Reverse recognition method for key factors of warhead

Abstract

The invention discloses a reverse recognition method for key factors of a warhead, and the method comprises the steps: determining key indexes and influence factors of the warhead, and constructing an implicit function relation; performing quantitative measurement on the influence factors, and obtaining a warhead index implementation value corresponding to each group of parameters through a test; carrying out reverse regression analysis on the influence factors and the warhead index implementation values; and carrying out importance sorting on all the influence factors. According to the method, the key factors of the warhead can be quickly identified, and the calculation efficiency is high.

Description

Technical Field

[0001] The present invention belongs to the technical field of warhead design and relates to a reverse identification method for key factors of a warhead. Background Art

[0002] The influencing factors of warhead performance include many parameters such as materials, structures, processes, manufacturing, etc. In the process of optimizing warhead design, optimizing all parameters is not only time-consuming and laborious, but also impossible to achieve due to its overly complex optimization model. Therefore, it is necessary to screen the key factors of the warhead, grasp the main factors affecting the warhead, so as to obtain a balance between performance and cost. At present, the grey correlation method is mostly used to analyze the correlation degree between influencing factors and warhead performance. This method lacks a theoretical basis and scientific proof, and the conversion process is prone to loss of original information. Summary of the Invention

[0003] Aiming at the deficiencies existing in the prior art, the purpose of the present invention is to provide a reverse identification method for key factors of a warhead. This method can utilize the contribution of each influencing factor to warhead performance, and rank the importance of influencing factors through a reverse regression method, so as to efficiently identify key factors.

[0004] To solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0005] A reverse identification method for key factors of a warhead includes the following steps:

[0006] Step S10: Determine the key indicators of the warhead and their influencing factors, and construct an implicit function relationship;

[0007] Step S20: Quantitatively measure the influencing factors, and obtain the realized values of the warhead indicators corresponding to each group of parameters through experiments;

[0008] Step S30: Conduct a reverse regression analysis on the influencing factors and the realized values of the warhead indicators;

[0009] Step S40: Rank the importance of all influencing factors.

[0010] The present invention further includes the following technical features:

[0011] Specifically, the step S10 includes: Analyze the influencing factors according to the requirements of the key indicators of the warhead. The influencing factors in a fragment warhead include fragment shape, fragment material, charge length, and charge diameter;

[0012] Let the key indicator of the warhead be Y, and its influencing factor be X. Construct its implicit function relationship Y = g(X), where X = {X1,..., X n} is an n-dimensional influencing factor.

[0013] Specifically, the step S20 includes: quantitatively measuring n-dimensional influencing factors and establishing a parameter matrix:

[0014]

[0015] where represents the j-th measurement value of the i-th dimensional influencing factor;

[0016] Correspondingly, the achieved value of its warhead index is y = {y (1) ,..., y (j) ,..., y (N)} T .

[0017] Specifically, the step S30 includes:

[0018] Sort y = {y (1) ,..., y (j) ,..., y (N)}, and the sorted achieved value is denoted as T Sort x according to the order of y

[0019] s so that the measurement values of the influencing factors correspond to the test results, denoted as x s ;

[0020] Divide the N groups of samples into h slices, and the number of samples in each slice is N h = N / h;

[0021] In each slice, calculate the sample mean, where is the k-th slice among the h slices, and

[0022]

[0023]

[0024]

[0025] where is the mean of all measurement values,

[0026] Calculate the covariance matrix:

[0027]

[0028] Perform eigenvalue decomposition:

[0029]

[0030] where β l is the l-th eigenvector, is the l-th eigenvalue.

[0031] Specifically, the step S40 includes: sorting the influencing factors according to the sorting of the eigenvalues, and the sorting principle is: the larger the eigenvalue of the influencing factor, the higher its importance.

[0032] Compared with the prior art, the present invention has the following technical effects:

[0033] The present invention realizes the importance ranking of the influencing factors considering the warhead performance through reverse regression, and measures the contribution degree of each influencing factor to the warhead performance. This method can sort all influencing factors through clear numerical values, so as to screen out the influencing factors that are more important to the warhead performance. In addition, this method has a strict mathematical basis, and the calculation is simple, convenient and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flowchart of the method for reverse identification of key factors of the warhead of the present invention.

[0035] Figure 2 is a schematic structural diagram of Example 1.

[0036] The meanings of the various reference numerals in the figure are as follows:

[0037] 1 - front end cover, 2 - housing, 3 - booster charge, 4 - fragment, 5 - main charge. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The following are specific embodiments of the present invention. It should be noted that the present invention is not limited to the following specific embodiments, and all equivalent transformations made on the basis of the technical solutions of the present application fall within the protection scope of the present invention.

[0039] Example 1:

[0040] This embodiment provides a method for reverse identification of key factors of a warhead, including: determining the key indicators of the warhead and their influencing factors, and constructing an implicit function relationship Y = g(X); quantitatively measuring the influencing factors, and obtaining the achieved values of the warhead indicators corresponding to each set of parameters through experiments; performing reverse regression analysis on the influencing factors and the achieved values of the warhead indicators; and sorting the importance of all influencing factors. Through this method, the contribution degree of each influencing factor to the warhead performance can be measured, so as to judge the importance of each influencing factor, and provide guidance for the selection of influencing factors in the design optimization of the warhead.

[0041] Such as Figure 1As shown in the figure, a reverse identification method for key factors of a warhead includes the following steps:

[0042] Step S10: Determine the key indicators of the warhead and their influencing factors, and construct an implicit function relationship Y = g(X);

[0043] Step S20: Quantitatively measure the influencing factors, and obtain the achieved values of the warhead indicators corresponding to each set of parameters through experiments;

[0044] Step S30: Conduct a reverse regression analysis on the influencing factors and the achieved values of the warhead indicators;

[0045] Step S40: Rank the importance of all influencing factors.

[0046] The above steps will be described in detail below with reference to an example.

[0047] Consider a typical fragment warhead, whose structure is as Figure 2 shown; the typical structure includes a front cover 1, a shell 2, an expanding charge column 3, fragments 4, and a main charge 5.

[0048] In step S10, determine the key indicators of the warhead and their influencing factors. In this embodiment, the fragment density is taken as the key indicator, and its influencing factors include the diameter of the main charge, the height of the main charge, the thickness of the shell, the charge density, the side length of the fragment, the thickness of the fragment, etc. The relationship between the fragment density and each influencing factor can be expressed as Y = g(X), where X = {X1,..., X n} is an n-dimensional influencing factor.

[0049] It should be noted that in this embodiment, the fragment density is taken as the key indicator, and 6-dimensional influencing factors are selected for analysis, but it is not limited to this. Other key indicators or influencing factors can be used as an application of this method, which are all within the protection scope of this disclosure.

[0050] In step S20, quantitatively measure the influencing factors, and obtain the achieved values of the warhead indicators corresponding to each set of parameters through experiments, including:

[0051] Measure the values of N groups of influencing factors, and establish a parameter matrix:

[0052]

[0053] where represents the jth measured value of the ith-dimensional influencing factor.

[0054] Correspondingly, the achieved value of its warhead indicator is y = {y (1) ,..., y (j) ,..., y (N)} T .

[0055] In step S30, inverse regression analysis is performed on the influencing factors and the achieved values of the warhead indicators, including:

[0056] Set the number of key factors, denoted as d. In this embodiment, d is taken as 2;

[0057] Sort y = {y (1) ,..., y (j) ,..., y (N)}, T and the sorted achieved values are denoted as

[0058] Sort x according to the order of y s so that the measured values of the influencing factors correspond to the test results, denoted as x s ;

[0059] Divide the N groups of samples into h slices, and the number of samples in each slice is N h = N / h;

[0060] Within each slice, calculate the sample mean, where is the k-th slice among the h slices, is the sample mean of the k-th slice:

[0061]

[0062] Calculate the covariance matrix of the slice means:

[0063]

[0064] where, is the mean of all measured values,

[0065] Calculate the covariance matrix

[0066]

[0067] Perform eigenvalue decomposition:

[0068]

[0069] where β l =(β l,1 ,..., β l,i ,..., β l,n ) T is the l-th eigenvector, is the l-th eigenvalue.

[0070] Step S40, perform importance ranking on all influencing factors, including:

[0071] According to the sorting of the eigenvalue, sort the influencing factors according to their importance. The sorting principle is: for an influencing factor, the larger its eigenvalue , the higher its importance.

[0072] The calculation results of this embodiment are shown in Table 1:

[0073] Table 1 Calculation Results

[0074]

[0075]

[0076] According to the calculation results, among the six influencing factors considered in this embodiment, the importance ranking of the contribution to the fragment density of the fragment warhead is: fragment thickness > fragment side length > main charge diameter > charge density > shell thickness > main charge height. Therefore, in the design optimization, the fragment thickness and the fragment side length should be considered first.

[0077] The preferred embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solutions of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0078] In addition, it should be noted that, among the various specific technical features described in the above specific embodiments, they can be combined in any appropriate manner without contradiction. To avoid unnecessary repetition, the present invention will not separately describe various possible combination methods.

[0079] Furthermore, any combination can be made between various different embodiments of the present invention as long as it does not violate the idea of the present invention, and it should also be regarded as the content disclosed by the present invention.

Claims

1. A reverse identification method for key factors of a warhead, characterized in that, It includes the following steps: Step S10: Determine the key indicators of the warhead and their influencing factors, and construct an implicit functional relationship; Step S20: Quantitatively measure the influencing factors, and obtain the achieved values of the warhead indicators corresponding to each set of parameters through experiments; Step S30: Conduct reverse regression analysis on the influencing factors and the achieved values of the warhead indicators; Step S40: Rank the importance of all influencing factors.

2. The reverse identification method for key factors of a warhead according to claim 1, characterized in that The said Step S10 includes: Analyze the influencing factors according to the requirements of the key indicators of the warhead. The influencing factors in the fragment warhead include fragment shape, fragment material, charge length, and charge diameter; Let the key index of the warhead be Y, and its influencing factors be X. Construct its implicit function relationship Y = g(X), where X = {X1,..., X n} is an n-dimensional influencing factor.

3. The reverse identification method for key factors of a warhead according to claim 1, characterized in that The said Step S20 includes: Quantitatively measure the n-dimensional influencing factors and establish a parameter matrix: wherein represents the j-th measured value of the i-th dimensional influencing factor; Correspondingly, the achieved values of its warhead indicators are y = {y (1) ,..., y (j) ,..., y (N)} T .

4. The reverse identification method for key factors of the warhead according to claim 3, characterized in that The said Step S30 includes: Sort y = {y (1) ,..., y (j) ,..., y (N)}, and denote the realized value after sorting as T ​ Sort x according to y s so that the measured values of influencing factors correspond to the test results, denoted as x s ; Divide N groups of samples into h slices, and the number of samples in each slice is N h = N / h; Within each slice, calculate the sample mean, where is the k-th slice out of h slices, is the sample mean of the k-th slice: Calculate the covariance matrix of the slice means: Among them, is the mean value of all measured values, Calculate the covariance matrix: Conduct eigenvalue decomposition: where β l is the l-th eigenvector, is the l-th eigenvalue.

5. The reverse identification method for key factors of a warhead according to claim 1, characterized in that, The said Step S40 includes: Rank the importance of the influencing factors according to the sorting of the eigenvalues, and the sorting principle is: The larger the eigenvalue of the influencing factor, the higher its importance.

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