Method for simulating impact initiation of energetic material under assistance of computer simulation

Abstract

The invention relates to the technical field of data acquisition, in particular to an energetic material impact detonation simulation method under the assistance of computer simulation, which comprises the following steps: acquiring a first clustering index and a second clustering index of each sampling position point relative to other sampling position points in a simulation area; performing clustering analysis on all the sampling position points by using the first clustering index and the second clustering index to obtain each clustering cluster, determining an initial interpolation value of each to-be-interpolated position point on a connecting line of every two first reference position points, and determining a final interpolation value of each target intersection point corresponding to the connecting line according to the clustering cluster and the initial interpolation value; and obtaining a final interpolation value of each to-be-interpolated position point on a connecting line of every two second reference position points, and performing iterative execution continuously until the final interpolation values of all to-be-interpolated position points in the simulation area are obtained. According to the method, the authenticity of an energetic material impact detonation simulation result is improved by improving the obtaining reliability and accuracy of simulation related parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and particularly to a method for simulating the shock initiation of energetic materials assisted by computer simulation. Background Art

[0002] As a special type of high-energy material, energetic materials are widely used in military, aerospace, civilian explosives and other fields. Their main characteristic is that they can rapidly release a large amount of energy under specific external stimuli. Therefore, the safety and reliability assessment of the shock initiation of energetic materials is extremely important.

[0003] In recent years, with the development of computer technology and numerical simulation technology, predicting and analyzing the shock initiation characteristics of energetic materials based on computer simulation means has become an important research direction. With the help of computer simulation, a large amount of data can be obtained in a short time, greatly improving the efficiency and safety of simulating the shock initiation of energetic materials.

[0004] To obtain the air density distribution required for shock initiation simulation, during the actual acquisition process, the geological environment will affect the sampling position points, such as vegetation occlusion and obstacles. Therefore, the data of some sampling position points may not be successfully collected, or even there are gaps, so interpolation processing is required.

[0005] Since the air density distribution at different sampling position points is non-uniform at different heights and weather conditions, interpolation processing is likely to cause large errors in the air density distribution corresponding to some sampling position points; in addition, when evaluating the explosion influence range of energetic materials, as the distribution of each sampling position point in the same cluster or different clusters gradually approaches the edge between different clusters, different types of geological information parameters have varying degrees of changes. Therefore, interpolation processing based only on clusters will produce certain errors. Further, the data acquisition results with interpolation errors will not be able to accurately simulate the actual shock initiation environment of energetic materials, resulting in distortion of the final explosion influence effect and the actual influence boundary. Summary of the Invention

[0006] In order to solve the technical problem that the data acquisition results with interpolation errors cannot accurately simulate the actual shock initiation environment of energetic materials, resulting in distortion of the explosion influence effect and the actual influence boundary, the purpose of the present invention is to provide a method for simulating the shock initiation of energetic materials assisted by computer simulation. The specific technical solution adopted is as follows: An embodiment of the present invention provides a method for simulating the shock initiation of energetic materials assisted by computer simulation. The method includes the following steps: Step S1: Obtain the simulation-related parameters of each sampling position point within the simulation area of the shock initiation of energetic materials; wherein, the sampling position point is a position point to be interpolated or a first reference position point; Step S2: Determine the first clustering index and the second clustering index of each sampling position point relative to other sampling position points according to the simulation-related parameters of each sampling position point; perform clustering analysis on all sampling position points by using the first clustering index and the second clustering index to obtain each clustering cluster; Step S3: Determine the initial insertion value of each position point to be interpolated on the line connecting every two first reference position points according to the first clustering index, the second clustering index and the simulation-related parameters corresponding to every two first reference position points; Step S4: Obtain each target intersection corresponding to all the connections, and determine the final insertion value of each target intersection according to the initial insertion value of each target intersection on different connections and the average value of the simulation-related parameters corresponding to all the first reference position points within the clustering cluster to which each target intersection belongs; the target intersection is a position point to be interpolated; Step S5: Take the position points to be interpolated with known insertion values as second reference position points, execute Steps S3 to S4, obtain the final insertion value of each position point to be interpolated on the line connecting every two second reference position points, and continuously iterate and execute Step S5 until the final insertion values of all the position points to be interpolated within the simulation area are obtained.

[0007] Further, the obtaining of each sampling position point within the simulation area of the shock initiation of energetic materials includes: Collect the remote sensing data of the actual geographical environment of the simulation area of the shock initiation of energetic materials, and draw a three-dimensional image of the actual geographical environment based on the remote sensing data; Construct a three-dimensional coordinate system of the three-dimensional image, and set a number of sampling position points on the three-dimensional coordinate system at a preset interval distance.

[0008] Further, the determining of the first clustering index and the second clustering index of each sampling position point relative to other sampling position points according to the simulation-related parameters of each sampling position point includes: The simulation-related parameters are several kinds of air state parameters, related material parameters and several kinds of geological information parameters; according to each kind of air state parameter of each first reference position point, use neural network technology to obtain the air density parameter of each first reference position point; Determine the first clustering index and the second clustering index of each sampling position point relative to other sampling position points according to each kind of geological information parameter of each position point to be interpolated, each kind of geological information parameter and the air density parameter of each first reference position point.

[0009] Further, determining the first clustering index and the second clustering index of each sampling position point relative to other sampling position points according to each geological information parameter of each interpolation position point to be interpolated, each geological information parameter of each first reference position point, and the air density parameter includes: For a sampling position point located at the center where there is a first reference position point within the range of a preset radius, determining the correlation index of the air information of the sampling position point located at the center relative to each geological information according to each geological information parameter and the air density parameter of each first reference position point within the range of the preset radius; For a sampling position point with a correlation index, determining the first clustering index and the second clustering index of the sampling position point relative to other sampling position points according to the correlation index of the air information of the sampling position point relative to each geological information and the difference situation of the sampling position point and other sampling position points in each geological information parameter; For a sampling position point without a correlation index, determining the first clustering index and the second clustering index of the sampling position point relative to other sampling position points according to the difference situation of the sampling position point and other sampling position points in each geological information parameter.

[0010] Further, for a sampling position point with a correlation index, the calculation formulas of the first clustering index and the second clustering index are: ; where represents the first clustering index of the i-th sampling position point relative to the z-th sampling position point, N represents the number of types of geological information, k represents the k-th geological information parameter, represents the correlation index of the air information of the i-th sampling position point relative to the k-th geological information, represents the k-th geological information parameter of the i-th sampling position point, represents the k-th geological information parameter of the z-th sampling position point, represents the absolute value of the difference between the i-th sampling position point and the z-th sampling position point in the k-th geological information parameter, and c represents a non-zero constant; ; where represents the second clustering index of the i-th sampling position point relative to the z-th sampling position point.

[0011] Further, the implementation steps of the step S3 include: For any two first reference position points, calculating the product of the first clustering index and the second clustering index corresponding to every two adjacent sampling position points on the line connecting the two first reference position points, and taking the mean of all the products as the slope of the line connecting the two first reference position points; Determine the initial insertion value of each position point to be interpolated on the line connecting two first reference position points based on the slope of the line connecting the two first reference position points and the air density parameters of the two first reference position points.

[0012] Further, the determining the initial insertion value of each position point to be interpolated on the line connecting two first reference position points based on the slope of the line connecting the two first reference position points and the air density parameters of the two first reference position points includes: Determine a linear equation based on the slope of the line connecting two first reference position points and the air density parameters of the two first reference position points located on the line; Substitute the coordinates of each position point to be interpolated on the line connecting two first reference position points into the linear equation to obtain the air density parameter of each position point to be interpolated on the line as the initial insertion value.

[0013] Further, the determining the final insertion value of each target intersection point based on the initial insertion values of each target intersection point on different lines and the mean value of the simulated correlation parameters corresponding to all first reference position points within the cluster to which each target intersection point belongs includes: For any target intersection point, obtain the range corresponding to the initial insertion values of the target intersection point on different lines, and select several candidate insertion values from the range corresponding to the range; For any candidate insertion value, determine the selection probability value of the candidate insertion value according to the difference between the air density mean value corresponding to all first reference position points within the cluster to which the target intersection point belongs and the candidate insertion value, and the difference between the initial insertion values of the target intersection point on different lines and the candidate insertion value; Obtain the selection probability value of each candidate insertion value, and use the candidate insertion value corresponding to the maximum selection probability value as the final insertion value of the target intersection point.

[0014] Further, the calculation formula for the selection probability value of the candidate insertion value is: ; where represents the selection probability value of the u-th candidate insertion value of the y-th target intersection point, represents the air density mean value corresponding to all first reference position points within the cluster to which the y-th target intersection point belongs, represents the u-th candidate insertion value of the y-th target intersection point, j represents the j-th line of the target intersection point, represents the number of lines corresponding to the y-th target intersection point, represents the initial insertion value of the y-th target intersection point on the j-th line, represents the absolute value function, and c represents a non-zero constant.

[0015] Further, after implementing step S5, it further includes: Based on the relevant material parameters and air density parameters at each sampling position point within the simulation area, simulate the shock initiation of energetic materials in the simulation area to obtain the explosion performance and influence range of the simulation area.

[0016] The present invention has the following beneficial effects: The present invention provides a method for simulating the shock initiation of energetic materials assisted by computer simulation. This method determines the first clustering index and the second clustering index through the simulation-related parameters at each sampling position point, and then obtains each clustering cluster. Based on the clustering cluster, calculate the final insertion value of the subsequent target intersection point, which can avoid the large error in the final insertion value caused by the influence of the change of geological information on the air density parameters corresponding to different sampling position points, and improve the authenticity and accuracy of the final insertion value of the target intersection point; based on the first clustering index, the second clustering index and the simulation-related parameters corresponding to every two first reference position points, determine the initial insertion value of each interpolation position point on the line connecting every two first reference position points, which can avoid the error generated by data collection at different positions at different time points, making the simulation-related parameters unable to accurately reflect the actual influence on the boundary distortion, and effectively improving the accuracy and authenticity of the final simulation of the environment where the energetic material shock initiation occurs and the explosion performance. By overcoming the defect that the existing interpolation method ignores the actual data characteristics of the simulation-related parameters for the shock initiation of energetic materials during the interpolation process, the present invention effectively improves the accuracy of the simulation results of the shock initiation of energetic materials achieved based on highly reliable simulation-related parameters. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a flowchart of the steps of a method for simulating the shock initiation of energetic materials assisted by computer simulation according to the present invention; Figure 2 It is a flowchart of the steps of step S2 in the embodiment of the present invention; Figure 3 It is a flowchart of the steps of step S3 in the embodiment of the present invention; Figure 4 It is a flowchart of the steps of step S4 in the embodiment of the present invention. Detailed Embodiments

[0019] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes the specific implementation manners, structures, features, and effects of the technical solutions proposed according to the present invention in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0021] The application scenario targeted by the present invention can be: during the simulation of shock initiation of existing energetic materials, multiple different types of relevant parameters are input, such as the characteristic data of the material itself. Based on the relevant parameters, existing explosion platforms and algorithms, such as AUTODYN, which are specialized for the simulation of shock, explosion, and other transient dynamic processes, are used to evaluate the performance parameters of the shock initiation simulation of the energetic material with the current parameters, and the explosion influence radius under the current material parameters is obtained. Therefore, collecting complete and reliable relevant parameters is particularly important for the accuracy of the initiation simulation results.

[0022] In order to improve the integrity and reliability of the collected simulation-related parameters, the present embodiment provides a method for simulating shock initiation of energetic materials assisted by computer simulation, as Figure 1 shown, including the following steps: Step S1: Obtain the simulation-related parameters of each sampling position point within the simulation area of the shock initiation of the energetic material.

[0023] The above step S1 can be implemented through steps S11 to S12 (not shown in the figure): S11: Obtain all the sampling position points within the simulation area of the shock initiation of the energetic material.

[0024] Since the actual application scenarios required during the initiation simulation are different, for example, for fixed-point initiation on a mine, the geological information at different initiation positions is different. At the same time, the energy propagation of the energetic material will attenuate due to the influence of air density after explosion, and its actual explosion influence boundary will change to varying degrees. Therefore, it is necessary to determine all the sampling position points of the simulation-related parameters according to the actual geographical environment of the simulation area.

[0025] Specifically, first, use a drone to collect remote sensing data of the actual geographical environment of the simulation area of the shock initiation of the energetic material, and draw a three-dimensional image of the actual geographical environment based on the remote sensing data. Secondly, construct a three-dimensional coordinate system for the three-dimensional image, and set a number of sampling position points at a preset interval distance on the three-dimensional coordinate system.

[0026] Among them, in the three-dimensional image, the non-ground spatial region is extracted and converted into three-dimensional point cloud data. The point cloud data not only represents the sampling position points but also can represent the air density parameters. Therefore, the construction of the three-dimensional image is to determine all the sampling position points of the air state parameters within the simulation region. The determination process of the three-dimensional image is a prior art and will not be elaborated in detail here. Additionally, the preset interval distance between two adjacent sampling position points can be set to 1 m, and the implementer can set it according to the specific actual situation.

[0027] Since, when actually collecting the simulation-related data of each sampling position point, affected by the geological environment, such as the existence of vegetation occlusion, obstacles, etc., the air-related parameters of some sampling position points cannot be collected, that is, there are gaps in the air-related parameters of some sampling position points. Therefore, all sampling position points include the interpolation position points and the first reference position points. Among them, the interpolation position points refer to the sampling position points lacking air-related parameters, and the first reference position points refer to the sampling position points where the air-related parameters can be collected currently and historically.

[0028] S12: Obtain the simulation-related parameters of each sampling position point.

[0029] Here, the simulation-related parameters can be several kinds of air state parameters, related material parameters, and several kinds of geological information parameters.

[0030] Among them, regarding the air state parameters, by randomly collecting various relevant air data, such as temperature, humidity, and other multi-dimensional air-related data characteristics, at different heights and positions in the simulation region through various types of sensors, these characteristics can be collectively referred to as air state parameters, and the air state parameters are assigned to the sampling position points corresponding to the positions on the three-dimensional coordinate system.

[0031] Regarding the related material parameters and several kinds of geological information parameters, the related material parameters and various geological information parameters can be collected through relevant equipment. The geological information parameters can be vegetation height, vegetation density, rock types, etc. The types and numbers of the air state parameters and geological information parameters can be set by the implementer according to the specific actual situation, and no specific limitation is made here.

[0032] It should be noted that the geological information parameters are two-dimensional data. That is, on the three-dimensional coordinate system, the projections of the sampling position points with the same horizontal and vertical axes but different heights on the plane where the horizontal and vertical axes are located only correspond to a set of geological information parameters.

[0033] So far, this embodiment has obtained the simulation-related parameters of each sampling position point within the simulation region.

[0034] Step S2: Determine the first clustering index and the second clustering index of each sampling position point relative to other sampling position points according to the simulated correlation parameters of each sampling position point; perform clustering analysis on all sampling position points by using the first clustering index and the second clustering index to obtain each clustering cluster.

[0035] The above step S2 can be implemented through Figure 2 the steps S21 to S23 shown as follows: S21: According to each air state parameter of each first reference position point, use neural network technology to obtain the air density parameter of each first reference position point.

[0036] Since the energy propagation of energetic materials will decay due to the influence of air density after explosion, and its actual explosion impact boundary will also change to varying degrees, the air state parameter is used as an important input data for the simulation of shock initiation of energetic materials, that is, interpolation processing is performed on the sampling position points lacking air state parameters. There are multiple different types of data for air state parameters. In order to facilitate the comprehensive analysis of the air density situation, dimensionality reduction processing is performed based on multiple air state parameters to obtain the air density parameter of each first reference position point.

[0037] Specifically, according to the existing neural network technology, input multiple air state parameters of a currently collected first reference position point, and output the air density parameter of this first reference position point.

[0038] Among them, the process of using neural network to achieve dimensionality reduction processing is prior art and not within the protection scope of the present invention, so it will not be elaborated in detail here; the value range of the air density parameter is between 0 and 100, and each first reference position point has its corresponding air density parameter; each sampling process can be accurate to every minute and is stored in the computer.

[0039] S22: Determine the first clustering index and the second clustering index of each sampling position point relative to other sampling position points according to each geological information parameter of each interpolation position point to be interpolated, each geological information parameter of each first reference position point, and the air density parameter.

[0040] Here, since the air density distribution of different sampling position points is uneven at different heights and different weather states, and due to the influence of the geological environment, there are data vacancies at some sampling position points. Therefore, when interpolating all interpolation position points on the entire three-dimensional coordinate, it is easy to cause large errors in the air density parameters corresponding to some interpolation position points, and it is necessary to analyze the influence of the actual geological environment on the air density parameters in the space where they are located.

[0041] In this embodiment, the amplitude of the change in geological information parameters over time is small, and the air density parameters corresponding to the sampling position points under the condition of having approximately the same geological information also have an approximately the same trend.

[0042] The above step S22 can be implemented through steps S221 to S223 (not shown in the figure): S221: For a sampling position point located at the center where there is a first reference position point within a preset radius, determine the correlation index of the air information of the sampling position point located at the center with respect to each type of geological information according to each type of geological information parameter and air density parameter of each first reference position point within the preset radius.

[0043] Specifically, taking any sampling position point as the center, determine whether there is a first reference position point among the sampling position points within the preset radius. If there is, determine the correlation index of the air information of the sampling position point located at the center with respect to each type of geological information according to each type of geological information parameter and air density parameter of each first reference position point within the preset radius. If not, there is no need to calculate the correlation index.

[0044] As a specific implementation manner, sort the air density parameters of all the first reference position points within the preset radius from large to small or from small to large according to the magnitude of the air density parameters to obtain an air density parameter sequence; obtain each type of geological information parameter sequence according to the sorting method of each first reference position point in the air density parameter sequence; use the Pearson correlation coefficient to obtain the correlation between the air density parameter sequence and each type of geological information parameter sequence, and take the absolute value of the correlation as the correlation index of the air information of the sampling position point located at the center with respect to each type of geological information. Among them, the preset radius can be set to 3, and the implementer can set it according to the specific actual situation.

[0045] S222: For the sampling position points with a correlation index, determine the first clustering index and the second clustering index of the sampling position points with respect to other sampling position points according to the correlation index of the air information of the sampling position points with respect to each type of geological information and the difference situation of the sampling position points and other sampling position points in each type of geological information parameter.

[0046] Here, the first clustering index represents the similarity degree of the address information between two sampling position points, and the second clustering index represents the change trend of the air density parameters of two sampling position points under the influence of geological information parameters. Other sampling position points refer to the remaining sampling position points except the sampling position point itself.

[0047] As a specific calculation method, for the sampling position points with a correlation index, the calculation formulas of the first clustering index and the second clustering index are: ; In the formula, represents the first clustering index of the \(i\)-th sampling position point relative to the \(z\)-th sampling position point, \(N\) represents the number of types of geological information, \(k\) represents the \(k\)-th geological information parameter, represents the correlation index of the air information at the \(i\)-th sampling position point relative to the \(k\)-th geological information, represents the \(k\)-th geological information parameter at the \(i\)-th sampling position point, represents the \(k\)-th geological information parameter at the \(z\)-th sampling position point, represents the absolute value of the difference between the \(k\)-th geological information parameters at the \(i\)-th sampling position point and the \(z\)-th sampling position point, and \(c\) represents a non-zero constant.

[0048] In the calculation formula of the first clustering index, is the weight. The larger the weight, the greater the amplitude of the influence of the current \(k\)-th geological information parameter on the air density. The numerical similarity of the two sampling position points in the \(k\)-th geological information parameter is more important for clustering, the higher the necessity of clustering, and the larger the first clustering index; represents the similarity situation of the two sampling position points in the \(k\)-th geological information parameter, The larger it is, the greater the similarity of the two sampling position points in the \(k\)-th geological information parameter, and the larger the first clustering index; \(c\) can be set to 0.01 to avoid the possibility of the denominator of the fraction being zero.

[0049] ; In the formula, represents the second clustering index of the \(i\)-th sampling position point relative to the \(z\)-th sampling position point.

[0050] In the calculation formula of the second clustering index, the second clustering index may be equal to -1, 0, or 1. The positive or negative of the second clustering index indicates that between the \(i\)-th sampling position point and the \(z\)-th sampling position point, the air density parameters affected by the geological information parameters show an upward trend or a downward trend respectively.

[0051] S223. For sampling position points without a correlation index, according to the difference situation of the sampling position point and other sampling position points in each geological information parameter, determine the first clustering index and the second clustering index of the sampling position point relative to other sampling position points.

[0052] Referring to the calculation processes of the first clustering index and the second clustering index in step S222, the first clustering index and the second clustering index of the sampling position point without a correlation index relative to other sampling position points can be obtained.

[0053] As a specific calculation method, for the sampling position points without correlation indicators, the calculation formulas of the first clustering indicator and the second clustering indicator are as follows: ; .

[0054] S23: Perform clustering analysis on all sampling position points by using the first clustering indicator and the second clustering indicator to obtain each clustering cluster.

[0055] Specifically, obtain the optimal number of clusters K based on the elbow method, and combine the optimal number of clusters K with the first clustering indicator and the second clustering indicator of all sampling position points on the three-dimensional coordinate system relative to other sampling position points to perform Kmeans clustering on the spatial region in the entire three-dimensional coordinate system to obtain K clustering clusters.

[0056] Among them, the implementation processes of the elbow method and Kmeans clustering are both prior arts and not within the protection scope of the present invention, so no detailed description will be given here.

[0057] It should be noted that each clustering cluster is obtained by the difference in geological information between different sampling position points on the three-dimensional coordinate system, and sampling position points with similar geological information are obtained based on the clustering clusters, which can avoid large errors in the final interpolation caused by the influence of changes in geological information on the air density parameters corresponding to different sampling position points, and help improve the authenticity and accuracy of the finally determined interpolation value.

[0058] Step S3: Determine the initial interpolation value of each interpolation position point on the line connecting every two first reference position points according to the first clustering indicator, the second clustering indicator, and the simulation correlation parameters corresponding to every two first reference position points.

[0059] According to prior knowledge, when evaluating the explosion influence range of energetic materials, whether it is the same clustering cluster or different clustering clusters, as the sampling position points in the cluster gradually approach the edge between different clustering clusters, the corresponding different geological information parameters change to varying degrees. At this time, when interpolating the air density parameters of different interpolation position points only based on the mean value of the air density parameters corresponding to each clustering cluster obtained in step S2, certain errors will occur, resulting in the inability of the finally constructed point cloud data to accurately simulate the actual environment, and the finally obtained explosion influence effect and actual influence boundary are distorted.

[0060] In this embodiment, the geological information changes from the high vegetation coverage area to the barren area. Accordingly, the air state parameters such as temperature and humidity also change in the same trend. This shows that the change of air state parameters is related to the actual geological information parameters. The change of air density parameters between two different sampling position points can be regarded as a local linear change, while the change of air density parameters along the line connecting any two sampling position points can be regarded as an overall non-linear change. The change characteristics of air density parameters at sampling position points in the entire three-dimensional coordinate system can be analyzed by analyzing the geological information between different sampling position points.

[0061] For any two first reference position points in the three-dimensional coordinate system, the above step S3 can be implemented by Figure 3 the steps S31 to S32 shown below: S31: Calculate the product of the first clustering index and the second clustering index corresponding to every two adjacent sampling position points on the line connecting the two first reference position points, and take the mean of all products as the slope of the line connecting the two first reference position points.

[0062] As a specific implementation manner, connect all reference position points in the three-dimensional coordinate system in pairs to obtain the lines corresponding to every two first reference position points; calculate the product of the first clustering index and the second clustering index corresponding to every two adjacent sampling position points on the line corresponding to the two first reference position points, and take the mean of all products as the slope of the line connecting the two first reference position points.

[0063] Here, multiplying the first clustering index representing the similarity of geological conditions and the second clustering index representing the trend direction can obtain the theoretical change slope of the air density parameters between two adjacent sampling position points on the line. The reason is that the smaller the first clustering index, the smaller the similarity of geological conditions between two adjacent sampling position points on the line, and the greater the change amplitude of the air density between them; at the same time, when the second clustering index is positive, it indicates that the air density parameters of two adjacent sampling position points on the line show an upward trend; multiplying the first clustering index and the second clustering index can represent the theoretical change slope of the air density parameters between two adjacent sampling position points.

[0064] It should be noted that there may be other first reference position points on the line connecting the two first reference position points, and there may also be interpolation position points without historical air state parameters.

[0065] S32: Determine the initial insertion value of each interpolation position point on the line connecting the two first reference position points according to the slope of the line connecting the two first reference position points and the air density parameters of the two first reference position points.

[0066] Specifically, a linear equation is determined based on the slope of the line connecting two first reference position points and the air density parameters of the two first reference position points located on the line; the coordinates of each interpolation position point on the line connecting the two first reference position points are substituted into the linear equation to obtain the air density parameter of each interpolation position point on the line as the initial insertion value. Among them, the implementation process of determining the linear equation is a prior art and not within the protection scope of the present invention, so it will not be elaborated in detail here.

[0067] So far, this embodiment has obtained the initial insertion value of each interpolation position point on the line connecting every two first reference position points.

[0068] Step S4: Obtain each target intersection corresponding to all the lines, and determine the final insertion value of each target intersection according to the initial insertion value of each target intersection on different lines and the mean value of the simulated correlation parameters corresponding to all the first reference position points within the clustering cluster to which each target intersection belongs.

[0069] When actually collecting the simulated correlation parameters of all sampling position points, the collection times of different sampling position points are different, and the collected positions also change randomly with time. During the attenuation process of the influence range, one or several identical sampling position points may correspond to the line connecting every two first reference position points. Based on the interpolation analysis of the line connecting two first reference position points, it may lead to different geological information changes of the same sampling position point on different lines, that is, the initial insertion value of the same sampling position point cannot represent its geological information change characteristics on other lines. Therefore, it is necessary to optimize the initial insertion value of the target intersection to obtain the final insertion value of the target intersection. Among them, the target intersection is an interpolation position point, and each target intersection corresponding to all the lines can be directly obtained.

[0070] In this embodiment, for any target intersection, the more real the candidate insertion value of the target intersection is, the greater the possibility that the candidate insertion value is the final insertion value. The above step S4 can be implemented through Figure 4 the steps S41 to S43 shown as follows: S41: Obtain the range corresponding to the initial insertion values of the target intersection on different lines, and select several candidate insertion values from the range corresponding to the range.

[0071] Specifically, obtain the initial insertion value sequence of the target intersection on different lines, and determine the maximum value and the minimum value in the initial insertion value sequence; according to the preset interval, obtain each candidate insertion value within the interval corresponding to the maximum value and the minimum value. Among them, the interval between two adjacent candidate insertion values can be 0.1, and the implementer can set it according to the specific actual situation without specific limitation.

[0072] S42: For any candidate insertion value, the selection probability value of the candidate insertion value is determined based on the difference between the mean air density corresponding to all first reference position points in the cluster to which the target intersection belongs and the candidate insertion value, and the difference between the initial insertion value of the target intersection on different connecting lines and the candidate insertion value.

[0073] As a specific calculation method, the calculation formula for the selection probability value of the candidate insertion value is: ; In the formula, represents the probability value of selecting the u-th candidate insertion value of the y-th target intersection point, It represents the mean air density corresponding to all the first reference positions in the cluster to which the y-th target intersection belongs. represents the u-th candidate insertion value of the y-th target intersection, j represents the j-th link of the target intersection, Indicates the number of lines corresponding to the y-th target intersection, represents the initial insertion value of the yth target intersection on the jth line, represents the absolute value function, and c represents a non-zero constant.

[0074] In the calculation formula for selecting the probability value, It represents the difference between the u-th candidate insertion value of the y-th target intersection point and the mean air density corresponding to all the first reference positions in the cluster to which the y-th target intersection point belongs. The mean air density can indicate that the air density parameters corresponding to the sampling positions under the condition of approximately the same geological information also have approximately the same trend. The smaller it is, the more the u-th candidate insertion value conforms to the approximate geological information cluster to which the y-th target intersection belongs, and the higher the authenticity of the u-th candidate insertion value; represents the difference between the u-th candidate insertion value of the y-th target intersection and the initial insertion value of the y-th target intersection on different lines, The smaller it is, the more the u-th candidate insertion value of the y-th target intersection point conforms to the characteristics of the air density parameter on the line where the y-th target intersection point is located changing with the geological information; the two equations are combined and the selection probability value is obtained by using a fraction according to the direct and inverse ratio. The larger the selection probability value is, the higher the probability that the u-th candidate insertion value of the y-th target intersection point is optimal; the constant c can be set to 0.01, which is used to avoid the possibility of the denominator of the fraction being zero.

[0075] S43: Obtain a selection probability value for each candidate insertion value, and use the candidate insertion value corresponding to the maximum selection probability value as the final insertion value of the target intersection.

[0076] Specifically, according to the calculation method of the selection probability value of the u-th candidate insertion value at the y-th target intersection as described above, obtain the selection probability values of all candidate insertion values corresponding to the y-th target intersection; take the candidate insertion value corresponding to the maximum selection probability value as the final insertion value of the y-th target intersection.

[0077] It should be noted that for the interpolation position points on the line that are not target intersections, the initial insertion value is taken as the final insertion value of the corresponding interpolation position points.

[0078] It should be noted that based on the air density parameters of different reference position points in multiple straight lines in the three-dimensional coordinate system, combined with the variation law of air density, an optimization function is constructed to obtain the air density parameters at different sampling position points in the entire three-dimensional coordinate system, which can avoid the error caused by data collection at different positions at different time points, resulting in inaccurate actual influence boundary distortion of the finally constructed point cloud data, and effectively improve the accuracy and authenticity of the simulation of the environment where the energetic material is impacted and detonated and its explosion performance.

[0079] So far, the first interpolation operation of this embodiment is completed.

[0080] Step S5: Take the interpolation position points with known insertion values as the second reference position points, execute steps S3 to S4, obtain the final insertion values of each interpolation position point on the line connecting every two second reference position points, and continuously iterate and execute step S5 until the final insertion values of all interpolation position points in the simulation area are obtained.

[0081] In this embodiment, after the first interpolation operation is completed, the interpolation position points with known insertion values are taken as new reference position points, denoted as the second reference position points; after obtaining the second reference position points, execute steps S3 to S4 for a new round of interpolation processing, that is, determine the initial insertion values of each interpolation position point on the line connecting every two second reference position points according to the first clustering index, the second clustering index, and the simulation-related parameters corresponding to every two second reference position points; obtain all the target intersections corresponding to the connections, and determine the final insertion values of each target intersection according to the initial insertion values of each target intersection on different connections and the mean value of the simulation-related parameters corresponding to all the first reference position points within the clustering cluster to which each target intersection belongs, so as to complete the second interpolation operation. Continuously iterate and execute step S5 until the final insertion values of all interpolation position points in the simulation area are obtained, that is, each sampling position point in the entire three-dimensional coordinate system corresponds to an air density parameter.

[0082] So far, this embodiment has completed the interpolation processing of the air density parameters and obtained the air density parameters of each sampling position point in the simulation area.

[0083] After obtaining the air density parameters at each sampling position point within the simulation area, the explosion performance and influence boundary of the energetic material are simulated.

[0084] Specifically, determine the initiation position of the energetic material in the three-dimensional coordinate system. Based on the relevant material parameters and air density parameters at each sampling position point within the simulation area, use the existing AUTODYN to simulate the impact initiation situation of the energetic material in the simulation area, and obtain the explosion performance and influence range of the simulation area.

[0085] It should be noted that when the influence radius contacts non-air components such as the ground or mountain during the extension process in a certain direction from the initiation position point, the formation of the initiation influence boundary is stopped, thereby outputting the impact initiation influence boundary of the entire energetic material.

[0086] The above-described embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention and should all be included within the protection scope of the present invention.

Claims

1. A computer simulation-assisted method for simulating the impact detonation of energetic materials, characterized in that: The following steps are involved: Step S1: obtaining simulation related parameters of each sampling position point in the energetic material impact detonation simulation area; wherein the sampling position point is a position point to be interpolated or a first reference position point; Step S2: determining a first clustering index and a second clustering index of each sampling location point relative to other sampling location points according to simulation-related parameters of each sampling location point; performing cluster analysis on all sampling location points using the first clustering index and the second clustering index to obtain various cluster clusters; Step S3: determining an initial interpolation value of each to-be-interpolated position point on the line connecting every two first reference position points according to the first clustering index, the second clustering index and simulation-related parameters corresponding to every two first reference position points; Step S4: Obtain each target intersection point corresponding to all the connecting lines, and determine the final interpolation value of each target intersection point according to the initial interpolation value of each target intersection point on different connecting lines and the mean value of the simulation-related parameters corresponding to all the first reference position points in the cluster to which each target intersection point belongs; the target intersection point is the position point to be interpolated; Step S5: Take the position point to be interpolated with a known interpolation value as the second reference position point, execute steps S3 to S4, obtain the final interpolation value of each position point to be interpolated on the line connecting every two second reference position points, and continuously iterate step S5 until the final interpolation values ​​of all the position points to be interpolated in the simulation area are obtained.

2. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 1, characterized in that: The step of obtaining each sampling position point in the energetic material impact detonation simulation area comprises: Collecting remote sensing data of the actual geographical environment of the energetic material impact detonation simulation area, and drawing a three-dimensional image of the actual geographical environment based on the remote sensing data; A three-dimensional coordinate system of the three-dimensional image is constructed, and a plurality of sampling position points are set on the three-dimensional coordinate system according to preset intervals.

3. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 1, characterized in that: The step of determining the first clustering index and the second clustering index of each sampling location point relative to other sampling location points according to the simulation related parameters of each sampling location point includes: The simulation related parameters are several air state parameters, related material parameters and several geological information parameters; according to each air state parameter of each first reference position point, the air density parameter of each first reference position point is obtained by using neural network technology; According to each geological information parameter of each to-be-interpolated position point and each geological information parameter and air density parameter of each first reference position point, a first clustering index and a second clustering index of each sampling position point relative to other sampling position points are determined.

4. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 3, characterized in that: The method of determining the first clustering index and the second clustering index of each sampling location point relative to other sampling location points according to each geological information parameter of each to-be-interpolated location point and each geological information parameter and air density parameter of each first reference location point comprises: For a sampling location point located at the center where a first reference location point exists within a preset radius, a correlation index of the air information of the sampling location point located at the center relative to each type of geological information is determined based on each type of geological information parameter and air density parameter of each first reference location point within the preset radius; For sampling locations with correlation indicators, the first clustering indicator and the second clustering indicator of the sampling location relative to other sampling locations are determined based on the correlation indicator of the air information of the sampling location relative to each type of geological information and the difference between the sampling location and other sampling locations in each type of geological information parameter; For sampling locations without correlation indicators, the first clustering indicator and the second clustering indicator of the sampling location relative to other sampling locations are determined according to the difference between the sampling location and other sampling locations in each geological information parameter.

5. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 4, characterized in that: For sampling locations with correlation indicators, the calculation formulas for the first clustering indicator and the second clustering indicator are: ; In the formula, represents the first clustering index of the i-th sampling position relative to the z-th sampling position, N represents the number of types of geological information, k represents the k-th geological information parameter, Represents the correlation index of the air information at the i-th sampling location with respect to the k-th geological information, represents the kth geological information parameter of the i-th sampling location, represents the kth geological information parameter of the zth sampling location, represents the absolute value of the difference between the i-th sampling location and the z-th sampling location in the k-th geological information parameter, and c represents a non-zero constant; ; In the formula, Represents the second clustering index of the ith sampling position point relative to the zth sampling position point.

6. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 3, characterized in that: The implementation steps of step S3 include: For any two first reference position points, calculate the product of the first clustering index and the second clustering index corresponding to every two adjacent sampling position points on the line connecting the two first reference position points, and take the average of all the products as the slope of the line connecting the two first reference position points; An initial interpolation value of each position point to be interpolated on the line connecting the two first reference position points is determined according to the slope of the line connecting the two first reference position points and the air density parameters of the two first reference position points.

7. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 6, characterized in that: The step of determining the initial interpolation value of each to-be-interpolated position point on the line connecting the two first reference position points according to the slope of the line connecting the two first reference position points and the air density parameters of the two first reference position points comprises: Determine a linear equation according to the slope of a line connecting the two first reference position points and air density parameters of the two first reference position points located on the line; The coordinates of each to-be-interpolated position point on the line connecting the two first reference position points are substituted into the linear equation to obtain the air density parameter of each to-be-interpolated position point on the line connecting the two first reference position points as the initial interpolation value.

8. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 3, characterized in that: The method of determining the final insertion value of each target intersection point according to the initial insertion value of each target intersection point on different connecting lines and the mean value of the simulation related parameters corresponding to all the first reference position points in the cluster to which each target intersection point belongs includes: For any target intersection point, obtain the range corresponding to the initial insertion value of the target intersection point on different connecting lines, and select several candidate insertion values ​​from the interval corresponding to the range; For any candidate insertion value, the probability value of selecting the candidate insertion value is determined according to the difference between the mean value of the air density corresponding to all the first reference position points in the cluster to which the target intersection point belongs and the candidate insertion value, and the difference between the initial insertion value of the target intersection point on different connecting lines and the candidate insertion value; The selection probability value of each candidate insertion value is obtained, and the candidate insertion value corresponding to the maximum selection probability value is used as the final insertion value of the target intersection.

9. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 8, characterized in that: The calculation formula for the selection probability value of the candidate insertion value is: ; In the formula, represents the probability value of selecting the u-th candidate insertion value of the y-th target intersection point, It represents the mean air density corresponding to all the first reference positions in the cluster to which the y-th target intersection belongs. represents the u-th candidate insertion value of the y-th target intersection, j represents the j-th link of the target intersection, Indicates the number of lines corresponding to the y-th target intersection, represents the initial insertion value of the yth target intersection on the jth line, represents the absolute value function, and c represents a non-zero constant.

10. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 3, characterized in that: After implementing step S5, the method further includes: Based on the relevant material parameters and air density parameters of each sampling position point in the simulation area, the impact detonation of energetic materials in the simulation area is simulated to obtain the explosion performance and impact range of the simulation area.

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