A computer simulation-assisted method for simulating the impact detonation of energetic materials

Abstract

The present invention relates to the field of data acquisition technology, and specifically to a computer simulation-assisted method for simulating the impact detonation of energetic materials, comprising: obtaining a first clustering index and a second clustering index for each sampling position point relative to other sampling position points within a simulation area; performing cluster analysis on all sampling position points using the first clustering index and the second clustering index to obtain individual cluster clusters, and determining an initial interpolation value for each position point to be interpolated on a line connecting every two first reference position points, and then determining a final interpolation value for each target intersection point corresponding to the line based on the cluster cluster and the initial interpolation value; obtaining a final interpolation value for each position point to be interpolated on a line connecting every two second reference position points, and continuously iterating the execution until the final interpolation values ​​for all position points to be interpolated within the simulation area are obtained. The present invention improves the authenticity of the results of the impact detonation simulation of energetic materials by improving the reliability and accuracy of obtaining simulation-related parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and in particular to a computer simulation-assisted method for simulating the impact detonation of energetic materials. Background Art

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

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

[0004] To obtain the air density distribution required for impact detonation simulation, in the actual collection process, the geological environment will affect the sampling location, such as vegetation obstruction and obstacles. Therefore, the data of some sampling locations may not be successfully collected or even be missing, so interpolation processing is required.

[0005] Because the air density distribution at different sampling locations is non-uniform at different altitudes and under different weather conditions, interpolation can easily lead to large errors in the air density distribution corresponding to some sampling locations. Furthermore, when assessing the explosive impact range of energetic materials, different types of geological information parameters vary to varying degrees as the distribution of sampling locations within the same cluster or across different clusters approaches the edge between clusters. Therefore, interpolation based solely on clusters will introduce certain errors. Furthermore, data collection results with interpolation errors cannot accurately simulate the actual explosive detonation environment of energetic materials, distorting the final explosive impact effect and the actual impact 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 impact detonation environment of energetic materials, resulting in distortion of the explosion effect and the actual impact boundary, the purpose of the present invention is to provide a computer simulation-assisted method for simulating the impact detonation of energetic materials. The technical solution adopted is as follows:

[0007] An embodiment of the present invention provides a computer simulation-assisted method for simulating the impact detonation of energetic materials, the method comprising the following steps:

[0008] 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;

[0009] Step S2: determining a first clustering index and a second clustering index of each sampling location relative to other sampling locations based on simulation-related parameters of each sampling location; performing cluster analysis on all sampling locations using the first clustering index and the second clustering index to obtain clusters;

[0010] Step S3: determining an initial interpolation value for each to-be-interpolated position point on the line connecting each two first reference position points based on the first clustering index, the second clustering index, and simulation-related parameters corresponding to each two first reference position points;

[0011] Step S4: Obtaining each target intersection point corresponding to all connecting lines, and determining the final interpolation value of each target intersection point based on 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 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;

[0012] 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 position points to be interpolated in the simulation area are obtained.

[0013] Furthermore, obtaining each sampling position point in the energetic material impact detonation simulation area includes:

[0014] 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;

[0015] 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.

[0016] Furthermore, determining the first clustering index and the second clustering index of each sampling location point relative to other sampling location points based on the simulation-related parameters of each sampling location point includes:

[0017] The simulation-related parameters are several air state parameters, relevant material parameters, and several geological information parameters; based on each air state parameter at each first reference position, the air density parameter at each first reference position is obtained using neural network technology;

[0018] 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, a first clustering index and a second clustering index of each sampling location point relative to other sampling location points are determined.

[0019] Furthermore, the method of determining the first clustering index and the second clustering index of each sampling location point relative to other sampling location points based on 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 includes:

[0020] For a sampling location point located at the center and having a first reference location point within a preset radius, determining a correlation index of the air information at the sampling location point located at the center with respect to each type of geological information based on each geological information parameter and air density parameter of each first reference location point within the preset radius;

[0021] For sampling locations with correlation indicators, determine the first clustering indicator and the second clustering indicator of the sampling location relative to other sampling locations 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;

[0022] For sampling locations that do not have 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 differences in each geological information parameter between the sampling location and other sampling locations.

[0023] Furthermore, for sampling locations with correlation indices, the calculation formulas for the first clustering index and the second clustering index are:

[0024] Where, represents the first clustering index of the i-th sampling location relative to the z-th sampling location, 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;

[0025] Where, Represents the second clustering index of the i-th sampling location relative to the z-th sampling location.

[0026] Furthermore, the implementation steps of step S3 include:

[0027] 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 use the average of all the products as the slope of the line connecting the two first reference position points;

[0028] An initial interpolation value of each to-be-interpolated position point 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.

[0029] Furthermore, determining the initial interpolation value of each to-be-interpolated position point on the line connecting the 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:

[0030] Determining a linear equation based on a slope of a line connecting the two first reference positions and air density parameters of the two first reference positions located on the line;

[0031] 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 as the initial interpolation value.

[0032] Furthermore, determining the final interpolation value of each target intersection point based on the initial interpolation value of each target intersection point on different lines and the mean value of the simulation-related parameters corresponding to all first reference position points in the cluster to which each target intersection point belongs includes:

[0033] 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;

[0034] For any candidate insertion value, the probability value of selecting the candidate insertion value is determined based on the difference between the mean air density corresponding to all first reference positions in the cluster to which the target intersection point belongs and the candidate insertion value, as well as the difference between the initial insertion value of the target intersection point on different connecting lines and the candidate insertion value;

[0035] 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.

[0036] Furthermore, the calculation formula for the selection probability value of the candidate insertion value is:

[0037] Where, 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 connection of the target intersection, Indicates the number of lines corresponding to the y-th target intersection point, Indicates the initial insertion value of the y-th target intersection on the j-th line, Represents the absolute value function, and c represents a non-zero constant.

[0038] Furthermore, after implementing step S5, the method further includes:

[0039] 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.

[0040] The present invention has the following beneficial effects:

[0041] The present invention provides a computer simulation-assisted method for simulating the impact detonation of energetic materials. The method determines a first clustering index and a second clustering index based on simulation-related parameters at each sampling location, thereby obtaining clusters. The final interpolation value of subsequent target intersections is calculated based on the clusters, thereby avoiding the large error in the final interpolation value caused by the air density parameters corresponding to different sampling locations being affected by changes in geological information, thereby improving the authenticity and accuracy of the final interpolation value of the target intersection. The method determines the initial interpolation value of each location to be interpolated on the line connecting each two first reference locations based on the first clustering index, the second clustering index, and simulation-related parameters corresponding to each first reference location. This method avoids the error caused by data collection at different times and locations, which prevents the simulation-related parameters from accurately affecting boundary distortion. The method effectively improves the accuracy and authenticity of the final simulation of the environment and explosive performance of the impact detonation of energetic materials. The present invention overcomes the defect of existing interpolation methods that ignores the actual data characteristics of the simulation-related parameters when interpolating the simulation-related parameters, thereby effectively improving the accuracy of the energetic material impact detonation simulation results achieved based on high-reliability simulation-related parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a flowchart of the steps of a computer simulation-assisted method for simulating the impact detonation of energetic materials according to the present invention;

[0044] Figure 2 This is a flowchart of step S2 in an embodiment of the present invention;

[0045] Figure 3 This is a flowchart of step S3 in an embodiment of the present invention;

[0046] Figure 4 4 is a flowchart of step S4 in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] To further illustrate the technical means and effects employed by the present invention to achieve its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementations, structures, features, and effects of the technical solutions proposed by the present invention. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0048] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0049] The present invention is targeted at applications where, during existing energetic material impact detonation simulations, various types of relevant parameters, such as the material's own characteristic data, are input. These parameters are then used to evaluate the performance of the energetic material impact detonation simulation under the current parameters, based on existing explosion platforms and algorithms, such as AUTODYN, specifically designed for simulating impact, explosion, and other transient dynamic processes. The resulting explosion radius is then determined based on the current material parameters. Therefore, collecting complete and reliable relevant parameters is crucial for the accuracy of detonation simulation results.

[0050] In order to improve the integrity and reliability of the collected simulation-related parameters, this embodiment provides a computer simulation-assisted method for simulating the impact detonation of energetic materials. Figure 1 As shown, the following steps are included:

[0051] Step S1: obtaining simulation-related parameters of each sampling position point within the energetic material impact detonation simulation area.

[0052] The above step S1 can be implemented through steps S11 to S12 (not shown):

[0053] S11: Acquire all sampling locations within the energetic material impact detonation simulation area.

[0054] Due to the diverse application scenarios required for detonation simulation, such as targeted detonation in a mine, geological information varies at different detonation locations. Furthermore, the energy propagation of energetic materials after detonation is attenuated by air density, resulting in varying degrees of change in the actual explosion impact boundary. Therefore, all sampling locations for simulation-related parameters must be determined based on the actual geographic environment of the simulation area.

[0055] Specifically, the method involves first using drones to collect remote sensing data of the actual geographic environment of the area where the energetic material impact detonation is simulated. Based on this data, a three-dimensional image of the actual geographic environment is created. Next, a three-dimensional coordinate system is constructed for the image, and a number of sampling locations are set at predetermined intervals within the three-dimensional coordinate system.

[0056] Among them, in the three-dimensional image, the non-ground spatial area is extracted and converted into three-dimensional point cloud data. The point cloud data represents both the sampling location points and the air density parameters. Therefore, the three-dimensional image is constructed to determine all the sampling location points of the air state parameters in the simulation area. The determination process of the three-dimensional image is an existing technology and will not be elaborated in detail. In addition, the preset interval distance between two adjacent sampling location points can be set to 1m, and the implementer can set it according to the specific actual situation.

[0057] When collecting simulated data from various sampling locations, due to the influence of the geological environment, such as vegetation obstruction and obstacles, some sampling locations cannot collect air-related parameters. In other words, some sampling locations have missing air-related parameters. Therefore, all sampling locations include interpolation locations and first reference locations. Among them, interpolation locations refer to sampling locations where air-related parameters are missing, while first reference locations refer to sampling locations where air-related parameters are currently collected and can also be collected historically.

[0058] S12: Obtain simulation-related parameters for each sampling location point.

[0059] Here, the simulation-related parameters may be several air state parameters, relevant material parameters, and several geological information parameters.

[0060] Among them, regarding the air state parameters, various types of sensors are used to randomly collect relevant air data at different heights and different positions in the simulation area, such as temperature, humidity and other multi-dimensional air-related data characteristics. These characteristics can be collectively referred to as air state parameters, and the air state parameters are assigned to the sampling position points at the corresponding positions in the three-dimensional coordinate system.

[0061] Regarding relevant material parameters and several types of geological information parameters, these parameters can be collected through relevant equipment. Geological information parameters may include vegetation height, vegetation density, rock type, etc. The number of types of air state parameters and geological information parameters can be set by the implementer based on specific actual conditions and are not specifically limited here.

[0062] It should be noted that geological information parameters are two-dimensional data, that is, in a three-dimensional coordinate system, the projections of sampling points at different heights on the same horizontal and vertical axes on the planes where the horizontal and vertical axes are located only correspond to one set of geological information parameters.

[0063] So far, this embodiment has obtained simulation-related parameters for each sampling location point in the simulation area.

[0064] Step S2: determining a first clustering index and a second clustering index of each sampling location relative to other sampling locations based on simulation-related parameters of each sampling location; performing cluster analysis on all sampling locations using the first clustering index and the second clustering index to obtain various cluster clusters.

[0065] The above step S2 can be Figure 2 The steps S21 to S23 shown implement:

[0066] S21: According to each air state parameter at each first reference position point, using a neural network technology, obtain an air density parameter at each first reference position point.

[0067] Because the energy propagation of energetic materials after explosion is affected by air density, which attenuates the energy transfer, and the actual explosion boundary also changes to varying degrees, air state parameters are used as important input data for the impact detonation simulation of energetic materials. This involves interpolating the sampling locations where air state parameters are missing. There are many different types of air state parameters. To facilitate a comprehensive analysis of air density, a dimensionality reduction process is performed based on these multiple air state parameters to obtain the air density parameters for each first reference location.

[0068] Specifically, according to the existing neural network technology, a plurality of air state parameters of a first reference position point currently collected are input, and the air density parameter of the first reference position point is output.

[0069] Among them, the process of using neural networks to achieve dimensionality reduction processing is a prior art and is not within the scope of protection of the present invention, and will not be elaborated here in detail; the value range of the air density parameter is made 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 stored in a computer.

[0070] S22: Determine a first clustering index and a second clustering index of each sampling location point relative to other sampling location points based on 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.

[0071] Here, the air density distribution at different sampling locations is uneven at different altitudes and under different weather conditions, and some sampling locations have data gaps due to the influence of the geological environment. Therefore, when all interpolation locations on the entire three-dimensional coordinate system are interpolated, it is easy to cause large errors in the air density parameters corresponding to some interpolation locations. It is necessary to analyze the impact of the actual geological environment on the air density parameters in the space.

[0072] In this embodiment, the amplitude of the change of the geological information parameter over time is relatively small, and the air density parameters corresponding to the sampling locations under the condition of approximately the same geological information also have approximately the same trend.

[0073] The above step S22 can be implemented through steps S221 to S223 (not shown):

[0074] S221: For a sampling position point located at the center and having a first reference position point within a preset radius, determine a correlation index of the air information of the sampling position point located at the center relative to each type of geological information based on each geological information parameter and air density parameter of each first reference position point within the preset radius.

[0075] Specifically, with any sampling location as the center, determine whether a first reference location exists within a preset radius. If so, then determine the correlation index of the air information at the central sampling location with respect to each type of geological information based on each geological information parameter and air density parameter at each first reference location within the preset radius. If no first reference location exists, no correlation index needs to be calculated.

[0076] As a specific implementation, the air density parameters of all first reference locations within a preset radius are sorted from largest to smallest or from smallest to largest according to the magnitude of the air density parameters to obtain an air density parameter sequence. A sequence of each geological information parameter is obtained based on the sorting of each first reference location in the air density parameter sequence. The Pearson correlation coefficient is used to determine the correlation between the air density parameter sequence and each geological information parameter sequence, and the absolute value of the correlation is used as an indicator of the correlation of the air information at the central sampling location with respect to each geological information parameter. The preset radius can be set to 3, and the implementer can adjust this value based on specific circumstances.

[0077] S222: For sampling locations with correlation indicators, determine the first clustering indicator and the second clustering indicator of the sampling location relative to other sampling locations 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.

[0078] Here, the first clustering index represents the similarity of the address information between the two sampling locations, and the second clustering index represents the change trend of the air density parameters of the two sampling locations under the influence of the geological information parameters. The other sampling locations refer to the remaining sampling locations except the sampling location itself.

[0079] As a specific calculation method, for sampling location points with correlation indicators, the calculation formulas for the first clustering indicator and the second clustering indicator are:

[0080] Where, represents the first clustering index of the i-th sampling location relative to the z-th sampling location, 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, It represents the absolute value of the difference between the i-th sampling location point and the z-th sampling location point in the k-th geological information parameter, and c represents a non-zero constant.

[0081] In the calculation formula of the first clustering index, for The weight of the k-th geological information parameter is larger, indicating that the influence of the current k-th geological information parameter on the air density is greater, the numerical similarity of the two sampling locations on the k-th geological information parameter is more important for clustering, the higher the necessity of clustering, and the larger the first clustering index is; Indicates the similarity between two sampling locations in terms of the kth geological information parameter, The larger the value is, the greater the similarity between the two sampling locations in the kth geological information parameter is, and the larger the first clustering index is. c can be set to 0.01 to avoid the possibility of the denominator of the fraction being zero.

[0082] Where, Represents the second clustering index of the i-th sampling location relative to the z-th sampling location.

[0083] 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 value of the second clustering index represents that the air density parameter affected by the geological information parameters between the i-th sampling location point and the z-th sampling location point is on an upward trend or a downward trend, respectively.

[0084] S223, for a sampling location point that does not have a correlation index, determine a first clustering index and a second clustering index of the sampling location point relative to other sampling location points based on the difference in each geological information parameter between the sampling location point and other sampling location points.

[0085] With reference to the calculation process 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 location point without the correlation index relative to other sampling location points can be obtained.

[0086] As a specific calculation method, for sampling locations where there is no correlation index, the calculation formulas for the first clustering index and the second clustering index are:

[0087] ; .

[0088] S23: Perform cluster analysis on all sampling locations using the first clustering index and the second clustering index to obtain clusters.

[0089] Specifically, the optimal number of clusters K is obtained based on the elbow method. Combining the optimal number of clusters K and the first clustering index and the second clustering index of all sampling positions relative to other sampling positions in the three-dimensional coordinate system, Kmeans clustering is performed on the spatial area in the entire three-dimensional coordinate system to obtain K clusters.

[0090] Among them, the implementation processes of the elbow method and Kmeans clustering are both existing technologies and are beyond the scope of protection of the present invention, and will not be elaborated here.

[0091] It should be noted that each cluster is obtained by the difference in geological information between different sampling locations in the three-dimensional coordinate system, and sampling locations with similar geological information are obtained based on the clusters. This can avoid the air density parameters corresponding to different sampling locations being affected by changes in geological information, resulting in large errors in the final interpolation, which helps to improve the authenticity and accuracy of the final interpolation value determined subsequently.

[0092] Step S3: determining an initial interpolation value of each to-be-interpolated position point on the line connecting each two first reference position points according to the first clustering index, the second clustering index and simulation-related parameters corresponding to each two first reference position points.

[0093] According to prior knowledge, when evaluating the explosion impact range of energetic materials, regardless of the same cluster or different clusters, as the sampling locations in the cluster gradually approach the edge between different clusters, the corresponding different types of geological information parameters change to varying degrees. At this time, only the mean of the air density parameters corresponding to each cluster obtained in step S2 will produce certain errors when interpolating the air density parameters of different interpolation locations, resulting in the final constructed point cloud data being unable to accurately simulate the actual environment, causing the final explosion impact effect and the actual impact boundary to be distorted.

[0094] In this example, as geological information changes from areas with high vegetation coverage to areas with low vegetation coverage, air state parameters such as temperature and humidity also change in the same trend. This indicates that the changes in air state parameters are related to the actual geological information parameters. The change in air density parameters between two different sampling locations can be considered a local linear change, while the change in air density parameters along any segment of the line connecting the sampling locations can be considered an overall nonlinear change. By analyzing the geological information between different sampling locations, the characteristics of air density parameter changes at the sampling locations across the entire three-dimensional coordinate system can be analyzed.

[0095] For any two first reference position points on the three-dimensional coordinate system, the above step S3 can be Figure 3 The steps S31 to S32 shown implement:

[0096] 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 use the mean of all products as the slope of the line connecting the two first reference position points.

[0097] As a specific implementation method, all reference position points in the three-dimensional coordinate system are connected in pairs to obtain a connecting line corresponding to every two first reference position points; the product of the first clustering index and the second clustering index corresponding to every two adjacent sampling position points on the connecting line corresponding to the two first reference position points is calculated, and the mean of all products is used as the slope of the connecting line between the two first reference position points.

[0098] Here, by multiplying the first clustering index, which represents the similarity of geological conditions, and the second clustering index, which represents the trend direction, we can obtain the theoretical slope of change of the air density parameter between two adjacent sampling locations on the connecting line. This is because the smaller the first clustering index, the less similar the geological conditions between the two adjacent sampling locations on the connecting line, and the greater the amplitude of the change in air density between them. At the same time, a positive second clustering index indicates that the air density parameters between the two adjacent sampling locations on the connecting line are showing an upward trend. Multiplying the first and second clustering indices together can express the theoretical slope of change of the air density parameter between the two adjacent sampling locations.

[0099] 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 position points to be interpolated that do not have historical air state parameters.

[0100] S32: Determine an initial interpolation value for 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.

[0101] Specifically, a linear equation is determined based on the slope of the line connecting the two first reference points and the air density parameters of the two first reference points located on the line. The coordinates of each to-be-interpolated point on the line connecting the two first reference points are substituted into the linear equation to obtain the air density parameters of each to-be-interpolated point on the line as initial interpolation values. The implementation process for determining the linear equation is prior art and falls outside the scope of protection of the present invention, and will not be elaborated upon herein.

[0102] So far, this embodiment has obtained the initial interpolation value of each to-be-interpolated position point on the line connecting every two first reference position points.

[0103] Step S4: Obtain each target intersection point corresponding to all connecting lines, and determine the final insertion value of each target intersection point based on 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 first reference position points in the cluster to which each target intersection point belongs.

[0104] When actually collecting simulation-related parameters for all sampling locations, the collection time of different sampling locations is different, and the collection location also changes randomly over time. In the process of influence range attenuation, each line connecting two first reference locations may correspond to the same one or several sampling locations. Interpolation analysis based on the line connecting the two first reference locations may cause the geological information of the same sampling location to change differently on different lines, that is, the initial interpolation value of the same sampling location cannot reflect the geological information change characteristics on other lines. Therefore, it is necessary to optimize the initial interpolation value of the target intersection to obtain the final interpolation value of the target intersection. Among them, the target intersection is the location point to be interpolated, and the target intersection points corresponding to all lines can be directly obtained.

[0105] In this embodiment, for any target intersection, the more realistic the candidate insertion value of the target intersection is, the greater the possibility that the candidate insertion value is the final insertion value. Figure 4 The steps S41 to S43 shown implement:

[0106] S41: Obtain the ranges corresponding to the initial insertion values ​​of the target intersection point on different lines, and select several candidate insertion values ​​from the interval corresponding to the ranges.

[0107] Specifically, the initial interpolation values ​​of the target intersection point on different connecting lines are obtained to form an initial interpolation value sequence. The maximum and minimum values ​​in the initial interpolation value sequence are determined. At preset intervals, candidate interpolation values ​​within the corresponding intervals of the maximum and minimum values ​​are obtained. The interval between two adjacent candidate interpolation values ​​can be 0.1, which can be set by the implementer based on specific circumstances and is not specifically limited.

[0108] 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.

[0109] As a specific calculation method, the calculation formula for the selection probability value of the candidate insertion value is:

[0110] Where, 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 connection of the target intersection, Indicates the number of lines corresponding to the y-th target intersection point, Indicates the initial insertion value of the y-th target intersection on the j-th line, Represents the absolute value function, and c represents a non-zero constant.

[0111] In the calculation formula of the selected 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 point 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 point and the initial insertion value of the y-th target intersection point 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 changes with the geological information; the two equations are combined and the selection probability value is obtained by using a fractional expression according to the direct and inverse proportion. The larger the selection probability value, 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.

[0112] 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.

[0113] Specifically, according to the calculation method of the selection probability value of the u-th candidate insertion value of the y-th target intersection mentioned above, the selection probability values ​​of all candidate insertion values ​​corresponding to the y-th target intersection are obtained; and the candidate insertion value corresponding to the maximum selection probability value is used as the final insertion value of the y-th target intersection.

[0114] It is worth noting that, for a position point to be interpolated that is not a target intersection point on the line, the initial interpolation value is used as the final interpolation value of the corresponding position point to be interpolated.

[0115] It should be noted that, based on the air density parameters of different reference positions in multiple straight lines in the three-dimensional coordinate system, combined with the law of change of air density, an optimization function is constructed to obtain the air density parameters of different sampling positions in the entire three-dimensional coordinate system. This can avoid the errors caused by data collection at different positions at different time points, which makes the final constructed point cloud data unable to accurately affect the boundary distortion. It effectively improves the accuracy and authenticity of the final simulation of the environment where the energetic material impact detonation is located and the explosion performance.

[0116] At this point, this embodiment completes the first interpolation operation.

[0117] 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 position points to be interpolated in the simulation area are obtained.

[0118] In this embodiment, after completing the first interpolation operation, the position point to be interpolated with a known interpolation value is used as a new reference position point, recorded as the second reference position point. After obtaining the second reference position point, steps S3 to S4 are executed to perform a new round of interpolation processing. Specifically, the initial interpolation value of each position point to be interpolated on the line connecting each two second reference position points is determined based on the first clustering index, the second clustering index, and the simulation-related parameters corresponding to each second reference position point. The target intersection points corresponding to all connecting lines are obtained, and the final interpolation value of each target intersection point is determined based on the initial interpolation value of each target intersection point on different connecting lines and the average of the simulation-related parameters corresponding to all first reference position points in the cluster to which each target intersection point belongs, thereby completing the second interpolation operation. Step S5 is continuously iterated until the final interpolation value of all the position points to be interpolated in the simulation area is obtained, that is, each sampling position point in the entire three-dimensional coordinate system corresponds to an air density parameter.

[0119] At this point, this embodiment completes the interpolation process of the air density parameter and obtains the air density parameter of each sampling position point in the simulation area.

[0120] After obtaining the air density parameters of each sampling location in the simulation area, the explosion performance and impact boundary of the energetic material are simulated.

[0121] Specifically, the detonation position of the energetic material in the three-dimensional coordinate system is determined. Based on the relevant material parameters and air density parameters of each sampling position point in the simulation area, the impact detonation of the energetic material in the simulation area is simulated through the existing AUTODYN to obtain the explosion performance and impact range of the simulation area.

[0122] It should be noted that when the impact radius contacts non-air components such as the ground and mountains while extending from the detonation position in a certain direction, the formation of the detonation impact boundary is stopped, thereby outputting the impact detonation impact boundary of the entire energetic material.

[0123] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in 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 relative to other sampling locations based on simulation-related parameters of each sampling location; performing cluster analysis on all sampling locations using the first clustering index and the second clustering index to obtain clusters; Step S3: determining an initial interpolation value for each to-be-interpolated position point on the line connecting each two first reference position points based on the first clustering index, the second clustering index, and simulation-related parameters corresponding to each two first reference position points; Step S4: Obtaining each target intersection point corresponding to all connecting lines, and determining the final interpolation value of each target intersection point based on 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 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: Using the position point to be interpolated with a known interpolation value as the second reference position point, executing steps S3 to S4 to obtain the final interpolation value of each position point to be interpolated on the line connecting every two second reference position points, and continuously iterating step S5 until the final interpolation values ​​of all the position points to be interpolated in the simulation area are obtained; The step of determining the first clustering index and the second clustering index of each sampling location point relative to other sampling location points based on the simulation-related parameters of each sampling location point includes: The simulation-related parameters are several air state parameters, relevant material parameters, and several geological information parameters; based on each air state parameter at each first reference position, the air density parameter at each first reference position is obtained using neural network technology; Determine a first clustering index and a second clustering index of each sampling location point relative to other sampling location points based on 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; The method of determining the first clustering index and the second clustering index of each sampling location point relative to other sampling location points based on 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 includes: For a sampling location point located at the center and having a first reference location point within a preset radius, determining a correlation index of the air information at the sampling location point located at the center with respect to each type of geological information based on each geological information parameter and air density parameter of each first reference location point within the preset radius; For sampling locations with correlation indicators, determine the first clustering indicator and the second clustering indicator of the sampling location relative to other sampling locations 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 that do not have 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 differences in each geological information parameter between the sampling location and other sampling locations.

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 includes: 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: For sampling locations with correlation indices, the calculation formulas for the first clustering index and the second clustering index are: Where, represents the first clustering index of the i-th sampling location relative to the z-th sampling location, 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; Where, Represents the second clustering index of the i-th sampling location relative to the z-th sampling location.

4. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 1, 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 use 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 to-be-interpolated position point 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.

5. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 4, 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 includes: Determining a linear equation based on a slope of a line connecting the two first reference positions and air density parameters of the two first reference positions 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 as the initial interpolation value.

6. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 1, characterized in that: The method of determining 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 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 based on the difference between the mean air density corresponding to all first reference positions in the cluster to which the target intersection point belongs and the candidate insertion value, as well as the difference between the initial insertion value of the target intersection point on different connecting 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.

7. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 6, characterized in that: The calculation formula for the selection probability value of the candidate insertion value is: Where, 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 connection of the target intersection, Indicates the number of lines corresponding to the y-th target intersection point, Indicates the initial insertion value of the y-th target intersection on the j-th line, Represents the absolute value function, and c represents a non-zero constant.

8. The computer simulation-assisted method for simulating the impact detonation of energetic materials according to claim 1, 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.

Images