Insulation blanket performance detection and evaluation method based on macro-micro fusion analysis

Abstract

The invention discloses an insulation blanket performance detection and evaluation method based on macro-micro fusion analysis, and relates to the field of insulation material performance tests.The insulation blanket performance detection and evaluation method comprises the steps that S1, multi-temperature mechanical performance detection is conducted on an insulation test sample based on temperature control, and a first performance detection result is obtained; s2, carrying out microscopic performance detection on the insulation test sample subjected to multi-temperature treatment based on a multi-element physical and chemical test, obtaining physical and chemical test data, and processing the data to obtain a second performance detection result; s3, performing fusion calculation on the first performance detection result and the second performance detection result to obtain a comprehensive evaluation result; the performance of an insulation test sample is comprehensively evaluated by obtaining a first performance detection result representing macroscopic mechanical performance and fusing the first performance detection result with a second performance detection result representing the influence degree of the microstructure change of the insulation sample to be tested on the insulation performance and mechanical performance of the insulation sample. And the performance evaluation efficiency and the comprehensiveness and accuracy of the evaluation result are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of insulation material performance testing, and in particular to an insulation blanket performance detection and evaluation method based on macro-micro fusion analysis. Background Art

[0002] As a material widely used in the power and electronics industries, EVA insulation blankets are favored for their excellent insulation properties and good flexibility. In power systems, EVA insulation blankets are often used for insulation shielding in live operations to ensure the safety of operators and equipment. However, high temperature environments can significantly affect the performance of EVA insulation blankets, causing degradation of their mechanical and electrical properties, and even problems such as heat melting and mechanical damage, which affect their normal use and safety performance. Therefore, in-depth research on the performance changes of EVA insulation blankets after experiencing high temperature conditions is of great significance for improving the reliability and safety of power systems.

[0003] Traditional EVA insulation blanket performance evaluation methods often rely on a single test method, resulting in low detection accuracy, such as only evaluating its mechanical properties through macroscopic mechanical performance tests, or only evaluating its molecular structure changes through microscopic chemical structure analysis. Although these methods can provide certain information, they cannot fully reflect the comprehensive performance of EVA insulation blankets at high temperatures. For example, macroscopic mechanical performance tests can provide data such as tensile strength and elongation of materials, but cannot reveal the changes in the chemical structure inside the material; and although microscopic chemical structure analysis can provide information at the molecular level, it often ignores the mechanical performance of the material in practical applications. If the traditional mechanical performance test is combined with the chemical structure analysis, it is equivalent to superimposing the two tests, which prolongs the detection time, reduces the detection efficiency, and complicates the operation and cannot guarantee the detection accuracy.

[0004] Chinese patent, publication number: CN201589844U, publication date: February 2, 2021, discloses a high tensile strength aluminum alloy flexible cable and its preparation method, using an insulation extruder to make an insulated wire core from an annealed aluminum alloy conductor, and then conducting a spark breakdown test on the insulated wire core, using a shielding unit to perform secondary processing on the qualified insulated wire core, and then performing quality inspection on the shielded insulated wire core. However, this scheme only detects the insulation performance of the insulation blanket from a microscopic perspective through breakdown, and cannot obtain the macroscopic mechanical properties of the insulation blanket and the microscopic effect on the mechanical properties, and has poor adaptability.

[0005] Chinese patent, publication number: CN118758839A, publication date: October 11, 2024, discloses an accelerated thermal oxidative aging test method for thermoplastic cable insulation materials, by measuring the melting temperature and aging activation energy of the thermoplastic cable insulation materials, subjecting the thermoplastic cable insulation materials to an accelerated thermal oxidative aging test at temperature T and in an air flow environment, determining the accelerated thermal oxidative aging test time, and the thermal oxidative aging life at the working temperature T, and characterizing the test endpoint by the elongation at break or the carboxyl index of the thermoplastic cable insulation materials, which can further shorten the test cycle and improve the accuracy of the test results; however, this scheme only detects the heat resistance of the insulation material at the test temperature, and cannot comprehensively evaluate the performance changes of the EVA insulation blanket after experiencing high temperature conditions. Summary of the invention

[0006] The purpose of the present invention is to address the problem that the existing insulation blanket performance detection technology lacks a fusion analysis of mechanical properties and chemical structure changes, resulting in low detection efficiency and poor effect; an insulation blanket performance detection and evaluation method based on macro-micro fusion analysis is proposed, by performing mechanical property tests on insulation test samples in multiple temperature environments, a first performance test result characterizing the macroscopic mechanical performance is obtained; after performing multivariate physical and chemical tests on the insulation test samples, the physical and chemical test data are processed to obtain a second performance test result characterizing the influence of microstructural changes on the insulation properties and mechanical properties of the insulation test samples to be tested; based on the first performance test result and the second performance test result, the performance of the insulation test sample is comprehensively evaluated, which significantly improves the performance evaluation efficiency and the comprehensiveness and accuracy of the evaluation results.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for detecting and evaluating the performance of an insulation blanket with macro-micro fusion analysis, comprising the following steps: S1. Performing a multi-temperature mechanical property test on the insulation test sample based on temperature control to obtain a first performance test result; S2. Based on multivariate physical and chemical tests, microscopic performance tests are performed on the insulation test samples that have been subjected to multiple temperatures, and the physical and chemical test data are obtained and processed to obtain a second performance test result; S3. Fusion calculation is performed on the first performance test result and the second performance test result to obtain a comprehensive evaluation result.

[0008] In this scheme, by conducting mechanical property tests at multiple temperatures (different high temperatures) on the insulation test samples, the first performance test results obtained can more accurately evaluate the ductility and mechanical strength of the insulation test samples after being subjected to high temperature conditions; based on multivariate physical and chemical tests, the chemical structure changes of the insulation test samples in a high temperature environment are detected from a microscopic perspective, and the molecular structure information provided by the chemical structure changes is used to evaluate the factors affecting the mechanical properties, and obtain the second performance test results, which is helpful to comprehensively and accurately reflect the mechanical performance of the insulation material in practical applications; the first performance test results and the second performance test results are integrated to achieve a macro-micro combination of evaluation of the performance of the insulation blanket in a high temperature environment, which solves the problem of low detection efficiency and poor effect due to the lack of fusion analysis of mechanical properties and chemical structure changes, and greatly improves the performance evaluation efficiency and the comprehensiveness and accuracy of the evaluation results.

[0009] Preferably, S1 comprises the following sub-steps: Obtain an insulation test sample of an insulation blanket that has undergone non-stop power supply operation, mark the positions thereof in accordance with the boundary spacing at both ends, perform a tensile test in a multi-temperature test environment, and record the first distance value between the marked positions; Calculating the elongation at break of the insulation test sample according to the tensile test; The first performance test result characterizing the macroscopic mechanical properties of the insulation test sample is calculated based on the elongation at break and the corresponding force at break.

[0010] Preferably, the calculating the elongation at break of the insulation test sample according to the tensile test comprises: Obtaining a second distance value between the marked positions when the insulation test sample is broken, and calculating a difference between the second distance value and the first distance value; The elongation at break is calculated based on the percentage of the ratio of the difference to the first distance value.

[0011] Preferably, the calculation formula of the first performance test result is as follows: In the formula, represents the first performance test result, S represents the elongation at break under the tensile test, and F D It represents the breaking force corresponding to the breaking elongation, F0 represents the initial breaking force standard value of the insulation blanket at room temperature, S0 represents the initial breaking elongation standard value of the insulation blanket at room temperature, α1 represents the breaking force correction factor in the breaking test, and α2 represents the breaking elongation correction factor in the breaking test.

[0012] Preferably, S2 comprises the following sub-steps: S21, performing a multivariate physical and chemical test on the insulation test sample at multiple temperatures to obtain physical and chemical test data; S22, performing microscopic performance evaluation calculation according to the physical and chemical test data to obtain a microscopic performance evaluation factor characterizing the influence of microstructural changes on insulation performance; S23, performing a mechanical property impact assessment based on the physical and chemical test data to obtain a mechanical property impact factor characterizing the impact of microstructure changes on mechanical properties; S24. Use the microscopic performance evaluation factor and the mechanical performance influencing factor as the second performance test result.

[0013] Preferably, the S21 includes the following sub-steps: Perform a first physical and chemical test on the insulation test sample based on Fourier transform infrared spectroscopy to generate a first test graph to obtain first physical and chemical test data, wherein the physical and chemical data at least includes a molecular structure; Perform a second physical and chemical test on the insulation test sample based on differential scanning calorimetry to obtain second physical and chemical test data, wherein the second physical and chemical test data at least includes a glass transition temperature, a melting temperature, and a crystallinity; Performing a third physical and chemical test on the insulation test sample based on X-ray diffraction, generating a second test graph to obtain third physical and chemical test data, wherein the third physical and chemical test data at least includes a crystal structure and a grain size of the insulation test sample; Performing a fourth physical and chemical test on the insulation test sample based on dynamic mechanical thermal analysis, generating a third test graph to obtain fourth physical and chemical test data, wherein the fourth physical and chemical test data at least includes a complex modulus of the insulation test sample; A fifth physical and chemical test is performed on the insulation test sample based on a field emission scanning electron microscope to generate a fourth test graph to identify the surface morphology change characteristics of the insulation test sample and obtain the surface roughness of the insulation test sample.

[0014] Preferably, the S22 comprises the following sub-steps: Extracting the absorbance peak within the test band based on the first physical and chemical test data; Assigning first weight parameters to the absorbance peak, the second physical and chemical test data, and the third physical and chemical test data respectively; calculating the microscopic properties of the insulation test sample based on the first weight parameters combined with the physical and chemical test data to obtain the microscopic property evaluation factor; The calculation formula of the micro-performance evaluation factor is as follows: Wherein, η is the microscopic performance evaluation factor, ω1 represents the correction factor for microscopic performance evaluation, represents the absorbance peak value, wherein j represents the four bands of the first physical and chemical test, and the value is (j=1, 2, 3, 4), i represents the two band intervals of the four bands for obtaining the absorbance peak value, and the value is (i=1, 2), χ is the crystallinity in the second physical and chemical test data, D hkl is the grain size of the insulation test sample perpendicular to the crystal plane, D0 represents the grain size of the insulation blanket after heat treatment at 20°C, γ1 is the first weight parameter of the absorbance peak, γ2 is the first weight parameter of the crystallinity, and γ3 is the first weight parameter of the grain size. It represents the two corresponding absorbance standard values ​​of the insulation blanket in the four bands, and χ0 represents the crystallinity of the insulation blanket under heat treatment at 20℃.

[0015] Preferably, the S23 includes the following sub-steps: Extracting storage modulus based on the fourth physical and chemical test data; assigning a second weight parameter to the storage modulus and the surface roughness; The mechanical property influencing factor is calculated based on the second weight parameter combined with the storage modulus and the surface roughness; The calculation formula of the mechanical property influencing factor is as follows: In the formula, is the mechanical property influencing factor, E D is the storage modulus, υ is the surface roughness, E0 is the standard value of the storage modulus for the insulation blanket to ensure normal mechanical properties, υ0 is the standard value of the surface roughness for the insulation blanket to ensure normal mechanical properties, β1 represents the correction factor for the storage modulus, and β2 represents the correction factor for the surface roughness.

[0016] Preferably, the calculation formula of the comprehensive evaluation result is as follows: Where e is the base of the natural logarithm, δ1 is the macro correction factor for the mechanical properties of the insulation blanket, and δ2 is the micro correction factor for the mechanical properties of the insulation blanket.

[0017] Preferably, the method further comprises: A temperature warning mechanism is configured based on the performance changes of the insulation test sample under multiple temperature environments, and a safety warning is issued when the test temperature exceeds the target temperature or / and the stress of the insulation test sample exceeds the target breaking force threshold; In response to the safety warning, a detection environment of the insulation test sample is regulated.

[0018] Beneficial effects of the present invention: 1. Through mechanical property testing, the mechanical performance of the insulation blanket can be evaluated from a macroscopic perspective. The first performance test results obtained can more accurately evaluate the ductility and mechanical strength of the insulation test sample after being subjected to high temperature conditions; 2. Through multi-element physical and chemical tests, the chemical structure changes of the insulation test samples under high temperature environment can be detected from a microscopic perspective, and the factors affecting the mechanical properties can be evaluated based on the molecular structure information provided by the chemical structure changes, which helps to fully and accurately reflect the mechanical performance of the insulation materials in practical applications; 3. By integrating the first performance test results and the second performance test results, a macro-micro combination of evaluation of the performance of the insulation blanket in a high temperature environment can be achieved, and it can be more accurately judged whether the performance of the insulation blanket after high-temperature work meets the requirements for re-operation. This not only simplifies the complex operation of the insulation blanket performance test, but also provides a more scientific evaluation basis for material selection, design and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0020] Figure 1 The present invention is a flowchart of a method for detecting and evaluating the performance of an insulation blanket based on macro-micro fusion analysis according to an embodiment of the present invention.

[0021] Figure 2 A schematic diagram of a position mark of an insulation test sample according to an embodiment of the present invention.

[0022] Figure 3 This is a comparison chart of multi-temperature macroscopic mechanical property test results of an embodiment of the present invention.

[0023] Figure 4 This is a structural block diagram of a macro-micro fusion analysis according to an embodiment of the present invention.

[0024] Figure 5 This is a field emission scanning electron microscope test image of a multi-temperature insulating toilet sample according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] Example 1: Figure 1 As shown, a method for detecting and evaluating the performance of an insulation blanket based on macro-micro fusion analysis includes steps S1-S3, wherein: S1. Perform multi-temperature mechanical property testing on the insulation test sample based on temperature control to obtain a first performance test result.

[0027] In this embodiment, the mechanical properties testing step includes measuring the tensile elongation and tensile force at break on a series of pre-temperature treated EVA insulation blanket samples (here, insulation test samples) to evaluate their ductility and mechanical strength after being subjected to high temperature conditions, wherein the pre-temperature treatment is performed by controlling the test temperature to different high temperature values.

[0028] Specifically, S1 includes the following sub-steps: Obtain an insulation test sample of an insulation blanket that has undergone non-stop power supply operation, mark the positions thereof in accordance with the boundary spacing at both ends, perform a tensile test in a multi-temperature test environment, and record the first distance value between the marked positions; Calculating the elongation at break of the insulation test sample according to the tensile test; The first performance test result characterizing the macroscopic mechanical properties of the insulation test sample is calculated based on the elongation at break and the corresponding force at break.

[0029] Specifically, the calculating the elongation at break of the insulation test sample according to the tensile test includes: Obtaining a second distance value between the marked positions when the insulation test sample is broken, and calculating a difference between the second distance value and the first distance value; The elongation at break is calculated based on the percentage of the ratio of the difference to the first distance value.

[0030] In this embodiment, the elongation at break can be expressed by the following calculation formula: Wherein, S is the elongation at break, L0 is the distance between the two marks before breaking (the first distance value), and L is the distance between the two marks after breaking (the second distance value).

[0031] Furthermore, the calculation formula of the first performance test result is as follows: In the formula, represents the first performance test result, S represents the elongation at break under the tensile test, and F D It represents the breaking force corresponding to the breaking elongation, F0 represents the initial breaking force standard value of the insulation blanket at room temperature, S0 represents the initial breaking elongation standard value of the insulation blanket at room temperature, α1 represents the breaking force correction factor in the breaking test, and α2 represents the breaking elongation correction factor in the breaking test.

[0032] In this embodiment, the breaking force correction factor is mainly used to adjust or correct the breaking force value applied by the insulation blanket sample in the breaking test. Due to the differences in the breaking test conditions, the breaking force application accuracy and other factors, the breaking force applied at the corresponding test temperature has errors, which in turn leads to inaccurate calculation results of the breaking elongation, which may not accurately reflect the true mechanical strength of the insulation blanket. By introducing the breaking force correction factor, these differences can be compensated, thereby providing more accurate and reliable breaking force data. Similarly, the calculation of the breaking elongation is determined by the mark spacing before breaking and the mark spacing after breaking. Considering the quality differences caused to the insulation blanket by factors such as different manufacturers, different batches and raw materials, the stress conditions of different insulation blankets under the same breaking force may be different. Therefore, the calculated elongation is corrected by the breaking elongation correction factor to achieve compensation for the calculation error, thereby obtaining more accurate and reliable elongation data, further ensuring the accuracy of mechanical property evaluation, and improving the reliability and adaptability of macro detection.

[0033] As an implementation method, Figure 2 and Figure 3 As shown, by selecting two axisymmetric positions in the test sample as marking points, the breaking test effect can be clearly reflected by comparing the spacing between the marking positions before and after the breaking test, so as to more accurately calculate the breaking force and breaking elongation, and realize the quantification of the ductility and mechanical strength of the test sample.

[0034] Furthermore, a specific performance threshold can be set according to the safety requirements for using the insulation blanket, and then the first performance test result can be compared with the performance threshold to determine whether the current EVA insulation blanket is suitable for next use, thereby ensuring the safety of power operations.

[0035] S2. Based on multivariate physical and chemical tests, microscopic performance tests are performed on the insulation test samples that have undergone multiple temperatures, and the physical and chemical test data are obtained and processed to obtain the second performance test results.

[0036] Specifically, S2 includes the following sub-steps: S21, performing a multivariate physical and chemical test on the insulation test sample at multiple temperatures to obtain physical and chemical test data; S22, performing microscopic performance evaluation calculation according to the physical and chemical test data to obtain a microscopic performance evaluation factor characterizing the influence of microstructural changes on insulation performance; S23, performing a mechanical property impact assessment based on the physical and chemical test data to obtain a mechanical property impact factor characterizing the impact of microstructure changes on mechanical properties; S24. Use the microscopic performance evaluation factor and the mechanical performance influencing factor as the second performance test result.

[0037] Specifically, the S21 includes the following sub-steps: Perform a first physical and chemical test on the insulation test sample based on Fourier transform infrared spectroscopy to generate a first test graph to obtain first physical and chemical test data, wherein the physical and chemical data at least includes a molecular structure; Perform a second physical and chemical test on the insulation test sample based on differential scanning calorimetry to obtain second physical and chemical test data, wherein the second physical and chemical test data at least includes a glass transition temperature, a melting temperature, and a crystallinity; Performing a third physical and chemical test on the insulation test sample based on X-ray diffraction, generating a second test graph to obtain third physical and chemical test data, wherein the third physical and chemical test data at least includes a crystal structure and a grain size of the insulation test sample; Performing a fourth physical and chemical test on the insulation test sample based on dynamic mechanical thermal analysis, generating a third test graph to obtain fourth physical and chemical test data, wherein the fourth physical and chemical test data at least includes a complex modulus of the insulation test sample; A fifth physical and chemical test is performed on the insulation test sample based on a field emission scanning electron microscope to generate a fourth test graph to identify the surface morphology change characteristics of the insulation test sample and obtain the surface roughness of the insulation test sample.

[0038] In this embodiment, the chemical structure analysis of the insulation test sample is carried out by Fourier Transform Infrared Spectrometer (FTIR) test, and the advantage of being able to non-destructively detect the molecular structure and chemical bonding state of the insulation blanket is utilized to identify the changes in functional groups in the sample, so as to evaluate the effect of high temperature on the stability of functional groups in the chemical structure of the EVA insulation blanket, thereby evaluating the heat resistance and chemical stability of the EVA insulation blanket.

[0039] The Differential Scanning Calorimetry (DSC) test can provide important information about the thermal performance of the insulation blanket, help understand the thermal stability of the EVA insulation blanket at high temperatures, and help evaluate the heat resistance grade and operating temperature range of the insulation blanket based on parameters such as the glass transition temperature.

[0040] X-ray diffraction (XRD) tests can provide an observable understanding of the crystal structure and grain size changes of the insulation blanket, which helps to understand the impact of high temperature on the microstructure of the EVA insulation blanket, and then by analyzing the changes in the crystal structure, the mechanical properties and stability of the insulation blanket can be evaluated.

[0041] The dynamic mechanical analysis (DMA) was used to simulate the multi-temperature working environment and measure the storage modulus and mechanical loss factor, which helps to understand the elasticity and rigidity of the EVA insulation blanket at high temperature and further evaluate the durability and service life of the insulation blanket.

[0042] The surface micromorphology of the insulation test sample was observed by scanning electron microscope (SEM), which can intuitively observe the surface micromorphology changes of the insulation blanket, help to understand the influence of high temperature on the surface structure of the EVA insulation blanket, so as to quantify the surface roughness of the insulation blanket into a specific value, and realize the evaluation of the surface quality and wear resistance of the insulation blanket.

[0043] Specifically, the S22 includes the following sub-steps: Extracting the absorbance peak within the test band based on the first physical and chemical test data; Assigning first weight parameters to the absorbance peak, the second physical and chemical test data, and the third physical and chemical test data respectively; calculating the microscopic properties of the insulation test sample based on the first weight parameters combined with the physical and chemical test data to obtain the microscopic property evaluation factor; The calculation formula of the micro-performance evaluation factor is as follows: Wherein, η is the microscopic performance evaluation factor, ω1 represents the correction factor for microscopic performance evaluation, represents the absorbance peak value, wherein j represents the four bands of the first physical and chemical test, and the value is (j=1, 2, 3, 4), i represents the two band intervals of the four bands for obtaining the absorbance peak value, and the value is (i=1, 2), χ is the crystallinity in the second physical and chemical test data, D hklis the grain size of the insulation test sample perpendicular to the crystal plane, D0 represents the grain size of the insulation blanket after heat treatment at 20°C, γ1 is the first weight parameter of the absorbance peak, γ2 is the first weight parameter of the crystallinity, and γ3 is the first weight parameter of the grain size. It represents the two corresponding absorbance standard values ​​of the insulation blanket in the four bands, and χ0 represents the crystallinity of the insulation blanket under heat treatment at 20℃.

[0044] Further, the crystallinity calculation formula is as follows: In the formula, ΔH f is the melting enthalpy, It is the equivalent melting enthalpy when the crystallinity is 100%, and its value is 277 / J / g.

[0045] Furthermore, the grain size change of the crystalline region of the insulation test sample is calculated using the Scherrer equation to calculate the grain size of different diffraction peaks. The calculation formula is as follows: Wherein, λ is the wavelength of X-rays, which is 0.154 nm in the experiment, FWHM is the half-maximum width of the diffraction peak, is the Bragg angle, and K is a constant.

[0046] In this embodiment, the microscopic performance evaluation factor is calculated by using the absorbance peak, crystallinity and grain size. It is possible to reflect the chemical stability of the insulation blanket in a high temperature environment from the changes in functional groups at the molecular level of the insulation blanket, reflect the thermal stability of the insulation blanket from the crystallinity and obtain the heat resistance grade, and reflect the changes in the crystal structure from the changes in the grain size and obtain the mechanical performance of the insulation blanket, thereby achieving a more comprehensive and accurate microscopic performance evaluation of the insulation blanket. Specifically, the S23 includes the following sub-steps: Extracting storage modulus based on the fourth physical and chemical test data; assigning a second weight parameter to the storage modulus and the surface roughness; The mechanical property influencing factor is calculated based on the second weight parameter combined with the storage modulus and the surface roughness; The calculation formula of the mechanical property influencing factor is as follows: In the formula, is the mechanical property influencing factor, E Dis the storage modulus, υ is the surface roughness, E0 is the standard value of the storage modulus for the insulation blanket to ensure normal mechanical properties, υ0 is the standard value of the surface roughness for the insulation blanket to ensure normal mechanical properties, β1 represents the correction factor for the storage modulus, and β2 represents the correction factor for the surface roughness.

[0047] S3. Fusion calculation is performed on the first performance test result and the second performance test result to obtain a comprehensive evaluation result.

[0048] In this embodiment, the mechanical property influencing factor is calculated by the storage modulus and the surface roughness. The elasticity and rigidity of the insulation blanket at high temperature can be accurately reflected according to the storage modulus, and the current surface wear degree of the insulation blanket can be accurately reflected according to the surface roughness, thereby providing reliable data support for the evaluation of wear resistance, ensuring the accuracy of the evaluation, and further realizing the evaluation of mechanical properties from a microscopic perspective.

[0049] Specifically, the calculation formula of the comprehensive evaluation result is as follows: Where e is the base of the natural logarithm, δ1 is the macro correction factor for the mechanical properties of the insulation blanket, and δ2 is the micro correction factor for the mechanical properties of the insulation blanket.

[0050] In this embodiment, by integrating the first performance test result and the second performance test result, a comprehensive evaluation of the macro and micro performance changes of the EVA insulation blanket in a high temperature environment is achieved, providing a scientific basis for the safe operation of the power system and ensuring the safety and reliability of the use of the EVA insulation blanket.

[0051] Furthermore, the configuration of the first weight parameter and the second weight parameter can determine the degree of association between the test data according to the correlation between them, and then set the weight value of each test data according to the value of the degree of association, and dynamically adjust it based on the different changes in the chemical structure corresponding to the test data at different high temperatures, so as to fully consider the quality and reliability of each test data, improve the accuracy of the first performance test results and the second performance test results, and the adaptability of the macro-micro evaluation method. For example, data with strong correlation can be assigned a lower weight to avoid repeated calculations; while data with weak correlation can be assigned a higher weight to fully reflect its uniqueness; or, according to the importance of the test data in the evaluation target, the weight value can be set, for example, data with a greater impact on the evaluation results should be assigned a higher weight, and data with a smaller impact on the evaluation results can be assigned a lower weight.

[0052] As an implementation method, Figure 4 As shown in the figure, the specific steps of the insulation blanket performance detection and evaluation method based on macro-micro fusion analysis are as follows: Step 1: According to the mechanical test requirements in the DL / T 803-2015 standard, samples were collected from the EVA insulation blanket that had been subjected to non-stop operation, and the elongation at break and the force at break were measured. The elongation at break S and the force at break FD of the sample at different temperatures were measured and substituted into formula (2) to calculate the macroscopic mechanical property evaluation factor (the first performance test result) to evaluate its mechanical properties; Step 2: Conduct FTIR tests on EVA insulation blanket samples that have undergone different high-temperature treatments, identify functional group changes in the samples through infrared spectra (first test graph), evaluate the effect of high temperature on the chemical structure of EVA insulation blankets, and extract the absorbance in four bands, and select two interval absorbance peaks respectively; Step 3: Perform a DSC test on the EVA insulation blanket sample to determine its thermal performance parameters such as glass transition temperature, melting temperature and crystallinity. Analyze the thermal stability and thermal performance changes of the sample through the DSC curve; Step 4: Perform an XRD test on the EVA insulation blanket sample to obtain an X-ray diffraction pattern (the second test pattern), analyze the crystal structure and grain size changes of the EVA insulation blanket sample, and evaluate the effect of high temperature on the microstructure of the EVA insulation blanket; Step 5: Perform a DMA test on the EVA insulation blanket sample to measure the storage modulus and mechanical loss factor of the EVA insulation blanket sample, and evaluate the changes in elasticity and rigidity of the sample at high temperature through the DMA curve (the third test figure); Step 6: Perform SEM test on the EVA insulation blanket sample, through SEM image (the fourth test figure, such as Figure 5 The surface microstructure changes of the EVA insulation blanket samples were observed and the surface roughness values ​​were obtained to evaluate the effect of high temperature on the surface structure of the EVA insulation blanket. Step 7: Substitute the peak value of the absorbance obtained in step 2, the crystallinity measured in step 3, and the grain size of the insulating blanket perpendicular to the crystal plane obtained in step 4 into formula (3), and assign corresponding weight parameters to calculate the micro-performance evaluation factor; Step 8: Substitute the storage modulus in step 5 and the surface roughness of the insulation blanket sample in step 6 into formula (6), assign weight parameters to each data object, and calculate the micromechanical property evaluation factor (mechanical property influencing factor); Step 9: Substitute the macroscopic mechanical property evaluation factor, microscopic performance evaluation factor, and microscopic mechanical property evaluation factor into formula (7) and fuse them to obtain the comprehensive evaluation result.

[0053] In this embodiment, by conducting systematic mechanical property tests and physical and chemical tests at multiple predetermined temperatures, the comprehensive performance of the EVA insulation blanket in a high temperature environment can be comprehensively evaluated, thereby providing more accurate performance data for the use of the insulation blanket. This precise evaluation method ensures that the performance of the EVA insulation blanket in a high temperature environment is accurately monitored, significantly improving the safety and reliability of the use of the material. Through the automated performance testing and early warning system, the operator's reliance on the performance judgment of the EVA insulation blanket in a high temperature environment is reduced, and the safety risks caused by human factors are reduced. The automated testing method and early warning mechanism that integrates mechanical property detection, microscopic performance evaluation, and microscopic evaluation of mechanical property influencing factors can ensure that timely measures are taken when the performance of the EVA insulation blanket exceeds the safety threshold, protect the operator from the influence of the high temperature environment, and improve the safety of the operation process.

[0054] Specifically, the method further includes: A temperature warning mechanism is configured based on the performance changes of the insulation test sample under multiple temperature environments, and a safety warning is issued when the test temperature exceeds the target temperature or / and the stress of the insulation test sample exceeds the target breaking force threshold; In response to the safety warning, a detection environment of the insulation test sample is regulated.

[0055] As an implementation method, a temperature warning system is set according to the performance changes of the EVA insulation blanket in a high temperature environment. When the temperature exceeds the preset temperature, or the force exceeds the set threshold, or the first performance test result exceeds the performance threshold, the system will automatically issue a warning to prompt the user to take corresponding safety measures, thereby avoiding performance degradation and safety accidents caused by high temperature.

[0056] Furthermore, temperature, elongation at break and breaking force are selected as key monitoring parameters. By real-time monitoring and analysis of these parameters, the performance of the EVA insulation blanket during use is controlled. Specifically, when any parameter exceeds the preset safety range, the system will automatically issue an early warning, prompting you to take necessary safety measures. For example, at a specific temperature a°C, if the measured value of the elongation at break reaches b%, the system will automatically control the breaking force not to exceed cN to ensure the safe use of the EVA insulation blanket. This method effectively prevents the degradation of the performance of the EVA insulation blanket in high temperature environments, reduces material loss, and improves the safety of non-stop operations.

[0057] Beneficial effects of the embodiment: by performing mechanical property tests at multiple temperatures (different high temperatures) on the insulation test sample, the first performance test result obtained can more accurately evaluate the ductility and mechanical strength of the insulation test sample after being subjected to high temperature conditions; based on multivariate physical and chemical tests, the chemical structure changes of the insulation test sample in a high temperature environment are detected from a microscopic perspective, and the molecular structure information provided by the chemical structure changes is used to evaluate the factors affecting the mechanical properties, and the second performance test result is obtained, which helps to comprehensively and accurately reflect the mechanical performance of the insulation material in practical applications; the first performance test result and the second performance test result are integrated to achieve a macro-micro combination evaluation of the performance of the insulation blanket in a high temperature environment, which solves the problem of low detection efficiency and poor effect due to the lack of fusion analysis of mechanical properties and chemical structure changes, and significantly improves the efficiency of performance evaluation and the comprehensiveness and accuracy of the evaluation results. Through the accurate evaluation of the performance of EVA insulation blankets in high temperature environments, more reliable insulation material use guidance can be provided for the power system. Based on the data-driven method in this embodiment, the power system's ability to respond to changes in the performance of EVA insulation blankets is improved, ensuring the stable operation of the power system, reducing power outages caused by degradation of the performance of insulation blankets, and improving the reliability of power supply. Among them, by configuring the temperature warning mechanism and setting the performance threshold, the performance degradation of the EVA insulation blanket can be identified in time, thereby avoiding safety accidents caused by performance degradation. This method not only reduces the premature scrapping of EVA insulation blankets due to high temperature, but also extends the service life of EVA insulation blankets through accurate performance monitoring, reducing the operation and maintenance costs of the power system.

[0058] The above specific embodiments are preferred embodiments of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the present specific embodiments. All equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. The insulation blanket performance detection and evaluation method based on macro-micro fusion analysis is characterized by: The steps include: S1. Performing a multi-temperature mechanical property test on the insulation test sample based on temperature control to obtain a first performance test result; S2. Based on multivariate physical and chemical tests, microscopic performance tests are performed on the insulation test samples that have been subjected to multiple temperatures, and the physical and chemical test data are obtained and processed to obtain a second performance test result; S3. Fusion calculation is performed on the first performance test result and the second performance test result to obtain a comprehensive evaluation result.

2. The insulation blanket performance detection and evaluation method based on macro-micro fusion analysis according to claim 1 is characterized in that: The S1 comprises the following sub-steps: Obtain an insulation test sample of an insulation blanket that has undergone non-stop power supply operation, mark the positions thereof in accordance with the boundary spacing at both ends, perform a tensile test in a multi-temperature test environment, and record the first distance value between the marked positions; Calculating the elongation at break of the insulation test sample according to the tensile test; The first performance test result characterizing the macroscopic mechanical properties of the insulation test sample is calculated based on the elongation at break and the corresponding force at break.

3. The insulation blanket performance detection and evaluation method based on macro-micro fusion analysis according to claim 2 is characterized in that: The calculating the elongation at break of the insulation test sample according to the tensile test comprises: Obtaining a second distance value between the marked positions when the insulation test sample is broken, and calculating a difference between the second distance value and the first distance value; The elongation at break is calculated based on the percentage of the ratio of the difference to the first distance value.

4. The insulation blanket performance detection and evaluation method based on macro-micro fusion analysis according to claim 3 is characterized in that: The calculation formula of the first performance test result is as follows: In the formula, represents the first performance test result, S represents the elongation at break under the tensile test, and F D It represents the breaking force corresponding to the breaking elongation, F0 represents the initial breaking force standard value of the insulation blanket at room temperature, S0 represents the initial breaking elongation standard value of the insulation blanket at room temperature, α1 represents the breaking force correction factor in the breaking test, and α2 represents the breaking elongation correction factor in the breaking test.

5. The insulation blanket performance detection and evaluation method based on macro-micro fusion analysis according to claim 1 is characterized in that: The S2 comprises the following sub-steps: S21, performing multivariate physical and chemical tests on the insulation test samples at multiple temperatures to obtain physical and chemical test data; S22, performing microscopic performance evaluation calculation according to the physical and chemical test data to obtain a microscopic performance evaluation factor characterizing the influence of microstructural changes on insulation performance; S23, performing a mechanical property impact assessment based on the physical and chemical test data to obtain a mechanical property impact factor characterizing the impact of microstructure changes on mechanical properties; S24. Use the microscopic performance evaluation factor and the mechanical performance influencing factor as the second performance test result.

6. The insulation blanket performance detection and evaluation method based on macro-micro fusion analysis according to claim 5 is characterized in that: The S21 includes the following sub-steps: Perform a first physical and chemical test on the insulation test sample based on Fourier transform infrared spectroscopy to generate a first test graph to obtain first physical and chemical test data, wherein the physical and chemical data at least includes a molecular structure; Perform a second physical and chemical test on the insulation test sample based on differential scanning calorimetry to obtain second physical and chemical test data, wherein the second physical and chemical test data at least includes a glass transition temperature, a melting temperature, and a crystallinity; Performing a third physical and chemical test on the insulation test sample based on X-ray diffraction, generating a second test graph to obtain third physical and chemical test data, wherein the third physical and chemical test data at least includes a crystal structure and a grain size of the insulation test sample; Performing a fourth physical and chemical test on the insulation test sample based on dynamic mechanical thermal analysis, generating a third test graph to obtain fourth physical and chemical test data, wherein the fourth physical and chemical test data at least includes a complex modulus of the insulation test sample; A fifth physical and chemical test is performed on the insulation test sample based on a field emission scanning electron microscope to generate a fourth test graph to identify the surface morphology change characteristics of the insulation test sample and obtain the surface roughness of the insulation test sample.

7. The method for detecting and evaluating the performance of an insulation blanket based on macro-micro fusion analysis according to claim 6 is characterized in that: The S22 comprises the following sub-steps: Extracting the absorbance peak within the test band based on the first physical and chemical test data; assigning first weight parameters to the absorbance peak value, the second physical and chemical test data, and the third physical and chemical test data respectively; Calculating the microscopic properties of the insulation test sample based on the first weight parameter in combination with the physical and chemical test data to obtain the microscopic property evaluation factor; The calculation formula of the micro-performance evaluation factor is as follows: Wherein, η is the microscopic performance evaluation factor, ω1 represents the correction factor for microscopic performance evaluation, represents the absorbance peak value, wherein j represents the four bands of the first physical and chemical test, and the value is (j=1, 2, 3, 4), i represents the two band intervals of the four bands for obtaining the absorbance peak value, and the value is (i=1, 2), χ is the crystallinity in the second physical and chemical test data, D hkl is the grain size of the insulation test sample perpendicular to the crystal plane, D0 represents the grain size of the insulation blanket after heat treatment at 20°C, γ1 is the first weight parameter of the absorbance peak, γ2 is the first weight parameter of the crystallinity, and γ3 is the first weight parameter of the grain size. It represents the two corresponding absorbance standard values ​​of the insulation blanket in the four bands, and χ0 represents the crystallinity of the insulation blanket under heat treatment at 20℃.

8. The method for detecting and evaluating the performance of an insulation blanket based on macro-micro fusion analysis according to claim 6 is characterized in that: The S23 comprises the following sub-steps: Extracting storage modulus based on the fourth physical and chemical test data; assigning a second weight parameter to the storage modulus and the surface roughness; The mechanical property influencing factor is calculated based on the second weight parameter combined with the storage modulus and the surface roughness; The calculation formula of the mechanical property influencing factor is as follows: In the formula, is the mechanical property influencing factor, E D is the storage modulus, υ is the surface roughness, E0 is the standard value of the storage modulus for the insulation blanket to ensure normal mechanical properties, υ0 is the standard value of the surface roughness for the insulation blanket to ensure normal mechanical properties, β1 represents the correction factor for the storage modulus, and β2 represents the correction factor for the surface roughness.

9. The insulation blanket performance detection and evaluation method based on macro-micro fusion analysis according to any one of claims 4 to 8 is characterized in that: The calculation formula of the comprehensive evaluation result is as follows: Where e is the base of the natural logarithm, δ1 is the macro correction factor for the mechanical properties of the insulation blanket, and δ2 is the micro correction factor for the mechanical properties of the insulation blanket.

10. The insulation blanket performance detection and evaluation method based on macro-micro fusion analysis according to claim 1 is characterized in that: The method further comprises: A temperature warning mechanism is configured based on the performance changes of the insulation test sample under multiple temperature environments, and a safety warning is issued when the test temperature exceeds the target temperature or / and the stress of the insulation test sample exceeds the target breaking force threshold; In response to the safety warning, a detection environment of the insulation test sample is regulated.

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