Self-adaptive analysis method for SHPB test data

Abstract

The invention discloses a self-adaptive analysis method for SHPB test data. The self-adaptive analysis method comprises the following steps: step 1, preprocessing the data of sand gravel subjected to an SHPB test by using a Kalman filtering algorithm; and 2, carrying out adaptive adjustment analysis on the preprocessed SHPB test data by using a pollen algorithm. The SHPB experimental data is preprocessed by using Kalman filtering, so that the influence of noise on the experimental data is reduced, the data obtained by the test becomes smooth and stable, and the experimental error is reduced; and performing global optimization on the preprocessed data in a set parameter value range by adopting an adaptive pollen algorithm to obtain an optimal parameter combination, optimizing rising time points of an incident wave, a reflected wave and a transmitted wave, performing global optimization in the set parameter value range, and finally obtaining the optimal parameter combination. The adjusting efficiency is obviously improved, and the accuracy of test data processing is improved.

Description

Technical Field

[0001] The invention relates to an adaptive analysis method for SHPB test data, and belongs to the field of SHPB data analysis and processing. Background Art

[0002] In the data obtained after the SHPB (Split Hopkinson Pressure Bar) test, noise often appears in the test data due to various factors such as signal transmission delay, sensor accuracy limitation and environmental interference. These noises sometimes cause a sudden rise or fall in the data, which in turn has an adverse effect on the accuracy of the experimental data and subsequent data analysis. In order to effectively deal with this problem, Kalman filtering, as an efficient and widely used filtering algorithm in the field of signal processing, has been introduced into the preprocessing of SHPB test data. Kalman filtering demonstrates its excellent ability in handling signal noise and uncertainty by dynamically updating state estimation and error covariance. It can significantly reduce the impact of noise on experimental data, making the data obtained from the experiment smoother and more stable, thus providing a reliable foundation for subsequent data analysis.

[0003] After completing the preprocessing of the SHPB test data by the Kalman filter, the subsequent data analysis phase begins. In this phase, a key step is to fine-tune the starting rise time points of the incident wave, reflected wave, and transmitted wave after the Kalman filter preprocessing. The purpose of this adjustment is to verify whether the front and rear surfaces of the specimen have reached a stress equilibrium state, thereby ensuring that the specimen can truly reflect its dynamic mechanical properties during the test, which is crucial to ensuring the accuracy and reliability of the test results.

[0004] However, when processing SHPB test data, existing technologies usually rely on manually adjusting the rise time points of the incident wave, reflected wave and transmitted wave to achieve stress balance. This method is not only time-consuming and labor-intensive, but also difficult to ensure the optimality and accuracy of the obtained parameter combination. Summary of the invention

[0005] The technical problem to be solved by the present invention is that when processing SHPB test data, the existing technology uses manual adjustment of the rising time points of the incident wave, reflected wave and transmitted wave to achieve stress balance, which is not only time-consuming and labor-intensive, but also difficult to ensure the optimality and accuracy of the obtained parameter combination.

[0006] In order to solve the above technical problems, the present invention provides an adaptive analysis method for SHPB test data, comprising:

[0007] Step 1. Use Kalman filter algorithm to pre-process the data of gravel after SHPB test;

[0008] Step 2. Use the pollen algorithm to perform adaptive adjustment analysis on the preprocessed SHPB test data.

[0009] In the aforementioned adaptive analysis method for SHPB test data, in step 1, the preprocessing includes:

[0010] S00: raw data after inputting SHPB test;

[0011] S01: The incident wave and the transmitted wave in the SHPB test data are taken as the observed values ​​respectively;

[0012] S02: Set up a memory pool 1 to record the posterior estimate after each filtering and calculate the prior estimate at the current kth estimation , prior error covariance matrix ;

[0013] S03: Use the standard Kalman filter algorithm to recursively update the corrected Kalman gain , and then the observed value and the prior estimate Perform data fusion to determine the posterior estimate , then update the error covariance matrix , put the posterior estimate after the kth Kalman filter into memory pool 1;

[0014] S04: After the k-th Kalman filter update and correction is completed, return to step S02 to update the k+1-th prior estimation value and the prior error covariance matrix, then substitute the k+1-th prior estimation value and the prior error covariance matrix into step S03 for update and correction, put the k+1-th posterior estimation value calculated in step S03 into memory pool 1, and recursively calculate in sequence;

[0015] S05: Compare the current estimated number and the size of the observation value array. If the current estimated number is less than the size of the observation value array, execute step S30; if the current sampling number is equal to the size of the observation value array, end the operation, complete the Kalman filter operation, and output the memory pool 1 obtained after the Kalman filter processes the SHPB test data. Execute steps S01-S05 on the incident wave and transmitted wave data respectively to complete the SHPB test data preprocessing.

[0016] The aforementioned adaptive analysis method for SHPB test data, in step S01, initializes the filter, and sets the initial values ​​of the parameters of the Kalman filter for the incident wave and the reflected wave, respectively, including setting the initial values ​​of the posterior estimation vector, state transfer matrix, observation matrix, control matrix, control input, posterior error covariance matrix, observation noise matrix and process noise covariance matrix at the time corresponding to k=0, and determining the size N of the observation array.

[0017] In the aforementioned adaptive analysis method for SHPB test data, in step S02, the prior estimation value at the current k-th estimation is calculated according to the following formula: , prior error covariance matrix ;

[0018] (1)

[0019] (2)

[0020] in: is the prior estimate of the system at time k, is the posterior estimate of the system at time k, is the prior error covariance matrix of the system at time k, is the error covariance matrix of the system at time k-1, is the state transfer matrix of the system at time k-1, is the control matrix of the system at time k-1, is the control input of the system at time k-1, is the process noise covariance matrix of the system at time k.

[0021] In the aforementioned adaptive analysis method for SHPB test data, in step S03, the calculation formula is:

[0022] (3)

[0023] (4)

[0024] (5)

[0025] in: is the observed value of the system at time k, is the observation matrix of the system at time k, is the observation noise covariance matrix of the system at time k.

[0026] The aforementioned adaptive analysis method for SHPB test data, in step 2, includes:

[0027] S06: Initialize the rising time points of each wave in the SHPB test data after Kalman filter preprocessing, and calculate the relationship between the stress of the front and rear surfaces of the specimen and time. Subtract the starting rising time point of the reflected wave in the input SHPB test raw data from the time point of the reflected wave through formula (6): , the starting rise time of the transmitted wave minus , initialize the rising time point of the reflected wave and the transmitted wave,

[0028] (6)

[0029] The following formula is used to calculate the stress variation over time of the front and rear surfaces of the sample without the adaptive pollen algorithm adjustment:

[0030] (7)

[0031] (8)

[0032] Where t is the time series, is the length from the strain gauge to the edge of the incident rod, is the length from the strain gauge to the edge of the transmission rod, is the length of the specimen, is the wave velocity of the stress wave propagating in the incident rod, is the wave velocity of stress wave propagating in the specimen, is the cross-sectional area of ​​the rod, is the cross-sectional area of ​​the specimen, is the stress on the front surface of the specimen at time t, is the stress on the back surface of the specimen at time t, is the elastic modulus of the rod, Calculate the strain for the incident wave at time t, Calculate the strain for the reflected wave at time t, Calculate the strain for the transmitted wave at time t;

[0033] S07: Select the index value range included in the front surface and back surface stress calculation, and take the front surface and back surface stress images calculated as needed. , The index value of the rising segment in the data is used as the adjustment calculation segment;

[0034] S08: Define the loss function as the mean square error function, that is

[0035] (9)

[0036] in, is the stress on the front surface of the specimen in the index value of the ascending section, is the stress on the rear surface of the specimen in the index value of the ascending segment, and n is the number of arrays;

[0037] S09: Initialize pollen algorithm parameters;

[0038] S10: Initialize pollen population;

[0039] S11: perform pollination operation;

[0040] S12: Evaluate the new pollen group after pollination, update the optimal pollen, and calculate the loss value after pollination;

[0041] S13: Determine whether the current number of iterations meets the maximum number of iterations Maxgen. If so, the iteration ends. Otherwise, return to step S11 to pollinate again to find the optimal pollen group.

[0042] S14: After the iteration is finished, the adjustment effect of the adaptive pollen algorithm is evaluated.

[0043] In the above-mentioned adaptive analysis method for SHPB test data, in step S09, the pollen algorithm parameters include setting the flower population size N, the conversion probability p, the total number of iterations Maxgen, and the upper and lower limits of the three parameters of the incident wave adjustment time IncidentTimeAdjust, the reflected wave adjustment time ReflectTimeAdjust, and the transmitted wave adjustment time TransmittedTimeAdjust in the SHPB test data; wherein the conversion probability p is dynamically adjusted using the following formula:

[0044] p ( t ) = exp [ − ( t T ) r ] (10)

[0045] Where: p(t) is the conversion probability at the tth iteration, t and T are the current iteration number and the total number of iterations respectively, and r is a random number that follows a uniform distribution on [0.2, 1.0].

[0046] In the above-mentioned adaptive analysis method of SHPB test data, in step S10, within the value range of the three parameters IncidentTimeAdjust, ReflectTimeAdjust and TransmittedTimeAdjust, the pollen individuals X1, X2, ..., the stress on the front surface of the sample are randomly initialized and each group of pollen individuals is substituted into equations (7) and (8) to calculate and obtain and the stress on the back surface of the specimen Then, according to step S07, the stress on the front surface of the sample is selected and the stress on the back surface of the specimen The stress rise index value segment is then substituted into formula (9) to obtain the loss value corresponding to each group of pollen individuals. The calculated loss values ​​are sorted, and the pollen individual information and loss value corresponding to the current minimum loss are recorded as the optimal pollen individual.

[0047] Set up memory pool 2 to record the current optimal individual information and its loss value.

[0048] In the above-mentioned adaptive analysis method of SHPB test data, in step S11, a random number rand is randomly generated. If the conversion probability p ≥ rand is established, cross-pollination is performed on the pollen individual, that is,

[0049] (11)

[0050] in: is the pollen group generated at the tth iteration, is the position of the pollen group generated at the t+1th iteration, is the best solution found by iterating all solutions, and the parameter L is the step length of pollination, which obeys the Levy distribution, that is,

[0051] (12)

[0052] in: is the minimum threshold of the step size, is the moving step length, is the standard gamma function, taking ;

[0053] If the conversion probability p < rand holds, the pollen individual is self-pollinated, and the new pollen individual generated is processed accordingly, that is,

[0054] (13)

[0055] and is the pollen set from any two flowers of the same plant species at the tth iteration, is the final pollen group generated at the tth iteration, is the pollen group finally generated at the t+1th iteration, is a proportional coefficient that follows a (0, 1) uniform distribution.

[0056] In the above-mentioned adaptive analysis method of SHPB test data, in step S12, the stress of the front surface of the sample of the pollen group newly generated after pollination in step S11 is calculated according to the stress calculation formula in step S06. and the stress on the back surface of the specimen The value is then calculated through the formula in step S08 to obtain the loss value of the newly generated pollen group, and the optimal pollen individual corresponding to the minimum loss value in the newly generated pollen group is selected. If the optimal pollen loss value after pollination is less than the minimum loss value in step S10, the minimum loss value and its corresponding optimal pollen individual are updated, otherwise they are discarded, and the optimal pollen individual information and its loss value in memory pool 2 are synchronously updated.

[0057] The beneficial effects achieved by the present invention are as follows: an adaptive analysis and processing method for SHPB experimental data of the present invention, by using Kalman filtering to pre-process the SHPB experimental data, significantly reduces the influence of noise on the experimental data, makes the data obtained from the experiment smooth and stable, and reduces the experimental error; then, an adaptive pollen algorithm is used to perform global optimization within a set parameter value range on the pre-processed data to obtain the optimal parameter combination, optimize the rising time points of the incident wave, the reflected wave, and the transmitted wave, and can perform global optimization within the set parameter value range, and finally obtain the optimal parameter combination by iteratively searching and comparing the model performance under different parameter combinations. This optimization process not only significantly improves the performance of the model and the reliability of the results, but also significantly improves the adjustment efficiency and the accuracy of the experimental data processing. The introduction of the adaptive pollen algorithm solves the shortcomings of traditional manual adjustment and provides a more efficient and accurate method for the processing and analysis of SHPB experimental data. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the adaptive analysis method of SHPB test data of the present invention;

[0059] Figure 2 Schematic diagram of the original data of incident wave and reflected wave and the data after adjustment by Kalman filter;

[0060] Figure 3 This is the stress balance effect diagram of the sample before adjustment by the adaptive pollen algorithm;

[0061] Figure 4 This is a graph showing the change of MSE value with the number of iterations in the adaptive algorithm adjustment;

[0062] Figure 5 This is a diagram showing the stress balance effect of the sample after adjustment by the adaptive pollen algorithm. DETAILED DESCRIPTION

[0063] The method of the present invention is described in detail below with reference to the accompanying drawings.

[0064] Example 1

[0065] like Figure 1 As shown, this embodiment provides an adaptive analysis method for SHPB test data, including:

[0066] Step 1. Use Kalman filter algorithm to pre-process the data of gravel after SHPB test;

[0067] Step 2. Use the pollen algorithm to perform adaptive adjustment analysis on the preprocessed SHPB test data.

[0068] In step one, the preprocessing includes:

[0069] S00: raw data after inputting SHPB test;

[0070] S01: The incident wave and the transmitted wave in the SHPB test data are taken as the observed values ​​respectively;

[0071] Initialize the filter and set the initial values ​​of the parameters of the Kalman filter for the incident wave and the reflected wave, including setting the initial values ​​of the posterior estimation vector, state transfer matrix, observation matrix, control matrix, control input, posterior error covariance matrix, observation noise matrix and process noise covariance matrix at the time corresponding to k=0, and determine the size of the observation array N; the setting of these initial values ​​determines the performance of the Kalman filter, including filtering accuracy, convergence speed and noise suppression effect. Reasonable selection of these parameters can enable the Kalman filter to more accurately estimate the true state of the waveform when processing SHPB test data, ensuring the best filtering effect of the incident wave and the reflected wave.

[0072] In this embodiment, N is 19996, k is the number of estimations, k=0, 1, 2, ...N;

[0073] S02: Set up a memory pool 1 to record the posterior estimation value after each filtering, and calculate the prior estimation value at the current kth estimation according to the following formula , prior error covariance matrix ;

[0074] (1)

[0075] (2)

[0076] in: is the prior estimate of the system at time k, is the posterior estimate of the system at time k, is the prior error covariance matrix of the system at time k, is the error covariance matrix of the system at time k-1, is the state transfer matrix of the system at time k-1, is the control matrix of the system at time k-1, is the control input of the system at time k-1, is the process noise covariance matrix of the system at time k; by calculating the k-th prior estimation value and the prior error covariance matrix, these values ​​provide the calculation basis for the subsequent update and correction steps.

[0077] S03: Use the standard Kalman filter algorithm to recursively update the corrected Kalman gain , and then the observed value and the prior estimate Perform data fusion to determine the posterior estimate , then update the error covariance matrix ,Right now

[0078] (3)

[0079] (4)

[0080] (5)

[0081] in: is the observed value of the system at time k, is the observation matrix of the system at time k, is the observation noise covariance matrix of the system at time k; the main effect of this step is to realize the update and correction of the system state by the Kalman filter in the current iteration number, and strengthen the dynamic correction function of the Kalman filter on the state estimation during the iteration process. And output the posterior estimate after the Kalman filter algorithm updates and corrects it.

[0082] Put the posterior estimate after the kth Kalman filter into memory pool one.

[0083] S04: After the k-th Kalman filter update and correction is completed, return to step S02 to update the k+1-th prior estimation value and the prior error covariance matrix, then substitute the k+1-th prior estimation value and the prior error covariance matrix into step S03 for update and correction, put the k+1-th posterior estimation value calculated in step S03 into memory pool 1, and recursively calculate in sequence;

[0084] S05: Compare the current estimated number and the size of the observation value array. If the current estimated number is less than the size of the observation value array, execute step S30; if the current sampling number is equal to the size of the observation value array, end the operation, complete the Kalman filter operation, and output the memory pool 1 obtained after the Kalman filter processes the SHPB test data. Execute steps S01-S05 on the incident wave and transmitted wave data respectively to complete the SHPB test data preprocessing.

[0085] like Figure 2 As shown by the dotted line in the middle, the solid lines in the left and right figures are the original data of the incident wave and the transmitted wave changing with time of the SHPB test without Kalman filtering processing, and the dotted lines are the images of the incident wave and the transmitted wave changing with time after processing. It can be seen that the noise of the data after processing is significantly reduced, and the waveform is smoother.

[0086] In step 2, the SHPB test data after Kalman filter preprocessing is analyzed using the pollen algorithm for adaptive adjustment, including:

[0087] S06: Initialize the rising time points of each wave in the SHPB test data after Kalman filter preprocessing, and calculate the relationship between the stress of the front and rear surfaces of the sample and time. Subtract the starting rising time point of the reflected wave in the SHPB test raw data input in step S00 from , the starting rise time of the transmitted wave minus , initialize the rising time point of the reflected wave and the transmitted wave,

[0088] (6)

[0089] The following formula is used to calculate the stress variation over time of the front and rear surfaces of the sample without the adaptive pollen algorithm adjustment:

[0090] (7)

[0091] (8)

[0092] Where t is the time series, is the length from the strain gauge to the edge of the incident rod, is the length from the strain gauge to the edge of the transmission rod, is the length of the specimen, is the wave velocity of the stress wave propagating in the incident rod, is the wave velocity of stress wave propagating in the specimen, is the cross-sectional area of ​​the rod, is the cross-sectional area of ​​the specimen, is the stress on the front surface of the specimen at time t, is the stress on the back surface of the specimen at time t, is the elastic modulus of the rod, Calculate the strain for the incident wave at time t, Calculate the strain for the reflected wave at time t, Calculate the strain for the transmitted wave at time t. Move the theoretical rising points of the three to the same time to ensure the timing alignment of the incident wave, reflected wave, and transmitted wave, reduce the time error, and provide a condition for the subsequent optimization algorithm to change the initial stress of the front and rear surfaces of the sample with time.

[0093] S07: Select the index value range included in the front surface and back surface stress calculation, and take the front surface and back surface stress images calculated as needed. , The index value of the rising segment in the data is used as the adjustment calculation segment. Here, the calculation index range is selected as [0:5000], and the data in other parts are not included in the calculation;

[0094] S08: Define the loss function as the mean square error function, that is

[0095] (9)

[0096] in, is the stress on the front surface of the specimen in the index value of the ascending section, is the stress on the rear surface of the specimen in the index value of the ascending segment, and n is the number of arrays;

[0097] S09: Initialize pollen algorithm parameters;

[0098] The pollen algorithm parameters include setting the flower population size N, conversion probability p, total number of iterations Maxgen, and the upper and lower limits of the three parameters IncidentTimeAdjust, ReflectTimeAdjust, and TransmittedTimeAdjust in the SHPB test data; among them, the conversion probability p is dynamically adjusted using the following formula:

[0099] p ( t ) = exp [ − ( t T ) r ] (10)

[0100] Where: p(t) is the conversion probability at the tth iteration, t and T are the current iteration number and the total number of iterations respectively, and r is a random number that follows a uniform distribution on [0.2, 1.0];

[0101] S10: Initialize pollen population;

[0102] In the range of the three parameters IncidentTimeAdjust, ReflectTimeAdjust and TransmittedTimeAdjust, the pollen individuals X1, X2, ..., the stress on the front surface of the sample are randomly initialized and each group of pollen individuals is substituted into equations (7) and (8) to obtain the sum. and the stress on the back surface of the specimen Then, according to step S07, the stress on the front surface of the sample is selected and the stress on the back surface of the specimen The stress rise index value segment is then substituted into formula (9) to obtain the loss value corresponding to each group of pollen individuals. The calculated loss values ​​are sorted, and the pollen individual information and loss value corresponding to the current minimum loss are recorded as the optimal pollen individual.

[0103] Set up memory pool 2 to record the current optimal individual information and its loss value;

[0104] S11: perform pollination operation;

[0105] A random number rand is randomly generated. If the conversion probability p ≥ rand holds, cross-pollination is performed on the pollen individual, that is,

[0106] (11)

[0107] in: is the pollen group generated at the tth iteration, is the position of the pollen group generated at the t+1th iteration, is the best solution found by iterating all solutions, and the parameter L is the step length of pollination, which obeys the Levy distribution, that is,

[0108] (12)

[0109] in: is the minimum threshold of the step size, is the moving step length, is the standard gamma function, taking ;

[0110] If the conversion probability p < rand holds, the pollen individual is self-pollinated, and the new pollen individual generated is processed accordingly, that is,

[0111] (13)

[0112] and is the pollen set from any two flowers of the same plant species at the tth iteration, is the final pollen group generated at the tth iteration, is the pollen group finally generated at the t+1th iteration, is the proportional coefficient that follows the (0, 1) uniform distribution;

[0113] S12: Evaluate the new pollen group after pollination, update the optimal pollen, and calculate the loss value after pollination

[0114] The stress on the front surface of the sample of the newly generated pollen group after pollination in step S11 is calculated according to the stress calculation formula in step S06. and the stress on the back surface of the specimen The value of is then calculated by the formula in step S08 to obtain the loss value of the newly generated pollen group, and the optimal pollen individual corresponding to the minimum loss value in the newly generated pollen group is selected. If the optimal pollen loss value after pollination is less than the minimum loss value in step S10, the minimum loss value and its corresponding optimal pollen individual are updated, otherwise they are discarded, and the optimal pollen individual information and its loss value in the memory pool 2 are synchronously updated;

[0115] S13: Determine whether the current number of iterations meets the maximum number of iterations Maxgen. If the end condition is met, the iteration can be ended. Otherwise, return to step S11 to pollinate again to find the optimal pollen group.

[0116] S14: After the iteration is finished, the adjustment effect of the adaptive pollen algorithm is evaluated.

[0117] The final loss value is used to judge the effect of the algorithm on the model adjustment after adaptive adjustment. If the loss value is still greater than a relative value determined according to specific test data after multiple iterations, it is considered that the initial data error of the SHPB test is large, and the output model adjustment effect is unqualified. Otherwise, the optimal pollen individual parameter combination (IncidentTimeAdjust, ReflectTimeAdjust, TransmittedTimeAdjust) after the model adjustment is output, and the optimal pollen individual parameter combination is substituted into formula (6) for stress calculation to obtain the final adjusted and , draw the corresponding stress balance image, and complete the adjustment of the SHPB test data by the adaptive flower pollination algorithm.

[0118] like Figure 3 As shown, the two solid lines are images of the front and rear surface stress changes over time of the sample after the Kalman filter preprocessed the SHPB test data and before the adaptive pollen algorithm was adjusted. It can be seen that there is a large difference between the front and rear surface stresses of the sample, and the stress balance is poor.

[0119] Figure 4 This is an image of the change in loss value with the number of iterations during the adaptive analysis of SHPB experimental data by the adaptive pollen algorithm. It can be seen that with the increase in the number of iterations, the loss value drops from the highest point 17 to the lowest point 0.343.

[0120] Figure 5 This is the image of the change of the front and rear surface stress over time after 1000 iterations of the adaptive pollen algorithm on the SHPB test data. It can be seen that the image of the change of the front and rear surface stress of the sample over time is different from that of Figure 3 It is obviously close to the stress balance condition, but due to the experimental conditions, the test data itself may have some defects, so this group of data still does not completely overlap after analysis by the adaptive pollen algorithm.

[0121] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0124] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An adaptive analysis method for SHPB test data, characterized in that: include: Step 1. Use Kalman filter algorithm to pre-process the data of gravel after SHPB test; Step 2. Use the pollen algorithm to perform adaptive adjustment analysis on the preprocessed SHPB test data.

2. The adaptive analysis method of SHPB test data according to claim 1, characterized in that: In step one, the preprocessing includes: S00: raw data after inputting SHPB test; S01: The incident wave and the transmitted wave in the SHPB test data are taken as the observed values ​​respectively; S02: Set up a memory pool 1 to record the posterior estimate after each filtering and calculate the prior estimate at the current kth estimation , prior error covariance matrix ; S03: Use the standard Kalman filter algorithm to recursively update the corrected Kalman gain , and then the observed value and the prior estimate Perform data fusion to determine the posterior estimate , then update the error covariance matrix , put the posterior estimate after the kth Kalman filter into memory pool 1; S04: After the k-th Kalman filter update and correction is completed, return to step S02 to update the k+1-th prior estimation value and the prior error covariance matrix, then substitute the k+1-th prior estimation value and the prior error covariance matrix into step S03 for update and correction, put the k+1-th posterior estimation value calculated in step S03 into memory pool 1, and recursively calculate in sequence; S05: Compare the current estimated number and the size of the observation value array. If the current estimated number is less than the size of the observation value array, execute step S30; if the current sampling number is equal to the size of the observation value array, end the operation, complete the Kalman filter operation, and output the memory pool 1 obtained after the Kalman filter processes the SHPB test data. Execute steps S01-S05 on the incident wave and transmitted wave data respectively to complete the SHPB test data preprocessing.

3. The adaptive analysis method of SHPB test data according to claim 2, characterized in that: In step S01, the filter is initialized and the initial values ​​of the parameters of the Kalman filter for the incident wave and the reflected wave are set respectively, including setting the initial values ​​of the posterior estimation vector, state transfer matrix, observation matrix, control matrix, control input, posterior error covariance matrix, observation noise matrix and process noise covariance matrix at the time corresponding to k=0, and determining the size N of the observation value array.

4. The adaptive analysis method of SHPB test data according to claim 2, characterized in that: In step S02, the prior estimate value at the current k-th estimation is calculated according to the following formula: , prior error covariance matrix ; (1) (2) in: is the prior estimate of the system at time k, is the posterior estimate of the system at time k, is the prior error covariance matrix of the system at time k, is the error covariance matrix of the system at time k-1, is the state transfer matrix of the system at time k-1, is the control matrix of the system at time k-1, is the control input of the system at time k-1, is the process noise covariance matrix of the system at time k.

5. The adaptive analysis method of SHPB test data according to claim 4, characterized in that: In step S03, the calculation formula is: (3) (4) (5) in: is the observed value of the system at time k, is the observation matrix of the system at time k, is the observation noise covariance matrix of the system at time k.

6. The adaptive analysis method of SHPB test data according to claim 1, characterized in that: In step 2, it includes: S06: Initialize the rising time points of each wave in the SHPB test data after Kalman filter preprocessing, and calculate the relationship between the stress of the front and rear surfaces of the specimen and time. Subtract the starting rising time point of the reflected wave in the input SHPB test raw data from the time point of the reflected wave through formula (6): , the starting rise time of the transmitted wave minus , initialize the rising time point of the reflected wave and the transmitted wave, （6） The following formula is used to calculate the stress variation over time of the front and rear surfaces of the sample without the adaptive pollen algorithm adjustment: （7） （8） Where t is the time series, is the length from the strain gauge to the edge of the incident rod, is the length from the strain gauge to the edge of the transmission rod, is the length of the specimen, is the wave velocity of the stress wave propagating in the incident rod, is the wave velocity of stress wave propagating in the specimen, is the cross-sectional area of ​​the rod, is the cross-sectional area of ​​the specimen, is the stress on the front surface of the specimen at time t, is the stress on the back surface of the specimen at time t, is the elastic modulus of the rod, Calculate the strain for the incident wave at time t, Calculate the strain for the reflected wave at time t, Calculate the strain for the transmitted wave at time t; S07: Select the index value range included in the front surface and back surface stress calculation, and take the front surface and back surface stress images obtained by calculation. , The index value of the rising segment in the data is used as the adjustment calculation segment; S08: Define the loss function as the mean square error function, that is （9） in, is the stress on the front surface of the specimen in the index value of the ascending section, is the stress on the rear surface of the specimen in the index value of the ascending segment, and n is the number of arrays; S09: Initialize pollen algorithm parameters; S10: Initialize pollen population; S11: perform pollination operation; S12: Evaluate the new pollen group after pollination, update the optimal pollen, and calculate the loss value after pollination; S13: Determine whether the current number of iterations meets the maximum number of iterations Maxgen. If so, the iteration ends. Otherwise, return to step S11 to pollinate again to find the optimal pollen group. S14: After the iteration is finished, the adjustment effect of the adaptive pollen algorithm is evaluated.

7. The method for adaptively analyzing SHPB test data according to claim 6, characterized in that: In step S09, the pollen algorithm parameters include setting the flower population size N, the conversion probability p, the total number of iterations Maxgen, and the upper and lower limits of the three parameters of the incident wave adjustment time IncidentTimeAdjust, the reflected wave adjustment time ReflectTimeAdjust, and the transmitted wave adjustment time TransmittedTimeAdjust in the SHPB test data; among which, the conversion probability p is dynamically adjusted using the following formula: （10） Where: p(t) is the conversion probability at the tth iteration, t and T are the current iteration number and the total number of iterations respectively, and r is a random number that follows a uniform distribution on [0.2, 1.0].

8. The method for adaptive analysis of SHPB test data according to claim 7, characterized in that: In step S10, within the value range of the three parameters IncidentTimeAdjust, ReflectTimeAdjust and TransmittedTimeAdjust, the pollen individuals X1, X2, ..., the stress on the front surface of the sample are randomly initialized and each group of pollen individuals is substituted into equations (7) and (8) to obtain the sum. and the stress on the back surface of the specimen Then, according to step S07, the stress on the front surface of the sample is selected and the stress on the back surface of the specimen The stress rise index value segment is then substituted into formula (9) to obtain the loss value corresponding to each group of pollen individuals. The calculated loss values ​​are sorted, and the pollen individual information and loss value corresponding to the current minimum loss are recorded as the optimal pollen individual. Set up memory pool 2 to record the current optimal individual information and its loss value.

9. The method for adaptive analysis of SHPB test data according to claim 8, characterized in that: In step S11, a random number rand is randomly generated. If the conversion probability p ≥ rand holds, cross-pollination is performed on the pollen individual, that is, (11) in: is the pollen group generated at the tth iteration, is the position of the pollen group generated at the t+1th iteration, is the best solution found by iterating all solutions, and the parameter L is the step length of pollination, which obeys the Levy distribution, that is, (12) in: is the minimum threshold of the step size, is the moving step length, is the standard gamma function, take ; If the conversion probability p < rand holds, the pollen individual is self-pollinated, and the new pollen individual generated is processed accordingly, that is, (13) and is the pollen set from any two flowers of the same plant species at the tth iteration, is the final pollen group generated at the tth iteration, is the pollen group finally generated at the t+1th iteration, is a proportional coefficient that follows a (0, 1) uniform distribution.

10. The method for adaptive analysis of SHPB test data according to claim 9, characterized in that: In step S12, the stress calculation formula in step S06 is used to calculate the stress of the front surface of the pollen group newly generated after pollination in step S11. and the stress on the back surface of the specimen The value is then calculated through the formula in step S08 to obtain the loss value of the newly generated pollen group, and the optimal pollen individual corresponding to the minimum loss value in the newly generated pollen group is selected. If the optimal pollen loss value after pollination is less than the minimum loss value in step S10, the minimum loss value and its corresponding optimal pollen individual are updated, otherwise they are discarded, and the optimal pollen individual information and its loss value in memory pool 2 are synchronously updated.

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