Method and system for calculating ignition delay and combustion temperature of energetic metal

Abstract

The invention relates to the technical field of energy and combustion, in particular to an energetic metal ignition delay and combustion temperature calculation method and system.The method comprises the steps that initial parameters used for calculating the ignition delay and combustion temperature of energetic metal are set; original spectral data of the energetic metal in the combustion process are obtained in real time and preprocessed; constructing an ignition delay calculation model, and calculating the ignition delay time of the energetic metal; performing multi-method fitting on the spectral data of the selected wavelength interval based on the Planck law, constructing a combustion temperature calculation model, and calculating the combustion temperature; and according to real-time feedback, the sampling frequency is dynamically adjusted, the step length is calculated, and calculation results of ignition delay time and combustion temperature are output. According to the method, the problems that in the prior art, experimental determination of the ignition delay and the combustion temperature of the energetic metal is complex, long in consumed time, large in error and poor in universality are effectively solved, and real-time accurate calculation of the ignition delay and the combustion temperature of the energetic metal is achieved.

Description

Technical Field

[0001] The invention relates to the field of energy and combustion technology, and in particular to a method and system for calculating ignition delay and combustion temperature of energetic metals. Background Art

[0002] Energetic metals have high energy density and are widely used in solid propellants, burners, fireworks and other fields to improve combustion efficiency and energy output. In these applications, ignition delay and combustion temperature are key parameters for evaluating the combustion performance of energetic metals, which directly affect their combustion efficiency, stability and application safety. At present, the ignition delay and combustion temperature of energetic metals mostly rely on experimental methods for determination. This method has the disadvantages of complex experiments and long time consumption. Traditional determination methods require strict control of experimental conditions and repeated experiments. The process is cumbersome and easily affected by environmental factors. At the same time, due to the huge amount of spectral data in the combustion process, the manual processing error is large, and it is difficult to calculate the accurate ignition delay and combustion temperature in real time. In addition, the existing calculation methods are usually designed for specific metals, which cannot adapt to the diversity of different energetic metals and lack universality. Summary of the invention

[0003] The present invention provides a method and system for calculating the ignition delay and combustion temperature of energetic metals, thereby effectively solving the problems pointed out in the background technology.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] A method for calculating ignition delay and combustion temperature of energetic metals, comprising:

[0006] According to the characteristics of the energetic metal material, initial parameters for calculating the ignition delay and combustion temperature of the energetic metal are set;

[0007] Real-time acquisition of raw spectrum data of the energetic metal during the combustion process and preprocessing to obtain combustion characteristic data;

[0008] Based on the extracted combustion characteristic data, an ignition delay calculation model is constructed to calculate the ignition delay time of the energetic metal, wherein the ignition delay calculation model optimizes the accuracy of inflection point recognition by a machine learning algorithm;

[0009] Based on Planck's law, a multi-method fitting is performed on the spectral data in the selected wavelength range to construct a combustion temperature calculation model, calculate the combustion temperature, and generate the combustion temperature calculation result;

[0010] The sampling frequency and calculation step size are dynamically adjusted based on real-time feedback, the calculation results of ignition delay time and combustion temperature are output, and a graphical display of temperature changes over time is generated.

[0011] Furthermore, the preprocessing includes:

[0012] Removing noise from the raw spectral data by data screening and filtering techniques;

[0013] Filter the spectral data according to the preset wavelength range and time interval to extract the key data that meets the analysis requirements during the combustion process;

[0014] The spectral intensity at a specific wavelength is selected, and the rows and columns of the filtered spectral data are exchanged.

[0015] Furthermore, the ignition delay calculation model is constructed, including:

[0016] Extract key characteristic data for ignition delay calculation from preprocessed spectral data to generate an input data set;

[0017] Based on the extracted key feature data, an inflection point recognition algorithm is designed to detect the position where the spectral slope changes, and the starting point and the change point in the combustion process are determined;

[0018] Setting the threshold standard for ignition delay calculation and unifying the calculation parameters of the starting point and the end point in the model;

[0019] The inflection point recognition algorithm is trained through a machine learning algorithm, and the optimized inflection point recognition model is generated using historical spectral data to obtain a high-precision ignition delay calculation model.

[0020] Furthermore, based on the extracted key feature data, an inflection point recognition algorithm is designed to detect the position where the spectral slope changes, and the starting point and the change point in the combustion process are determined, including:

[0021] According to the rate of change of spectral intensity over time, a slope detection mechanism is constructed to monitor the change of spectral slope in real time;

[0022] By setting an initial threshold value of the spectral intensity, identifying the position where the spectral intensity first reaches the initial threshold value as the starting point of combustion;

[0023] Set a slope change threshold to identify the change point of the combustion process by monitoring the spectral data slope exceeding the preset threshold;

[0024] Based on the smoothing algorithm, the interference of noise on the inflection point identification is eliminated, and an inflection point sequence is generated by identifying the starting point and the change point.

[0025] Furthermore, the inflection point recognition algorithm is trained by a machine learning algorithm, and an optimized inflection point recognition model is generated using historical spectral data to obtain a high-precision ignition delay calculation model, including:

[0026] Based on the historical spectral data, construct a training data set and a validation set containing various combustion process and inflection point characteristics;

[0027] Extracting key features from the historical spectral data and performing standardization and normalization processing;

[0028] Based on the machine learning algorithm, the model is trained using the training data set, and the trained model is evaluated and optimized using the validation set to obtain the ignition delay calculation model with high accuracy.

[0029] Furthermore, constructing the combustion temperature calculation model includes:

[0030] According to the combustion characteristics of energetic metals, the wavelength range for temperature calculation is selected to establish a specific spectral range for the model;

[0031] Based on Planck's law, a mathematical relationship model between spectral irradiance and temperature is constructed to generate the basis for initial temperature calculation;

[0032] Based on the mathematical relationship model, multiple methods are used for fitting to form a multiple method fitting module;

[0033] Introduce the physical constraints of energy conservation and mass conservation into the model to generate a temperature calculation framework with physical consistency;

[0034] The model is trained through deep learning, and an optimized combustion temperature calculation model is generated through historical temperature data.

[0035] Furthermore, a mathematical relationship model between spectral irradiance and temperature is constructed based on Planck's law to generate the basis for initial temperature calculation, including:

[0036] Based on Krump's law, the relationship between spectral irradiance and temperature is mathematically modeled to obtain a mathematical relationship model;

[0037] The measured spectral irradiance data is standardized and the inverse temperature calculation formula is derived based on Planck's law;

[0038] Calibrate the parameters in Planck's law based on actual observation data obtained from the combustion process;

[0039] The mathematical relationship model, standardization processing and calibration parameters are integrated to obtain a complete initial temperature calculation framework.

[0040] Furthermore, the sampling frequency and calculation step size are dynamically adjusted according to the real-time feedback, the calculation results of the ignition delay time and the combustion temperature are output, and a graphical display of the temperature change over time is generated, including:

[0041] Based on the real-time feedback information of the combustion process, analyze the data change trend of the combustion process and dynamically adjust the sampling frequency;

[0042] Adjusting the calculation step size according to the rate of change of the combustion state, and outputting the final calculation results of the ignition delay time and the combustion temperature according to the adjusted sampling frequency;

[0043] Based on the calculation results and visualization techniques, an image of temperature change over time is generated.

[0044] An energetic metal ignition delay and combustion temperature calculation system, the system comprising:

[0045] An initial parameter setting module, which sets initial parameters for calculating the ignition delay and combustion temperature of the energetic metal according to the characteristics of the energetic metal material;

[0046] A combustion characteristic data acquisition module is used to acquire the original spectrum data of the energetic metal during the combustion process in real time and perform preprocessing to obtain the combustion characteristic data;

[0047] An ignition delay model building module, based on the extracted combustion characteristic data, builds an ignition delay calculation model to calculate the ignition delay time of energetic metals, wherein the ignition delay calculation model optimizes the accuracy of inflection point recognition by a machine learning algorithm;

[0048] The temperature calculation result generation module performs multi-method fitting on the spectral data of the selected wavelength range based on Planck's law, builds a combustion temperature calculation model, calculates the combustion temperature, and generates the combustion temperature calculation result;

[0049] The calculation result visual output module dynamically adjusts the sampling frequency and calculation step size according to real-time feedback, outputs the calculation results of ignition delay time and combustion temperature, and generates a graphical display of temperature changes over time.

[0050] Furthermore, the calculation result visual output module includes:

[0051] The sampling frequency adjustment unit analyzes the data change trend during the combustion process based on the real-time feedback information during the combustion process and dynamically adjusts the sampling frequency;

[0052] A calculation result output unit, which adjusts the calculation step size according to the rate of change of the combustion state, and outputs the final calculation results of the ignition delay time and the combustion temperature according to the adjusted sampling frequency;

[0053] The visualization image generation unit generates an image showing the temperature changing over time based on the calculation results and visualization technology.

[0054] The technical solution of the present invention can achieve the following technical effects:

[0055] The method effectively solves the problems in the prior art of experimental determination of ignition delay and combustion temperature of energetic metals, which are complex, time-consuming, with large errors and poor universality, and realizes real-time and accurate calculation of ignition delay and combustion temperature of multiple energetic metals. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] 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 some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A schematic diagram of a flow chart of a method for calculating ignition delay and combustion temperature of energetic metals;

[0058] Figure 2 is a schematic diagram of the process of the pretreatment step;

[0059] Figure 3 A schematic diagram of the process for constructing an ignition delay calculation model;

[0060] Figure 4 Schematic diagram of the process for determining the starting and changing points in the combustion process;

[0061] Figure 5 A schematic diagram of the process of training and optimizing the inflection point recognition algorithm through a machine learning algorithm;

[0062] Figure 6 A schematic diagram of the process for constructing a combustion temperature calculation model;

[0063] Figure 7 Schematic diagram of the process flow for generating the basis for the initial temperature calculation;

[0064] Figure 8 A flow chart for outputting calculation results and displaying them graphically. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0067] Embodiment 1

[0068] like Figure 1 As shown, the present invention provides a method for calculating the ignition delay and combustion temperature of energetic metals, the method comprising:

[0069] S1: setting initial parameters for calculating the ignition delay and combustion temperature of the energetic metal according to the characteristics of the energetic metal material;

[0070] Specifically, the initial parameters of ignition delay and combustion temperature are set according to the characteristics of different energetic metal materials. For example, for a typical aluminum-based energetic material, the specific heat capacity is set to 900 J / (kg·K) and the density is set to 2700 kg / m 3 , thermal conductivity is 237W / (m·K), and the initial conditions of the combustion model are set in combination with the activation energy and pre-exponential factor of the material. At the same time, a suitable spectral monitoring wavelength range (e.g. 200-900nm) is selected to facilitate the extraction of spectral characteristic data during the combustion process. By setting these initial parameters, the calculation model of ignition delay time and combustion temperature can adapt to the combustion behavior of the material, so that the ignition delay and combustion temperature can be accurately calculated after real-time data acquisition. By combining the thermodynamic and kinetic properties of the material, the applicability and calculation accuracy of the model are improved, and the combustion performance evaluation of energetic metals is effectively optimized.

[0071] S2: Real-time acquisition of raw spectrum data of energetic metals during combustion and preprocessing to obtain combustion characteristic data;

[0072] Specifically, the original spectral data of energetic metals during the combustion process is acquired in real time and preprocessed to extract key characteristic data of the combustion process, such as light intensity changes, characteristic peak positions, peak widths and peak areas. These combustion characteristic data reflect the temperature, combustion rate, chemical reaction progress and other information of the energetic metals during the combustion process, and are the basis for the subsequent calculation of ignition delay time and combustion temperature.

[0073] S3: Based on the extracted combustion characteristic data, an ignition delay calculation model is constructed to calculate the ignition delay time of energetic metals. The ignition delay calculation model uses a machine learning algorithm to optimize the accuracy of inflection point recognition;

[0074] Specifically, by constructing an ignition delay calculation model based on combustion characteristic data, the ignition delay time of energetic metals can be accurately calculated. The ignition delay time is one of the key parameters for evaluating the combustion performance of energetic metals, which directly affects their combustion efficiency and safety. By using machine learning algorithms to optimize the accuracy of inflection point identification, the reaction start time in the combustion process can be more accurately detected, reducing errors caused by manual identification or traditional algorithms.

[0075] S4: Based on Planck's law, a multi-method fitting is performed on the spectral data in the selected wavelength range to construct a combustion temperature calculation model, calculate the combustion temperature, and generate the combustion temperature calculation result;

[0076] Specifically, Planck's law is used to fit the spectral data in the selected wavelength range to construct an accurate combustion temperature calculation model, thereby accurately calculating the temperature of energetic metals during the combustion process. Combustion temperature is one of the key parameters for evaluating the combustion performance of energetic metals and has a direct impact on combustion efficiency and energy output. Through multi-method fitting, the spectral data can be more accurately solved for temperature, effectively reducing the calculation errors caused by data noise or external interference, and generating more reliable combustion temperature results.

[0077] S5: Dynamically adjust the sampling frequency and calculation step size according to real-time feedback, output the calculation results of ignition delay time and combustion temperature, and generate a graphical display of temperature changes over time.

[0078] Specifically, by dynamically adjusting the sampling frequency and calculation step size based on real-time feedback to improve the accuracy and efficiency of the calculation, more real-time and accurate ignition delay time and combustion temperature results can be obtained. The temperature and reaction rate during the combustion process may change dramatically, so dynamically adjusting the sampling frequency and calculation step size can ensure sufficient resolution in the critical reaction stage to capture subtle changes. In addition, by generating a graphical display of temperature changes over time, the temperature change trend of energetic metals during the combustion process can be intuitively reflected, which is helpful to analyze their combustion characteristics and provide support for the performance evaluation and improvement of energetic metals.

[0079] As a preferred embodiment of the above, Figure 2 As shown, the preprocessing includes:

[0080] A10: Remove noise from raw spectral data through data screening and filtering techniques;

[0081] A20: Filter the spectral data according to the preset wavelength range and time interval to extract the key data that meets the analysis requirements during the combustion process;

[0082] A30: Select the spectral intensity at a specific wavelength and swap the rows and columns of the filtered spectral data.

[0083] Specifically, the raw spectral data is processed through a variety of filtering techniques (such as Gaussian filtering, bandpass filtering, etc.) to remove noise data introduced by environmental interference, instrument noise and other factors. This step ensures the purity of the data and makes subsequent analysis more accurate; according to the preset wavelength range (for example, 200-900nm) and time interval, the raw spectral data is screened to extract key data with obvious characteristics in the combustion process that meet the analysis requirements. This step focuses on data at specific wavelengths and time periods to exclude irrelevant or low-impact data and enhance the validity of the data; in the screened spectral data, the spectral intensity at a specific wavelength (such as the combustion characteristic band) is selected as the main analysis basis, and the data matrix is ​​exchanged in rows and columns to make the data structure more in line with subsequent calculation requirements. This operation helps to focus on the analysis of specific features in the calculation model and improve calculation efficiency and accuracy.

[0084] As a preferred embodiment of the above, Figure 3 As shown, an ignition delay calculation model is constructed, including:

[0085] B10: Extract key feature data for ignition delay calculation from the preprocessed spectral data to generate input data set;

[0086] B20: Based on the extracted key feature data, an inflection point recognition algorithm is designed to detect the position of the spectral slope change and determine the starting point and change point in the combustion process;

[0087] B30: Set the threshold standard for ignition delay calculation and unify the calculation parameters of the starting point and end point in the model;

[0088] B40: The inflection point recognition algorithm is trained through a machine learning algorithm, and the optimized inflection point recognition model is generated using historical spectral data to obtain a high-precision ignition delay calculation model.

[0089] Specifically, key feature data related to ignition delay are extracted from the preprocessed spectral data, including the change trend of spectral intensity, characteristic peak position, peak width, and change of spectral slope. Through data annotation, these feature data are integrated into an input data set for subsequent model training and calculation. Based on the extracted key feature data, an inflection point recognition algorithm is developed to detect the position of the slope inflection point of the spectral intensity change. The algorithm focuses on identifying the starting point (the moment when the combustion reaction starts) and the change point (the moment when the reaction accelerates or stabilizes) of the combustion process as the key reference for ignition delay. According to experimental data and material properties, a unified threshold standard for ignition delay calculation is set (such as the critical value of spectral intensity change or the minimum value of slope change) to standardize the judgment criteria of the starting point and end point to ensure the universality and consistency of the model. The designed inflection point recognition algorithm is trained using a machine learning algorithm (such as a support vector machine, random forest or neural network), and the performance of the model is continuously optimized using historical spectral data. The trained model can significantly improve the accuracy and robustness of inflection point recognition and generate a final high-precision ignition delay calculation model.

[0090] As a preferred embodiment of the above, Figure 4 As shown, step S20, based on the extracted key feature data, designs an inflection point recognition algorithm for detecting the position where the spectral slope changes, and determines the starting point and the change point in the combustion process, including:

[0091] B21: Based on the rate of change of spectral intensity over time, a slope detection mechanism is constructed to monitor the change of spectral slope in real time;

[0092] B22: By setting an initial threshold of the spectral intensity, the position where the spectral intensity first reaches the initial threshold is identified as the starting point of combustion;

[0093] B23: Set the slope change threshold to identify the change point of the combustion process by monitoring the slope of the spectral data exceeding the preset threshold;

[0094] B24: Based on the smoothing algorithm, the interference of noise on inflection point identification is eliminated, and an inflection point sequence is generated through the identified starting points and change points.

[0095] Specifically, according to the rate of change of spectral intensity over time, a slope detection mechanism is constructed to calculate the speed of change of spectral intensity (i.e., slope) at different time points. By real-time monitoring of the slope change of spectral intensity, the key time points in the combustion process are captured, providing a basis for identifying the starting point and the change point; an initial threshold of spectral intensity is set (for example, the minimum effective intensity determined by material properties). When the spectral intensity reaches or exceeds the position of the initial threshold for the first time, the position is marked as the starting point of the combustion process. This step determines the starting point of the ignition moment through early recognition of spectral intensity changes, providing a basis for calculating the ignition delay; a threshold of slope change is set to identify the combustion process. The conversion of different stages in the combustion process, for example, when the slope of the spectral intensity exceeds the set change threshold, the position is marked as the change point in the combustion process, indicating that the reaction rate has changed significantly. By monitoring the degree of slope change, the conversion points of different stages of combustion (such as initial combustion, accelerated combustion or stable combustion stage) can be identified; in order to eliminate the interference of noise on inflection point identification, a smoothing algorithm (such as sliding average, low-pass filtering, etc.) is used to smooth the spectral data, thereby reducing the impact of random noise on slope and inflection point identification. In the smoothed data, the identified starting points and change points are summarized to generate an inflection point sequence to ensure the stability and accuracy of inflection point detection.

[0096] As a preferred embodiment of the above, Figure 5 As shown, step B40, training the inflection point recognition algorithm through a machine learning algorithm, generating an optimized inflection point recognition model using historical spectral data, and obtaining a high-precision ignition delay calculation model, including:

[0097] B41: Based on historical spectral data, construct training data sets and validation sets containing various combustion process and inflection point characteristics;

[0098] B42: Extract key features from historical spectral data and perform standardization and normalization;

[0099] B43: Based on the machine learning algorithm, the model is trained using the training data set, and the trained model is evaluated and optimized using the validation set to obtain a high-precision ignition delay calculation model.

[0100] Specifically, historical spectral data are collected and organized to construct training data sets and validation sets containing a variety of combustion processes and corresponding inflection point features. The data sets should include spectral data of different types of energetic metal materials and various stages of their combustion process to ensure the versatility and robustness of the model. By including a rich range of combustion conditions, the model's ability to recognize different combustion behaviors can be enhanced. Key features required for ignition delay calculation (such as spectral intensity change rate, characteristic peak position, slope change, etc.) are extracted from historical spectral data, and these feature data are standardized and normalized. This step converts the feature data to the same scale range and eliminates the magnitude differences between different features, thereby improving the convergence speed and prediction accuracy of the machine learning algorithm. Select a suitable machine learning algorithm (such as support vector machine, random forest or deep neural network) to train the inflection point recognition model, input the standardized training data set into the model for learning, and adjust the model's parameters so that it can accurately identify the starting point and change point in the combustion process. Then, the performance of the model is evaluated through the validation set, and the parameters of the trained model are optimized and errors are corrected to ensure the accuracy and stability of the model in identifying inflection points.

[0101] As a preferred embodiment of the above, Figure 6 As shown, a combustion temperature calculation model is constructed, including:

[0102] C10: According to the combustion characteristics of energetic metals, the wavelength range for temperature calculation is selected to establish a specific spectral range for the model;

[0103] C20: Construct a mathematical relationship model between spectral irradiance and temperature based on Planck's law to generate the basis for initial temperature calculation;

[0104] C30: Based on the mathematical relationship model, multiple methods are used for fitting to form a multi-method fitting module;

[0105] C40: Introducing the physical constraints of energy conservation and mass conservation into the model to generate a temperature calculation framework with physical consistency;

[0106] C50: Perform deep learning training on the model and generate an optimized combustion temperature calculation model based on historical temperature data.

[0107] Specifically, according to the combustion characteristics of energetic metals and the needs of spectral analysis, a wavelength range suitable for temperature calculation is selected (such as infrared, visible light or ultraviolet bands). Through the selected wavelength range, spectral information related to temperature changes can be captured during the combustion process, providing a data basis for temperature calculation; based on Planck's law, a mathematical relationship model between spectral irradiance and temperature is established to convert spectral radiation intensity into temperature information. Planck's law describes the spectral distribution of radiation from objects at different temperatures, which provides theoretical support for temperature calculation. Through this model, the correspondence between spectral data and combustion temperature can be preliminarily realized; combined with different mathematical fitting methods (such as least squares method, piecewise fitting, nonlinear fitting, etc.), a multi-method fitting of the mathematical relationship model between spectral irradiance and temperature is performed. , forming a multi-method fitting module. The use of multiple fitting methods helps to process complex spectral data in the combustion process and improve the applicability and accuracy of the temperature calculation model; the physical constraints of energy conservation and mass conservation are introduced into the temperature calculation model to ensure that the calculation process conforms to the laws of physics. These constraints can help the model eliminate unreasonable results when calculating temperature, thereby improving the physical consistency and reliability of the model and making the temperature calculation more stable and reliable; through the deep learning algorithm, the model is trained and optimized using historical temperature data. Through the learning of a large amount of actual combustion data, the model can more accurately adapt to temperature changes under different combustion conditions. The introduction of deep learning can further improve the model's temperature prediction accuracy for complex combustion processes, making the model more robust and applicable.

[0108] As a preferred embodiment of the above, Figure 7 As shown, step C20, based on Planck's law, a mathematical relationship model between spectral irradiance and temperature is constructed to generate an initial temperature calculation basis, including:

[0109] C21: Based on Kramp's law, the relationship between spectral irradiance and temperature is mathematically modeled to obtain a mathematical relationship model;

[0110] C22: Standardize the measured spectral irradiance data and derive the inverse temperature calculation formula based on Planck's law;

[0111] C23: Calibrate the parameters in Planck's law based on actual observation data obtained from the combustion process;

[0112] C24: Integrate mathematical relationship models, standardization processing and calibration parameters to obtain a complete initial temperature calculation framework.

[0113] Specifically, based on Planck's law, a mathematical relationship model between spectral irradiance and temperature is established. Planck's law describes the spectral radiation intensity distribution of an object at a specific temperature, which can be expressed as: Where I is the irradiance (W·m-2 ); λ is the wavelength (m); T is the temperature (K); ε is the emissivity; C1 and C2 are the first radiation constants (3.7419×10 -16 W·m 2 ) and the second radiation constant (1.4388×10 -2 W·K), during the combustion stage, assuming that the particle emission rate is independent of the wavelength (gray body assumption), Planck's formula can be written as The actual measured spectral irradiance data is standardized to eliminate the magnitude differences and external interference of different data during the measurement process. Based on Planck's law, the spectral irradiance formula is reversely derived to obtain the formula for temperature calculation, which is suitable for real-time temperature estimation in the combustion process of energetic metals; the parameters in the Planck's law model are calibrated using the actual observed data in the combustion process. For example, the constants or coefficients in Planck's law are adjusted for parameter deviations caused by specific environments (such as high temperature or high pressure) in the combustion to ensure the accuracy of the model under actual combustion conditions; the above mathematical relationship model, standardization process and calibrated parameters are integrated into a complete initial temperature calculation framework, which can be directly applied to real-time spectral data input to generate temperature calculation results, providing a basis for subsequent fitting optimization.

[0114] As a preferred embodiment of the above, Figure 8 As shown, step S5 dynamically adjusts the sampling frequency and calculation step size according to real-time feedback, outputs the calculation results of the ignition delay time and the combustion temperature, and generates a graphical display of the temperature change over time, including:

[0115] S51: Based on the real-time feedback information of the combustion process, analyze the data change trend of the combustion process and dynamically adjust the sampling frequency;

[0116] S52: adjusting the calculation step size according to the rate of change of the combustion state, and outputting the final calculation results of the ignition delay time and the combustion temperature according to the adjusted sampling frequency;

[0117] S53: Generate an image of temperature change over time based on the calculation results and visualization technology.

[0118] Specifically, based on the real-time feedback of the combustion process, the changing trend of the data is analyzed. For example, in the stage where the combustion reaction is violent or the data changes rapidly, the sampling frequency is increased to capture more detailed data. In the relatively stable combustion stage, the sampling frequency is reduced to reduce the burden of data processing. The purpose of dynamically adjusting the sampling frequency is to ensure that the precision of data collection matches the rate of change of the combustion process, thereby improving the computational efficiency and real-time performance of the model; according to the rate of change of the state in the combustion process, the calculation step size is dynamically adjusted. When the combustion rate changes violently, the calculation step size is shortened to obtain more accurate results; when the combustion rate is stable, the calculation step size is increased to improve the computational efficiency. The adjusted sampling frequency and calculation step size work together to The calculation model of ignition delay time and combustion temperature outputs the final calculation results. This dynamic adjustment mechanism ensures the real-time and accuracy of the calculation results, making the evaluation of ignition delay time and combustion temperature more consistent with the actual combustion process. Based on the calculated temperature data and ignition delay time, visualization technology is used to generate an image of temperature changes over time. This image intuitively shows the temperature change trend and key time points (such as ignition delay time) during the combustion process, helping users to better understand the thermodynamic characteristics of the combustion process. At the same time, this graphical display can also be used for subsequent analysis and performance evaluation as a key data reference for the energetic metal combustion process.

[0119] Embodiment 2

[0120] Based on the same inventive concept as the method for calculating the ignition delay and combustion temperature of an energetic metal in the aforementioned embodiment, the present invention further provides a system for calculating the ignition delay and combustion temperature of an energetic metal, the system comprising:

[0121] An initial parameter setting module, which sets initial parameters for calculating the ignition delay and combustion temperature of the energetic metal according to the characteristics of the energetic metal material;

[0122] The combustion characteristic data acquisition module acquires the original spectrum data of energetic metals in the combustion process in real time and performs preprocessing to obtain the combustion characteristic data;

[0123] Ignition delay model building module, based on the extracted combustion characteristic data, builds an ignition delay calculation model to calculate the ignition delay time of energetic metals. The ignition delay calculation model uses a machine learning algorithm to optimize the accuracy of inflection point recognition;

[0124] The temperature calculation result generation module performs multi-method fitting on the spectral data of the selected wavelength range based on Planck's law, builds a combustion temperature calculation model, calculates the combustion temperature, and generates the combustion temperature calculation result;

[0125] The calculation result visual output module dynamically adjusts the sampling frequency and calculation step size according to real-time feedback, outputs the calculation results of ignition delay time and combustion temperature, and generates a graphical display of temperature changes over time.

[0126] The above-mentioned evaluation system in the present invention can effectively implement the calculation method of the ignition delay and combustion temperature of energetic metals, and the technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.

[0127] As a preferred embodiment of the above, the calculation result visual output module includes:

[0128] The sampling frequency adjustment unit analyzes the data change trend during the combustion process based on the real-time feedback information during the combustion process and dynamically adjusts the sampling frequency;

[0129] The calculation result output unit adjusts the calculation step size according to the rate of change of the combustion state, and outputs the final calculation results of the ignition delay time and the combustion temperature according to the adjusted sampling frequency;

[0130] The visualization image generation unit generates an image showing the temperature changing over time based on the calculation results and visualization technology.

[0131] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the optimization effects corresponding to the method in Example 1, which will not be repeated here.

[0132] Although the present application has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined herein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application.

[0133] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for calculating the ignition delay and combustion temperature of energetic metals, characterized in that: include: According to the characteristics of the energetic metal material, setting initial parameters for calculating the ignition delay and combustion temperature of the energetic metal; Real-time acquisition of raw spectrum data of the energetic metal during the combustion process and preprocessing to obtain combustion characteristic data; Based on the extracted combustion characteristic data, an ignition delay calculation model is constructed to calculate the ignition delay time of the energetic metal, wherein the ignition delay calculation model optimizes the accuracy of inflection point recognition by a machine learning algorithm; Based on Planck's law, multi-method fitting is performed on the spectral data of the selected wavelength range to construct a combustion temperature calculation model, calculate the combustion temperature, and generate the combustion temperature calculation result; The sampling frequency and calculation step size are dynamically adjusted based on real-time feedback, the calculation results of ignition delay time and combustion temperature are output, and a graphical display of temperature changes over time is generated.

2. The method for calculating the ignition delay and combustion temperature of energetic metals according to claim 1, characterized in that: The pre-processing comprises: Removing noise from the raw spectral data by data screening and filtering techniques; Filter the spectral data according to the preset wavelength range and time interval to extract the key data that meets the analysis requirements during the combustion process; The spectral intensity at a specific wavelength is selected, and the rows and columns of the filtered spectral data are exchanged.

3. The method for calculating the ignition delay and combustion temperature of energetic metals according to claim 1, characterized in that: Constructing the ignition delay calculation model includes: Extract key characteristic data for ignition delay calculation from preprocessed spectral data to generate an input data set; Based on the extracted key feature data, an inflection point recognition algorithm is designed to detect the position where the spectral slope changes, and the starting point and the change point in the combustion process are determined; Setting the threshold standard for ignition delay calculation and unifying the calculation parameters of the starting point and the end point in the model; The inflection point recognition algorithm is trained through a machine learning algorithm, and the optimized inflection point recognition model is generated using historical spectral data to obtain a high-precision ignition delay calculation model.

4. The method for calculating the ignition delay and combustion temperature of energetic metals according to claim 3, characterized in that: Based on the extracted key feature data, an inflection point recognition algorithm is designed to detect the position of the spectral slope change, and the starting point and change point in the combustion process are determined, including: According to the rate of change of spectral intensity over time, a slope detection mechanism is constructed to monitor the change of spectral slope in real time; By setting an initial threshold value of the spectral intensity, identifying the position where the spectral intensity first reaches the initial threshold value as the starting point of combustion; Set a slope change threshold to identify the change point of the combustion process by monitoring the spectral data slope exceeding the preset threshold; Based on the smoothing algorithm, the interference of noise on the inflection point identification is eliminated, and an inflection point sequence is generated by identifying the starting point and the change point.

5. The method for calculating the ignition delay and combustion temperature of energetic metals according to claim 3, characterized in that: The inflection point recognition algorithm is trained through machine learning algorithms, and the optimized inflection point recognition model is generated using historical spectral data to obtain a high-precision ignition delay calculation model, including: Based on the historical spectral data, construct a training data set and a validation set containing various combustion process and inflection point characteristics; Extracting key features from the historical spectral data and performing standardization and normalization processing; Based on the machine learning algorithm, the model is trained using the training data set, and the trained model is evaluated and optimized using the validation set to obtain the ignition delay calculation model with high accuracy.

6. The method for calculating the ignition delay and combustion temperature of energetic metals according to claim 1, characterized in that: Constructing the combustion temperature calculation model includes: According to the combustion characteristics of energetic metals, the wavelength range for temperature calculation is selected to establish a specific spectral range for the model; Based on Planck's law, a mathematical relationship model between spectral irradiance and temperature is constructed to generate the basis for initial temperature calculation; Based on the mathematical relationship model, multiple methods are used for fitting to form a multiple method fitting module; Introduce the physical constraints of energy conservation and mass conservation into the model to generate a temperature calculation framework with physical consistency; The model is trained through deep learning, and an optimized combustion temperature calculation model is generated through historical temperature data.

7. The method for calculating the ignition delay and combustion temperature of energetic metals according to claim 1, characterized in that: Based on Planck's law, a mathematical relationship model between spectral irradiance and temperature is constructed to generate the basis for initial temperature calculation, including: Based on Krump's law, the relationship between spectral irradiance and temperature is mathematically modeled to obtain a mathematical relationship model; The measured spectral irradiance data is standardized and the inverse temperature calculation formula is derived based on Planck's law; Calibrate the parameters in Planck's law based on actual observation data obtained from the combustion process; The mathematical relationship model, standardization processing and calibration parameters are integrated to obtain a complete initial temperature calculation framework.

8. The method for calculating the ignition delay and combustion temperature of energetic metals according to claim 1, characterized in that: Dynamically adjust the sampling frequency and calculation step size based on real-time feedback, output the calculation results of ignition delay time and combustion temperature, and generate a graphical display of temperature changes over time, including: Based on the real-time feedback information of the combustion process, analyze the data change trend of the combustion process and dynamically adjust the sampling frequency; Adjusting the calculation step size according to the rate of change of the combustion state, and outputting the final calculation results of the ignition delay time and the combustion temperature according to the adjusted sampling frequency; Based on the calculation results and visualization techniques, an image of temperature change over time is generated.

9. An energetic metal ignition delay and combustion temperature calculation system, characterized in that: The system comprises: An initial parameter setting module, which sets initial parameters for calculating the ignition delay and combustion temperature of the energetic metal according to the characteristics of the energetic metal material; A combustion characteristic data acquisition module is used to acquire the original spectrum data of the energetic metal during the combustion process in real time and perform preprocessing to obtain the combustion characteristic data; An ignition delay model building module, based on the extracted combustion characteristic data, builds an ignition delay calculation model to calculate the ignition delay time of energetic metals, wherein the ignition delay calculation model optimizes the accuracy of inflection point recognition by a machine learning algorithm; The temperature calculation result generation module performs multi-method fitting on the spectral data of the selected wavelength range based on Planck's law, builds a combustion temperature calculation model, calculates the combustion temperature, and generates the combustion temperature calculation result; The calculation result visual output module dynamically adjusts the sampling frequency and calculation step size according to real-time feedback, outputs the calculation results of ignition delay time and combustion temperature, and generates a graphical display of temperature changes over time.

10. The energetic metal ignition delay and combustion temperature calculation system according to claim 9, characterized in that: The calculation result visual output module includes: The sampling frequency adjustment unit analyzes the data change trend during the combustion process based on the real-time feedback information during the combustion process and dynamically adjusts the sampling frequency; A calculation result output unit, which adjusts the calculation step size according to the rate of change of the combustion state, and outputs the final calculation results of the ignition delay time and the combustion temperature according to the adjusted sampling frequency; The visualization image generation unit generates an image showing the temperature changing over time based on the calculation results and visualization technology.

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