A method and device for detecting the particle size of green silicon carbide powder

Abstract

The application relates to the technical field of particle size detection, and discloses a green silicon carbide micro-powder particle size detection method and device.The method comprises the following steps: collecting particle size distribution data to obtain two-dimensional detection data sets; obtaining particle size distribution data sets after pretreatment; performing dimension reduction analysis on the particle size distribution data sets to form a particle size distribution feature vector; acquiring a ratio parameter time sequence of silicon and carbon contents; calculating a correlation coefficient of the particle size distribution feature vector and the ratio parameter, and establishing a dynamic particle size prediction model; inputting real-time silicon and carbon content data into the dynamic particle size prediction model to obtain a particle size distribution feature; when a main particle size interval deviates from a preset target particle size range, adjusting the silicon and carbon contents to obtain an optimized ratio parameter time sequence; updating the model based on the optimized ratio parameter time sequence to obtain an adjusted particle size prediction model, and repeatedly predicting to obtain an optimized particle size distribution feature.The method can realize accurate prediction and dynamic adjustment of the particle size distribution, and improves the product particle size stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-powder particle size detection, and in particular to a green silicon carbide micro-powder particle size detection method and device. BACKGROUND

[0002] Green silicon carbide micro-powder, as a key component of high-end abrasives and functional materials, plays an irreplaceable role in precision machining, electronic device manufacturing, and new energy fields. The particle size distribution directly affects product quality and application performance. The uniformity of the particle size distribution and the precise control of the main particle size range are the core prerequisites for ensuring that the micro-powder meets the design requirements in terms of polishing accuracy and device performance.

[0003] In the prior art, the particle size detection of green silicon carbide micro-powder relies on single devices such as laser particle size analyzers and sedimentation instruments. Particle size distribution data is obtained through single or batch detection. In terms of production control, process parameters such as raw material ratio and grinding time are adjusted empirically, and the particle size distribution is optimized based on offline detection results. In this mode, the correlation analysis of detection data and process parameters only stays on the surface. Neither does it systematically aggregate multiple batches of data to uncover the rules, nor does it quantify the internal relationship between raw material ratio and particle size output through mathematical modeling. It is difficult to achieve active prediction and dynamic optimization of particle size characteristics.

[0004] In summary, the prior art lacks dynamic correlation between raw material ratio and final product particle size output, and there is a problem of insufficient stability of particle size distribution characteristics. SUMMARY

[0005] The present application provides a green silicon carbide micro-powder particle size detection method and device to improve the stability of particle size distribution and production efficiency, and reduce quality deviations caused by equipment jitter or ratio fluctuations.

[0006] In a first aspect, to solve the above technical problems, the present application provides a green silicon carbide micro-powder particle size detection method, comprising:

[0007] Collect and analyze multiple batches of particle size distribution data to obtain a two-dimensional detection data set containing particle size range and distribution ratio;

[0008] Preprocess the two-dimensional detection data set to obtain a final particle size distribution data set;

[0009] Perform dimensionality reduction analysis on the particle size distribution data set to determine the main particle size range and the corresponding particle volume proportion, and linearly transform to form a particle size distribution feature vector;

[0010] Obtain raw material ratio change data containing silicon content and carbon content ratio from production records, and aggregate the raw material ratio change data in time sequence to obtain a ratio parameter time series;

[0011] calculating a correlation coefficient between the particle size distribution feature vector and the proportioning parameter time series, and when the correlation coefficient is greater than a preset correlation threshold, establishing a nonlinear mapping relationship to obtain a dynamic particle size prediction model;

[0012] obtaining real-time silicon content and carbon content proportion data, inputting the real-time silicon content and carbon content proportion data into the dynamic particle size prediction model, and obtaining a predicted particle size distribution feature;

[0013] when a main particle size interval in the particle size distribution feature deviates from a preset target particle size range, adjusting the silicon content and carbon content proportion to obtain an optimized proportioning parameter time series;

[0014] updating the dynamic particle size prediction model based on the optimized proportioning parameter time series to obtain an adjusted particle size prediction model, and repeatedly predicting a main particle size interval and distribution proportion based on the adjusted particle size prediction model to obtain an optimized particle size distribution feature.

[0015] In an optional implementation, the pre-processing of the two-dimensional detection data set to obtain a final particle size distribution data set comprises:

[0016] calculating an absolute value of a deviation of a volume proportion value of each particle size interval in the two-dimensional detection data set from a mean value of the volume proportion in the data set, and when the absolute value of the deviation is greater than a preset deviation threshold, marking as an abnormal data point, and removing the abnormal data point to obtain a first data set;

[0017] calculating a relative position of each data point in the first data set relative to a minimum value and a maximum value of the data set, mapping the value to a 0-1 interval, and obtaining a normalized second data set;

[0018] according to the second data set, calculating a mean value of data points in a preset sliding window and replacing a center point data value, and after a preset number of iterations, obtaining the final particle size distribution data set.

[0019] In an optional implementation, the dimensionality reduction analysis of the particle size distribution data set to determine a main particle size interval and a corresponding particle volume proportion, linear transformation to form a particle size distribution feature vector comprises:

[0020] obtaining an original particle size range and an original distribution proportion from the particle size distribution data set, and calculating a particle size mean value;

[0021] based on a principal component analysis method, mapping a multi-dimensional feature composed of the original particle size range, the original distribution proportion, and the particle size mean value to a low-dimensional space, and calculating a characteristic value;

[0022] Screening the particle size interval with the feature value greater than the preset feature value threshold to determine as a main particle size interval, and extracting a particle volume proportion corresponding to the main particle size interval;

[0023] Performing linear transformation on the main particle size interval and the particle volume proportion to form the particle size distribution feature vector after combination.

[0024] In an optional implementation, the correlation coefficient of the particle size distribution feature vector and the proportioning parameter time series is calculated, when the correlation coefficient is greater than a preset correlation threshold, a nonlinear mapping relationship is established to obtain a dynamic particle size prediction model, including:

[0025] According to the proportioning parameter time series, silicon content proportion data and carbon content proportion data are obtained;

[0026] The correlation coefficient of the silicon content proportion data and the carbon content proportion data and the particle size distribution feature vector is calculated to obtain a silicon content correlation coefficient value and a carbon content correlation coefficient value;

[0027] When the silicon content correlation coefficient value or the carbon content correlation coefficient value exceeds a preset correlation threshold, a support vector regression algorithm is used to fit the nonlinear mapping relationship between the silicon content and the carbon content proportion and the particle size distribution to obtain a main particle size interval prediction value and a distribution proportion prediction value;

[0028] The main particle size interval prediction value and the distribution proportion prediction value are weighted and fused with the particle size distribution feature vector to obtain the dynamic particle size prediction model.

[0029] In an optional implementation, real-time silicon content and carbon content proportion data are obtained and input into the dynamic particle size prediction model to obtain a predicted particle size distribution feature, including:

[0030] Real-time silicon content and carbon content proportion data are collected, and outliers exceeding a preset reasonable range are removed. Taking a time point corresponding to a missing value as a center, an average value of valid data in a preset time period is taken to fill in the vacancy to obtain a clean data set;

[0031] A principal component analysis method is used to extract time series features of the clean data set to obtain an element proportion feature set containing silicon content and carbon content proportion change trends;

[0032] The variance contribution rate of the element proportion feature set is calculated, when the variance contribution rate exceeds a preset variance contribution threshold, the element proportion feature set is input into the dynamic particle size prediction model to obtain the predicted particle size distribution feature.

[0033] In an optional implementation, when the main particle size interval in the particle size distribution feature deviates from the preset target particle size range, the silicon content and carbon content ratio is adjusted to obtain an optimized proportioning parameter time sequence, including:

[0034] The main particle size interval is extracted from the particle size distribution feature, and a deviation degree of the main particle size interval from the preset target particle size range is calculated to obtain a particle size deviation degree;

[0035] When the particle size deviation degree is greater than a preset deviation degree threshold, the silicon content and carbon content ratio is linearly regressed and adjusted to generate an optimized proportioning parameter;

[0036] The optimized proportioning parameter is aggregated in time sequence to obtain the optimized proportioning parameter time sequence.

[0037] In an optional implementation, characterized in that, the dynamic particle size prediction model is updated based on the optimized proportioning parameter time sequence to obtain an adjusted particle size prediction model, including:

[0038] Based on the optimized proportioning parameter time sequence, mean and variance features of the optimized proportioning parameter are extracted;

[0039] The optimized proportioning parameter is applied to actual production, and the green silicon carbide micro powder produced is detected to obtain an optimized particle size distribution feature;

[0040] When the mean and variance features meet preset mean and variance threshold values respectively, the optimized proportioning parameter and the optimized particle size distribution feature are linearly regressed and fitted to update the dynamic particle size prediction model to obtain the adjusted particle size prediction model.

[0041] In a second aspect, the present application provides a green silicon carbide micro powder particle size detection device, comprising:

[0042] A data acquisition module acquires and analyzes multiple batches of particle size distribution data to obtain a two-dimensional detection data set containing particle size range and distribution ratio;

[0043] A data preprocessing module preprocesses the two-dimensional detection data set to obtain a final particle size distribution data set;

[0044] A feature extraction module performs dimensionality reduction analysis on the particle size distribution data set to determine a main particle size interval and a corresponding particle volume ratio, and linearly transforms to form a particle size distribution feature vector;

[0045] The ratio polymerization module obtains raw material ratio change data containing silicon content and carbon content ratio from production records, aggregates the raw material ratio change data in chronological order to obtain a ratio parameter time sequence;

[0046] The model construction module calculates a correlation coefficient of the particle size distribution feature vector and the ratio parameter time sequence, establishes a nonlinear mapping relationship when the correlation coefficient is greater than a preset correlation threshold, and obtains a dynamic particle size prediction model;

[0047] The real-time prediction module obtains real-time silicon content and carbon content ratio data, inputs the real-time silicon content and carbon content ratio data into the dynamic particle size prediction model, and obtains a predicted particle size distribution feature;

[0048] The ratio optimization module adjusts the silicon content and carbon content ratio when the main particle size interval in the particle size distribution feature deviates from a preset target particle size range, and obtains an optimized ratio parameter time sequence;

[0049] The model updating module updates the dynamic particle size prediction model based on the optimized ratio parameter time sequence, obtains an adjusted particle size prediction model, repeatedly predicts the main particle size interval and distribution ratio based on the adjusted particle size prediction model, and obtains an optimized particle size distribution feature.

[0050] Compared with the prior art, the present application has the following beneficial effects:

[0051] (1) The present application obtains a two-dimensional detection data set by collecting and analyzing multiple batches of particle size distribution data, and obtains a final particle size distribution data set by preprocessing the two-dimensional detection data set. This process can integrate multiple batches of data and eliminate noise interference, comprehensively capture the overall characteristics of the particle size distribution, and more accurately reflect the true particle size state of green silicon carbide micro powder, thereby providing a high-quality data basis for subsequent analysis.

[0052] (2) The present application determines the main particle size interval and the corresponding particle volume ratio by performing dimensionality reduction analysis on the particle size distribution data set, and forms a particle size distribution feature vector after linear transformation. This operation can strip redundant information, focus on the core particle size characteristics that affect the performance of the micro powder, and convert complex data into a structured feature vector, which facilitates efficient mining of particle size distribution rules, thereby more directly associating raw material ratio with key particle size characteristics and clearly and accurately controlling the direction.

[0053] (3) The application calculates the correlation coefficient of the particle size distribution feature vector and the proportioning parameter time series, and when the coefficient exceeds the preset correlation threshold, a nonlinear mapping relationship is established to obtain a dynamic particle size prediction model. This mechanism can quantify the internal relationship between the proportion of raw materials containing silicon and carbon and the particle size distribution, break the limitations of empirical control, realize active prediction of particle size characteristics based on raw material parameters, predict the particle size change trend in advance, and then adjust the process parameters in a timely and scientific manner.

[0054] (4) The application obtains real-time silicon-carbon ratio data, inputs the dynamic particle size prediction model to obtain a prediction result, adjusts the proportion when the target particle size range is deviated, and updates the model to repeat optimization. This closed-loop process can dynamically optimize the proportioning parameters according to real-time production data, continuously adapt to process fluctuations through model iteration, ensure that the particle size distribution is always stable within the target range, and significantly improve the quality stability and production efficiency of green silicon carbide micro powder products. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is a flowchart of an embodiment of the green silicon carbide micro powder particle size detection method provided by the application;

[0056] Figure 2 is a structural schematic diagram of an embodiment of the green silicon carbide micro powder particle size detection device provided by the application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0058] Referring to Figure 1 , the first embodiment of the application provides a green silicon carbide micro powder particle size detection method, including steps S11 to S18:

[0059] S11, collecting and analyzing multiple batches of particle size distribution data to obtain a two-dimensional detection data set containing particle size range and distribution ratio;

[0060] S12, preprocessing the two-dimensional detection data set to obtain a final particle size distribution data set;

[0061] S13, performing dimensionality reduction analysis on the particle size distribution data set to determine the main particle size interval and the corresponding particle volume ratio, and linearly transforming to form a particle size distribution feature vector;

[0062] S14, obtain raw material ratio change data containing silicon content and carbon content ratio from production records, and aggregate the raw material ratio change data in chronological order to obtain a ratio parameter time series;

[0063] S15, calculate the correlation coefficient of the particle size distribution feature vector and the ratio parameter time series, and when the correlation coefficient is greater than a preset correlation threshold, a nonlinear mapping relationship is established to obtain a dynamic particle size prediction model;

[0064] S16, obtain real-time silicon content and carbon content ratio data and input it into the dynamic particle size prediction model to obtain a predicted particle size distribution feature;

[0065] S17, when the main particle size interval in the particle size distribution feature deviates from a preset target particle size range, adjust the silicon content and carbon content ratio to obtain an optimized ratio parameter time series;

[0066] S18, update the dynamic particle size prediction model based on the optimized ratio parameter time series to obtain an adjusted particle size prediction model, and repeatedly predict the main particle size interval and distribution ratio based on the adjusted particle size prediction model to obtain an optimized particle size distribution feature.

[0067] In step S11, multiple batches of particle size distribution data are collected and analyzed to obtain a two-dimensional detection data set containing particle size range and distribution ratio.

[0068] It should be noted that the data collection work depends on a laser particle size analyzer, and its working principle is to use the scattering characteristics of particles on laser, by measuring the scattering angle and scattering intensity of laser irradiated to green silicon carbide micro-powder particles, to deduce the size distribution of particles. The reason for selecting multiple batches is that single batch detection data may be affected by accidental factors (such as uneven sampling of samples, instantaneous fluctuations of equipment, etc.), and multiple batch data can more comprehensively reflect the overall characteristics and fluctuation range of particle size distribution in the production process, thereby improving the reliability of subsequent analysis. The particle size range is represented by the size interval of particles, such as 0.5-1 microns, 1-5 microns, etc., which reflects the distribution span of micro-powder particle size. The distribution ratio is measured by particle volume ratio, which represents the percentage of particle volume in a certain particle size range in the total volume.

[0069] In order to ensure the consistency and comparability of the data, the particle size range of the collected original data is divided into several fixed intervals according to a unified particle size division standard, for example, 0.1-100 microns is divided into 0.1-1 micron, 1-10 microns, and 10-100 microns three intervals, and then the volume proportion of each interval in each batch is calculated. Through this process, the original scattered data is integrated into a structured two-dimensional detection data set, and each dimension corresponds to the particle size range and the distribution ratio in the range respectively.

[0070] In step S12, the two-dimensional detection data set is preprocessed to obtain the final particle size distribution data set, including:

[0071] The absolute value of the deviation of the volume proportion of each particle size interval in the two-dimensional detection data set from the average value of the volume proportion in the data set is calculated, and when the absolute value of the deviation is greater than a preset deviation threshold, it is marked as an abnormal data point, and the abnormal data point is removed to obtain a first data set;

[0072] The relative position of each data point in the first data set with respect to the minimum and maximum values of the data set is calculated, and the value is mapped to the 0-1 interval to obtain a normalized second data set;

[0073] According to the second data set, the average value of the data points in a preset sliding window is calculated and the center point data value is replaced, and after a preset number of iterations, the final particle size distribution data set is obtained.

[0074] It should be noted that when calculating the absolute value of the deviation, the volume proportion of all data points is first calculated to obtain the overall average, and then the absolute value of the difference between the volume proportion of each data point and the average is calculated. The preset deviation threshold is set based on production experience or statistical rules. The first data set filters out extreme values caused by accidental factors such as equipment instantaneous failure and sample contamination, and is closer to the true characteristics of the particle size distribution of the micro powder.

[0075] Next, the volume proportion of each data point is converted to the proportion with respect to the minimum and maximum values of the data point volume proportion in the data set by the minimum-maximum normalization method, and the calculation formula of the method is: (data point volume proportion-minimum value) / (maximum value-minimum value). The normalized value obtained is uniformly mapped to the 0-1 interval to form a second data set. For example, if the minimum value of the volume proportion in the first data set is 5% and the maximum value is 20%, the volume proportion of a certain particle size interval is 15%, then its normalized value is (15%-5%) / (20%-5%) = 0.667. This not only facilitates direct comparison of data from different batches, but also provides standardized input for subsequent model training, avoiding model bias caused by differences in numerical scales.

[0076] Further, the size of the preset sliding window can be set according to the data density (such as containing 3 or 5 consecutive particle size interval data points), and the principle is to weaken the influence of instantaneous fluctuations through local averaging. For example, if the normalized value sequence of the second data set is 0.2, 0.5, 0.8, 0.6 (corresponding to four consecutive particle size intervals), and the size of the sliding window is set to 3, the first window contains 0.2, 0.5, 0.8, and the average value is 0.5, which replaces the center point 0.5; the second window contains 0.5, 0.8, 0.6, and the average value is 0.63, which replaces the center point 0.8, obtaining the preliminary smoothed sequence: 0.2, 0.5, 0.633, 0.6. Performing a preset number of iterations will further enhance the smoothing effect, and the final particle size distribution data set is obtained. The setting of the preset number of iterations needs to be combined with the data noise intensity, the continuity requirement of the particle size distribution, and the calculation efficiency, and is mostly 2-4 times. Through multiple iterations, the sudden rise or sudden drop mutation points caused by detection errors in the data set will be gradually weakened, highlighting the overall trend of the particle size distribution.

[0077] In step S13, the particle size distribution data set is subjected to dimensionality reduction analysis to determine the main particle size interval and the corresponding particle volume proportion, and linear transformation is performed to form a particle size distribution feature vector, including:

[0078] The original particle size range and the original distribution ratio are obtained from the particle size distribution data set, and the particle size average value is calculated;

[0079] Based on the principal component analysis method, the multi-dimensional features composed of the original particle size range, the original distribution ratio, and the particle size average value are mapped to a low-dimensional space, and the characteristic values are calculated;

[0080] The particle size interval with a characteristic value greater than a preset characteristic value threshold is selected as the main particle size interval, and the particle volume proportion corresponding to the main particle size interval is extracted;

[0081] The main particle size interval and the particle volume proportion are subjected to linear transformation, and are combined to form the particle size distribution feature vector.

[0082] It should be noted that the original particle size range refers to the particle size range retained after pretreatment, which is used to represent the span of the particle size distribution; the original distribution ratio is the volume percentage corresponding to each interval. The average particle size is a single value representing the particle size characteristics of a certain interval, which can be obtained by calculating the arithmetic mean of the upper and lower limits of the interval. In the particle size distribution data, the multi-dimensional feature refers to the combination of characteristic parameters of multiple particle size intervals, and there is a correlation between these characteristics. For example, when the proportion of large particle size intervals is high, the proportion of small particle size intervals will be relatively low, which can easily lead to data redundancy. Principal component analysis (PCA) is a statistical method that maps high-dimensional particle size feature data to a low-dimensional space through linear transformation, which can reduce the dimensionality of the data while retaining the core information of the data (such as the key rules of the particle size distribution) and eliminating redundant features. The main operation steps are as follows:

[0083] First, the original multi-dimensional features (such as the average value and volume percentage of each particle size interval) are standardized by subtracting the average value of each feature and dividing by the standard deviation of the feature, so that features of different dimensions are unified to the same scale. Then, the covariance matrix of the standardized data is calculated to describe the strength of the correlation between the features. Then, the characteristic equation of the covariance matrix is solved to obtain the corresponding eigenvalues and eigenvectors, where the eigenvectors represent the direction of the principal components, and the eigenvalues represent the size of the original data variance explained by the principal component. The larger the value, the more original particle size data information the corresponding principal component contains. Sort the eigenvalues from large to small, and select the principal components with eigenvalues greater than the preset eigenvalue threshold. Since these principal components contain the main information of the original data, the particle size intervals with high weights in the corresponding eigenvectors play a leading role in the particle size distribution, and are therefore determined as the main particle size intervals. The preset eigenvalue threshold is mainly determined according to the application scenario requirements, production process stability and data information. For the field of precision machining that requires high precision, more particle size details need to be retained, and the threshold needs to be set relatively high (such as 0.8-1).

[0084] After determining the main particle size intervals, the corresponding distribution ratio is extracted from the final particle size distribution data set to obtain the particle volume percentage. The volume percentage is normalized to the 0-1 interval through linear transformation. The lower limit and upper limit of the main particle size interval are combined with the corresponding standardized volume percentage to form a particle size distribution feature vector, achieving dimensionality reduction from high-dimensional original data to low-dimensional space.

[0085] Exemplarily, after preprocessing, the particle size distribution data set of a certain green silicon carbide powder contains five particle size intervals: 0.1-1 μm, 1-10 μm, 10-20 μm, 20-50 μm and 50-100 μm, and the corresponding original distribution proportions are 10%, 50%, 20%, 15% and 5%, respectively. The average particle sizes of the intervals are 0.55 μm, 5.5 μm, 15 μm, 35 μm and 75 μm, respectively. Based on the principal component analysis method, the average particle sizes and distribution proportions of the particle size intervals are standardized, and the covariance matrix is calculated to obtain three principal components, with eigenvalues of 0.88, 0.09 and 0.03, respectively. The preset eigenvalue threshold is 0.85, so the principal component corresponding to the eigenvalue 0.88 is retained, which is mainly associated with the 1-10 μm interval. The characteristic vector of the principal component shows that the distribution proportion of the 1-10 μm interval is 50%, which is significantly higher than that of other intervals, so the 1-10 μm interval is determined as the main particle size interval. The volume proportion of 50% is converted to 0.5, and the lower limit 1 μm and the upper limit 10 μm are retained to form the particle size distribution characteristic vector [1, 10, 0.5], which clearly reflects the particle size characteristics that play a leading role in the performance of the powder.

[0086] In step S14, raw material ratio change data containing silicon content and carbon content proportions are obtained from production records, and the raw material ratio change data are aggregated in time sequence to obtain a ratio parameter time sequence.

[0087] It should be noted that the silicon content and the carbon content proportion refer to the percentage of the mass of silicon and carbon elements in the total mass of raw materials, respectively. As core raw material parameters for green silicon carbide powder production, they can directly affect the particle size distribution of the powder generated by the reaction. The raw material ratio change data are derived from real-time collection by production line sensors or manual records, and need to be preprocessed, such as checking whether the time stamp is complete, whether the content proportion is within a reasonable range, and whether the value is a non-negative floating point number. If there are records with missing time stamps, the time stamp of the last valid record can be filled in. If there are negative values or proportion values outside the preset reasonable range, they are determined as abnormal values, and the average value of the normal data at the three adjacent time points before and after the abnormal value is taken as a replacement. After preprocessing, according to the time granularity requirements of the production process (such as every 4 hours, every day, etc.), the time intervals are set, and the average values of the silicon content and the carbon content proportion in the intervals are calculated as the representative values of the intervals. In this way, discrete data are aggregated into a ratio parameter time sequence arranged in time order, each data point contains a time interval and the corresponding silicon and carbon content representative values, and the trend of the change of the raw material ratio with time can be clearly presented.

[0088] In step S15, a correlation coefficient of the particle size distribution feature vector and the proportioning parameter time series is calculated, and when the correlation coefficient is greater than a preset correlation threshold, a nonlinear mapping relationship is established to obtain a dynamic particle size prediction model, including:

[0089] According to the proportioning parameter time series, silicon content proportion data and carbon content proportion data are obtained.

[0090] Correlation coefficients of the silicon content proportion data and the carbon content proportion data and the particle size distribution feature vector are calculated to obtain silicon content correlation coefficient values and carbon content correlation coefficient values.

[0091] When the silicon content correlation coefficient value or the carbon content correlation coefficient value exceeds a preset correlation threshold, a support vector regression algorithm is used to fit the nonlinear mapping relationship between the silicon content and carbon content proportions and the particle size distribution to obtain main particle size interval prediction values and distribution proportion prediction values.

[0092] The main particle size interval prediction values and the distribution proportion prediction values are weighted and fused with the particle size distribution feature vector to obtain the dynamic particle size prediction model.

[0093] It should be noted that the corresponding silicon content proportion representative values and carbon content proportion representative values are extracted from the time sequence of the proportioning parameter time series data points to obtain silicon content proportion data sequences and carbon content proportion data, respectively. The correlation coefficient is a statistical quantity for measuring the linear correlation degree between two variables, and the value range is [-1, 1]. The absolute value is closer to 1, indicating a stronger correlation. A positive value indicates a positive correlation, and a negative value indicates a negative correlation. For example, if the silicon content increases and the main particle size interval increases, it indicates a positive correlation; if the carbon content increases and the main particle size interval decreases, it indicates a negative correlation. The silicon and carbon content proportion data are one-dimensional data sequences, and the particle size distribution feature vector contains multi-dimensional data. The calculation of the correlation coefficient value needs to be performed by dimension. Taking the calculation of the silicon content correlation coefficient value as an example, first, the average value of the silicon content data and the average value of the volume proportion dimension are calculated, and then the deviation of each data point from the corresponding average value is calculated to obtain two deviation sequences. Then, the sum of the product of the two deviation sequences is calculated as the numerator, and the square root of the sum of the squares of the two deviation sequences, i.e., the standard deviation, is calculated as the denominator. Finally, the numerator is divided by the denominator to obtain the silicon content correlation coefficient value.

[0094] In the present embodiment, the preset correlation threshold is adjusted according to the process precision requirement, and is generally set to 0.6-0.8. When the correlation coefficients of silicon or carbon do not exceed the threshold, it indicates that the correlation between the raw material ratio and the particle size in the current data is weak, and more data needs to be supplemented. If the correlation coefficient exceeds the threshold, it indicates that there is a significant correlation between the ratio change of the element and the particle size distribution characteristics, and further modeling analysis is needed. The support vector regression (SVR) algorithm is a regression algorithm based on statistical learning theory. The core is to find the optimal hyperplane to control the deviation of the training data points to the hyperplane within a preset range, so as to realize accurate prediction of continuous values. The silicon content ratio and the carbon content ratio are taken as input features, and the corresponding main particle size interval and distribution ratio are taken as output labels. After alignment by time, the training set and the validation set are divided, and standardized processing is performed to eliminate the dimension effect. Since the raw material ratio and the particle size distribution have a nonlinear relationship, a radial basis kernel function is introduced to map the low-dimensional input to a high-dimensional space, so that the nonlinear relationship can be captured by a linear model. By optimizing the penalty coefficient for controlling the error tolerance and the kernel parameter for adjusting the mapping complexity, the model training is completed by minimizing the prediction error. When new silicon and carbon content ratios are input, the model calculates the output of the main particle size interval prediction value and the distribution ratio prediction value through linear regression in the high-dimensional space.

[0095] Further, according to the importance of the characteristics on the particle size distribution, higher weights (such as 0.6-0.8) are assigned to the main particle size interval prediction value and the distribution ratio prediction value, and lower weights (such as 0.2-0.4) are assigned to the secondary features in the original particle size distribution feature vector. The upper and lower limits of the interval and the distribution ratio after weighted fusion are combined to form a dynamic particle size prediction model.

[0096] For example, in the time series of the ratio parameters of a certain green silicon carbide production, the silicon content ratio data is 25%, 26%, 27% and 28%, the carbon content ratio data is 15%, 14%, 13% and 12%, and the corresponding particle size distribution feature vector is [50, 100, 0.6], [55, 105, 0.58], [60, 110, 0.56] and [65, 115, 0.55]. The correlation coefficients are calculated: the correlation coefficients of the silicon content and the carbon content with the particle size feature vector are 0.82 and 0.45 respectively, and the preset correlation coefficient threshold is 0.7, so the correlation of the silicon content is significant. The support vector regression algorithm is used to fit the nonlinear relationship. When the silicon content is 26.5% and the carbon content is 13.5%, the main particle size interval prediction value is 62-112 μm and the distribution ratio prediction value is 0.57. The prediction value weight is set to 0.7, and the weight of the closest original feature vector [60-110 μm, 0.56] is set to 0.3. The dynamic particle size prediction model obtained after weighted fusion has an output of main particle size interval 61.4-111.4 μm and distribution ratio 0.567. This model can accurately predict the particle size distribution in real time according to the raw material ratio.

[0097] In step S16, real-time silicon content and carbon content ratio data are obtained and input into the dynamic particle size prediction model to obtain predicted particle size distribution characteristics, including:

[0098] The silicon content and carbon content ratio data are collected in real time, and abnormal values exceeding a preset reasonable range are removed. Taking the time point corresponding to the missing value as the center, the average value of the effective data in a preset time period is taken to fill in the vacancy to obtain a clean data set.

[0099] The time series characteristics of the clean data set are extracted using principal component analysis to obtain an element ratio feature set containing the variation trend of the silicon content and carbon content ratio.

[0100] The variance contribution rate of the element ratio feature set is calculated, and when the variance contribution rate exceeds a preset variance contribution threshold, the element ratio feature set is input into the dynamic particle size prediction model to obtain the predicted particle size distribution characteristics.

[0101] It should be noted that the real-time acquisition of silicon content and carbon content ratio data is realized by deploying high-precision sensors on the production line, and time stamps are used as identifiers. Due to factors such as sensor fluctuations and equipment interference, there are abnormal values or missing values not recorded in the original real-time data. First, abnormal values exceeding a preset reasonable range are removed. For missing values, taking the time point corresponding to the missing value as the center, the average value of the effective data in a preset time period is taken to fill in the vacancy, for example, the silicon content is missing at a certain time point, and the effective data in the previous and next 30 minutes is 25%, 26%, and 24%. The filling value is 25%, and finally an accurate and reliable clean data set is obtained. Since the clean data set contains multi-dimensional information such as element content variation trend and fluctuation amplitude, directly inputting the prediction model leads to dimension redundancy, therefore, principal component analysis is used to map the multi-dimensional time series to a low-dimensional space through linear transformation. The principal components generated are sorted in descending order of variance contribution, and each principal component is a linear combination of the original silicon and carbon content time series. The two principal components with the largest explained variance are retained to form the element ratio feature set.

[0102] In this embodiment, the variance contribution rate represents the proportion of the extracted principal components that cumulatively explain the variance of the original time series data, ranging from 0 to 100%. When the variance contribution rate does not exceed the preset variance contribution threshold, the number of principal components needs to be increased to ensure information integrity; if it exceeds the threshold, it indicates that the feature set has retained sufficient original data information, which can be input into the dynamic particle size prediction model. After inputting the model, the model outputs the predicted particle size distribution characteristics based on the established nonlinear mapping relationship, including the main particle size interval and the particle volume proportion, which provides a direct basis for real-time adjustment of raw material ratio and stabilization of product particle size.

[0103] Exemplarily, the silicon content data collected by a certain production line in real time are 25%, 26%, 35% (abnormal), 27%, missing and 28%, and the carbon content data are 15%, 14%, 13%, 12%, 14% and 13%. During processing, the abnormal value of 35% of the silicon content is removed, and the average value of 27.5% is filled in for the missing value by taking the average of the data before and after (27% and 28%), to obtain a clean data set, the silicon content ratio data are 25%, 26%, 27%, 27.5% and 28%, and the carbon content ratio data are 15%, 14%, 13%, 14% and 13%. The time series characteristics are extracted by using PCA to obtain two principal components, the first principal component reflects the overall trend of the increase of the silicon content and the decrease of the carbon content, and the variance contribution rate is 72%; the second principal component reflects the short-term fluctuation characteristics, and the variance contribution rate is 18%, and the cumulative variance contribution rate is 90%, which exceeds the preset threshold of 85%, to form an element ratio feature set. The element ratio feature set is input into the dynamic particle size prediction model, and the output predicted particle size distribution characteristics are that the main particle size interval is 61-118 μm, and the volume ratio is 57%.

[0104] In step S17, when the main particle size interval in the particle size distribution characteristics deviates from the preset target particle size range, the silicon content and the carbon content ratio are adjusted to obtain an optimized proportioning parameter time series, including:

[0105] The main particle size interval is extracted from the particle size distribution characteristics, and the deviation degree of the main particle size interval from the preset target particle size range is calculated to obtain a particle size deviation degree;

[0106] When the particle size deviation degree is greater than a preset deviation threshold, the silicon content and the carbon content ratio are adjusted by linear regression to generate an optimized proportioning parameter;

[0107] The optimized proportioning parameter is aggregated in time sequence to obtain the optimized proportioning parameter time series.

[0108] It should be noted that the preset target particle size range is an ideal interval set according to the product application scene, such as a more fine particle size required for precision grinding. When calculating the deviation degree of the main particle size interval from the preset target particle size range, the difference between the actual predicted interval and the target interval needs to be quantified, and the weighted sum of the interval center deviation and the range deviation is used to represent: the interval center deviation is the difference between the predicted interval center value and the target interval center value, and the weight is 0.8; the range deviation is the difference between the predicted interval width and the target interval width, and the weight is 0.2. After weighting, the particle size deviation degree is obtained, and the greater the deviation degree, the more significant the deviation.

[0109] In the embodiment, based on historical production data, a linear correspondence relationship between silicon content variation and carbon content variation and particle size variation is summarized, when the particle size deviation degree is greater than a preset deviation threshold, based on the linear correspondence relationship, the proportion data of the proportion of silicon content and carbon content that needs to be increased or decreased is calculated by reverse deduction, so that the particle size returns to the preset target particle size range. At the same time, the constraint range of silicon content and carbon content in the production process needs to be strictly followed, and the single adjustment amplitude needs to be controlled to avoid large process fluctuations, and finally the silicon and carbon content proportion that meets the constraint and can realize particle size correction is the optimized matching parameter. The optimized matching parameter is recorded according to the production time interval to form a sequence aligned with time, each data point contains a time interval and a corresponding optimized silicon and carbon content proportion value, which is the optimized matching parameter time sequence. The time sequence can not only reflect the dynamic process of matching adjustment, but also provide continuous parameter execution basis for the production line, realizing closed-loop control from deviation detection to parameter optimization.

[0110] Illustratively, the preset target particle size range is 50-100 μm, the center value is 75 μm, the predicted main particle size interval at a certain time is 60-110 μm, the center value is 85 μm, the center deviation is calculated as 85 μm-75 μm=10 μm, the range deviation is 0 μm, and the weighted deviation is 8 μm, which is greater than the preset deviation threshold of 5 μm. According to historical production data, for every 1% increase in silicon content, the particle size center value increases by 2 μm, and for every 1% increase in carbon content, the particle size center value decreases by 1.5 μm. To reduce the center value by 8 μm, the silicon content needs to be reduced from 26% to 24%, which contributes to-4 μm; the carbon content needs to be increased from 14% to 16.7%, which contributes to-4.05 μm, and the total adjustment amount meets the requirements. The production time interval is 1 hour, so the adjusted parameters in the 10:00-12:00 period are recorded in time order as ["10:00-11:00, silicon 24%, carbon 16.7%", "11:00-12:00, silicon 24%, carbon 16.7%"], forming the optimized matching parameter time sequence to guide real-time adjustment of the production line.

[0111] In step S18, the dynamic particle size prediction model is updated based on the optimized matching parameter time sequence to obtain an adjusted particle size prediction model, including:

[0112] Based on the optimized matching parameter time sequence, the mean and variance features of the optimized matching parameter are extracted;

[0113] The optimized matching parameter is applied to actual production, and the green silicon carbide micro powder produced is detected to obtain an optimized particle size distribution feature;

[0114] When the mean value and variance characteristics meet the preset mean value threshold and the preset variance threshold respectively, the optimized proportioning parameters are linearly regressed and fitted with the optimized particle size distribution characteristics, the dynamic particle size prediction model is updated, and the adjusted particle size prediction model is obtained.

[0115] It should be noted that the mean value characteristic of the optimized proportioning parameters refers to the arithmetic mean value of the optimized silicon and carbon content ratio in the time sequence, reflecting the overall level of the adjusted proportioning parameters. The variance characteristic refers to the average value of the square of the deviation of the parameter value at each time point from the mean value, reflecting the stability of the parameter in the time dimension. The smaller the variance, the more stable the production process after the proportioning adjustment. The optimized proportioning parameters are applied to actual production, and the green silicon carbide micro-powder particle size produced is measured by a laser particle size analyzer to obtain the optimized particle size distribution characteristics. The role is to form a new "input-output" data pair with the optimized proportioning parameters, and to provide the latest correlation samples for model updating. The preset mean value threshold is the silicon and carbon content reference range allowed by the process, such as the silicon content generally being 22%-28% and the carbon content generally being 12%-18%. The preset variance threshold represents the upper limit of the allowed fluctuation. When the mean value and variance characteristics meet the corresponding preset threshold, the optimized proportioning parameters are within a reasonable range and have stability, and the corresponding particle size characteristics are reliable.

[0116] It should be noted that the linear regression fitting method uses the least squares method to update the mapping coefficients in the model, i.e. the influence coefficients of silicon and carbon content on particle size and distribution ratio, which are in units of μm / %, indicating the change amount of the particle size center value / distribution ratio corresponding to a 1% change in silicon / carbon content. Specifically, the optimized silicon and carbon content ratio is taken as the independent variable, and the corresponding main particle size interval center value and distribution ratio are taken as the dependent variable. The least squares method is used to adjust the coefficients and constant term of the independent variable, so that the fitted linear equation can most accurately reflect the correlation between the proportioning parameters and the particle size characteristics in the new samples. The final adjusted particle size prediction model has a prediction accuracy that is more consistent with the current production state, and can support more accurate real-time control.

[0117] Exemplarily, in the optimized proportioning parameter time sequence, the silicon content average is 25%, which is within the preset silicon content average threshold of 22%-28%, and the variance is 0.8%2, which is less than the preset silicon content variance threshold of 1.5%2; the carbon content average is 15%, which is within the preset carbon content average threshold of 12%-18%, and the variance is 0.5%2, which is less than the preset carbon content variance threshold of 1%2. According to the optimized proportioning parameter, production is carried out, and the optimized particle size distribution characteristics are detected: the main particle size interval center value is 75 μm, and the distribution proportion is 60%. The least square method is used to fit the new sample of "silicon 25%, carbon 15%, center value 75 μm, and distribution proportion 60%", and the coefficient of silicon content in the model is adjusted from 2 μm / % to 1.9 μm / %, and the coefficient of carbon content is adjusted from-1.5 μm / % to-1.4 μm / %, to obtain the adjusted particle size prediction model. In the prediction of the subsequent silicon content of 25.5% and the carbon content of 14.8%, the particle size center value error is reduced from the original 2 μm to 0.8 μm, which significantly improves the prediction accuracy.

[0118] In summary, the present application discloses a green silicon carbide micro-powder particle size detection method, which comprises collecting and analyzing multiple batches of particle size distribution data to obtain a two-dimensional detection data set containing particle size range and distribution proportion; preprocessing the two-dimensional detection data set to obtain the final particle size distribution data set; performing dimensionality reduction analysis on the particle size distribution data set to determine the main particle size interval and the corresponding particle volume proportion, and linearly transforming to form a particle size distribution feature vector; obtaining raw material proportioning change data containing silicon content and carbon content proportion from production records, and performing data aggregation on the raw material proportioning change data in chronological order to obtain a proportioning parameter time sequence; calculating the correlation coefficient of the particle size distribution feature vector and the proportioning parameter time sequence, and when the correlation coefficient is greater than a preset correlation threshold, a nonlinear mapping relationship is established to obtain a dynamic particle size prediction model; real-time silicon content and carbon content proportion data are input into the dynamic particle size prediction model to obtain a predicted particle size distribution feature; when the main particle size interval in the particle size distribution feature deviates from a preset target particle size range, the silicon content and carbon content proportion are adjusted to obtain an optimized proportioning parameter time sequence; the dynamic particle size prediction model is updated based on the optimized proportioning parameter time sequence to obtain an adjusted particle size prediction model, and the main particle size interval and the distribution proportion are repeatedly predicted based on the adjusted particle size prediction model to obtain an optimized particle size distribution feature. The present application dynamically correlates the raw material proportioning parameters with the particle size distribution characteristics, and continuously optimizes the model parameters according to real-time data feedback, thereby realizing accurate prediction and dynamic adjustment of the particle size distribution of green silicon carbide micro-powder, and effectively improving the product particle size stability.

[0119] Referring Figure 2 , the second embodiment of the present application provides a green silicon carbide micro-powder particle size detection device, which comprises:

[0120] a data collection module, which collects and analyzes multi-batch particle size distribution data to obtain a two-dimensional detection data set containing a particle size range and a distribution ratio;

[0121] a data preprocessing module, which pre-processes the two-dimensional detection data set to obtain a final particle size distribution data set;

[0122] a feature extraction module, which performs dimensionality reduction analysis on the particle size distribution data set to determine a main particle size interval and a corresponding particle volume ratio, and forms a particle size distribution feature vector after linear transformation;

[0123] a ratio aggregation module, which obtains raw material ratio change data containing silicon content and carbon content ratios from production records, and aggregates the raw material ratio change data in chronological order to obtain a ratio parameter time sequence;

[0124] a model construction module, which calculates a correlation coefficient of the particle size distribution feature vector and the ratio parameter time sequence, establishes a nonlinear mapping relationship when the correlation coefficient is greater than a preset correlation threshold, and obtains a dynamic particle size prediction model;

[0125] a real-time prediction module, which obtains real-time silicon content and carbon content ratio data, inputs the data into the dynamic particle size prediction model, and obtains a predicted particle size distribution feature;

[0126] a ratio optimization module, which adjusts the silicon content and carbon content ratios when the main particle size interval in the particle size distribution feature deviates from a preset target particle size range, and obtains an optimized ratio parameter time sequence;

[0127] a model updating module, which updates the dynamic particle size prediction model based on the optimized ratio parameter time sequence, obtains an adjusted particle size prediction model, repeatedly predicts the main particle size interval and the distribution ratio based on the adjusted particle size prediction model, and obtains an optimized particle size distribution feature.

[0128] It should be noted that the green silicon carbide micro-powder particle size detection device provided by the embodiments of the present application is used to perform all process steps of the green silicon carbide micro-powder particle size detection method of the above-mentioned embodiments, and the working principles and beneficial effects of the two are one-to-one correspondence, so they will not be repeated.

[0129] The embodiments of the present application also provide an electronic device. The electronic device includes a processor, a memory, and a computer program, such as an abnormal data rejection program, stored in the memory and executable on the processor. The processor implements the steps in each of the above green silicon carbide micro-powder particle size detection method embodiments when executing the computer program, for example Figure 1The step S11 is shown. Alternatively, the processor implements the functions of the modules / units in each of the above apparatus embodiments when executing the computer program, such as the data acquisition module.

[0130] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0131] The electronic device can be a desktop computer, a notebook computer, a palm computer, a smart tablet and the like. The electronic device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device, and can include more or less components than the above, or combine certain components, or different components, for example, the electronic device can also include an input / output device, a network access device, a bus and the like.

[0132] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like. The processor is the control center of the electronic device, and connects all parts of the electronic device through various interfaces and lines.

[0133] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function (such as a sound playing function, an image playing function, etc.), and the like; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), and the like. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0134] The modules / units integrated in the electronic device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can realize the steps of the above-mentioned various method embodiments when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0135] It should be noted that the apparatus embodiments described above are merely illustrative, and the units described as separate units can or can not be physically separate, and the units displayed as units can or can not be physical units, i.e. can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the apparatus embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0136] The above specific embodiments further illustrate the purpose, technical scheme and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting the particle size of green silicon carbide powder, characterized by, The method comprises the following steps: Collect and analyze multiple batches of particle size distribution data to obtain a two-dimensional detection data set containing particle size range and distribution proportion; Preprocess the two-dimensional detection data set to obtain a final particle size distribution data set; Perform dimensionality reduction analysis on the particle size distribution data set to determine the main particle size interval and the corresponding particle volume proportion, and linearly transform to form a particle size distribution feature vector; Obtain raw material ratio change data containing silicon content and carbon content proportion from production records, and aggregate the data in time sequence to obtain a ratio parameter time sequence; Calculate the correlation coefficient of the particle size distribution feature vector and the ratio parameter time sequence, and when the correlation coefficient is greater than a preset correlation threshold, establish a nonlinear mapping relationship to obtain a dynamic particle size prediction model; Input real-time silicon content and carbon content proportion data into the dynamic particle size prediction model to obtain a predicted particle size distribution feature; When the main particle size interval in the particle size distribution feature deviates from a preset target particle size range, adjust the silicon content and carbon content proportion to obtain an optimized ratio parameter time sequence; Update the dynamic particle size prediction model based on the optimized ratio parameter time sequence to obtain an adjusted particle size prediction model, and repeatedly predict the main particle size interval and distribution proportion based on the adjusted particle size prediction model to obtain an optimized particle size distribution feature; When the main particle size interval in the particle size distribution feature deviates from a preset target particle size range, adjust the silicon content and carbon content proportion to obtain an optimized ratio parameter time sequence, which comprises: Extract the main particle size interval from the particle size distribution feature, calculate the deviation of the main particle size interval from the preset target particle size range to obtain a particle size deviation; When the particle size deviation is greater than a preset deviation threshold, linearly regress the silicon content and carbon content proportion to generate an optimized ratio parameter; Aggregate the optimized ratio parameter in time sequence to obtain the optimized ratio parameter time sequence; The dimensionality reduction analysis of the particle size distribution data set to determine the main particle size interval and the corresponding particle volume proportion, and linearly transform to form a particle size distribution feature vector, comprises: Obtain the original particle size range and the original distribution proportion from the particle size distribution data set, and calculate the particle size average value; Map the multi-dimensional features composed of the original particle size range, the original distribution proportion, and the particle size average value to a low-dimensional space based on principal component analysis, and calculate the feature values; Screen out the particle size intervals with feature values greater than a preset feature value threshold to determine the main particle size interval, and extract the corresponding particle volume proportion of the main particle size interval; Linearly transform the main particle size interval and the particle volume proportion to form the particle size distribution feature vector after combination; The calculation of the correlation coefficient of the particle size distribution feature vector and the ratio parameter time sequence, and when the correlation coefficient is greater than a preset correlation threshold, the establishment of a nonlinear mapping relationship to obtain a dynamic particle size prediction model, comprises: Obtain silicon content proportion data and carbon content proportion data according to the ratio parameter time sequence; Correlation coefficients of the silicon content proportion data and the carbon content proportion data and the particle size distribution feature vector are calculated to obtain silicon content correlation coefficient values and carbon content correlation coefficient values; When the silicon content correlation coefficient value or the carbon content correlation coefficient value exceeds a preset correlation threshold, a support vector regression algorithm is used to fit a nonlinear mapping relationship between the silicon content proportion, the carbon content proportion and the particle size distribution, to obtain a main particle size interval prediction value and a distribution proportion prediction value; The main particle size interval prediction value and the distribution proportion prediction value are weighted and fused with the particle size distribution feature vector to obtain the dynamic particle size prediction model.

2. The green silicon carbide powder particle size detection method according to claim 1, characterized by, The two-dimensional detection data set is preprocessed to obtain a final particle size distribution data set, including: The absolute value of the deviation of the volume proportion of each particle size interval in the two-dimensional detection data set from the average volume proportion in the data set is calculated. When the absolute value of the deviation is greater than a preset deviation threshold, the abnormal data point is marked and removed to obtain a first data set; The relative position of each data point in the first data set relative to the minimum and maximum values of the data set is calculated, and the numerical value is mapped to the 0-1 interval to obtain a normalized second data set; According to the second data set, the average value of the data points in a preset sliding window is calculated and the center point data value is replaced, and after a preset number of iterations, the final particle size distribution data set is obtained.

3. The green silicon carbide powder particle size detection method according to claim 1, characterized by, The real-time silicon content and carbon content proportion data are input into the dynamic particle size prediction model to obtain a predicted particle size distribution feature, including: The silicon content and carbon content proportion data are collected in real time, and abnormal values exceeding a preset reasonable range are removed. The average value of the valid data in a preset time period is taken as the center point of the missing value to fill in the vacancy to obtain a clean data set; The time series features of the clean data set are extracted using principal component analysis to obtain an element proportion feature set containing the change trend of the silicon content and carbon content proportion; The variance contribution rate of the element proportion feature set is calculated. When the variance contribution rate exceeds a preset variance contribution threshold, the element proportion feature set is input into the dynamic particle size prediction model to obtain the predicted particle size distribution feature.

4. The green silicon carbide powder particle size detection method according to claim 1, characterized by, The dynamic particle size prediction model is updated based on the optimized proportioning parameter time series to obtain an adjusted particle size prediction model, including: Based on the optimized proportioning parameter time series, the mean value and variance features of the optimized proportioning parameter are extracted; The optimized proportioning parameter is applied to actual production, and the green silicon carbide micro powder produced is detected to obtain an optimized particle size distribution feature; When the mean value and variance features meet preset mean value and variance thresholds, respectively, the optimized proportioning parameter and the optimized particle size distribution feature are linearly regressed and fitted to update the dynamic particle size prediction model to obtain the adjusted particle size prediction model.

5. A particle size detection device for green silicon carbide micro powder, characterized in that, A green silicon carbide micro powder particle size detection method is implemented, including: A data acquisition module acquires and analyzes multiple batches of particle size distribution data to obtain a two-dimensional detection data set containing particle size ranges and distribution proportions; A data preprocessing module pre-processes the two-dimensional detection data set to obtain a final particle size distribution data set; A feature extraction module performs dimensionality reduction analysis on the particle size distribution data set to determine a main particle size interval and a corresponding particle volume proportion, and linearly transforms the main particle size interval and the corresponding particle volume proportion to form a particle size distribution feature vector; A proportion aggregation module obtains raw material proportion change data including silicon content and carbon content proportions from production records, aggregates the raw material proportion change data in chronological order, and obtains a proportion parameter time series; A model construction module calculates a correlation coefficient of the particle size distribution feature vector and the proportion parameter time series, establishes a nonlinear mapping relationship when the correlation coefficient is greater than a preset correlation threshold, and obtains a dynamic particle size prediction model; A real-time prediction module obtains real-time silicon content and carbon content proportion data, inputs the real-time silicon content and carbon content proportion data into the dynamic particle size prediction model, and obtains a predicted particle size distribution feature; A proportion optimization module adjusts silicon content and carbon content proportions when a main particle size interval in the particle size distribution feature deviates from a preset target particle size range, and obtains an optimized proportion parameter time series; A model updating module updates the dynamic particle size prediction model based on the optimized proportion parameter time series, obtains an adjusted particle size prediction model, repeatedly predicts a main particle size interval and a distribution proportion based on the adjusted particle size prediction model, and obtains an optimized particle size distribution feature.

6. An electronic device, comprising: The computer readable storage medium includes a stored computer program, wherein the computer readable storage medium controls a device in which the computer readable storage medium is located to execute the green silicon carbide powder particle size detection method according to any one of claims 1 to 4 when the computer program runs.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium includes a stored computer program, wherein the computer readable storage medium controls a device in which the computer readable storage medium is located to execute the green silicon carbide powder particle size detection method according to any one of claims 1 to 4 when the computer program runs.

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