Insulator life prediction method and device and storage medium

Abstract

The invention relates to the technical field of power equipment detection, in particular to an insulator life prediction method and device and a storage medium. Comprising the following steps: acquiring aging parameters corresponding to a target insulator having the same composition as a to-be-predicted insulator at different time points; determining a first correlation result and a second correlation result based on the aging parameters corresponding to different time points; determining at least two key aging parameters from the aging parameters according to the first correlation result and the second correlation result; performing multivariate linear fitting processing on the at least two key aging parameters based on different time points to obtain a fitting result; and determining the residual life of the insulator to be predicted according to the fitting result, the total life of the target insulator and the current operation time of the insulator to be predicted. According to the embodiment of the invention, the accuracy of insulator life prediction can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment detection, and in particular to an insulator life prediction method, device and storage medium. Background Art

[0002] As a key component in transmission lines, insulators need to meet the dual requirements of mechanical support and electrical insulation. Since insulators need to be exposed to complex outdoor environments for a long time, it is difficult to monitor their status. Therefore, life prediction of insulators can ensure the safe and stable operation of the power system.

[0003] At present, the life prediction method of insulators is usually to predict based on appearance characteristics, physical characteristics and electrical characteristics combined with the on-site operating environment. However, the model established with a single aging feature is difficult to fully reflect the actual aging status of the insulator, resulting in a one-sided model evaluation. Although the life prediction model based on multiple aging characteristics has improved this deficiency, it often generates redundancy due to ignoring the correlation between the characteristics, which limits the versatility and accuracy of the model and is difficult to be widely used. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides an insulator life prediction method, device and storage medium, which can improve the accuracy of insulator life prediction.

[0005] In a first aspect, the present application provides an insulator life prediction method, comprising: obtaining aging parameters corresponding to a target insulator having the same components as an insulator to be predicted at different time points; determining a first correlation result and a second correlation result based on the aging parameters corresponding to the different time points; the first correlation result is a correlation result between the aging parameter and time, and the second correlation result is a correlation result between any two aging parameters; determining at least two key aging parameters from the aging parameters based on the first correlation result and the second correlation result; performing multivariate linear fitting processing on at least two key aging parameters based on different time points to obtain fitting results; the fitting results include a weight and a constant parameter for each key aging parameter; determining the remaining life of the insulator to be predicted based on the fitting results, the total life of the target insulator, and the current operating time of the insulator to be predicted.

[0006] In a second aspect, the present application provides an insulator life prediction device, comprising: an acquisition module, used to obtain aging parameters corresponding to a target insulator having the same composition as the insulator to be predicted at different time points; a determination module, used to determine a first correlation result and a second correlation result based on the aging parameters corresponding to different time points; the first correlation result is a correlation result between the aging parameter and time, and the second correlation result is a correlation result between any two aging parameters; the determination module is also used to determine at least two key aging parameters from the aging parameters based on the first correlation result and the second correlation result; a processing module, used to perform multivariate linear fitting processing on at least two key aging parameters based on different time points to obtain a fitting result; the fitting result includes a weight and a constant parameter of each key aging parameter; a calculation module, used to determine the remaining life of the insulator to be predicted based on the fitting result, the total life of the target insulator, and the current operating time of the insulator to be predicted.

[0007] In a third aspect, the present application provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the insulator life prediction method of the first aspect is implemented.

[0008] In a fourth aspect, the present application provides a computer-readable storage medium, comprising: a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the insulator life prediction method as in the first aspect is implemented.

[0009] In a fifth aspect, the present application provides a computer program product, comprising: when the computer program product is run on a computer, the computer is enabled to implement the insulator life prediction method as in the first aspect.

[0010] Compared with the prior art, the technical solution provided by the present application has the following advantages: First, the aging parameters corresponding to the target insulator with the same composition as the insulator to be predicted at different time points are obtained, and the first correlation result and the second correlation result are determined based on the aging parameters corresponding to the different time points. Among them, the first correlation result is the correlation result between the aging parameter and time, and the second correlation result is the correlation result between any two aging parameters. Afterwards, at least two key aging parameters are determined from the aging parameters based on the first correlation result and the second correlation result, and multivariate linear fitting processing is performed on the at least two key aging parameters based on different time points to obtain fitting results. Among them, the fitting results include the weight and constant parameters of each key aging parameter. Finally, the remaining life of the insulator to be predicted is determined based on the fitting results, the total life of the target insulator, and the current operating time of the insulator to be predicted. In this way, the remaining life of the insulator to be predicted can be predicted based on the correlation results between multiple aging parameters of the target insulator with the same composition as the insulator to be predicted at different time points. On the one hand, multiple aging parameters can be used to predict the life of the insulator, avoiding the problem that the model established using a single aging feature is difficult to fully reflect the actual aging state of the insulator, resulting in one-sided model evaluation, thereby improving the accuracy of insulator life prediction; on the other hand, the insulator life can also be predicted through the correlation results between multiple aging parameters at different time points, avoiding the problem of feature redundancy caused by ignoring the correlation between the aging characteristics, and the limited versatility and accuracy of the model, thereby further improving the accuracy of predicting the insulator life. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0013] Figure 1 A schematic diagram of an application scenario of an insulator life prediction method provided in an embodiment of the present application;

[0014] Figure 2 One of the flow charts of the insulator life prediction method provided in the embodiment of the present application;

[0015] Figure 3 A second flow chart of the insulator life prediction method provided in an embodiment of the present application;

[0016] Figure 4 A third flow chart of the insulator life prediction method provided in an embodiment of the present application;

[0017] Figure 5 A schematic diagram of the structure of an insulator life prediction device provided in an embodiment of the present application;

[0018] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only part of the embodiments of the present application, rather than all of the embodiments.

[0021] Insulators are a type of device used in electrical engineering. They are mainly used to support or suspend conductors and insulate the conductors from poles or other supporting structures. Insulators are usually made of materials with excellent electrical insulation properties. For example, composite insulators are mainly composed of hardware, core rods, and silicone rubber sheds. The outer silicone rubber shed is a composite of polysiloxane, flame retardants, and reinforcing agents. It is a key protective layer to ensure the long-term and stable operation of insulators. However, when insulators are exposed to complex outdoor environments for a long time, they will be affected by various aging factors such as ultraviolet radiation, humidity, and electric fields, which will cause their performance to gradually degrade, thereby affecting the service life of the insulators. Therefore, accurately assessing the aging state of insulators and effectively predicting their service life are of great significance to formulating maintenance, repair, and replacement strategies for insulators.

[0022] At present, scholars have conducted research on the aging state of composite insulators from multiple dimensions such as appearance characteristics, physical properties and electrical properties, and conducted accelerated aging tests in combination with the on-site operating environment to reveal the aging laws of insulators. However, the model established with a single aging feature is difficult to fully reflect the actual aging state of silicone rubber, resulting in a one-sided model evaluation; and although the life prediction model based on multiple aging features has improved this deficiency, it often generates redundancy due to ignoring the correlation between the features, which limits the versatility and accuracy of the model and is difficult to be widely applied.

[0023] In view of the above problems, an embodiment of the present application provides an insulator life prediction method, including first, obtaining aging parameters corresponding to target insulators with the same components as the insulator to be predicted at different time points, and determining a first correlation result and a second correlation result based on the aging parameters corresponding to the different time points. The first correlation result is the correlation result between the aging parameter and time, and the second correlation result is the correlation result between any two aging parameters. Afterwards, at least two key aging parameters are determined from the aging parameters based on the first correlation result and the second correlation result, and multivariate linear fitting processing is performed on the at least two key aging parameters based on different time points to obtain fitting results. The fitting results include the weight and constant parameters of each key aging parameter. Finally, the remaining life of the insulator to be predicted is determined based on the fitting results, the total life of the target insulator, and the current operating time of the insulator to be predicted. In this way, the remaining life of the insulator to be predicted can be predicted based on the correlation results between multiple aging parameters of the target insulator with the same composition as the insulator to be predicted at different time points. On the one hand, multiple aging parameters can be used to predict the life of the insulator, avoiding the problem that the model established using a single aging feature is difficult to fully reflect the actual aging state of the insulator, resulting in one-sided model evaluation, thereby improving the accuracy of insulator life prediction; on the other hand, the insulator life can also be predicted through the correlation results between multiple aging parameters at different time points, avoiding the problem of feature redundancy caused by ignoring the correlation between the aging characteristics, and the limited versatility and accuracy of the model, thereby further improving the accuracy of predicting the insulator life.

[0024] The insulator life prediction method provided by the embodiment of the present disclosure is mainly applicable to the scenario of life prediction of insulators that operate outdoors for a long time. Figure 1 This is an application scenario diagram of an insulator life prediction method provided by an embodiment of the present disclosure. Figure 1 As shown, the application scenario includes a terminal 102 and a server 104. The terminal 102 can communicate with the server 104 through a network. The terminal 102 can include but is not limited to various personal computers, laptops, smart phones, tablet computers, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0025] It should be noted that the insulator life prediction method provided in the embodiment of the present application can be executed by an electronic device, and the electronic device can be a server 104, a terminal 102, or a device integrating the functions of the server 104 and the terminal 102. The present application does not limit this.

[0026] The insulator life prediction method provided in the embodiment of the present application can be executed by an insulator life prediction device, which can be hardware or software. When the insulator life prediction device is hardware, it can be various electronic devices with the function of running the insulator life prediction, including but not limited to mobile phones, computers, tablet computers, etc. When the insulator life prediction device is software, it can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules, or it can be implemented as a single software or software module. No specific limitation is made here.

[0027] Figure 2 A schematic diagram of a flow chart of an insulator life prediction method provided in an embodiment of the present application, such as Figure 3 As shown, the insulator life prediction method may include the following steps.

[0028] S11, obtaining aging parameters corresponding to target insulators having the same components as the insulator to be predicted at different time points.

[0029] First, an insulator having the same composition as the insulator to be predicted is determined as a target insulator. For example, when the insulator to be detected is a composite insulator, the target insulator also needs to be a composite insulator. In some embodiments, the target insulator has the same operating environment as the insulator to be predicted. For example, the operating environment such as humidity, light, temperature, salt spray, and altitude is the same.

[0030] Secondly, determine the detection position for obtaining the aging parameters. Specifically, since the first part of the insulator affected by the environment is usually the surface part in contact with the external environment, the surface position of the insulator is used as the detection position.

[0031] Finally, the aging parameters corresponding to the target insulator with the same composition as the insulator to be predicted at different time points are obtained.

[0032] The aging parameters include physical characteristic parameters and chemical characteristic parameters of the target insulator. For example, the aging parameters include at least Shore hardness, tensile strength, elongation at break, tear strength, Si-O-Si relative peak area, Si(CH3)2 relative peak area, Si(-O)2 relative content, etc.

[0033] Specifically, the method of obtaining the corresponding aging parameters of the target insulator at different time points can be to use relevant instruments to measure the corresponding aging parameters of the target insulator at different time points. The unit of the time point is year, that is, when the target insulator is operated in different years, the aging parameters of the corresponding years are collected.

[0034] For example, a Shore hardness tester (such as LX-A) can be used to measure the Shore hardness of the target insulator. Specifically, to ensure the measurement accuracy, the Shore hardness corresponding to each time point should be collected by collecting multiple average values. For example, when collecting the Shore hardness of a target insulator that has been in operation for the 5th year, multiple Shore hardnesses can be collected in that year (the 5th year), and then the average of the multiple Shore hardnesses can be used as the Shore hardness of the target insulator in the 5th year. Among them, the specific values ​​of the multiple values ​​can be set according to the actual situation, such as 3, 5, etc.

[0035] For example, a machine-controlled electronic tensile testing machine (such as TSKL-500N) can be used to measure the tensile strength, elongation at break, and tear strength of the target insulator. Specifically, during the measurement, the target insulator is cut into dumbbell shapes and right-angle shapes for testing, and it is necessary to ensure that the surface of the material is not affected by the cutting. Similarly, during the measurement, the tensile strength, elongation at break, and tear strength corresponding to each time point are collected by collecting multiple average values.

[0036] For example, an infrared spectrometer (such as Thermo Scientific Nicolet iS10) can be used to measure the relative peak area of ​​Si-O-Si and the relative peak area of ​​Si(CH3)2 of the target insulator. Specifically, the relative peak area of ​​Si-O-Si and the relative peak area of ​​Si(CH3)2 are measured using the attenuated total reflection (ATR) mode, and the characteristic peak range of the main functional groups is 500cm -1 Up to 4000cm -1 ,During the measurement process, the peak area of ​​the non-aged target insulator was used as a reference value, and the ,peak area ratio between the non-aged target insulator and the aged target insulator was used to ,quantitatively analyze the changes of the functional groups.

[0037] For example, an X-ray photoelectron spectrometer (such as Thermo Scientific K-Alpha) can be used to measure the relative content of Si(-O)2 in the target insulator. Specifically, the contaminated carbon C1s peak (~284.80 eV) is used for target insulator binding energy (BE) charge correction, and Si2p is subjected to peak separation analysis. The relative peak area is defined as the peak area of ​​the intact target insulator divided by the peak area of ​​the target insulator at different actual operating times, which is used to characterize the Si-O bond transformation during the aging process and record the relative content change of Si(-O)2.

[0038] In some embodiments, the insulator to be predicted is a composite insulator.

[0039] S12. Determine a first correlation result and a second correlation result based on aging parameters corresponding to different time points.

[0040] Among them, the first correlation result is the correlation result between the aging parameter and time, and the second correlation result is the correlation result between any two aging parameters. For example, when the aging parameters corresponding to different time points are shown in Table 1 below, the Shore hardness, tensile strength, elongation at break, tear strength, Si-O-Si relative peak area, Si(CH3)2 relative peak area, and Si(-O)2 relative content corresponding to the 1st, 3rd, 5th, and 7th years are collected. In this way, the first correlation results include the correlation results between Shore strength and time (a1-a4 and year 1-year 7), the correlation results between tensile strength and time (b1-b4 and year 1-year 7), the correlation results between elongation at break and time (c1-c4 and year 1-year 7), the correlation results between tear strength and time (d1-d4 and year 1-year 7), the correlation results between Si-O-Si relative peak area and time (e1-e4 and year 1-year 7), the correlation results between Si(CH3)2 relative peak area and time (f1-f4 and year 1-year 7), and the correlation results between Si(-O)2 relative content and time (g1-g4 and year 1-year 7). The second correlation results include the correlation results between Shore strength and tensile strength (a1-a4 and b1-b4), Shore strength and elongation at break (a1-a4 and c1-c4), ..., the relative peak area of ​​Si(CH3)2 and the relative content of Si(-O)2 (f1-f4 and g1-g4).

[0041] Table 1

[0042]

[0043] Specifically, a correlation algorithm may be used to calculate and determine the correlation result between the aging parameter and time, and the correlation result between any two aging parameters. For example, the correlation algorithm may be a lag correlation algorithm, a cross-correlation function algorithm, an autocorrelation algorithm, and the like.

[0044] S13. Determine at least two key aging parameters from the aging parameters according to the first correlation result and the second correlation result.

[0045] The correlation results include correlation and non-correlation.

[0046] Specifically, multiple groups of aging parameters that are related to time and unrelated to each other can be determined as key aging parameters. For example, the aging parameters corresponding to different time points are shown in Table 1 above, and when the Shore hardness, Si(-O)2 relative content, and Si-O-Si relative peak area are all related to time, the Shore hardness and Si(-O)2 relative content are unrelated, the Shore hardness and Si-O-Si relative peak area are related, and the Si(-O)2 relative content and Si-O-Si relative peak area are related, the Shore hardness and Si(-O)2 relative content are selected as key aging parameters.

[0047] In some embodiments, according to the first correlation result and the second correlation result, at least two key aging parameters are determined from the aging parameters by determining the time-related aging parameters as the time-related aging parameters according to the first correlation result, and then determining the irrelevant time-related aging parameters among the time-related aging parameters as the key aging parameters according to the second correlation result.

[0048] In some embodiments, based on the second correlation result, the unrelated aging parameters among the aging parameters are determined as key aging parameters by determining any two unrelated aging parameters among the aging parameters as key aging parameters.

[0049] Exemplarily, when the aging parameters include A, B, C, D, and E, and A, B, C, and D are all related to time, A, B, C, and D are determined as time-dependent aging parameters. Then, any two unrelated time-dependent aging parameters among A, B, C, and D are determined as key aging parameters. For another example, when the time-dependent aging parameters are A, B, C, and D, and A is unrelated to B, A is unrelated to C, B is unrelated to C, A is related to D, B is unrelated to D, and C is related to D, any group of A and B, A and C, B and C, and B and D is determined as the key aging parameter.

[0050] In some embodiments, when the aging parameters include multiple groups of irrelevant aging parameters with different numbers, the group with the largest number of irrelevant aging parameters is determined as the key aging parameter.

[0051] For another example, when the aging parameters are A, B, C, and D, and A is uncorrelated with B, A is uncorrelated with C, B is uncorrelated with C, A is correlated with D, B is uncorrelated with D, and C is correlated with D, it can be obtained that A, B, and C are uncorrelated with each other, and B is uncorrelated with D. Then the group of A, B, and C with the most uncorrelated aging parameters is determined as the key aging parameters.

[0052] S14. Perform multivariate linear fitting processing on at least two key aging parameters based on different time points to obtain fitting results.

[0053] The fitting results include the weight and constant parameters of each key aging parameter, and the constants include regression coefficients and constants.

[0054] First, the equivalent operating time is established with the key aging parameters as independent variables and time as dependent variable. Specifically, the equivalent time expression is as follows: T C / A =β0+β1x1+β2x2+…+β k x k +ε, where T C / A is the equivalent operating time of the target insulator, x k is the key aging parameter, β is the regression coefficient, and ε is a constant.

[0055] Secondly, the key aging parameters corresponding to different time points are fitted according to the equivalent running time formula to obtain fitting results. Specifically, a tool capable of performing multivariate linear fitting can be used to perform multivariate linear fitting on at least two key aging parameters to obtain fitting results. For example, the tool for multivariate linear fitting can be SPSS software, Origin software, etc.

[0056] S15. Determine the remaining life of the insulator to be predicted according to the fitting result, the total life of the target insulator, and the current operating time of the insulator to be predicted.

[0057] In some embodiments, the remaining life of the insulator to be predicted can be determined based on the fitting result, the total life of the target insulator, and the current operating time of the insulator to be predicted. The method can be to calculate the ratio of the current operating time and the fitting result, and calculate the product of the ratio and the total life of the target insulator; and determine the difference between the product and the current operating time of the insulator to be predicted as the remaining life of the insulator to be predicted.

[0058] In some embodiments, the formula Determine the remaining life of the insulator to be predicted. Among them, T R is the remaining life of the insulator to be predicted, T C is the current operating time of the insulator to be tested, A is the fitting result of the equivalent operating time, T F / A (x) is the total life of the target insulator.

[0059] Specifically, the remaining life of the insulator to be predicted can be expressed as Formula 1: T R =T F -T C , where T R is the remaining life of the insulator to be predicted, T F is the total life of the insulator to be tested, T Cis the current operating time of the insulator to be detected. In this way, the ratio between the current operating time of the insulator to be detected and the total life of the insulator to be detected satisfies the following formula 2: Among them, T C is the current operating time of the insulator to be tested, T F is the total life of the insulator to be tested, T C / A (x) is the equivalent operating time of the target insulator, T F / A (x) is the total life of the target insulator. Then, substituting Formula 2 into Formula 1, we can get: Then the fitting result (T C / A Substituting (x) into So we can use the formula Determine the remaining life of the insulator to be predicted.

[0060] In the above scheme, first, the aging parameters corresponding to the target insulator with the same composition as the insulator to be predicted at different time points are obtained, and the first correlation result and the second correlation result are determined based on the aging parameters corresponding to the different time points. The first correlation result is the correlation result between the aging parameter and time, and the second correlation result is the correlation result between any two aging parameters. Afterwards, at least two key aging parameters are determined from the aging parameters based on the first correlation result and the second correlation result, and multivariate linear fitting processing is performed on the at least two key aging parameters based on different time points to obtain fitting results. The fitting results include the weight and constant parameters of each key aging parameter. Finally, the remaining life of the insulator to be predicted is determined based on the fitting results, the total life of the target insulator, and the current operating time of the insulator to be predicted. In this way, the remaining life of the insulator to be predicted can be predicted based on the correlation results between multiple aging parameters of the target insulator with the same composition as the insulator to be predicted at different time points. On the one hand, multiple aging parameters can be used to predict the life of the insulator, avoiding the problem that the model established using a single aging feature is difficult to fully reflect the actual aging state of the insulator, resulting in one-sided model evaluation, thereby improving the accuracy of insulator life prediction; on the other hand, the insulator life can also be predicted through the correlation results between multiple aging parameters at different time points, avoiding the problem of feature redundancy caused by ignoring the correlation between the aging characteristics, and the limited versatility and accuracy of the model, thereby further improving the accuracy of predicting the insulator life.

[0061] In some embodiments, the correlation results include correlation and non-correlation, such as Figure 3 As shown, the method of determining the first correlation result and the second correlation result based on the aging parameters corresponding to different time points includes the following steps:

[0062] S121. For any two sets of parameters, calculate the Pearson coefficient between the two sets of parameters according to different time points and the two sets of parameters.

[0063] The two sets of parameters are two sets of parameters corresponding to the first correlation result, or two sets of parameters corresponding to the second correlation result.

[0064] Specifically, according to the formula Calculate the Pearson coefficient between two sets of parameters. i One of the first set of parameters in the two sets of parameters, y i is one of the second set of parameters in the two sets of parameters, x is the mean of the first set of parameters, y is the mean of the second set of parameters, r i For x i and i The Pearson coefficient between .

[0065] S122. Calculate the test value of the Pearson coefficient using a test algorithm.

[0066] Specifically, according to the formula Calculate the test value of Pearson's coefficient, where r i For x i and i Pearson coefficient between i For r i The test value of , n is the number of parameters in a set of parameters, which can also be understood as the number of samples in a set of parameters.

[0067] S123. Calculate the correlation factor of the two sets of parameters based on the test value and the sample size.

[0068] In some embodiments, corresponding tools can be used to calculate the correlation factor of two sets of parameters according to the test value and the sample size, for example, the dt(t, df) function in R language, the tpdf(t, nu) function in MATLAB, the online tool WolframAlpha, etc.

[0069] In some embodiments, the correlation factor of the two sets of parameters may also be calculated according to the following formula: p=2×(1-F(|t|, df)).

[0070] Among them, p is the correlation factor of the two sets of parameters, df=n-2, n is the number of samples in a set of parameters, t is the test value, and Γ is the gamma function.

[0071] S124. When the correlation factor is less than a first threshold, it is determined that the two groups of parameters are correlated; when the correlation factor is greater than a second threshold, it is determined that the two groups of parameters are uncorrelated.

[0072] The second threshold is greater than or equal to the first threshold, and both the first threshold and the second threshold are preset, for example, default values, or values ​​calculated by relevant personnel according to actual conditions. For example, the first threshold is 0.01, and the second threshold is 0.05.

[0073] In some embodiments, the correlation results include significantly correlated, correlated, and not correlated. Figure 4 As shown, the insulator life prediction method also includes:

[0074] S1241. Determine whether the correlation factor is less than a first threshold, and when the correlation factor is less than the first threshold, execute step S1242; when the correlation factor is greater than or equal to the first threshold, execute step S1243.

[0075] S1242. Determine whether two sets of parameters are significantly correlated.

[0076] S1243. Determine whether the correlation factor is less than the second threshold, and when the correlation factor is greater than or equal to the first threshold and less than or equal to the second threshold, execute step S1244; when the correlation factor is greater than the second threshold, execute step S1245.

[0077] S1244. Determine whether the two sets of parameters are related.

[0078] S1245. Determine that the two sets of parameters are unrelated.

[0079] Exemplarily, when the first threshold is 0.01 and the second threshold is 0.05, if the correlation factor is less than 0.01, it is determined that the two sets of parameters are significantly correlated; if the correlation factor is greater than or equal to 0.01 and less than or equal to 0.05, it is determined that the two sets of parameters are correlated; if the correlation factor is greater than 0.05, it is determined that the two sets of parameters are uncorrelated.

[0080] In this way, when determining the aging parameters in step S13, the aging parameters that are significantly correlated with time and those that are correlated with time are determined as aging parameters; when determining the key aging parameters, if there are no completely irrelevant aging parameters, the correlated aging parameters are determined as the key aging parameters.

[0081] In some embodiments, the correlation between the two sets of parameters can also be determined based on the positive or negative situation of the Pearson coefficient. When the Pearson coefficient is a positive number and the two sets of parameters are correlated, it is determined that the two sets of parameters are positively correlated; when the Pearson coefficient is a negative number and the two sets of parameters are correlated, it is determined that the two sets of parameters are negatively correlated.

[0082] In the above scheme, for any two sets of parameters, the Pearson coefficient between the two sets of parameters can be calculated according to different time points and two sets of parameters; the test value of the Pearson coefficient is calculated by a test algorithm; the correlation factor of the two sets of parameters is calculated according to the test value and the number of samples; when the correlation factor is less than the first threshold, the two sets of parameters are determined to be correlated; when the correlation factor is greater than the second threshold, the two sets of parameters are determined to be uncorrelated; the second threshold is greater than or equal to the first threshold. In this way, the significance test is introduced to screen the aging parameters that are significantly correlated with time to predict the remaining life, which reduces the redundancy and deviation between variables and ensures that the calculation process has better versatility and accuracy.

[0083] The embodiment of the present application can divide the functional modules of the insulator life prediction device according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0084] like Figure 5 As shown, it is a structural schematic diagram of the insulator life prediction device provided in an embodiment of the present application, which is applied to a security operation center. The insulator life prediction device includes an acquisition module 51, a determination module 52, a processing module 53 and a calculation module 54.

[0085] The acquisition module 51 is used to obtain the aging parameters corresponding to the target insulator with the same composition as the insulator to be predicted at different time points; the determination module 52 is used to determine the first correlation result and the second correlation result based on the aging parameters corresponding to the different time points; the first correlation result is the correlation result between the aging parameter and time, and the second correlation result is the correlation result between any two aging parameters; the determination module 52 is also used to determine at least two key aging parameters from the aging parameters according to the first correlation result and the second correlation result; the processing module 53 is used to perform multivariate linear fitting processing on at least two key aging parameters based on different time points to obtain fitting results; the fitting results include the weight and constant parameters of each key aging parameter; the calculation module 54 is used to determine the remaining life of the insulator to be predicted according to the fitting results, the total life of the target insulator, and the current operating time of the insulator to be predicted.

[0086] In the above scheme, first, the aging parameters corresponding to the target insulator with the same composition as the insulator to be predicted at different time points are obtained, and the first correlation result and the second correlation result are determined based on the aging parameters corresponding to the different time points. The first correlation result is the correlation result between the aging parameter and time, and the second correlation result is the correlation result between any two aging parameters. Afterwards, at least two key aging parameters are determined from the aging parameters based on the first correlation result and the second correlation result, and multivariate linear fitting processing is performed on the at least two key aging parameters based on different time points to obtain fitting results. The fitting results include the weight and constant parameters of each key aging parameter. Finally, the remaining life of the insulator to be predicted is determined based on the fitting results, the total life of the target insulator, and the current operating time of the insulator to be predicted. In this way, the remaining life of the insulator to be predicted can be predicted based on the correlation results between multiple aging parameters of the target insulator with the same composition as the insulator to be predicted at different time points. On the one hand, multiple aging parameters can be used to predict the life of the insulator, avoiding the problem that the model established using a single aging feature is difficult to fully reflect the actual aging state of the insulator, resulting in one-sided model evaluation, thereby improving the accuracy of insulator life prediction; on the other hand, the insulator life can also be predicted through the correlation results between multiple aging parameters at different time points, avoiding the problem of feature redundancy caused by ignoring the correlation between the aging characteristics, and the limited versatility and accuracy of the model, thereby further improving the accuracy of predicting the insulator life.

[0087] In some embodiments, the correlation results include correlation and irrelevance; the determination module 52 includes a calculation submodule, a test submodule and a determination submodule; the calculation submodule is used to calculate the Pearson coefficient between any two sets of parameters according to different time points and the two sets of parameters; the two sets of parameters are the two sets of parameters corresponding to the first correlation result, or the two sets of parameters corresponding to the second correlation result; the test submodule is used to calculate the test value of the Pearson coefficient using a test algorithm; the calculation submodule is also used to calculate the correlation factor of the two sets of parameters based on the test value and the sample size; the determination submodule is used to determine that the two sets of parameters are correlated when the correlation factor is less than a first threshold; when the correlation factor is greater than a second threshold, determine that the two sets of parameters are irrelevant; the second threshold is greater than or equal to the first threshold.

[0088] In some embodiments, the correlation results include correlation and irrelevance; the determination module 52 is specifically used to: determine the time-related aging parameters as aging parameters according to the first correlation results; and determine the irrelevant aging parameters among the aging parameters as key aging parameters according to the second correlation results.

[0089] In some embodiments, the determination module 52 is specifically configured to determine a group with the largest number of irrelevant aging parameters as the key aging parameter when the aging parameters include multiple groups of irrelevant aging parameters of different numbers.

[0090] In some embodiments, the determination module 52 is specifically used to: calculate the ratio of the current operating time and the fitting result, and calculate the product of the ratio and the total life of the target insulator; and determine the difference between the product and the current operating time of the insulator to be predicted as the remaining life of the insulator to be predicted.

[0091] In some embodiments, the aging parameters include at least Shore hardness, tensile strength, elongation at break, tear strength, relative peak area of ​​Si-O-Si, relative peak area of ​​Si(CH3)2, and relative content of Si(-O)2.

[0092] In some embodiments, the operating environment of the target insulator is the same as that of the insulator to be predicted.

[0093] The insulator life prediction device provided in this embodiment can execute the insulator life prediction method provided in the above method embodiment. Its implementation principle and technical effect are similar to the above method and will not be repeated here.

[0094] Figure 6 An electronic device according to an exemplary embodiment may include a processor 802, and the processor 802 is used to execute application code to implement the insulator life prediction method in the present application.

[0095] The processor 802 may be a central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present application.

[0096] like Figure 6 As shown, the electronic device may further include a memory 803. The memory 803 is used to store application program codes for executing the solution of the present application, and the execution is controlled by the processor 802.

[0097] The memory 803 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 803 may exist independently and be connected to the processor 802 via the bus 804. The memory 803 may also be integrated with the processor 802.

[0098] like Figure 6 As shown, the electronic device may further include a communication interface 801, wherein the communication interface 801, the processor 802, and the memory 803 may be coupled to each other, for example, via a bus 804. The communication interface 801 is used to exchange information with other devices, for example, to support information exchange between the electronic device and other devices.

[0099] It should be pointed out that Figure 6 The device structure shown in the figure does not constitute a limitation on the electronic device, except Figure 6 In addition to the components shown, the electronic device may include more or fewer components than shown, or combine certain components, or arrange the components differently. The electronic device provided in this embodiment can execute the insulator life prediction method provided in the above method embodiment, and its implementation principle and technical effect are similar to the above method, which will not be repeated here.

[0100] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the insulator life prediction method in the above method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0101] The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0102] An embodiment of the present application provides a computer program product, which stores a computer program. When the computer program is executed by a processor, each process of the insulator life prediction method in the above method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0103] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media that include computer-usable program code.

[0104] In the present application, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0105] In this application, memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0106] In this application, computer-readable media includes permanent and non-permanent, removable and non-removable storage media. Storage media can be implemented by any method or technology to store information, and the information can be computer-readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. According to the definition in this article, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data and carrier waves.

[0107] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0108] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting insulator life, characterized in that: include: Obtaining aging parameters corresponding to target insulators having the same composition as the insulator to be predicted at different time points; Based on the aging parameters corresponding to the different time points, determine a first correlation result and a second correlation result; the first correlation result is a correlation result between the aging parameter and time, and the second correlation result is a correlation result between any two aging parameters; determining at least two key aging parameters from the aging parameters according to the first correlation result and the second correlation result; Performing multivariate linear fitting processing on the at least two key aging parameters based on the different time points to obtain fitting results; the fitting results include a weight and a constant parameter of each key aging parameter; The remaining life of the insulator to be predicted is determined according to the fitting result, the total life of the target insulator, and the current operating time of the insulator to be predicted.

2. The insulator life prediction method according to claim 1, characterized in that: The correlation results include correlation and non-correlation; the determining of the first correlation result and the second correlation result based on the aging parameters corresponding to the different time points includes: For any two sets of parameters, according to the different time points and the two sets of parameters, the Pearson coefficient between the two sets of parameters is calculated; the two sets of parameters are the two sets of parameters corresponding to the first correlation result, or the two sets of parameters corresponding to the second correlation result; Calculating the test value of the Pearson coefficient using a test algorithm; Calculate the correlation factor of the two sets of parameters according to the test value and the sample size; When the correlation factor is less than a first threshold, it is determined that the two groups of parameters are correlated; when the correlation factor is greater than a second threshold, it is determined that the two groups of parameters are uncorrelated; and the second threshold is greater than or equal to the first threshold.

3. The insulator life prediction method according to claim 1, characterized in that: The correlation results include correlation and non-correlation; and determining at least two key aging parameters from the aging parameters according to the first correlation result and the second correlation result includes: Determining the time-related aging parameter as a time-dependent aging parameter according to the first correlation result; According to the second correlation result, irrelevant aging parameters among the aging parameters are determined as key aging parameters.

4. The insulator life prediction method according to claim 3, characterized in that: When the aging parameters include multiple groups of irrelevant aging parameters of different numbers, the group with the largest number of irrelevant aging parameters is determined as the key aging parameter.

5. The insulator life prediction method according to claim 1, characterized in that: Determining the remaining life of the insulator to be predicted according to the fitting result, the total life of the target insulator, and the current operating time of the insulator to be predicted includes: Calculating a ratio of the current operating time to the fitting result, and calculating a product of the ratio and a total life of the target insulator; The difference between the product and the current operating time of the insulator to be predicted is determined as the remaining life of the insulator to be predicted.

6. The insulator life prediction method according to any one of claims 1 to 5, characterized in that: The aging parameters include at least Shore hardness, tensile strength, elongation at break, tear strength, Si-O-Si relative peak area, Si(CH3)2 relative peak area, and Si(-O)2 relative content.

7. The insulator life prediction method according to any one of claims 1 to 5, characterized in that: The target insulator and the insulator to be predicted have the same operating environment.

8. An insulator life prediction device, characterized in that: include: An acquisition module, used for acquiring aging parameters corresponding to target insulators having the same components as the insulator to be predicted at different time points; A determination module, configured to determine a first correlation result and a second correlation result based on the aging parameters corresponding to the different time points; the first correlation result is a correlation result between the aging parameter and time, and the second correlation result is a correlation result between any two aging parameters; The determination module is further configured to determine at least two key aging parameters from the aging parameters according to the first correlation result and the second correlation result; A processing module, configured to perform multivariate linear fitting processing on the at least two key aging parameters based on the different time points to obtain a fitting result; the fitting result includes a weight and a constant parameter of each key aging parameter; The calculation module is used to determine the remaining life of the insulator to be predicted according to the fitting result, the total life of the target insulator, and the current operation time of the insulator to be predicted.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the insulator life prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: include: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the insulator life prediction method according to any one of claims 1 to 7 is implemented.

11. A computer program product, characterized in that When the computer program product runs on a computer, the computer is enabled to implement the insulator life prediction method according to any one of claims 1 to 7.

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