Combustion rate regulation and control method and system for composite solid propellant

Abstract

The invention discloses a method for regulating and controlling the combustion rate of a composite solid propellant, and relates to the technical field of composite solid propellants, and the method comprises the following steps: collecting the combustion rate of the composite solid propellant under different conditions and the torque value of a mixer; constructing a correlation model of the combustion rate and the torque value of the mixing machine; inputting a mixer torque value measured in the production process into the associated model for calculation to obtain a propellant burning rate prediction value under the conditions of different oxidant particle sizes and oxidant contents; adjusting the grading of the oxidizing agent according to the conformity and dispersity of the combustion rate predicted value and the design index; the invention further discloses a composite solid propellant burning rate regulation and control system. The system comprises a data acquisition module, a data analysis module, a burning rate prediction module and an oxidant regulation module. According to the method, by combining multivariable regression analysis, real-time data prediction and refined oxidant grading adjustment, high-precision combustion speed control can be achieved, and high adaptability and optimization space are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of composite solid propellants, and specifically relates to a method and system for regulating the burning rate of composite solid propellants. Background Technique

[0002] Composite solid propellants are a widely used type of high-energy propulsion material, which are widely used in the propulsion systems of rockets, missiles, and other spacecraft. The quality of their performance directly affects the effectiveness of the propulsion system. Among them, the burning rate is one of the important parameters of the propellant performance. The burning rate of the propellant not only affects the thrust of the thruster but is also closely related to factors such as the ratio of oxidizer to fuel, particle size, and curing conditions. Therefore, precisely controlling the burning rate is crucial for the design and production of propellants.

[0003] Traditional burning rate regulation methods rely on experience and experimental data. However, with the increase in production scale and the complexity of the production process, simply relying on experiments often fails to achieve the ideal regulation effect. How to achieve accurate prediction of the burning rate and real-time regulation has become an important challenge. Existing technologies mainly rely on simple physical modeling of the burning rate of solid propellants and lack in-depth analysis of the complex relationships between multiple factors. Methods such as linear regression analysis and mathematical modeling have been gradually applied to this field, but there is still much room for improvement in multi-variable control and high-precision regulation. Therefore, developing a method for regulating the burning rate of composite solid propellants based on multi-variable regression analysis, combining actual production data with prediction models to optimize the ratio and performance of the propellant, has become a key technology for improving production efficiency and product quality. Summary of the Invention

[0004] To solve the above technical problems, a method and system for regulating the burning rate of composite solid propellants are provided, and the present technical solution solves the above problems.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method and system for regulating the burning rate of composite solid propellants, including:

[0007] Collect the burning rates and mixer torque values of composite solid propellants under different oxidizer particle sizes, oxidizer contents, and curing conditions, and obtain a sample set through preprocessing;

[0008] Perform mathematical regression on the burning rates and mixer torque values in the sample set to obtain a linear regression equation, describe the relationship between the propellant burning rate and the mixer torque, and construct an associated model of the burning rate and the mixer torque value based on the obtained regression results;

[0009] The mixer torque value measured during the production process is input into the correlation model for calculation to obtain the predicted value of the propellant burning rate under different oxidizer particle sizes and oxidizer contents;

[0010] Adjust the oxidant grading according to the compliance and dispersion of the predicted burning rate with the design indicators.

[0011] Preferably, the burning rate and mixer torque values ​​of the composite solid propellant under different oxidizer particle sizes, oxidizer contents and curing conditions are collected, and the sample set obtained after preprocessing specifically includes:

[0012] Check whether there are missing values ​​in the collected data and process the missing values ​​based on interpolation method;

[0013] The interpolation formula is:

[0014]

[0015] In the formula, x1x2 is the x coordinate of the two known data points before and after the missing value, y1y2 is the corresponding y value, and x i is the i-th position that needs to be interpolated, y i The i-th estimated missing value;

[0016] The moving average method is used to remove noise caused by sensor fluctuations and external interference to ensure data quality. The standard deviation statistical method is used to identify outliers in the data. For each set of data on the burning rate and mixer torque value, the deviation from the overall data distribution is calculated. If a data point deviates from the overall distribution by more than the set threshold, it is considered to be abnormal data. Combined with the physical laws in the propellant production process and the working parameters of the equipment, according to the preset physical constraints, data points that cannot meet the physical constraints are eliminated;

[0017] The mixer torque and combustion rate data are standardized and converted to the range of 0-1.

[0018] Preferably, the method of identifying outliers in data based on standard deviation statistics specifically includes:

[0019] Among them, the formula of standard deviation statistical method is:

[0020]

[0021] In the formula, x i is the i-th data point, μ is the mean of the data set, σ is the standard deviation of the data set, and Z i The standard deviation of the ith data point;

[0022] Based on the standard deviation of data points, abnormal data is judged. If the absolute value of the standard deviation of a data point exceeds 2 times the set threshold, then this data point is considered an outlier.

[0023] Preferably, the burning rate and the mixer torque value in the sample set are subjected to mathematical regression to obtain a linear regression equation to describe the relationship between the propellant burning rate and the mixer torque. Based on the obtained regression result, constructing the associated model of the burning rate and the mixer torque value specifically includes:

[0024] Obtain a sample set containing the burning rate and the mixer torque value, and construct a linear regression model. Among them, the formula of the linear regression model is:

[0025] Y = a + bX

[0026] In the formula, Y is the propellant burning rate, X is the mixer torque value, a is the constant term of the regression equation, and b is the regression coefficient;

[0027] Based on the linear regression model, find the straight line that best fits the data and minimize the sum of the squares of the errors between the actual observed values and the predicted values;

[0028] Take the derivatives of a and b and set the derivatives to zero to obtain the regression coefficient;

[0029] Use the obtained regression equation to predict the burning rate for different mixer torque values.

[0030] Preferably, the step of based on the linear regression model to find the straight line that best fits the data and minimize the sum of the squares of the errors between the actual observed values and the predicted values specifically includes:

[0031] Among them, the formula for the sum of the squares of the errors is:

[0032]

[0033] In the formula, E is the sum of the squares of the errors, i is the index, n is the total number of data points, y i the i-th observed value, a is the intercept of the regression equation, b is the slope of the regression equation, x i the value of the i-th independent variable;

[0034] Minimize the value of E by taking the derivatives of a and b.

[0035] Preferably, the step of taking the derivatives of a and b and setting the derivatives to zero to obtain the regression coefficient specifically includes:

[0036] Take the partial derivatives of a and b respectively and set them to zero. Among them, the partial derivative calculation formula is:

[0037]

[0038] In the formula, represents the partial derivative of the sum of squared errors \(E\) with respect to the regression coefficient \(a\), \(y\) i is the true dependent variable value corresponding to the \(i\)-th observation, \(a\) is the intercept in the regression equation, \(b\) is the slope in the regression equation, \(x\) i is the \(i\)-th independent variable value, represents the partial derivative of the sum of squared errors \(E\) with respect to the regression coefficient \(a\);

[0039] By solving these two equations, the regression coefficients \(a\) and \(b\) are obtained, thus obtaining the linear regression equation.

[0040] Preferably, the step of inputting the mixer torque value measured during the production process into the associated model for calculation to obtain the predicted propellant burning rate values under different oxidizer particle sizes and oxidizer contents specifically includes:[[]]

[0041] Input the mixer torque value measured in real time during the production process into the trained associated model. The model predicts the burning rate based on the input torque value, oxidizer particle size, and oxidizer content, and the obtained predicted value is the propellant burning rate under different oxidizer particle sizes and oxidizer contents;

[0042] Compare the predicted burning rate with the design target value to check whether it meets the index requirements.

[0043] Preferably, the step of adjusting the oxidizer grading according to the compliance and dispersion of the predicted burning rate value and the design index specifically includes:[[]]

[0044] Calculate the current burning rate error and dispersion, compare the prediction result of the regression model with the design index, calculate the error between the actual burning rate and the design index, and calculate the dispersion of the burning rate at the same time;

[0045] Among them, the calculation formula for the dispersion of the burning rate is:[[]]

[0046]

[0047] In the formula, \(n\) is the total number of sample data, is the predicted burning rate of the \(i\)-th observation, is the average value of all observations, and \(J\) is the dispersion of the burning rate;

[0048] Analyze the different grading ratios of the oxidizer based on the experimental data and production data, including the influence of the mass ratio of the oxidizer and the fuel on the burning rate and dispersion;

[0049] Based on the error between the predicted burning rate value and the design target, adjust the proportion of the oxidizer to make the burning rate closer to the design index;

[0050] By gradually changing the grading ratio of the oxidizer and making predictions, observing the differences between the predicted burning rates and the design indicators, as well as the changes in dispersion, continuously adjusting the grading of the oxidizer until the burning rate meets the design requirements and the dispersion reaches the requirements.

[0051] Preferably, the step of gradually changing the grading ratio of the oxidizer and making predictions, observing the differences between the predicted burning rates and the design indicators, as well as the changes in dispersion, and continuously adjusting the grading of the oxidizer until the burning rate meets the design requirements and the dispersion reaches the requirements specifically includes:

[0052] By adjusting the oxidizer grading, reducing the error between the predicted burning rate and the design indicator, making the predicted burning rate as close as possible to the target burning rate, setting a tolerance range to ensure that the burning rate is within this range, and adjusting the oxidizer ratio if it exceeds this range;

[0053] Through actual production verification, check whether the burning rate of the propellant meets the design indicators and has small dispersion under the adjusted oxidizer grading. If the requirements are still not met, continue to adjust. If the adjusted burning rate meets the target but there is still a certain dispersion, further refine the adjustment of the oxidizer grading to reduce the dispersion;

[0054] After multiple rounds of optimization, obtain the optimal grading ratio of the oxidizer.

[0055] A composite solid propellant burning rate control system, comprising:

[0056] A data acquisition module: The data acquisition module is used to collect the burning rates and mixer torque values of the composite solid propellant under different oxidizer particle sizes, oxidizer contents, and curing conditions, and obtain a sample set after preprocessing;

[0057] A data analysis module: The data analysis module is electrically connected to the data acquisition module. The data analysis module is used to perform mathematical regression on the burning rates and mixer torque values in the sample set to obtain a linear regression equation, describe the relationship between the propellant burning rate and the mixer torque, and construct an associated model of the burning rate and the mixer torque value based on the obtained regression results;

[0058] A burning rate prediction module: The burning rate prediction module is electrically connected to the data analysis module. The burning rate prediction module is used to input the mixer torque value measured during the production process into the associated model for calculation to obtain the predicted burning rate value of the propellant under different oxidizer particle sizes and oxidizer contents;

[0059] An oxidizer adjustment module: The oxidizer adjustment module is electrically connected to the burning rate prediction module. The oxidizer adjustment module is used to adjust the oxidizer grading according to the compliance and dispersion of the predicted burning rate value with the design indicators.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] The present invention proposes that by combining multivariate regression analysis, real-time data prediction and refined oxidizer grading adjustment, not only high-precision burning rate control can be achieved, but also strong adaptability and optimization space are provided, thereby significantly improving the production efficiency and product quality of the propellant. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a step flow framework diagram of the present invention;

[0063] Figure 2 It is a system framework diagram of the present invention. DETAILED DESCRIPTION

[0064] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0065] Reference Figure 1 As shown, a composite solid propellant burning rate control method and system, comprising:

[0066] A composite solid propellant burning rate control method and system, comprising:

[0067] Step 1:

[0068] Check whether there are missing values ​​in the collected data and process the missing values ​​based on interpolation method;

[0069] The interpolation formula is:

[0070]

[0071] In the formula, x1x2 is the x coordinate of the two known data points before and after the missing value, y1y2 is the corresponding y value, and x i is the i-th position that needs to be interpolated, y i The i-th estimated missing value;

[0072] The moving average method is used to remove noise caused by sensor fluctuations and external interference to ensure data quality. The standard deviation statistical method is used to identify outliers in the data. For each set of data on the burning rate and mixer torque value, the deviation from the overall data distribution is calculated. If a data point deviates from the overall distribution by more than the set threshold, it is considered to be abnormal data. Combined with the physical laws in the propellant production process and the working parameters of the equipment, according to the preset physical constraints, data points that cannot meet the physical constraints are eliminated;

[0073] Among them, the formula of standard deviation statistical method is:

[0074]

[0075] where x i is the i-th data point, μ is the mean of the data set, σ is the standard deviation of the data set, and Z i is the standard deviation of the i-th data point;

[0076] Based on the standard deviation of the data points, abnormal data is judged. If the absolute value of the standard deviation of the data point exceeds 2 times the set threshold, then the data point is considered an outlier;

[0077] The torque of the mixer and the burn rate data are standardized and the data is converted to the range of 0-1;

[0078] Step 2:

[0079] Obtain a sample set containing burn rate and mixer torque values, and construct a linear regression model. Among them, the linear regression model formula is:

[0080] Y = a + bX

[0081] where Y is the burn rate of the propellant, X is the mixer torque value, a is the constant term of the regression equation, and b is the regression coefficient;

[0082] Based on the linear regression model, find the straight line that best fits the data, and minimize the sum of the squares of the errors between the actual observed values and the predicted values;

[0083] Among them, the formula for the sum of the squares of the errors is:

[0084]

[0085] where E is the sum of the squares of the errors, i is the index, n is the total number of data points, and y i is the i-th observed value, a is the intercept of the regression equation, b is the slope of the regression equation, and x i is the value of the i-th independent variable;

[0086] By taking the derivatives of a and b, the value of E is minimized;

[0087] Take the partial derivatives of a and b respectively and set them to zero. Among them, the partial derivative calculation formula is:

[0088]

[0089] where represents the partial derivative of the sum of the squares of the errors E with respect to the regression coefficient a, and y i is the true dependent variable value corresponding to the i-th observed value, a is the intercept in the regression equation, b is the slope in the regression equation, and x i is the value of the i-th independent variable, represents the partial derivative of the sum of the squares of the errors E with respect to the regression coefficient a;

[0090] By solving these two equations, the regression coefficients a and b are obtained, thus obtaining the linear regression equation;

[0091] Using the obtained regression equation, the burning rate is predicted for different mixer torque values.

[0092] Step Three:

[0093] Input the mixer torque values measured in real time during the production process into the trained associated model. The model predicts the burning rate based on the input torque values, oxidizer particle size, and oxidizer content. The obtained prediction value is the burning rate of the propellant under different oxidizer particle sizes and oxidizer content conditions;

[0094] Compare the predicted burning rate with the design target value to check whether it meets the index requirements.

[0095] Step Four:

[0096] Calculate the current burning rate error and dispersion. Use the prediction results of the regression model to compare with the design index, calculate the error between the actual burning rate and the design index, and at the same time calculate the dispersion of the burning rate;

[0097] Among them, the calculation formula for the dispersion of the burning rate is:

[0098]

[0099] In the formula, n is the total number of sample data, is the predicted burning rate of the i-th observation value, is the average value of all observation values, and J is the dispersion of the burning rate;

[0100] Based on the experimental data and production data, analyze the different grading ratios of the oxidizer, including the influence of the mass ratio of the oxidizer to the fuel on the burning rate and dispersion;

[0101] Based on the error between the predicted burning rate and the design target, adjust the proportion of the oxidizer to make the burning rate closer to the design index;

[0102] By gradually changing the grading ratio of the oxidizer and making predictions, observe the differences between the predicted burning rate and the design index, as well as the changes in dispersion, and continuously adjust the grading of the oxidizer until the burning rate meets the design requirements and the dispersion reaches the requirements;

[0103] By adjusting the oxidizer grading, reduce the error between the predicted burning rate and the design index, make the predicted burning rate as close as possible to the target burning rate, set a tolerance range, ensure that the burning rate is within this range, and adjust the oxidizer ratio if it exceeds this range;

[0104] Through actual production verification, whether the propellant burning rate meets the design index and has a small dispersion under the adjusted oxidizer gradation. If it still does not meet the requirements, continue to adjust. The adjusted burning rate meets the target, but there is still a certain dispersion. The dispersion can be reduced by further refining the adjustment of the oxidizer gradation.

[0105] After multiple rounds of optimization, the optimal oxidant grading ratio was obtained.

[0106] A composite solid propellant burning rate control system, comprising:

[0107] Data acquisition module: The data acquisition module is used to collect the burning rate and mixer torque values ​​of the composite solid propellant under different oxidizer particle sizes, oxidizer contents and curing conditions, and obtain a sample set after preprocessing;

[0108] Data analysis module: The data analysis module is electrically connected to the data acquisition module, and is used to perform mathematical regression on the burning rate and the mixer torque value in the sample set to obtain a linear regression equation to describe the relationship between the propellant burning rate and the mixer torque, and to construct a correlation model between the burning rate and the mixer torque value based on the obtained regression results;

[0109] Burning rate prediction module: The burning rate prediction module is electrically connected to the data analysis module, and is used to input the mixer torque value measured during the production process into the associated model for calculation, so as to obtain the predicted value of the propellant burning rate under different oxidant particle sizes and oxidant contents;

[0110] Oxidant adjustment module: The oxidant adjustment module is electrically connected to the burning rate prediction module, and is used to adjust the oxidant gradation according to the compliance and dispersion of the burning rate prediction value with the design index.

[0111] The use process of the present invention is:

[0112] Step 1: Collect the propellant burning rate and mixer torque values ​​under different oxidizer particle sizes, oxidizer contents, and curing conditions;

[0113] Step 2: Conduct a preliminary check on the collected sample data to identify whether there are missing values;

[0114] Step 3: Use interpolation to fill in missing data;

[0115] Step 4: Use the moving average method to remove the noise caused by sensor fluctuations and external interference;

[0116] Step 5: Identify abnormal data points through standard deviation statistics;

[0117] Step 6: Eliminate abnormal data that does not meet production conditions according to preset physical constraints;

[0118] Step 7: Standardize the mixer torque value and burn rate data and convert them into the range of 0 - 1;

[0119] Step 8: Based on the processed data, construct a linear regression model of the burn rate and the mixer torque value;

[0120] Step 9: Use the linear regression model formula to establish the mathematical relationship between the burn rate and the mixer torque value;

[0121] Step 10: Use the method of minimizing the sum of squared errors to optimize the regression coefficients in the regression equation;

[0122] Step 11: Take the partial derivatives of the constant term and regression coefficients of the regression equation and set them to zero to solve for the optimal regression coefficients;

[0123] Step 12: Use the optimized regression equation to predict the burn rate;

[0124] Step 13: Input the mixer torque value measured in real time during the production process into the trained regression model;

[0125] Step 14: Predict the propellant burn rate based on the input mixer torque value, oxidizer particle size, and oxidizer content;

[0126] Step 15: Compare the predicted burn rate with the designed target burn rate to check compliance;

[0127] Step 16: Calculate the error between the actual burn rate and the design index and calculate the dispersion of the burn rate;

[0128] Step 17: Based on the experimental data, analyze the effects of the mass ratios of different oxidizers and fuels on the burn rate and dispersion;

[0129] Step 18: Adjust the proportion of the oxidizer according to the error between the predicted burn rate and the design target;

[0130] Step 19: By gradually adjusting the grading ratio of the oxidizer, observe the difference between the burn rate and the design target, and make adjustments to reduce the error and dispersion;

[0131] Step 20: After multiple rounds of optimization, verify whether the burn rate meets the design index and ensure the optimization of the oxidizer grading until the dispersion reaches the predetermined requirements.

[0132] In summary, the advantages of the present invention are as follows:

[0133] By collecting burning rate and mixer torque data under different oxidizer particle sizes, oxidizer contents and curing conditions, combined with mathematical regression analysis, an accurate correlation model between propellant burning rate and mixer torque can be established. This model can not only predict the burning rate in the experimental stage, but also adjust the oxidizer ratio in the production process in real time to ensure that the burning rate reaches the predetermined design target.

[0134] By preprocessing the collected data, including missing value interpolation, noise removal and outlier elimination, the high quality of the model input data is ensured, thereby improving the prediction accuracy and reliability of the regression model. This process ensures the accuracy of the data, effectively avoids errors caused by data fluctuations or interference, and enhances the stability of the model;

[0135] By using the standard deviation statistical method to identify and eliminate outliers, data anomalies caused by sensor errors or external interference are reduced, and the burning rate control in the production process is optimized, which helps to ensure the consistency and stability of propellant performance and reduce uncertainty in production;

[0136] By predicting the error and dispersion between the burning rate and the design index through the model, the gradation of the oxidant can be adjusted in real time so that the burning rate gradually approaches the target value. Through multiple rounds of optimization, the proportion of the oxidant can be continuously adjusted until the design requirements are met and the dispersion reaches the minimum value. This process makes the production process more flexible and accurate, and can quickly respond to deviations in the production process.

[0137] This method can predict and adjust the burning rate of the propellant in real time, greatly shortening the production cycle and reducing the workload of experimental verification. Since the relationship between the burning rate and the oxidizer gradation is accurately modeled, frequent experimental debugging and excessive manual intervention are no longer required in the production process, which improves the automation and efficiency of production.

[0138] By continuously optimizing the oxidizer grading, we can not only ensure that the burning rate of the propellant meets the design requirements, but also effectively control the dispersion and ensure that the performance of the propellant remains consistent during the production process, which is crucial for high-precision aerospace and missile applications.

[0139] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for regulating the burning rate of a composite solid propellant, characterized in that, include: The burning rate and mixer torque values ​​of composite solid propellant under different oxidizer particle sizes, oxidizer contents and curing conditions were collected, and a sample set was obtained after preprocessing. Mathematical regression is performed on the burning rate and mixer torque values ​​in the sample set to obtain a linear regression equation to describe the relationship between the propellant burning rate and the mixer torque. Based on the obtained regression results, a correlation model between the burning rate and the mixer torque value is constructed; The mixer torque value measured during the production process is input into the correlation model for calculation to obtain the predicted value of the propellant burning rate under different oxidizer particle sizes and oxidizer contents; Adjust the oxidant grading according to the compliance and dispersion of the predicted burning rate with the design indicators.

2. A method for regulating the burning rate of a composite solid propellant according to claim 1, characterized in that The burning rate and mixer torque values ​​of the composite solid propellant under different oxidizer particle sizes, oxidizer contents and curing conditions are collected, and the sample set obtained after preprocessing specifically includes: Check whether there are missing values ​​in the collected data and process the missing values ​​based on interpolation method; The interpolation formula is: Wherein, x1x2 are the x coordinates of two known data points before and after the point where the missing value is located, y1y2 are the corresponding y values, x i is the position of the i-th value to be interpolated, y i is the estimated missing value for the i-th position; The moving average method is used to remove noise caused by sensor fluctuations and external interference to ensure data quality. The standard deviation statistical method is used to identify outliers in the data. For each set of data on the burning rate and mixer torque value, the deviation from the overall data distribution is calculated. If a data point deviates from the overall distribution by more than the set threshold, it is considered to be abnormal data. Combined with the physical laws in the propellant production process and the working parameters of the equipment, according to the preset physical constraints, data points that cannot meet the physical constraints are eliminated; The mixer torque and combustion rate data are standardized and converted to the range of 0-1.

3. A method for regulating the burning rate of a composite solid propellant according to claim 2, characterized in that, The method of identifying outliers in data based on standard deviation statistics is specifically include: Among them, the formula of standard deviation statistical method is: where x i is the i-th data point, μ is the mean of the data set, σ is the standard deviation of the data set, and Z i is the standard deviation of the i-th data point; Abnormal data is judged based on the standard deviation of the data point. If the absolute value of the standard deviation of a data point exceeds the set threshold by 2 times, the data point is considered to be an outlier.

4. A method for controlling the burning rate of a composite solid propellant according to claim 3, characterized in that, The burning rate and the mixer torque values ​​in the sample set are mathematically regressed to obtain a linear regression equation to describe the relationship between the burning rate of the propellant and the mixer torque. Based on the obtained regression results, a correlation model between the burning rate and the mixer torque value is constructed, which specifically includes: A sample set containing combustion rate and mixer torque values ​​is obtained, and a linear regression model is constructed, wherein the linear regression model formula is: Y=a+bX In the formula, Y is the propellant burning rate, X is the mixer torque value, a is the constant term of the regression equation, and b is the regression coefficient; Find the line that best fits the data based on the linear regression model, minimizing the sum of squared errors between the actual observed values ​​and the predicted values; Take the derivative of a and b and set the derivative to zero to get the regression coefficient; The obtained regression equation was used to predict the burning rate for different mixer torque values.

5. A method for regulating the burning rate of a composite solid propellant according to claim 4, characterized in that, The linear regression model is based on finding the line that best fits the data and minimizes the sum of squares of the errors between the actual observed values ​​and the predicted values. include: The formula for the sum of squares of errors is: where E is the sum of squared errors, i is the index, n is the total number of data points, y i is the i-th observed value, a is the intercept of the regression equation, b is the slope of the regression equation, and x i is the value of the i-th independent variable; By taking the derivative of a and b, the value of E is minimized.

6. A method for controlling the burning rate of a composite solid propellant according to claim 5, characterized in that, The step of taking the derivative of a and b and setting the derivative to zero to obtain the regression coefficient specifically includes: Take the partial derivatives of a and b respectively and set them to zero, where the partial derivative calculation formula is: In the formula, represents the partial derivative of the sum of squared errors E with respect to the regression coefficient a, and y i is the true dependent variable value corresponding to the i-th observation value, a is the intercept in the regression equation, b is the slope in the regression equation, and x i is the i-th independent variable value, represents the partial derivative of the sum of squared errors E with respect to the regression coefficient a; By solving these two equations, the regression coefficients a and b are obtained, and thus the linear regression equation is obtained.

7. A method for regulating the burning rate of a composite solid propellant according to claim 6, characterized in that The step of inputting the measured mixer torque value during the production process into the associated model for calculation to obtain the predicted value of the propellant burning rate under different oxidizer particle sizes and oxidizer contents specifically includes: Input the mixer torque value measured in real time during the production process into the trained associated model. The model predicts the burning rate based on the input torque value, oxidizer particle size, and oxidizer content, and the obtained predicted value is the propellant burning rate under different oxidizer particle sizes and oxidizer contents; Compare the predicted burning rate with the design target value to check whether it meets the index requirements.

8. A method and system for controlling the burning rate of a composite solid propellant according to claim 7, characterized in that, The step of adjusting the oxidizer grading according to the compliance and dispersion of the predicted burning rate and the design index specifically includes: Calculate the current burning rate error and dispersion, compare the prediction result of the regression model with the design index, calculate the error between the actual burning rate and the design index, and calculate the dispersion of the burning rate at the same time; Among them, the dispersion calculation formula of the burning rate is: where n is the total number of sample data, is the predicted burning rate of the i-th observation, is the average value of all observations, and J is the dispersion of the burning rate; Analyze the different grading ratios of the oxidizer based on the experimental data and production data, including the influence of the mass ratio of the oxidizer and the fuel on the burning rate and dispersion; Based on the error between the predicted burning rate and the design target, adjust the proportion of the oxidizer to make the burning rate closer to the design index; By gradually changing the oxidizer grading ratio and making predictions, observe the difference between the predicted burning rate and the design index, as well as the change in dispersion, and continuously adjust the oxidizer grading until the burning rate meets the design requirements and the dispersion reaches the requirements.

9. A method and system for controlling the burning rate of a composite solid propellant according to claim 8, characterized in that, The step of by gradually changing the oxidizer grading ratio and making predictions, observing the difference between the predicted burning rate and the design index, as well as the change in dispersion, and continuously adjusting the oxidizer grading until the burning rate meets the design requirements and the dispersion reaches the requirements specifically includes: By adjusting the oxidizer grading, reduce the error between the predicted burning rate and the design index, make the predicted burning rate as close as possible to the target burning rate, set a tolerance range, ensure that the burning rate is within this range, and adjust the oxidizer ratio if it exceeds this range; Verify through actual production whether the propellant burning rate meets the design index and has a small dispersion under the adjusted oxidizer grading. If the requirements are still not met, continue to adjust. The adjusted burning rate meets the target, but there is still a certain dispersion. Further refine the adjustment of the oxidizer grading to reduce the dispersion; After multiple rounds of optimization, the optimal oxidizer grading ratio is obtained.

10. A burning rate control system for composite solid propellants, characterized in that, Including: Data acquisition module: The data acquisition module is used to collect the burning rate and mixer torque values of the composite solid propellant under different oxidizer particle sizes, oxidizer contents, and curing conditions, and obtain a sample set after preprocessing; Data analysis module: The data analysis module is electrically connected to the data acquisition module. The data analysis module is used to perform mathematical regression on the burning rate and mixer torque values in the sample set to obtain a linear regression equation, describe the relationship between the propellant burning rate and the mixer torque, and construct an associated model of the burning rate and the mixer torque value based on the obtained regression result; Burning rate prediction module: The burning rate prediction module is electrically connected to the data analysis module. The burning rate prediction module is used to input the measured mixer torque value during the production process into the associated model for calculation, and obtain the predicted values of the propellant burning rate under different oxidizer particle sizes and oxidizer content conditions; Oxidizer adjustment module: The oxidizer adjustment module is electrically connected to the burning rate prediction module. The oxidizer adjustment module is used to adjust the oxidizer grading according to the compliance and dispersion of the burning rate prediction value and the design index.

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