A method for constructing fault criteria for automatic weapon automata based on design parameter association

Abstract

This invention proposes a method for constructing fault criteria for an automatic weapon automaton based on design parameter associations, relating to the field of computer simulation technology. The method includes: constructing a sample matrix based on the distribution and statistics of the automatic weapon's design parameters, inputting the sample matrix into a dynamic simulation model of the automatic weapon automaton, extracting motion parameter responses, and fitting them into a proxy model; establishing correlations among the design parameters, calculating the contribution of each design parameter based on the correlations, and identifying design parameters whose contributions exceed a contribution threshold as key design parameters; constructing a Bayesian network based on the key design parameters and the proxy model, and then tracing back the design parameters that affect the fault through forward and reverse reasoning to obtain the design parameter constraint ranges and motion parameter constraint ranges, thereby establishing the automaton fault criteria. This method can be used to quickly locate the cause of the fault, improve design parameters, and thus guide the reliability design of automatic weapons.

Description

Technical Field

[0001] The present invention relates to the technical field of computer simulation, and in particular to a method for constructing a fault criterion of an automatic weapon automaton based on design parameter association. Background Art

[0002] The existing technology provides a fault diagnosis method for complex equipment based on a fault tree using fuzzy Bayesian network reasoning. By analyzing the structural composition of the complex equipment, a fault tree model of the complex equipment is established. The fault tree transformation method is used to construct a Bayesian network topology node based on the fault tree. The causal reasoning and diagnostic reasoning in the fuzzy Bayesian network reasoning are used to diagnose the fault (potential fault) nodes in the case.

[0003] The existing technology uses causal reasoning and diagnostic reasoning in Bayesian network reasoning to diagnose the fault (potential fault) nodes in the case. However, since the association relationship between design parameters is not established and the design parameters are relatively independent, it can only be used to diagnose faults, but cannot form fault judgment criteria and is difficult to guide design. Summary of the Invention

[0004] In view of the current technical problems that the failure mechanism of automatic weapon automatons is unclear, the failure formation mechanism is not clear, the fault location is difficult, the fault judgment criteria have not yet been formed, the analysis and solution of faults rely only on qualitative analysis, and the design parameters and motion parameters that affect the faults have not yet been quantified, the present invention proposes a method and system for constructing automatic weapon automatons fault judgment criteria based on design parameter association.

[0005] The first aspect of the present invention discloses a method for constructing a fault criterion of an automatic weapon automaton based on design parameter association, the method comprising:

[0006] Step S1, constructing a sample matrix based on the design parameter distribution and statistics of the automatic weapon, and inputting the sample matrix into the automatic weapon automaton dynamics simulation model to extract the motion parameter response, and then fitting the automatic weapon automaton motion parameter response proxy model;

[0007] Step S2: establishing an association relationship among the design parameters, and calculating the contribution of each design parameter based on the association relationship, and determining the design parameter whose contribution is greater than a contribution threshold as a key design parameter;

[0008] In step S3, a Bayesian network is constructed based on the key design parameters and the automatic weapon automaton motion parameter response agent model, and then the key design parameters that affect the fault are traced back through forward and reverse reasoning to obtain the constraint range of the key design parameters and the constraint range of the motion parameters, and establish the automaton fault judgment criteria.

[0009] Optionally, step S1 specifically includes:

[0010] Step S11, constructing a sample matrix based on the design parameter distribution and statistics of the automatic weapon through Latin hypercube sampling;

[0011] Step S12, inputting the sample matrix into the automatic weapon automaton dynamics simulation model to calculate and extract motion parameter responses;

[0012] Step S13, based on the correspondence between each sample in the sample matrix and the extracted motion parameter response, a proxy model is fitted to obtain a motion parameter response proxy model of an automatic weapon automaton; the motion parameter response proxy model of an automatic weapon automaton is used to obtain a motion parameter response corresponding to a fault criterion according to the input design parameters.

[0013] Optionally, in step S13, the type of the automatic weapon automaton motion parameter response agent model is one or more of a Kriging model, a radial basis neural network, a support vector regression model, an extended adaptive hybrid agent model, an optimization-based two-layer integrated agent model, a weighted average combination agent model, a weighted average combination agent model, a moving least squares-based variable-fidelity agent model, and a Gaussian process regression model.

[0014] Optionally, step S1 further includes:

[0015] Step S14, calculate and output at least one motion parameter response through the automatic weapon automaton motion parameter response proxy model, and perform a one-to-one comparison analysis with the motion parameter response calculated by the automatic weapon automaton dynamics simulation model; if the errors are all less than the preset values, it is determined that the automatic weapon automaton motion parameter response proxy model meets the requirements; otherwise, repeat steps S11-S13 until the errors are all less than the preset values.

[0016] Optionally, in step S2, establishing an association relationship of design parameters specifically includes:

[0017] Step S21, performing dimensionless and normalization processing on the design parameters to obtain standardized design parameters;

[0018] Step S22: By dividing the normalized design parameters by evaluation thresholds, a transaction database is obtained, and then data combinations are performed on the design parameters in the transaction database, and the support of each data combination is calculated to screen out all data combinations greater than the minimum support threshold as frequent itemsets, thereby obtaining a frequent pattern growth tree;

[0019] Step S23 , performing association rule mining on the frequent pattern growth tree in a bottom-up manner to obtain a corresponding design parameter set; wherein the design parameter set includes a redundant association design parameter set M, a complementary association design parameter set N and an independent design parameter set K.

[0020] Optionally, in step S2, the contribution of each design parameter is calculated based on the association relationship, specifically including:

[0021] Step S24, solving the nominal contribution of the design parameters in the redundant associated design parameter set M and the complementary associated design parameter set N by maximizing the Marichal entropy; wherein,

[0022] The formula for maximizing Marichal entropy is:

[0023]

[0024] Where, The actual contribution is The value of Maricha entropy of redundant associated design parameter set M when Actual contribution Hyperparameters of is the u-th design parameter in the redundant associated design parameter set M, i is numerically equal to |M|, which represents the number of all design parameters in the redundant associated design parameter set M; S is any subset of all subsets in the redundant associated design parameter set M; The weight coefficient of the subset S in the parameter set M is designed for redundant association; Calculate the entropy value; is the nominal contribution; To merge, is a design parameter The Shapley value in the cooperative game of ; T is a specific subset of size t in the redundant association parameter set M;

[0025] Step S25, based on the pointers of the single design parameter frequent itemsets in all design parameter sets, the nominal contributions of the design parameters in the redundant associated design parameter set M and the complementary associated design parameter set N are corrected to obtain the actual contributions of the design parameters; wherein,

[0026] The u-th design parameter in the redundant associated design parameter set M The actual contribution of for:

[0027]

[0028] Where, is the u-th design parameter in the redundant associated design parameter set M Nominal contribution of P vis the pointer to the frequent item set of a single design parameter; m1, m2, ..., mi are the serial numbers of the design parameters in the redundant associated design parameter set M; n1, n2, ..., nj are the serial numbers of the design parameters in the complementary associated design parameter set N; k1, k2, ..., kl are the serial numbers of the design parameters in the independent design parameter set K;

[0029] Complementary association design parameter set The u-th design parameter in The actual contribution of for:

[0030]

[0031] Where, Design parameter sets for complementary associations The u-th design parameter in Nominal contribution;

[0032] Step S26, based on the pointers of the single design parameter frequent item sets in all design parameter sets, calculate the actual contribution of the design parameters in the independent design parameter set K; wherein,

[0033]

[0034] Where, is the u-th design parameter in the independent design parameter set K The actual contribution of P ku is the u-th design parameter in the independent design parameter set K Pointer to the frequent itemsets.

[0035] Optionally, in step S3, key design parameters that affect the fault are traced back through forward and reverse reasoning to obtain the constraint range of the key design parameters and the constraint range of the motion parameters, and establish the automaton fault judgment criteria, which specifically includes:

[0036] Step S31, changing the values ​​of key design parameters, and inputting the changed key design parameters into the automatic weapon automaton motion parameter response agent model to obtain the range of motion parameter changes, and determining the automaton fault type based on the forward reasoning capability of the Bayesian network;

[0037] Step S32, changing the fault type, using the reverse reasoning capability of the Bayesian network to obtain the variation range of the motion parameters, and inputting the variation range of the motion parameters into the automatic weapon automaton motion parameter response agent model, thereby obtaining the constraint range of the key design parameters;

[0038] In step S33, based on the reasoning results of step S31 and step S32, a causal traceability analysis is performed to obtain the constraint range of key design parameters and the constraint range of motion parameters, and to establish the automaton fault judgment criteria.

[0039] Optionally, the key design parameters include: extraction timing, chamfer of the outer edge of the shell bottom rim, extractor spring force, ejection port size, distance between the extractor and the ejection port, bullet pushing height, bullet holding port size, return spring force, extractor mirror distance, support spring force and feeding resistance.

[0040] A second aspect of the present invention discloses a system for constructing fault criteria for an automatic weapon automaton based on design parameter association, the system comprising:

[0041] The first processing module is configured to construct a sample matrix based on the distribution and statistics of the design parameters of the automatic weapon, and input the sample matrix into the dynamic simulation model of the automatic weapon automaton to extract the motion parameter response, thereby fitting the automatic weapon automaton motion parameter response proxy model;

[0042] The second processing module is configured to establish an association relationship among the design parameters, calculate a contribution of each design parameter based on the association relationship, and determine a design parameter whose contribution is greater than a contribution threshold as a key design parameter;

[0043] The third processing module is configured to construct a Bayesian network based on the key design parameters and the automatic weapon automaton motion parameter response agent model, and then trace the key design parameters that affect the fault through forward and reverse reasoning, obtain the constraint range of the key design parameters and the constraint range of the motion parameters, and establish the automaton fault judgment criteria.

[0044] A third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the steps of the method for constructing a fault criterion for an automatic weapon automaton based on design parameter association described in the first aspect of the present invention.

[0045] A fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the steps of the method for constructing a fault criterion for an automatic weapon automaton based on design parameter association described in the first aspect of the present invention.

[0046] In summary, the present invention establishes the correlation between design parameters and parameter weights (i.e., parameter contribution), establishes the constraint range of design parameters through fault reproduction, reverse reasoning and tracing, establishes the constraint range of automatic machine motion parameters, and forms fault judgment criteria, which can be used to quickly locate the cause of the fault, improve the design parameters, and further guide the reliability design of automatic weapons. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0048] Figure 1 A flow chart is constructed for the fault judgment criteria of the automatic weapon automaton according to an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the proxy model fitting process according to an embodiment of the present invention;

[0050] Figure 3 This is a flow chart of forward reasoning analysis of automatic weapon automaton faults according to an embodiment of the present invention;

[0051] Figure 4 This is a flow chart of reverse reasoning analysis of automatic weapon automaton faults according to an embodiment of the present invention;

[0052] Figure 5 This is a flowchart of the causal tracing analysis of the automatic weapon automaton failure according to an embodiment of the present invention;

[0053] Figure 6 This is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0055] The first aspect of the present invention discloses a method for constructing fault criteria of an automatic weapon automaton based on the association of design parameters, see Figure 1 , the method comprising:

[0056] Step S1, constructing a sample matrix based on the design parameter distribution and statistics of the automatic weapon, and inputting the sample matrix into the automatic weapon automaton dynamics simulation model to extract the motion parameter response, and then fitting the automatic weapon automaton motion parameter response proxy model;

[0057] Alternatively, see Figure 2 , the step S1 specifically includes:

[0058] Step S11, constructing a sample matrix based on the design parameter distribution and statistics of the automatic weapon through Latin hypercube sampling;

[0059] Step S12, inputting the sample matrix into the automatic weapon automaton dynamics simulation model to calculate and extract motion parameter responses;

[0060] Optionally, the extracted motion parameter response includes shell ejection speed, recoil speed, projectile ejection speed, return speed, recoil displacement, return displacement, shell position coordinates, bullet position coordinates, etc.

[0061] Step S13, based on the correspondence between each sample in the sample matrix and the extracted motion parameter response, a proxy model is fitted to obtain a motion parameter response proxy model of an automatic weapon automaton; the motion parameter response proxy model of an automatic weapon automaton is used to obtain a motion parameter response corresponding to a fault criterion according to the input design parameters.

[0062] Optionally, in step S13, the type of the automatic weapon automaton motion parameter response agent model is one or more of a Kriging model, a radial basis neural network, a support vector regression model, an extended adaptive hybrid agent model, an optimization-based two-layer integrated agent model, a weighted average combination agent model, a weighted average combination agent model, a moving least squares-based variable-fidelity agent model, and a Gaussian process regression model.

[0063] Optionally, the agent model is prioritized in the following order: Kriging model, radial basis neural network, support vector regression, extended adaptive hybrid agent model, optimization-based two-layer ensemble agent model, weighted average combination agent model, weighted average combination agent model, moving least squares-based variable-fidelity agent model, and Gaussian process regression.

[0064] Optionally, step S1 further includes:

[0065] Step S14, calculate and output at least one motion parameter response through the automatic weapon automaton motion parameter response proxy model, and perform a one-to-one comparison analysis with the motion parameter response calculated by the automatic weapon automaton dynamics simulation model; if the errors are all less than the preset values, it is determined that the automatic weapon automaton motion parameter response proxy model meets the requirements; otherwise, repeat steps S11-S13 until the errors are all less than the preset values.

[0066] In this step, the proxy model calculates and outputs four parameters: shell ejection speed, recoil speed, projectile push speed, and return speed. These parameters are compared and analyzed one-to-one with the four parameters calculated by the automatic weapon automaton dynamics simulation model. If the error is less than 10%, the requirement is met. If the requirement is not met, the proxy model needs to be rebuilt by increasing the sample size and checking the response extraction until the error is less than 10%.

[0067] Step S2: establishing an association relationship among the design parameters, and calculating the contribution of each design parameter based on the association relationship, and determining the design parameter whose contribution is greater than a contribution threshold as a key design parameter;

[0068] Automatic weapons have a large number of design parameters with complex relationships. These parameters are primarily categorized into three main categories: key component dimensions, spring parameters, and chamber pressure load parameters. Accurately identifying associations and optimizing key design parameters are crucial for constructing automatic weapon fault criteria. Optionally, the FP-Tree algorithm (Frequent Pattern Growing) can be used to discover the correlations and types of associations between design parameters.

[0069] Optionally, in step S2, establishing an association relationship of design parameters specifically includes:

[0070] Step S21, performing dimensionless and normalization processing on the design parameters to obtain standardized design parameters;

[0071] Step S22: By dividing the normalized design parameters by evaluation thresholds, a transaction database is obtained. Then, data combinations are performed on the design parameters in the transaction database, and the support of each data combination is calculated to screen out all data combinations with a support greater than a minimum threshold as frequent itemsets, thereby obtaining a frequent pattern growth tree (FP-Free). Each branch of the FP-Free tree is a frequent itemset.

[0072] Step S23, performing association rule mining on the frequent pattern growth tree in a bottom-up manner to obtain a corresponding design parameter set;

[0073] Expert knowledge is used to screen and determine the association rules to be mined, and finally the association types (complementary and redundant) between the design parameters are determined. Design parameter association rule mining can be divided into two stages. The first stage is to find all frequent item sets that meet the minimum support threshold from the design parameter set. The second stage is to generate association rules that meet the minimum confidence level from the frequent item sets.

[0074] After mining association rules, three types of design parameter sets are obtained, namely, redundant associated design parameter set M with redundant associations between design parameters, complementary associated design parameter set N with complementary associations between design parameters, and independent design parameter set K with independent design parameters.

[0075] Optionally, in step S2, the contribution of each design parameter is calculated based on the association relationship, specifically including:

[0076] Step S24, solving the nominal contribution of the design parameters in the redundant associated design parameter set M and the complementary associated design parameter set N by maximizing the Marichal entropy; wherein,

[0077] The formula for maximizing Marichal entropy is:

[0078]

[0079] Where, The actual contribution is The value of Maricha entropy of redundant associated design parameter set M when Actual contribution Hyperparameters of is the u-th design parameter in the redundant associated design parameter set M, i is numerically equal to |M|, which represents the number of all design parameters in the redundant associated design parameter set M; S is any subset of all subsets in the redundant associated design parameter set M; The weight coefficient of the subset S in the parameter set M is designed for redundant association; Calculate the entropy value; is the nominal contribution; To merge, is a design parameter The Shapley value in the cooperative game of ; T is a specific subset of size t in the redundant association parameter set M;

[0080] In this step, the fuzziness This modeling approach is used to represent the combined importance of multiple design parameters and to express the relationships between them, i.e., their actual contributions. Marichal entropy (based on information entropy) is used to construct the objective function and establish an optimization model. The contribution of design parameters in a set of redundantly associated design parameters, M, is determined by maximizing Marichal entropy.

[0081] In the above formula:

[0082] ;

[0083] ;

[0084] Step S25, based on the pointers of the single design parameter frequent itemsets in all design parameter sets, the nominal contributions of the design parameters in the redundant associated design parameter set M and the complementary associated design parameter set N are corrected to obtain the actual contributions of the design parameters; wherein,

[0085] The u-th design parameter in the redundant associated design parameter set M The actual contribution of for:

[0086]

[0087] Where, is the u-th design parameter in the redundant associated design parameter set M Nominal contribution of P v It is the pointer of the frequent item set of a single design parameter, which is used to normalize the contribution value; m1, m2, ..., mi are the serial numbers of the design parameters in the redundant associated design parameter set M; n1, n2, ..., nj are the serial numbers of the design parameters in the complementary associated design parameter set N; k1, k2, ..., kl are the serial numbers of the design parameters in the independent design parameter set K;

[0088] The u-th design parameter in the complementary associated design parameter set N The actual contribution of for:

[0089]

[0090] Where, is the u-th design parameter in the complementary associated design parameter set N Nominal contribution;

[0091] The solution of the contribution of design parameters in the complementary association design parameter set N is similar to the type of redundant association design parameters. The optimization model is constructed with the maximum Marichal entropy as the objective function to solve the contribution of complementary association design parameters. The nominal contribution is corrected to obtain the u-th design parameter in the complementary association design parameter set N. The actual contribution of .

[0092] Step S26, based on the pointers of the single design parameter frequent item sets in all design parameter sets, calculate the actual contribution of the design parameters in the independent design parameter set K; wherein,

[0093]

[0094] Where, is the u-th design parameter in the independent design parameter set K The actual contribution of P kuis the u-th design parameter in the independent design parameter set K Pointer to the frequent itemsets.

[0095] In step S3, a Bayesian network is constructed based on the key design parameters and the automatic weapon automaton motion parameter response agent model, and then the key design parameters that affect the fault are traced back through forward and reverse reasoning to obtain the constraint range of the key design parameters and the constraint range of the motion parameters, and establish the automaton fault judgment criteria.

[0096] Optionally, in step S3, key design parameters that affect the fault are traced back through forward and reverse reasoning to obtain the constraint range of the key design parameters and the constraint range of the motion parameters, and establish the automaton fault judgment criteria, which specifically includes:

[0097] Step S31, changing the values ​​of key design parameters, and inputting the changed key design parameters into the automatic weapon automaton motion parameter response agent model to obtain the range of motion parameter changes, and determining the automaton fault type based on the forward reasoning capability of the Bayesian network;

[0098] See Figure 3 In this step, we focus on four typical faults: jammed shells, jammed bullets, empty chambers, and incomplete recoil. We make full use of the Bayesian network forward reasoning model to change the values ​​of design parameters such as the extraction timing, recoil spring force, bullet pushing height, and ejection window size of automatic weapons. We analyze the changes in motion parameters such as shell ejection angle, ejection speed, and bullet pushing speed, as well as the occurrence of faults, and obtain the range of motion parameters. We analyze the occurrence of automatic weapon automaton faults, determine the range of motion parameters based on the fault boundary, and obtain the fault judgment criteria.

[0099] Step S32, changing the fault type, using the reverse reasoning capability of the Bayesian network to obtain the variation range of the motion parameters, and inputting the variation range of the motion parameters into the automatic weapon automaton motion parameter response agent model, thereby obtaining the constraint range of the key design parameters;

[0100] See Figure 4 ,In this step, the reverse reasoning mode makes full use of the reverse ,reasoning capability of the Bayesian network. By focusing on ,four types of faults including jammed, jammed, empty chamber, and incomplete recoil, the ,four faults are set separately. ,According to the changes of motion parameters such as shell ejection angle, ,ejection speed, and shell pushing speed, the constraint range of ,design parameters such as shell extraction timing, recoil spring force, shell pushing height, and ,shell ejection window size of the automatic weapon is ,analyzed, and thus guides the design.

[0101] In step S33, based on the reasoning results of step S31 and step S32, a causal traceability analysis is performed to obtain the constraint range of key design parameters and the constraint range of motion parameters, and to establish the automaton fault judgment criteria.

[0102] See Figure 5In this step, the reasoning results of step S31 and step S32 are fully utilized to carry out causal tracing analysis. By constraining the value ranges of design parameters such as the extraction timing, recoil spring force, projectile ejection height, and ejection window size of the automatic weapon, the influence of motion parameters such as the shell ejection angle, shell ejection speed, and projectile ejection speed is analyzed. Combined with fault reproduction, the value ranges of motion parameters such as the shell ejection angle, shell ejection speed, and projectile ejection speed that are closely related to the fault are determined.

[0103] Optionally, the key design parameters include: extraction timing, chamfer of the outer edge of the shell bottom rim, extractor spring force, ejection port size, distance between the extractor and the ejection port, bullet pushing height, bullet holding port size, return spring force, extractor mirror distance, support spring force and feeding resistance.

[0104] A second aspect of the present invention discloses a system for constructing fault criteria for an automatic weapon automaton based on design parameter association, the system comprising:

[0105] The first processing module is configured to construct a sample matrix based on the distribution and statistics of the design parameters of the automatic weapon, and input the sample matrix into the dynamic simulation model of the automatic weapon automaton to extract the motion parameter response, thereby fitting the automatic weapon automaton motion parameter response proxy model;

[0106] The second processing module is configured to establish an association relationship among the design parameters, calculate a contribution of each design parameter based on the association relationship, and determine a design parameter whose contribution is greater than a contribution threshold as a key design parameter;

[0107] The third processing module is configured to construct a Bayesian network based on the key design parameters and the automatic weapon automaton motion parameter response agent model, and then trace the key design parameters that affect the fault through forward and reverse reasoning, obtain the constraint range of the key design parameters and the constraint range of the motion parameters, and establish the automaton fault judgment criteria.

[0108] A third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the steps of the method for constructing a fault criterion for an automatic weapon automaton based on design parameter association described in the first aspect of the present invention.

[0109] Figure 6 FIG. 1 is a structural diagram of an electronic device according to an embodiment of the present invention. Figure 6As shown, the electronic device includes a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal via wired or wireless communication. The wireless communication method can be achieved through Wi-Fi, a carrier network, near-field communication (NFC), or other technologies. The display of the electronic device can be a liquid crystal display or an electronic ink display. The input device of the electronic device can be a touch layer covering the display, or it can be buttons, a trackball, or a touchpad provided on the electronic device housing, or it can be an external keyboard, touchpad, or mouse.

[0110] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a structural diagram of the part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0111] A fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the steps of the method for constructing a fault criterion for an automatic weapon automaton based on design parameter association described in the first aspect of the present invention.

[0112] The above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may be modified or some or all of the technical features thereof may be replaced with equivalents, and such modifications or replacements do not deviate from the essence of the corresponding technical solutions within the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing fault criteria of an automatic weapon automaton based on design parameter association, characterized in that: The method comprises: Step S1, constructing a sample matrix based on the design parameter distribution and statistics of the automatic weapon, and inputting the sample matrix into the automatic weapon automaton dynamics simulation model to extract the motion parameter response, and then fitting the automatic weapon automaton motion parameter response proxy model; Step S2: establishing an association relationship among the design parameters, and calculating the contribution of each design parameter based on the association relationship, and determining the design parameter whose contribution is greater than a contribution threshold as a key design parameter; In step S2, establishing an association relationship of design parameters specifically includes: Step S21, performing dimensionless and normalization processing on the design parameters to obtain standardized design parameters; Step S22: By dividing the normalized design parameters by evaluation thresholds, a transaction database is obtained, and then data combinations are performed on the design parameters in the transaction database, and the support of each data combination is calculated to screen out all data combinations greater than the minimum support threshold as frequent itemsets, thereby obtaining a frequent pattern growth tree; Step S23, performing association rule mining on the frequent pattern growth tree in a bottom-up manner to obtain a corresponding design parameter set; wherein the design parameter set includes a redundant association design parameter set M, a complementary association design parameter set N, and an independent design parameter set K; Step S3: constructing a Bayesian network based on the key design parameters and the automatic weapon automaton motion parameter response agent model, and then tracing the key design parameters that affect the failure through forward and reverse reasoning to obtain the constraint range of the key design parameters and the constraint range of the motion parameters, and establishing the automaton failure judgment criteria; In step S3, the key design parameters that affect the fault are traced back through forward and reverse reasoning to obtain the constraint range of the key design parameters and the constraint range of the motion parameters, and establish the automaton fault judgment criteria, which specifically include: Step S31, changing the values ​​of key design parameters, and inputting the changed key design parameters into the automatic weapon automaton motion parameter response agent model to obtain the range of motion parameter changes, and determining the automaton fault type based on the forward reasoning capability of the Bayesian network; Step S32, changing the fault type, using the reverse reasoning capability of the Bayesian network to obtain the variation range of the motion parameters, and inputting the variation range of the motion parameters into the automatic weapon automaton motion parameter response agent model, thereby obtaining the constraint range of the key design parameters; In step S33, based on the reasoning results of step S31 and step S32, a causal traceability analysis is performed to obtain the constraint range of key design parameters and the constraint range of motion parameters, and to establish the automaton fault judgment criteria.

2. The method according to claim 1, characterized in that The step S1 specifically includes: Step S11, constructing a sample matrix based on the design parameter distribution and statistics of the automatic weapon through Latin hypercube sampling; Step S12, inputting the sample matrix into the automatic weapon automaton dynamics simulation model to calculate and extract motion parameter responses; Step S13, based on the correspondence between each sample in the sample matrix and the extracted motion parameter response, a proxy model is fitted to obtain a motion parameter response proxy model of an automatic weapon automaton; the motion parameter response proxy model of an automatic weapon automaton is used to obtain a motion parameter response corresponding to a fault criterion according to the input design parameters.

3. The method according to claim 2, characterized in that In step S13, the type of the automatic weapon automaton motion parameter response agent model is one or more of a Kriging model, a radial basis neural network, a support vector regression model, an extended adaptive hybrid agent model, an optimization-based two-layer integrated agent model, a weighted average combination agent model, a moving least squares-based variable-fidelity agent model, and a Gaussian process regression model.

4. The method according to claim 3, characterized in that The step S1 further includes: Step S14, calculate and output at least one motion parameter response through the automatic weapon automaton motion parameter response proxy model, and perform a one-to-one comparison analysis with the motion parameter response calculated by the automatic weapon automaton dynamics simulation model; if the errors are all less than the preset values, it is determined that the automatic weapon automaton motion parameter response proxy model meets the requirements; otherwise, repeat steps S11-S13 until the errors are all less than the preset values.

5. The method according to claim 1, wherein In step S2, the contribution of each design parameter is calculated based on the correlation relationship, specifically including: Step S24, solving the nominal contribution of the design parameters in the redundant associated design parameter set M and the complementary associated design parameter set N by maximizing the Marichal entropy; wherein, The formula for maximizing Marichal entropy is: ， Where, The actual contribution is The value of Maricha entropy of redundant associated design parameter set M when Actual contribution Hyperparameters of is the u-th design parameter in the redundant associated design parameter set M, i is numerically equal to |M|, which represents the number of all design parameters in the redundant associated design parameter set M; S is any subset of all subsets in the redundant associated design parameter set M; The weight coefficient of the subset S in the parameter set M is designed for redundant association; Calculate the entropy value; is the nominal contribution; To merge, is a design parameter The Shapley value in the cooperative game of ; T is a specific subset of size t in the redundant association parameter set M; Step S25, based on the pointers of the single design parameter frequent itemsets in all design parameter sets, the nominal contributions of the design parameters in the redundant associated design parameter set M and the complementary associated design parameter set N are corrected to obtain the actual contributions of the design parameters; Among them, the u-th design parameter in the redundant associated design parameter set M is The actual contribution of for: ， Where, is the u-th design parameter in the redundant associated design parameter set M Nominal contribution; P v is the pointer to the frequent item set of a single design parameter, v is the serial number variable; m1, m2, ..., mi are the serial numbers of each design parameter in the redundant associated design parameter set M; n1, n2, ..., nj are the serial numbers of each design parameter in the complementary associated design parameter set N; k1, k2, ..., kl are the serial numbers of each design parameter in the independent design parameter set K; The u-th design parameter in the complementary associated design parameter set N The actual contribution of for: ， Where, is the u-th design parameter in the complementary associated design parameter set N Step S26, based on the pointers of the single design parameter frequent item sets in all design parameter sets, calculate the actual contribution of the design parameters in the independent design parameter set K; wherein, ， Where, is the u-th design parameter in the independent design parameter set K The actual contribution of P ku is the u-th design parameter in the independent design parameter set K Pointer to the frequent itemsets.

6. The method according to any one of claims 1 to 5, characterized in that The key design parameters include: extraction timing, chamfer of the outer edge of the shell base, extractor spring force, ejection port size, distance between the extractor and the ejection port, bullet ejection height, bullet mouth size, return spring force, extractor mirror distance, support spring force and feeding resistance.

7. A system for constructing fault criteria for automatic weapon automata based on design parameter association, characterized in that: The system comprises: The first processing module is configured to construct a sample matrix based on the distribution and statistics of the design parameters of the automatic weapon, and input the sample matrix into the dynamic simulation model of the automatic weapon automaton to extract the motion parameter response, thereby fitting the automatic weapon automaton motion parameter response proxy model; The second processing module is configured to establish an association relationship among the design parameters, calculate the contribution of each design parameter based on the association relationship, and determine the design parameter whose contribution is greater than a contribution threshold as a key design parameter; the second processing module is specifically configured to: Perform dimensionless and normalized processing on the design parameters to obtain standardized design parameters; By dividing the standardized design parameters into evaluation thresholds, a transaction database is obtained. Then, the design parameters in the transaction database are combined and the support of each data combination is calculated to screen out all data combinations with a support greater than the minimum threshold as frequent itemsets to obtain a frequent pattern growth tree. Performing association rule mining on the frequent pattern growth tree in a bottom-up manner to obtain the corresponding design parameter set; wherein the design parameter set includes a redundant association design parameter set M, a complementary association design parameter set N and an independent design parameter set K; The third processing module is configured to construct a Bayesian network based on the key design parameters and the automatic weapon automaton motion parameter response agent model, and then trace the key design parameters that affect the failure through forward and reverse reasoning to obtain the constraint range of the key design parameters and the constraint range of the motion parameters, and establish the automaton failure judgment criteria. The third processing module is specifically configured to: Changing the values ​​of key design parameters and inputting the changed key design parameters into the automatic weapon automaton motion parameter response agent model to obtain the range of motion parameter changes and determine the automaton fault type based on the forward reasoning capability of the Bayesian network; By changing the fault type and using the reverse reasoning capability of the Bayesian network, the variation range of the motion parameters is obtained, and the variation range of the motion parameters is input into the motion parameter response agent model of the automatic weapon automaton to obtain the constraint range of the key design parameters; Based on the above reasoning results, causal tracing analysis is carried out to obtain the constraint range of key design parameters and the constraint range of motion parameters, and the automaton fault judgment criteria are established.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the steps in the method for constructing an automatic weapon automatic machine fault judgment criterion based on design parameter association as described in claim 6.

Images