Confidence evaluation method and device based on strong robust grey correlation analysis

Abstract

The application provides a confidence evaluation method and device based on strong robust grey correlation analysis, and relates to the technical field of data processing. The original test data corresponding to a target research object and the original simulation data corresponding to a simulation model to be evaluated can be obtained. The expected valve penetration rate corresponding to the simulation model is calculated according to the original test data and the original simulation data. The original test data and the original simulation data are updated by interpolation according to the expected valve penetration rate corresponding to the simulation model, so as to obtain target test data and target simulation data. The target confidence of the simulation model is determined by grey correlation analysis according to the target test data, the target simulation data and the preset deviation acceptability. In this way, the expected valve penetration rate is introduced to assist interpolation calculation, and the deviation acceptability is introduced as a distance threshold index, so that the anti-noise performance and the anti-disturbance performance of the evaluation result are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a confidence evaluation method and device based on strong robust grey correlation analysis. BACKGROUND

[0002] The development process of equipment (such as weapon equipment, etc.) usually has characteristics such as high cost, small sample, short development cycle, etc. Simulation test, as one of the indispensable test methods, runs through the whole development process from scheme design to system finalization. The simulation model is an approximate representation of the physical model, and has certain similarity with the physical model. In order to effectively verify and use the simulation test results, it is necessary to carry out confidence quantitative evaluation on the simulation model.

[0003] Confidence evaluation is an important part of simulation model and simulation system construction. Different simulation model indexes correspond to different confidence evaluation methods. For dynamic data, if the simulation model confidence is analyzed by time sequence characteristics, the grey correlation analysis method can be used. This method quantifies the consistency of the geometric shape of the data sequence in space through the grey correlation degree to analyze the correlation of the data sequence, and is a powerful tool for simulation confidence verification.

[0004] In the research of grey correlation analysis method, it is found that the grey correlation degree has the limitation of not being able to guarantee its order preserving, which leads to poor robustness of the grey correlation analysis method in terms of noise resistance and disturbance resistance, and further leads to the simulation model confidence obtained not being consistent with the actual situation; only by adjusting parameters such as correlation coefficient and resolution coefficient in order to achieve the approximation of simulation results and actual test results, which may have adverse effects on the reliability evaluation and applicability of the model, leading to prediction deviation and application limitations in subsequent model evaluation and actual application. SUMMARY

[0005] The purpose of the present application is to provide a confidence evaluation method and device based on strong robust grey correlation analysis, in order to improve the noise resistance and disturbance resistance.

[0006] In a first aspect, the present application provides a confidence evaluation method based on strong robust grey correlation analysis, comprising:

[0007] Obtaining the original test data corresponding to the target research object and the original simulation data corresponding to the simulation model to be evaluated; wherein the original test data is a set of time series data obtained by testing the physical model of the target research object, and the original simulation data is a set of time series data obtained by simulating the corresponding simulation model of the target research object;

[0008] According to the original test data and the original simulation data, the expected penetration rate corresponding to the simulation model is calculated;

[0009] According to the expected valve penetration rate corresponding to the simulation model, the original test data and the original simulation data are interpolated and updated to obtain target test data and target simulation data;

[0010] According to the target test data, the target simulation data and the preset acceptable deviation, the target confidence of the simulation model is determined through grey correlation analysis.

[0011] Further, the expected valve penetration rate corresponding to the simulation model is calculated according to the original test data and the original simulation data, comprising:

[0012] The original test data is curve-fitted to obtain a threshold curve;

[0013] The original simulation data is curve-fitted to obtain a disturbance sequence curve;

[0014] The expected valve penetration rate corresponding to the simulation model is calculated according to the threshold curve and the disturbance sequence curve.

[0015] Further, the expected valve penetration rate corresponding to the simulation model is calculated according to the threshold curve and the disturbance sequence curve, comprising:

[0016] The expected valve penetration rate is calculated by the following formula v a :

[0017] ;

[0018] Wherein, y represents the disturbance sequence curve, represents the derivative of y , represents the threshold curve, a represents time, t represents the probability density of three variables , a , , t .

[0019] Further, the original test data and the original simulation data are interpolated and updated to obtain target test data and target simulation data according to the expected valve penetration rate corresponding to the simulation model, comprising:

[0020] When the target is to evaluate the absolute confidence of the simulation model, the valve penetration point is determined according to the difference data corresponding to the original simulation data; wherein the difference data is obtained by subtracting the original simulation data from the original test data, and the valve penetration point is the midpoint of adjacent data points with different signs of data values in the difference data;

[0021] When there are not less than two simulation models, and the target is to determine the relative confidence among the simulation models, it is determined whether the expected valve penetration rates corresponding to the simulation models are all equal; if not, the valve penetration point is determined according to the difference data corresponding to the original simulation data of each group;

[0022] The interpolation of the neighborhood space position range of the valve penetration point is performed on the original test data and the original simulation data according to the expected valve penetration rate corresponding to the simulation model, to obtain a corresponding interpolation data sequence; wherein the neighborhood space position range of the valve penetration point is the range between two adjacent data points corresponding to the valve penetration point;

[0023] The original test data and the original simulation data are respectively combined with the corresponding interpolation data sequence to obtain target test data and target simulation data.

[0024] Further, the above method further comprises:

[0025] If yes, the original test data and the original simulation data of each group are respectively determined as the target test data and the target simulation data of each group.

[0026] Further, the above method further comprises:

[0027] Based on the target test data, the target simulation data and the deviation acceptable degree, the gray correlation degree corresponding to the simulation model is calculated;

[0028] According to the gray correlation degree corresponding to the simulation model and the preset gray correlation degree-mean square error distance model, the mean square error distance corresponding to the simulation model is determined;

[0029] According to the mean square error distance corresponding to the simulation model and the preset mean square error distance-confidence degree model, the target confidence degree of the simulation model is determined.

[0030] Further, the above method further comprises:

[0031] According to the deviation acceptable degree and the target test data, the perturbation tolerance data is calculated;

[0032] The gray correlation degree is calculated by the following formula:

[0033] ;

[0034] Wherein, The gray correlation degree between the target test data X and the target simulation data Y , , , denotes the i-th data value in the i-th data point in the target test data, i k denotes the i-th data value in the i-th data point in the target test data, denotes the i-th data value in the i-th data point in the target test data, i k denotes the i-th data value in the i-th data point in the target test data, n denotes the number of data points, τ denotes the preset resolution coefficient, r i denotes the i-th perturbation tolerance in the perturbation tolerance data r, i , denotes the weight function, and satisfies:

[0035] .

[0036] In a second aspect, the present application further provides a confidence evaluation device based on strong robust grey correlation analysis, comprising:

[0037] an acquisition module configured to acquire original test data corresponding to a target research object and original simulation data corresponding to a simulation model to be evaluated; wherein the original test data is a set of time series data obtained by testing a physical model of the target research object, and the original simulation data is a set of time series data obtained by simulating a corresponding simulation model of the target research object;

[0038] a calculation module configured to calculate an expected valve penetration rate corresponding to the simulation model according to the original test data and the original simulation data;

[0039] an interpolation module configured to interpolate and update the original test data and the original simulation data according to the expected valve penetration rate corresponding to the simulation model, to obtain target test data and target simulation data;

[0040] a determination module configured to determine a target confidence of the simulation model by grey correlation analysis according to the target test data, the target simulation data and a preset deviation acceptability.

[0041] In a third aspect, the present application further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor implements the confidence evaluation method based on strong robust grey correlation analysis of the first aspect when executing the computer program.

[0042] In a fourth aspect, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the confidence evaluation method based on strong robust grey correlation analysis of the first aspect.

[0043] ​​​The application provides a confidence evaluation method and device based on strong robust grey correlation analysis, which can obtain original test data corresponding to a target research object and original simulation data corresponding to a simulation model to be evaluated; wherein the original test data is a group of time series data obtained by testing a physical model of the target research object, and the original simulation data is a group of time series data obtained by simulating a corresponding simulation model of the target research object; the expected valve penetration rate corresponding to the simulation model is calculated according to the original test data and the original simulation data; the original test data and the original simulation data are updated by interpolation according to the expected valve penetration rate corresponding to the simulation model, so as to obtain target test data and target simulation data; and the target confidence of the simulation model is determined by grey correlation analysis according to the target test data, the target simulation data and a preset deviation acceptability. In this way, the expected valve penetration rate is introduced to assist interpolation calculation, which indirectly increases the influence weight of distance on the grey correlation degree, effectively makes up for the distance proximity ignored when the traditional grey correlation analysis method judges the similarity of the sequence curve, and at the same time, the deviation acceptability is introduced as a distance threshold index, which increases the grey correlation coefficients of the data points within a certain deviation range, and the more stable confidence evaluation is realized by combining the grey correlation analysis, so that the anti-noise performance and anti-disturbance performance of the evaluation result are effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the specific embodiments of the application or the technical solutions in the prior art, the drawings needed to be used in the specific embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0045] Figure 1 A flowchart of a confidence evaluation method based on strong robust grey correlation analysis provided by an embodiment of the application is shown in the figure.

[0046] Figure 2 A schematic diagram of a grey correlation degree-median square deviation distance-confidence conversion model provided by an embodiment of the application is shown in the figure.

[0047] Figure 3 An example of a plurality of data sequences provided by an embodiment of the application is shown in the figure.

[0048] Figure 4 A comparison schematic diagram of random data sequences of the Gramacy&Lee function provided by an embodiment of the application is shown in the figure.

[0049] Figure 5 An example data sequence schematic diagram provided by an embodiment of the application is shown in the figure.

[0050] Figure 6The global grey correlation coefficient distribution diagram of data points under the traditional grey correlation analysis method provided by the embodiment of the present application is provided.

[0051] Figure 7 The local grey correlation coefficient distribution diagram of data points under the traditional grey correlation analysis method provided by the embodiment of the present application is provided.

[0052] Figure 8 The grey correlation coefficient diagram of local data points after linear interpolation provided by the embodiment of the present application is provided.

[0053] Figure 9 The global grey correlation coefficient distribution diagram of data points after adding the bias acceptability provided by the embodiment of the present application is provided.

[0054] Figure 10 The local grey correlation coefficient distribution diagram of data points after adding the bias acceptability provided by the embodiment of the present application is provided.

[0055] Figure 11 Another flowchart of the confidence evaluation method based on the strong robust grey correlation analysis provided by the embodiment of the present application is provided.

[0056] Figure 12 The structural diagram of the confidence evaluation device based on the strong robust grey correlation analysis provided by the embodiment of the present application is provided.

[0057] Figure 13 The structural diagram of the electronic device provided by the embodiment of the present application is provided. DETAILED DESCRIPTION

[0058] The technical solutions of the present application will be described clearly and completely in combination with the embodiments. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0059] At present, the traditional grey correlation analysis method does not consider the geometric similarity between dynamic indexes and the proximity of data curve distance at the same time when evaluating dynamic simulation data, and has the defects of poor robustness of anti-noise and anti-interference, thereby leading to the inconsistency between the model confidence and the actual situation. Based on this, the confidence evaluation method and device based on the strong robust grey correlation analysis provided by the embodiment of the present application introduces the expected valve penetration rate auxiliary interpolation calculation, takes the bias acceptability as the distance threshold index, and realizes more stable confidence evaluation by combining the grey correlation degree-median square deviation distance-confidence model, improves the defects of the traditional grey correlation analysis method which pays more attention to the sequence curve similarity, and enhances the anti-noise and anti-interference ability of the grey correlation coefficient.

[0060] For the convenience of understanding the present embodiment, firstly, a kind of confidence evaluation method based on strong robust grey correlation analysis disclosed in the present embodiment is introduced in detail.

[0061] The present embodiment provides a kind of confidence evaluation method based on strong robust grey correlation analysis, which can be executed by electronic equipment with data processing capability.Referring to Figure 1 The flowchart of a kind of confidence evaluation method based on strong robust grey correlation analysis shown in the figure, the method mainly includes the following steps S110 to step S140:

[0062] Step S110, the original test data corresponding to target research object and the original simulation data corresponding to the simulation model to be evaluated are acquired.

[0063] The original test data is a set of time series data obtained by testing the physical model of the target research object, and the original simulation data is a set of time series data obtained by simulating the corresponding simulation model of the target research object.

[0064] The target research object can be any equipment, such as missile and other weapon equipment, and in the following, the target research object will be taken as solid-liquid rocket engine as an example, and the strong robustness of the method will be verified using certain solid-liquid rocket engine interior ballistic experiment-simulation dynamic data. The simulation model to be evaluated can be one or more, and the absolute confidence of the simulation model can be evaluated, or the relative confidence between simulation models can be evaluated. In the original test data and the original simulation data, each set of time series data includes data values of multiple data points (i.e. time points), and each data point can include one or more data values.

[0065] Step S120, according to the original test data and the original simulation data, the expected crossing rate corresponding to the simulation model is calculated.

[0066] The expected crossing rate (ECR for short) refers to the frequency of data points crossing a certain set threshold in a given time sequence. Including ECR in the calculation can more accurately reflect the data fluctuation characteristics and be used to assist interpolation calculation. In the present embodiment, the expected crossing rate will be used to calculate the number of times the space curve formed by the disturbance sequence data crosses the reference sequence curve and the number of interpolation points in the neighborhood space position range of the crossing point, to provide position reference for subsequent construction of interpolation points.

[0067] In some possible embodiments, the above step S120 can include: performing curve fitting on the original test data to obtain a threshold curve; performing curve fitting on the original simulation data to obtain a disturbance sequence curve; and calculating the expected crossing rate corresponding to the simulation model according to the threshold curve and the disturbance sequence curve.

[0068] It should be noted that the obtaining step of the threshold curve and the obtaining step of the perturbation sequence curve are not executed in a sequence, and in other embodiments, the perturbation sequence curve can be obtained first, and then the threshold curve is obtained.

[0069] Optionally, the expected valve passing rate can be calculated by the following formula v a :

[0070] ;

[0071] wherein, y represents the perturbation sequence curve, represents the derivative of y , a represents the threshold curve, t represents time, represents the probability density of three variables a , , t .

[0072] The probability density can be obtained in the following manner: first, difference is obtained between the original test data and the original simulation data; then, according to the positive and negative transformation between adjacent differences in the difference sequence, the number of times of different output values is counted for each sub-interval of the time axis to obtain statistical data of each sub-interval; wherein, the output values include positive valve passing, negative valve passing and no valve passing, and the statistical data includes the number of times of positive valve passing, the number of times of negative valve passing and the number of times of no valve passing; finally, according to the statistical data of each sub-interval, the probability density of different output values in each sub-interval is estimated using a preset estimation algorithm, and the probability density is obtained; wherein, the preset estimation algorithm can use maximum likelihood estimation or kernel density estimation.

[0073] The time axis is divided into a plurality of non-overlapping time sub-intervals in advance, and the number of times of function values (i.e. the above-mentioned output values) is counted in each sub-interval, and then the probability density function of the corresponding output value in each sub-interval is estimated using maximum likelihood estimation or kernel density estimation. The explanations of the above-mentioned positive valve passing, negative valve passing and no valve passing are as follows: if the previous difference is positive and the next difference is negative, there is positive valve passing between the two data points; if the previous difference is negative and the next difference is positive, there is negative valve passing between the two data points; if the signs of the previous and next differences are the same (such as both positive, both negative or both 0), there is no valve passing between the two data points.

[0074] It should be noted that the preset estimation algorithm is not limited in the embodiments of the present application, and other types of estimation algorithms can also be used in other embodiments.

[0075] In step S130, the original test data and the original simulation data are interpolated and updated according to the expected valve passing rate corresponding to the simulation model, to obtain target test data and target simulation data.

[0076] In this embodiment, different interpolation and updating methods are provided for the following two targets:

[0077] Interpolation and updating method one:

[0078] When the target is to evaluate the absolute confidence of the simulation model, a valve passing point is determined according to difference data corresponding to the original simulation data, wherein the difference data is obtained by subtracting the original test data from the original simulation data, and the valve passing point is the midpoint of adjacent data points with different signs of data values in the difference data; the original test data and the original simulation data are respectively interpolated in the neighborhood spatial position range of the valve passing point according to the expected valve passing rate corresponding to the simulation model, to obtain a corresponding interpolation data sequence; wherein the neighborhood spatial position range of the valve passing point is the range between two adjacent data points corresponding to the valve passing point, and the expected valve passing rate is used to determine the number of interpolation points in the neighborhood spatial position range of each valve passing point; the original test data and the original simulation data are respectively combined with the corresponding interpolation data sequence, to obtain target test data and target simulation data.

[0079] Interpolation and updating method two:

[0080] When there are no less than two simulation models, and the target is to judge the relative confidence among the simulation models, it is judged whether the expected valve passing rates corresponding to the simulation models are all equal; if yes, interpolation is not performed, and the original test data and each group of original simulation data are directly determined as target test data and each group of target simulation data; if not, valve passing points are determined according to difference data corresponding to each group of original simulation data; the original test data and the original simulation data are respectively interpolated in the neighborhood spatial position range of the valve passing point according to the expected valve passing rate corresponding to the simulation model, to obtain a corresponding interpolation data sequence; the original test data and the original simulation data are respectively combined with the corresponding interpolation data sequence, to obtain target test data and target simulation data.

[0081] Optionally, when interpolating in the neighborhood spatial position range of the valve passing point, the number of interpolation points in the neighborhood spatial position range of each valve passing point can be the average of the expected valve passing rate and the number of valve passing points, that is, the number of interpolation points = (expected valve passing rate + number of valve passing points) / 2. This calculation method of the number of interpolation points is relatively simple and has high calculation efficiency.

[0082] It should be noted that the calculation method of the number of interpolation points is not limited in the embodiment, and other calculation methods can also be used in other embodiments.

[0083] Step S140: Based on the target experimental data, target simulation data, and preset acceptable deviation, determine the target confidence level of the simulation model through grey relational analysis.

[0084] The confidence assessment principle based on grey relational analysis involves constructing a grey relational degree-mean squared error distance model and a mean squared error distance-confidence model. Simulation data (such as target simulation data) and physical experiment data (such as target experimental data) are substituted into the grey relational degree-mean squared error distance model. The resulting mean squared error distance is then converted into confidence scores using the mean squared error distance-confidence model. Here, grey relational degree measures the similarity between two sequences; mean squared error (MSE) distance reflects the difference between the actual experimental data and the simulation data predicted by the simulation model.

[0085] In this embodiment, to address the issue of low confidence assessment results, a grey relational degree calculation method based on acceptable deviation is proposed. Therefore, step S140 may include the following steps S141 to S143:

[0086] Step S141: Based on the target experimental data, target simulation data, and acceptable deviation, calculate the grey relational degree corresponding to the simulation model.

[0087] In practice, the perturbation tolerance data can be calculated first based on the acceptable deviation and the target experimental data. Specifically, the target experimental data is used as the baseline sequence b, and the acceptable deviation is denoted as... p The perturbation tolerance data r is calculated using the following formula:

[0088] ;

[0089] Then, the grey relational degree is calculated using the following formula:

[0090] ;

[0091] in, Represents target test data X With target simulation data Y The degree of gray correlation between them , , Indicating the first in the target experimental data i The data point of the th data point k One data value, Indicating the first in the target simulation data i The data point of the th data point k One data value, n Indicates the number of data points. τ This represents the preset resolution coefficient.r i denotes the i-th perturbation tolerance in the perturbation tolerance data r, i , denotes a weight function, satisfying:

[0092] .

[0093] Optionally, the weight function can be based on a Sigmoid function, and is fitted based on the above formula satisfied by the weight function as a boundary condition. Based on this, the calculation formula of the grey correlation degree can be as follows:

[0094] ;

[0095] wherein, .

[0096] In step S142, the mean square error distance corresponding to the simulation model is determined according to the grey correlation degree corresponding to the simulation model and the preset grey correlation degree-mean square error distance model.

[0097] The grey correlation degree-mean square error distance model represents the conversion relationship between the grey correlation degree and the mean square error distance, and the grey correlation degree corresponding to the simulation model can be brought into the grey correlation degree-mean square error distance model to obtain the mean square error distance corresponding to the simulation model.

[0098] In step S143, the target confidence of the simulation model is determined according to the mean square error distance corresponding to the simulation model and the preset mean square error distance-confidence model.

[0099] The mean square error distance-confidence model represents the conversion relationship between the mean square error distance and the confidence, and the mean square error distance corresponding to the simulation model can be brought into the mean square error distance-confidence model to obtain the target confidence of the simulation model.

[0100] ​The confidence evaluation method based on the strong robust grey correlation analysis provided by the application can obtain original test data corresponding to a target research object and original simulation data corresponding to a simulation model to be evaluated; wherein the original test data is a set of time series data obtained by testing a physical model of the target research object, and the original simulation data is a set of time series data obtained by simulating a corresponding simulation model of the target research object; the expected valve penetration rate corresponding to the simulation model is calculated according to the original test data and the original simulation data; the target test data and the target simulation data are obtained by interpolating and updating the original test data and the original simulation data according to the expected valve penetration rate corresponding to the simulation model; and the target confidence of the simulation model is determined through grey correlation analysis according to the target test data, the target simulation data and the preset deviation acceptability. In this way, the expected valve penetration rate is introduced to assist interpolation calculation, which indirectly increases the influence weight of the distance on the grey correlation degree, effectively makes up for the distance proximity ignored when the traditional grey correlation analysis method judges the similarity of the sequence curve, and at the same time, the deviation acceptability is introduced as a distance threshold index, which increases the grey correlation coefficients of each data point within a certain deviation range, and the more stable confidence evaluation is realized in combination with the grey correlation analysis, thereby effectively improving the anti-noise performance and anti-disturbance performance of the evaluation result.

[0101] For the sake of understanding, the above-mentioned confidence evaluation method based on the strong robust grey correlation analysis will be described in detail below.

[0102] This embodiment aims at the shortcomings of poor robustness of the grey correlation analysis method, introduces the expected valve penetration rate and combines the interpolation function to calculate the sequence distance correction point; proposes the deviation acceptability control index to filter and select the time series data, and constructs the distance weight function to realize the confidence evaluation in combination with the grey correlation degree-median square deviation distance-confidence model, and finally verifies it by using the experiment-simulation dynamic data instance.

[0103] 1. Confidence evaluation based on grey correlation analysis.

[0104] The principle of the confidence evaluation based on the grey correlation analysis is to construct a grey correlation degree-median square deviation distance model and a median square deviation distance-confidence model, to substitute the simulation test data (corresponding to the simulation model) and the physical test data (corresponding to the physical model) into the grey correlation degree-median square deviation distance model, and then to convert the obtained median square deviation distance into the confidence through the median square deviation distance-confidence model.

[0105] 1.1 Solution of grey correlation degree and median square deviation distance:

[0106] Suppose that the reference time series data measured by the physical test under the same time interval is , the time series data generated by the simulation model is , and the calculation formula of the grey correlation degree of the two time series is:

[0107] (1);

[0108] In formula (1), represents time series data X and Y between the grey correlation degree; τ represents the resolution coefficient, , determines the maximum distance on the influence of the grey correlation degree, according to the grey correlation space theory proposed by Deng Julong, the overall and anti-interference effect of the correlation degree is considered, τ is usually taken as 0.5.

[0109] According to the characteristics of the grey correlation coefficient, the greater the grey correlation degree, the higher the consistency degree between the simulation output time series and the reference data; on the contrary, the smaller the grey correlation degree, the lower the consistency degree between the simulation output time series and the reference data.

[0110] The mean square error distance between two time series is:

[0111] (2).

[0112] According to the research of Sun Yugen on grey correlation analysis, a threshold of mean square error distance is introduced, denoted as d ref , called reference mean square error distance. When , the consistency degree between X and Y is low, and the confidence is low; when , the consistency degree between X and Y is high, and the confidence is high.

[0113] Definition d max is the maximum mean square error distance between X and Y at any time t, called the upper limit of the mean square error distance; d min is the minimum mean square error distance between X and Y at any time t, called the lower limit of the mean square error distance.

[0114] 1.2 Grey correlation degree-mean square error distance model:

[0115] Reference grey correlation degree γ refThe value is determined by the selected correlation model and actual needs. The higher the value, the higher the consistency requirement for the model, and the more gentle the change of the confidence near the reference grey correlation when converting the grey correlation to the confidence. The grey correlation corresponding to the upper limit of the mean square deviation distance is defined as γ min , which is called the minimum grey correlation; the grey correlation corresponding to the lower limit of the mean square deviation distance is γ max , which is called the maximum grey correlation.

[0116] To represent the relationship between the grey correlation and the mean square deviation distance, the reference mean square deviation distance d ref corresponding to the reference grey correlation is defined as γ ref When , X the consistency between Y and is higher; when X , Y the consistency between γ and is lower. According to Li's research, for the ideal model,

[0117] ref =0.5. Therefore, when , corresponds to γ , and X ( Y , d ) and X ( Y , Figure 2 ) have a linear relationship, and the model curve is shown in the grey correlation-mean square deviation distance model in

[0118] (3).

[0119] 1.3 Mean square deviation distance-confidence model:

[0120] The function of converting the mean square deviation distance to the confidence can be selected according to the specific situation. Generally, the mean square deviation distance d ( X , Y ) is converted to the distance d 0( X , Y ) under the standard model (i.e. normalized under the standard model), and the conversion relationship is:

[0121] (4);

[0122] In formula (4), d max0 is the maximum mean square deviation distance under the standard model; d min0 is the minimum mean square deviation distance under the standard model. At the same time, the median of the mean square deviation distance under the standard model is defined as d mean0 is:

[0123] (5).

[0124] The confidence degree in the corresponding case is C mean =0.5; at the same time, the upper limit of the mean square deviation distance d max The corresponding confidence degree is called the lower limit of the confidence degree, denoted as C min ; and the lower limit of the mean square deviation distance d min The corresponding confidence degree is called the upper limit of the confidence degree, denoted as C max .

[0125] The reference mean square deviation distance d ref The corresponding confidence degree is called the reference confidence degree C ref The passing line of the simulation confidence degree is 0.6 in engineering.

[0126] Further, the mean square deviation distance-confidence degree standard model is:

[0127] (6);

[0128] In the formula, α is the shape coefficient of the curve, which changes the shape of the curve by changing α The shape coefficient can be 0.5, which has good universality. The standard model curve is shown in the mean square deviation distance-confidence degree model in Figure 2 .

[0129] Using the above two models, the grey correlation degree is converted into the mean square deviation distance, and further converted into the confidence degree, that is, the confidence degree evaluation based on time domain analysis under the dynamic time series of the simulation model can be completed.

[0130] One of the reasons that grey correlation degree cannot determine the geometric similarity of time series data is that the grey correlation degree cannot guarantee its order preserving when calculating, that is, it cannot determine the positive or negative deviation of simulation and test data, and further cannot determine the consistency of geometric shape on continuous time-varying data. When applying grey correlation analysis method to evaluate model confidence, potential disturbance factors should be considered. If the disturbance factor is within the error tolerance threshold, or acceptable disturbance is inevitably introduced in actual engineering test, the robustness of traditional grey correlation analysis method is low, and ideal evaluation results cannot be provided. Therefore, in order to ensure the accuracy and effectiveness of evaluation, additional measures need to be taken to control or correct these disturbance factors.

[0131] 2. Confidence evaluation based on strong robust grey correlation analysis.

[0132] The essence of traditional grey correlation analysis method is to judge the similarity of the shape of the space curve constructed by time series data according to the distance between the corresponding data points of time series. The problem of this method is that the relative distance between two sequences is not considered, and further the calculation result cannot accurately evaluate the correlation of sequences, such as Figure 3 , as shown in the case, Figure 3 , where X0, X1, X2, X3 are different data sequences. It can be concluded that the traditional grey correlation analysis method has the following problems:

[0133] (1) Intuitively, the consistency of sequence X1 and X2 compared with sequence X0 can be obtained. The consistency of sequence X2 and sequence X0 should be better than that of X1 and sequence X0, and further it can be inferred that However, the difference of sequence X1 and X2 at each data point corresponding to the reference sequence X0 is equal or opposite, which leads to the calculation result of traditional grey correlation analysis in this case , which is inconsistent with the actual situation.

[0134] (2) For sequences X1 and X3, the displacement difference of each sampling point is constant, that is, the two-level minimum difference is equal to the two-level maximum difference , and the calculation result of traditional grey correlation analysis method is , which ignores the influence of sequence relative distance and is inconsistent with the actual situation.

[0135] (3) There is a relatively close distance between sequence data points, but due to the difference between two-level range, the grey correlation coefficient is low, and further the overall grey correlation degree is low.

[0136] The embodiment aims at problem (1), introduces the expected valve crossing rate combined with the interpolation function to calculate the sequence distance correction point, and increases the weight of the grey correlation coefficient in the valve crossing point neighborhood; for problems (2) and (3), the deviation acceptance control index is proposed to filter and select the time series data, to judge the distance proximity of the data sequence, to construct the distance weight function, to correct the time series data distance and geometric morphology in a small scale range to enhance the robustness of the grey correlation degree evaluation result, and to realize the confidence evaluation combined with the grey correlation degree-median square deviation distance-confidence model.

[0137] 2.1 Expected valve crossing rate:

[0138] In the embodiment, the expected valve crossing rate will be used to calculate the number of times that the space curve formed by the disturbed sequence data crosses the reference sequence curve and the space position range of the valve crossing point neighborhood, to provide a position reference for subsequent construction of the interpolation point.

[0139] Suppose Y(t) is a continuous time continuous state random sequence, and at least once the mean square is differentiable, then the number of times that the sequence Y(t) crosses the valve a in the time interval [t1, t2] is the valve crossing number, and the expected value can be constructed by means of the unit step function u to construct a 0-1 process function Z(t):

[0140] (7);

[0141] The formal derivative of Z(t) is:

[0142] (8);

[0143] In formula (8), δ(t-t1) and δ(t-t2) are Dirac functions. δ

[0144] Then the number of times that the random sequence Y(t) crosses the valve a in the time interval [t1, t2] is :

[0145] (9);

[0146] The expected valve crossing number is:

[0147] (10);

[0148] In formula (10), y is a function expression of Y .

[0149] The expected valve crossing number per unit time, i.e. the expected valve crossing rate is:

[0150] (11).

[0151] ​The higher the expected valve crossing rate, the more times the disturbance sequence curve crosses the reference sequence curve in a unit of time, the smaller the relative position distance between the disturbance sequence and the reference sequence within a certain disturbance threshold range at the valve crossing point, and the gray correlation degree should be given a positive gain, such as Figure 3 In the reference example, the X1, X2 curves are within the defined range, But the gray correlation degree calculation result of X1, X2 is Therefore, the influence weight of the gray correlation degree can be increased indirectly by increasing the distance between the calculation data points in the valve crossing point neighborhood. To ensure that the spatial morphology of the data sequence curve does not change, and to avoid the influence of the local range size in the valve crossing point neighborhood on the distribution of the inserted data points, single linear interpolation, bilinear interpolation or trilinear interpolation is used to create data points according to different data dimensions.

[0152] Figure 4 The example shown uses Gramacy & Lee Function as the basis function (i.e. as the reference sequence) for verification:

[0153] (12).

[0154] On the basis of this function, a random disturbance is added to construct a disturbance sequence data. The disturbance sequence one is a random disturbance result with a positive proportional weight coefficient. The difference between the corresponding data points of the reference sequence and the disturbance sequence one is used as a benchmark to construct the disturbance sequence two with a random ±1 gain. The difference between the corresponding data points of the disturbance sequence one and the disturbance sequence two is equal to or opposite to the reference sequence.

[0155] The gray correlation degree of the two groups of sequence data is calculated, and the results are shown in Table 1 (Table 1 is a comparison of the evaluation results of the two kinds of gray correlation analysis methods under the condition of equal distance difference value), it is found that under the traditional gray correlation analysis method, the gray correlation degrees of the two groups of sequence data are equal, both are 87.3134%, and the confidence after model mapping is 97.1851%, which is inconsistent with the actual situation; by judging the expected valve crossing rate of the disturbance sequence one and the disturbance sequence two respectively, it is obtained that After calculating the valve crossing point sequence respectively, the sequence is merged, the original disturbance sequence one and the disturbance sequence two are linearly interpolated by the merged sequence index, and the gray correlation degrees of the interpolated sequence one and the interpolated sequence two are calculated, which are 87.1090% and 89.7850% respectively. After model mapping, the sequence confidence is 97.1377% and 97.7010% respectively. Through TIC (Theil's Inequality Coefficient, also known as Theil's U, which is a statistical quantity for measuring the inequality between predicted values and actual values), the inequality coefficient method is used for calculation and verification, and it is obtained that That is, the fitting accuracy of the second disturbance sequence to the reference sequence is greater than that of the first disturbance sequence, which is consistent with the evaluation results of the improved method of the embodiment.

[0156] Table 1

[0157]

[0158] 2.2 Acceptability of deviation:

[0159] Figure 5 Taking the disturbance sequence constructed by using a sine function as a base function (i.e., a reference sequence) and adding random disturbance as an example, it can be seen from Figure 5 that, when there is disturbance, at a larger geometric scale, the disturbance sequence is similar in geometric shape to the test data sequence (i.e., the reference sequence), and it can be seen that the simulation model is acceptable, but at a smaller geometric scale, the simulation confidence of the grey correlation degree analysis is not acceptable. Therefore, in the face of too "rigorous" geometric similarity evaluation conditions, qualified simulation models often get unqualified evaluation results in engineering.

[0160] Figure 5 The grey correlation degree result shown in the example is about 54.315%, and the corresponding evaluation confidence result is only about 68.312%, but such disturbance is completely acceptable for actual application.

[0161] Figure 5 The global grey correlation coefficient distribution of the data points in the example is shown in Figure 6 , and the local grey correlation coefficient distribution of the data points is shown in Figure 7 . It can be found from Figure 6 and Figure 7 that, due to the excessively large two-stage range and the selection error of the resolution coefficient, there are data points with a small distance difference from the reference sequence, but a small grey correlation coefficient, which affects the overall grey correlation degree.

[0162] The sequence curve in Figure 5 is evaluated using the expected valve penetration rate, and after linear interpolation, the evaluated grey correlation degree result is about 62.048%, and the corresponding evaluation confidence result is about 80.504%, which has a certain increase compared with the evaluation result of the traditional grey correlation analysis method, but as shown in Figure 8 , the local grey correlation coefficient of the data points obtained after linear interpolation of the sequence in Figure 8 is shown in Figure 5 , and the data points within the 1% deviation range of the original data still have a slightly low evaluation result.

[0163] It can be concluded that the use of the expected valve rate evaluation sequence curve valve situation and linear interpolation and then the grey correlation analysis leads to the grey correlation degree, confidence results rise. The essence is that the increase of valve interpolation points makes the evaluation data points in a certain neighborhood of the reference sequence data increase, and the distance weight increases when calculating the grey correlation degree, which effectively makes up for the distance similarity ignored by the traditional grey correlation analysis method when judging the similarity of the sequence curve. However, it still belongs to the macro improvement method, and the optimization object is the whole data sequence, and the evaluation results of single data points are not improved.

[0164] Therefore, the embodiment further proposes a dimensionless perturbation tolerance based on the valve interpolation method , , n is the number of data points in the sequence, and its meaning is that for an acceptable error size of a simulation result, which is equivalent to adding an error tolerance band, the simulation results in the error tolerance band are all reliable, and the single data point grey correlation coefficient is modified. After adding the perturbation tolerance index, the original grey correlation analysis method evaluation formula becomes:

[0165] (13);

[0166] wherein, is the weight function, which satisfies:

[0167] (14).

[0168] In this embodiment, the Sigmoid function is used as the basis, and the function fitting is performed based on the boundary condition of formula (14) to achieve a small slope and smooth transformation near the boundary of the error tolerance band, which is used to construct the weight S-shaped distribution, so that the output is limited to a specific range, which is beneficial to the stability and interpretability of the model weight update.

[0169] Taking the experimental time series data as the reference sequence b, the dimensionless parameter deviation acceptable degree p of the perturbation tolerance is added, which satisfies formula (15):

[0170] (15);

[0171] The grey correlation degree calculation formula after adding the fitted weight function is shown in formula (16):

[0172] ;

[0173] wherein, .

[0174] In summary, for different examples, the appropriate p value is added to (100± pThe reference value is taken as an error tolerance boundary, a linear difference method is combined with the expected valve passing rate to determine the grey correlation coefficient of each point in the data sequence, and a confidence evaluation method based on strong robust grey correlation analysis is constructed.

[0175] The valve passing interpolation method corrects the distance weight of the grey correlation degree calculation formula, and the deviation acceptability index filters and corrects the grey correlation coefficient. Figure 5 The global average deviation of 2% is taken as the deviation acceptability selection standard. The grey correlation coefficients of the data points with 2% deviation acceptability added after the valve passing interpolation are as shown in Figure 9 and Figure 10 The calculation results are shown in Table 2. Figure 9 Comparing Figure 6 It is found that the use of the method of the embodiment increases the grey correlation coefficients of each data point in a certain deviation range of the reference sequence, enhances the weight coefficient of the distance in calculating the grey correlation coefficient, and obtains TIC = 0.01032, proving that the fitting accuracy of the sequence is high; in the case of using the valve passing interpolation method and the deviation acceptability at the same time, the overall grey correlation degree of the sequence reaches 68.439%, and the confidence reaches 87.602%, compared with the traditional grey correlation analysis method, the method of the embodiment is more consistent with the TIC determination conclusion.

[0176] Table 2

[0177]

[0178] 2.3 Confidence evaluation process based on strong robust grey correlation analysis:

[0179] The confidence evaluation steps based on strong robust grey correlation analysis are specifically summarized as shown in Figure 11 First, the expected valve passing rate v a and the valve interpolation point data sequence N are calculated according to the test data and the simulation data; the disturbance tolerance r and the tolerance reference value b are selected according to the engineering practical application, and the non-dimensional deviation acceptability p is obtained; the grey correlation degree-median square deviation distance model and the median square deviation distance-confidence model are constructed according to the simulation-test time sequence data, the grey correlation coefficient (i.e. the weight function ) of the simulation-experimental time sequence data (the sequence data after merging the interpolation point data sequence) is calculated based on the deviation acceptability, and then the grey correlation degree is calculated, the grey correlation degree is mapped to the median square deviation distance combined with the grey correlation degree-median square deviation distance model, and the median square deviation distance is mapped to the confidence evaluation result combined with the median square deviation distance-confidence model.

[0180] The embodiment aims at the poor robustness of the grey correlation analysis method in processing the confidence evaluation of the data containing noise, introduces the interpolation method guided by the expected valve penetration rate, indirectly increases the weight of the data distance for the grey correlation analysis calculation by increasing the number of data points in the small distance difference range on the basis of not changing the original data sequence curve appearance, proposes the deviation acceptability index p as the data distance threshold, increases the weight correction after the judgment disturbance tolerance on the basis of the difference taking the absolute value for the grey correlation analysis interpolation, maps the grey correlation degree to the confidence by constructing the grey correlation degree-mean square deviation distance-confidence model; microscopically corrects the grey correlation coefficient of a single data point, macroscopically increases the weight of the data point distance, effectively improves the anti-noise performance and anti-disturbance performance of the evaluation result, and is helpful to accurately and clearly reflect the confidence of the simulation model.

[0181] Corresponding to the confidence evaluation method based on the strong robust grey correlation analysis, the embodiment of the application further provides a confidence evaluation device based on the strong robust grey correlation analysis. Figure 12 As shown in the structural schematic diagram of the confidence evaluation device based on the strong robust grey correlation analysis, the device comprises:

[0182] The acquisition module 1201 is configured to acquire the original test data corresponding to the target research object and the original simulation data corresponding to the simulation model to be evaluated; wherein the original test data is a set of time series data obtained by testing the physical model of the target research object, and the original simulation data is a set of time series data obtained by simulating the corresponding simulation model of the target research object;

[0183] The calculation module 1202 is configured to calculate the expected valve penetration rate corresponding to the simulation model according to the original test data and the original simulation data.

[0184] The interpolation module 1203 is configured to update the original test data and the original simulation data by interpolation according to the expected valve penetration rate corresponding to the simulation model, to obtain the target test data and the target simulation data.

[0185] The determination module 1204 is configured to determine the target confidence of the simulation model by the grey correlation analysis according to the target test data, the target simulation data and the preset deviation acceptability.

[0186] The confidence evaluation device based on the strong robust grey correlation analysis provided by the application can obtain original test data corresponding to a target research object and original simulation data corresponding to a simulation model to be evaluated; the original test data is a set of time series data obtained by testing a physical model of the target research object, and the original simulation data is a set of time series data obtained by simulating a corresponding simulation model of the target research object; the expected valve penetration rate corresponding to the simulation model is calculated according to the original test data and the original simulation data; the original test data and the original simulation data are updated by interpolation to obtain target test data and target simulation data according to the expected valve penetration rate corresponding to the simulation model; and the target confidence of the simulation model is determined by grey correlation analysis according to the target test data, the target simulation data and the preset deviation acceptability. In this way, the expected valve penetration rate is introduced to assist interpolation calculation, which indirectly increases the influence weight of distance on the grey correlation degree, effectively makes up for the distance proximity ignored when the traditional grey correlation analysis method judges the similarity of the sequence curve, and at the same time, the deviation acceptability is introduced as a distance threshold index, which increases the grey correlation coefficient of each data point within a certain deviation range, and the more stable confidence evaluation is realized by combining the grey correlation analysis, thereby effectively improving the anti-noise performance and anti-disturbance performance of the evaluation result.

[0187] Further, the calculation module 1202 is specifically configured to: perform curve fitting on the original test data to obtain a threshold curve; perform curve fitting on the original simulation data to obtain a disturbance sequence curve; and calculate the expected valve penetration rate corresponding to the simulation model according to the threshold curve and the disturbance sequence curve.

[0188] Further, the calculation module 1202 is further configured to calculate the expected valve penetration rate by the following formula v a :

[0189] ;

[0190] wherein, y represents the disturbance sequence curve, represents y derivative of the disturbance sequence curve, a represents the threshold curve, t represents time, represents the probability density of three variables a , , t .

[0191] Furthermore, the interpolation module 1203 is specifically used to: when the objective is to evaluate the absolute confidence of the simulation model, determine the valve penetration point based on the difference data corresponding to the original simulation data; wherein, the difference data is obtained by subtracting the original simulation data and the original experimental data, and the valve penetration point is the midpoint of adjacent data points with different signs in the difference data;

[0192] When there are at least two simulation models and the objective is to determine the relative confidence level between each simulation model, determine whether the expected valve penetration rate corresponding to each simulation model is equal; if not, determine the valve penetration point based on the difference data corresponding to each set of original simulation data.

[0193] Based on the expected valve penetration rate corresponding to the simulation model, the neighborhood spatial location range of the valve penetration point is interpolated for both the original experimental data and the original simulation data to obtain the corresponding interpolated data sequence; where the neighborhood spatial location range of the valve penetration point is the range between two adjacent data points corresponding to the valve penetration point;

[0194] The original experimental data and original simulation data are merged with the corresponding interpolated data sequences to obtain the target experimental data and target simulation data.

[0195] Furthermore, the interpolation module 1203 is also used to: if so, determine the original experimental data and each group of original simulation data as the target experimental data and each group of target simulation data, respectively.

[0196] Furthermore, the aforementioned determining module 1204 is specifically used for: calculating the grey relational degree corresponding to the simulation model based on the target experimental data, the target simulation data, and the acceptable deviation; determining the mean squared distance corresponding to the simulation model based on the grey relational degree corresponding to the simulation model and the preset grey relational degree-mean squared distance model; and determining the target confidence level of the simulation model based on the mean squared distance corresponding to the simulation model and the preset mean squared distance-confidence model.

[0197] Furthermore, the aforementioned determining module 1204 is also used to: calculate the disturbance tolerance data based on the deviation acceptability and the target test data;

[0198] The grey relational degree is calculated using the following formula:

[0199] ;

[0200] in, Represents target test data X With target simulation data Y The degree of gray correlation between them , , Indicating the first in the target experimental data i The data point of the th data pointk a data value, represents a target simulation data, i a data value, k a data value, n represents a data point number, τ represents a preset resolution coefficient, r i represents a perturbation tolerance data r, i a perturbation tolerance, , represents a weight function, and satisfies:

[0201] .

[0202] The device provided by the embodiment has the same implementation principle and technical effects as the foregoing method embodiment, and for brief description, the part not mentioned in the device embodiment can be referred to the corresponding content in the foregoing method embodiment.

[0203] As shown in Figure 13 , the electronic device 1300 provided by the embodiment of the present application comprises a processor 1301, a memory 1302 and a bus, the memory 1302 stores a computer program capable of running on the processor 1301, the processor 1301 and the memory 1302 communicate through the bus when the electronic device 1300 runs, and the processor 1301 executes the computer program to implement the confidence evaluation method based on the strong robust grey correlation analysis.

[0204] Specifically, the memory 1302 and the processor 1301 can be general memory and processor, which are not specifically limited here.

[0205] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is run by a processor to execute the confidence evaluation method based on the strong robust grey correlation analysis in the foregoing method embodiment. The computer readable storage medium comprises a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM for short), RAM, a magnetic disk or an optical disk and various media capable of storing program codes.

[0206] The term "and / or" in the present document is only used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" in the present document means any one of the plurality or any combination of at least two of the plurality, for example, at least one of A, B and C includes any one or more elements selected from the set consisting of A, B and C.

[0207] In all of the examples shown and described herein, any specific values should be interpreted as merely exemplary and not limiting, and thus other examples of the example embodiments can have different values.

[0208] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of apparatuses, methods and computer program products according to the present application. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0209] Finally, it should be noted that the above-described embodiments are merely intended for describing the technical solutions of the present application, but not limit the technical solutions of the present application; even though the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can be modified, or some or all of the technical features can be replaced by equivalent replacements; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A confidence assessment method based on robust grey relational analysis, characterized in that, include: The process involves acquiring raw experimental data corresponding to the target research object and raw simulation data corresponding to the simulation model to be evaluated. The target research object is a weapon system; the raw experimental data is a set of time-series data obtained by testing a physical model of the target research object; and the raw simulation data is a set of time-series data obtained by simulating the corresponding simulation model of the target research object. The raw experimental data includes internal ballistic experimental data of a rocket engine, and the raw simulation data includes internal ballistic simulation data of a rocket engine. Based on the original experimental data and the original simulation data, the expected valve penetration rate corresponding to the simulation model is calculated; Based on the expected valve penetration rate corresponding to the simulation model, the original experimental data and the original simulation data are interpolated and updated to obtain the target experimental data and the target simulation data. Based on the target experimental data, the target simulation data, and the preset acceptable deviation, the target confidence level of the simulation model in the process of constructing the simulation system is determined by grey relational analysis. The step of calculating the expected valve penetration rate corresponding to the simulation model based on the original experimental data and the original simulation data includes: The original experimental data were subjected to curve fitting to obtain the threshold curve; Curve fitting is performed on the original simulation data to obtain the perturbation sequence curve; Based on the threshold curve and the disturbance sequence curve, the expected valve penetration rate corresponding to the simulation model is calculated.

2. The method according to claim 1, characterized in that, The step of calculating the expected threshold rate corresponding to the simulation model based on the threshold curve and the disturbance sequence curve includes: The desired valve penetration rate is calculated using the following formula. v a : ； in, y This represents the perturbation sequence curve. express y The derivative, a This represents the threshold curve. t Indicates time, Representing three variables a , , t The probability density is given below.

3. The method according to claim 1, characterized in that, The step of interpolating and updating the original experimental data and the original simulation data according to the expected valve penetration rate corresponding to the simulation model to obtain target experimental data and target simulation data includes: When the objective is to evaluate the absolute confidence of the simulation model, the valve penetration point is determined based on the difference data corresponding to the original simulation data; wherein, the difference data is obtained by subtracting the original simulation data and the original experimental data, and the valve penetration point is the midpoint of adjacent data points with different signs in the difference data; When there are at least two simulation models and the objective is to determine the relative confidence level between the simulation models, determine whether the expected valve penetration rates corresponding to each simulation model are equal; if not, determine the valve penetration point based on the difference data corresponding to each group of original simulation data. Based on the expected valve penetration rate corresponding to the simulation model, the original experimental data and the original simulation data are respectively interpolated for the neighborhood spatial location range of the valve penetration point to obtain the corresponding interpolated data sequence; wherein, the neighborhood spatial location range of the valve penetration point is the range between two adjacent data points corresponding to the valve penetration point; The original experimental data and the original simulation data are merged with the corresponding interpolated data sequences to obtain the target experimental data and the target simulation data.

4. The method according to claim 3, characterized in that, The method further includes: If so, the original experimental data and the original simulation data of each group are respectively determined as the target experimental data and the target simulation data of each group.

5. The method according to claim 1, characterized in that, The step of determining the target confidence level of the simulation model through grey relational analysis based on the target experimental data, the target simulation data, and a preset acceptable deviation level includes: Based on the target experimental data, the target simulation data, and the acceptable deviation, the grey relational degree corresponding to the simulation model is calculated. Based on the grey relational degree corresponding to the simulation model and the preset grey relational degree-mean squared error distance model, determine the mean squared error distance corresponding to the simulation model; The target confidence level of the simulation model is determined based on the mean squared error distance corresponding to the simulation model and the preset mean squared error distance-confidence level model.

6. The method according to claim 5, characterized in that, The calculation of the grey relational degree corresponding to the simulation model based on the target experimental data, the target simulation data, and the acceptable deviation includes: Based on the acceptable deviation and the target test data, the perturbation tolerance data is calculated. The grey relational degree is calculated using the following formula: ； in, Represents target test data X With target simulation data Y The degree of gray correlation between them , , Indicating the first in the target experimental data i The data point of the th data point k One data value, Indicating the first in the target simulation data i The data point of the th data point k One data value, n Indicates the number of data points. τ This represents the preset resolution coefficient. r i The perturbation tolerance data r represents the first... i A perturbation tolerance, , The weight function is defined as follows: 。 7. A confidence assessment device based on robust grey relational analysis, characterized in that, include: The acquisition module is used to acquire the original experimental data corresponding to the target research object and the original simulation data corresponding to the simulation model to be evaluated; wherein, the target research object is a weapon system, the original experimental data is a set of time-series data obtained by testing the physical model of the target research object, and the original simulation data is a set of time-series data obtained by simulating the corresponding simulation model of the target research object; the original experimental data includes internal ballistic experimental data of rocket engines, and the original simulation data includes internal ballistic simulation data of rocket engines. The calculation module is used to calculate the expected valve penetration rate corresponding to the simulation model based on the original experimental data and the original simulation data. An interpolation module is used to interpolate and update the original experimental data and the original simulation data according to the expected valve penetration rate corresponding to the simulation model, so as to obtain target experimental data and target simulation data. The determination module is used to determine the target confidence level of the simulation model during the construction of the simulation system by means of grey relational analysis, based on the target experimental data, the target simulation data and the preset acceptable deviation. The calculation module is specifically used for: performing curve fitting on the original experimental data to obtain a threshold curve; performing curve fitting on the original simulation data to obtain a disturbance sequence curve; and calculating the expected threshold rate corresponding to the simulation model based on the threshold curve and the disturbance sequence curve.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the confidence assessment method based on strong robust grey relational analysis as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program, when executed by the processor, performs the confidence assessment method based on strong robust grey relational analysis as described in any one of claims 1-6.

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