Comprehensive evaluation method for aging behavior of submarine cable insulating material

Abstract

The invention discloses a comprehensive evaluation method for the aging behavior of a submarine cable insulating material, and the method comprises the following steps: carrying out an electric heating power multi-physics field accelerated aging experiment, obtaining the performance parameters of the submarine cable insulating material, carrying out the same direction and standardization of the performance parameters, constructing an evaluation index system through employing an analytic hierarchy process, quantifying the importance of each index through a judgment matrix, and carrying out the comprehensive evaluation of the aging behavior of the submarine cable insulating material. The aging behavior of the submarine cable insulation material in multiple physical fields is deeply analyzed, the performance change rule in the aging process is revealed, and a scientific basis is provided for performance evaluation and life prediction of the material. The subjective weight is calculated, the scientificity of weight distribution is ensured, the principal component factor is extracted, the score variance is calculated, and the weight is determined. According to the invention, the aging characteristics of the submarine cable insulating material in multiple physical fields can be reflected, and a scientific basis is provided for multi-dimensional evaluation and performance optimization of the aging performance of a high polymer material.

Description

Technical Field

[0001] The present invention belongs to the field of material science and engineering technology, and specifically relates to a comprehensive evaluation method for aging behavior of submarine cable insulation materials. Background Art

[0002] Insulating materials, especially polyethylene materials, play an important role in many modern engineering and technical fields due to their wide application. However, during the use of polyethylene materials, they will inevitably be affected by multi-physical field factors such as thermal and electrical factors, resulting in a gradual decline in material performance. This process is called aging. Therefore, studying the mechanical properties and microstructural changes of polyethylene materials under multi-physical field artificial accelerated aging conditions has important scientific research and practical application value for in-depth understanding of material aging characteristics, improving their environmental adaptability and extending their service life. The Chinese invention patent with publication number CN114966338 A describes a TOPSIS model for evaluating cable insulation materials with different voltage stabilizing components. It comprehensively evaluates cable insulation materials with different voltage stabilizing components while considering electrical and thermal characteristics, which can ensure that the cable insulation materials have more comprehensive insulation performance; the Chinese invention patent with publication number CN112949099B describes a power cable life prediction equation that can simultaneously characterize electrical aging, thermal aging life and the combined effect between the two, so as to achieve the effect of evaluating the life of power cables at any time without conducting destructive experiments; the Chinese invention patent with publication number CN111339679B describes a method for establishing a fuzzy recognition model for predicting the aging grade of LDPE in a long-term natural environment, and uses fuzzy recognition technology to establish an LDPE material aging grade evaluation model based on environmental parameters. However, existing studies focus more on the changes in a single performance parameter, and there is a lack of research on the correlation between parameters and the commonality of changes. Moreover, when describing the aging behavior of polyethylene, the common evaluation model fails to fully consider the importance of different aging characteristic indicators, resulting in the evaluation results may not accurately reflect the actual aging behavior of the material. In common evaluation models, since the changing trends of characteristic indicators may be opposite, negative characteristic values ​​may occur during data processing, which needs to be improved in practical applications. In long-term natural behavior, complex physical processes may change under huge temperature differences. For example, the difference between the annual maximum temperature and the annual minimum temperature in the northwest region can reach 70°C. At the same time, the establishment of the model under long-term and complex working conditions requires a large sample size, and its applicability needs further evaluation. Summary of the invention

[0003] The purpose of the present invention is to provide a comprehensive evaluation method for the aging behavior of submarine cable insulation materials. The present invention can reflect the aging characteristics of submarine cable insulation materials under multi-physical fields and provide a scientific basis for the multi-dimensional evaluation and performance optimization of polymer material aging performance.

[0004] The technical solution of the present invention is a comprehensive evaluation method for the aging behavior of submarine cable insulation materials, comprising the following steps:

[0005] Step 1: Conduct an electro-thermal multi-physics field accelerated aging experiment on the submarine cable insulation material to obtain macroscopic and microscopic performance parameters;

[0006] Step 2: Perform homogeneous processing on the macroscopic and microscopic performance parameters, and standardize the performance parameters of the submarine cable insulation materials;

[0007] Step 3: Construct a hierarchical structure of evaluation indicators based on the hierarchical analysis method, and construct a quantitative judgment matrix by comparing the importance of each evaluation indicator pairwise;

[0008] Step 4: Calculate the eigenvalues ​​and eigenvectors of the judgment matrix to obtain the subjective weights of each evaluation index, and ensure the rationality of the weight distribution through consistency test;

[0009] Step 5: Use the standardized data to extract the principal component factors and calculate the score variance of each principal component factor;

[0010] Step 6: Based on the results of principal component analysis and the weights determined by the hierarchical analysis method, a comprehensive evaluation index for multi-physical field aging of submarine cable insulation materials is established;

[0011] Step 7: Based on the comprehensive evaluation indicators, the aging behavior of submarine cable insulation materials under multi-physical fields is analyzed to achieve comprehensive performance evaluation of submarine cable insulation materials.

[0012] In the aforementioned comprehensive evaluation method for the aging behavior of submarine cable insulation materials, in step one, the macroscopic and microscopic performance parameters include: tensile strength X1, breakdown strength X2, crystallinity X3, carbonyl index X4, hydroxyl index X5, thermal decomposition temperature X6 and melting peak temperature X7.

[0013] In the aforementioned comprehensive evaluation method for the aging behavior of submarine cable insulation materials, in step 2, since the carbonyl index X4 and the hydroxyl index X5 are inverse indicators in the multi-physical field aging process of submarine cables, they need to be converted into the same direction using the following formula:

[0014] Y ij =1 / Y′ ij (i=1,2,…,m;j=1,2,…,n);

[0015] Among them, i and j are observation values, m and n are indicator numbers;

[0016] If a moderate index appears, set the optimal value α, and use the following formula for homogeneity:

[0017] Y ij=1 / |α-Y′ ij |(i=1,2,…,m;=j1,2,…,n).

[0018] In the above-mentioned comprehensive evaluation method of the aging behavior of submarine cable insulation materials, in step 3, the 1-9 and its reciprocal scaling method are used for quantification: if n indicators belong to a certain criterion layer, then the n indicators are compared with each other by comparing the importance of each evaluation indicator to construct a judgment matrix C = (C ij ) n×n .

[0019] In the aforementioned comprehensive evaluation method for the aging behavior of submarine cable insulation materials, in step 4, the square root method is used to conduct a hierarchical analysis method study, and the process is as follows:

[0020] Calculate the product M of each row of the judgment matrix C i :

[0021]

[0022] Calculate M i The nth root W i :

[0023]

[0024] To W i Normalize and get the characteristic vector W of n indicators i :

[0025]

[0026] Solution judgment matrix C = (C ij ) n×n , find the maximum eigenvalue, the eigenvector corresponding to the maximum eigenvalue is the weight vector of the principal component factor, and the CI value is calculated using the maximum eigenvalue:

[0027] CI = (largest characteristic root - n) / (n - 1);

[0028] Find the random consistency RI table, combine the judgment matrix order to get the RI value, which is used for consistency verification, and calculate the consistency index CR value by combining the following formula:

[0029] CR = CI / RI;

[0030] By comparing the CR values, the consistency check of the judgment matrix is ​​performed.

[0031] In the aforementioned comprehensive evaluation method for the aging behavior of submarine cable insulation materials, in step 5, the score variance of each principal component factor is obtained according to the following formula, and the score variance includes the variance contribution rate and the cumulative variance contribution rate:

[0032]

[0033]

[0034] Where Y i represents the variance contribution rate of the i-th principal component factor; i Represents Y i The size of the eigenvalue of the principal component factor; Y m It represents the cumulative variance contribution rate of the first m principal component factors.

[0035] In the aforementioned comprehensive evaluation method for the aging behavior of submarine cable insulation materials, in step 6, a comprehensive evaluation index Z for evaluating the multi-physical field aging of submarine cable insulation materials is established based on the results of principal component analysis and the weights determined by the hierarchical analysis method. The expression is:

[0036]

[0037] Among them, P i is the extracted principal component factor.

[0038] In the aforementioned comprehensive evaluation method for the aging behavior of submarine cable insulation materials, in step seven, a relationship curve between the comprehensive evaluation index Z and the aging time t is drawn according to the formula in step six to reveal the regularity of performance changes during the aging process.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention provides a comprehensive evaluation method for the aging behavior of submarine cable insulation materials under multi-physical fields, which can comprehensively reflect the aging characteristics of the materials, use an improved principal component analysis method to reduce the dimension of performance parameters, extract key factors, simplify the analysis of multi-dimensional performance parameters, and combine the hierarchical analysis method to determine the weight of each evaluation index, thereby ensuring the rationality and scientificity of the evaluation results. The present invention also performs isotropic and standardized processing on the performance parameters, eliminating the influence of different dimensions and magnitudes. In addition, the present invention describes in detail how to construct a judgment matrix and ensure the rationality of weight distribution through consistency testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a graph showing changes in the comprehensive aging evaluation index of the present invention;

[0042] Figure 2 This is the scree plot of the principal component analysis of the present invention. DETAILED DESCRIPTION

[0043] The present invention is further described below in conjunction with the accompanying drawings and embodiments, but they are not intended to limit the present invention.

[0044] Embodiment: A comprehensive evaluation method for aging behavior of submarine cable insulation materials comprises the following steps:

[0045] Step 1: Conduct an electrothermal multi-physics field accelerated aging experiment on the submarine cable insulation material for 192 hours to obtain macroscopic and microscopic performance parameters such as mechanical properties, chemical structure, thermal stability and melting characteristics;

[0046] In step 1, the macroscopic and microscopic performance parameters include: tensile strength X1, breakdown strength X2, crystallinity X3, carbonyl index X4, hydroxyl index X5, thermal decomposition temperature X6 and melting peak temperature X7.

[0047] Among them, tensile strength is one of the most important and basic properties of polymers, and is also the dominant factor that determines the rational application of polymer materials. The specific measurement process is: using the international standard CigréTB 852 for testing, and using the universal material testing machine Zwick / Roell Z020 to carry out mechanical property testing.

[0048] The DC breakdown field strength is a technical indicator that reflects the DC breakdown resistance of submarine cable insulation materials. The specific measurement process is: use a ball-column electrode to test the breakdown electric field of the submarine cable insulation sample under DC voltage, and apply a rapidly increasing DC voltage to the measured sample at room temperature until the sample breaks down, with a voltage increase rate of 1kV / s. Repeat the test 15 times to obtain the DC breakdown field strength Weibu11 distribution diagram of the sample, and take the breakdown field strength at the 63.2% probability as the breakdown result obtained by the statistical result.

[0049] The crystallinity and melting peak temperature properties of the samples were studied and analyzed using a TA-Q200 differential scanning calorimeter. Under an argon protective atmosphere, the sample was heated from room temperature 30°C to 200°C at a rate of 10°C / min, and then changed to an oxygen atmosphere and heated to 250°C at a rate of 10°C / min.

[0050] The carbonyl index and hydroxyl index were characterized by attenuated total reflection infrared spectrometer (Nicolet 6700 with ATR accessory) on the exposed surface of the sample for chemical structure characterization. The reflective crystal was ZnSe, the incident angle was 45°, the number of scans was 32, the scanning range was 700 to 4000 cm-1, and the resolution was 4 cm-1.

[0051] The thermal decomposition temperature was analyzed using an SDT-Q600 DSC-TG thermogravimetric analyzer with a sample mass of 5.0-6.0 mg. The sample was heated from room temperature 30°C to 700°C at a rate of 10°C / min under a protective nitrogen atmosphere to obtain thermogravimetric curves under different aging time conditions.

[0052] The test was carried out according to the test scheme of the above method, and the change values ​​of 7 performance parameters of the submarine cable insulation material before and after aging under multi-physical fields were recorded. The performance parameter measurement results are shown in Table 1.

[0053] Table 1 Original data of aging index performance parameters

[0054]

[0055] Step 2: Use the improved principal component analysis method to homogenize the performance parameters and standardize the performance parameters of the submarine cable insulation materials to eliminate the influence of different dimensions and magnitudes.

[0056] Since the carbonyl index X4 and the hydroxyl index X5 are inverse indices in the multi-physical field aging process of submarine cables, they need to be converted into the same direction using the following formula:

[0057] Y ij =1 / Y′ ij (i=1,2,…,m;j=1,2,…,n);

[0058] Among them, i and j are observation values, m and n are indicator numbers;

[0059] The indicators in this model can be replaced. If a moderate indicator appears, the optimal value α is set and can be replaced according to the following formula:

[0060] Y ij =1 / |α-Y′ ij |(i=1,2,…,m;j=1,2,…,n).

[0061] According to the above method, the data in Table 1 are homogenized and standardized, and the results are shown in Table 2:

[0062] Table 2 Data after homogeneity and standardization of performance parameters

[0063]

[0064] Step 3: Construct a hierarchical structure of evaluation indicators based on the analytic hierarchy process, and build a quantitative judgment matrix by comparing the importance of each evaluation indicator pairwise;

[0065] The quantification is performed using the 1-9 and its inverse scaling method; if there are n indicators belonging to a certain criterion layer, then the n indicators are compared with each other to construct a judgment matrix C = (C ij ) n×n The main component of submarine cable insulation material is cross-linked polyethylene. The order of importance of the indicators obtained from the test in step 1 is: tensile strength = breakdown strength > crystallinity > carbonyl index = hydroxyl index > thermal decomposition starting temperature = melting peak temperature;

[0066] According to the above method, the judgment matrix C of this embodiment is determined, and the result is shown in the following formula:

[0067]

[0068] Step 4: Calculate the eigenvalues ​​and eigenvectors of the judgment matrix to obtain the subjective weights of each evaluation index, and ensure the rationality of the weight distribution through consistency test;

[0069] Use the square root method to study the hierarchical analysis method and calculate the product M of each row of the judgment matrix C i :

[0070]

[0071] Calculate M i The nth root W i :

[0072]

[0073] To W i After normalization, we can get the characteristic vector W of n indicators i :

[0074]

[0075] Solve the judgment matrix C = (C ij ) n×n Among all the calculated eigenvalues, find the largest one. The eigenvector corresponding to the largest eigenvalue is the weight vector of the principal component.

[0076] Then use the maximum eigenvalue to calculate the CI value:

[0077] CI = (maximum eigenvalue - n) / (n - 1);

[0078] Find the random consistency RI table in Table 3, combine it with the judgment matrix order to get the RI value for consistency verification, and finally calculate the consistency index CR value by combining the following formula:

[0079] CR = CI / RI;

[0080] Table 3 Random consistency RI table

[0081] nth order 3 4 5 6 7 8 9 10 11 12 13 14 15 16 RI value 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59 1.5943

[0082] Generally speaking, the smaller the CR value, the better the consistency of the judgment matrix. Generally speaking, if the CR value is less than 0.1, the judgment matrix satisfies the consistency test. If the CR value is greater than 0.1, it means that there is no consistency, and the judgment matrix should be appropriately adjusted and analyzed again.

[0083] According to the above method, the square root method was used to conduct hierarchical analysis, and the analysis results are shown in Table 4.

[0084]

[0085]

[0086] In this study, a 7-order judgment matrix was constructed. The random consistency RI value obtained by querying Table 3 is 1.360. The RI value is used for the following consistency test calculation. The consistency test results are shown in Table 5.

[0087]

[0088] The CI value calculated for the 7-order judgment matrix is ​​0.055, and the RI value is 1.360. Therefore, the calculated CR value is 0.041<0.1, which means that the judgment matrix of this study meets the consistency test and the calculated weights are consistent.

[0089] Step 5: Using the standardized data, extract the principal component factors through principal component analysis, and calculate the score variance of each principal component factor;

[0090] The KMO value or Bartlett test was used to determine whether the principal component analysis could be condensed into principal component factors.

[0091] Calculation of KMO value: First, you need to collect the data set for analysis. Use statistical software (such as SPSS, SAS, etc.) to calculate the correlation coefficients between the variables in the data set to obtain a correlation coefficient matrix. The partial correlation coefficient measures the strength of the correlation between two variables after controlling the influence of other variables. The simple correlation coefficient is the direct correlation between two variables, while the partial correlation coefficient is the correlation after excluding the influence of other variables. The size of the KMO value depends on the comparison between the partial correlation coefficient and the simple correlation coefficient. The calculation formula of the KMO value is the difference between the sum of squares of the simple correlation coefficients and the sum of squares of the partial correlation coefficients of all variables, divided by the sum of squares of the simple correlation coefficients. Specifically, KMO = (sum of squares of simple correlation coefficients - sum of squares of partial correlation coefficients) / sum of squares of simple correlation coefficients.

[0092] The calculation of the Bartlett test first requires the collection of the data set for analysis. The Bartlett test is used to test whether the correlation matrix of the data is the unit matrix, that is, to test whether the variables are independent. If it is not the unit matrix, it means that there is a correlation between the variables and the data may be suitable for factor analysis. The statistical software will give the statistic of the Bartlett sphericity test and the corresponding p-value. The larger the statistic and the p-value is less than the set significance level (such as 0.05), the null hypothesis is rejected (that is, the correlation matrix of the data is not the unit matrix), and the data is considered suitable for factor analysis.

[0093] KMO value or Bartlett's test is used for weight calculation:

[0094] 1. Analyze the KMO value; if this value is higher than 0.8, it means that it is very suitable for analysis; if this value is between 0.7 and 0.8, it means that it is relatively suitable for analysis; if this value is between 0.6 and 0.7, it means that it can be analyzed; if this value is less than 0.6, it means that it is not suitable for analysis;

[0095] 2. If the p-value corresponding to the Bartlett test is less than 0.05, it also indicates that principal component analysis is suitable;

[0096] 3. If there are only two analysis items, KMO is 0.5 anyway.

[0097] The score variance of each principal component factor is obtained according to the following formula, and the score variance includes the variance contribution rate and the cumulative variance contribution rate:

[0098]

[0099]

[0100] Where Y i represents the variance contribution rate of the i-th principal component factor; i Represents Y i The size of the eigenvalue of the principal component factor; Y m It represents the cumulative variance contribution rate of the first m principal component factors.

[0101] According to step 5 of the method provided by the present invention, KMO and Bartlett's test are first used to determine whether the analysis data is suitable for principal component analysis. From Table 6 below, it can be seen that KMO is 0.720, which is greater than 0.6, meeting the prerequisite requirements of principal component analysis, which means that the data can be used for principal component analysis research. And the data passed the Bartlett sphericity test (p<0.05), indicating that the research data is suitable for principal component analysis.

[0102] Table 6 KMO and Bartlett's test

[0103]

[0104] Table 7 Variance contribution rate table

[0105]

[0106]

[0107] Table 7 analyzes the principal component extraction and the amount of information extracted from the principal component. The corresponding scree plot is attached. Figure 2 , from the table, we can see that: the principal component analysis extracted a total of 3 principal component factors, the variance contribution rates of these 3 principal component factors are 90.432%, 7.889%, 1.470%, and the cumulative variance contribution rate is 99.791%. In addition, this principal component analysis extracted a total of 3 principal component factors, and their corresponding weighted variance contribution rates, i.e., weights, are: 90.432 / 99.791=90.62%; 7.889 / 99.791=7.91%; 1.470 / 99.791=1.47%.

[0108] Step 6: Based on the results of principal component analysis and the weights determined by the hierarchical analysis method, a comprehensive evaluation index for multi-physical field aging of submarine cable insulation materials is established;

[0109] The expression of comprehensive evaluation index Z is:

[0110]

[0111] Among them, P i is the extracted principal component factor.

[0112] Each principal component factor is defined as a linear combination of multiple variables with optimal weights, and the eigenvector is obtained based on the component matrix and the eigenvalues ​​of the principal component factors.

[0113] According to the above method, the principal component factors and eigenvectors of several indicators are calculated, such as

[0114] As shown in Table 8.

[0115]

[0116] The load coefficient reflects the degree of correlation between the original variables and the principal component factors. In principal component analysis, the load coefficient is the projection of the original variables on the principal component factors, which is used to indicate the contribution of each original variable to the principal component factors. The linear combination coefficient refers to the coefficient that converts the original variables into principal component factors. These coefficients indicate the weights of the principal component factors as linear combinations of the original variables. The size of the eigenvalue determines the importance of the principal component factors. The calculation of the load coefficient and the linear combination coefficient both depend on the eigenvalue. For example, when calculating the load coefficient, it is necessary to divide the elements in the component matrix by the square root of the corresponding principal component eigenvalue.

[0117] According to the weights of the seven variables in the principal components P1, P2, and P3, the relationship between the principal components and the variables can be obtained as follows:

[0118] P1=0.386*X1+0.378*X2+0.390*X3+0.339*X4+0.372*X5+0.384*X6+0.394*X7;

[0119] P2=-0.253*X1-0.408*X2-0.220*X3+0.699*X4+0.461*X5-0.050*X6-0.132*X7;

[0120] P3 = 0.422*X1+0.101*X2+0.264*X3+0.042*X4+0.226*X5-0.792*X6-0.249*X7;

[0121] The calculation formula for the current data is:

[0122]

[0123] Step 7: Based on the comprehensive evaluation indicators, the aging behavior of submarine cable insulation materials under multi-physical fields is analyzed to achieve comprehensive performance evaluation of submarine cable insulation materials.

[0124] According to the formula in step 6, draw the relationship curve between the comprehensive evaluation index Z and the aging time t, such as Figure 1 As shown in the figure, the regularity of performance changes during the aging process is revealed. The Z value of the comprehensive aging evaluation index shows a three-stage change trend as a whole: in the early aging period of 0 to 48 hours, the Z value drops sharply, and the aging effect is strong; in the middle aging period of 48 to 168 hours, the Z value changes very little and tends to be flat, and the aging is slow; in the late aging period of 168 to 192 hours, the Z value drops rapidly again, and the aging rate gradually increases.

[0125] The present invention provides a comprehensive evaluation method for the aging behavior of submarine cable insulation materials under multi-physical fields, which can comprehensively reflect the aging characteristics of the materials, use an improved principal component analysis method to reduce the dimension of performance parameters, extract key factors, simplify the analysis of multi-dimensional performance parameters, and combine the hierarchical analysis method to determine the weight of each evaluation index, thereby ensuring the rationality and scientificity of the evaluation results. The present invention also performs isotropic and standardized processing on the performance parameters, eliminating the influence of different dimensions and magnitudes. In addition, the present invention describes in detail how to construct a judgment matrix and ensure the rationality of weight distribution through consistency testing.

[0126] In summary, the present invention can reflect the aging characteristics of submarine cable insulation materials under multi-physical fields, and provide a scientific basis for the multi-dimensional evaluation and performance optimization of polymer material aging performance.

Claims

1. A comprehensive evaluation method for aging behavior of submarine cable insulation materials, characterized in that: The following steps are involved: Step 1: Conduct an electro-thermal multi-physics field accelerated aging test on the submarine cable insulation material to obtain macroscopic and microscopic performance parameters; Step 2: Perform homogeneous processing on the macroscopic and microscopic performance parameters, and standardize the performance parameters of the submarine cable insulation materials; Step 3: Construct a hierarchical structure of evaluation indicators based on the hierarchical analysis method, and construct a quantitative judgment matrix by comparing the importance of each evaluation indicator pairwise; Step 4: Calculate the eigenvalues ​​and eigenvectors of the judgment matrix to obtain the subjective weights of each evaluation index, and ensure the rationality of the weight distribution through consistency test; Step 5: Use the standardized data to extract the principal component factors and calculate the score variance of each principal component factor; Step 6: Based on the results of principal component analysis and the weights determined by the hierarchical analysis method, a comprehensive evaluation index for multi-physical field aging of submarine cable insulation materials is established; Step 7: Based on the comprehensive evaluation indicators, the aging behavior of submarine cable insulation materials under multi-physical fields is analyzed to achieve comprehensive performance evaluation of submarine cable insulation materials.

2. The comprehensive evaluation method for aging behavior of submarine cable insulation materials according to claim 1 is characterized by: In step 1, the macroscopic and microscopic performance parameters include: tensile strength X1, breakdown strength X2, crystallinity X3, carbonyl index X4, hydroxyl index X5, thermal decomposition temperature X6 and melting peak temperature X7.

3. The comprehensive evaluation method for aging behavior of submarine cable insulation materials according to claim 1 is characterized by: In step 2, since the carbonyl index X4 and the hydroxyl index X5 are inverse indices in the multi-physical field aging process of submarine cables, they need to be converted into the same direction using the following formula: Y ij =1 / Y′ ij (i=1,2,…,m;j=1,2,…,n); Among them, i and j are observation values, m and n are indicator numbers; If a moderate index appears, set the optimal value α, and use the following formula for homogeneity: Y ij =1 / |α-Y′ ij |(i=1,2,…,m;j=1,2,…,n)。 4. The comprehensive evaluation method for aging behavior of submarine cable insulation materials according to claim 1 is characterized in that: In step 3, the quantification is performed using the 1-9 and its inverse scaling method: Suppose there are n indicators belonging to a certain criterion layer, then the n indicators are compared with each other to construct a judgment matrix C = (C ij ) n×n .

5. The comprehensive evaluation method for aging behavior of submarine cable insulation materials according to claim 4 is characterized by: In step 4, the square root method is used to conduct AHP research, and the process is as follows: Calculate the product M of each row of the judgment matrix C i : Calculate M i The nth root W i : To W i Normalize and get the characteristic vector W of n indicators i : Solve the judgment matrix C = (C ij ) n×n , find the maximum eigenvalue, the eigenvector corresponding to the maximum eigenvalue is the weight vector of the principal component factor, and the CI value is calculated using the maximum eigenvalue: CI = (maximum eigenvalue - n) / (n - 1). Find the random consistency RI table, combine the judgment matrix order to get the RI value, which is used for consistency verification, and calculate the consistency index CR value by combining the following formula: CR = CI / RI; By comparing the CR values, the consistency check of the judgment matrix is ​​performed.

6. The comprehensive evaluation method for aging behavior of submarine cable insulation materials according to claim 5 is characterized by: In step 5, the score variance of each principal component factor is obtained according to the following formula, and the score variance includes the variance contribution rate and the cumulative variance contribution rate: Where Y i represents the variance contribution rate of the i-th principal component factor; i Represents Y i The eigenvalue of the principal component factor; Ym represents the cumulative variance contribution rate of the first m principal component factors.

7. The comprehensive evaluation method for aging behavior of submarine cable insulation materials according to claim 1 is characterized by: In step six, the comprehensive evaluation index Z for evaluating the multi-physical field aging of submarine cable insulation materials is established based on the results of principal component analysis and the weights determined by the hierarchical analysis method. The expression is: Among them, P i is the extracted principal component factor.

8. The comprehensive evaluation method for aging behavior of submarine cable insulation materials according to claim 1, characterized in that: In step seven, a relationship curve between the comprehensive evaluation index Z and the aging time t is drawn according to the formula in step six to reveal the regularity of performance changes during the aging process.

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