Method and system for analyzing consistency of Hugoniot experimental data of small-sample explosive
A method for analyzing Hugoniot experimental data of small samples through linear fitting and deviation calculation addresses quality differences, improving the calibration and accuracy of physical models by assigning appropriate weights to data points.
Patent Information
- Application Number
- CN202411176271.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-26
- Publication Date
- 2024-12-17
AI Technical Summary
Existing methods struggle to effectively quantify and address quality differences in small sample explosive Hugoniot experimental data, which can significantly impact the calibration and accuracy of physical models.
A method involving linear fitting of shock wave velocity and particle velocity data, calculating fitting deviations, and identifying data with significant differences to assign appropriate weights, thereby improving the generalization and prediction accuracy of physical models.
Effectively identifies and quantifies quality differences in Hugoniot experimental data, enhancing the generalization ability and prediction accuracy of physical models by providing weighted data analysis.
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Figure CN119150067A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of Hugoniot test data quality identification, and in particular to a method and system for analyzing consistency of small sample explosive Hugoniot test data. Background Art
[0002] Data quality has a direct impact on data modeling, data analysis, and physical model evaluation. Data quality control and management is one of the key links in experiments. Currently, researchers are mostly focusing on the study of abnormal data identification methods in experiments. The more commonly used abnormal data detection methods include 3 sigma, Z score, etc. However, there are few studies on the quality difference evaluation between different experimental data.
[0003] For many experiments, it is difficult to obtain a large amount of sample experimental data, usually small sample experimental data, due to experimental cost, repeatability, etc. When calibrating a physical model with small sample experimental data, there may be large quality differences in individual data. If these differences are not identified, they will have a significant impact on the physical model calibration, thereby directly affecting the generalization ability and prediction accuracy of the calibrated physical model.
[0004] The shock Hugoniot relation of explosives is a basic performance parameter that characterizes the performance of explosives under the action of shock waves. From this relation, the state parameters such as wave velocity, post-wave particle velocity, specific volume and specific internal energy of unreacted explosives under the action of shock waves can be obtained; the state equation of unreacted explosives is also calibrated through its shock Hugoniot relation. However, the shock Hugoniot relation is usually measured experimentally. Based on the linear relationship assumption of the shock-shock Hugoniot, it is easy to judge abnormal data in the experiment, but it is difficult to identify the quality differences of normal data due to various experimental devices, measurements, etc. Summary of the invention
[0005] In order to solve the above problems, the present invention provides a consistency analysis method for Hugoniot experimental data of small sample explosives, the method comprising: step 1, for n Hugoniot experimental data of explosives, removing the i-th experimental data, and using the remaining n-1 experimental data to fit the linear relationship between shock wave velocity and particle velocity; step 2, calculating the fitting deviation according to the linear relationship; step 3, changing the value of i, cyclically executing steps 1-2 to traverse the n Hugoniot experimental data of explosives to obtain n fitting deviations; step 4, judging whether the n fitting deviations meet the obvious difference condition; step 5, if the obvious difference condition is met, marking the i-th experimental data corresponding to the obvious difference value in the fitting deviation as experimental data with large quality difference.
[0006] The small sample explosive Hugoniot experimental data consistency analysis method provided in the embodiment of the present invention can perform linear fitting on the explosive Hugoniot experimental data and calculate the fitting deviation, and quantitatively characterize the quality of the Hugoniot experimental data by the fitting deviation, so as to effectively identify the quality difference of the explosive Hugoniot experimental data, give appropriate weights to different experimental data in subsequent Hugoniot relationship fitting and physical model calibration, fully mine the experimental data information, and effectively improve the generalization ability and prediction accuracy of the calibration model.
[0007] Optionally, after step 5, the method further includes: step 6, removing the i-th experimental data corresponding to the obvious difference value in the fitting deviation, setting n: =n-1, returning to execute step 1 for the remaining n-1 samples, and looping through steps 1-6 until the fitting deviation no longer meets the obvious difference condition.
[0008] In the embodiment of the present invention, after the i-th experimental data corresponding to the obvious difference value in the fitting deviation is removed, the steps of linear fitting and calculating the fitting deviation are executed cyclically to identify all the sample data with large data differences.
[0009] Optionally, calculating the fitting deviation according to the linear relationship includes: calculating the shock wave velocity fitting values corresponding to the remaining n-1 experimental data according to the linear relationship; respectively calculating the absolute difference between each shock wave velocity fitting value and the corresponding shock wave velocity experimental value in the remaining n-1 Hugoniot experimental data; and calculating the average of the n-1 differences as the fitting deviation.
[0010] The embodiment of the present invention provides a specific calculation method for the fitting deviation, and the remaining n-1 experimental data are substituted into the calculation to obtain the fitting deviation, and then the identification is performed.
[0011] Optionally, the judging whether the n fitting deviations meet the significant difference condition includes: calculating the difference between the maximum value and the minimum value among the n fitting deviations; if the difference is greater than a threshold, determining that the significant difference condition is met; marking the i-th experimental data corresponding to the significant difference value in the fitting deviation as experimental data with large quality difference includes: marking the i-th experimental data corresponding to the minimum value of the fitting deviation as experimental data with large quality difference.
[0012] In the embodiment of the present invention, if there is a large gap between the maximum value and the minimum value of the n fitting deviations, it can be considered that there is an obvious quality difference between the experimental data corresponding to the minimum value and the other experimental data.
[0013] Optionally, calculating the fitting deviation according to the linear relationship includes: calculating a shock wave velocity fitting value corresponding to the ith experimental data according to the linear relationship; and calculating a difference between the shock wave velocity fitting value and a shock wave velocity experimental value of the ith experimental data as the fitting deviation.
[0014] The embodiment of the present invention provides a specific calculation method for the fitting deviation, and the removed i-th experimental data is substituted into the calculation to obtain the fitting deviation, and then identification is performed.
[0015] Optionally, the judging whether the n fitting deviations meet the significant difference condition comprises: sorting the n fitting deviations by size; if the maximum value among the fitting deviations is greater than a threshold, determining that the significant difference condition is met; marking the i-th experimental data corresponding to the significant difference value in the fitting deviations as experimental data with large quality difference comprises: marking the i-th experimental data corresponding to the maximum value of the fitting deviations as experimental data with large quality difference.
[0016] In the embodiment of the present invention, the larger the value of the fitting deviation of the experimental data removed, the greater the quality difference. By sorting by the size of the fitting deviation, the sample points with large quality difference can be found based on the maximum value of the fitting deviation.
[0017] Optionally, the linear relationship between the shock wave velocity and the particle velocity is:
[0018] U S =c0 (-i) +s (-i) u p
[0019] Among them, U s is the shock wave velocity, c0 and s are the parameters of the fitted linear Hugoniot relationship, and u p is the particle velocity after the wave;
[0020] The calculation formula for the fitting deviation is as follows:
[0021] err j (-i) =|U S j -U S (-i) (u p j )|, j = 1, 2, i-1, i+1, n;
[0022]
[0023] Among them, err j (-i) To remove the fitting deviation after the i-th Hugoniot experimental data, err(-i) is its mean value, U S (-i) is the shock wave velocity of the Hugoniot relationship fitted after removing the i-th Hugoniot experimental data, is the particle velocity after the wave measured in the jth experiment.
[0024] Optionally, the linear relationship between the shock wave velocity and the particle velocity is:
[0025] U S =c0 (-i) +s (-i) u p
[0026] Among them, U s is the shock wave velocity, c0 and s are the parameters of the fitted linear Hugoniot relationship, and u p is the particle velocity after the wave;
[0027] The calculation formula for the fitting deviation is as follows:
[0028] err i (-i) =|U S i -U S (-i) (u p i )|
[0029] Among them, err j (-i) To remove the fitting deviation after the i-th Hugoniot experimental data, U S (-i) is the shock wave velocity of the Hugoniot relationship fitted after removing the i-th Hugoniot experimental data, is the particle velocity after the wave measured in the jth experiment.
[0030] Optionally, the threshold is half of the maximum value.
[0031] The embodiment of the present invention provides a small sample explosive Hugoniot experiment data consistency analysis system, which is used to execute the small sample explosive Hugoniot experiment data consistency analysis method.
[0032] The small sample explosive Hugoniot experiment data consistency analysis system provided by the embodiment of the present invention can achieve the same technical effect as the small sample explosive Hugoniot experiment data consistency analysis method mentioned above. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0034] Figure 1 A schematic diagram of a flow chart of a method for analyzing consistency of Hugoniot experimental data of a small sample of explosives provided in an embodiment of the present invention;
[0035] Figure 2 A schematic diagram of the absolute deviation of the fitting without the shot-1 experiment provided in an embodiment of the present invention;
[0036] Figure 3 A schematic diagram of the absolute deviation of the fitting without the shot-2 experiment provided in an embodiment of the present invention;
[0037] Figure 4 A schematic diagram of the absolute deviation of the fitting without the shot-3 experiment provided in an embodiment of the present invention;
[0038] Figure 5 A schematic diagram of the absolute deviation of the fitting without the shot-4 experiment provided in an embodiment of the present invention;
[0039] Figure 6 A schematic diagram of the absolute deviation of the fitting without the shot-5 experiment provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0040] In order to make the above-mentioned objects, features and advantages of the present invention more clearly understood, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.
[0041] In previous studies, researchers focused on the detection and identification of abnormal experimental data, and it was difficult to effectively quantify the quality differences of normal experimental data.
[0042] The embodiments of the present invention can effectively identify the quality differences of explosive Hugoniot experimental data, assign appropriate weights to different experimental data in subsequent Hugoniot relationship fitting and physical model calibration, fully mine experimental data information, and effectively improve the generalization ability and prediction accuracy of the calibration model.
[0043] The analysis method of the present method embodiment can effectively quantify and evaluate the quality difference of Hugoniot experimental data of explosives, quickly evaluate the consistency of experimental data, identify experimental data points with large quality differences in experimental data, and provide a weighted basis for subsequent Hugoniot relationship and physical model evaluation.
[0044] The impact Hugoniot relation of explosives refers to the set of explosives that start from the same initial state and reach the final state after different impact compressions. It reflects the relationship between the thermodynamic state quantities of the explosives after the shock wave. It is of great significance to the parameters of the state equation of explosives, the study of the impact initiation of explosives, and the detonation simulation. The most commonly used Hugoniot relation of explosives in practical applications is the shock wave velocity U in the explosive. s and the particle velocity u p The relationship between the two, the study found that most of the explosives U s and u p An approximate linear relationship, that is
[0045] U S =c0+su p
[0046] Among them, c0 and s are unknown parameters, which are determined based on the fitting of the impact Hugoniot experimental data.
[0047] For the small sample explosive Hugoniot experimental data, the following consistency analysis method is established in the embodiment of the present invention.
[0048] Figure 1 A schematic flow chart of a method for analyzing consistency of Hugoniot experimental data of a small sample of explosive provided by an embodiment of the present invention is shown. The method comprises the following steps:
[0049] Step 1: For n explosive Hugoniot experimental data, remove the ith experimental data and use the remaining n-1 experimental data to fit the linear relationship between shock wave velocity and particle velocity.
[0050] This embodiment is for small sample data, so n is generally small. Each explosive Hugoniot experimental data includes shock wave velocity and particle velocity. In order to identify experimental data points with large mass differences in the experimental data, any experimental data is removed, and the remaining data is used for linear fitting. The linear relationship after fitting is used to determine whether the above-mentioned removed experimental data is data with large differences. If so, the experimental data is marked as experimental data with large mass differences, so that in the process of implementing physical analysis using the above-mentioned n explosive Hugoniot experimental data, the influence weight of the experimental data marked as having large mass differences is reduced. If not, it is considered that the quality of the experimental data is relatively close and can be treated equally.
[0051] Step 2: Calculate the fitting deviation based on the above linear relationship.
[0052] In this embodiment, based on the linear relationship, the remaining n-1 experimental data can be substituted into the calculation to obtain the fitting deviation, and then identification can be performed; or based on the linear relationship, the i-th experimental data that has been removed can be substituted into the calculation to obtain the fitting deviation, and then identification can be performed.
[0053] As a feasible method, the fitting deviation is calculated based on the following process:
[0054] First, the shock wave velocity fitting values corresponding to the remaining n-1 experimental data are calculated according to the above linear relationship. The particle velocities of the remaining n-1 experimental data are substituted into the above linear relationship to calculate n-1 shock wave velocity fitting values.
[0055] Secondly, the absolute difference between each shock wave velocity fitting value and the corresponding shock wave velocity experimental value in the remaining n-1 Hugoniot experimental data is calculated. After calculating the n-1 shock wave velocity fitting values, the difference is calculated with the corresponding shock wave velocity experimental value to obtain n-1 differences.
[0056] Then, the mean of the n-1 above differences is calculated as the fitting deviation.
[0057] As a second feasible method, the fitting deviation is calculated based on the following process:
[0058] First, the shock wave velocity fitting value corresponding to the i-th experimental data is calculated according to the above linear relationship. The particle velocity of the i-th experimental data is substituted into the above linear relationship to calculate the shock wave velocity fitting value of the i-th experimental data.
[0059] Then, the difference between the shock wave velocity fitting value and the shock wave velocity experimental value of the i-th experimental data is calculated as the fitting deviation.
[0060] Step 3, change the value of i, and execute steps 1-2 repeatedly to traverse n explosive Hugoniot experimental data to obtain n fitting deviations.
[0061] For example, in step 1, initially i=1, and in this step, i:=i+1. The above steps 1 and 2 are cyclically executed by changing the value of i until each experimental data is removed and fitted to obtain a linear relationship and then calculate the fitting deviation, and finally obtain n fitting deviations.
[0062] Step 4: Determine whether the n fitting deviations meet the significant difference condition.
[0063] After obtaining n fitting deviations of n experimental data, we can compare their sizes to determine whether they meet the obvious difference conditions. If they meet the obvious difference conditions, the corresponding experimental data need to be marked to provide a weighted basis for the subsequent Hugoniot relationship and physical model evaluation; if they do not meet the obvious difference conditions, it means that the quality of all experimental data is relatively close and can be treated equally.
[0064] Step 5: If the significant difference condition is met, the i-th experimental data corresponding to the significant difference value in the fitting deviation is marked as the experimental data with large quality difference.
[0065] For the first feasible approach, judging whether the n fitting deviations meet the obvious difference condition includes: calculating the difference between the maximum value and the minimum value of the n fitting deviations; if the difference is greater than a threshold, determining that the obvious difference condition is met. Exemplarily, the threshold may be half of the maximum value.
[0066] Among them, if there is a large gap between the maximum and minimum values of the n fitting deviations, it can be considered that there is an obvious quality difference between the experimental data corresponding to the minimum value and other experimental data.
[0067] Further, marking the i-th experimental data corresponding to the obvious difference value in the fitting deviation as the experimental data with large quality difference includes: marking the i-th experimental data corresponding to the minimum value of the fitting deviation as the experimental data with large quality difference.
[0068] For the above-mentioned feasible method 2, judging whether the n fitting deviations meet the obvious difference condition includes: sorting the n fitting deviations by size; if the maximum value among the fitting deviations is greater than a threshold, determining that the obvious difference condition is met.
[0069] The larger the value of the fitting deviation of the removed experimental data is, the greater the quality difference is. By sorting the fitting deviations, the sample points with large quality differences can be found based on the maximum value of the fitting deviation.
[0070] Further, marking the i-th experimental data corresponding to the obvious difference value in the fitting deviation as the experimental data with large quality difference includes: marking the i-th experimental data corresponding to the maximum value of the fitting deviation as the experimental data with large quality difference.
[0071] The small sample explosive Hugoniot experimental data consistency analysis method provided in the embodiment of the present invention can perform linear fitting on the explosive Hugoniot experimental data and calculate the fitting deviation, and quantitatively characterize the quality of the Hugoniot experimental data by the fitting deviation, so as to effectively identify the quality difference of the explosive Hugoniot experimental data, give appropriate weights to different experimental data in subsequent Hugoniot relationship fitting and physical model calibration, fully mine the experimental data information, and effectively improve the generalization ability and prediction accuracy of the calibration model.
[0072] Optionally, after step 5, the method further includes:
[0073] Step 6, remove the ith experimental data corresponding to the obvious difference value in the fitting deviation, set n: = n-1, return to step 1 for the remaining n-1 samples, and loop through steps 1-6 until the fitting deviation no longer meets the obvious difference condition. In each loop, n fitting deviations are obtained to judge the data quality, and the experimental data with large quality differences are marked and removed until all experimental data with large quality differences are marked.
[0074] The following example illustrates the above consistency analysis process.
[0075] Assume that there are n explosive Hugoniot experimental data points, each experimental point includes shock wave velocity and particle velocity. This embodiment is aimed at small sample data, so n is generally small. Initially i=1.
[0076] Step 1: Remove the ith experimental data from the Hugoniot experimental data and use the remaining n-1 Hugoniot experimental data to fit U s and u p The linear relationship is:
[0077] U S =c0 (-i) +s (-i) u p
[0078] Among them, U s is the shock wave velocity, c0 and s are the parameters of the fitted linear Hugoniot relationship, and u p is the particle velocity after the wave;
[0079] Step 2, calculate the fitting deviation and mean of the n-1 experimental data excluding the i-th experimental data.
[0080] err j (-i) =|U S j -U S(-i) (u p j )|, j=1,2,i-1,i+1,n.
[0081]
[0082] Among them, err j (-i) To remove the fitting deviation after the i-th Hugoniot experimental data, err (-i) is its mean value, U S (-i) is the shock wave velocity of the Hugoniot relationship fitted after removing the i-th Hugoniot experimental data, is the particle velocity after the wave measured in the jth experiment.
[0083] Step 3, if i=n, continue to step 4. Otherwise: i:=i+1, go to step 1.
[0084] Step 4: Sort by mean from large to small.
[0085] If the maximum mean With the minimum mean There is a significant difference between the i-th explosive and the i-th explosive (for example, a difference of more than 0.5 times, which is determined by the object explosive and the user). m There is a significant quality difference between the i-th sample and the other samples. Then, remove the i-th sample. m samples, let n:=n-1, and for the remaining n-1 samples, return to step 1.
[0086] If the maximum mean With the minimum mean If the data are relatively close and the difference does not exceed 0.5 times, then the quality of the Hugoniot experimental data of this group is considered to be relatively close and can be treated equally.
[0087] The following uses five Hugoniot test data of a certain explosive as an example to illustrate the implementation process of the method. The five Hugoniot test data of the explosive are shown in Table 1.
[0088]
[0089]
[0090] Table 1
[0091] The following results were obtained by implementing the above analysis method. Figure 2 The figure shows the absolute deviation of the fitting without the shot-1 experiment. Figure 3The figure shows the absolute deviation of the fitting without the shot-2 experiment. Figure 4 The figure shows the absolute deviation of the fitting without the shot-3 experiment. Figure 5 The figure shows the absolute deviation of the fitting without the shot-4 experiment. Figure 6 A schematic diagram of the absolute deviation of the fit after removing the shot-5 experiment is shown.
[0092] like Figure 2 As shown, the average absolute deviation of the fitting (Mean of Absolute Deviations) err of the shot-1 experiment is removed (-1) =0.0236; Figure 3 As shown, the fitting absolute deviation err of the shot-2 experiment is removed (-2) =0.0241; Figure 4 As shown, the fitting absolute deviation err of the shot-3 experiment is removed (-3) =0.0079; Figure 5 As shown, the fitting absolute deviation err of the shot-4 experiment is removed (-4) =0.0209; Figure 6 As shown, the absolute deviation err of the fitting of the shot-5 experiment is removed (-5) =0.0241.
[0093] The mean absolute deviations are sorted as follows:
[0094] err (-2) =err (-5) >err (-1) >err (-4) >>err (-3)
[0095] Based on the above results, it can be judged that there is a quality difference between the shot-3 experiment and the other four experimental data. In the subsequent physical analysis process using the five experimental data, the impact weight of the shot-3 experimental point is appropriately reduced.
[0096] The embodiment of the present invention can determine the quality difference of explosive Hugoniot experimental data, and the calculation is simple and efficient. The embodiment of the present invention only needs to carry out linear fitting and fitting deviation calculation, and the calculation process is simple and the calculation amount is small. Especially for small sample data, the calculation amount is even smaller.
[0097] Optionally, the calculation of the mean value of the fitting deviation in the above embodiment can also be replaced by the fitting deviation of the removed point (i.e. the removed i-th experimental data), that is:
[0098] err i (-i) =|U Si -U S (-i) (u p i )|
[0099] In the embodiment of the present invention, the above err i (-i) The larger the value, the greater the quality difference, so as to judge the quality consistency and find the sample points with large quality difference.
[0100] The embodiment of the present invention further provides a small sample explosive Hugoniot experiment data consistency analysis system, which is used to execute the above-mentioned small sample explosive Hugoniot experiment data consistency analysis method.
[0101] The small sample explosive Hugoniot experiment data consistency analysis system provided by the embodiment of the present invention can achieve the same technical effect as the small sample explosive Hugoniot experiment data consistency analysis method mentioned above.
[0102] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the control device through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the above-mentioned method embodiments, wherein the storage medium may be a memory, a disk, an optical disk, etc.
[0103] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.
[0104] The above contents described in this specification are merely examples of the present invention. The present invention is not limited by the above embodiments. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.
Claims
1. A method for consistency analysis of Hugoniot test data of small sample explosives, characterized in that: The method comprises: Step 1, for n explosive Hugoniot experimental data, remove the i-th experimental data, and use the remaining n-1 experimental data to fit the linear relationship between shock wave velocity and particle velocity; Step 2, calculating the fitting deviation according to the linear relationship; Step 3, changing the value of i, cyclically executing the steps 1-2 to traverse the n explosive Hugoniot experimental data to obtain n fitting deviations; Step 4, determining whether the n fitting deviations meet the obvious difference condition; Step 5: If the significant difference condition is met, the i-th experimental data corresponding to the significant difference value in the fitting deviation is marked as experimental data with large quality difference.
2. The method according to claim 1, characterized in that After step 5, the method further comprises: Step 6, remove the i-th experimental data corresponding to the obvious difference value in the fitting deviation, set n: =n-1, return to execute step 1 for the remaining n-1 samples, and loop through steps 1-6 until the fitting deviation no longer meets the obvious difference condition.
3. The method according to claim 1, characterized in that The calculating the fitting deviation according to the linear relationship comprises: Calculate the shock wave velocity fitting values corresponding to the remaining n-1 experimental data according to the linear relationship; Calculate the difference between each of the shock wave velocity fitting values and the corresponding shock wave velocity experimental values in the remaining n-1 Hugoniot experimental data respectively; The mean of the n-1 differences is calculated as the fitting deviation.
4. The method according to claim 3, characterized in that The determining whether the n fitting deviations meet the obvious difference condition comprises: calculating the difference between the maximum value and the minimum value of the n fitting deviations; if the difference is greater than a threshold, determining that the obvious difference condition is met; The marking of the i-th experimental data corresponding to the obvious difference value in the fitting deviation as experimental data with large quality difference includes: marking the i-th experimental data corresponding to the minimum value of the fitting deviation as experimental data with large quality difference.
5. The method according to claim 1, characterized in that The calculating the fitting deviation according to the linear relationship comprises: Calculate the shock wave velocity fitting value corresponding to the i-th experimental data according to the linear relationship; The absolute difference between the shock wave velocity fitting value and the shock wave velocity experimental value of the i-th experimental data is calculated as the fitting deviation.
6. The method according to claim 5, characterized in that The determining whether the n fitting deviations meet the obvious difference condition comprises: sorting the n fitting deviations by size; if the maximum value among the fitting deviations is greater than a threshold, determining that the obvious difference condition is met; The marking of the i-th experimental data corresponding to the obvious difference value in the fitting deviation as experimental data with large quality difference includes: marking the i-th experimental data corresponding to the maximum value of the fitting deviation as experimental data with large quality difference.
7. The method according to claim 1, characterized in that The linear relationship between the shock wave velocity and the particle velocity is: The S =c0 (-i) +s (-i) the p Among them, U s is the shock wave velocity, c0 and s are the parameters of the fitted linear Hugoniot relationship, and u p is the particle velocity after the wave; The calculation formula for the fitting deviation is as follows: err j (-i) =|U s j -THE s (-i) (the p j )|,j=1,2,i-1,i+1,n; Among them, err j (-i) To remove the fitting deviation after the i-th Hugoniot experimental data, err (-i) is its mean value, U S (-i) is the shock wave velocity of the Hugoniot relationship fitted after removing the i-th Hugoniot experimental data, is the particle velocity after the wave measured in the jth experiment.
8. The method according to claim 1, characterized in that The linear relationship between the shock wave velocity and the particle velocity is: The S =c0 (-i) +s (-i) the p Among them, U s is the shock wave velocity, c0 and s are the parameters of the fitted linear Hugoniot relationship, and u p is the particle velocity after the wave; The calculation formula for the fitting deviation is as follows: err i (-i) =|U S i -THE S (-i) (the p i )| Among them, err j (-i) To remove the fitting deviation after the i-th Hugoniot experimental data, U S (-i) is the shock wave velocity of the Hugoniot relationship fitted after removing the i-th Hugoniot experimental data, is the particle velocity after the wave measured in the jth experiment.
9. The method according to claim 4, characterized in that The threshold is half of the maximum value.
10. A small sample explosive Hugoniot test data consistency analysis system, characterized in that: Used to execute the small sample explosive Hugoniot test data consistency analysis method described in any one of claims 1-9.