A method and system for controlling circularity in silicon carbide production

Abstract

The present invention relates to the field of control and regulation technology, and in particular to a method and system for controlling circularity in silicon carbide production. The collected information data is comprehensively processed, and the comprehensively processed information data is analyzed using a three-dimensional morphological analysis algorithm to obtain the circularity of silicon carbide particles, and then analyzed in combination with the historical data of silicon carbide production to obtain analysis results; an optimization control strategy is generated based on the analysis results; a multi-stage adaptive multi-dimensional optimization control algorithm is used to adjust production parameters according to the circularity data of silicon carbide particles, and then the production parameters are optimized and adjusted according to the optimization control strategy to obtain control signals of optimized production parameters; the control signals of optimized production parameters are transmitted to the production line in real time to adjust the production parameters in real time. The technical problem of inaccurate control of the circularity of silicon carbide particles due to inaccurate information data processing of silicon carbide particles in silicon carbide production is solved.

Description

Technical Field

[0001] The present invention relates to the field of control and regulation technology, and in particular to a method and system for controlling circularity in silicon carbide production. Background Art

[0002] In the production process of silicon carbide (SiC) materials, there are great technical challenges in controlling the roundness of silicon carbide particles. Existing technologies often rely on two-dimensional image analysis and simple PID control algorithms for morphology monitoring, which makes it difficult to achieve precise control in a multi-variable, complex coupled production environment. This makes it difficult to stably control the morphology of silicon carbide particles during the production process, affecting the quality and performance of the final product, especially in high-end application scenarios.

[0003] In terms of automation and optimization of production control, traditional monitoring methods are mostly offline analysis, which cannot provide real-time feedback on the morphological changes of silicon carbide particles during the production process, resulting in delayed adjustment of production parameters. Therefore, the existing technology urgently needs to introduce more advanced automatic control methods and morphological analysis technologies into the circularity control system of silicon carbide particles to improve the circularity control accuracy of silicon carbide particles. In particular, the introduction of three-dimensional morphological analysis and multivariable control algorithms will help to achieve real-time monitoring and dynamic adjustment of the production process, thereby effectively improving the production stability and application performance of silicon carbide particles.

[0004] This roundness control system for silicon carbide particles can continuously and automatically monitor and adjust the shape of silicon carbide particles during the production process, ensuring the morphological stability and consistency of silicon carbide particles to meet the strict requirements of applications such as high-performance electronic devices and optical materials. Summary of the invention

[0005] The present invention provides a method and system for controlling circularity in silicon carbide production, so as to solve the technical problem of inaccurate circularity control of silicon carbide particles due to inaccurate information data processing of silicon carbide particles in silicon carbide production.

[0006] A method and system for controlling circularity in silicon carbide production of the present invention specifically includes the following technical solutions:

[0007] A method for controlling circularity in silicon carbide production comprises the following steps:

[0008] S1. Collect and comprehensively process information data of silicon carbide particles in real time, analyze the comprehensively processed information data using a three-dimensional morphological analysis algorithm to obtain the roundness of the silicon carbide particles, and then analyze the historical data of silicon carbide production to obtain analysis results; generate an optimization control strategy based on the analysis results;

[0009] S2. A multi-stage adaptive multi-dimensional optimization control algorithm is used to adjust the production parameters according to the circularity data of the silicon carbide particles, and then the production parameters are optimized and adjusted according to the optimization control strategy to obtain the control signal of the optimized production parameters; the control signal of the optimized production parameters is transmitted to the production line in real time to adjust the production parameters in real time.

[0010] Preferably, the S1 specifically includes:

[0011] Comprehensive processing includes denoising, grayscale processing, edge detection and feature extraction.

[0012] Preferably, the S1 specifically includes:

[0013] In the process of feature extraction, multi-dimensional surface fitting and shape feature enhancement algorithms are introduced.

[0014] Preferably, the S1 specifically includes:

[0015] In the process of implementing the multidimensional surface fitting and shape feature enhancement algorithm, edge point sampling is performed on the information data after edge detection; a fitting model is established by introducing multidimensional surface fitting, and the fitting model is optimized by a multidimensional transformation minimization algorithm; the fitting surface area is calculated by multidimensional integration; based on the fitting surface area, the surface normal vector of each edge point is calculated; based on the surface normal vector, the shape complexity is calculated; based on the fitting surface area and the shape complexity, the information data after comprehensive processing is obtained.

[0016] Preferably, the S2 specifically includes:

[0017] The multi-stage adaptive multi-dimensional optimization control algorithm controls and optimizes the production parameters in the silicon carbide particle production process through multi-dimensional error calculation and generalized nonlinear Lagrange multiplier method.

[0018] Preferably, the S2 specifically includes:

[0019] In the process of implementing the multi-stage adaptive multi-dimensional optimization control algorithm, the control signal of each production parameter is used as the preliminary adjustment value, and the multi-dimensional error is calculated based on the optimization control strategy. The multi-dimensional error calculation formula is as follows:

[0020] ,

[0021] in, is the multidimensional error; , , is the coupling coefficient; , Respectively represent , control signal of a production parameter.

[0022] Preferably, the S2 specifically includes:

[0023] Based on the multi-dimensional error, a multi-stage adaptive optimization algorithm is used to adjust the control signals of the optimized production parameters.

[0024] Preferably, the S2 specifically includes:

[0025] In the process of implementing the multi-stage adaptive optimization algorithm, a multidimensional objective function is constructed. Based on the multidimensional objective function, the generalized nonlinear Lagrange multiplier method is introduced, and the optimal solution of the production parameters is obtained; based on the optimal solution of the production parameters, the control signal of the optimized production parameters is obtained.

[0026] A circularity control system in silicon carbide production includes the following parts:

[0027] Data acquisition module, data processing module, preliminary analysis module, feedback control module, intelligent analysis module, data storage module;

[0028] A data acquisition module collects information data of silicon carbide particles in real time and sends the information data of silicon carbide particles to a data processing module;

[0029] A data processing module performs comprehensive processing on the information data of the silicon carbide particles to obtain the comprehensively processed information data, and sends the comprehensively processed information data to the preliminary analysis module;

[0030] The preliminary analysis module uses a three-dimensional morphological analysis algorithm to analyze the information data after comprehensive processing to obtain the circularity of the silicon carbide particles, and transmits the circularity data of the silicon carbide particles to the feedback control module and the intelligent analysis module;

[0031] Data storage module, used to store and periodically update all detection and control data in silicon carbide production, and provide historical data for the intelligent analysis module;

[0032] An intelligent analysis module analyzes the circularity data of the silicon carbide particles and the historical data of the data storage module to obtain analysis results, generates an optimization control strategy based on the analysis results, and transmits the optimization control strategy to the feedback control module;

[0033] The feedback control module uses a multi-stage adaptive multi-dimensional optimization control algorithm to adjust the production parameters in the silicon carbide production process according to the circularity data of the silicon carbide particles. The production parameters in the silicon carbide production process are then optimized and adjusted according to the optimization control strategy of the intelligent analysis module to ensure that the circularity of the silicon carbide particles is within the target range.

[0034] The beneficial effects of the technical solution of the present invention are:

[0035] The circularity control method in silicon carbide production adjusts production parameters through a multi-stage adaptive multidimensional optimization control algorithm, thereby improving the accuracy and stability of the production process; through multidimensional surface fitting, shape complexity analysis and circularity calculation, the circularity of silicon carbide particles is ensured, thereby improving the quality of silicon carbide; the coupling effect of multidimensional production parameters such as temperature, pressure, reaction time, etc. is comprehensively considered, and multidimensional optimization adjustment is performed through the generalized nonlinear Lagrange multiplier method, so that the production parameters are optimized and controlled, further improving the consistency and stability of the product.

[0036] By analyzing the historical data of silicon carbide production and continuously optimizing the control strategy, the circularity control system in silicon carbide production is made adaptive and can dynamically adjust the production parameters to cope with different production conditions, thus improving the intelligence level of the control system. The control signal of the optimized production parameters is transmitted to the production line in real time through the feedback control module to adjust the production parameters in real time. Through the multi-dimensional objective function, the circularity deviation is minimized and the production cost is minimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a structural diagram of a circularity control system in silicon carbide production according to the present invention;

[0038] Figure 2 The present invention is a flow chart of a method for controlling circularity in silicon carbide production. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0041] The specific scheme of the method and system for controlling circularity in silicon carbide production provided by the present invention is described in detail below with reference to the accompanying drawings.

[0042] Refer to the attached Figure 1 , which shows a structural diagram of a circularity control system in silicon carbide production provided by an embodiment of the present invention, the system includes the following parts:

[0043] Data acquisition module, data processing module, preliminary analysis module, feedback control module, intelligent analysis module, data storage module;

[0044] A data acquisition module, using a collection device to collect information data of silicon carbide particles in real time, the collection device is such as a camera, and the collected information data of silicon carbide particles is image data of silicon carbide particles; the collected image data of silicon carbide particles is sent to a data processing module;

[0045] The data processing module performs comprehensive processing including denoising, graying, edge detection and feature extraction on the collected image data of silicon carbide particles to obtain the image data after comprehensive processing, and sends the image data after comprehensive processing to the preliminary analysis module;

[0046] A preliminary analysis module analyzes the image data after comprehensive processing using a three-dimensional morphological analysis algorithm to obtain the circularity of the silicon carbide particles, and transmits the circularity data of the silicon carbide particles to the feedback control module and the intelligent analysis module;

[0047] The data storage module is used to store and periodically update all detection and control data in silicon carbide production, including image data, circularity data, production parameters and control strategies, and provide historical data for the intelligent analysis module;

[0048] An intelligent analysis module analyzes the circularity data of the silicon carbide particles and the historical data of the data storage module to obtain analysis results, generates an optimization control strategy based on the analysis results, and transmits the optimization control strategy to the feedback control module;

[0049] The feedback control module uses a multi-stage adaptive multi-dimensional optimization control algorithm to adjust the production parameters in the silicon carbide production process according to the circularity data of the silicon carbide particles, and then optimizes and adjusts the production parameters in the silicon carbide production process according to the optimization control strategy of the intelligent analysis module to ensure that the circularity of the silicon carbide particles is within the target range;

[0050] The production parameters are such as temperature, pressure and reaction time.

[0051] Refer to the attached Figure 2 , which shows a flow chart of a method for controlling circularity in silicon carbide production provided by an embodiment of the present invention, the method comprising the following steps:

[0052] S1. Collect and comprehensively process information data of silicon carbide particles in real time, analyze the comprehensively processed information data using a three-dimensional morphological analysis algorithm to obtain the roundness of the silicon carbide particles, and then analyze the historical data of silicon carbide production to obtain analysis results; generate an optimization control strategy based on the analysis results;

[0053] At the key position of the production line determined by professional technicians, information data of silicon carbide particles are collected in real time using a collection device; the collection device is such as a camera, and the information data of silicon carbide particles collected specifically refers to the image data of silicon carbide particles, and the collected image data is subjected to denoising by using existing image processing algorithms such as Gaussian filtering or median filtering; the denoised image data is subjected to grayscale processing to obtain grayscale image data, and the Canny edge detection algorithm or Sobel operator is used to perform edge detection on the grayscale image data, and the image data after edge detection is subjected to feature extraction using a multi-dimensional surface fitting and shape feature enhancement algorithm to obtain image feature data, i.e., image data after comprehensive processing; the specific implementation process is as follows:

[0054] First, in order to extract representative edge points from the image after edge detection and provide a data basis for subsequent processing, edge point sampling is performed on the image data after edge detection. The existing random sampling method is used to select edge points on the image after edge detection to ensure that the edge points are evenly distributed and representative. The coordinates of the edge points are marked as ,in, It is The coordinates of the edge points; It is edge points Coordinates and coordinate; , is the total number of edge points.

[0055] Furthermore, by introducing multi-dimensional surface fitting, a fitting model of edge points in three-dimensional space is established to facilitate subsequent surface analysis. By multi-dimensional surface fitting, edge points are extended to three-dimensional space. The formula is as follows:

[0056] ,

[0057] in, is the multidimensional surface fitting function, i.e. the fitting model; is the fitting coefficient of the polynomial part; is the fitting coefficient of the logarithmic term; is the order of the polynomial and logarithmic terms; is the highest order of polynomial and logarithmic terms;

[0058] In order to optimize the fitting model, a multidimensional transformation minimization algorithm is introduced to minimize the error function. The minimized error function is expressed as:

[0059] ,

[0060] in, The fitting surface is Edge points The value at is the third-dimensional coordinate value of the actual edge point, which can be taken as ; is a regularization parameter used to control the size of the polynomial coefficients to avoid overfitting; is a regularization parameter used to control the size of the logarithmic term coefficient to avoid overfitting; by minimizing the error function, the optimal polynomial and logarithmic term coefficients can be found to make the fitting surface close to the distribution of the actual edge points.

[0061] Furthermore, the area of ​​the fitted surface is calculated, and the actual area of ​​the fitted surface is calculated through multi-dimensional integration to provide a quantitative index for subsequent shape feature extraction. The integral formula is:

[0062] ,

[0063] in, is the area of ​​the fitted surface; is the integration area; and are the partial derivatives of the fitted surface.

[0064] After calculating the fitted surface area, the surface normal vector of each edge point is further calculated to enhance the shape features. The calculation formula of the surface normal vector is:

[0065] ,

[0066] in, To fit the surface The surface normal vector at . The local geometric features of the fitted surface are captured by calculating the surface normal vector, providing basic data for the calculation of shape complexity.

[0067] Furthermore, the shape complexity is calculated by combining the surface normal vector. The calculation formula of shape complexity is:

[0068] ,

[0069] in, is the shape complexity; is the dot product of the surface normal vector; The surface is The surface normal vector at ; It is a small increment of the integral variable and is used for multi-dimensional integral calculations. It calculates the local changes of the surface and quantifies the complexity of the shape by using the dot product of the surface normal vector.

[0070] After the above processing, image feature data such as the fitted surface area and shape complexity are obtained, that is, the image data after comprehensive processing;

[0071] Finally, the three-dimensional morphological analysis algorithm was introduced to analyze the image data after comprehensive processing to obtain the circularity of silicon carbide particles. The calculation formula of circularity is:

[0072] ,

[0073] in, is the circumference of a circle, To fit the surface area, is the shape complexity, is the volume, which represents the volume of space enclosed by the fitting surface.

[0074] Furthermore, the circularity of the silicon carbide particles and the historical data of silicon carbide production of the data storage module are analyzed using an existing neural network algorithm (such as a feedforward neural network or a convolutional neural network), and then the analysis results are obtained through processing such as an input layer, a hidden layer, and an output layer. An optimization control strategy is generated based on the analysis results. The optimization control strategy includes: if the circularity of the silicon carbide particles is If the roundness of the silicon carbide particles is too low, it may be necessary to increase the reaction time or adjust the temperature. If it is too high, you may need to lower the pressure or shorten the reaction time.

[0075] S2. Use a multi-stage adaptive multi-dimensional optimization control algorithm to adjust the production parameters according to the roundness data of silicon carbide particles, and then optimize and adjust the production parameters according to the optimization control strategy to obtain the control signal of the optimized production parameters; transmit the control signal of the optimized production parameters to the production line in real time to adjust the production parameters in real time.

[0076] The multi-stage adaptive multi-dimensional optimization control algorithm realizes the precise control and optimization of the production parameters in the silicon carbide production process through complex multi-dimensional error calculation and generalized nonlinear Lagrange multiplier method. The production parameters are such as temperature, pressure and reaction time. The specific implementation process is as follows:

[0077] Initial adjustment phase: Use PID control algorithm according to the current moment Circularity data Make preliminary adjustments. Make adjustments in stages through multiple sub-controllers. Calculate the preliminary adjustment values ​​for each production parameter:

[0078] ,

[0079] in, is the control signal; Indicates the current time In Dimension The circularity error, , is the target circularity, For the current moment In Dimension Circularity; , , The dimensions are Proportional, integral, and differential coefficients; is the integration variable; Indicates the current moment. Through the PID control algorithm, the initial adjustment of production parameters can be achieved to reduce errors.

[0080] The continuous addition calculation setting of the above formula is accumulated to 3, which specifically represents the dimension of the production parameter. Three different production parameters are considered here, namely temperature, pressure and reaction time.

[0081] Furthermore, based on the optimized control strategy, a multi-dimensional error calculation formula is introduced to consider the coupling effect between production parameters. The multi-dimensional error calculation formula is as follows:

[0082] ,

[0083] in, is the multidimensional error; , , is the coupling coefficient; , Respectively represent , control signal of a production parameter.

[0084] Based on the multi-dimensional error calculation results, a multi-stage adaptive optimization algorithm is used for adjustment. A multi-dimensional objective function is constructed to minimize circularity deviation and production cost, while considering the coupling relationship between multi-dimensional parameters:

[0085] ,

[0086] in, is the objective function; is the target circularity; It is the current moment Circularity; For the The weight coefficients of the production parameters; For the Optimal production parameters; Represents the total time. By optimizing the objective function, the influence of production parameters on circularity and their mutual coupling relationship are comprehensively considered.

[0087] Furthermore, in order to solve the optimization problem, the generalized nonlinear Lagrange multiplier method is introduced:

[0088] ,

[0089] in, is the generalized nonlinear Lagrangian function; It is The generalized nonlinear Lagrange multipliers of the production parameters; For the and The generalized nonlinear Lagrange multiplier of the production parameters, For the moment Time control signal of production parameters The dynamic change function is fitted by experimental data of multiple production parameters; For the and Production parameters in time The multi-dimensional parameter coupling constraint function is obtained through joint experiments and identification of multiple production parameters.

[0090] Further, solve the generalized nonlinear Lagrangian equation, that is, solve the generalized nonlinear Lagrangian function The partial derivative of is set equal to zero to obtain the optimal solution of the production parameters. Based on the optimal solution of the production parameters, the control signal adjustment formula of the optimized production parameters is finally obtained:

[0091] ,

[0092] in, It is A control signal of an optimized production parameter; It is The initial value of the control signal of the production parameter; No. The proportional coefficient of the production parameter is used to adjust the proportional relationship between the production parameter and the error; It is The integration coefficient of each production parameter; It is Differentiation coefficients of production parameters; It is The error of production parameters; To optimize the adjustment items, it is used to refine and adjust the production parameters to ensure the optimality of the production parameter adjustment; is the coupling adjustment coefficient, It is a parameter coupling adjustment item, which is used to consider the mutual influence between different production parameters. By calculating the ratio difference between production parameters and adjusting them, it ensures the coordinated change of various production parameters. is the integration variable; is time. Through the above formula, multi-dimensional optimization adjustment of production parameters can be achieved to improve the accuracy and stability of control.

[0093] The control signal of the optimized production parameters is transmitted to the production line in real time through the feedback control module, and the production parameters are adjusted in real time to ensure that the roundness of the silicon carbide particles is within the target range. The feedback control module continuously adjusts according to the roundness data of the real-time silicon carbide particles, and the intelligent analysis module continuously optimizes based on the historical data in silicon carbide production and the roundness data of the current silicon carbide particles. The optimized production parameters output by the feedback control module are used as the input of the data storage module to ensure that the data after each adjustment is recorded and stored.

[0094] The historical data in silicon carbide production, such as all detection and control data, including image data, circularity data, production parameters and control strategies, are stored and periodically updated in the data storage module. The historical data of silicon carbide production in the data storage module is used to further optimize the control strategy and continuously improve production efficiency and product quality. The historical data in the data storage module can also be used for algorithm training and model updating in the intelligent analysis module to ensure the adaptability and dynamic optimization capabilities of the control system.

[0095] In summary, a method and system for controlling circularity in silicon carbide production have been completed.

[0096] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0097] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0098] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for controlling circularity in silicon carbide production, characterized in that: The following steps are involved: S1. Real-time collection and comprehensive processing of information data of silicon carbide particles, analysis of the comprehensively processed information data using a three-dimensional morphological analysis algorithm to obtain the roundness of the silicon carbide particles, and then analysis combined with historical data of silicon carbide production to obtain analysis results; and generation of an optimization control strategy based on the analysis results; S2. Based on the circularity data of silicon carbide particles, the multi-stage adaptive multi-dimensional optimization control algorithm controls and optimizes the production parameters in the silicon carbide particle production process through multi-dimensional error calculation and generalized nonlinear Lagrange multiplier method. In the implementation process of the multi-stage adaptive multi-dimensional optimization control algorithm, the control signal of each production parameter is used as the preliminary adjustment value, and the multi-dimensional error is calculated based on the optimization control strategy. The multi-dimensional error calculation formula is as follows: ， in, is the multidimensional error; , , is the coupling coefficient; , Respectively represent , Control signals for production parameters; Based on multi-dimensional errors, a multi-stage adaptive optimization algorithm is used to adjust the control signal of the optimized production parameters; In the process of implementing the multi-stage adaptive optimization algorithm, a multidimensional objective function is constructed. Based on the multidimensional objective function, the generalized nonlinear Lagrange multiplier method is introduced to obtain the optimal solution of the production parameters. Based on the optimal solution of the production parameters, the control signal of the optimized production parameters is obtained. The control signal of the optimized production parameters is transmitted to the production line in real time to adjust the production parameters in real time.

2. The method for controlling circularity in silicon carbide production according to claim 1, characterized in that: The S1 specifically includes: Comprehensive processing includes denoising, grayscale processing, edge detection and feature extraction.

3. The method for controlling circularity in silicon carbide production according to claim 2, characterized in that: The S1 specifically includes: In the process of feature extraction, multi-dimensional surface fitting and shape feature enhancement algorithms are introduced.

4. The method for controlling circularity in silicon carbide production according to claim 3, characterized in that: The S1 specifically includes: In the process of implementing the multidimensional surface fitting and shape feature enhancement algorithm, edge point sampling is performed on the information data after edge detection; a fitting model is established by introducing multidimensional surface fitting, and the fitting model is optimized by a multidimensional transformation minimization algorithm; the fitting surface area is calculated by multidimensional integration; based on the fitting surface area, the surface normal vector of each edge point is calculated; based on the surface normal vector, the shape complexity is calculated; based on the fitting surface area and the shape complexity, the information data after comprehensive processing is obtained.

5. A control system for circularity in silicon carbide production, applied to the method for controlling circularity in silicon carbide production as claimed in claim 1, characterized in that: Includes the following parts: Data acquisition module, data processing module, preliminary analysis module, feedback control module, intelligent analysis module, data storage module; A data acquisition module collects information data of silicon carbide particles in real time and sends the information data of silicon carbide particles to a data processing module; A data processing module performs comprehensive processing on the information data of the silicon carbide particles to obtain the comprehensively processed information data, and sends the comprehensively processed information data to the preliminary analysis module; The preliminary analysis module uses a three-dimensional morphological analysis algorithm to analyze the information data after comprehensive processing to obtain the circularity of the silicon carbide particles, and transmits the circularity data of the silicon carbide particles to the feedback control module and the intelligent analysis module; Data storage module, used to store and periodically update all detection and control data in silicon carbide production, and provide historical data for the intelligent analysis module; An intelligent analysis module analyzes the circularity data of the silicon carbide particles and the historical data of the data storage module to obtain analysis results, generates an optimization control strategy based on the analysis results, and transmits the optimization control strategy to the feedback control module; The feedback control module uses a multi-stage adaptive multi-dimensional optimization control algorithm to adjust the production parameters in the silicon carbide production process according to the circularity data of the silicon carbide particles. The production parameters in the silicon carbide production process are then optimized and adjusted according to the optimization control strategy of the intelligent analysis module to ensure that the circularity of the silicon carbide particles is within the target range.

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