A multivariate coupling analysis method for detecting thermal stability of plastic pipe

Abstract

The application discloses a kind of plastic pipe thermal stability detection multivariate coupling analysis method, specifically relates to the field of polymer material performance testing technology, first, the experimental condition parameters of the present application are designed, and the plastic pipe experimental sample data is obtained, multidimensional performance data is collected, and multidimensional performance index is obtained;Then, by principal component analysis algorithm, the principal component dataset of multidimensional performance index is obtained, and the comprehensive thermal stability index dataset of experimental group sample is calculated;Finally, the critical temperature of thermal stability detection is judged, and the critical temperature output of thermal stability detection is obtained to the management end for human-computer interaction;By collecting mechanical property data, physical and chemical performance data, micro-morphology data and micro-chemical performance data, the multidimensional performance index is calculated, and the limitations of single index evaluation are abandoned, and the thermal failure behavior of material is fully described;At the same time, the correlation of variables is revealed by covariance matrix, which is more in line with the actual thermal aging law.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of polymer material performance testing, in particular to a multivariate coupling analysis method for detecting the thermal stability of plastic pipes. BACKGROUND

[0002] Plastic pipes are widely used in the fields of building water supply and drainage, heating, ventilation and air conditioning, etc. During their long-term service, they will be subjected to continuous thermal stress, resulting in problems such as mechanical property degradation, discoloration, and microstructure deterioration. Therefore, thermal stability is a core performance indicator, which is suitable for evaluating the thermal aging performance of various thermoplastic plastic pipes such as PP-R, PVC, and PE-Xa, and can be used for pipe production quality control, engineering material selection, and long-term service life prediction.

[0003] Existing thermal stability detection methods, such as thermal aging experiments and thermogravimetric analysis methods, etc. The thermal aging experiment method places the plastic pipe sample in a constant temperature oven (such as 150℃), records the time of initial decomposition or observes the appearance change, thereby realizing the thermal aging experiment. The thermogravimetric analysis method measures the mass loss of the plastic pipe during heating to evaluate the thermal decomposition temperature or thermal degradation stability, which is usually carried out in an inert gas or air, and it is difficult to simulate the complex environment in actual use.

[0004] Although the existing thermal stability detection methods can realize thermal stability detection in practical application, they have some limitations, such as only focusing on a single control variable (such as heat distortion temperature, tensile strength), which cannot reflect the synergistic degradation law of "strength-toughness-appearance-microstructure"; the coupling relationship between multiple performance variables is not established; there is a lack of quantitative comprehensive index, making it difficult to realize the horizontal comparison of different pipe materials and the accurate positioning of the critical failure temperature; therefore, in order to solve the above problems, the present application proposes a method combining multi-temperature interval step experiment, multi-dimensional data acquisition and matrix coupling analysis, which realizes comprehensive, quantitative and traceable evaluation of the thermal stability of plastic pipes. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a multivariate coupling analysis method for detecting the thermal stability of plastic pipes to solve the problems raised in the background art.

[0006] To achieve the above purpose, the present application provides the following technical scheme: a multivariate coupling analysis method for detecting the thermal stability of plastic pipes, comprising:

[0007] S1: using random sampling technology, screening plastic pipe samples of the same batch, designing thermal stability detection experiment condition parameters, and obtaining plastic pipe experimental sample data for detecting thermal stability;

[0008] S2: Based on the plastic pipe experimental sample data, multi-dimensional performance data is collected through a data acquisition device to obtain plastic pipe experimental sample performance data;

[0009] S3: Based on the plastic pipe experimental sample performance data, the multi-dimensional performance indicators of the plastic pipe experimental sample data are calculated, and after preprocessing, a multi-dimensional performance indicator coupling matrix of the plastic pipe experimental group sample is constructed;

[0010] S4: Through principal component analysis algorithm, the multi-dimensional performance indicator coupling matrix of the experimental group sample is subjected to characteristic decomposition to obtain a multi-dimensional performance indicator principal component data set of the plastic pipe experimental group sample;

[0011] S5: Based on the multi-dimensional performance indicator principal component data set, the comprehensive thermal stability index of each experimental group sample is calculated through a weighted summation algorithm to obtain a comprehensive thermal stability index data set of the experimental group sample;

[0012] S6: Based on the comprehensive thermal stability index data set and the tensile strength retention rate in the multi-dimensional performance indicator coupling matrix, the critical temperature of the thermal stability detection is determined to obtain the critical temperature output of the thermal stability detection to the management end for human-computer interaction.

[0013] Technical effects and advantages of the present application:

[0014] 1、The present application collects mechanical property data, physical and chemical property data, micro-morphology data and micro-chemical property data, calculates multi-dimensional performance indicators, discards the limitations of single indicator evaluation, and comprehensively describes the thermal failure behavior of the material through multi-dimensional performance detection; meanwhile, the covariance matrix reveals the correlation of variables, which is more in line with the actual thermal aging law;

[0015] 2、The present application reveals the synergistic decay law of different performance variables under thermal stress through principal component analysis algorithm, which helps to understand the thermal aging mechanism of the material; meanwhile, the global principal components are extracted and the comprehensive thermal stability index is constructed, so that the multi-dimensional performance information is converted into quantitative values which can be directly compared, avoiding misjudgment caused by isolated analysis of indicators;

[0016] 3、The present application realizes full coverage of thermal degradation scene by combining the quantitative comprehensive thermal stability index and the accurate critical temperature determination, on the one hand through TSI-temperature curve and taking tensile strength retention rate less than or equal to 50% as the determination standard, so that the thermal stability detection is more scientific and objective, and the comparison between different materials and product quality control are facilitated. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 It is the overall flowchart of the present application.

[0018] Figure 2A flow chart of the method of the present application is shown in the figure.

[0019] Figure 3 A flow chart of the multi-dimensional performance index coupling matrix construction of the present application is shown in the figure. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0021] Please refer to Figure 1 As shown in the figure, the present application provides a plastic pipe thermal stability detection multi-variable coupling analysis system, which comprises a thermal stability detection experiment design module, a multi-dimensional performance data acquisition module, a multi-dimensional performance matrix construction module, a multi-variable coupling analysis module, a thermal stability detection comprehensive evaluation module and a thermal stability detection coupling analysis result output module.

[0022] The thermal stability detection experiment design module is connected with the multi-dimensional performance data acquisition module, the multi-dimensional performance matrix construction module is connected with the multi-dimensional performance data acquisition module and the multi-variable coupling analysis module, and the thermal stability detection comprehensive evaluation module is connected with the multi-variable coupling analysis module and the thermal stability detection coupling analysis result output module.

[0023] The thermal stability detection experiment design module: adopts random sampling technology, screens the same batch of plastic pipe samples, designs thermal stability detection experiment condition parameters, obtains plastic pipe experiment sample data for detecting thermal stability, and transmits to the multi-dimensional performance data acquisition module;

[0024] The multi-dimensional performance data acquisition module: based on the plastic pipe experiment sample data, through a data acquisition device, acquires multi-dimensional performance data, obtains plastic pipe experiment sample performance data, and transmits to the multi-dimensional performance matrix construction module;

[0025] The multi-dimensional performance matrix construction module: based on the plastic pipe experiment sample performance data, calculates the multi-dimensional performance index of the plastic pipe experiment sample data, constructs the multi-dimensional performance index coupling matrix of the plastic pipe experiment group sample after pretreatment, and transmits to the multi-variable coupling analysis module;

[0026] The multi-variable coupling analysis module: through principal component analysis algorithm, performs characteristic decomposition on the multi-dimensional performance index coupling matrix of the experiment group sample, obtains the multi-dimensional performance index principal component data set of the plastic pipe experiment group sample, and transmits to the thermal stability detection comprehensive evaluation module;

[0027] The thermal stability detection comprehensive evaluation module: based on the multi-dimensional performance index principal component dataset, the comprehensive thermal stability index of each experimental group sample is calculated by a weighted summation algorithm, the comprehensive thermal stability index dataset of the experimental group sample is obtained, and is transmitted to the thermal stability detection coupling analysis result output module;

[0028] The thermal stability detection coupling analysis result output module: based on the comprehensive thermal stability index dataset and the tensile strength retention rate in the multi-dimensional performance index coupling matrix, the critical temperature of the thermal stability detection is determined, and the critical temperature of the thermal stability detection is output to the management end for human-computer interaction.

[0029] Please refer to Figure 2 A plastic pipe thermal stability detection multivariate coupling analysis method is shown in FIG. 1, which comprises the following steps: S1: adopting a random sampling technique, screening plastic pipe samples of the same batch, designing thermal stability detection experimental condition parameters, and obtaining plastic pipe experimental sample data for detecting thermal stability; S2: based on the plastic pipe experimental sample data, collecting multi-dimensional performance data through a data acquisition device, and obtaining plastic pipe experimental sample performance data; S3: based on the plastic pipe experimental sample performance data, calculating multi-dimensional performance indexes of the plastic pipe experimental sample data, and constructing a multi-dimensional performance index coupling matrix of the plastic pipe experimental group sample after preprocessing; S4: through a principal component analysis algorithm, characteristic decomposition is performed on the multi-dimensional performance index coupling matrix of the experimental group sample, and a multi-dimensional performance index principal component dataset of the plastic pipe experimental group sample is obtained; S5: based on the multi-dimensional performance index principal component dataset, the comprehensive thermal stability index of each experimental group sample is calculated by a weighted summation algorithm, and a comprehensive thermal stability index dataset of the experimental group sample is obtained; S6: based on the comprehensive thermal stability index dataset and the tensile strength retention rate in the multi-dimensional performance index coupling matrix, the critical temperature of the thermal stability detection is determined, and the critical temperature of the thermal stability detection is output to the management end for human-computer interaction.

[0030] S1: adopting a random sampling technique, screening plastic pipe samples of the same batch, designing thermal stability detection experimental condition parameters, and obtaining plastic pipe experimental sample data for detecting thermal stability, comprising the following steps:

[0031] S1.1: first, adopting a random sampling technique, screening plastic pipe samples of the same batch, and dividing them into n experimental groups and 1 room temperature control group, with 3 parallel samples in each group; then, based on the plastic pipe glass transition temperature T g , the melting temperature T m , and the actual use temperature T u of the plastic pipe, obtaining the temperature interval experimental condition upper and lower limit parameters T max and T min , and T min =T u+ ΔT1, ΔT1 is the temperature lower limit increment (ΔT1 makes the temperature lower limit T min satisfies that the thermal aging performance change of the plastic pipe can be observed, generally 5℃-20℃, T max = T m - ΔT2, ΔT2 is a safety margin to ensure that the plastic pipe does not melt during the experiment and can maintain the thermal aging process in a solid state, and the gradient temperature points T i , i = 1, 2,..., n, to avoid the temperature interval from deviating from the actual application scenario;

[0032] It needs to be specifically explained in this embodiment that the basic information of the same batch of plastic pipes is the same, including but not limited to material (such as PP-R, PVC), grade (such as PP-R pipe grade S4), additive composition (antioxidant type such as 1010, light stabilizer type such as UV-531), size, etc.; the sample is a plastic pipe without defects; the actual use temperature of the plastic pipe is the environmental temperature under long-term use; taking PP-R as an example, T g ≈-10℃, T m ≈160℃, the actual use temperature is about 60℃, the lower limit can be set to 60℃, the upper limit is set to 150℃, and then the gradient temperature points are divided between 60℃ and 150℃, T1=60℃, T2=90℃, T3=120℃, etc.

[0033] S1.2: Design the experimental condition parameters of the constant temperature test box, including humidity (for example, 50%±5%RH), air flow rate (for example, 0.5%), heat treatment constant time t (for example, 1000h), and the control group is placed in a room temperature environment of 23℃±2℃; then the experimental group samples are respectively placed in the constant temperature test box with a temperature of T i for heat treatment operation for a time of t, and after the heat treatment operation, the experimental group samples are cooled to room temperature for a cooling time of t1 (for example, 24h); finally, the experimental sample data of the plastic pipe for detecting thermal stability are obtained, including the experimental group samples at n gradient temperature points and 1 room temperature control group sample after cooling to room temperature;

[0034] S2: Based on the experimental sample data of the plastic pipe, through the data acquisition equipment, multi-dimensional performance data are collected to obtain the performance data of the experimental sample of the plastic pipe, including the following steps:

[0035] S2.1: Based on the experimental sample data of the plastic pipe, through the data acquisition equipment, the multi-dimensional performance data of the i-th temperature group sample are collected, including mechanical performance data, physical and chemical performance data, microscopic morphology data and microscopic chemical performance data; the mechanical performance data includes tensile strength a i , impact toughness b i and hardness h i ; the physical and chemical performance data include color difference value ΔEi and mass m i ; micro-morphology data includes crack density C i and bubble area proportion SA i ; micro-chemical performance data includes infrared spectrum of functional groups;

[0036] It needs to be specifically explained in this embodiment that the data acquisition equipment includes a universal testing machine to obtain tensile strength, a pendulum impact testing machine to obtain impact toughness, a Shore hardness tester to obtain hardness, a color difference meter to obtain color difference value, and a precision electronic balance to obtain mass; a scanning electron microscope (SEM) combined with quantitative metallographic analysis software to obtain crack density and bubble area proportion; and a Fourier transform infrared spectrometer to obtain the infrared spectrum of functional groups.

[0037] It needs to be specifically explained in this embodiment that the SEM+Image-Pro Plus combined analysis method is prior art, and in the field of material science (such as failure analysis and performance characterization of plastics and metals), professional software is used for quantitative statistics after images are acquired by SEM; SEM first acquires micro-morphology images of the sample, then the images are imported into Image-Pro Plus software, and after steps such as “image preprocessing (gray scale adjustment, noise reduction), threshold segmentation (separation of cracks / bubbles and matrix), feature statistics (counting, length / area measurement), unit conversion and proportion / density calculation”, the software outputs the quantitative results of crack density and bubble area proportion.

[0038] S2.2: traversing the multi-dimensional performance data of the n+1 plastic pipe experimental samples in groups to obtain the performance data of the plastic pipe experimental samples, including the multi-dimensional performance data of the n experimental groups and the multi-dimensional performance data of the 1 control group;

[0039] Please refer to Figure 3 , S3: based on the performance data of the plastic pipe experimental samples, calculating the multi-dimensional performance indicators of the plastic pipe experimental sample data, and constructing the multi-dimensional performance indicator coupling matrix of the plastic pipe experimental group samples after preprocessing, including the following steps:

[0040] S3.1: based on the performance data of the plastic pipe experimental samples, calculating the multi-dimensional performance indicators of the i-th temperature group of the plastic pipe experimental sample data, including mechanical performance indicators, physical and chemical performance indicators, micro-morphology performance indicators and micro-chemical performance indicators; the mechanical performance indicators include tensile strength retention rate R(a i ), impact toughness retention rate R(b i ) and hardness retention rate R(h i ), R(a i )=μ(a i ) / μ(a0), μ(a i) and μ(a0) are the average tensile strength (obtained from the mean value of 3 parallel samples) of the i-th temperature group sample and the average tensile strength of the control group sample, respectively, R(a i )=μ(a i ) / μ(a0), μ(a i ) and μ(a0) are the average impact toughness of the i-th temperature group sample and the average impact toughness of the control group sample, respectively, R(h i )=μ(h i ) / μ(h0), μ(h i ) and μ(h0) are the average hardness of the i-th temperature group sample and the average hardness of the control group sample, respectively; the physical and chemical performance indicators include the color difference value ΔE i and the mass loss rate R(m i ), R(m i )=[μ(m0)-μ(m i )] / μ(m0), μ(m i ) and μ(m0) are the average mass of the i-th temperature group sample and the average initial mass of the control group sample, respectively; the micro-morphology performance indicators include the crack density C i and the bubble area ratio SA i ; the micro-chemical performance indicators include the carbonyl index I C=O,i , which is obtained based on the infrared spectrum of the functional groups obtained by the Fourier transform infrared spectrometer, and the ratio of the carbonyl absorption peak area to the methylene absorption peak area is calculated to represent the degree of oxidation.

[0041] S3.2: Traverse the plastic pipe multi-dimensional performance indicators of n+1 groups to obtain the multi-dimensional performance indicators of the plastic pipe experimental sample data, including the multi-dimensional performance indicators of n experimental groups and the multi-dimensional performance indicators of 1 control group;

[0042] S3.3: Perform Z-score standardization processing on the plastic pipe multi-dimensional performance indicators R(a i ), R(b i ), R(h i ), C i , SA i and I C=O,i of the n experimental groups, and then use matrix structuring technology, with the temperature group as the row, the row index i corresponding to each temperature group T i , i=1, 2,..., n, and the column index j, j=1, 2,..., m, corresponding to each performance indicator, for example, j=1 for R(a i ), j=2 for R(b i ), etc., to construct an n×m multi-dimensional performance indicator coupling matrix Y of the plastic pipe experimental group samples, and the elements X ijThe multi-dimensional performance index of the control group is taken as the benchmark and does not enter the matrix Y for the standardized i th temperature group and j th performance index;

[0043] S4: Through the principal component analysis algorithm, the multi-dimensional performance index coupling matrix of the experimental group sample is characteristic decomposed, and the multi-dimensional performance index principal component data set of the experimental group sample of the plastic pipe is obtained, including the following steps:

[0044] S4.1: First, based on the multi-dimensional performance index coupling matrix Y of the experimental group sample, the covariance matrix S of all temperature groups is calculated, the covariance matrix S is an m×m symmetric matrix, the main diagonal elements are the variances of the performance indexes, and the non-main diagonal elements are the covariances between two performance indexes, the elements S jk is the covariance of the j th and k th performance indexes, , n is the number of experimental group samples, X ij is the j th performance index of the i th temperature group, μ(X j ) is the mean of the standardized value of the j th performance index, j∈m, k∈m; S jk >0 indicates that the two indexes are positively correlated (for example, the more cracks, the greater the quality loss), S jk <0 indicates that the two indexes are negatively correlated (for example, the more serious the oxidation, the lower the retention rate);

[0045] S4.2: The eigenvalue decomposition is performed on the covariance matrix S through S×a=λ×a, λ is the eigenvalue vector, a is the eigenvector, and the j th principal component variance contribution rate ω j , , λ j is the j th eigenvalue, the m eigenvalues are sorted from large to small, the number of eigenvalues is equal to the number of performance indexes, and the first J principal component data set with a principal component variance contribution rate greater than or equal to the corresponding threshold value (for example, the threshold value is 0.9) is screened, the elements PC j in the first J principal component data set are PC j =a j1 ×X1+a j2 ×X2+...+a jm ×X m , a jm is the weight of the j th principal component, representing the contribution degree of the j th performance index to the principal component, X m is the standardized value of the m th performance index; finally, the multi-dimensional performance index principal component data set of the experimental group sample of the plastic pipe is obtained, including the principal component data set, the principal component weight and the principal component variance contribution rate;

[0046] S5: Based on the multi-dimensional performance index principal component dataset, the comprehensive thermal stability index of each experimental group sample is calculated by weighted summation algorithm, and the comprehensive thermal stability index dataset of the experimental group sample is obtained, including the following steps:

[0047] S5.1: Based on the multi-dimensional performance index principal component dataset, the comprehensive thermal stability index TSI of each experimental group sample is calculated by weighted summation algorithm i , , ω j is the variance contribution rate of the jth principal component PC j i is the jth principal component score of the ith experimental group, and J is the number of principal components;

[0048] It is particularly pointed out in this embodiment that the larger the comprehensive thermal stability index TSI i , the worse the overall thermal stability at this temperature; because in the thermal stability detection experiment, the common mode of all performance indicators (strength retention rate, color change, mass loss, crack, etc.) is that the material performance gradually deteriorates as the temperature rises, good performance (such as strength retention rate) will decrease, and bad performance (such as crack density, mass loss rate) will increase, and the numerical size is positively correlated with the performance degradation, so the larger the index, the worse the overall thermal stability at this temperature;

[0049] S5.2: Traverse n experimental group samples to obtain the comprehensive thermal stability index dataset TSI of the experimental group samples, TSI = [TSI1, TSI2, …, TSI i , …, TSI n ], n is the number of experimental group samples;

[0050] S6: Based on the comprehensive thermal stability index dataset and the tensile strength retention rate in the multi-dimensional performance index coupling matrix, the critical temperature of thermal stability detection is determined, and the critical temperature of thermal stability detection is output to the management end for human-computer interaction, including the following steps:

[0051] S6.1: First, the mapping relationship R(a, T) between tensile strength retention rate R(a) and temperature T is fitted by Logistic regression analysis algorithm, , A is the fitting constant (usually close to the tensile strength retention rate of the control group), B is the decay rate constant (positive number, the larger B, the faster R(a) decreases when the temperature rises), T c is the critical temperature predicted by the model, when T = T c , R(a, T) = A / 2, that is, the temperature when the strength retention rate decreases to 50%, which is the upper limit T c of thermal stability detection (indicating that the strength is lost by half, which is a recognized and symbolic material performance failure threshold).

[0052] S6.2: Then draw the comprehensive thermal stability index TSI of all experimental group samples i The change curve of T i , calculate the TSI of the next gradient temperature point i+1 The difference between the TSI of the previous gradient temperature point i The ratio of the TSI of the previous gradient temperature point i The growth rate of the comprehensive thermal stability index Δ(TSI) is obtained; according to the equal interval ΔT, the growth rate Δ(TSI) between two gradient temperature points is calculated in the order of temperature from small to large, if Δ(TSI) is greater than the corresponding threshold (for example, 50%), it is considered that the TSI i There is a sharp increase inflection point, and the corresponding temperature is the upper limit T i of the thermal stability test. d ;

[0053] S6.3: If the tensile strength retention rate R(a) is less than or equal to 50% or the TSI i There is a sharp increase inflection point, the corresponding temperature is the critical temperature of the thermal stability test, and is output to the management end for human-computer interaction.

[0054] Secondly, the present application discloses the structure involved in the embodiment of the present application, and other structures can refer to the usual design, and in the case of no conflict, the same embodiment and different embodiments of the present application can be combined with each other;

[0055] Finally, the above-mentioned is only the preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be

[0056] included in the protection scope of the present application.

Claims

1. A method for multivariate coupled analysis of plastic pipe thermal stability detection, characterized in that: The method comprises the following steps: S1: adopt random sampling technology to screen plastic pipe samples of the same batch, design thermal stability detection experiment condition parameters, and obtain plastic pipe experiment sample data for detecting thermal stability; S2: based on the plastic pipe experiment sample data, collect multi-dimensional performance data through a data acquisition device, and obtain plastic pipe experiment sample performance data; S3: based on the plastic pipe experiment sample performance data, calculate multi-dimensional performance indexes of the plastic pipe experiment sample data, and construct a multi-dimensional performance index coupling matrix of the plastic pipe experiment group samples after preprocessing; S4: perform characteristic decomposition on the multi-dimensional performance index coupling matrix of the experiment group samples through a principal component analysis algorithm, and obtain a multi-dimensional performance index principal component data set of the plastic pipe experiment group samples; S5: based on the multi-dimensional performance index principal component data set, calculate a comprehensive thermal stability index of each experiment group sample through a weighted summation algorithm, and obtain a comprehensive thermal stability index data set of the experiment group samples; S6: based on the comprehensive thermal stability index data set and the tensile strength retention rate in the multi-dimensional performance index coupling matrix, determine a critical temperature for thermal stability detection, and output the critical temperature for thermal stability detection to a management end for human-computer interaction.

2. The multivariate coupled analysis method for detecting the thermal stability of plastic pipes according to claim 1, characterized in that: The plastic pipe experiment sample data for detecting thermal stability obtained in S1 comprises: S1.1: Firstly, the same batch of plastic pipe samples were screened by random sampling technique and divided into n experimental groups and 1 room temperature control group, with 3 parallel samples in each group; then based on the plastic pipe glass transition temperature T g , melting temperature T m and actual use temperature T u of the plastic pipe, the upper and lower limit parameters T max and T min of the temperature interval experimental conditions were obtained, T min =T u +ΔT1, ΔT1 is the lower limit increment of temperature, T max =T m -ΔT2, ΔT2 is the safety margin, and the gradient temperature points T i are set according to equal interval ΔT, i=1,2,...,n; S1.2: Design the experimental condition parameters of the constant temperature test chamber, including humidity, air flow rate, and constant time t of heat treatment. The control group is placed in a room temperature environment of 23°C ± 2°C. Then the experimental group samples are placed in the constant temperature test chamber at a temperature of T i for a heat treatment operation for a time of t. After the heat treatment operation, the experimental group samples are cooled to room temperature for a cooling time of t1. Finally, the experimental sample data of the plastic pipe for detecting thermal stability are obtained, including the experimental group samples at n gradient temperature points and 1 room temperature control group sample cooled to room temperature.

3. The multivariate coupled analysis method for detecting the thermal stability of plastic pipes according to claim 1, characterized in that: The multi-dimensional performance index of the plastic pipe experimental sample data in S3 is calculated: S3.1: based on the performance data of the plastic pipe experimental sample, the multi-dimensional performance index of the i th temperature group of the plastic pipe experimental sample data is calculated, including mechanical performance index, physical and chemical performance index, micro-morphology performance index and micro-chemical performance index; the mechanical performance index includes tensile strength retention rate R(a i ), impact toughness retention rate R(b i ) and hardness retention rate R(h i ); the physical and chemical performance index includes color difference value ΔE i and mass loss rate R(m i ); the micro-morphology performance index includes crack density C i and bubble area ratio SA i ; the micro-chemical performance index includes carbonyl index I C=O,i ; S3.2: traverse n+1 plastic pipe multi-dimensional performance indexes of groups to obtain multi-dimensional performance indexes of the plastic pipe experiment sample data, including n experiment group multi-dimensional performance indexes and 1 control group multi-dimensional performance index.

4. The multivariate coupled analysis method for detecting the thermal stability of plastic pipes according to claim 1, characterized in that: The multi-dimensional performance index coupling matrix of the experimental group sample of the plastic pipe in S3: S3.3: the multi-dimensional performance indexes R(a i ), R(b i ), R(h i ), C i , SA i and I C=O,i of n experimental groups of plastic pipes are subjected to Z-score standardization processing, and then matrix structuring technology is used, with the temperature groups as rows, the row index i corresponding to each temperature group T i , i = 1, 2,..., n, and the column index j, j = 1, 2,..., m, corresponding to each performance index, to construct an n x m multi-dimensional performance index coupling matrix Y of the experimental group sample of the plastic pipe, the element X ij in the matrix being the i th temperature group j th performance index after standardization. The multi-dimensional performance indexes of the control group are not entered into the matrix Y as a benchmark.

5. The multivariate coupled analysis method for detecting the thermal stability of plastic pipes according to claim 1, characterized in that: The S4 implementation includes: S4.1: firstly, based on the multi-dimension performance index coupling matrix Y of the experimental group sample, the covariance matrix S of all temperature groups is calculated, the covariance matrix S is an m x m symmetric matrix, the main diagonal elements are the variances of the performance indexes, and the non-main diagonal elements are the covariances between different performance indexes, the element S jk is the covariance of the jth and kth performance indexes, , n is the number of experimental group samples, X ij is the jth performance index of the ith temperature group, μ(X j ) is the mean of the standardized value of the jth performance index, j ∈ m, k ∈ m.

6. The multivariate coupled analysis method for detecting the thermal stability of plastic pipes according to claim 1, characterized in that: The S4 implementation further comprises: S4.2: eigenvalue decomposition is performed on the covariance matrix S through Sx a = λx a, λ is an eigenvalue vector, a is an eigenvector, and a variance contribution rate ω of a jth principal component is obtained j , a first J principal component dataset with a principal component variance contribution rate greater than or equal to a corresponding threshold value is screened, elements PC j in the first J principal component dataset j , PC j1 = a j2 x X1 + a jm x X2 +... + a m x Xm, a jm is a weight of the jth principal component, and X m is a standardized value of the mth performance index; finally, a multi-dimensional performance index principal component dataset of the plastic pipe experimental group sample is obtained, including a principal component dataset, a principal component weight and a principal component variance contribution rate.

7. The multivariate coupled analysis method for detecting the thermal stability of plastic pipes according to claim 1, characterized in that: The comprehensive thermal stability index data set of the experiment group samples obtained in S5 comprises: S5.1: Based on the multi-dimensional performance index principal component dataset, the comprehensive thermal stability index TSI of each experimental group sample is calculated by weighted summation algorithm i , , ω j is the variance contribution rate of the jth principal component, PC j i is the jth principal component score of the ith experimental group, and J is the number of principal components; S5.2: Traverse the m experimental group samples to obtain the comprehensive thermal stability index dataset TSI of the experimental group samples, TSI = [TSI1, TSI2,..., TSI i ,...,TSI n ], n is the number of experimental group samples.

8. The multivariate coupled analysis method for detecting the thermal stability of plastic pipes according to claim 1, characterized in that: The critical temperature for thermal stability detection obtained in S6 comprises: S6.1: Firstly, the mapping relationship R(a, T) between the tensile strength retention rate R(a) and temperature T is fitted by Logistic regression analysis algorithm, , A is the fitting constant, B is the decay rate constant, T c is the critical temperature predicted by the model, when T = T c , R(a, T) = A / 2, that is, the temperature at which the strength retention rate is reduced to 50%, which is the upper limit T c of the thermal stability test; S6.2: Then plot the composite thermal stability index (TSI) for all experimental group samples. i With T i The curve of change is used to calculate the TSI at the next gradient temperature point. i+1 TSI at the previous gradient temperature point i The difference between the TSI at the previous gradient temperature point and the TSI at the previous gradient temperature point i The ratio is used to obtain the growth rate Δ(TSI) of the comprehensive thermal stability index; the growth rate Δ(TSI) between each pair of gradient temperature points is calculated according to the equal interval ΔT, in ascending order of temperature. If Δ(TSI) is greater than the corresponding threshold, it indicates that TSI i If a sudden inflection point appears, then TSI is determined. i The corresponding temperature is the upper limit T for thermal stability testing. d ; S6.3: If the tensile strength retention rate R(a) is less than or equal to 50% or TSI i A sharp increase inflection point occurs, and the corresponding temperature is the critical temperature of the thermal stability test, and is output to the management end for human-computer interaction.

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