A method, device, equipment and medium for determining a transmission line crosstalk characteristic parameter

Abstract

The application discloses a kind of transmission line crosstalk characteristic parameter determination method, device, equipment and medium.The method comprises the following steps: obtaining the line attribute parameter set of at least two adjacent transmission lines with crosstalk;Determine the fitting relationship between each attribute parameter in the line attribute parameter set and line operation parameter;According to each attribute parameter in the line attribute parameter set and the fitting relationship between each attribute parameter in the line attribute parameter set and line operation parameter, determine the characteristic parameter corresponding to the at least two adjacent transmission lines with crosstalk, by the technical scheme of the application, the characteristic parameter corresponding to the transmission line with crosstalk can be determined, so as to carry out rectification and optimization according to the characteristic parameter corresponding to the transmission line with crosstalk.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of vehicles, and particularly relate to a method and device for determining characteristic parameters of transmission line crosstalk, equipment and a medium. BACKGROUND

[0002] With the progress of science and technology and the pursuit of intelligent and automated lifestyle, various electrical and electronic devices and large systems (such as cars, airplanes and ships, etc.) required for life have increased significantly, and due to the increasing richness of functions of various products, the number of multi-conductor transmission lines such as cable harnesses and microstrip lines in printed circuit boards in each device has also increased significantly.

[0003] The crosstalk problem between multi-conductor transmission lines is one of the main reasons for the failure or failure of electrical and electronic devices, so the crosstalk problem of multi-conductor transmission lines has become the focus of researchers. In actual engineering application and people's life, the errors existing in the production and manufacturing process of transmission lines will cause the uncertainty of the geometric parameters (such as the length, radius, etc. of the transmission line) of the transmission line, and the displacement or jolt that may exist in the actual use of electrical and electronic devices will cause the uncertainty of the position parameters (such as the height of the transmission line to the ground, the distance between the transmission lines, etc.) of the transmission line, and Paul found that the change of the distance between the transmission lines will cause the crosstalk to change by as much as 20dB, and the uncertainty of the above transmission lines will be propagated to the crosstalk, causing the electrical and electronic devices to fail, seriously affecting the normal use of the product, and even causing property loss or personal safety problems.

[0004] Therefore, how to obtain the characteristic parameters of the transmission line with crosstalk is a problem that needs to be solved at present. SUMMARY

[0005] Embodiments of the present application provide a method and device for determining characteristic parameters of transmission line crosstalk, equipment and a medium, which can determine the characteristic parameters of the transmission line with crosstalk, so as to rectify and optimize the multi-conductor transmission line crosstalk problem according to the characteristic parameters of the transmission line with crosstalk.

[0006] According to an aspect of the present application, a method for determining characteristic parameters of transmission line crosstalk is provided, comprising:

[0007] Obtaining a set of line attribute parameters of at least two adjacent transmission lines with crosstalk;

[0008] Determining a fitting relationship between each attribute parameter in the set of line attribute parameters and a line operation parameter;

[0009] The characteristic parameters of the at least two adjacent transmission lines with crosstalk are determined according to each attribute parameter in the set of line attribute parameters and the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter.

[0010] According to another aspect of the present application, a transmission line crosstalk characteristic parameter determination device is provided, which comprises:

[0011] A set of line attribute parameters acquisition module is configured to acquire a set of line attribute parameters of at least two adjacent transmission lines with crosstalk.

[0012] A fitting relationship determination module is configured to determine a fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter.

[0013] A characteristic parameter determination module is configured to determine the characteristic parameters of the at least two adjacent transmission lines with crosstalk according to each attribute parameter in the set of line attribute parameters and the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter.

[0014] According to another aspect of the present application, an electronic device is provided, which comprises:

[0015] at least one processor; and

[0016] a memory connected to the at least one processor in communication; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the transmission line crosstalk characteristic parameter determination method according to any one of the embodiments of the present application.

[0018] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the transmission line crosstalk characteristic parameter determination method according to any one of the embodiments of the present application when executed.

[0019] The embodiments of the present application can acquire a set of line attribute parameters of at least two adjacent transmission lines with crosstalk, determine a fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter, and determine the characteristic parameters of the at least two adjacent transmission lines with crosstalk according to each attribute parameter in the set of line attribute parameters and the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter, so as to facilitate the rectification and optimization of the multi-conductor transmission line crosstalk problem according to the characteristic parameters of the transmission lines with crosstalk.

[0020] It is to be understood that the description of the background of the application is not an acknowledgement or admission that any of the information provided in the description of the background of the application is prior art to the application. The information in the description of the background of the application may contain ideas, concepts and / or discoveries not yet known to be prior art. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0022] Figure 1 is a flow chart of a transmission line crosstalk characteristic parameter determination method in the embodiments of the present application;

[0023] Figure 2 is a hyperbolic truncation scheme effect comparison diagram of different dimension variable models in the embodiments of the present application;

[0024] Figure 3 is a sparse effect diagram of a two-dimensional variable hyperbolic truncation scheme in the embodiments of the present application;

[0025] Figure 4 is a structure diagram of a multi-conductor transmission line crosstalk model in the embodiments of the present application;

[0026] Figure 5 is a comparison diagram of discrete data set quantity selection in the embodiments of the present application;

[0027] Figure 6 is a total sensitivity index diagram of input variables of different frequency multi-conductor transmission line crosstalk uncertainty quantification in the embodiments of the present application;

[0028] Figure 7 is a structure diagram of a transmission line crosstalk characteristic parameter determination device in the embodiments of the present application;

[0029] Figure 8 is a structure diagram of an electronic device in the embodiments of the present application. DETAILED DESCRIPTION

[0030] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", and the like in the description and in the claims of the present application and above-described accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular sequential or chronological order. It should be understood that the data thus used can be interchanged under appropriate circumstances so that the embodiments of the application described herein can be implemented in other than the order illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a list of steps or units as non-limiting to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products, or apparatuses.

[0032] It can be understood that before using the technical solutions disclosed in the embodiments of the present disclosure, the type, scope of use, and use scenario of personal information involved in the present disclosure should be informed to the user and the authorization of the user should be obtained according to relevant laws and regulations.

[0033] Embodiment one

[0034] Figure 1 A flowchart of a transmission line crosstalk characteristic parameter determination method provided by the embodiment of the present application, the embodiment can be applicable to the case of transmission line crosstalk characteristic parameter determination, and the method can be executed by the transmission line crosstalk characteristic parameter determination device in the embodiment of the present application. The device can be realized in the form of software and / or hardware, for example, as shown in the figure, and the method specifically includes the following steps: Figure 1

[0035] S110, obtaining a line attribute parameter set of at least two adjacent transmission lines with crosstalk.

[0036] The line attribute parameter set includes at least two attribute parameters, and the attribute parameters include at least one of the radius of the transmission line, the lateral distance between any two adjacent transmission lines, and the height of the transmission line to the ground.

[0037] S120, determining the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter.

[0038] ​Specifically, the manner of determining the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter can be: establishing an orthogonal polynomial basis corresponding to each attribute parameter based on data driving, and determining the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter according to the orthogonal polynomial basis corresponding to each attribute parameter. The manner of determining the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter can also be: obtaining the mean and statistical moment corresponding to each attribute parameter in the line attribute parameter set; determining the coefficient of the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set according to the mean and statistical moment corresponding to each attribute parameter in the line parameter set; and determining the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter according to the coefficient of the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set.

[0039] Optionally, the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter is determined, including:

[0040] The mean and statistical moment corresponding to each attribute parameter in the line attribute parameter set are obtained.

[0041] The coefficient of the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set is determined according to the mean and statistical moment corresponding to each attribute parameter in the line parameter set.

[0042] The fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter is determined according to the coefficient of the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set.

[0043] Specifically, the orthogonal polynomial basis corresponding to each attribute parameter is constructed by data rather than random distribution type, that is, the orthogonal polynomial basis is established based on data driving, and the data driving satisfies any distribution form of the attribute parameter. The corresponding orthogonal polynomial basis is established by using limited data of one-dimensional variable, which can effectively extend the limitation of the chaotic polynomial expansion method to any probability distribution. Assuming that Q (k) (ξ) is the k-th order orthogonal polynomial basis corresponding to the attribute parameter:

[0044]

[0045] wherein, is the coefficient of the orthogonal polynomial basis, ξ is the attribute parameter, and d is the dimension of the attribute parameter.

[0046] The corresponding orthogonal polynomial basis is constructed according to the moments of each attribute parameter. In the case of the first orthogonal polynomial, according to the orthogonal condition of the polynomial, the following can be obtained:

[0047]

[0048]

[0049]

[0050] where Q (l) is the lth order orthogonal polynomial basis corresponding to the property parameter.

[0051] According to the above formula, we can directly get and further get According to the orthogonality between the zero-order polynomial and the first-order polynomial, we can get:

[0052]

[0053] According to the orthogonality between any k-order polynomial Q (k) and all its lower-order polynomials, we continue the above recursive process to get:

[0054]

[0055]

[0056] M

[0057]

[0058]

[0059] In the above formula, the definition of the k-order orthogonal polynomial Q (k) involves all the lower-order polynomials Q (k-1) , Q (1) , Q (0) , which can be simplified as:

[0060]

[0061]

[0062] M

[0063]

[0064]

[0065] Therefore, the k-order moment of the property parameter ξ can be defined as:

[0066]

[0067] where μ is the mean value and statistical moment corresponding to the property parameter.

[0068] Based on the above formula, simplification is obtained:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] Simplifying the above formula, we get:

[0075]

[0076] The data-driven arbitrary distribution orthogonal polynomial basis coefficient exists.

[0077] S130, according to each attribute parameter in the line attribute parameter set and the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter, determine the characteristic parameters corresponding to the at least two adjacent transmission lines with crosstalk.

[0078] Among them, the characteristic parameters corresponding to the at least two adjacent transmission lines with crosstalk include: the sensitivity corresponding to the line attribute parameter set and / or the statistical characteristic parameters of the line operation parameters.

[0079] Specifically, the way of determining the characteristic parameters corresponding to the at least two adjacent transmission lines with crosstalk according to each attribute parameter in the line attribute parameter set and the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter can be: determining the fitting relationship between the line attribute parameter set and the line operation parameter according to the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter; determining the characteristic parameters corresponding to the at least two adjacent transmission lines with crosstalk according to each attribute parameter in the line attribute parameter set and the fitting relationship between the line attribute parameter set and the line operation parameter. The way of determining the characteristic parameters corresponding to the at least two adjacent transmission lines with crosstalk according to each attribute parameter in the line attribute parameter set and the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter can also be: determining the chaotic polynomial corresponding to the line attribute parameter set according to the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter, and determining the characteristic parameters corresponding to the at least two adjacent transmission lines with crosstalk according to the chaotic polynomial corresponding to the line attribute parameter set.

[0080] Optionally, the characteristic parameters corresponding to the at least two adjacent transmission lines with crosstalk are determined according to each attribute parameter in the set of line attribute parameters and the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter, including:

[0081] The fitting relationship between the set of line attribute parameters and the line operation parameter is determined according to the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter.

[0082] The characteristic parameters corresponding to the at least two adjacent transmission lines with crosstalk are determined according to each attribute parameter in the set of line attribute parameters and the fitting relationship between the set of line attribute parameters and the line operation parameter.

[0083] The fitting relationship between the set of line attribute parameters and the line operation parameter includes: the fitting relationship between each attribute parameter and the line operation parameter, the fitting relationship between any two attribute parameters and the line operation parameter, the fitting relationship between any three attribute parameters and the line operation parameter, …, the fitting relationship between any d attribute parameters and the line operation parameter, where d is the dimension of the attribute parameter.

[0084] Specifically, the manner of determining the fitting relationship between the set of line attribute parameters and the line operation parameter according to the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter can be: determining the orthogonal polynomial basis corresponding to each attribute parameter according to the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter, and determining the fitting relationship between the set of line attribute parameters and the line operation parameter according to the orthogonal polynomial basis corresponding to each attribute parameter. The manner of determining the fitting relationship between the set of line attribute parameters and the line operation parameter according to the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter can also be: determining the chaotic polynomial corresponding to the set of line attribute parameters according to the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter, and determining the fitting relationship between the set of line attribute parameters and the line operation parameter according to the chaotic polynomial corresponding to the set of line attribute parameters. The manner of determining the fitting relationship between the set of line attribute parameters and the line operation parameter according to the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter can also be: determining the chaotic polynomial expansion term corresponding to the set of line attribute parameters according to the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameter, and determining the fitting relationship between the set of line attribute parameters and the line operation parameter according to the chaotic polynomial expansion term corresponding to the set of line attribute parameters.

[0085] Specifically, the way of determining the characteristic parameter corresponding to the at least two adjacent transmission lines with crosstalk according to each attribute parameter in the line attribute parameter set and the fitting relationship between the line attribute parameter set and the line operation parameter can be: when the characteristic parameter corresponding to the at least two adjacent transmission lines with crosstalk is the sensitivity corresponding to the line attribute parameter set, determining the coefficient of the chaotic polynomial expansion term corresponding to the line attribute parameter set according to the fitting relationship between the line attribute parameter set and the line operation parameter; determining the sensitivity corresponding to the line attribute parameter set according to the coefficient of the chaotic polynomial expansion term corresponding to the line attribute parameter set, the chaotic polynomial corresponding to the line attribute parameter set, and each attribute parameter in the line attribute parameter set. When the characteristic parameter corresponding to the at least two adjacent transmission lines with crosstalk is the statistical characteristic parameter of the line operation parameter, determining the chaotic polynomial according to each attribute parameter in the line attribute parameter set and the fitting relationship between the line attribute parameter set and the line operation parameter, and determining the mean, variance, and probability density of the line attribute parameter set according to the chaotic polynomial and the line attribute parameter set.

[0086] Optionally, determining the fitting relationship between the line attribute parameter set and the line operation parameter according to the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter comprises:

[0087] constructing the orthogonal polynomial basis corresponding to each attribute parameter in the line parameter set according to the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter;

[0088] determining the chaotic polynomial corresponding to the line attribute parameter set according to the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set;

[0089] determining the fitting relationship between the line attribute parameter set and the line operation parameter according to the chaotic polynomial corresponding to the line attribute parameter set.

[0090] wherein the fitting relationship between the attribute parameter and the line operation parameter can comprise the coefficient of the orthogonal polynomial basis.

[0091] Specifically, the way of constructing the orthogonal polynomial basis corresponding to each attribute parameter in the line parameter set according to the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter can be: determining the coefficient of the orthogonal polynomial basis corresponding to each attribute parameter according to the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter, and constructing the orthogonal polynomial basis corresponding to each attribute parameter in the line parameter set according to the coefficient of the orthogonal polynomial basis corresponding to each attribute parameter.

[0092] Specifically, the way of determining the chaotic polynomial corresponding to the line attribute parameter set according to the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set can be: determining the chaotic polynomial expansion term corresponding to the line attribute parameter set according to the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set, and determining the chaotic polynomial corresponding to the line attribute parameter set according to the chaotic polynomial expansion term corresponding to the line attribute parameter set. The way of determining the chaotic polynomial corresponding to the line attribute parameter set according to the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set can also be: determining the chaotic polynomial expansion term corresponding to the line attribute parameter set according to the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set, and determining the initial chaotic polynomial according to the chaotic polynomial expansion term corresponding to the line parameter set; truncating the initial chaotic polynomial to obtain the chaotic polynomial corresponding to the line parameter set.

[0093] Optionally, the determining of the chaotic polynomial corresponding to the line parameter set according to the orthogonal polynomial basis corresponding to each attribute parameter in the line parameter set comprises:

[0094] The chaotic polynomial expansion term corresponding to the line attribute parameter set is determined according to the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set, wherein the attribute parameters include at least one of the radius of the transmission line, the lateral distance between any two adjacent transmission lines, and the height of the transmission line above the ground.

[0095] The chaotic polynomial corresponding to the line attribute parameter set is determined according to the chaotic polynomial expansion term corresponding to the line attribute parameter set.

[0096] Specifically, the way of determining the chaotic polynomial expansion term corresponding to the line attribute parameter set according to the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set can be: determining the sum of the orthogonal polynomial basis corresponding to each attribute parameter, the product of the orthogonal polynomial bases corresponding to any two attribute parameters, and the product of the orthogonal polynomial bases corresponding to any d attribute parameters as the chaotic polynomial expansion term corresponding to the line attribute parameter set, wherein d is the dimension of the attribute parameter.

[0097] Optionally, the characteristic parameter corresponding to the at least two adjacent transmission lines with crosstalk includes the sensitivity corresponding to the line attribute parameter set.

[0098] The characteristic parameter corresponding to the at least two adjacent transmission lines with crosstalk is determined according to each attribute parameter in the line attribute parameter set and the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter, comprising:

[0099] The coefficient of the chaotic polynomial expansion term corresponding to the line attribute parameter set is determined according to the fitting relationship between the line attribute parameter set and the line operation parameter.

[0100] determine the first variance according to the coefficient of the chaos polynomial expansion term corresponding to the line attribute parameter set;

[0101] determine the second variance according to the chaos polynomial corresponding to the line attribute parameter set and each attribute parameter in the line attribute parameter set;

[0102] determine the sensitivity corresponding to the line attribute parameter set as the ratio of the first variance and the second variance.

[0103] Specifically, in order to facilitate the optimization and rectification of the crosstalk problem of the multi-conductor transmission line, quantifying the influence degree of different input variables can provide a reasonable basis, therefore, on the basis of the OMP-aPC, the embodiment of the application combines the Sobol method to solve the global sensitivity index of different variables. The Sobol method is based on the idea of variance decomposition, and calculates the global sensitivity index by quantifying the contribution degree of the interaction between a single variable and multiple variables to the output variance, and is a widely used global sensitivity analysis method. The original model is decomposed into the form of the sum of incremental terms:

[0104]

[0105] In the above formula, each decomposition term is mutually orthogonal, y0 is a constant, in order to obtain the variance decomposition formula, the variance of the left and right sides of the above formula is taken at the same time:

[0106]

[0107] In the above formula, each decomposition term represents the influence of different attribute parameters and the interaction between attribute parameters on the output variance, and the Sobol sensitivity index is defined as:

[0108]

[0109] Where, S i is the first-order sensitivity index, representing the contribution of a single attribute parameter to the output variance, and the sum of the first-order sensitivity index of the attribute parameter and the sensitivity index of the interaction between the attribute parameter and other attribute parameters is defined as the total sensitivity index:

[0110]

[0111] On the basis of the chaos polynomial proxy model and in combination with the Sobol method, the calculation efficiency can be significantly improved, and the chaos polynomial expansion formula is rewritten into the form of incremental summation:

[0112]

[0113] Where,

[0114] Further calculations can be performed to obtain:

[0115]

[0116] Based on the orthogonality of the basis functions of chaotic polynomials, we can obtain:

[0117]

[0118] The global sensitivity indices of each variable in the chaotic polynomial surrogate model can then be calculated, including the first-order sensitivity index and the total sensitivity index.

[0119] The key to constructing a generalized chaotic polynomial lies in solving c. α Typically, regression methods are used for calculation. Based on the random distribution of each input variable, N samples are taken using the Latin hypervariable method, ensuring that the number of sampling points N conforms to the oversampling principle (N ≥ 2P). Finally, c can be calculated using the least quadratic regression method. α Considering that the traditional chaotic polynomial expansion method is limited by the probability distribution type of the input variables, resulting in a certain difference between the uncertainty quantification analysis results of crosstalk in multi-conductor transmission lines and actual engineering applications, this invention proposes to establish an orthogonal polynomial basis corresponding to the input variables using a data-driven method, and construct a chaotic polynomial that satisfies arbitrary distribution to calculate the characteristic parameters corresponding to at least two adjacent transmission lines with crosstalk.

[0120] Optionally, the coefficients of the chaotic polynomial expansion terms corresponding to the set of line attribute parameters are determined based on the fitting relationship between the set of line attribute parameters and the line operating parameters, including:

[0121] Based on the fitting relationship between the set of line attribute parameters and the line operation parameters, the chaotic polynomial expansion terms corresponding to the set of line parameters are filtered to obtain the target expansion term set.

[0122] The coefficients of each target expansion term in the target expansion term set are determined based on the least squares method, and the coefficients of each target expansion term in the target expansion term set are determined as the coefficients of the chaotic polynomial expansion term corresponding to the line attribute parameter set.

[0123] The target expansion set includes at least one target expansion item.

[0124] Specifically, the method for obtaining the target set of expanded terms by filtering the chaotic polynomial expansion terms corresponding to the set of line attribute parameters based on the fitting relationship between the set of line attribute parameters and the line operating parameters can be as follows: determine all expanded terms of the chaotic polynomial based on the fitting relationship between the set of line attribute parameters and the line operating parameters, filter all expanded terms of the chaotic polynomial, and obtain the target set of expanded terms.

[0125] It should be noted that, to alleviate the "curse of dimensionality" problem, this invention employs the OMP algorithm to calculate the coefficients of the chaotic polynomial expansion terms. OMP is a greedy algorithm based on compressed sensing theory, which can accurately reconstruct the model's output when N is relatively small. OMP is a sequential process that greedily selects terms (target expansion terms) from the dictionary D of basis functions, where dictionary D includes all expansion terms of the chaotic polynomial. In the t-th iteration, OMP calculates the coefficients of the chaotic polynomial expansion terms by selecting the target expansion term from the candidate set D. (t-1) Find the basis function φ that is most relevant to the residual r. j And add it to the activity set, where j∈ D(t-1) The selection process can be represented as:

[0126]

[0127] The polynomial coefficients corresponding to the t-th iteration are c. (t-1) At this time, the matrix Φ (t-1) The columns in the table contain the selection results of the current basis function (where Φ (t-1) (where r is the matrix formed by the chaotic polynomial expansion terms at the t-th iteration), the training residual can be obtained through r (t-1) =φ (t-1) c (t-1) -y is obtained. After selecting the basis function, the new PC coefficients can be calculated using least squares. Repeat the above steps until the l2 norm of the residuals meets the requirements. To achieve loop termination in OMP, a tolerance ε needs to be defined to minimize the return error of the input during training. Cross-validation is usually used for estimation.

[0128] Optionally, the chaotic polynomial corresponding to the line parameter set is determined based on the chaotic polynomial expansion term corresponding to the line parameter set, including:

[0129] The initial chaotic polynomial is determined based on the chaotic polynomial expansion term corresponding to the set of line parameters.

[0130] The initial chaotic polynomial is truncated to obtain the chaotic polynomial corresponding to the set of line parameters.

[0131] It should be noted that in solving c αAt this time, since the number of expansion terms in the above formula is infinite, it cannot be directly solved, and considering the calculation cost in practical application analysis, the chaotic polynomial in the above formula should be truncated,

[0132] In order to improve the calculation efficiency of the chaotic polynomial surrogate model and alleviate the "dimension disaster" problem, the truncation scheme can be used to truncate the expansion terms of the chaotic polynomial of arbitrary distribution.

[0133] Scheme one, the hyperbolic truncation scheme is used to truncate the expansion terms of the chaotic polynomial of arbitrary distribution. First, define the highest truncation order of φ i (ξ) as p i , and the order of the kth random variable is t k When the traditional truncation scheme is used:

[0134]

[0135] In the traditional truncation scheme, the highest order of the polynomial is equal to the sum of the orders of the variables. In the traditional truncation scheme, the norm concept is introduced, and the norm is f. The above formula is rewritten to obtain:

[0136]

[0137] Obviously, when f=1, the hyperbolic truncation scheme is the traditional truncation scheme, so the traditional truncation scheme can be said to be a special case of the hyperbolic truncation scheme. When f<1, the hyperbolic truncation scheme can remove the high-order mutual effect in the model and retain the low-order mutual effect.

[0138] Scheme two, the ordinary truncation scheme is used to truncate the expansion terms of the chaotic polynomial of arbitrary distribution.

[0139] The truncated chaotic polynomial can also be expressed as:

[0140]

[0141] Where p is the truncation order, and the multi-index Λ p,d after truncation It should be noted that the embodiments of the present application can use any one of the above two truncation methods.

[0142] In a specific example, taking a two-dimensional variable model and a three-dimensional variable model as an example, with the change of f, the effect of the hyperbolic truncation scheme is as follows: Figure 2As shown, the hyperbolic curve effect in the hyperbolic truncation scheme is obvious, whether it is a two-dimensional variable or an input variable, the high-order interaction effect in the model can be truncated, and with the decrease of the norm f, the curvature of the hyperbolic curve also increases. In order to more obviously show the sparsification effect of the hyperbolic truncation on the model, also taking the two-dimensional input variable as an example, the effect is shown as Figure 3 As shown in the figure, the hyperbolic curve effect in the hyperbolic truncation scheme is obvious, whether it is a two-dimensional variable or an input variable, the high-order interaction effect in the model can be truncated, and with the decrease of the norm f, the curvature of the hyperbolic curve also increases. In order to more obviously show the sparsification effect of the hyperbolic truncation on the model, also taking the two-dimensional input variable as an example, the effect is shown as Figure 2 As shown in the figure, the hyperbolic curve effect in the hyperbolic truncation scheme is obvious, whether it is a two-dimensional variable or an input variable, the high-order interaction effect in the model can be truncated, and with the decrease of the norm f, the curvature of the hyperbolic curve also increases. In order to more obviously show the sparsification effect of the hyperbolic truncation on the model, also taking the two-dimensional input variable as an example, the effect is shown as Figure 3 As shown in the figure, the hyperbolic curve effect in the hyperbolic truncation scheme is obvious, whether it is a two-dimensional variable or an input variable, the high-order interaction effect in the model can be truncated, and with the decrease of the norm f, the curvature of the hyperbolic curve also increases. In order to more obviously show the sparsification effect of the hyperbolic truncation on the model, also taking the two-dimensional input variable as an example, the effect is shown as

[0143] Optionally, it also includes:

[0144] Obtain the truncation order and the dimension corresponding to each parameter in the line parameter set;

[0145] Determine the number of truncated chaotic polynomial expansion terms according to the truncation order and the dimension corresponding to each parameter in the line parameter set.

[0146] Optionally, the characteristic parameters corresponding to the at least two adjacent transmission lines with crosstalk further include: statistical characteristic parameters of line operation parameters.

[0147] Specifically, in the traditional chaotic polynomial expansion method, let the original model be Y=y(ξ), and use multi-dimensional orthogonal polynomials to expand it, which can obtain:

[0148]

[0149] Wherein, c α is the coefficient of the chaotic polynomial expansion term, d is the dimension of the attribute parameter, ξ is the d-dimensional attribute parameter [ξ1, ξ2,..., ξ d ], is a multi-index with a size of d.

[0150] The number of truncated chaotic polynomial expansion terms P is:

[0151]

[0152] In order to verify that the data-driven arbitrary distribution sparse chaotic polynomial expansion method adopted in the embodiment of the present application can quantify the uncertainty of various multi-conductor transmission line crosstalk, and can provide reasonable suggestions for electromagnetic protection of various equipment, a multi-conductor transmission line model is established, Figure 4 The load of the multi-conductor transmission line in the model is 50Ω, r is the radius of the transmission line, L is the lateral distance between the transmission lines, h1 and h2 are the heights of the transmission lines to the ground, and the above four variables are regarded as line attribute parameters in the embodiment of the present application.

[0153] Since the method proposed in this embodiment of the invention satisfies the condition that the line attribute parameters follow an arbitrary distribution, and the data measured in actual experiments is discrete and finite, in order to facilitate comparison and verification of the calculation results of the scheme provided in this embodiment of the invention, random sampling will be performed from some classical probability distribution types. The sampled data will be used to establish an orthogonal polynomial basis through a data-driven approach, thereby constructing a chaotic polynomial surrogate model for calculation. The sampling probability distribution of the input variable parameters of the multi-conductor transmission line crosstalk model is shown in Table 1, where U represents a uniform distribution and N represents a normal distribution.

[0154] Table 1

[0155] Distributed and parameters Unit r N(0.4,0.1) mm h1 U(2,2.5) cm h2 U(2,2.5) cm L U(5,7) mm

[0156] Based on the multi-conductor transmission line crosstalk model established in this invention embodiment, firstly, to select an appropriate number of discrete datasets, taking the mean value of the induced current at the far end of the second transmission line at 10MHz as an example, 15, 20, 25, 30, and 35 discrete data points were selected respectively. Orthogonal polynomial sets were constructed using different methods, and chaotic polynomials were formed for calculation. The calculation results of different arbitrary distribution methods under different discrete data were compared, such as... Figure 5 As shown, the truncation order of the chaotic polynomial is 4, and the f of the hyperbolic truncation scheme in OMP-aPC is 0.75.

[0157] according to Figure 5 The comparison results show that all three data-driven arbitrary distribution chaotic polynomials can obtain relatively stable results. Considering that the average induced current obtained from 10,000 MC calculations is 0.0019A, it is clear that the OMP-aPC method used in this embodiment is closer to the target. Furthermore, when the discrete dataset used to construct the data-driven method reaches 35, it can obtain stable and accurate results. Therefore, in the following embodiment of this invention, 35 discrete datasets are selected to construct an orthogonal polynomial set.

[0158] For different frequencies, the probability density distribution function of the induced current at the far end of the second transmission line is calculated. The method used in this embodiment of the invention is compared with existing arbitrary distribution chaotic polynomial expansion methods, namely the Stieltjes method and the Lanzcos method, and the calculation results of 10,000 Monte Carlo methods are used as verification.

[0159] According to the comparison result, it can be seen that the calculation effect of OMP-aPC is obviously better than that of Stieltjes method and Lanzcos method, and better results can be obtained at different frequency points. It should be noted that the data required for OMP-aPC is only 35, which is relatively easy to realize in actual engineering application. In order to further verify the effectiveness of OMP-aPC in quantifying the uncertainty problem of multi-conductor transmission line crosstalk, the mean and standard deviation of the far-end induced current of the second transmission line in the frequency band [1MHz, 1GHz] are calculated, and the calculation results are compared with 10000 times MC method.

[0160] The mean and standard deviation of the multi-conductor transmission line crosstalk calculated by OMP-aPC are consistent with MC, further proving the effectiveness of OMP-aPC, and in terms of calculation efficiency, the mean and standard deviation of the induced current in the calculation frequency band by OMP-aPC only needs 28.65s, while MC method needs 785.46s, and the calculation efficiency is increased by about 27 times. In order to quantify the influence degree of each input variable on the multi-conductor transmission line crosstalk, OMP-aPC is combined with Sobol to calculate the global sensitivity index of the input variable at different frequencies, and the calculation results of MC method are compared, as shown in Figure 6

[0161] According to Figure 6 , the total sensitivity index of each input variable of the multi-conductor transmission line crosstalk calculated by OMP-aPC is consistent with that of MC method, proving the effectiveness of OMP-aPC. It should be noted here that when combining arbitrary distribution chaotic polynomial method with Sobol to calculate global sensitivity index, Stieltjes method and Lanzcos method cannot obtain correct results, so only the calculation results of OMP-aPC and MC are compared. The calculation frequency is expanded to the frequency band [1MHz, 1GHz], and the global sensitivity index of each variable of the multi-conductor transmission line crosstalk is calculated.

[0162] In the uncertainty quantification problem of multi-conductor transmission line crosstalk, the total sensitivity index and the first-order sensitivity index of the lateral distance between transmission lines are large, which means that the influence degree on the crosstalk is also the largest, while the influence degree of the height of the transmission line to the ground is relatively small, which has important guiding significance for solving the rectification and optimization problem of multi-conductor transmission line crosstalk in the future. The above calculation results prove that the OMP-aPC method proposed in the embodiment of the application can effectively calculate the uncertainty quantification problem of multi-conductor transmission line crosstalk, significantly improve the calculation efficiency under the premise of ensuring the calculation accuracy, and expand the limitation of traditional probability distribution to arbitrary distribution, which has stronger applicability.

[0163] ​The technical scheme of the embodiment comprises the following steps: acquiring a line attribute parameter set of at least two adjacent transmission lines with crosstalk; determining a fitting relationship between each attribute parameter in the line attribute parameter set and a line operation parameter; and determining a characteristic parameter corresponding to the at least two adjacent transmission lines with crosstalk according to each attribute parameter in the line attribute parameter set and the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter.

[0164] Embodiment two

[0165] Figure 7 A structural schematic diagram of a transmission line crosstalk characteristic parameter determination device provided by the embodiment of the application is shown in the figure. The embodiment can be applied to the case of transmission line crosstalk characteristic parameter determination. The device can be realized in the form of software and / or hardware, and can be integrated in any device providing the function of transmission line crosstalk characteristic parameter determination, such as a computer. Figure 7 As shown in the figure, the transmission line crosstalk characteristic parameter determination device specifically comprises a line attribute parameter set acquisition module 210, a fitting relationship determination module 220, and a characteristic parameter determination module 230.

[0166] The line attribute parameter set acquisition module is configured to acquire a line attribute parameter set of at least two adjacent transmission lines with crosstalk.

[0167] The fitting relationship determination module is configured to determine a fitting relationship between each attribute parameter in the line attribute parameter set and a line operation parameter.

[0168] The characteristic parameter determination module is configured to determine a characteristic parameter corresponding to the at least two adjacent transmission lines with crosstalk according to each attribute parameter in the line attribute parameter set and the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter.

[0169] The product described above can execute the method provided by any embodiment of the application, and has the corresponding function modules and beneficial effects of the execution method.

[0170] The technical scheme of the embodiment comprises the following steps: acquiring a line attribute parameter set of at least two adjacent transmission lines with crosstalk; determining a fitting relationship between each attribute parameter in the line attribute parameter set and a line operation parameter; and determining a characteristic parameter corresponding to the at least two adjacent transmission lines with crosstalk according to each attribute parameter in the line attribute parameter set and the fitting relationship between each attribute parameter in the line attribute parameter set and the line operation parameter.

[0171] Embodiment three

[0172] Figure 8 A structural diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0173] As shown in Figure 8 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., connected in communication with the at least one processor 11, where the memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0174] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0175] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the transmission line crosstalk signature determination method.

[0176] In some embodiments, the transmission line crosstalk signature determination method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the above-described transmission line crosstalk signature determination method can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the transmission line crosstalk signature determination method by way of other any suitable means, e.g., by way of firmware.

[0177] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0178] Computer programs used to implement the processes of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0179] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0180] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0181] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0182] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0183] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0184] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for determining transmission line crosstalk characteristic parameters, characterized in that, include: Obtain the set of line attribute parameters for at least two adjacent transmission lines that have crosstalk. Determine the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operating parameters; Construct an orthogonal polynomial basis for each attribute parameter in the set of line attribute parameters based on the fitting relationship between each attribute parameter and the line operation parameters; Determining the chaotic polynomial corresponding to the line attribute parameter set based on the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set includes: determining the chaotic polynomial expansion term corresponding to the line attribute parameter set based on the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set, wherein the attribute parameters include at least one of the following: the radius of the transmission line, the lateral distance between any two adjacent transmission lines, and the height of the transmission line above ground; and determining the chaotic polynomial corresponding to the line attribute parameter set based on the chaotic polynomial expansion term corresponding to the line attribute parameter set. The fitting relationship between the set of line attribute parameters and the line operation parameters is determined based on the chaotic polynomial corresponding to the set of line attribute parameters. The characteristic parameters corresponding to at least two adjacent transmission lines with crosstalk are determined based on each attribute parameter in the line attribute parameter set and the fitting relationship between the line attribute parameter set and the line operating parameters; wherein, the characteristic parameters corresponding to at least two adjacent transmission lines with crosstalk include: the sensitivity corresponding to the line attribute parameter set. Specifically, the characteristic parameters corresponding to at least two adjacent transmission lines with crosstalk are determined based on each attribute parameter in the line attribute parameter set and the fitting relationship between the line attribute parameter set and the line operating parameters, including: The coefficients of the chaotic polynomial expansion term corresponding to the set of line attribute parameters are determined based on the fitting relationship between the set of line attribute parameters and the line operation parameters. The first variance is determined based on the coefficients of the chaotic polynomial expansion terms corresponding to the set of line attribute parameters. The second variance is determined based on the chaotic polynomial corresponding to the set of line attribute parameters and each attribute parameter in the set of line attribute parameters; The ratio of the first variance to the second variance is determined as the sensitivity corresponding to the set of line attribute parameters.

2. The method according to claim 1, characterized in that, The coefficients of the chaotic polynomial expansion terms corresponding to the set of line attribute parameters are determined based on the fitting relationship between the set of line attribute parameters and the line operating parameters, including: Based on the fitting relationship between the set of line attribute parameters and the line operation parameters, the chaotic polynomial expansion terms corresponding to the set of line parameters are filtered to obtain the target expansion term set. The coefficients of each target expansion term in the target expansion term set are determined based on the least squares method, and the coefficients of each target expansion term in the target expansion term set are determined as the coefficients of the chaotic polynomial expansion term corresponding to the line attribute parameter set.

3. The method according to claim 1, characterized in that, Determine the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operating parameters, including: Obtain the mean and statistical moments for each attribute parameter in the set of line attribute parameters; The coefficients of the orthogonal polynomial basis corresponding to each attribute parameter in the set of line attribute parameters are determined by the mean and statistical moments of each attribute parameter in the set of line attribute parameters. The fitting relationship between each attribute parameter in the set of line attribute parameters and the line operation parameters is determined by the coefficients of the orthogonal polynomial basis corresponding to each attribute parameter in the set of line attribute parameters.

4. A device for determining transmission line crosstalk characteristic parameters, characterized in that, include: The line attribute parameter set acquisition module is used to acquire the line attribute parameter set of at least two adjacent transmission lines with crosstalk. The fitting relationship determination module is used to determine the fitting relationship between each attribute parameter in the set of line attribute parameters and the line operating parameters. The feature parameter determination module is used for: Construct an orthogonal polynomial basis for each attribute parameter in the set of line attribute parameters based on the fitting relationship between each attribute parameter and the line operation parameters; Determining the chaotic polynomial corresponding to the line attribute parameter set based on the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set includes: determining the chaotic polynomial expansion term corresponding to the line attribute parameter set based on the orthogonal polynomial basis corresponding to each attribute parameter in the line attribute parameter set, wherein the attribute parameters include at least one of the following: the radius of the transmission line, the lateral distance between any two adjacent transmission lines, and the height of the transmission line above ground; and determining the chaotic polynomial corresponding to the line attribute parameter set based on the chaotic polynomial expansion term corresponding to the line attribute parameter set. The fitting relationship between the set of line attribute parameters and the line operation parameters is determined based on the chaotic polynomial corresponding to the set of line attribute parameters. The characteristic parameters corresponding to at least two adjacent transmission lines with crosstalk are determined based on each attribute parameter in the line attribute parameter set and the fitting relationship between the line attribute parameter set and the line operating parameters; wherein, the characteristic parameters corresponding to at least two adjacent transmission lines with crosstalk include: the sensitivity corresponding to the line attribute parameter set. Specifically, the characteristic parameters corresponding to at least two adjacent transmission lines with crosstalk are determined based on each attribute parameter in the line attribute parameter set and the fitting relationship between the line attribute parameter set and the line operating parameters, including: The coefficients of the chaotic polynomial expansion term corresponding to the set of line attribute parameters are determined based on the fitting relationship between the set of line attribute parameters and the line operation parameters. The first variance is determined based on the coefficients of the chaotic polynomial expansion terms corresponding to the set of line attribute parameters. The second variance is determined based on the chaotic polynomial corresponding to the set of line attribute parameters and each attribute parameter in the set of line attribute parameters; The ratio of the first variance to the second variance is determined as the sensitivity corresponding to the set of line attribute parameters.

5. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the transmission line crosstalk characteristic parameter determination method according to any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for determining transmission line crosstalk characteristic parameters as described in any one of claims 1-3.

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