Testing technology of tensile and compressive properties of pipeline cold insulation materials

Abstract

The invention discloses a process for detecting the tensile and compressive properties of pipeline cold insulation materials, and relates to the technical field of pipeline tensile and compressive properties detection. An image acquisition module is used to acquire images of the surface of the pipeline cold insulation material, and contrast enhancement processing is performed on the image. A comprehensive image texture intensity unit is used to evaluate the comprehensive strength of the image, and a comprehensive strength evaluation value is outputted by using a comprehensive strength unit in the tensile and compressive process of the evaluation material. Based on the degradation rate and the strength evaluation value, and using a performance degradation prediction unit under a cycle, a degradation prediction value of the next tensile and compressive performance is calculated and outputted. According to the difference between the actual test of the tensile and compressive performance and the degradation prediction value, the relevant weights of the comprehensive evaluation image texture intensity unit are maintained and adjusted, so that the accuracy and reliability of the material performance evaluation are comprehensively improved, and a scientific basis is provided for the selection, design, use and maintenance of the material.

Description

Technical Field

[0001] The invention relates to the technical field of pipeline tension and compression performance detection, in particular to a pipeline cold insulation material tension and compression performance detection process. Background Art

[0002] Pipeline insulation materials play a vital role in various industrial systems, especially in situations where a low temperature environment needs to be maintained, such as the petroleum, chemical, and refrigeration industries. The tensile and compressive properties of these materials are directly related to the stability and safety of the pipeline system. Therefore, accurate performance testing is particularly important. With the advancement of science and technology, especially the rapid development of image processing technology and computer technology, the tensile and compressive performance testing method of pipeline insulation materials based on image analysis has gradually attracted attention. This method collects the surface image of the material non-contactly and uses image processing technology to extract the material's texture, strain and other characteristics, thereby achieving a rapid and accurate evaluation of the material's performance.

[0003] Traditional tensile and compressive performance testing often relies only on a single mechanical indicator, including maximum stress and fracture strain, while ignoring the impact of the important factor of material surface texture on performance. Its physical testing machine detection requires complex equipment, professional operators and a large number of samples, resulting in long testing cycles and high costs. In addition, traditional methods make it difficult to monitor the performance changes of materials in real time during use, and cannot effectively predict the performance degradation of materials under long-term use. In addition, the existing technology lacks a comprehensive evaluation system that combines texture characteristics, strain changes and comprehensive strength evaluation, and cannot fully reflect the tensile and compressive performance of materials. Summary of the invention

[0004] The purpose of the present invention is to provide a process for testing the tensile and compressive properties of pipeline cold insulation materials, thereby solving the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution, and the specific implementation steps are as follows: Step 1: Use the image acquisition module to acquire images of the surface of the pipeline cold insulation material, and perform contrast enhancement processing on the images; Step 2: Using the comprehensive evaluation image texture intensity unit, the initial and contrast-enhanced image texture intensities are calculated and output respectively, and the texture analysis method of the gray-level co-occurrence matrix is ​​used to calculate the evaluation result of the output texture direction, and finally the comprehensive image texture intensity is output in combination with the above results; Step 3-1. Use the comprehensive strength unit of the material during tension and compression to output a comprehensive strength evaluation value in combination with the current strain value measured during the tension and compression process of the material; Step 3-2, based on the strength evaluation value, calculate and output the degradation rate of the current texture degradation condition; Step 4: Based on the degradation rate and strength evaluation value, and using the cycle performance degradation prediction unit, calculate and output the degradation prediction value of the next tensile and compressive performance; Step 5: According to the difference between the actual test of tensile and compressive properties and the degradation prediction value, the relevant weights of the texture intensity units of the comprehensive evaluation image are maintained and adjusted; Step 6: Use the data analysis and reporting module to organize, analyze and generate reports on the above-mentioned test-related data.

[0006] Optionally, the equipment used by the image acquisition module includes a high-resolution camera and an image sensor; The equipment used by the comprehensive evaluation image texture strength unit, the comprehensive strength unit for evaluating the tension and compression process of the material and the performance degradation prediction unit under the cycle include a computer and image processing software, a strain gauge and a testing machine; The equipment used in the data analysis and reporting module includes a data acquisition card and data recording software; The high-resolution camera and image sensor are used for image acquisition; The computer and image processing software are used for image preprocessing, texture evaluation and comprehensive evaluation calculation; The strain gauge is used to measure the strain value of the material in the tension and compression process in real time; The testing machine is used to perform tension and compression tests on pipeline cold insulation materials; The data acquisition card and data recording software are used to record the data during the experiment and perform subsequent analysis.

[0007] Optionally, the calculation formula of the comprehensive evaluation image texture intensity unit is as follows: ; in: WQ is the comprehensive evaluation value of image texture intensity; WQ^ is the initial evaluation value of image texture intensity; WDB is the texture intensity evaluation value after contrast enhancement; WF is the texture directionality evaluation value; a, b and c are all weight coefficients, which are used to adjust the contribution of different evaluation values ​​in the comprehensive evaluation; N is the total number of pixels; HD i is the gray value of the i-th pixel; HD avg is the average grayscale value of pixels; is the gray value of the i-th pixel after contrast enhancement; is the average grayscale value of pixels after contrast enhancement; θ(i, j) is the angle value. θ(i, j) refers to the angle value θ used in calculating JZ(i, j, σ, θ). This angle defines the relative direction between pixel pairs, that is, the co-occurrence probability of pixel pairs (i, j) with a distance of δ in a specific direction of different angle values ​​θ. J avg is the average value of angle θ; JZ(i, j, σ, θ) is the co-occurrence probability, specifically the probability that a pixel with gray level i and a pixel with gray level j appear at the same time under the given distance σ and angle θ. This probability value reflects the spatial relationship between gray levels in the image, especially their co-occurrence in different directions and distances.

[0008] Optionally, the calculation formula of the comprehensive strength unit in the tension and compression process of the evaluation material is as follows: ZWQ=WQ×(1+DY / DY max )-(WQ / WQ max ) 0.5 ×DY; in: ZWQ is the comprehensive strength assessment value; DY is the current strain value; DY max is the maximum strain value, DY max Reflects the maximum strain value that the pipeline cold insulation material can withstand during the tension and compression process; WQ max is the maximum texture intensity evaluation value.

[0009] Optionally, based on the comprehensive strength evaluation value ZWQ, the calculation formula of the degradation rate of the current material texture as the number of cycles degrades is as follows: THZWQn→n+1=ZWQ n -ZWQ n+1 ; TL=(THZWQn→n+1 / ZWQ frist )×100%; THZWQn→n+1 is the degradation amount, and THZWQn→n+1 reflects the decrease in the comprehensive strength evaluation value ZWQ from cycle n to cycle n+1; TL is the texture degradation rate; ZGar n is the nth comprehensive strength evaluation value; QUR n+1 is the n+1th comprehensive strength evaluation value; QUR frist is the initial evaluation value of comprehensive strength, ZWQ frist It reflects the first comprehensive strength assessment value ZWQ of the material under cyclic testing.

[0010] Optionally, the calculation formula of the performance degradation prediction unit under the cycle is as follows: YT=[(TL×(S+1)) / (ZWQ frist -ZWQ)]×[(S+ZWQ frist / ZWQ) / (S+1)]; in: YT is the predicted value of performance degradation; S is the number of cycles, and S reflects the total number of tension and compression cycles that the material has experienced; Within the number of cycles S, any performance degradation prediction value YT is used to predict the degradation of the next material tensile and compressive performance and serves as a prediction and detection reference for actual tests.

[0011] Optionally, the prediction adjustment based on the performance degradation prediction value YT is as follows: If the predicted performance degradation value YT is lower than the actual test result value of the next test, it means that the performance degradation of the material is slow under the current tension-compression cycle and remains relatively stable during the cycle; If the predicted performance degradation value YT is higher than the actual test result value of the next time, it means that the material performance degrades rapidly under the current tension-compression cycle and changes significantly during the cycle. The weight coefficients a, b and c should be increased.

[0012] Optionally, the test sample of the actual test and the sample used for the performance degradation prediction value YT must have the same material properties, size and processing technology, and be tested multiple times, and the average value is taken to compare with the performance degradation prediction value YT.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention comprehensively considers the influence of material surface texture on tensile and compressive properties by comprehensively evaluating multiple dimensions of image texture intensity evaluation introduced by the image texture intensity unit, texture intensity evaluation after contrast enhancement, and texture directionality evaluation. This multi-dimensional evaluation method can more accurately reflect the actual performance of the material.

[0014] 2. The cycle performance degradation prediction unit in the present invention converts the initial evaluation value ZWQ of the comprehensive strength into frist , comprehensive strength evaluation value ZWQ, texture degradation rate TL and number of cycles S are taken into comprehensive consideration, and a comprehensive strength evaluation model based on texture and strain changes is established. This model can monitor the performance changes of materials in real time and predict their performance degradation under long-term use.

[0015] 3. Compared with traditional physical testing machine detection, the present invention utilizes image processing technology to achieve a non-contact, fast, and low-cost detection method, which greatly reduces the detection cost, shortens the detection cycle, and improves the detection efficiency.

[0016] 4. The present invention can guide the design and selection of materials more scientifically through a comprehensive evaluation model. For example, in the material research and development stage, the texture characteristics of the material can be optimized by adjusting the formula and process parameters, thereby improving its tensile and compressive properties. In the material selection stage, the material with the best comprehensive performance can be selected according to the needs of the specific application scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the method for testing the tensile and compressive properties of pipeline cold insulation materials; Figure 2 This is a schematic diagram of the evaluation structure of the comprehensive evaluation image texture intensity unit; Figure 3 It is a schematic diagram of the structure of the loop feedback 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.

[0019] Regarding the tensile and compressive performance testing process of the pipeline cold insulation material, it is different from the traditional testing method. The traditional testing method often relies on physical testing machines to directly measure the mechanical response of the material. However, this method has the disadvantages of high cost, long time consumption, and difficulty in fully reflecting the impact of the surface microstructure of the material on the performance. The algorithm unit introduces multi-dimensional evaluation indicators, establishes a comprehensive evaluation model, and optimizes the detection method, which effectively solves the problems and shortcomings of the existing technology and provides a more scientific, efficient and low-cost solution for the tensile and compressive performance testing of pipeline cold insulation materials.

[0020] For example, see Figures 1 to 3 This implementation provides a pipeline cold insulation material tensile and compressive performance testing process, and the specific implementation steps are as follows: Step 1: Use the image acquisition module to acquire images of the surface of the pipeline cold insulation material, and perform contrast enhancement processing on the images; Step 2: Using the comprehensive evaluation image texture intensity unit, the initial and contrast-enhanced image texture intensities are calculated and output respectively, and the texture analysis method of the gray-level co-occurrence matrix is ​​used to calculate the evaluation result of the output texture direction, and finally the comprehensive image texture intensity is output in combination with the above results; Step 3-1. Use the comprehensive strength unit of the material during tension and compression to output a comprehensive strength evaluation value in combination with the current strain value measured during the tension and compression process of the material; Step 3-2, based on the strength evaluation value, calculate and output the degradation rate of the current texture degradation condition; Step 4: Based on the degradation rate and strength evaluation value, and using the cycle performance degradation prediction unit, calculate and output the degradation prediction value of the next tensile and compressive performance; Step 5: According to the difference between the actual test of tensile and compressive properties and the degradation prediction value, the relevant weights of the texture intensity units of the comprehensive evaluation image are maintained and adjusted; Step 6: Use the data analysis and reporting module to organize, analyze and generate reports on the above test-related data; The equipment used in the image acquisition module includes a high-resolution camera and an image sensor, which are used for image acquisition; The equipment used in the comprehensive evaluation image texture strength unit, the comprehensive strength unit in the process of evaluating material tension and compression, and the performance degradation prediction unit under cycles include computers and image processing software, strain gauges and testing machines. The computer and image processing software are used for image preprocessing, texture evaluation and calculation of comprehensive evaluation. The strain gauge is used to measure the strain value of the material in the process of tension and compression in real time. The testing machine is used to perform tension and compression tests on pipeline cold insulation materials. The equipment used in the data analysis and reporting module includes data acquisition cards and data recording software, which are used to record data during the experiment and perform subsequent analysis.

[0021] In this embodiment, the system comprehensively improves the accuracy and reliability of material performance evaluation through the mutual cooperation of three algorithm units and the combination of the three operation results of WQ, ZWQ and YT, provides a scientific basis for the selection, design, use and maintenance of materials, and promotes the progress of material science research and development. WQ is a comprehensive evaluation value of image texture intensity. When combining the texture intensity after contrast enhancement with the texture directionality evaluation result, it can balance the influence of the two on the final evaluation result by setting different weight coefficients a, b and c. ZWQ is a comprehensive strength evaluation value, which can more comprehensively reflect the performance of the material under stress. YT is a performance degradation prediction value, which is predicted based on the cumulative effect of multiple cycles, thereby providing guidance for the long-term use and maintenance of the material. The calculation result of YT can also affect the calculation of WQ and ZWQ, so that the three algorithms of this system have a high correlation and entanglement, so that the overall algorithm system can be closer to reality by automatic feedback and optimization according to actual conditions.

[0022] See also Figures 1 to 3 , the calculation formula for the comprehensive evaluation image texture intensity unit is as follows: ; in: WQ is the comprehensive evaluation value of image texture intensity; WQ^ is the initial evaluation value of image texture intensity; WDB is the texture intensity evaluation value after contrast enhancement; WF is the texture directionality evaluation value; a, b and c are all weight coefficients, which are used to adjust the contribution of different evaluation values ​​in the comprehensive evaluation; N is the total number of pixels; HD i is the gray value of the i-th pixel; HD avg is the average grayscale value of pixels; is the gray value of the i-th pixel after contrast enhancement; is the average grayscale value of pixels after contrast enhancement; θ(i, j) is the angle value. θ(i, j) refers to the angle value θ used in calculating JZ(i, j, σ, θ). This angle defines the relative direction between pixel pairs, that is, the co-occurrence probability of pixel pairs (i, j) with a distance of δ in a specific direction of different angle values ​​θ. J avg is the average value of angle θ; JZ(i, j, σ, θ) is the co-occurrence probability, specifically the probability that a pixel with gray level i and a pixel with gray level j appear at the same time under the given distance σ and angle θ. This probability value reflects the spatial relationship between gray levels in the image, especially their co-occurrence in different directions and distances.

[0023] In this embodiment: First, the algorithm unit calculates a value for comprehensively evaluating the image texture intensity based on the initial image texture intensity evaluation value WQ^, the contrast enhanced texture intensity evaluation value WDB and the texture directionality evaluation value WF, and in combination with the weight coefficients a, b and c; in, The calculation formula provides a highly quantitative evaluation standard for the surface texture strength of the material by accurately calculating the variance of the pixel grayscale value in the image. This quantitative method not only reduces the influence of subjective judgment, but also makes the evaluation results more objective and accurate. In the early stage of material research and development, it enables it to quickly screen out materials with potential excellent tensile and compressive properties, providing strong support for subsequent in-depth research. avg As a benchmark, it ensures that the texture strength assessment results of materials are comparable under different lighting conditions, shooting angles and equipment resolutions. This stability is crucial for data sharing and comparative analysis across laboratories and equipment, and helps promote the standardization and internationalization of materials science. The calculation formula significantly improves the visibility of subtle textures in images through contrast enhancement technology, allowing evaluators to analyze the microstructural characteristics of materials more deeply. This in-depth analysis helps to reveal the intrinsic connection between material performance and microstructure, providing a scientific basis for the optimization of material performance. It not only improves the accuracy of texture strength evaluation, but also makes the evaluation results closer to the actual performance of the material. In application scenarios that require high-precision evaluation, such as aerospace and nuclear energy, the application of texture strength evaluation values ​​after contrast enhancement can ensure reliable evaluation of material performance and provide strong protection for engineering safety. The calculation formula comprehensively evaluates the directional characteristics of the material surface texture by calculating the directional parameters in the gray-level co-occurrence matrix. This evaluation helps to deeply understand the anisotropic properties of the material, that is, the different mechanical properties that the material may exhibit in different directions. This is of great significance for predicting the response of the material under different force directions and optimizing material design. By introducing the contrast-enhanced texture intensity evaluation value and texture directionality evaluation value WF, not only the basic intensity of the texture is taken into account, but also the contrast and directionality of the texture, so that the surface characteristics of the material can be more comprehensively reflected. This comprehensive evaluation helps to more accurately understand the physical and chemical properties of the material and provide stronger support for subsequent performance testing; Contrast enhancement processing can improve the visual effect of the image, making the originally difficult-to-distinguish texture details clearer, thereby improving the accuracy of texture intensity assessment. At the same time, the evaluation of texture directionality reveals the regularity of texture spatial distribution, which is of great significance for understanding the microstructure and mechanical properties of materials. Based on the comprehensive evaluation value WQ of image texture intensity based on various texture evaluation results, the surface structure of the material can be optimized to improve its tensile and compressive properties and other related properties. For example, by adjusting the production process and surface treatment method of the material, its texture characteristics can be changed to meet specific application requirements.

[0024] See also Figures 1 to 3 , the calculation formula for evaluating the comprehensive strength unit of the material in the tension and compression process is as follows: ZWQ=WQ×(1+DY / DY max )-(WQ / WQ max ) 0.5 ×DY; in: ZWQ is the comprehensive strength assessment value; DY is the current strain value; DY max is the maximum strain value, DY max Reflects the maximum strain value that the pipeline cold insulation material can withstand during the tension and compression process; WQ max is the maximum texture intensity evaluation value.

[0025] In this embodiment, first; This algorithm unit combines the initial evaluation value WQ^ of the image texture strength, the evaluation value WDB of the texture strength after contrast enhancement, and the evaluation value WF of the texture directionality with the strain value to achieve a comprehensive evaluation of the comprehensive strength of the material in the tension and compression process. This evaluation method not only considers the influence of the surface texture characteristics of the material on the performance, but also considers the strain response of the material in the stress process. This comprehensiveness makes the evaluation result closer to the actual performance of the material and provides a reliable reference for engineering applications. By measuring the current strain value DY in real time and substituting it into the comprehensive strength unit for evaluating the material during tension and compression, dynamic monitoring of the comprehensive strength of the material during tension and compression can be achieved. This real-time performance helps to promptly discover abnormal changes in material properties, predict material failure behavior, and provide early warning for engineering safety. At the same time, dynamic monitoring data can also provide feedback support for the continuous optimization of material performance.

[0026] See also Figures 1 to 3 , the calculation formula of the performance degradation prediction unit under the cycle is as follows: YT=[(TL×(S+1)) / (ZWQ frist -ZWQ)]×[(S+ZWQ frist / ZWQ) / (S+1)]; in: YT is the predicted value of performance degradation; S is the number of cycles, and S reflects the total number of tension and compression cycles that the material has experienced; Within the number of cycles S, any performance degradation prediction value YT is used to predict the degradation of the next material tensile and compressive performance and serves as a prediction and detection reference for actual tests.

[0027] In this embodiment, the algorithm unit achieves accurate prediction of the performance degradation of the material during long-term use by introducing the parameters of the texture degradation rate TL and the number of cycles S. This prediction capability helps to evaluate the durability of the material and provide a scientific basis for formulating a reasonable maintenance plan. At the same time, accurate performance degradation prediction also helps to reduce engineering risks and economic losses caused by material failure. This embodiment can optimize the selection and design of materials by comparing the performance degradation prediction values ​​YT of different materials under the same conditions. For example, materials with lower performance degradation rates can be selected for application scenarios that need to withstand tensile and compressive stresses for a long time. This optimization not only improves the efficiency and economy of material use, but also promotes the innovation and development of materials science.

[0028] See also Figures 1 to 3 , the prediction adjustment based on the performance degradation prediction value YT is as follows: If the predicted performance degradation value YT is lower than the actual test result value of the next test, it means that the performance degradation of the material is slow under the current tension-compression cycle and remains relatively stable during the cycle; If the predicted performance degradation value YT is higher than the actual test result value of the next test, it means that the performance of the material degrades rapidly under the current tension-compression cycle and changes significantly during the cycle. When the weight coefficients a, b and c are increased; The test samples used in the actual test and the samples used for the performance degradation prediction value YT must have the same material properties, dimensions and processing technology, and be tested multiple times, and the average value is taken to compare with the performance degradation prediction value YT.

[0029] In this embodiment, the algorithm unit makes it possible to evaluate the performance of the material under multiple tension and compression cycles based on the cycle number S parameter. By continuously substituting the new cycle number into the formula and calculating the performance degradation prediction value YT, the performance degradation of the material after multiple cycles can be deeply evaluated. This in-depth analysis helps to reveal the performance change law of the material during long-term use and provides important information for the continuous optimization of material performance. The calculation result of the performance degradation prediction value YT can be fed back into the evaluation process of the comprehensive evaluation image texture intensity unit. For example, if it is found that the performance of a certain material is seriously degraded after multiple cycles, the changes in its surface texture characteristics can be further analyzed. This feedback and optimization mechanism not only helps to reveal the internal mechanism of material performance degradation, but also provides a scientific basis for the optimization of material surface treatment processes and the selection of more suitable material formulas. Through this continuous improvement and optimization mechanism, the tensile and compressive properties and durability of the material can be continuously improved to meet the higher requirements of engineering applications for material performance. By comparing and verifying the predicted results of the performance degradation prediction value YT with the actual test results, the deviations in the prediction model can be discovered in time, and these deviations can be corrected by adjusting the evaluation parameters or improving the model. This continuous feedback loop mechanism helps to continuously improve the accuracy of the prediction, making the prediction results closer to the actual situation. The loop feedback of this embodiment also promotes the continuous optimization of the evaluation method. In each cycle, based on the new experimental data and the performance degradation prediction value YT, the evaluation parameters in the comprehensive evaluation image texture intensity unit, such as the calculation method of the weight coefficients a, b and c, can be adjusted to better reflect the actual performance changes of the material. This optimization helps to improve the scientificity and effectiveness of the evaluation method. Materials may exhibit different performance degradation characteristics under different use conditions. The circular feedback mechanism enables the performance degradation prediction value YT model to be dynamically adjusted according to the actual test results, thereby enhancing its adaptability to different use conditions and material properties. This adaptability is the key to ensuring that the model maintains accurate prediction capabilities in complex and changing engineering environments. Through circular feedback, a large amount of experimental data and prediction results can be accumulated. These data provide strong support for data-driven decision-making. For example, companies can formulate reasonable maintenance plans and replacement cycles based on the performance degradation prediction value YT to reduce operating costs and improve equipment efficiency. The circular feedback mechanism not only helps to optimize existing evaluation methods and prediction models, but also stimulates new technological innovations. In the process of solving prediction bias and performance degradation problems, new ideas and methods will be generated. These new ideas and methods may promote technological progress in related fields of materials science and mechanical engineering. Ultimately, the circular feedback mechanism helps to improve product quality and reliability by improving prediction accuracy and optimizing evaluation methods. Companies can more accurately understand the performance degradation of products and take corresponding measures to delay the degradation process or increase the degradation threshold, thereby extending the service life of products and improving user satisfaction. In summary, loop feedback has many beneficial effects in the performance degradation prediction value YT process. It not only improves the prediction accuracy and model adaptability, but also promotes the optimization of evaluation methods and the development of technological innovation, ultimately improving product quality and reliability.

[0030] For example 2, please refer to Figures 1 to 3 , based on the comprehensive strength evaluation value ZWQ, the calculation formula of the degradation rate of the current material texture with the number of cycles is as follows: THZWQn→n+1=ZWQ n -ZWQ n+1 ; TL=(THZWQn→n+1 / ZWQ frist )×100%; THZWQn→n+1 is the degradation amount, and THZWQn→n+1 reflects the decrease in the comprehensive strength evaluation value ZWQ from cycle n to cycle n+1; TL is the texture degradation rate; ZGar n is the nth comprehensive strength evaluation value; ZGar n+1 is the n+1th comprehensive strength evaluation value; ZGar frist is the initial evaluation value of comprehensive strength, ZWQ frist It reflects the first comprehensive strength assessment value ZWQ of the material under cyclic testing.

[0031] In this embodiment, by calculating the degradation rate after each cycle, the changing trend of material performance can be monitored in real time. This real-time monitoring helps to find signs of performance degradation in time, so that intervention measures can be taken at an early stage to prevent further deterioration of performance. Continuous calculation of the degradation rate can provide accurate information about the degradation rate, which helps to understand the degradation behavior of the material at different cycle stages and which factors will have a significant impact on the degradation rate. If the experiment involves multiple conditions, calculating the degradation rate for each cycle can easily compare the degradation behavior under different conditions. This comparison helps to reveal which conditions are most sensitive to material performance degradation, thereby guiding material design and process optimization. In material science research, it is often necessary to establish a mathematical model to predict the performance degradation behavior of the material. By calculating the degradation rate for each cycle and comparing the actual measured value with the model predicted value, the accuracy and reliability of the model can be verified. This verification is crucial for improving the model and improving the prediction accuracy. Based on the degradation rate data of each cycle, the effectiveness and efficiency of the current test plan can be evaluated. If the degradation rate data is found to fluctuate greatly and is difficult to interpret, the test plan needs to be adjusted, including adding measurement points and changing the measurement method to obtain more accurate and reliable results. Understanding the degradation behavior of materials is crucial in material selection, process optimization or product design. Calculating the degradation rate for each cycle can provide decision makers with important information about the stability and reliability of material performance, thus supporting more informed decision making. In summary, recalculating the current degradation rate at each cycle is of great significance for gaining a deeper understanding of material performance degradation behavior, optimizing test plans, validating model predictions, and supporting decision making.

[0032] 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 can be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The tensile and compressive performance testing process of pipeline cold insulation materials is characterized by: The specific implementation steps are as follows: Step 1: Use the image acquisition module to acquire images of the surface of the pipeline cold insulation material, and perform contrast enhancement processing on the images; Step 2: Using the comprehensive evaluation image texture intensity unit, the initial and contrast-enhanced image texture intensities are calculated and output respectively, and the texture analysis method of the gray-level co-occurrence matrix is ​​used to calculate the evaluation result of the output texture direction, and finally the comprehensive image texture intensity is output in combination with the above results; Step 3-1. Use the comprehensive strength unit of the material during tension and compression to output a comprehensive strength evaluation value in combination with the current strain value measured during the tension and compression process of the material; Step 3-2, based on the strength evaluation value, calculate and output the degradation rate of the current texture degradation condition; Step 4: Based on the degradation rate and strength evaluation value, and using the cycle performance degradation prediction unit, calculate and output the degradation prediction value of the next tensile and compressive performance; Step 5: According to the difference between the actual test of tensile and compressive properties and the degradation prediction value, the relevant weights of the texture intensity units of the comprehensive evaluation image are maintained and adjusted; Step 6: Use the data analysis and reporting module to organize, analyze and generate reports on the above-mentioned test-related data.

2. The process for testing the tensile and compressive properties of pipeline cold insulation materials according to claim 1 is characterized in that: The equipment used in the image acquisition module includes a high-resolution camera and an image sensor; The equipment used by the comprehensive evaluation image texture strength unit, the comprehensive strength unit for evaluating the tension and compression process of the material and the performance degradation prediction unit under the cycle include a computer and image processing software, a strain gauge and a testing machine; The equipment used in the data analysis and reporting module includes a data acquisition card and data recording software.

3. The process for testing the tensile and compressive properties of pipeline cold insulation materials according to claim 2 is characterized by: The high-resolution camera and image sensor are used for image acquisition; The computer and image processing software are used for image preprocessing, texture evaluation and comprehensive evaluation calculation; The strain gauge is used to measure the strain value of the material in the tension and compression process in real time; The testing machine is used to perform tension and compression tests on pipeline cold insulation materials; The data acquisition card and data recording software are used to record the data during the experiment and perform subsequent analysis.

4. The process for testing the tensile and compressive properties of pipeline cold insulation materials according to claim 3 is characterized by: The calculation formula of the comprehensive evaluation image texture intensity unit is as follows: ； in: WQ is the comprehensive evaluation value of image texture intensity; WQ^ is the initial evaluation value of image texture intensity; WDB is the texture intensity evaluation value after contrast enhancement; WF is the texture directionality evaluation value; a, b and c are all weight coefficients, which are used to adjust the contribution of different evaluation values ​​in the comprehensive evaluation; N is the total number of pixels; HD i is the gray value of the i-th pixel; HD avg is the average grayscale value of pixels; is the gray value of the i-th pixel after contrast enhancement; is the average grayscale value of pixels after contrast enhancement; θ(i, j) is the angle value. θ(i, j) refers to the angle value θ used in calculating JZ(i, j, σ, θ). This angle defines the relative direction between pixel pairs, that is, the co-occurrence probability of pixel pairs (i, j) with a distance δ in a specific direction with different angle values ​​θ. J avg is the average value of angle θ; JZ(i, j, σ, θ) is the co-occurrence probability, specifically the probability that a pixel with gray level i and a pixel with gray level j appear at the same time under the given distance σ and angle θ. This probability value reflects the spatial relationship between gray levels in the image.

5. The process for testing the tensile and compressive properties of pipeline cold insulation materials according to claim 4 is characterized by: The calculation formula of the comprehensive strength unit in the tension and compression process of the evaluation material is as follows: ZWQ=WQ×(1+DY / DY max )-(WQ / WQ max ) 0.5 ×DY; in: ZWQ is the comprehensive strength assessment value; DY is the current strain value; DY max is the maximum strain value, DY max Reflects the maximum strain value that the pipeline cold insulation material can withstand during the tension and compression process; WQ max is the maximum texture intensity evaluation value.

6. The process for testing the tensile and compressive properties of pipeline cold insulation materials according to claim 5, characterized in that: Based on the comprehensive strength evaluation value ZWQ, the calculation formula of the degradation rate of the current material texture as the number of cycles degrades is as follows: THZWQn→n+1=ZWQ n -ZWQ n+1 ; <h2 style=";text-align:left;direction:ltr">TL=(THZWQn→n+1 / ZWQ<h2 style=";text-align:left;direction:ltr"> frist <h2 style=";text-align:left;direction:ltr"> )×100%; THZWQn→n+1 is the degradation amount, and THZWQn→n+1 reflects the decrease in the comprehensive strength evaluation value ZWQ from cycle n to cycle n+1; TL is the texture degradation rate; QUR n is the nth comprehensive strength evaluation value; QUR n+1 is the n+1th comprehensive strength evaluation value; QUR frist is the initial evaluation value of comprehensive strength, ZWQ frist It reflects the first comprehensive strength assessment value ZWQ of the material under cyclic testing.

7. The process for testing the tensile and compressive properties of pipeline cold insulation materials according to claim 6, characterized in that: The calculation formula of the performance degradation prediction unit under the cycle is as follows: YT=[(TL×(S+1)) / (ZWQ frist -ZWQ)]×[(S+ZWQ frist / ZWQ) / (S+1)]; in: YT is the predicted value of performance degradation; S is the number of cycles, and S reflects the total number of tension and compression cycles that the material has experienced; Within the number of cycles S, any performance degradation prediction value YT is used to predict the degradation of the next material tensile and compressive performance and serves as a prediction and detection reference for actual tests.

8. The process for testing the tensile and compressive properties of pipeline cold insulation materials according to claim 7, characterized in that: The prediction adjustment based on the performance degradation prediction value YT is as follows: If the predicted performance degradation value YT is lower than the actual test result value of the next test, it means that the performance degradation of the material is slow under the current tension-compression cycle and remains relatively stable during the cycle; If the predicted performance degradation value YT is higher than the actual test result value of the next time, it means that the material performance degrades rapidly under the current tension-compression cycle and changes significantly during the cycle. The weight coefficients a, b and c should be increased.

9. The process for testing the tensile and compressive properties of pipeline cold insulation materials according to claim 8, characterized in that: The test samples of the actual test and the samples used for the performance degradation prediction value YT must have the same material properties, dimensions and processing technology, and be tested multiple times, and the average value is taken to compare with the performance degradation prediction value YT.

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