemical pipeline anti-leakage safety detection method, comprising: collecting an infrared image of the surface of a chemical pipeline, dividing the infrared image of the chemical pipeline into multiple windows, merging the windows according to the grayscale value differences of all pixels in the windows to obtain a thermal radiation dispute area, then determining the subordinate voting score corresponding to the pixel in combination with the pixel grayscale value, thereby determining all thermal poles and subordinate pixels in the thermal radiation dispute area, then determining the local abnormal temperature parameters of multiple pixels in combination with the grayscale value changes of the pixel at different distances, and finally determining the leakage possibility of the thermal radiation dispute area. The present invention effectively reduces the difficulty of identifying possible leakage areas, thereby improving the accuracy of pipeline surface leakage monitoring, which is beneficial to the transportation and recovery of chemical materials and the intelligent detection of pipeline leakage.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and in particular to a chemical pipeline anti-leakage safety detection method. Background Art

[0002] Chemical products are widely used in modern industry, encompassing fields such as petrochemicals, pharmaceuticals, food, and agriculture. These chemicals provide essential raw materials and products for a wide range of industries, but they also pose safety risks. In chemical pipeline systems, the transportation and storage of chemicals often carries the risk of leakage. Pipeline leaks not only waste resources but can also cause fires and explosions, endangering human life and the environment.

[0003] Existing leak detection technology typically uses infrared to detect gas leaks in chemical pipelines. This method has certain limitations because chemical pipelines are not always a complete whole. They contain welds and areas of uneven thickness. Differences in pipe material and thickness in these areas lead to different absorption of thermal radiation from the chemical gas within the pipeline, resulting in different external thermal radiation. When using conventional infrared detection methods for leak detection in chemical pipelines, false detections are very likely to occur at welds and areas of uneven thickness, affecting the accuracy of actual detection. Summary of the Invention

[0004] The present invention provides a chemical pipeline anti-leakage safety detection method to solve the existing problems.

[0005] A chemical pipeline anti-leakage safety detection method of the present invention adopts the following technical solutions:

[0006] An embodiment of the present invention provides a chemical pipeline anti-leakage safety detection method, which includes the following steps:

[0007] Acquire infrared images of chemical pipelines;

[0008] The infrared image of the chemical pipeline is divided into several windows, and the windows are merged according to the grayscale value difference of the pixels between adjacent windows to obtain the thermal radiation dispute area;

[0009] Determine the membership parameters of the neighboring pixels of each pixel in the thermal radiation dispute area and a number of subordinate pixels of each pixel according to the differences between the grayscale values ​​of the pixels in the thermal radiation dispute area;

[0010] Determine the membership voting score corresponding to each pixel in the thermal radiation dispute area based on the membership parameter corresponding to each pixel point of its subordinate pixel points; and select several thermal poles from all pixels in the thermal radiation dispute area based on the membership voting score corresponding to each pixel point in the thermal radiation dispute area.

[0011] Determine the local abnormal temperature parameter corresponding to the thermal extreme point according to the membership parameter corresponding to each pixel point and the gray value of the pixel point corresponding to the lower-level pixel point of the thermal extreme point;

[0012] According to the local abnormal temperature parameters corresponding to all the thermal extremes in the thermal radiation dispute area, it is judged whether there is leakage in the thermal radiation dispute area.

[0013] Furthermore, the infrared image of the chemical pipeline is divided into a number of windows, and the windows are merged according to the difference in pixel grayscale values ​​between adjacent windows to obtain the thermal radiation dispute area. The specific steps include the following:

[0014] The infrared image of the chemical pipeline is divided into several equal parts of size Window, is the preset side length;

[0015] For the A window is formed, and the grayscale mean of all pixels in the window is calculated and recorded as The grayscale mean of the window;

[0016] According to the grayscale mean difference between adjacent windows, all the thermal radiation dispute windows in the infrared image of the chemical pipeline are determined;

[0017] Adjacent heat radiation dispute windows constitute a heat radiation dispute area.

[0018] Furthermore, the specific steps of determining all thermal radiation dispute windows in the infrared image of the chemical pipeline based on the grayscale mean difference between adjacent windows include the following:

[0019] For the window, calculate the The first window and the The absolute value of the difference between the grayscale mean values ​​of each window adjacent to the window, if the The first window and the The absolute value of the difference between the grayscale means of all adjacent windows is greater than the first preset threshold. When This window is a heat radiation dispute window.

[0020] Furthermore, the method of determining the membership parameters of the neighboring pixels of each pixel in the thermal radiation dispute area and a number of subordinate pixels of each pixel in the thermal radiation dispute area based on the differences between the grayscale values ​​of the pixels in the thermal radiation dispute area includes the following specific steps:

[0021] For the Thermal radiation dispute area, calculate the The gray value of the pixel is The first pixel in the eight-neighborhood The difference in the grayscale values ​​of pixels is recorded as the first grayscale difference;

[0022] Calculate the The average grayscale value of the pixels in the eight neighborhoods of the pixel point is recorded as the first grayscale mean;

[0023] For the The first pixel in the eight-neighborhood pixels, calculate the The average gray value of the pixel in the eight neighborhoods of the pixel is recorded as The first pixel in the eight-neighborhood The grayscale mean of the first neighborhood of pixels;

[0024] For the pixel points, calculate the first grayscale mean and the The first pixel in the eight-neighborhood The difference between the grayscale means of the first neighborhood of the pixel point is recorded as The first pixel in the eight-neighborhood Pixels for the The membership degree of each pixel;

[0025] Calculate the first preset selection weight The product of the grayscale difference and the first grayscale difference is recorded as the first product, and the second preset selection weight is calculated. With the The first pixel in the eight-neighborhood Pixels for the The product of the membership of the pixels is recorded as the second product, and the sum of the first product and the second product is calculated and recorded as the first product. The first pixel in the eight-neighborhood Pixels for the Membership parameters of pixels;

[0026] For the All pixels in the eight neighborhoods of the pixel are The membership parameters of pixels are selected, and the first Pixels in the eight-neighborhood of a pixel As the first The first subordinate pixel of pixels;

[0027] According to the The method of obtaining the first subordinate pixel of a pixel point is to obtain the pixel point The first lower-level pixel , as the first The second lower level pixel of the pixel, and then get the pixel The first lower-level pixel , as the first The third lower level pixel of the pixel; and so on, we can get several The lower level pixel of each pixel.

[0028] Furthermore, the step of determining the membership voting score corresponding to each pixel in the thermal radiation dispute area according to the membership parameters corresponding to each pixel of the subordinate pixels of each pixel in the thermal radiation dispute area includes the following specific steps:

[0029] For the pixels, calculate the All the lower level pixels of the pixel are The sum of the membership parameters of pixels is recorded as The membership value of the pixel point is calculated The number of all lower-level pixels of the pixel is the same as that of the The product of the membership values ​​of the pixels is recorded as the membership voting score.

[0030] Furthermore, the method of selecting a plurality of thermal extremes from all pixels in the thermal radiation dispute area according to the membership voting score corresponding to each pixel in the thermal radiation dispute area includes the following specific steps:

[0031] For the For each heat radiation dispute area, the membership voting scores of all pixels are arranged in descending order to obtain the pixel membership voting score sequence;

[0032] The first The value of one tenth of the area of ​​the heat radiation dispute area is rounded down and recorded as Recommended thermal extreme values ​​for thermal radiation dispute areas ;

[0033] For the pixel membership voting score sequence, select the first Pixels as the first A thermal pole in a controversial area of ​​thermal radiation.

[0034] Furthermore, the method of determining the local abnormal temperature parameter corresponding to the thermal extreme point according to the membership parameter corresponding to each pixel point and the grayscale value of the pixel point of the subordinate pixel points of the thermal extreme point includes the following specific steps:

[0035] For the Lower-level pixels, according to the The membership parameters and grayscale values ​​of all subordinate pixels of the hot point are obtained. The internal weight of each subordinate pixel;

[0036] For the The first hot point Lower-level pixel points, calculate the The internal weight of the lower level pixel is The product of the grayscale values ​​of the lower-level pixels is recorded as The temperature gradient of each lower-level pixel;

[0037] Calculate the The difference between the grayscale value of the pixel point at the first thermal extreme and the temperature gradient is recorded as The grayscale value of the new pixel of the next level pixel;

[0038] According to The grayscale values ​​of all the lower-level pixels of a thermal extreme point are used to determine the local abnormal temperature parameters corresponding to the thermal extreme point.

[0039] Furthermore, according to The membership parameters and grayscale values ​​of all subordinate pixels of the hot point are obtained. The specific steps of calculating the in-group weight of each subordinate pixel are as follows:

[0040] In the In the heat radiation dispute area, for the thermal extremes, calculate the All the lower level pixels of the hot point are The mean of the membership parameters of the hot extreme points is recorded as the first mean, and the The first hot point The lower level pixels are The ratio of the membership parameter of each hot point to the first mean is recorded as the relative membership parameter;

[0041] For the thermal extremes, calculate the The first hot point and the The first hot point The reciprocal of the Euclidean distance between the subordinate pixels is recorded as distance correlation;

[0042] For the The first hot point subordinate pixels, calculate the The grayscale value of the pixel point of the next lower level is the same as that of the The absolute value of the difference between the mean grayscale values ​​of the pixels in the eight neighborhoods of the lower-level pixel point is recorded as the second grayscale difference, and the reciprocal of the second grayscale difference is calculated and recorded as the temperature uniformity index;

[0043] For the The first hot point The product of the relative membership parameter, distance correlation and temperature uniformity index is calculated for each lower-level pixel point, and recorded as The internal weight of each subordinate pixel.

[0044] Furthermore, according to The grayscale values ​​of all lower-level pixels of a thermal extreme point are used to determine the local abnormal temperature parameters corresponding to the thermal extreme point, including the following specific steps:

[0045] For the thermal extremes, calculate the All the lower level pixels of the hot spot are D8 distance between the two hot spots;

[0046] Get the The maximum value Z of the D8 distance between all the lower-level pixels of the hot pole and the b-th hot pole is used to construct a preset distance sequence ;

[0047] Filter out all subordinate pixels whose D8 distance is the first value in the preset distance sequence and record them as target subordinate pixels. Calculate the variance of the temperature gradient of all target subordinate pixels and record it as the temperature variance of the first value in the preset distance sequence. Calculate the mean grayscale value of all target subordinate pixels and record it as the grayscale mean of the first value in the preset distance sequence.

[0048] Calculate the sum of the temperature variances of all values ​​in the preset distance sequence and record it as the temperature distribution consistency index;

[0049] The absolute value of the difference between the grayscale means of adjacent values ​​in the preset distance sequence, and the sum of the absolute value of the difference between the grayscale means of all adjacent values ​​in the preset distance sequence are used as the temperature distribution rationality index;

[0050] Calculate the ratio of the temperature distribution rationality index to the temperature distribution consistency index, record it as the first abnormal index, and write the first abnormal index The inverse proportional normalized value of is recorded as the local abnormal temperature parameter.

[0051] Furthermore, judging whether leakage occurs in the heat radiation dispute area based on the local abnormal temperature parameters corresponding to all the thermal extremes in the heat radiation dispute area includes the following specific steps:

[0052] For the Thermal radiation dispute area, calculate the The normalized value of the mean of the local abnormal temperature parameters of all thermal extremes in the thermal radiation dispute area is recorded as The possibility of pipeline leakage in the heat radiation dispute area;

[0053] For all heat radiation dispute areas, when the pipeline leakage probability of the tth heat radiation dispute area is greater than the second preset threshold When , it is determined that there is a pipeline leakage in the tth heat radiation dispute area.

[0054] The beneficial effects of the technical solution of the present invention are:

[0055] Infrared images of the chemical pipeline surface are collected and divided into multiple windows. Adjacent windows are merged based on the grayscale value differences of all pixels within each window to obtain the thermal radiation dispute area. This allows for the initial location of abnormal areas on the chemical pipeline surface, narrowing the scope of possible leaks and improving monitoring accuracy. Based on the grayscale values ​​of the pixels in the thermal radiation dispute area, the corresponding membership parameters are determined, which in turn leads to the membership voting scores of the pixels in the thermal radiation dispute area. Finally, all thermal extremes in the thermal radiation dispute area and the multiple subordinate pixels corresponding to each thermal extreme are determined. This identifies multiple abnormal points within the abnormal area, further narrowing the possible leak area and improving monitoring accuracy. Based on the membership parameters and grayscale values ​​of the multiple subordinate pixels corresponding to the thermal extreme, the relative pipeline temperature distribution within the local area of ​​the thermal extreme is constructed, and the local abnormal temperature parameters corresponding to the thermal extreme are determined. This allows for the characterization of the temperature characteristics of the abnormal area, reducing the difficulty of identifying possible leak areas. Based on the local abnormal temperature parameters corresponding to all thermal extremes in the thermal radiation dispute area, it is determined whether the thermal radiation dispute area has leaked and the leak area on the chemical pipeline surface is located. At this point, the present invention determines multiple controversial areas and abnormal pixel points on the surface of chemical pipelines by analyzing the grayscale values ​​of pixels in infrared images, effectively reducing the difficulty of identifying possible leakage areas, thereby improving the accuracy of pipeline surface leakage monitoring, which is beneficial to the transportation and recycling of chemical materials, as well as the intelligentization of pipeline leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 This is a flow chart of the steps of a chemical pipeline leakage prevention safety detection method of the present invention;

[0058] Figure 2 This is a schematic diagram of an infrared image of a chemical pipeline with a relatively uniform medium in this embodiment;

[0059] Figure 3 Schematic diagram of infrared image of chemical pipeline with inhomogeneous medium in this embodiment. DETAILED DESCRIPTION

[0060] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a chemical pipeline leak prevention and safety detection method according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0061] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0062] The specific scheme of the chemical pipeline anti-leakage safety detection method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0063] See also Figure 1 , which shows a flow chart of a chemical pipeline anti-leakage safety detection method provided by one embodiment of the present invention, the method comprising the following steps:

[0064] Step S001: Acquire an infrared image of a chemical pipeline.

[0065] In this embodiment, an infrared detector is used to capture infrared images of a chemical pipeline. The infrared detector is positioned parallel to the chemical pipeline and is held by a mobile robot equipped with a robotic arm. This captures an infrared image of a section of the chemical pipeline directly in front of it. The infrared image is then de-noised.

[0066] In this embodiment, the infrared image of each segment is de-noised using a Gaussian filter algorithm. The Gaussian filter de-noising algorithm is a well-known technique, and the specific algorithm will not be described here.

[0067] It should be noted that an infrared detector is a device that can sense and measure infrared radiation. It can detect infrared radiation emitted by objects or environments and convert this information into visual images or data.

[0068] The shooting process is repeated to obtain several infrared images of the chemical pipeline surface.

[0069] It should be noted that, in the infrared image of the chemical pipeline, the pipeline area occupies 1 / 3 of the infrared image.

[0070] Step S002: Divide the infrared image of the chemical pipeline into several windows, merge the windows according to the difference in pixel grayscale values ​​between adjacent windows, and obtain the thermal radiation dispute area.

[0071] It should be noted that when the medium is uniform, the temperature radiation is close because the internal pressure of the pipeline is constant and the flow rate of the chemical gas is close, that is, the difference in the mean grayscale values ​​in adjacent windows is small. When a disputed area appears, there is a certain difference in the mean grayscale values ​​in adjacent windows. Therefore, this difference is used to determine the thermal radiation dispute area.

[0072] The infrared image of the chemical pipeline is divided into several equal parts of size window.

[0073] What needs to be explained is that Indicates the preset side length. In this embodiment, This is described as an example. Other values ​​may be set in other embodiments, and this embodiment does not specifically limit this.

[0074] For the A window is formed, and the grayscale mean of all pixels in the window is calculated and recorded as Repeat the steps to get the grayscale mean of several windows.

[0075] Calculate the The first window and the The absolute value of the difference between the grayscale mean values ​​of each window adjacent to the window, if the The first window and the The absolute value of the difference between the grayscale means of all adjacent windows is greater than the first preset threshold. When This window is a heat radiation dispute window.

[0076] It should be noted that the first preset threshold is 50, which is used as an example for explanation.

[0077] Obtain all heat radiation dispute windows in the manner described above.

[0078] According to all the acquired heat radiation dispute windows, adjacent heat radiation dispute windows are combined to form a heat radiation dispute area.

[0079] It should be noted that the thermal radiation dispute area refers to the area where it is impossible to determine whether a chemical leak has occurred by absolute temperature, that is, it may be the welding area of ​​the pipeline or the area with unbalanced medium.

[0080] Step S003: Determine the membership parameters of the neighborhood pixels of each pixel in the thermal radiation dispute area and several subordinate pixels of each pixel according to the differences between the grayscale values ​​of the pixels in the thermal radiation dispute area; determine the membership voting score corresponding to each pixel in the thermal radiation dispute area according to the membership parameters corresponding to the subordinate pixels of each pixel in the thermal radiation dispute area; and select several thermal poles from all the pixels in the thermal radiation dispute area according to the membership voting score corresponding to each pixel in the thermal radiation dispute area.

[0081] It should be noted that for the disputed area of ​​thermal radiation, due to the complexity of its thermal radiation, it is impossible to perform effective thermal radiation calculation (i.e., the method for determining regional thermal radiation generated by a uniform medium). Therefore, this embodiment uses the neighborhood analysis method to analyze the thermal radiation in the disputed area by assuming a thermal extreme point and then using the relative temperature analysis method to analyze the normal thermal radiation in the local area within a smaller range. Schematic diagram of infrared image of a chemical pipeline with relatively uniform medium, as shown in Figure 2 shown.

[0082] It should also be noted that even if the medium in the pipeline is not uniform, within a smaller range, due to the obvious thermal superposition effect (i.e., mutual heat transfer), it can be regarded as a relatively stable thermal attenuation phenomenon within the smaller range. This is specifically manifested as a change in temperature gradient. Heat transfer tends to gradually reduce the temperature difference between different areas, thereby making the regional heat distribution reach a relatively stable state. Schematic diagram of infrared image of chemical pipeline with non-uniform medium, such as Figure 3 shown.

[0083] For the Thermal radiation dispute area, calculate the The gray value of the pixel is The first pixel in the eight-neighborhood The difference in the grayscale values ​​of the pixels is recorded as the first grayscale difference.

[0084] Determine the first preset selection weight according to the known size of the first grayscale difference and the second preset selection weight .

[0085] It should be noted that when the first grayscale difference is equal to zero, the first preset selection weight Equal to zero, second preset selection weight Equal to one, otherwise when the first grayscale difference is not equal to zero, the first preset selection weight Equal to the first and second preset selection weights Equal to zero, take this as an example to illustrate.

[0086] For the Thermal radiation dispute area, calculate the The average grayscale value of the pixels in the eight neighborhoods of the pixel is recorded as the first grayscale mean.

[0087] For the The first pixel in the eight-neighborhood pixels, calculate the The average gray value of the pixel in the eight neighborhoods of the pixel is recorded as The first pixel in the eight-neighborhood The first neighborhood grayscale mean of pixels.

[0088] For the pixel points, calculate the first grayscale mean and the The first pixel in the eight-neighborhood The difference between the grayscale means of the first neighborhood of the pixel point is recorded as The first pixel in the eight-neighborhood Pixels for the The membership degree of each pixel.

[0089] For the pixels, calculate the first preset selection weight The product of the grayscale difference and the first grayscale difference is recorded as the first product, and the second preset selection weight is calculated. With the The first pixel in the eight-neighborhood Pixels for the The product of the membership of the pixels is recorded as the second product, and the sum of the first product and the second product is calculated and recorded as the first product. The first pixel in the eight-neighborhood Pixels for the The membership parameters of each pixel.

[0090] It should be noted that since thermal radiation is transferred from a high-temperature area to a low-temperature area, the choice of the thermal pole is to fit the local maximum temperature point of the disputed area of ​​the pipeline.

[0091] Repeat the steps to get All pixels in the eight neighborhoods of the pixel are The membership parameters of pixels are selected, and the first Pixels in the eight-neighborhood of a pixel As the first The first lower-level pixel of pixels.

[0092] According to the The method of obtaining the first subordinate pixel of a pixel point is to obtain the pixel point The first lower-level pixel ( Not for the first pixels), as the The second lower level pixel of the pixel; according to the The method of obtaining the first subordinate pixel of a pixel point is to obtain the pixel point The first lower-level pixel ( Not for With the pixels), as the The third lower level pixel of the pixel; and so on, we can get several The lower level pixel of each pixel.

[0093] What needs to be explained is that when the first grayscale difference is greater than 0, and the larger it is, the The gray value of the pixel is much larger than the gray value of the pixel in its eight neighborhoods. pixels, and when the The first pixel in the eight-neighborhood Pixels for the The membership degree of each pixel is greater than 0, and the larger the degree, the higher the membership degree of the pixel. The grayscale mean of the neighborhood of a pixel is much larger than that of the The neighborhood grayscale mean of the pixel points, so the first grayscale difference and membership degree are obtained The first pixel in the eight-neighborhood Pixels for the The larger the membership parameter of a pixel point is, the more it can explain the The gray value of the pixel is much larger than the gray value of the pixel in its eight neighborhoods. pixels, and the The first subordinate pixel of the pixel is The pixel with the largest membership parameter in the eight neighborhoods of the pixel point, so the The first subordinate pixel of the pixel is The grayscale value of the eight neighborhoods of the pixel is much smaller than that of the pixels of pixels. Therefore, The more first-level pixels a pixel has, and the larger its membership parameter is, the more The larger the range from the pixel point to the surrounding area where the temperature gradually decreases, and the greater the temperature drop, the The more pixels there are, the more likely they are to be local thermal extremes.

[0094] What needs to be explained is: When the yth lower level pixel of the pixel point has no first lower level pixel point, the iteration stops, and the yth lower level pixel point is obtained. The y lower-level pixels of a pixel.

[0095] It should be noted that when the membership parameters of multiple pixels in the eight-neighborhood are all at the maximum value, one of the pixels is randomly selected as the first pixel. The lower level pixel of each pixel.

[0096] The first pixels as pixels The parent pixel of .

[0097] It should be noted that each pixel can have multiple subordinate pixels, but each pixel can only have one superior pixel. If a pixel belongs to multiple pixels, only the pixel corresponding to the maximum membership parameter is retained, and the rest are invalid.

[0098] According to the above method, a number of lower-level pixel points of each pixel point in the thermal radiation dispute area and a unique upper-level pixel point of each pixel point are obtained.

[0099] For the pixels, calculate the All the lower level pixels of the pixel are The sum of the membership parameters of pixels is recorded as The membership value of the pixel point is calculated The number of all lower-level pixels of the pixel is the same as that of the The product of the membership values ​​of the pixels is recorded as the membership voting score.

[0100] For the For each heat radiation dispute area, the steps are repeated to obtain the membership voting scores of different pixels, and the membership voting scores are arranged in descending order to obtain the pixel membership voting score sequence.

[0101] The first The value of one tenth of the area of ​​the heat radiation dispute area is rounded down and recorded as Recommended thermal extreme values ​​for thermal radiation dispute areas .

[0102] For the pixel membership voting score sequence, select the first Pixels as the first A thermal pole in a controversial area of ​​thermal radiation.

[0103] Step S004: determining the local abnormal temperature parameter corresponding to the thermal extreme point according to the membership parameter corresponding to each pixel point and the gray value of the pixel point corresponding to the subordinate pixel point of the thermal extreme point.

[0104] It should be noted that due to the scattered distribution of pixel grayscale values ​​in the disputed thermal radiation area, it is usually difficult to identify outliers in this local area. In this embodiment, by performing weighted blurring on the disputed thermal radiation area, a local area with a significant temperature gradient change centered at the thermal pole is obtained, making it easier to identify outliers in the local area.

[0105] In the In the heat radiation dispute area, for the thermal extremes, calculate the All the lower level pixels of the hot point are The mean of the membership parameters of the hot extreme points is recorded as the first mean, and the The first hot point The lower level pixels are The ratio of the membership parameter of a hot spot to the first mean is recorded as the relative membership parameter.

[0106] For the thermal extremes, calculate the The first hot point and the The first hot point The reciprocal of the Euclidean distance between the subordinate pixels is recorded as distance correlation.

[0107] For the The first hot point subordinate pixels, calculate the The grayscale value of the pixel point of the next lower level is the same as that of the The absolute value of the difference between the mean grayscale values ​​of the pixels in the eight neighborhoods of the lower-level pixel points is recorded as the second grayscale difference, and the reciprocal of the second grayscale difference is calculated and recorded as the temperature uniformity index.

[0108] It should be noted that when the relative membership parameter is larger and the temperature uniformity index is larger, it means that it is more consistent with the temperature change characteristics, and the adjustment range of the temperature gradient is smaller. When the distance correlation is small, the corresponding temperature echelon is smaller.

[0109] For the The first hot point The product of the relative membership parameter, distance correlation and temperature uniformity index is calculated for each lower-level pixel point, and recorded as The internal weight of each subordinate pixel.

[0110] What needs to be explained is that by adjusting the grayscale value of the disputed area, the temperature value (grayscale value) in the local area is made to reduce the heat transfer effect according to the uniform medium with the hot pole as the center as much as possible, that is, with the hot pole as the center, the pipeline medium at the hot pole is the thinnest and the temperature is the highest, and then gradually becomes uniformly thicker towards the outside and the temperature decreases uniformly.

[0111] For the The first hot point Lower-level pixel points, calculate the The internal weight of the lower level pixel is The product of the grayscale values ​​of the lower-level pixels is recorded as The temperature gradient of each lower-level pixel.

[0112] For the Repeat the steps to obtain the first hot spot. The temperature gradient of all subordinate pixels of a thermal pole.

[0113] For the The first hot point Lower-level pixel points, calculate the The difference between the grayscale value of the pixel point at the first thermal extreme and the temperature gradient is recorded as Repeat the steps to get the gray value of the new pixel point of the next level pixel point. The new pixel grayscale values ​​of all subordinate pixels of the hot extreme point.

[0114] What needs to be explained is that by obtaining The relative distribution of pipe temperature in the local area of ​​the thermal pole, the temperature change in the corresponding local neighborhood should ideally be related to the distance between the pixel point and the thermal pole in the local area, that is, when the distance from the thermal pole is equal, the corresponding temperature gradient is equal, and the farther the distance from the thermal pole, the lower the corresponding temperature gradient. In summary, combined with this feature, calculate the Abnormal temperature parameters in the local area of ​​a thermal extreme.

[0115] For the thermal extremes, calculate the All the lower level pixels of the hot spot are The D8 distance (chessboard distance) of the hot spots is a well-known technology and its details are not described here.

[0116] Get the The maximum value Z of the D8 distance between all the lower-level pixels of the hot pole and the b-th hot pole is used to construct a preset distance sequence .

[0117] For the Thermal poles are screened out, and all subordinate pixel points whose D8 distance is the first value in the preset distance sequence are recorded as target subordinate pixel points. The variance of the temperature gradient of all target subordinate pixel points is calculated and recorded as the temperature variance of the first value in the preset distance sequence. The mean grayscale value of all target subordinate pixel points is calculated and recorded as the grayscale mean of the first value in the preset distance sequence.

[0118] Repeat the steps to obtain the temperature variance and grayscale mean at all distances.

[0119] For the The sum of the temperature variances of all values ​​in the preset distance sequence is calculated and recorded as the temperature distribution consistency index.

[0120] For the The absolute value of the difference between the grayscale means of adjacent values ​​in the preset distance sequence is calculated, and the sum of the absolute value of the difference between the grayscale means of all adjacent values ​​in the preset distance sequence is used as the temperature distribution rationality index.

[0121] It should be noted that the smaller the temperature distribution consistency index is, the smaller the temperature difference is in the area with the same distance from the thermal pole, which is consistent with the local uniform heat transfer; when the temperature distribution rationality index is larger, it means that in the current local area, the farther away from the thermal pole, the lower the temperature is.

[0122] For the The ratio of the temperature distribution rationality index to the temperature distribution consistency index is calculated and recorded as the first abnormal index. The inverse proportional normalized value of is recorded as the local abnormal temperature parameter.

[0123] What needs to be explained is that is an exponential function with a natural constant as the base. Presentation The inverse proportional relationship and normalization processing of the , the implementer can set the inverse proportional function and normalization function according to the actual situation. Repeat the steps to get the The local abnormal temperature parameters of all thermal extremes in the thermal radiation dispute area.

[0124] Step S005: judging whether leakage occurs in the heat radiation dispute area according to the local abnormal temperature parameters corresponding to all the thermal extreme points in the heat radiation dispute area.

[0125] For the Thermal radiation dispute area, calculate the The mean of the local abnormal temperature parameters of all thermal extremes in the thermal radiation dispute area is recorded as the second mean, and the normalization function is used Normalize the second mean and record it as The possibility of pipeline leakage in the heat radiation dispute area.

[0126] What needs to be explained is that It is a linear normalization function that normalizes the data values ​​to the range [0, 1].

[0127] Repeat the steps to obtain the pipe leakage probability of all heat radiation dispute areas.

[0128] For all heat radiation dispute areas, when the pipeline leakage probability of the tth heat radiation dispute area is greater than the second preset threshold When , it is determined that there is a pipeline leakage in the tth heat radiation dispute area.

[0129] It should be noted that the second preset threshold for , take this as an example to illustrate.

[0130] It should be noted that when a pipeline leak is determined in the heat radiation dispute area, ultrasonic flaw detection equipment is used to conduct a detailed problem search in the leaking area, locate the leak point, and repair it. In this embodiment, when calculating the inverse or ratio, if the denominator is 0, it is set to 1 to ensure that the calculation is correct.

[0131] So far, the present invention is completed.

[0132] In summary, in an embodiment of the present invention, an infrared image of the surface of a chemical pipeline is collected, the infrared image of the chemical pipeline is divided into multiple windows, and the windows are merged according to the grayscale value differences of all pixels in the windows to obtain a thermal radiation dispute area. The grayscale values ​​of the pixels are then combined to determine the membership voting scores corresponding to the pixels, thereby determining all the thermal poles and subordinate pixels in the thermal radiation dispute area. The grayscale value changes of the pixels at different distances are then combined to determine the local abnormal temperature parameters of the multiple pixels, and finally determine the possibility of leakage in the thermal radiation dispute area. The present invention effectively reduces the difficulty of identifying possible leakage areas, thereby improving the accuracy of pipeline surface leakage monitoring, which is beneficial to the transportation and recovery of chemical materials and the intelligent detection of pipeline leakage.

[0133] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A chemical pipeline anti-leakage safety detection method, characterized in that: The method comprises the following steps: Acquire infrared images of chemical pipelines; The infrared image of the chemical pipeline is divided into several windows, and the windows are merged according to the grayscale value difference of the pixels between adjacent windows to obtain the thermal radiation dispute area; Determine the membership parameters of the neighboring pixels of each pixel in the thermal radiation dispute area and a number of subordinate pixels of each pixel according to the differences between the grayscale values ​​of the pixels in the thermal radiation dispute area; Determine the membership voting score corresponding to each pixel in the thermal radiation dispute area based on the membership parameter corresponding to each pixel point of its subordinate pixel points; and select several thermal poles from all pixels in the thermal radiation dispute area based on the membership voting score corresponding to each pixel point in the thermal radiation dispute area. Determine the local abnormal temperature parameter corresponding to the thermal extreme point according to the membership parameter corresponding to each pixel point and the gray value of the pixel point corresponding to the lower-level pixel point of the thermal extreme point; According to the local abnormal temperature parameters corresponding to all the thermal extreme points in the thermal radiation dispute area, it is judged whether there is leakage in the thermal radiation dispute area.

2. A chemical pipeline anti-leakage safety detection method according to claim 1, characterized in that: The infrared image of the chemical pipeline is divided into several windows, and the windows are merged according to the grayscale value difference of the pixels between adjacent windows to obtain the thermal radiation dispute area. The specific steps include the following: The infrared image of the chemical pipeline is divided into several equal parts of size Window, is the preset side length; For the A window is formed, and the grayscale mean of all pixels in the window is calculated and recorded as The grayscale mean of the window; According to the grayscale mean difference between adjacent windows, all the thermal radiation dispute windows in the infrared image of the chemical pipeline are determined; Adjacent heat radiation dispute windows constitute a heat radiation dispute area.

3. A chemical pipeline anti-leakage safety detection method according to claim 2, characterized in that: The specific steps of determining all thermal radiation dispute windows in the infrared image of the chemical pipeline based on the grayscale mean difference between adjacent windows are as follows: For the window, calculate the The first window and the The absolute value of the difference between the grayscale mean values ​​of each window adjacent to the window, if the The first window and the The absolute value of the difference between the grayscale means of all adjacent windows is greater than the first preset threshold. When This window is a heat radiation dispute window.

4. A chemical pipeline anti-leakage safety detection method according to claim 1, characterized in that: The method of determining the membership parameters of the neighboring pixels of each pixel in the thermal radiation dispute area and a number of subordinate pixels of each pixel in the thermal radiation dispute area based on the differences between the grayscale values ​​of the pixels in the thermal radiation dispute area includes the following specific steps: For the Thermal radiation dispute area, calculate the The gray value of the pixel is The first pixel in the eight-neighborhood The difference in the grayscale values ​​of pixels is recorded as the first grayscale difference; Calculate the The average grayscale value of the pixels in the eight neighborhoods of the pixel point is recorded as the first grayscale mean; For the The first pixel in the eight-neighborhood pixels, calculate the The average gray value of the pixel in the eight neighborhoods of the pixel is recorded as The first pixel in the eight-neighborhood The grayscale mean of the first neighborhood of pixels; For the pixel points, calculate the first grayscale mean and the The first pixel in the eight-neighborhood The difference between the grayscale means of the first neighborhood of the pixel point is recorded as The first pixel in the eight-neighborhood Pixels for the The membership degree of each pixel; Calculate the first preset selection weight The product of the grayscale difference and the first grayscale difference is recorded as the first product, and the second preset selection weight is calculated. With the The first pixel in the eight-neighborhood Pixels for the The product of the membership of the pixels is recorded as the second product, and the sum of the first product and the second product is calculated and recorded as the first product. The first pixel in the eight-neighborhood Pixels for the Membership parameters of pixels; For the All pixels in the eight neighborhoods of the pixel are The membership parameters of pixels are selected, and the first Pixels in the eight-neighborhood of a pixel As the first The first subordinate pixel of pixels; According to the The method of obtaining the first subordinate pixel of a pixel point is to obtain the pixel point The first lower-level pixel , as the first The second lower level pixel of the pixel, and then get the pixel The first lower-level pixel , as the first The third lower level pixel of the pixel; and so on, we can get several The lower level pixel of each pixel.

5. A chemical pipeline anti-leakage safety detection method according to claim 1, characterized in that: The method of determining the membership voting score corresponding to each pixel in the thermal radiation dispute area according to the membership parameters corresponding to each pixel corresponding to the subordinate pixel points of each pixel in the thermal radiation dispute area includes the following specific steps: For the pixels, calculate the All the lower level pixels of the pixel are The sum of the membership parameters of pixels is recorded as The membership value of the pixel point is calculated The number of all lower-level pixels of the pixel is the same as that of the The product of the membership values ​​of the pixels is recorded as the membership voting score.

6. A chemical pipeline anti-leakage safety detection method according to claim 1, characterized in that: The specific steps of selecting a plurality of thermal extreme points from all pixels in the thermal radiation dispute area according to the membership voting score corresponding to each pixel in the thermal radiation dispute area are as follows: For the For each heat radiation dispute area, the membership voting scores of all pixels are arranged in descending order to obtain the pixel membership voting score sequence; The first The value of one tenth of the area of ​​the heat radiation dispute area is rounded down and recorded as Recommended thermal extreme values ​​for thermal radiation dispute areas ; For the pixel membership voting score sequence, select the first Pixels as the first A thermal pole in a controversial area of ​​thermal radiation.

7. A chemical pipeline anti-leakage safety detection method according to claim 1, characterized in that: The specific steps of determining the local abnormal temperature parameter corresponding to the thermal extreme point according to the membership parameter corresponding to each pixel point and the gray value of the pixel point corresponding to the subordinate pixel point of the thermal extreme point are as follows: For the Lower-level pixels, according to the The membership parameters and grayscale values ​​of all subordinate pixels of the hot point are obtained. The internal weight of each subordinate pixel; For the The first hot point Lower-level pixel points, calculate the The internal weight of the lower level pixel is The product of the grayscale values ​​of the lower-level pixels is recorded as The temperature gradient of each lower-level pixel; Calculate the The difference between the grayscale value of the pixel point at the first thermal extreme and the temperature gradient is recorded as The grayscale value of the new pixel of the next level pixel; According to The grayscale values ​​of all the lower-level pixels of a thermal extreme point are used to determine the local abnormal temperature parameters corresponding to the thermal extreme point.

8. A chemical pipeline anti-leakage safety detection method according to claim 7, characterized in that: According to the The membership parameters and grayscale values ​​of all subordinate pixels of the hot point are obtained. The specific steps of calculating the in-group weight of each subordinate pixel are as follows: In the In the heat radiation dispute area, for the thermal extremes, calculate the All the lower level pixels of the hot point are The mean of the membership parameters of the hot extreme points is recorded as the first mean, and the The first hot point The lower level pixels are The ratio of the membership parameter of each hot point to the first mean is recorded as the relative membership parameter; For the thermal extremes, calculate the The first hot point and the The first hot point The reciprocal of the Euclidean distance between the subordinate pixels is recorded as distance correlation; For the The first hot point subordinate pixels, calculate the The grayscale value of the pixel point of the next lower level is the same as that of the The absolute value of the difference between the mean grayscale values ​​of the pixels in the eight neighborhoods of the lower-level pixel point is recorded as the second grayscale difference, and the reciprocal of the second grayscale difference is calculated and recorded as the temperature uniformity index; For the The first hot point The product of the relative membership parameter, distance correlation and temperature uniformity index is calculated for each lower-level pixel point, and recorded as The internal weight of each subordinate pixel.

9. A chemical pipeline anti-leakage safety detection method according to claim 7, characterized in that: According to the The grayscale values ​​of all lower-level pixels of a thermal extreme point are used to determine the local abnormal temperature parameters corresponding to the thermal extreme point, including the following specific steps: For the thermal extremes, calculate the All the lower level pixels of the hot spot are D8 distance between the two hot spots; Get the The maximum value Z of the D8 distance between all the lower-level pixels of the hot pole and the b-th hot pole is used to construct a preset distance sequence ; Filter out all subordinate pixels whose D8 distance is the first value in the preset distance sequence and record them as target subordinate pixels. Calculate the variance of the temperature gradient of all target subordinate pixels and record it as the temperature variance of the first value in the preset distance sequence. Calculate the mean grayscale value of all target subordinate pixels and record it as the grayscale mean of the first value in the preset distance sequence. Calculate the sum of the temperature variances of all values ​​in the preset distance sequence and record it as the temperature distribution consistency index; The sum of the absolute values ​​of the grayscale mean differences of all adjacent values ​​in the preset distance sequence is used as the temperature distribution rationality index; Calculate the ratio of the temperature distribution rationality index to the temperature distribution consistency index, record it as the first abnormal index, and write the first abnormal index The inverse proportional normalized value of is recorded as the local abnormal temperature parameter.

10. A chemical pipeline anti-leakage safety detection method according to claim 1, characterized in that: The specific steps of judging whether leakage occurs in the heat radiation dispute area based on the local abnormal temperature parameters corresponding to all the thermal extremes in the heat radiation dispute area are as follows: For the Thermal radiation dispute area, calculate the The normalized value of the mean of the local abnormal temperature parameters of all thermal extremes in the thermal radiation dispute area is recorded as The possibility of pipeline leakage in the heat radiation dispute area; For all heat radiation dispute areas, when the pipeline leakage probability of the tth heat radiation dispute area is greater than the second preset threshold When , it is determined that there is a pipeline leakage in the tth heat radiation dispute area.

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