Methods, apparatus, and computer equipment for crosstalk analysis of multi-conductor transmission lines

Abstract

This application relates to a method, apparatus, computer device, storage medium, and computer program product for analyzing crosstalk in multi-conductor transmission lines. The method includes: acquiring multiple sample induced currents generated on the multi-conductor transmission line; determining a chaotic polynomial corresponding to the multi-conductor transmission line based on multiple input variables; sparsifying the chaotic polynomial using a greedy algorithm based on each sample induced current to determine the coefficients of the chaotic polynomial; and determining a chaotic polynomial model based on the coefficients and the chaotic polynomial. The chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line, and the statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line. This significantly improves the efficiency of analyzing crosstalk in multi-conductor transmission lines.

Description

Technical Field

[0001] This application relates to the field of crosstalk technology for multi-conductor transmission lines, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for analyzing crosstalk in multi-conductor transmission lines. Background Technology

[0002] With the development of multi-conductor transmission line technology, electromagnetic interference (ECI) often affects the reliability and safety of electrical and electronic systems, causing them to malfunction. This ECI is generated by the interaction of electromagnetic fields between multi-conductor transmission lines. To analyze ECI in multi-conductor transmission lines, it is often necessary to quantify the uncertainties caused by this interference.

[0003] Traditionally, numerical integration methods are often used to determine the crosstalk model for multi-conductor transmission lines (MCLs) in order to quantify the uncertainties caused by MCLs. However, as the dimensionality of the input variables increases, the computational cost of determining the MCL crosstalk model increases significantly, making it difficult to quickly determine the uncertainties using the MCL crosstalk model. Therefore, there is a problem of low efficiency in MCL crosstalk analysis. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for analyzing crosstalk in multi-conductor transmission lines, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for crosstalk analysis of multi-conductor transmission lines. The method includes:

[0006] Acquire multiple sample induced currents generated on a multi-conductor transmission line;

[0007] Based on multiple input variables corresponding to the multi-conductor transmission line, a chaotic polynomial corresponding to the multi-conductor transmission line is determined. The input variables include at least one of the following: the length of each transmission line in the multi-conductor transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above the ground.

[0008] Based on the induced current of each sample, the chaotic polynomial is sparsified using a greedy algorithm to determine the coefficients of the chaotic polynomial.

[0009] Based on the coefficients and the chaotic polynomial, a chaotic polynomial model is determined; the chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line, and the statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

[0010] Secondly, this application also provides a multi-conductor transmission line crosstalk analysis device. The device includes:

[0011] The acquisition module is used to acquire multiple sample induced currents generated on the multi-conductor transmission line;

[0012] The determination module is used to determine the chaotic polynomial corresponding to the multi-conductor transmission line based on multiple input variables corresponding to the multi-conductor transmission line. The input variables include at least one of the following: the length of each transmission line in the multi-conductor transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above the ground.

[0013] The processing module is used to perform sparsification processing on the chaotic polynomial based on the induced current of each sample using a greedy algorithm, so as to determine the coefficients of the chaotic polynomial.

[0014] The module is used to determine a chaotic polynomial model based on the coefficients and the chaotic polynomial; the chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line, and the statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

[0015] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0016] Acquire multiple sample induced currents generated on a multi-conductor transmission line;

[0017] Based on multiple input variables corresponding to the multi-conductor transmission line, a chaotic polynomial corresponding to the multi-conductor transmission line is determined. The input variables include at least one of the following: the length of each transmission line in the multi-conductor transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above the ground.

[0018] Based on the induced current of each sample, the chaotic polynomial is sparsified using a greedy algorithm to determine the coefficients of the chaotic polynomial.

[0019] Based on the coefficients and the chaotic polynomial, a chaotic polynomial model is determined; the chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line, and the statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

[0020] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0021] Acquire multiple sample induced currents generated on a multi-conductor transmission line;

[0022] Based on multiple input variables corresponding to the multi-conductor transmission line, a chaotic polynomial corresponding to the multi-conductor transmission line is determined. The input variables include at least one of the following: the length of each transmission line in the multi-conductor transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above the ground.

[0023] Based on the induced current of each sample, the chaotic polynomial is sparsified using a greedy algorithm to determine the coefficients of the chaotic polynomial.

[0024] Based on the coefficients and the chaotic polynomial, a chaotic polynomial model is determined; the chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line, and the statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

[0025] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0026] Acquire multiple sample induced currents generated on a multi-conductor transmission line;

[0027] Based on multiple input variables corresponding to the multi-conductor transmission line, a chaotic polynomial corresponding to the multi-conductor transmission line is determined. The input variables include at least one of the following: the length of each transmission line in the multi-conductor transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above the ground.

[0028] Based on the induced current of each sample, the chaotic polynomial is sparsified using a greedy algorithm to determine the coefficients of the chaotic polynomial.

[0029] Based on the coefficients and the chaotic polynomial, a chaotic polynomial model is determined; the chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line, and the statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

[0030] The aforementioned method, apparatus, computer equipment, storage medium, and computer program products for analyzing crosstalk in multi-conductor transmission lines acquire multiple sample induced currents generated on the multi-conductor transmission line. Based on multiple input variables corresponding to the multi-conductor transmission line, a chaotic polynomial corresponding to the multi-conductor transmission line is determined. These input variables include at least one of the following: the length of each transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above ground. Thus, the chaotic polynomial can intuitively characterize the influence of multiple input variables on the multi-conductor transmission line, thereby preventing crosstalk. Based on each sample induced current, a greedy algorithm is used to sparsify the chaotic polynomial to determine its coefficients. Therefore, even with an increase in the dimensionality of the input variables, the sparsification process using the greedy algorithm significantly reduces computational costs, thereby greatly improving computational efficiency. Based on this coefficient and the chaotic polynomial, a chaotic polynomial model can be efficiently determined. This chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line. This statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line. In this way, based on the efficiently determined chaotic polynomial model, the uncertainty of the induced current caused by crosstalk in multi-conductor transmission lines can be accurately quantified, thereby greatly improving the efficiency of crosstalk testing in multi-conductor transmission lines. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating a crosstalk analysis method for multi-conductor transmission lines in one embodiment;

[0032] Figure 2 This is a three-dimensional schematic diagram of a calculation model for a multi-conductor transmission line in one embodiment;

[0033] Figure 3 This is a side view of a calculation model for a multi-conductor transmission line in one embodiment;

[0034] Figure 4 This is a flowchart illustrating the process of determining the coefficients of a chaotic polynomial in one embodiment.

[0035] Figure 5 This is a flowchart illustrating the process of determining whether the verification result meets the iteration stopping condition in one embodiment.

[0036] Figure 6 This is a distribution chart of sensitivity indices for various input variables in one embodiment;

[0037] Figure 7 a is a graph showing the change of far-end crosstalk induced current at a frequency of 400MHz in one embodiment;

[0038] Figure 7b is a graph showing the change of far-end crosstalk induced current at a frequency of 800MHz in one embodiment;

[0039] Figure 8 This is a schematic diagram illustrating the change in crosstalk induced current at the far end of the disturbed line in one embodiment;

[0040] Figure 9 This is a structural block diagram of a multi-conductor transmission line crosstalk analysis device in one embodiment;

[0041] Figure 10 This is a structural block diagram of a multi-conductor transmission line crosstalk analysis device in another embodiment;

[0042] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] In one embodiment, such as Figure 1 As shown, a method for crosstalk analysis of multi-conductor transmission lines is provided. This embodiment illustrates the application of this method to a computer device, which can specifically be a terminal or a server. It is understood that this method can also be applied to systems including terminals and servers, and implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. The server can be a standalone server or a server cluster composed of multiple servers. In this embodiment, the method includes the following steps:

[0045] Step S102: Obtain multiple sample induced currents generated on the multi-conductor transmission line.

[0046] The multi-conductor transmission line has multiple conductors and multiple layers of filling medium. The sample induced current is the induced current generated by crosstalk of the multi-conductor transmission line.

[0047] Specifically, the multi-conductor transmission line calculation model sends multiple sample induced currents generated on the multi-conductor transmission line to a computer device. The computer device acquires these multiple sample induced currents. The multi-conductor transmission line calculation model is based on multi-conductor transmission line theory. For example, ... Figure 2 and Figure 3 As shown, where, Figure 2 A three-dimensional diagram of the calculation model of a multi-conductor transmission line. Figure 3 This is a side view of the calculation model of a multi-conductor transmission line. The multi-conductor transmission line calculation model is a three-conductor transmission line model. In this figure, l is the transmission line length, R is the load impedance, V is the voltmeter, r is the radius of the transmission line, d is the distance between the transmission lines, and h1 and h2 are both distances from the transmission line to ground.

[0048] Step S104: Based on multiple input variables corresponding to the multi-conductor transmission line, determine the chaotic polynomial corresponding to the multi-conductor transmission line. The input variables include at least one of the following: the length of each transmission line in the multi-conductor transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above the ground.

[0049] Among them, the chaotic polynomial is a generalized chaotic polynomial (gPC) constructed based on different types of orthogonal polynomial basis functions, which is used to quantify the uncertainty of induced current caused by crosstalk in multi-conductor transmission lines.

[0050] Specifically, for each input variable corresponding to the target multiconductor, the computer device determines the distribution type corresponding to the corresponding input variable. For each distribution type, the computer device determines the orthogonal polynomial basis corresponding to the corresponding categorical variable, and multiplies the various orthogonal polynomial bases to obtain the chaotic polynomial to be processed corresponding to the target multiconductor transmission line. The computer device obtains the truncation order and processes the chaotic polynomial to be processed based on the truncation order to obtain the chaotic polynomial. The input variables include the length of each transmission line in the multiconductor transmission line, the radius of each transmission line, the distance between each transmission line, the height of each transmission line above ground, and the excitation source (e.g., excitation voltage).

[0051] For example, the computer device acquires input variables corresponding to the actual situation of a multi-conductor transmission line. These input variables include the transmission line length *l*, radius *r*, distance *d* between transmission lines, and heights *h1* and *h2* above ground of the two transmission lines. The dimension of these input variables is *m*. Based on these input variables, the computer device determines that the transmission line length *l* follows a normal distribution with a mean of 1 m and a standard deviation of 0.005 m; the transmission line radius *r* follows a normal distribution with a mean of 0.4 mm and a standard deviation of 0.1 mm; the distance *d* between transmission lines follows a uniform distribution in the interval [5 mm, 7 mm]; and the heights *h1* and *h2* above ground both follow a uniform distribution in the interval [0.2 m, 0.25 m]. According to the mapping relationship between distribution types and orthogonal polynomial bases, the computer device determines that the normal distribution corresponds to the Hermite orthogonal polynomial base, and the uniform distribution corresponds to the Legendre orthogonal polynomial base. The computer device multiplies the products of the various orthogonal polynomial bases to obtain the chaotic polynomial to be processed, such as:

[0052]

[0053] in, Let n be a mixed orthogonal polynomial of order n, and be a multidimensional standard random variable. The function is ∞, where ∞ is the highest power of the mixed orthogonal polynomial. and Φ i respectively with and correspond, Let Φ be the coefficients of the expansion terms of the chaotic polynomial to be processed. i (ξ) is the product of the one-dimensional orthogonal polynomial bases corresponding to each random variable.

[0054] If the truncation order is P, then the computer device will truncate the chaotic polynomial to order P, resulting in the chaotic polynomial:

[0055]

[0056] The number of terms q in the truncated chaotic polynomial expansion is:

[0057]

[0058] Step S106: Based on the induced current of each sample, the chaotic polynomial is sparsified using a greedy algorithm to determine the coefficients of the chaotic polynomial.

[0059] Greedy algorithms, when faced with a problem, make the best choice at the moment, without considering the overall optimal solution; thus, they arrive at a locally optimal solution in some sense. This particular algorithm is a weakly greedy algorithm based on orthogonal matching pursuit.

[0060] Specifically, based on the induced current of each sample, the computer device performs iterative calculations using a greedy algorithm. During each iteration, the index number most relevant to the residual is determined using a correlation function. Based on this index number, it is determined whether the iteration stopping condition is met. If it is met, the coefficients of the chaotic polynomial are determined based on the index number corresponding to the current iteration.

[0061] Step S108: Based on the coefficient and the chaotic polynomial, determine the chaotic polynomial model; the chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line, and the statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

[0062] Specifically, the computer equipment substitutes the determined coefficients into the chaotic polynomial to obtain a chaotic polynomial model. This chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line. This statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

[0063] It should be noted that if it is necessary to determine the uncertainty of the crosstalk induced voltage of a multi-conductor transmission line, multiple sample induced voltages of the multi-conductor transmission line are obtained, and the above steps are performed based on the multiple sample induced voltages to determine the chaotic polynomial coefficients corresponding to the induced voltages, thereby enabling the determination of the chaotic polynomial model used to quantify the crosstalk induced voltage of the multi-conductor transmission line.

[0064] In the aforementioned method for analyzing crosstalk in multi-conductor transmission lines, multiple sample induced currents generated on the multi-conductor transmission line are acquired. Based on multiple input variables corresponding to the multi-conductor transmission line, a chaotic polynomial corresponding to the multi-conductor transmission line is determined. These input variables include at least one of the following: the length of each transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above ground. Thus, the chaotic polynomial can intuitively characterize the influence of multiple input variables on the multi-conductor transmission line, thereby preventing crosstalk. Based on each sample induced current, a greedy algorithm is used to sparsify the chaotic polynomial to determine its coefficients. Even with an increase in the dimensionality of the input variables, the sparsification process using the greedy algorithm significantly reduces computational costs, thereby greatly improving computational efficiency. Based on these coefficients and the chaotic polynomial, a chaotic polynomial model can be efficiently determined. This chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line. This statistical information is used to quantify the uncertainty of the crosstalk induced current in the multi-conductor transmission line. In this way, based on the efficient and deterministic chaotic polynomial model, the uncertainty of the induced current caused by crosstalk in multi-conductor transmission lines can be accurately quantified, thereby greatly improving the efficiency of dealing with crosstalk in multi-conductor transmission lines.

[0065] In one embodiment, the method further includes: acquiring multiple sets of test data; wherein each set of test data contains values ​​of multiple input variables. For each set of test data, a chaotic multinomial model is used to process the corresponding test data to obtain test results corresponding to the corresponding test data. At least one of the following calculations is performed on the multiple test results: mean calculation, variance calculation, and probability distribution calculation, to obtain statistical information corresponding to the multi-conductor transmission line. This statistical information includes at least one of the mean result, variance result, and probability distribution result.

[0066] Specifically, the computer equipment acquires multiple sets of test data. Each set of test data contains values ​​for multiple input variables. For each set of test data, the computer equipment inputs each input variable from the corresponding test data into a chaotic polynomial model to obtain the test result corresponding to the test data. The computer equipment performs at least one calculation on the multiple test results, including mean calculation, variance calculation, and probability distribution calculation, to obtain the corresponding statistical results. The computer equipment integrates the statistical results to obtain the statistical information corresponding to the multi-conductor transmission line.

[0067] In this embodiment, by using a chaotic polynomial model to perform statistical calculations on multiple sets of test data containing values ​​of multiple input variables, the uncertainty of the induced current caused by crosstalk in multi-conductor transmission lines can be accurately quantified, thereby greatly improving the efficiency of dealing with crosstalk in multi-conductor transmission lines.

[0068] In one embodiment, such as Figure 4 As shown, based on the induced current of each sample, a greedy algorithm is used to sparsify the chaotic polynomial to determine its coefficients, including:

[0069] Step S402: Obtain the product of the historical polynomial bases corresponding to the previous iteration, and calculate the index number of the product of the historical polynomial bases.

[0070] Specifically, the computer device obtains the product of the historical polynomial bases corresponding to the previous iteration, as well as the historical residuals corresponding to the previous iteration. The computer device performs relevant calculations on the product of the historical polynomial bases and the historical residuals to obtain a sparsified index number corresponding to the current iteration. This index number is used to determine the active set and the filter set. This set is a subset of the dictionary composed of the products of the polynomial bases.

[0071] Step S404: Update the set based on the index number to obtain the updated set, and determine the product of the current polynomial basis corresponding to the current iteration based on the updated set; wherein, the set contains the product of the polynomial basis.

[0072] Specifically, the computer device obtains the previous set corresponding to the previous iteration, which is the set updated in the previous iteration. The computer device filters out the index number from the previous set to obtain the updated set. The computer device uses the elements in the updated set to determine the product of the current polynomial basis corresponding to the current iteration.

[0073] Step S406: Factorize the product of the current polynomial basis to obtain an orthogonal matrix and an upper triangular matrix.

[0074] Specifically, the computer device performs matrix factorization on the product of the current polynomial basis to obtain an orthogonal matrix and an upper triangular matrix. For example, the computer device performs QR factorization on the product of the current polynomial basis to obtain an orthogonal matrix Q and an upper triangular matrix R.

[0075] Step S408: Determine the current residual based on the induced current of each sample, the orthogonal matrix, and the historical residual corresponding to the previous iteration.

[0076] Specifically, the computer device transposes the orthogonal matrix to obtain the transposed orthogonal matrix. The computer device then performs matrix multiplication on the transposed orthogonal matrix, the induced currents of each sample, and the orthogonal matrix to obtain the orthogonality value. The computer device uses the difference between the historical residual corresponding to the previous iteration and this orthogonality value as the current residual corresponding to the current iteration. For example, based on this orthogonal matrix Q... (t)(:,t), determine the orthogonal matrix (Q) after transpose. (t) (:,t)) T Then, based on the transposed orthogonal matrix, the induced current Y of each sample, and the orthogonal matrix Q (t) (:,t), the historical residual r corresponding to the previous iteration (t-1) Then the current residual r corresponding to the current iteration (t) for:

[0077] r (t) =r (t-1) -(Q (t) (:,t)) T YQ (t) (:,t)

[0078] Step S410: Perform error verification on the current residual and the orthogonal matrix to obtain the verification result.

[0079] Specifically, the computer device performs error verification on the current residual and the orthogonal matrix using the leave-one-out cross-validation method, obtaining the verification result corresponding to the current iteration. The leave-one-out cross-validation method is as follows:

[0080]

[0081] Among them, in the formula Q represents the residual corresponding to the current iteration. (t) (j,;) is the orthogonal matrix corresponding to the current iteration.

[0082] Step S412: If the verification result determines that the iteration stopping condition is not met, then the product of the current polynomial basis is used as the product of the historical polynomial basis corresponding to the next iteration, and the current residual is used as the historical residual corresponding to the next iteration. The next iteration is then entered, and the step of calculating the index number of the product of the historical polynomial basis is returned to continue execution until the verification result output by the iterative calculation meets the iteration stopping condition.

[0083] Specifically, based on the verification result, the computer device determines whether the first iteration stopping condition of minimizing the generalization error is met, or whether the second iteration stopping condition of minimizing the LOO standard is met. If the computer device determines that at least one of the first and second iteration stopping conditions is not met, it uses the product of the current polynomial basis as the product of the historical polynomial basis corresponding to the next iteration, and uses the current residual as the historical residual corresponding to the next iteration, and performs the next iteration. It then returns to the step of calculating the index number of the product of the historical polynomial basis and continues execution until the verification result output by the iterative calculation meets the iteration stopping condition.

[0084] The first iteration stopping condition is determined by constructing two offset LOO errors, which are defined from two approximate windows across five iterations. The second iteration stopping condition is determined by judging the magnitude of the change in the verification result.

[0085] Step S414: Based on the sample induced current and the orthogonal matrix and upper triangular matrix from the last iteration calculation, determine the coefficients of the chaotic polynomial through matrix calculation.

[0086] Specifically, the computer device acquires a coefficient equation, which consists of the sample induced current, an orthogonal matrix, an upper triangular matrix, and coefficients. Based on the sample induced current and the orthogonal matrix and upper triangular matrix from the last iteration calculation, the computer device performs matrix calculations using the coefficient equation to determine the coefficients of the chaotic polynomial.

[0087] The equation for this coefficient is as follows:

[0088] R (t) c (t) =(Q (t) ) T Y

[0089] In this coefficient equation, Y represents the sample induced current, and R... (t) Q (t) c (t) All of these are the coefficients of the upper triangular matrix, orthogonal matrix, and chaotic polynomial corresponding to the last iteration.

[0090] In this embodiment, after processing using a greedy algorithm, the index number most relevant to the residual can be obtained. Thus, by using the index number with strong correlation to the residual, the set corresponding to the current iteration can be effectively selected, allowing for rapid updating of the residual through factorization. Based on the updated residual, a highly valid verification result is obtained through error checking to determine whether the iteration stopping condition is met, greatly improving the reliability and accuracy of the iteration result. Therefore, once the iteration stopping condition is met, the coefficients of the chaotic polynomial can be determined promptly and effectively based on the highly valid verification result.

[0091] In one embodiment, calculating the index number by multiplying the historical polynomial base includes: obtaining the historical norm parameter and historical residual corresponding to the previous iteration, squaring the product of the historical norm parameter and the historical residual, and dividing the squaring result by the product of the historical polynomial base to obtain the residual function, and performing relevant calculations on the residual function to obtain the index number.

[0092] Specifically, the computer device obtains the history norm parameter and history residual corresponding to the previous iteration, squares the product of the history norm parameter and the history residual, and multiplies the squared result by the product of the history polynomial basis to obtain the residual term function. The computer device then performs argmax calculations on this residual term function to obtain the index number. The history norm corresponding to the first iteration is obtained by performing norm calculations on the product of the polynomial basis in the first iteration set.

[0093] For example, the formula shown below calculates the index number corresponding to the current iteration:

[0094]

[0095] in, Let Φ be a subset, Φ be the product of polynomial bases, and r be a subset. (t-1) This is the historical residual corresponding to the previous iteration. This is the history norm parameter corresponding to the previous iteration.

[0096] In this embodiment, based on the product of the historical norm parameter, historical residual, and historical polynomial basis corresponding to the previous iteration, the residual term function corresponding to the current iteration can be determined. Furthermore, by applying a correlation function to the residual term function, the index number of the set with high correlation in the current iteration can be determined. This facilitates the effective selection of the set corresponding to the current iteration, enabling rapid updating of the residual through factorization.

[0097] In this embodiment, to facilitate understanding of the greedy algorithm of this application, the following code example is used for explanation. The computer device determines a dictionary D based on a chaotic polynomial, which is a set of products of one-dimensional orthogonal polynomial bases of all orders. The computer device determines the first and second iteration stopping conditions and determines the sample induced current (i.e., the function response value in the corresponding example) corresponding to each sampling point through sampling points. The computer device initializes the parameters in the greedy algorithm. During iteration, the computer device obtains the history norm parameter and history residual corresponding to the previous iteration, and performs a square operation on the product of the history norm parameter and the history residual to obtain the square operation result. The square operation result is divided by the product of the history polynomial bases to obtain the residual term function, and related calculations are performed on the residual term function to obtain the index number. The set is updated based on the index number to obtain the updated set, and the product of the current polynomial base corresponding to the current iteration is determined based on the updated set; wherein, the set contains the product of polynomial bases. By factoring the product of the current polynomial bases, an orthogonal matrix and an upper triangular matrix are obtained. Based on the induced current of each sample, the orthogonal matrix, and the historical residual corresponding to the previous iteration, the current residual is determined. The computer device, based on this verification result, determines whether the first iteration stopping condition of minimizing generalization error is met, or whether the second iteration stopping condition of minimizing the LOO criterion is met. If the computer device determines that at least one of the first and second iteration stopping conditions is not met, the product of the current polynomial basis is used as the product of the historical polynomial basis corresponding to the next iteration, and the current residual is used as the historical residual corresponding to the next iteration. The next iteration is then performed, returning to the step of calculating the index number of the product of the historical polynomial basis, until the verification result output by the iterative calculation meets the iteration stopping condition. Based on the induced current of the sample and the orthogonal matrix and upper triangular matrix from the last iteration calculation, the coefficients of the chaotic polynomial are determined through matrix calculation.

[0098] The code example is shown below:

[0099]

[0100]

[0101] In this embodiment, after processing using a greedy algorithm, the index number most relevant to the residual can be obtained. Thus, by using the index number with strong correlation to the residual, the set corresponding to the current iteration can be effectively selected, allowing for rapid updating of the residual through factorization. Based on the updated residual, a highly valid verification result is obtained through error checking to determine whether the iteration stopping condition is met, greatly improving the reliability and accuracy of the iteration result. Therefore, once the iteration stopping condition is met, the coefficients of the chaotic polynomial can be determined promptly and effectively based on the highly valid verification result.

[0102] In one embodiment, such as Figure 5 As shown, the method also includes:

[0103] Step S502: Based on the first iteration parameter and the number of iterations corresponding to the current iteration, determine the range of first iterations, and determine multiple first iterations based on the range of first iterations; wherein the first iterations are less than or equal to the number of iterations corresponding to the current iteration.

[0104] Specifically, the computer device uses the difference between the first iteration parameter and the number of iterations corresponding to the current iteration as the first iteration lower limit, and the number of iterations corresponding to the current iteration as the first iteration upper limit. Based on the first iteration lower limit and the first iteration upper limit, the computer device determines the range of the first iteration number, and determines the number of iterations that fall within the first iteration number range. This first iteration number is less than or equal to the number of iterations corresponding to the current iteration. The difference between the first iteration number range and the number of iterations is five iterations.

[0105] Step S504: Sum the first verification results corresponding to each first iteration number to obtain a first summation value; wherein the first verification result includes the verification result corresponding to the current iteration.

[0106] Specifically, the computer device acquires the first verification result corresponding to each first iteration number, and sums the first verification results to obtain a first summation value. For example, the first summation value is determined based on the following formula:

[0107]

[0108] Among them, LOO (i) This is the first verification result corresponding to the i-th iteration.

[0109] Step S506: Determine the range of the second iteration number based on the first iteration parameter and the second iteration parameter, and determine multiple second iteration numbers based on the range of the second iteration number; wherein the second iteration number is less than the number corresponding to the current iteration.

[0110] Specifically, the computer device determines the upper limit and lower limit of the second iteration based on the lower limit of the first iteration. The computer device then determines the range of the second iteration count based on the upper and lower limits of the second iteration, and determines the number of second iterations falling within this range. This number of second iterations is less than the number corresponding to the current iteration. The difference between the range of the second iteration count and the number of iterations is five iterations.

[0111] Step S508: Sum the second verification results corresponding to each second iteration number to obtain the second summation value.

[0112] Specifically, the computer device acquires the second verification results corresponding to each second iteration, and sums these results to obtain a second sum value. For example, the second sum value can be determined based on the following formula:

[0113]

[0114] Among them, LOO (i) This is the second verification result corresponding to the i-th iteration.

[0115] Step S510: If the first summation value is greater than the second summation value, then the verification result is determined to meet the iteration stopping condition.

[0116] Specifically, the first summation value and the second summation value are compared. If the first summation value is greater than the second summation value, then the verification result is determined to meet the iteration stopping condition.

[0117] Step S512: If the second summation value is less than or equal to the second summation value, then it is determined that the verification result does not meet the iteration stopping condition.

[0118] Specifically, the first summation value and the second summation value are compared. If the first summation value is less than or equal to the second summation value, then the verification result is determined to not meet the iteration stopping condition.

[0119] In this embodiment, by constructing two offset LOO errors, the corresponding first and second summations are calculated from two approximate windows in five iterations. Thus, the condition that the first summation is less than the second summation can be used as a reasonable condition for minimizing the generalization error, i.e., as the iteration stopping condition. This iteration stopping condition based on the minimum generalization error allows for effective judgment of the error verification results, greatly improving the reliability and accuracy of the iteration process.

[0120] In one embodiment, the method further includes: obtaining historical verification results corresponding to the previous iteration, and determining an error coefficient corresponding to the current iteration based on the historical verification results and the current verification result. If the error coefficient is less than a coefficient threshold, the verification result is determined to meet the iteration stopping condition. If the error coefficient is greater than or equal to the coefficient threshold, the verification result is determined not to meet the iteration stopping condition.

[0121] Specifically, the computer device acquires the historical verification results corresponding to the previous iteration and calculates the difference between these historical verification results and the verification results of the current iteration to obtain a verification difference value. The computer device divides this verification difference value by the historical verification result to obtain an error coefficient. The computer device compares this error coefficient with a coefficient threshold. If the error coefficient is less than the coefficient threshold, the verification result is determined to meet the iteration stopping condition. If the error coefficient is greater than or equal to the coefficient threshold, the verification result is determined not to meet the iteration stopping condition.

[0122] In this embodiment, by comparing the error between the verification result corresponding to the current iteration number and the historical verification result corresponding to the previous iteration number, it is possible to determine more quickly and accurately whether the verification result of the current iteration meets the iteration stopping condition, which greatly improves the efficiency of the iteration process.

[0123] In one embodiment, the method further includes: converting the chaotic polynomial model into an increasing form of the chaotic polynomial model, and taking the variance of the increasing form of the chaotic polynomial model to obtain a variance model. A computer device determines the input variable to be tested, and based on the input variable to be tested, determines at least one influence term related to the input variable to be tested from the variance model. The computer device calculates a sensitivity index for the influence term to determine the sensitivity of the input variable to be tested, which characterizes the impact of the input variable to be tested on crosstalk generated by the multi-conductor transmission line.

[0124] For example, the chaotic polynomial model can be transformed into an increasing form of the chaotic polynomial model as follows:

[0125]

[0126] in, The variance model is obtained by taking the variance of both sides of the chaotic polynomial model of the increasing motion. If the computer determines that the input variable to be tested is variable 1, then it determines multiple influencing terms related to variable 1, such as terms containing variable 1. The computer calculates the sensitivity index of these influencing terms to determine the sensitivity of the input variable to be tested.

[0127] In the sensitivity calculation of an increasing polynomial, if the increasing polynomial is Y(ξ), then the variance of both sides of the increasing polynomial is taken, as described below:

[0128]

[0129] The Sobel sensitivity calculation determines the impact of the input variable under test on crosstalk generated in a multi-conductor transmission line. Specifically, the Sobel sensitivity index is defined as:

[0130]

[0131] The above S i The first-order sensitivity index represents the contribution of a single input to the variance of the output response. The sum of the first-order sensitivity indices of each input variable and the sensitivity indices of the interactions between the variables is defined as the total sensitivity index.

[0132]

[0133] In this embodiment, such as Figure 6 As shown, for 6a, there are the total sensitivity index distribution diagrams of each input variable in the frequency band [1MHz, 1GHz] and the first-order sensitivity index distribution diagrams of each input variable in the frequency band [1MHz, 1GHz].

[0134] In this embodiment, based on the chaotic polynomial model, the influence of each input variable on crosstalk in multi-conductor transmission lines can be clearly and intuitively determined through sensitivity index calculation. This helps to reduce crosstalk in multi-conductor transmission lines and, consequently, ensures the normal operation of electrical or electronic systems.

[0135] To facilitate a clearer understanding of the technical solution of this application, a more detailed embodiment is provided for description. A computer device establishes a multi-conductor transmission line calculation model (i.e., a multi-conductor transmission line crosstalk induced current model) based on multi-conductor transmission line theory to obtain multiple sample induced currents. Each input variable is determined based on the actual usage of the multi-conductor transmission line, and for each input variable, a distribution type corresponding to the corresponding input variable is determined. For each distribution type, the computer device determines an orthogonal polynomial basis corresponding to the corresponding categorical variable, and multiplies the orthogonal polynomial bases to obtain a chaotic polynomial to be processed corresponding to the target multi-conductor transmission line. The computer device obtains the truncation order and processes the chaotic polynomial to be processed based on the truncation order to obtain a chaotic polynomial.

[0136] The computer device determines the first and second iteration stopping conditions and determines the sample induced current (i.e., the function response value in the corresponding example) corresponding to each sampling point through sampling points. The computer device initializes the parameters in the greedy algorithm. During the iteration process, the computer device obtains the historical norm parameter and historical residual corresponding to the previous iteration, and performs a square operation on the product of the historical norm parameter and the historical residual to obtain the square operation result. The square operation result is divided by the product of the historical polynomial basis to obtain the residual term function, and related calculations are performed on the residual term function to obtain the index number. The set is updated based on the index number to obtain the updated set, and the product of the current polynomial basis corresponding to the current iteration is determined based on the updated set; wherein, the set contains the product of the polynomial basis. By factoring the product of the current polynomial basis, an orthogonal matrix and an upper triangular matrix are obtained. Based on each sample induced current, the orthogonal matrix, and the historical residual corresponding to the previous iteration, the current residual is determined. Based on the verification result, the computer device determines whether the first iteration stopping condition of minimizing the generalization error is met, or whether the second iteration stopping condition of minimizing the LOO standard is met. If the computer device determines that at least one of the first and second iteration stopping conditions is not met, it uses the product of the current polynomial basis as the product of the historical polynomial basis corresponding to the next iteration, and uses the current residual as the historical residual corresponding to the next iteration, and performs the next iteration. It then returns to the step of calculating the index number of the product of the historical polynomial basis and continues execution until the verification result output by the iterative calculation meets the iteration stopping condition. Based on the sample induced current and the orthogonal matrix and upper triangular matrix in the last iteration calculation, the coefficients of the chaotic polynomial are determined through matrix calculation. Based on the coefficients and the chaotic polynomial, a chaotic polynomial model is determined. This chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line. This statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

[0137] The uncertainty of crosstalk induced current in multi-conductor transmission lines is quantified based on this chaotic polynomial model. Specifically, multiple sets of test data are acquired; each set of test data contains values ​​of multiple input variables. For each set of test data, the chaotic polynomial model is used to process the corresponding test data to obtain test results. At least one of the following calculations—mean calculation, variance calculation, and probability distribution calculation—is performed on the multiple test results to obtain statistical information corresponding to the multi-conductor transmission line. This statistical information includes at least one of the mean result, variance result, and probability distribution result.

[0138] Furthermore, to compare the differences between the computer equipment and the orthogonal matching pursuit method, chaotic polynomial models for quantifying induced current and quantifying induced voltage were determined based on the scheme of this application. The computation time of the chaotic polynomial model was compared with that of the orthogonal matching pursuit method and the Monte Carlo method, as well as the LOO error, under different cutoff orders P. The computation time of the 10,000th Monte Carlo method was 59.69 s. The comparison results are shown in Table 1; the comparison results of the probability distribution at different frequency points are attached. Figure 7 a and appendix Figure 7 As shown in b; the comparison results of the mean and variance over the frequency band are attached. Figure 8 As shown, the computational results of the RGA method provided by this invention are basically consistent with those of the Monte Carlo method (MC) (i.e., the two methods are consistent for the mean line and for the variance line). Furthermore, as the truncation order of the chaotic polynomial increases, the computation time of the method provided by this invention remains consistently low compared to the OMP method, effectively avoiding the curse of dimensionality. Based on a comprehensive comparison of the computational results in Table 1, the truncation order P in the embodiment provided by this invention is set to 5.

[0139] Table 1(a) Comparison of calculation times, unit: / s

[0140] 2 3 4 5 6 7 8 9 10 11 12 OMP 1.49 2.29 4.77 12.11 9.98 9.61 9.86 11.34 24.63 40.18 78.86 RGA 1.96 2.76 3.96 3.71 3.03 2.21 4.02 4.01 3.35 8.17 11.51

[0141] Table 1(b) Comparison of LOO errors, unit / le-5

[0142]

[0143] In this embodiment, multiple sample induced currents generated on a multi-conductor transmission line are acquired. Based on multiple input variables corresponding to the multi-conductor transmission line, a chaotic polynomial corresponding to the multi-conductor transmission line is determined. Thus, the chaotic polynomial can intuitively characterize the influence of multiple input variables on the multi-conductor transmission line, thereby preventing crosstalk. Based on each sample induced current, the chaotic polynomial is sparsified using a greedy algorithm to determine its coefficients. This way, even with an increase in the dimensionality of the input variables, the sparsification process using the greedy algorithm can significantly reduce computational costs, thereby greatly improving computational efficiency. Based on these coefficients and the chaotic polynomial, a chaotic polynomial model can be efficiently determined. This chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line. This statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line. Thus, based on the efficiently determined chaotic polynomial model, the uncertainty of the induced current caused by crosstalk in the multi-conductor transmission line can be accurately quantified, thereby greatly improving the efficiency of handling crosstalk in multi-conductor transmission lines.

[0144] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0145] Based on the same inventive concept, this application also provides a multi-conductor transmission line crosstalk analysis apparatus for implementing the multi-conductor transmission line crosstalk analysis method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the multi-conductor transmission line crosstalk analysis apparatus provided below can be found in the limitations of the multi-conductor transmission line crosstalk analysis method described above, and will not be repeated here.

[0146] In one embodiment, such as Figure 9 As shown, a multi-conductor transmission line crosstalk analysis device is provided. The device 900 includes: an acquisition module 902, a determination module 904, a processing module 906, and a obtaining module 908.

[0147] in:

[0148] The acquisition module 902 is used to acquire multiple sample induced currents generated on a multi-conductor transmission line.

[0149] The determination module 904 is used to determine the chaotic polynomial corresponding to the multi-conductor transmission line based on multiple input variables corresponding to the multi-conductor transmission line. The input variables include at least one of the following: the length of each transmission line in the multi-conductor transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above the ground.

[0150] The processing module 906 is used to perform sparsification processing on the chaotic polynomial based on the induced current of each sample using a greedy algorithm, so as to determine the coefficients of the chaotic polynomial.

[0151] The module 908 is used to determine the chaotic polynomial model based on the coefficient and the chaotic polynomial. The chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line. The statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

[0152] In one embodiment, such as Figure 10 As shown, the device 900 further includes a test module 910, which is used to acquire multiple sets of test data; each set of test data contains values ​​of multiple input variables. For each set of test data, a chaotic polynomial model is used to process the corresponding test data to obtain test results corresponding to the corresponding test data. At least one of the following calculations—mean calculation, variance calculation, and probability distribution calculation—is performed on the multiple test results to obtain statistical information corresponding to the multi-conductor transmission line. This statistical information includes at least one of the following results: mean result, variance result, and probability distribution result.

[0153] In one embodiment, the processing module 906 is configured to obtain the product of the historical polynomial basis corresponding to the previous iteration, and calculate the index number of the product of the historical polynomial basis. Based on the index number, the set is updated to obtain an updated set, and based on the updated set, the product of the current polynomial basis corresponding to the current iteration is determined; wherein, the set contains the product of polynomial bases. By factoring the product of the current polynomial basis, an orthogonal matrix and an upper triangular matrix are obtained. Based on the induced current of each sample, the orthogonal matrix, and the historical residual corresponding to the previous iteration, the current residual is determined. Error verification is performed on the current residual and the orthogonal matrix to obtain a verification result. If, based on the verification result, it is determined that the iteration stopping condition is not met, the product of the current polynomial basis is used as the product of the historical polynomial basis corresponding to the next iteration, and the current residual is used as the historical residual corresponding to the next iteration. The process proceeds to the next iteration, and the step of calculating the index number of the product of the historical polynomial basis is returned to continue execution until the verification result output by the iterative calculation meets the iteration stopping condition. Based on the sample induced current, and the orthogonal matrix and upper triangular matrix from the last iteration calculation, the coefficients of the chaotic polynomial are determined through matrix calculation.

[0154] In one embodiment, the processing module 906 is configured to obtain the history norm parameter and history residual corresponding to the previous iteration, and to square the product of the history norm parameter and the history residual to obtain the squared result. The squared result is then divided by the product of the history polynomial base to obtain the residual term function, and the residual term function is used for related calculations to obtain the index number.

[0155] In one embodiment, the processing module 906 is configured to determine a first iteration count range based on a first iteration parameter and the number of iterations corresponding to the current iteration, and to determine multiple first iteration counts based on the first iteration count range; wherein the first iteration count is less than or equal to the number of iterations corresponding to the current iteration. The first verification results corresponding to each first iteration count are summed to obtain a first summation value; wherein the first verification result includes the verification result corresponding to the current iteration. A second iteration count range is determined based on the first iteration parameter and the second iteration parameter, and multiple second iteration counts are determined based on the second iteration count range; wherein the second iteration count is less than the number of iterations corresponding to the current iteration. The second verification results corresponding to each second iteration count are summed to obtain a second summation value. If the first summation value is greater than the second summation value, the verification result is determined to satisfy the iteration stopping condition. If the second summation value is less than or equal to the second summation value, the verification result is determined not to satisfy the iteration stopping condition.

[0156] In one embodiment, the processing module 906 is configured to obtain the historical verification result corresponding to the previous iteration, and determine the error coefficient corresponding to the current iteration based on the historical verification result and the current verification result. If the error coefficient is less than a coefficient threshold, the verification result is determined to meet the iteration stopping condition. If the error coefficient is greater than or equal to the coefficient threshold, the verification result is determined not to meet the iteration stopping condition.

[0157] Each module in the aforementioned multi-conductor transmission line crosstalk analysis device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0158] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores multi-conductor transmission line crosstalk analysis data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-conductor transmission line crosstalk analysis method.

[0159] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0160] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0161] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0162] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0163] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0164] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0165] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for analyzing crosstalk in multi-conductor transmission lines, characterized in that, The method includes: Acquire multiple sample induced currents generated on a multi-conductor transmission line; Based on multiple input variables corresponding to the multi-conductor transmission line, a chaotic polynomial corresponding to the multi-conductor transmission line is determined. The input variables include at least one of the following: the length of each transmission line in the multi-conductor transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above the ground. Obtain the product of the historical polynomial bases corresponding to the previous iteration, and calculate the index number by multiplying the historical polynomial bases. The index number is filtered out from the set updated in the previous iteration to obtain the updated set, and the product of the current polynomial basis corresponding to the current iteration is determined based on the updated set; wherein, the set contains the product of polynomial basis; By factoring the product of the current polynomial basis, an orthogonal matrix and an upper triangular matrix are obtained. Multiply the transposed orthogonal matrix, the induced current of each sample, and the orthogonal matrix to obtain the orthogonal value. Use the difference between the historical residual corresponding to the previous iteration and the orthogonal value as the current residual. Error verification is performed on the current residual and the orthogonal matrix to obtain the verification result; If the verification result determines that the iteration stopping condition is not met, then the product of the current polynomial base is used as the product of the historical polynomial base corresponding to the next iteration, and the current residual is used as the historical residual corresponding to the next iteration. The next iteration is then initiated, and the step of calculating the index number by multiplying the historical polynomial base is returned to continue execution until the verification result output by the iterative calculation meets the iteration stopping condition. Based on the sample induced current, and the orthogonal matrix and upper triangular matrix in the last iteration calculation, the coefficients of the chaotic polynomial are determined by matrix calculation. Based on the coefficients and the chaotic polynomial, a chaotic polynomial model is determined; the chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line, and the statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

2. The method according to claim 1, characterized in that, The method further includes: Obtain multiple sets of test data; each set of test data contains the values ​​of multiple input variables; For each set of test data, the corresponding test data is processed using a chaotic multinomial model to obtain the test results corresponding to the test data. The mean, variance, and probability distribution of multiple test results are calculated to obtain statistical information corresponding to the multi-conductor transmission line. The statistical information includes at least one of the mean result, variance result, and probability distribution result.

3. The method according to claim 2, characterized in that, The calculation of the index number by multiplying the historical polynomial bases includes: Obtain the historical norm parameter and historical residual corresponding to the previous iteration, and perform a square operation on the product of the historical norm parameter and the historical residual to obtain the square operation result; Divide the result of the squaring operation by the product of the historical polynomial bases to obtain the residual term function, and perform related calculations on the residual term function to obtain the index number.

4. The method according to claim 2, characterized in that, The method further includes: Based on the first iteration parameter and the number of iterations corresponding to the current iteration, a range of first iteration counts is determined, and multiple first iteration counts are determined based on the first iteration count range; wherein, the first iteration count is less than or equal to the number of iterations corresponding to the current iteration. The first verification results corresponding to each first iteration number are summed to obtain a first sum value; wherein, the first verification result includes the verification result corresponding to the current iteration; The second iteration number range is determined based on the first iteration parameter and the second iteration parameter, and multiple second iteration numbers are determined based on the second iteration number range; wherein, the second iteration number is less than the number corresponding to the current iteration; The second verification results corresponding to each second iteration number are summed to obtain the second sum value; If the first summation value is greater than the second summation value, then the verification result is determined to meet the iteration stopping condition; If the second summation value is less than or equal to the second summation value, then the verification result is determined not to meet the iteration stopping condition.

5. The method according to claim 2, characterized in that, The method further includes: Obtain the historical verification results corresponding to the previous iteration, and determine the error coefficient corresponding to the current iteration based on the historical verification results and the verification results; If the error coefficient is less than the coefficient threshold, then the verification result is determined to meet the iteration stopping condition; If the error coefficient is greater than or equal to the coefficient threshold, then the verification result is determined not to meet the iteration stopping condition.

6. A multi-conductor transmission line crosstalk analysis device, characterized in that, The device includes: The acquisition module is used to acquire multiple sample induced currents generated on the multi-conductor transmission line; The determination module is used to determine the chaotic polynomial corresponding to the multi-conductor transmission line based on multiple input variables corresponding to the multi-conductor transmission line. The input variables include at least one of the following: the length of each transmission line in the multi-conductor transmission line, the radius of each transmission line, the distance between each transmission line, and the height of each transmission line above the ground. The processing module is used to obtain the product of the historical polynomial bases corresponding to the previous iteration, and calculate the index number of the product of the historical polynomial bases; filter out the index number from the set updated in the previous iteration to obtain the updated set, and determine the product of the current polynomial bases corresponding to the current iteration based on the updated set; wherein the set contains the product of polynomial bases; obtain an orthogonal matrix and an upper triangular matrix by factoring the product of the current polynomial bases; multiply the transposed orthogonal matrix, each sample induced current, and the orthogonal matrix to obtain the orthogonality value, and take the difference between the historical residual corresponding to the previous iteration and the orthogonality value as the current residual. The current residual and the orthogonal matrix are subjected to error verification to obtain a verification result. If the verification result determines that the iteration stopping condition is not met, the product of the current polynomial basis is used as the product of the historical polynomial basis corresponding to the next iteration, and the current residual is used as the historical residual corresponding to the next iteration. The next iteration is then initiated, and the step of calculating the index number by multiplying the historical polynomial basis is returned to continue execution until the verification result output by the iterative calculation meets the iteration stopping condition. Based on the sample induced current and the orthogonal matrix and upper triangular matrix in the last iteration calculation, the coefficients of the chaotic polynomial are determined by matrix calculation. The module is used to determine a chaotic polynomial model based on the coefficients and the chaotic polynomial; the chaotic polynomial model is used to process the values ​​of multiple input variables contained in multiple sets of test data to obtain statistical information corresponding to the multi-conductor transmission line, and the statistical information is used to quantify the uncertainty of the crosstalk induced current of the multi-conductor transmission line.

7. The apparatus according to claim 6, characterized in that, The device further includes a testing module for acquiring multiple sets of test data; each set of test data contains values ​​of multiple input variables; for each set of test data, a chaotic multinomial model is used to process the corresponding test data to obtain test results corresponding to the corresponding test data; at least one of mean calculation, variance calculation, and probability distribution calculation is performed on the multiple test results to obtain statistical information corresponding to the multi-conductor transmission line, wherein the statistical information includes at least one of mean result, variance result, and probability distribution result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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