Method for detecting heat insulation performance of pipeline cold insulation material

Abstract

The invention discloses a heat insulation performance detection method of a pipeline cold insulation material, and relates to the technical field of pipeline cold insulation, and the method comprises the following steps: preparation before detection, data acquisition, image data processing, heat insulation performance detection, and identification of temperature abnormal values in a temperature distribution image by using a local anomaly detection algorithm. Then, a heat insulation efficiency algorithm is used for evaluation to obtain heat insulation efficiency and uniformity evaluation of the pipeline cold insulation layer, and a heat insulation uniformity index and a detection report of the pipeline cold insulation layer are obtained through a uniformity evaluation algorithm in combination with the thickness, the temperature abnormal value and the heat insulation efficiency. According to the method, tiny temperature fluctuation can be captured, the detection accuracy of the heat insulation performance of the pipeline cold insulation layer is improved, the heat insulation uniformity index of the pipeline cold insulation layer is obtained through the uniformity evaluation algorithm in combination with the temperature abnormal value and the heat insulation efficiency, the heat insulation performance of the pipeline cold insulation layer is reflected more comprehensively, and the detection quality is improved.

Description

Technical Field

[0001] The invention relates to the technical field of pipeline cold insulation, and in particular to a method for detecting the thermal insulation performance of a pipeline cold insulation material. Background Art

[0002] Pipeline cold insulation materials are materials used to reduce the cold loss of cryogenic fluids during transportation, ensure that the outlet temperature of the medium meets the requirements, and save energy. These materials are mainly used for pipelines below room temperature. By covering the outside of the pipeline with insulation materials, the external heat is reduced from invading the inside, and condensation on the outer surface is prevented, thereby keeping the surface temperature of the pipeline above the dew point. Choosing appropriate cold insulation materials and structures is crucial to improving the cold insulation effect. It can significantly reduce the cold loss of cryogenic media during transportation, ensure that the outlet temperature of the medium meets the requirements, and save energy. In order to ensure the working effect of pipeline cold insulation materials, it is usually necessary to test their thermal insulation properties.

[0003] The current thermal insulation performance testing methods usually directly obtain the temperature distribution image of a large area of ​​the cold insulation material, which cannot accurately capture the slight changes in temperature distribution, resulting in low accuracy of the test data. The performance of the pipeline cold insulation layer is evaluated only by relying on the temperature data of the infrared thermal imager, which makes it difficult to fully reflect the key performance indicators of the pipeline cold insulation material, such as uniformity and thermal insulation efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a method for detecting the thermal insulation performance of pipeline cold insulation materials, which has the ability to identify temperature anomalies in a temperature distribution image through a local anomaly detection algorithm, can capture tiny temperature fluctuations, and improve the accuracy of thermal insulation performance detection of the pipeline cold insulation layer. The thermal insulation uniformity index of the pipeline cold insulation layer is obtained through a uniformity evaluation algorithm and combined with temperature anomalies and thermal insulation efficiency, which more comprehensively reflects the thermal insulation performance of the pipeline cold insulation layer, improves the detection quality, and solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for detecting the thermal insulation performance of a pipeline cold-insulating material, comprising the following steps: Preparation before testing: measure the length and thickness d of the pipeline insulation layer, and then install the temperature measuring instrument; Data collection: heat the pipeline insulation layer, use a temperature measuring instrument to fully scan the pipeline insulation layer, and collect images of the pipeline insulation layer multiple times at different time periods to obtain temperature distribution images of the pipeline insulation layer on the back and front sides of the fire; Image data processing: enhance and denoise the temperature distribution image to improve the quality of the temperature distribution image; Thermal insulation performance detection: Use the local anomaly detection algorithm to identify the temperature anomaly value LTA in the enhanced and denoised temperature distribution image, and set a threshold to determine whether it is an abnormal point. The abnormal point indicates that the thermal insulation performance of the pipeline insulation layer in this area is low. Then use the thermal insulation efficiency algorithm to evaluate the thermal insulation efficiency E of the pipeline insulation layer. Uniformity assessment: The thermal insulation uniformity index UAI of the pipeline insulation layer is obtained by combining the uniformity assessment algorithm with the thickness d, temperature anomaly value LTA and thermal insulation efficiency E to complete the thermal insulation performance test of the pipeline insulation layer; The inspection report records all inspection data and inspection environment information of the pipeline insulation layer, compiles it into an inspection report, and stores it.

[0006] Optionally, the temperature measuring instrument is an infrared thermal imager, which can detect the surface temperature of the pipeline insulation layer through infrared rays without direct contact with the pipeline insulation layer, thereby achieving a non-contact measurement effect without affecting the surface structure of the pipeline insulation layer.

[0007] Optionally, the local anomaly detection algorithm formula in the image data processing step is as follows:

[0008] Where (x, y) represents a pixel point; LTA(x,y) represents the temperature anomaly value of the pixel (x,y); T(x,y) is the temperature value of the pixel (x,y); It is the average temperature in the N*N neighborhood centered at (x,y); The standard deviation of temperature in a neighborhood of N*N centered at (x,y); By comparing the temperature difference between the pixel point (x, y) and its surrounding neighborhood, the sensitivity to temperature changes is improved, and tiny temperature fluctuations can be captured, and defects in the pipeline insulation layer can be discovered in time. The LTA (x, y) threshold is set to Y. When LTA (x, y) is greater than Y, the pixel point (x, y) is an abnormal point, and the abnormal point is displayed at the pixel level with high spatial resolution, which can accurately locate the specific location where the temperature anomaly occurs.

[0009] Optionally, the thermal insulation efficiency algorithm formula in the thermal insulation performance detection step is as follows: ;

[0010] E is the insulation efficiency; T amb is the ambient temperature; T back It is the average temperature of the non-fired surface of the pipeline insulation layer; T crit is the critical temperature; T fro is the average temperature on the front side of the pipe insulation layer; is the temperature influence coefficient, The initial value is 0.9; The thermal insulation efficiency algorithm expresses the thermal insulation efficiency E by comparing the temperature difference and expressing it in percentage form, making the result intuitive and easy to understand, and facilitating the understanding of the thermal insulation performance of the pipeline cold insulation layer.

[0011] Optionally, the uniformity evaluation algorithm formula in the uniformity evaluation step is as follows:

[0012] Where UAI is the insulation uniformity index; M is the number of areas divided on the surface of the pipeline insulation layer; LTA i is the set of LTA values ​​of all pixels (x, y) in the i-th region; MAX(LTA i ) is the maximum LTA value in the i-th region; MIN(LTA i ) is the minimum LTA value in the ith region; is the standard deviation of pipeline insulation thickness; is the average thickness of the pipeline insulation layer; E i is the insulation efficiency of the ith region; The smaller the insulation uniformity index UAI is, the better the insulation uniformity of the pipeline cold insulation layer is. Indicates the uniformity of the thickness of the pipeline insulation layer, which directly affects the insulation efficiency E. When it is greater than 0.6, the thermal insulation efficiency calculation formula The value changes, specifically: ; It is the adjustment coefficient, which is used to control the influence of the uniformity of the thickness of the pipeline insulation layer on the insulation efficiency E; When the thickness of the pipeline insulation layer is evenly distributed, The value is small, then The adjustment has little effect, but when the thickness of the pipeline insulation layer is uneven, The value is large, thus reducing the adjusted value, which reduces the insulation efficiency E, and ultimately affects the insulation uniformity index UAI.

[0013] Optionally, in order to ensure the accuracy of the temperature distribution image on both sides of the pipeline insulation layer in the data acquisition step, a semi-enclosed space that can control the temperature, humidity and wind speed is selected. The semi-enclosed space includes a laboratory. A thermometer, a hygrometer and anemometer are used to monitor the temperature, humidity and wind speed of the semi-enclosed space in real time. The environmental control equipment is adjusted according to the monitoring results. The environmental control equipment includes air conditioners and humidifiers, etc., so that the environmental parameters are stabilized within a predetermined range.

[0014] Optionally, in the image data processing step, the temperature distribution image enhancement processing selects a bilateral filtering method, and the image enhancement processing selects an adaptive histogram equalization method.

[0015] Optionally, the preparation before the inspection includes cleaning the pipeline cold insulation layer to remove dust and impurities to ensure that the surface of the pipeline cold insulation layer is clean.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention improves the sensitivity to temperature changes by comparing the temperature difference between the pixel point and its surrounding neighborhood through the local anomaly detection algorithm, can capture small temperature fluctuations, accurately identify local temperature anomalies, and promptly discover defects in the pipeline insulation layer. The thermal insulation efficiency algorithm expresses the thermal insulation efficiency E by comparing the temperature difference and in percentage form, and comprehensively considers the ambient temperature T amb , the back-fire surface temperature of the insulation material and the set critical temperature T crit factors, improve the accuracy of the thermal insulation efficiency E. Finally, the uniformity evaluation algorithm combines the temperature anomaly value LTA and the thermal insulation efficiency E, and considers the thickness factor of the pipeline cold insulation layer. It can more accurately evaluate the uniformity of the pipeline cold insulation layer, and does not rely solely on the temperature data of the infrared thermal imager for evaluation, making the thermal insulation performance detection of the pipeline cold insulation layer more comprehensive and accurate, and can fully reflect the thermal insulation performance of the pipeline cold insulation layer. At the same time, the thermal insulation efficiency E is adjusted in real time through the thickness factor of the pipeline cold insulation layer, strengthening the connection between algorithms and further improving the accuracy of the detection data. The above algorithms are interconnected and influence each other, and jointly improve the thermal insulation performance detection quality of pipeline cold insulation materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0019] See also Figure 1 This embodiment provides a method for detecting the thermal insulation performance of a pipeline cold insulation material, comprising the following steps: Preparation before testing: measure the length and thickness d of the pipeline insulation layer, and then install the temperature measuring instrument; Preparation before testing includes cleaning the pipe insulation layer, removing dust and impurities, and ensuring that the surface of the pipe insulation layer is clean; Data collection: heat the pipeline insulation layer, use a temperature measuring instrument to fully scan the pipeline insulation layer, and collect images multiple times at different time periods to obtain temperature distribution images on both sides of the pipeline insulation layer; In actual testing, the pipeline insulation layer is not only heated, but also cooled; Image data processing: enhance and denoise the temperature distribution image to improve the quality of the temperature distribution image; Thermal insulation performance detection: use the local anomaly detection algorithm to identify abnormal values ​​in the enhanced temperature distribution image, and set the abnormal threshold to identify whether it is an abnormal point. The abnormal point indicates that the thermal insulation performance of the pipeline insulation layer in this area is low. Then, the thermal insulation efficiency algorithm is used to evaluate the thermal insulation efficiency E of the pipeline insulation layer. Uniformity assessment: The thermal insulation uniformity index UAI of the pipeline insulation layer is obtained by combining the uniformity assessment algorithm with the thickness d, temperature anomaly value LTA and thermal insulation efficiency E to complete the thermal insulation performance test of the pipeline insulation layer; The inspection report records all inspection data and inspection environment information of the pipeline insulation layer, compiles it into an inspection report, and stores it.

[0020] More specifically, in this embodiment: by measuring the length of the pipeline cold insulation layer, and then installing the temperature measuring instrument, ensure that the temperature measuring instrument can cover the entire pipeline cold insulation layer to avoid missed detection, then collect images multiple times in different time periods to observe the trend of temperature change, and then enhance and denoise the temperature distribution image to improve the quality of the temperature distribution image, identify the temperature anomaly value LTA in the temperature distribution image through a local anomaly detection algorithm, and set a threshold Y to determine whether it is an abnormal point, and the abnormal point is displayed at the pixel level, which can capture tiny temperature fluctuations, thereby improving the accuracy of thermal insulation performance detection of the pipeline cold insulation layer, and the thermal insulation efficiency algorithm can directly reflect the thermal insulation efficiency of the pipeline cold insulation layer, and provide a quantitative evaluation standard for the performance of the pipeline cold insulation layer, so that the staff can understand the thermal insulation efficiency E of the pipeline cold insulation layer, and finally obtain the thermal insulation uniformity index UAI of the pipeline cold insulation layer through a uniformity evaluation algorithm and in combination with the temperature anomaly value LTA and the thermal insulation efficiency E, thereby more comprehensively reflecting the thermal insulation performance of the pipeline cold insulation layer and improving the detection effect.

[0021] Furthermore, the temperature measuring instrument is an infrared thermal imager, which can detect the surface temperature of the pipeline insulation layer through infrared rays without direct contact with the pipeline insulation layer, thereby achieving a non-contact measurement effect without affecting the surface structure of the pipeline insulation layer.

[0022] Specifically, infrared thermal imagers have a fast measurement speed and can usually obtain a temperature distribution image in a few milliseconds. In actual use, multiple sets of temperature distribution images of the pipeline insulation layer can be continuously obtained in a short period of time, and then compared, making it easier to determine the time when the change occurred for subsequent data analysis. Infrared thermometers have good adaptability to environmental conditions and are not affected by changes in light intensity or ambient lighting, thereby improving the stability of thermal insulation performance testing.

[0023] Furthermore, the local anomaly detection algorithm formula in the image data processing step is as follows: ;

[0024] Where (x, y) represents a pixel point; LTA(x,y) represents the temperature anomaly value of the pixel (x,y); T(x,y) is the temperature value of the pixel (x,y); It is the average temperature in the N*N neighborhood centered at (x,y); It is the standard deviation of temperature in a neighborhood of N*N centered at (x,y).

[0025] Specifically, by setting the LTA(x,y) threshold to Y to determine whether the pixel point (x,y) is an abnormal point, maintenance can be carried out on this area later. By comparing the temperature difference between the pixel point (x,y) and its surrounding neighborhood, the sensitivity to temperature changes is improved, and small temperature fluctuations can be captured, local temperature abnormalities can be accurately identified, and defects in the pipeline insulation layer can be discovered in time. The LTA(x,y) threshold is set to Y, and Y is determined according to actual conditions and experimental data to ensure that the real temperature abnormal points can be effectively identified and normal temperature fluctuations can be avoided from being misjudged as abnormalities. When LTA(x,y) is greater than Y, the pixel point (x,y) is an abnormal point. If there are many abnormal points in an area, it means that there are defects in this area of ​​the pipeline insulation layer, and the abnormal points are displayed at the pixel level, which has a high spatial resolution and can accurately locate the specific location where the temperature anomaly occurs, facilitating subsequent maintenance work.

[0026] Furthermore, the thermal insulation efficiency algorithm formula in the thermal insulation performance detection step is as follows: ;

[0027] E is the insulation efficiency; T amb is the ambient temperature; T back It is the average temperature of the non-fired surface of the pipeline insulation layer; T crit is the critical temperature; T fro is the average temperature on the front side of the pipe insulation layer; is the temperature influence coefficient, The initial value is 0.9; Specifically, the thermal insulation efficiency algorithm expresses the thermal insulation efficiency E by comparing the temperature difference and expressing it in percentage form, making the algorithm result intuitive and easy to understand, and facilitating the understanding of the thermal insulation performance of the pipeline insulation layer. In addition, the ambient temperature T is comprehensively considered in the calculation process. amb , the back-fire surface temperature of the insulation material and the set critical temperature T crit Factors such as the above can make the evaluation results more accurate and reliable, and can more truly reflect the thermal insulation performance of the pipeline cold insulation layer in actual applications.

[0028] Furthermore, the uniformity evaluation algorithm formula in the uniformity evaluation step is as follows:

[0029] Where UAI is the insulation uniformity index; M is the number of areas divided on the surface of the pipeline insulation layer; LTA i is the set of LTA values ​​of all pixels (x, y) in the i-th region; MAX(LTA i ) is the maximum LTA value in the i-th region; MIN(LTA i ) is the minimum LTA value in the i-th region; is the standard deviation of pipeline insulation thickness; is the average thickness of the pipeline insulation layer; E i is the insulation efficiency of the ith region The smaller the insulation uniformity index UAI is, the better the insulation uniformity of the pipeline cold insulation layer is. It indicates the uniformity of the thickness of the pipeline insulation layer, which will directly affect the insulation efficiency E. When it is greater than 0.6, the thermal insulation efficiency calculation formula The value changes, specifically: ; It is the adjustment coefficient, which is used to control the influence of the uniformity of the thickness of the pipeline insulation layer on the insulation efficiency E; When the thickness of the pipeline insulation layer is evenly distributed, The value is small, then The adjustment has little effect, but when the thickness of the pipeline insulation layer is uneven, The value is large, thus reducing the adjusted value, that is, the insulation efficiency E is reduced, which ultimately affects the insulation uniformity index UAI. By adjusting the insulation efficiency E data in real time according to the thickness uniformity of the pipeline insulation layer, the accuracy of the thermal insulation performance detection of the pipeline insulation layer can be further improved, and the connection between the algorithms can be strengthened.

[0030] Specifically, the uniformity evaluation algorithm can more accurately evaluate the uniformity of the pipeline insulation layer by combining the temperature anomaly value LTA and the insulation efficiency E, and considering key factors such as the thickness of the pipeline insulation layer. These quantitative indicators can capture tiny temperature fluctuations and material performance differences. The uniformity evaluation algorithm divides the pipeline insulation layer into multiple areas, allowing staff to fully understand the thermal insulation performance of each area of ​​the pipeline insulation layer, and then carry out subsequent integration. It does not rely solely on the temperature data of the infrared thermal imager for evaluation, making the thermal insulation performance detection of the pipeline insulation layer more comprehensive and accurate, and can fully reflect the thermal insulation performance of the pipeline insulation layer.

[0031] Furthermore, in the data collection step, in order to ensure the accuracy of the temperature distribution image on both sides of the pipeline insulation layer, a semi-enclosed space that can control the temperature, humidity and wind speed is selected. The semi-enclosed space includes a laboratory. A thermometer, a hygrometer and anemometer are used to monitor the temperature, humidity and wind speed of the semi-enclosed space in real time. The environmental control equipment is adjusted according to the monitoring results. The environmental control equipment includes air conditioners and humidifiers, etc., so that the environmental parameters are stabilized within a predetermined range.

[0032] Furthermore, by controlling temperature, humidity and wind speed, the interference of detection environment variables on temperature distribution images can be reduced. A stable detection environment ensures the consistency of data results, making the detection results more repeatable and reliable. Real-time monitoring of environmental data and use of environmental control equipment to adjust environmental parameters can ensure that the detection data is within a predetermined range, avoiding large errors in thermal insulation performance detection data due to environmental fluctuations, and improving the accuracy of thermal insulation performance detection of pipeline cold insulation materials.

[0033] Furthermore, in the image data processing step, the temperature distribution image enhancement process selects a bilateral filtering method, and the image enhancement process selects an adaptive histogram equalization method.

[0034] Specifically, compared with global histogram equalization, adaptive histogram equalization can perform histogram equalization on each local area of ​​the temperature distribution image, avoiding the problem of excessively high or low local contrast that may be caused by global equalization, and can better maintain the detail information of the temperature distribution image. Bilateral filtering can maintain edge information while removing noise, avoiding blurring of the temperature distribution image. Bilateral filtering combines spatial proximity and pixel value similarity, making the weight of pixels near the edge greater. By using bilateral filtering and adaptive histogram equalization to process the temperature distribution image, the quality of the temperature distribution image is effectively improved, and the accuracy of thermal insulation performance detection of the pipeline cold insulation layer is further improved.

[0035] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for testing the thermal insulation performance of pipeline cold insulation materials, characterized in that: The following steps are involved: Step S1: Preparation before testing, measuring the length and thickness d of the pipeline insulation layer, and then installing the temperature measuring instrument; Step S2: data collection, heating the pipeline cold insulation layer, using a temperature measuring instrument to fully scan the pipeline cold insulation layer, and collecting images of the pipeline cold insulation layer multiple times at different time periods to obtain temperature distribution images of the back-fire surface and the front surface of the pipeline cold insulation layer; Step S3: image data processing, enhancing and denoising the temperature distribution image to improve the quality of the temperature distribution image; Step S4: thermal insulation performance detection, using a local anomaly detection algorithm to identify the temperature anomaly value LTA in the enhanced and denoised temperature distribution image, and setting a threshold to determine whether it is an abnormal point. The abnormal point indicates that the thermal insulation performance of the pipeline cold insulation layer in this area is low, and then the thermal insulation efficiency algorithm is used to evaluate the thermal insulation efficiency E of the pipeline cold insulation layer; Step S5: uniformity assessment, using a uniformity assessment algorithm and combining the thickness d, temperature anomaly value LTA and thermal insulation efficiency E to obtain the thermal insulation uniformity index UAI of the pipeline cold insulation layer, and complete the thermal insulation performance test of the pipeline cold insulation layer; Step S6: Test report, recording all test data and test environment information of the pipeline cold insulation layer, compiling it into a test report, and storing it.

2. The method for detecting the thermal insulation performance of pipeline cold insulation materials according to claim 1, characterized in that: The temperature measuring instrument is an infrared thermal imager, which can detect the surface temperature of the pipeline insulation layer through infrared rays without direct contact with the pipeline insulation layer, thereby achieving a non-contact measurement effect without affecting the surface structure of the pipeline insulation layer.

3. The method for detecting the thermal insulation performance of pipeline cold insulation materials according to claim 2, characterized in that: The local anomaly detection algorithm formula in the image data processing step is as follows: ; Where (x, y) represents a pixel point; LTA (x, y) represents the temperature anomaly value of the pixel (x, y); T(x,y) is the temperature value of the pixel (x,y); is the average temperature in a neighborhood of N*N centered at (x,y); The standard deviation of temperature in a neighborhood of N*N centered at (x,y); By comparing the temperature difference between the pixel point (x, y) and its surrounding neighborhood, the sensitivity to temperature changes is improved, and it is possible to capture tiny temperature fluctuations and promptly discover defects in the pipeline insulation layer. The LTA (x, y) threshold is set to Y. When LTA (x, y) is greater than Y, the pixel point (x, y) is an abnormal point, and the abnormal point is displayed at the pixel level with high spatial resolution, which can accurately locate the specific location where the temperature anomaly occurs.

4. The method for detecting the thermal insulation performance of pipeline cold-insulating materials according to claim 3 is characterized in that: The thermal insulation efficiency algorithm formula in the thermal insulation performance detection step is as follows: ; E is the insulation efficiency; T amb is the ambient temperature; T back It is the average temperature of the non-fired surface of the pipeline insulation layer; T crit is the critical temperature; T fro is the average temperature on the front side of the pipe insulation layer; is the temperature influence coefficient, The initial value is 0.9; The thermal insulation efficiency algorithm expresses the thermal insulation efficiency E by comparing the temperature difference and expressing it in percentage form, making the result intuitive and easy to understand, and facilitating the understanding of the thermal insulation performance of the pipeline cold insulation layer.

5. The method for detecting the thermal insulation performance of pipeline cold-insulating materials according to claim 4, characterized in that: The uniformity evaluation algorithm formula in the uniformity evaluation step is as follows: ; Where UAI is the insulation uniformity index; M is the number of areas divided on the surface of the pipeline insulation layer; LTA i is the set of LTA values ​​of all pixels (x, y) in the i-th region; MAX(LTA i ) is the maximum LTA value in the i-th region; MIN(LTA i ) is the minimum LTA value in the i-th region; is the standard deviation of pipeline insulation thickness; is the average thickness of the pipeline insulation layer; E i is the insulation efficiency of the ith region; The smaller the insulation uniformity index UAI is, the better the insulation uniformity of the pipeline cold insulation layer is. Indicates the uniformity of the thickness of the pipeline insulation layer, which directly affects the insulation efficiency E. When it is greater than 0.6, the thermal insulation efficiency calculation formula The value changes, specifically: ; It is the adjustment coefficient, which is used to control the influence of the uniformity of the thickness of the pipeline insulation layer on the insulation efficiency E; When the thickness of the pipeline insulation layer is evenly distributed, The value is small, then The adjustment has little effect, but when the thickness of the pipeline insulation layer is uneven, The value is large, thus reducing the adjusted value, which reduces the insulation efficiency E, and ultimately affects the insulation uniformity index UAI.

6. The method for detecting the thermal insulation performance of pipeline cold insulation materials according to claim 1, characterized in that: In the data collection step, in order to ensure the accuracy of the temperature distribution image on both sides of the pipeline insulation layer, a semi-enclosed space capable of controlling temperature, humidity and wind speed is selected. The semi-enclosed space includes a laboratory. A thermometer, a hygrometer and anemometer are used to monitor the temperature, humidity and wind speed of the semi-enclosed space in real time. The environmental control equipment including air conditioners and humidifiers is adjusted according to the monitoring results to stabilize the environmental parameters within a predetermined range.

7. The method for testing the thermal insulation performance of pipeline cold insulation materials according to claim 1, characterized in that: In the image data processing step, the temperature distribution image enhancement process selects a bilateral filtering method, and the image enhancement process selects an adaptive histogram equalization method.

8. The method for testing the thermal insulation performance of pipeline cold-insulating materials according to claim 1, characterized in that: The preparation before the test includes cleaning the pipeline insulation layer, removing dust and impurities, and ensuring that the surface of the pipeline insulation layer is clean.

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