Kriging model-based artillery coordinator electro-hydraulic system motion reliability evaluation method

Abstract

The invention belongs to the technical field of weapons, and relates to a Krigi ng model-based artillery coordination electro-hydraulic system motion reliability evaluation method, which comprises the following steps of: selecting points through a Krigi ng method, an MCMC method, a K-means method and a sampling distance limiting method, designing a motion displacement precision evaluation index and a projectile line conveying angle precision evaluation index, and obtaining real response values of the two evaluation indexes; according to the method, coordinator motion precision evaluation indexes are designed, the importance degree of pose deviations in different directions on the reliability of the electromechanical hydraulic system is considered, information redundancy caused by training sample concentration is avoided in a point selection rule, parallel computing of multiple computers can be achieved through the K-means method, and the reliability of the electromechanical hydraulic system is improved. The artillery coordinator electromechanical hydraulic system motion reliability precision evaluation method is established, so that high calculation efficiency is achieved on the premise that reliability calculation precision is guaranteed.

Description

Technical Field

[0001] The invention belongs to the field of weapon technology, and in particular relates to a motion reliability assessment technology for an electromechanical-hydraulic system of a gun coordinator. Background Art

[0002] As a conventional weapon, artillery still plays an important role in modern wars. As an important mechanical substructure of self-propelled artillery, the coordinator electromechanical hydraulic system plays a very critical role. For the coordinated action of the coordinator, if the coordinated positioning error exceeds the specified coordinated positioning accuracy index, it will affect the firing mission of the artillery. The research on the motion reliability of the artillery coordinator is also of great significance to improving the survivability of self-propelled artillery on the battlefield.

[0003] The motion reliability calculation of the coordinator electromechanical hydraulic system is mainly through constructing a function, and then solving the structural reliability according to the primary reliability, secondary reliability or Monte Carlo method. In order to obtain high-precision reliability, the Monte Carlo method is generally used for reliability calculation. However, there are many parameters that affect the motion accuracy of the coordinator electromechanical hydraulic system, and the influencing relationship is complex. This leads to a large amount of calculation when using the Monte Carlo method to calculate the reliability, which consumes a lot of manpower and material resources.

[0004] At present, in order to solve the above problems, a proxy model is usually used to approximate the performance function, and then the reliability is solved by the Monte Carlo method. However, it is difficult to obtain good sample points with the traditional sampling method, which makes the accuracy of the constructed model poor. The accuracy of the proxy model can be improved by increasing the sample points, but this will increase the test cost. The calculation accuracy of the reliability depends to a large extent on the accuracy of the proxy model. Therefore, it is very important to obtain a high-precision proxy model while ensuring a low test cost for the calculation of the motion reliability of the coordinator electromechanical hydraulic system. At present, there is little research on the evaluation of the motion reliability of the coordinator electromechanical hydraulic system based on the Kriging model in China. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for evaluating the kinematic reliability of a gun coordination electromechanical and hydraulic system based on a Kriging model.

[0006] In order to achieve the purpose of the present invention, the present invention is implemented by adopting the following technical solution.

[0007] A method for evaluating the kinematic reliability of a gun coordination electromechanical and hydraulic system based on a Kriging model, characterized in that it comprises the following steps:

[0008] S1. Determine N random variables and distribution types according to relevant parameters affecting the coordinator motion;

[0009] S2. Use Latin hypercube sampling to extract N0 training samples that meet the distribution type for each random variable, and form the initial training sample set in the standard normal space. And get the corresponding true response value Get the initial true response value matrix;

[0010] S3, use Monte Carlo random sampling method to extract N random variables MC A random sample that meets the respective distribution type is used as the corresponding candidate sample point;

[0011] S4, based on the initial training sample set S DoE The Kriging model is established by using the sample data and true response values ​​in;

[0012] S5, using the Markov Chain Monte Carlo method to extract candidate sample points with U(x)<2 from the candidate sample points generated in step S3, and using the k-means clustering algorithm to divide the extracted candidate sample points into K groups;

[0013] S6. According to the method for selecting sample points, select K best training sample points from the K groups of candidate sample points formed in step S5. And obtain the true response value of each optimal training sample point

[0014] S7, judging whether the K best training sample points meet the requirements of the current S DoE The distance criterion for the training sample points in is:

[0015] If not, remove the non-conforming points from the corresponding K-means group. After removing the non-conforming points, go to step S6 and reselect according to the distance criterion.

[0016] If satisfied, execute step S8;

[0017] The distance criterion is expressed by a point selection formula, which is:

[0018] max{R(x new ,x DoE ;θ)}≥[r],

[0019] Where x DoE Indicates the training points that constitute the current Kriging model, [r] is usually between 0.95 and 1;

[0020] S8, K best training sample points and the true response value of each optimal training sample point Add them to the initial training sample set and the initial true response value matrix respectively, obtain the updated training sample set and the true response value matrix, and update the Kriging model;

[0021] S9. Calculate the estimated failure probability P of the coordinator motion reliability based on the current Kriging model and the training sample set. f , error coefficient e ε and coefficient of variation δP f ;

[0022] S10, judgment error coefficient e ε Is it satisfied? Requirements:

[0023] If not satisfied, return to step S5;

[0024] If satisfied, execute step S11;

[0025] In the formula, e ε is the error coefficient, [e ε ] is the threshold value, and the threshold value is determined according to the actual accuracy requirement;

[0026] S11. Determine the coefficient of variation δP f Whether it satisfies δP f Requirements for ≤[δ]:

[0027] If it is not satisfied, the number of random sample points will be increased through the Monte Carlo method;

[0028] If satisfied, execute step S12;

[0029] Where [δ] is the threshold;

[0030] S12. Calculate and output the coordinator motion reliability results: failure probability Coefficient of variation and error coefficient e ε .

[0031] As a preferred embodiment of the present invention, the related parameters include manufacturing errors of key dimensions of the coordinator, clearance errors of key hole-shaft matching, projectile mass errors, oil pump speed, leakage rate of the coordinating cylinder and leakage rate of the flipping cylinder.

[0032] As a further solution of the present invention, the key dimensions of the coordinator include the dimensions of the coordinating arm and the dimensions of the flipping mechanism.

[0033] As a preferred embodiment of the present invention, the true response value The method for obtaining comprises the following steps:

[0034] S41. Use Latin hypercube sampling to extract N0 training samples that meet the respective distribution types for each random variable, and form an initial training sample set in the standard normal space.

[0035] S42, the coordinator motion displacement accuracy γ D It is expressed as:

[0036]

[0037] Among them, η X , η Y , η Z Respectively represent the reliability error sensitivity coefficients corresponding to the three axes X, Y and Z;

[0038] S43, the coordinator motion feed line angle accuracy γ ε It is expressed as:

[0039]

[0040] Among them, α, β, are the angle deviations of the three directions of the bullet feeding line, X, Y, and Z, respectively, α , β , are the three angle deviations α, β, The corresponding sensitivity coefficient;

[0041] S44, the coordinator motion displacement accuracy γ D The angle accuracy of the coordinator motion feed line γ ε As a standard value, a function function of the coordinator motion accuracy is established, and the expression of the function function is:

[0042] y(x)=ξ-|γ-γ′|,

[0043] In the formula, ξ is the allowable error, which can be determined according to the accuracy requirements, and γ′ is the motion displacement γ D Or the angle of the moving bullet line γ ε The ideal value of γ is the displacement accuracy of the projectile. D Or the angle accuracy of the moving bullet feeding line γ ε .

[0044] As a preferred embodiment of the present invention, the method for establishing the Kriging model comprises the following steps:

[0045] S51. In the Kriging model, the performance function G(x) consists of two parts: a deterministic part and a random part, namely:

[0046] G(x)=Γ(β,x)+z(x)=gT (x)β+z(x),

[0047] S52, given an initial training sample set S DoE , basis function g(x) and correlation coefficient function R(x i ,x j ; θ), Kriging prediction value and Kriging predicted values and Kriging equation The expression of is completely dependent on the parameter θ. The widely accepted maximum likelihood method is used to determine the optimal θ value.

[0048] As a preferred solution of the present invention, the specific operation steps of step S5 are:

[0049] In the MCMC method (Markov chain), if the sequence x0, x1, x2, ..., x K+1 Produced in {x k+1 ∣x k}, and the conditional probability satisfies: P(x k+1 =x|x k ,x k-1 ,…)=P(x k+1 =x|x k ), x k+1 The distribution of depends only on the current state x k , does not depend on the historical state, then x0, x1, x2, …, x K+1 is a Markov chain. Where x0 is the given initial condition, according to the conditional probability distribution, the state x k+1 The desired probabilistic properties are conditioned by the previous state x k Given;

[0050] When k approaches infinity, x k It has nothing to do with the initial value, that is, when k increases, the random vector in the Markov chain will converge to the steady-state distribution π(x). At this time, the Markov chain is considered to be in a convergent state. When the Markov chain does not reach the convergent state, it is considered that the probability density distribution of each state cannot reach a stable distribution. When evaluating, the first M sampling values ​​that have not reached the steady-state distribution state should be removed, and the last nM sampling values ​​should be used for evaluation: The estimated value of E[F] The variance is: The estimated value of V(F) is From this, a Markov chain can be constructed;

[0051] Using the K-means mean clustering algorithm, according to the principle of minimum Euclidean distance from the data sample in each group of samples to the cluster center, under the condition of a given cluster group number K value, n sample data are divided into K groups through multiple iterations. The calculation formula of the Euclidean distance can be expressed as:

[0052]

[0053] Where: E is the sum of the mean square errors of all objects in the cluster sample; K is the number of clusters; xj is a point in the cluster; mi is the mean of the cluster Ci;

[0054] According to the calculated Euclidean distance from the data sample to the cluster center, the samples are classified according to the minimum distance principle, and the n sample data are divided into K groups through multiple iterations.

[0055] As a preferred solution of the present invention, the k-means clustering algorithm comprises the following steps:

[0056] S71, determine the number of clusters K, input a data vector group X, the data vectors included are X1, X2, X3, ..., X n ;

[0057] S72, randomly selecting K vectors from the n data vectors as initial clustering centers;

[0058] S73, calculating the Euclidean distance of each vector to the K initial cluster centers, and dividing the vector into the cluster where the initial cluster center with the smallest distance value is located;

[0059] S74. If the data vector in the cluster changes, recalculate the centers of the K clusters;

[0060] S75. Repeat steps S73 to S74 until the centers of the clusters no longer change or the center distance is less than a specified threshold.

[0061] As a preferred embodiment of the present invention, the method for selecting sample points comprises the following steps:

[0062] S81, calculating the learning function value of each candidate sample point corresponding to each random variable according to the initial Kriging model;

[0063] S82, finding the minimum learning function value corresponding to each group of random variables in the K groups;

[0064] S83. Determine whether the minimum learning function value corresponding to each group of random variables is less than 2:

[0065] If the minimum learning function value is less than 2, the K candidate sample points corresponding to the minimum learning function value in each group of candidate sample points corresponding to each random variable are used as the K best training sample points of the random variable;

[0066] If the minimum learning function value is ≥ 2, continue to judge.

[0067] As a preferred embodiment of the present invention, the specific process of establishing the point selection formula includes the following steps:

[0068] S91, use the threshold [L] (the threshold [L] is determined empirically according to different parameters and dimensions) to directly new And the training sample set x that constitutes the Kriging model j (j=1,…,N) is limited, that is:

[0069]

[0070] Based on the above formula, we get a new A hypersphere with centered at [L] and radius [L];

[0071] S92, convert the constraint of the distance between two points into a correlation function problem, and restrict the correlation function between training samples, that is:

[0072] max{R(x new ,x DoE ;θ)}≥[r],

[0073] In the formula, x DoE Represents the training points that constitute the current Kriging model, and [r] is usually between 0.95 and 1.

[0074] As a preferred solution of the present invention, the failure probability P f :

[0075] The coefficient of variation

[0076] The error coefficient e ε :

[0077] In the formula, x MC,n (n=1,…,N MC ) is the probability density function f X (x) is an independent and identically distributed random sample point, N MC is the number of sample points.

[0078] Beneficial Effects

[0079] Compared with the prior art, the present invention has the following advantages:

[0080] The present invention designs a coordinator motion accuracy evaluation index, takes into account the importance of posture deviations in different directions to the reliability of the electromechanical and hydraulic system, and uses point selection rules to avoid information redundancy caused by concentrated training samples. The K-means method can realize parallel calculation of multiple computers, and establishes a method for evaluating the motion reliability accuracy of the artillery coordinator electromechanical and hydraulic system, so that it has higher computing efficiency while ensuring the accuracy of reliability calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 is a basic flow chart of the method of the present invention;

[0082] Figure 2 is a structural diagram of the coordinator of the present invention;

[0083] Figure 3 This is a posture comparison diagram of the coordinator of the present invention;

[0084] Figure 4 This is a schematic diagram of the projectile mass center displacement error of the present invention;

[0085] Figure 5 This is a schematic diagram of the angle error of the bullet feeding line described in the present invention. DETAILED DESCRIPTION

[0086] The present invention will be further described with reference to the accompanying drawings and embodiments.

[0087] As an embodiment of the present invention, Figures 1 to 5 As shown, a method for evaluating the motion reliability of a coordinated electromechanical and hydraulic system of a gun based on a Kriging model includes the following steps:

[0088] S1. According to the relevant parameters affecting the movement of the coordinator: the manufacturing error of the key size of the coordinator, the clearance error of the key hole shaft matching, the mass error of the projectile, the oil pump speed, the leakage rate of the coordination cylinder, and the leakage rate of the flip cylinder, N random variables and distribution types are determined according to these parameters; wherein the manufacturing error of the key size of the coordinator includes the size of the coordination arm and the size of the flip mechanism, such as Figure 2 As shown;

[0089] S2. Use the LHS method to construct the initial training sample set in the standard normal space Set the coordinator motion displacement accuracy γ D It is expressed as:

[0090]

[0091] Where η X , η Y , η ZThey represent the reliability error sensitivity coefficients corresponding to the three axes X, Y, and Z, respectively. The angle deviations of the three directions of the bullet feeding line X, Y, and Z are α, β,

[0092] The coordinator motion feed line angle accuracy γ ε Expressed as

[0093]

[0094] In the formula, λ α , β , are the three angle deviations α, β, The corresponding sensitivity coefficient;

[0095] When obtaining the corresponding true function value, the motion displacement accuracy γ of the projectile at the end of the motion D And the angle accuracy of the moving bullet feeding line γ ε As a standard value, establish the function of the coordinator motion accuracy:

[0096] y(x)=ξ-|γ-γ′|,

[0097] In the formula, ξ is the allowable error, which can be determined according to the accuracy requirements, and γ′ is the motion displacement γ D Or the angle of the moving bullet line γ ε The ideal value of γ is the displacement accuracy of the projectile. D Or the angle accuracy of the moving bullet feeding line γ ε . And get the real function value corresponding to SDoE like Figures 3 to 5 shown.

[0098] S3, use Monte Carlo random sampling method to extract N random variables MC A random sample that meets the respective distribution type is used as the corresponding candidate sample point;

[0099] S4, based on the initial training sample set S DoE The Kriging model is established by using the sample data and true response values ​​in;

[0100] In the process of constructing the approximate expression of the state function using the Kriging method, the performance function G(x) consists of two parts: a deterministic part and a random part, namely: G(x) = Γ(β, x) + z(x) = g T (x)β+z(x).

[0101] In a given sample set S DoE , basis function g(x) and correlation coefficient function R(x i ,xj ; θ), Kriging predicted value and Kriging predicted values and Kriging equation The expression of is completely dependent on the parameter θ; this method uses the widely recognized maximum likelihood method to determine the optimal θ value

[0102] S5. Use MCMC method to generate sample points with U(x)<2. If the sequence x0, x1, ..., x K+1 Produced in {x k+1 ∣x k}, and the conditional probability satisfies: P(x k+1 =x|x k ,x k-1 ,…)=P(x k+1 =x|x k ), x k+1 The distribution of depends only on the current state x k , does not depend on the historical state, then x0, x1, x2, …, x K+1 is a Markov chain. Where x0 is the given initial condition, according to the conditional probability distribution, the state x k+1 The desired probabilistic properties are conditioned by the previous state x k Given;

[0103] When k approaches infinity, x k It has nothing to do with the initial value, that is, when k increases, the random vector in the Markov chain will converge to the steady-state distribution π(x). At this time, the Markov chain is considered to be in a convergent state. When the Markov chain does not reach the convergent state, it is considered that the probability density distribution of each state cannot reach a stable distribution. When evaluating, the first M sampling values ​​that have not reached the steady-state distribution state should be removed, and the last nM sampling values ​​should be used for evaluation: The estimated value of E[F] The variance is: The estimated value of V(F) is From this, a Markov chain can be constructed;

[0104] Using the K-Means clustering algorithm, under the condition of a given cluster group number K value, n sample data are divided into K groups through multiple iterations. The algorithm uses Euclidean distance as the similarity measurement method, and the objective function can be expressed as:

[0105] Where: E is the sum of the mean square errors of all objects in the cluster sample; K is the number of clusters; xj is a point in the cluster; mi is the mean of the cluster Ci;

[0106] The basic steps of the K-Means clustering algorithm are:

[0107] 1) Determine the number of clusters K, input the data vector group X, which contains data vectors X1, X2, X3, ..., X n ;

[0108] 2) Randomly select K vectors from n data vectors as initial cluster centers;

[0109] 3) Calculate the Euclidean distance of each vector to the K initial cluster centers, and divide it into the cluster where the initial cluster center with the smallest distance value is located;

[0110] 4) If the data vector in the cluster changes, recalculate the centers of the K clusters;

[0111] 5) Repeat steps 3) to 4) until the centers of each cluster no longer change or the center distance is less than the specified threshold.

[0112] S6. Calculate the learning function value of each candidate sample point corresponding to each random variable according to the initial Kriging model. After the candidate samples are divided into K groups, find the minimum learning function value corresponding to each group of random variables in the K groups. If there is a minimum learning function value less than 2, then find the K candidate sample points corresponding to the minimum learning function value among the candidate sample points corresponding to each group of random variables. As the best sample point of the random variable; and obtain the true response value of each best sample point

[0113] Point selection principle: interval P i Take Δ as an example. At this time, the accuracy of the model in the interval is Δ i N , if in the interval P i Select a new sample point within Updating the Kriging model will improve the model accuracy within this range by Δ i N+1 Indicates that the model accuracy is improved It is expressed as:

[0114]

[0115] In the formula, Δ i N can be regarded as a constant, is with Related functions;

[0116] The expression (point selection formula) is:

[0117]

[0118] Where NG Indicates the number of integral points, v j and w j are the integral points and their corresponding weights respectively;

[0119] S7. Use the point selection formula to select K candidate sample points With the current S DoE The points in the formula are judged: if not satisfied, go to step S6, remove the points that do not meet the requirements from the corresponding K-means group, and then reselect them according to the point selection formula; if satisfied, go to step S8;

[0120] Among them, the construction process of the point selection formula is:

[0121] Use the threshold [L] to directly adjust the training sample x new and the training samples x that constitute the Kriging model j (j=1,…,N) is limited in spacing, that is,

[0122]

[0123] Based on the above formula, we can get a new A hypersphere with centered at [L] and radius [L];

[0124] The constraint on the distance between two points is converted into a correlation function problem, and the correlation function between training samples is restricted, that is:

[0125]

[0126] Where x DoE Indicates the training points that constitute the current Kriging model, [r] is usually between 0.95 and 1;

[0127] S8, K best training sample points and the true response value of each optimal training sample point Add them to the initial training sample set and the initial true response value matrix respectively, obtain the updated training sample set and the true response value matrix, and update the Kriging model;

[0128] S9. Calculate the estimated failure probability P of the coordinator motion reliability based on the current Kriging model and the training sample set. f , error coefficient e ε and coefficient of variation δP f , the specific calculation process is:

[0129] The absolute error between the true value P of the failure probability and the estimated value P is:

[0130]

[0131] The I(x) value in the above formula cannot be obtained based on the Kriging model, but Satisfying the 0-1 distribution, it can be deduced according to the above formula expectations, that is

[0132]

[0133] Thus, we can get an upper limit value A of the expected absolute error of failure probability:

[0134]

[0135] Error coefficient e ε for:

[0136]

[0137] Multiple I G≤0 (X) The mean of the random sample values ​​is taken as P f The valuation of

[0138]

[0139] In the formula, x MC,n (n=1,…,N MC ) is the probability density function f X (x) is an independent and identically distributed random sample point, N MC is the number of sample points;

[0140] According to the central limit theorem, when N MC →∞, The distribution converges to the normal distribution.

[0141]

[0142] so,

[0143]

[0144] The coefficient of variation is,

[0145]

[0146] δP f ≤[δ];

[0147] S10, judgment error coefficient e ε Is it satisfied? Requirements:

[0148] If not satisfied, return to step S5;

[0149] If satisfied, execute step S11;

[0150] In the formula, e ε is the error coefficient, [e ε ] is the threshold value, and the threshold value is determined according to the actual accuracy requirement;

[0151] S11. Determine the coefficient of variation δP f Whether it satisfies δP f Requirements for ≤[δ]:

[0152] If it is not satisfied, the number of random sample points will be increased through the Monte Carlo method;

[0153] If satisfied, execute step S12;

[0154] Where [δ] is the threshold;

[0155] S12. Calculate and output the coordinator motion reliability results: failure probability Coefficient of variation and error coefficient e ε .

[0156] As application examples of the present invention:

[0157] Specify the mechanism to be analyzed as the artillery coordinator electromechanical hydraulic system, determine the variables of the functional function and its probability distribution information, such as Figure 2 The structure diagram of the gun coordinator is shown. The example takes into account the clearance between the two shaft holes of the coordination mechanism, the hydraulic oil temperature of the hydraulic system, the rotation speed of the hydraulic pump, the leakage rate of the coordination hydraulic cylinder and the leakage rate of the flip hydraulic cylinder.

[0158] Table 1 Random variable distribution table

[0159]

[0160]

[0161] The comparison of the calculation results of the Monte Carlo method (MCS), AK-MCS-U method, AK-MCS-EFF method and the calculation method of the present invention is shown in Table 2. The present invention takes three cases of k=2, k=3, and k=4 for calculation, and takes the calculation results obtained by MCS as a reference. The estimated value of the failure probability obtained by the present invention is closest to the failure probability calculation result of the reference method, which is less than the other two methods; at the same time, the number of calls of the function function is 336, 340, and 338 times, respectively, which is also the least among all methods. The results show that the accuracy and efficiency of the method proposed by the present invention are higher than those of the AK-MCS-U method and the AK-MCS-EFF method.

[0162] Table 2 Comparison of calculation results

[0163]

[0164] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

Claims

1. A method for evaluating the kinematic reliability of a gun coordination electromechanical and hydraulic system based on a Kriging model, characterized in that: The steps include: S1. Determine N random variables and distribution types according to relevant parameters affecting the coordinator motion; S2. Use Latin hypercube sampling to extract N0 training samples that meet the distribution type for each random variable, and form the initial training sample set in the standard normal space. And get the corresponding true response value Get the initial true response value matrix; S3, use Monte Carlo random sampling method to extract N random variables MC A random sample that meets the respective distribution type is used as the corresponding candidate sample point; S4, based on the initial training sample set S DoE The Kriging model is established by using the sample data and true response values ​​in; S5, using the Markov Chain Monte Carlo method to extract candidate sample points with U(x)<2 from the candidate sample points generated in step S3, and using the k-means clustering algorithm to divide the extracted candidate sample points into K groups; S6. According to the method for selecting sample points, select K best training sample points from the K groups of candidate sample points formed in step S5. And obtain the true response value of each optimal training sample point S7, judging whether the K best training sample points meet the requirements of the current S DoE The distance criterion for the training sample points in is: If not, remove the non-conforming points from the corresponding K-means group. After removing the non-conforming points, go to step S6 and reselect according to the distance criterion. If satisfied, execute step S8; The distance criterion is expressed by a point selection formula, which is: max{R(x new ,x DoE ;θ)}≥[r], Where x DoE Indicates the training points that constitute the current Kriging model, [r] is usually between 0.95 and 1; S8, K best training sample points and the true response value of each optimal training sample point Add them to the initial training sample set and the initial true response value matrix respectively, obtain the updated training sample set and the true response value matrix, and update the Kriging model; S9. Calculate the estimated failure probability P of the coordinator motion reliability based on the current Kriging model and the training sample set. f , error coefficient e ε and coefficient of variation δP f ; S10, judgment error coefficient e ε Is it satisfied? Requirements: If not satisfied, return to step S5; If satisfied, execute step S11; In the formula, e ε is the error coefficient, [e ε ] is the threshold value, and the threshold value is determined according to the actual accuracy requirement; S11. Determine the coefficient of variation δP f Whether it satisfies δP f Requirements for ≤[δ]: If it is not satisfied, the number of random sample points will be increased through the Monte Carlo method; If satisfied, execute step S12; Where [δ] is the threshold; S12. Calculate and output the coordinator motion reliability results: failure probability Coefficient of variation and error coefficient e ε .

2. According to claim 1, a method for evaluating the kinematic reliability of a gun coordination electro-hydraulic system based on a Kriging model is characterized in that: The related parameters include the manufacturing error of the key size of the coordinator, the clearance error of the key hole-shaft matching, the mass error of the projectile, the oil pump speed, the leakage rate of the coordinating cylinder and the leakage rate of the flipping cylinder.

3. The method for evaluating the kinematic reliability of the artillery coordination electro-hydraulic system based on the Kriging model according to claim 2 is characterized in that: The key dimensions of the coordinator include the dimensions of the coordinating arm and the dimensions of the flipping mechanism.

4. The method for evaluating the kinematic reliability of the artillery coordination electro-hydraulic system based on the Kriging model according to claim 1 is characterized in that: The true response value The method for obtaining comprises the following steps: S41. Use Latin hypercube sampling to extract N0 training samples that meet the distribution type for each random variable, and form an initial training sample set in the standard normal space. S42, the coordinator motion displacement accuracy γ D It is expressed as: Among them, η X , η Y , η Z Respectively represent the reliability error sensitivity coefficients corresponding to the three axes X, Y and Z; S43, the coordinator motion feed line angle accuracy γ ε It is expressed as: Among them, α, β, are the angle deviations of the three directions of the bullet feeding line, X, Y, and Z, respectively, α , β , are the three angle deviations α, β, The corresponding sensitivity coefficient; S44, the coordinator motion displacement accuracy γ D The angle accuracy of the coordinator motion feed line γ ε As a standard value, a function function of the coordinator motion accuracy is established, and the expression of the function function is: In the formula, ξ is the allowable error, which can be determined according to the accuracy requirements, and γ′ is the motion displacement γ D Or the angle of the moving bullet line γ ε The ideal value of γ is the displacement accuracy of the projectile. D Or the angle accuracy of the moving bullet feeding line γ ε .

5. The method for evaluating the kinematic reliability of the artillery coordination electro-hydraulic system based on the Kriging model according to claim 1 is characterized in that: The method for establishing the Kriging model comprises the following steps: S51. In the Kriging model, the performance function G(x) consists of two parts: a deterministic part and a random part, namely: G(x)=Γ(β,x)+z(x)=g T (x)β+z(x), S52, given an initial training sample set S DoE , basis function g(x) and correlation coefficient function R(x i ,x j ; θ), Kriging prediction value and Kriging predicted values and Kriging equation The expression of is completely dependent on the parameter θ; the optimal value of θ is determined by the widely accepted maximum likelihood method.

6. The method for evaluating the kinematic reliability of the artillery coordination electro-hydraulic system based on the Kriging model according to claim 1 is characterized in that: The specific operation steps of step S5 are as follows: In the MCMC method (Markov chain), if the sequence x0, x1, x2, ..., x K+1 Produced in {x k+1 ∣x k }, and the conditional probability satisfies: P(x k+1 =x|x k ,x k-1 ,…)=P(x k+1 =x|x k ), x k+1 The distribution of depends only on the current state x k , does not depend on the historical state, then x0, x1, x2, …, x K+1 is a Markov chain. Where x0 is the given initial condition, according to the conditional probability distribution, the state x k+1 The desired probabilistic properties are conditioned by the previous state x k Given; When k approaches infinity, x k It has nothing to do with the initial value, that is, when k increases, the random vector in the Markov chain will converge to the steady-state distribution π(x). At this time, the Markov chain is considered to be in a convergent state. When the Markov chain does not reach the convergent state, it is considered that the probability density distribution of each state cannot reach a stable distribution. When evaluating, the first M sampling values ​​that have not reached the steady-state distribution state should be removed, and the last nM sampling values ​​should be used for evaluation: The estimated value of E[F] The variance is: The estimated value of V(F) is From this, a Markov chain can be constructed; Using the K-means mean clustering algorithm, according to the principle of minimum Euclidean distance from the data sample in each group of samples to the cluster center, under the condition of a given cluster group number K value, n sample data are divided into K groups through multiple iterations. The calculation formula of the Euclidean distance can be expressed as: Where: E is the sum of the mean square errors of all objects in the cluster sample; K is the number of clusters; x j is a point in the cluster; m i is the mean of cluster Ci; According to the calculated Euclidean distance from the data sample to the cluster center, the samples are classified according to the minimum distance principle, and the n sample data are divided into K groups through multiple iterations.

7. The method for evaluating the kinematic reliability of the artillery coordination electro-hydraulic system based on the Kriging model according to claim 6 is characterized in that: The k-means clustering algorithm includes the following steps: S71, determine the number of clusters K, input a data vector group X, the data vectors included are X1, X2, X3, ..., X n ; S72, randomly selecting K vectors from the n data vectors as initial clustering centers; S73, calculating the Euclidean distance from each vector to the K initial cluster centers, and dividing the vector into the cluster where the initial cluster center with the smallest distance value is located; S74. If the data vector in the cluster changes, recalculate the centers of the K clusters; S75. Repeat steps S73 to S74 until the centers of the clusters no longer change or the center distance is less than a specified threshold.

8. The method for evaluating the kinematic reliability of the artillery coordination electro-hydraulic system based on the Kriging model according to claim 1 is characterized in that: The method for selecting sample points comprises the following steps: S81, calculating the learning function value of each candidate sample point corresponding to each random variable according to the initial Kriging model; S82, finding the minimum learning function value corresponding to each group of random variables in the K groups; S83. Determine whether the minimum learning function value corresponding to each group of random variables is less than 2: If the minimum learning function value is less than 2, the K candidate sample points corresponding to the minimum learning function value in each group of candidate sample points corresponding to each random variable are used as the K best training sample points of the random variable; If the minimum learning function value is ≥ 2, continue to judge.

9. The method for evaluating the kinematic reliability of the artillery coordination electro-hydraulic system based on the Kriging model according to claim 1 is characterized in that: The specific establishment process of the point selection formula includes the following steps: S91, use the threshold [L] to train the sample set x new And the training sample set x that constitutes the Kriging model j (j=1,…,N) is limited, that is: Based on the above formula, we get a new A hypersphere with centered at [L] and radius [L]; S92, convert the constraint of the distance between two points into a correlation function problem, and restrict the correlation function between training samples, that is: max{R(x new ,x DoE ;θ)}≥[r], In the formula, x Do E represents the training points that constitute the current Kriging model, and [r] is usually between 0.95 and 1.

10. The method for evaluating the kinematic reliability of the artillery coordination electro-hydraulic system based on the Kriging model according to claim 1 is characterized in that: The failure probability P f : The coefficient of variation The error coefficient e ε : In the formula, x MC,n (n=1,…,N MC ) is the probability density function f X (x) is an independent and identically distributed random sample point, N MC is the number of sample points.

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