Melting monitoring system and method for solid coloring agent

Abstract

The embodiment of the invention provides a melting monitoring system and method for a solid coloring agent. The method comprises the steps that a light source and a light sensor are arranged to generate an initial optical path, light intensity values are recorded and processed to form a time-light intensity curve, a fusion starting point is recognized through a support vector machine model, the fusion rate is calculated through a Kalman filter, and a quality evaluation report is generated. When the melting rate reaches a preset threshold value, the PID controller triggers a cooling program, the uniformity and purity of the coloring agent are analyzed in combination with the deep learning technology, and finally a melting monitoring report is generated. According to the technical scheme provided by the embodiment of the invention, the product quality and the production efficiency of the solid coloring agent are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of automation control technology, and in particular to a melting monitoring system and method for solid coloring agents. Background Art

[0002] In many industrial fields such as textiles, plastics, and coatings, the melting process of solid dyes is a key step to ensure the quality of the final product. Accurately monitoring the melting state of solid dyes is essential for controlling the production process, optimizing process parameters, and ensuring product consistency.

[0003] At present, there are some technologies that attempt to solve the problem of monitoring the melting of solid dyes. For example, optical sensors are used to record the changes in light intensity passing through the dye sample to form a time-light intensity curve; or temperature sensors are used to monitor the temperature changes in the heating container.

[0004] Existing solutions have several significant flaws: First, most of them rely on simple threshold judgments or linear regression models to identify the melting starting point and rate, which is inadequate when faced with complex nonlinear melting processes and is prone to misjudgment. Second, although traditional denoising methods (such as mean filtering) can reduce noise interference, they may also blur key feature information and affect the accuracy of subsequent analysis. Finally, existing systems rarely have intelligent adjustment functions and cannot automatically adjust production process parameters (such as cooling programs) based on real-time monitoring data, limiting their potential for application in automated production lines. Summary of the invention

[0005] The embodiments of the present application provide a melting monitoring system and method for a solid colorant, which are used to solve the problems of defective product quality and low production efficiency of solid colorants in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a melting monitoring method for a solid colorant, comprising: The solid dye to be monitored is placed in a transparent heating container, at least one light source is arranged on one side of the transparent heating container, at least one light sensor is arranged on the other side opposite thereto, a wavelength combination is selected according to the absorption spectrum of the solid dye, and a multi-wavelength light source is used to make the light path pass through the maximum cross section of the solid dye to generate an initial optical path; Recording the light intensity value transmitted through the solid dye received by the light sensor according to a preset time interval to form an initial time-light intensity curve, and performing denoising processing on the data of the initial optical path using a wavelet transform algorithm to obtain a time-light intensity curve; Based on the change trend of the time-light intensity curve, a preset support vector machine model is used for observation to obtain a turning point where the slope of the light intensity curve changes, and based on the turning point, the moment when the solid dye begins to melt is indicated to generate a melting start signal; Based on the data of the time-light intensity curve of the melting start signal at a fixed time, the melting rate is calculated using a Kalman filter, and the stability of the solid dye under different environmental conditions is predicted in combination with a thermodynamic analysis algorithm to generate a quality assessment report; When it is detected that the melting rate reaches a preset threshold, the cooling program is automatically triggered by the PID controller, and the uniformity and purity of the solid colorant are predicted and analyzed with the deep learning image analysis technology to obtain key parameters. The key parameters and quality assessment results of the solid colorant during the melting process are integrated to generate a melting monitoring report.

[0007] Optionally, based on the change trend of the time-light intensity curve, a preset support vector machine model is used for observation to obtain a turning point where the slope of the light intensity curve changes, and based on the turning point, the moment when the solid dye begins to melt is indicated to generate a melting start signal, including: The time-light intensity curve is smoothed by using a five-point cubic smoothing algorithm to obtain a smooth curve, and the slope of the smooth curve is calculated according to a central difference method combined with a least squares fitting technique to obtain the slope value corresponding to each time point; Performing dimensionality reduction processing on the slope value by using principal component analysis to obtain a dimensionality reduction slope value, inputting the dimensionality reduction slope value into a pre-trained support vector machine model, classifying and identifying the dimensionality reduction slope value, and obtaining an initial turning point where the slope of the light intensity curve changes; Based on the initial turning point, a dynamic time warping algorithm is used to match the time-light intensity curve within a specified time period, and an anomaly detection algorithm is used to monitor the model output to predict and locate the turning point where the slope of the light intensity curve changes; Based on the turning point, a Kalman filter is used to optimize the estimation of the melting start time to generate a melting start signal.

[0008] Optionally, principal component analysis is used to perform dimension reduction processing on the slope value to obtain a dimension-reduced slope value, including: Optimizing the slope value using a robust regression algorithm to form a high-dimensional slope value matrix, and standardizing the features of the high-dimensional slope value matrix according to a Z-Score standardization method to generate a standardized slope value matrix, wherein the features include a zero mean and a unit variance; According to the standardized slope value matrix, a predefined Mahalanobis distance-based covariance matrix is ​​calculated, and a random singular value decomposition algorithm is used to accelerate the decomposition process of the covariance matrix to obtain eigenvalues ​​and eigenvectors; Based on the eigenvalues ​​and the eigenvectors, a cross-validation technique is used to evaluate the performance of the support vector machine model under different numbers of principal components, and N principal components that can retain maximum information and verify the performance of the support vector machine model are selected. When determining the value of N, the least absolute shrinkage and selection operator regression is used to screen out the principal components that are helpful for the classification task, and a principal component set is generated; The standardized slope value matrix is ​​preliminarily reduced in dimension using a t-distributed random neighbor embedding algorithm to obtain preliminary reduced-dimensionality slope values, and the preliminary reduced-dimensionality slope values ​​are projected onto a new coordinate system defined by the principal component set to generate reduced-dimensionality slope values.

[0009] Optionally, when it is detected that the melting rate reaches a preset threshold, a cooling program is automatically triggered by a PID controller, and the uniformity and purity of the solid colorant are predicted and analyzed in combination with deep learning image analysis technology to obtain key parameters, and the key parameters and quality assessment results of the solid colorant during the melting process are integrated to generate a melting monitoring report, including: When the melting rate reaches a preset threshold, the parameter setting of the PID controller is optimized using fuzzy logic, and a cooling program is automatically triggered according to a preset temperature control strategy; During the cooling process, an initial image of the solid dye is obtained using a high-resolution camera, and the initial image is processed based on a multi-scale image fusion technology to obtain an optimized image; Preprocessing the optimized image according to a superpixel segmentation algorithm to obtain a processed image, and using an ensemble learning method to analyze the color distribution, particle size and distribution and other features extracted from the processed image to obtain key parameters including uniformity index, purity level and impurity content of the solid colorant; According to the key parameters, external data in the melting process are collected, and the melting rate and environmental condition changes in a fixed time in the future are predicted based on time series analysis to obtain analysis results, wherein the external data includes melting rate, environmental temperature and humidity; Based on the analysis results, natural language processing technology is used to automatically generate a melting monitoring report.

[0010] Optionally, preprocessing the optimized image according to a superpixel segmentation algorithm to obtain a processed image includes: Using a multi-view data set and a structural similarity index, a weighted average fusion process is performed on the initial image of the solid dye acquired by a high-resolution camera to generate an optimized image; Initializing a superpixel grid according to the optimized image, and iteratively optimizing the boundary of the superpixel grid based on an improved simple linear iterative clustering algorithm combined with a spectral clustering technique to obtain a preliminary segmentation result; Based on the preliminary segmentation result, an active contour model and a graph cut algorithm are applied to refine and smooth the boundaries of the superpixel grid, and shape regularity constraints such as ellipse fitting are introduced to generate an intermediate segmented image; According to the intermediate segmented image, a connected component labeling algorithm is used to remove isolated areas, and bilateral filtering and smoothing operations are applied to perform morphological operations to generate a processed image, wherein the morphological operations include opening operations and closing operations.

[0011] Optionally, based on the data of the time-light intensity curve of the melting start signal within a fixed time, the melting rate is calculated using a Kalman filter, comprising: The melting start signal is used in combination with an adaptive threshold algorithm to calculate a fixed time when the solid dye starts to melt, and based on the fixed time, a dynamic window selection method is used to automatically adjust the length of the time period according to the changing trend of the melting rate, thereby extracting initial time-light intensity curve data starting from the fixed time; The median filtering method is used to remove noise and outliers from the initial time-light intensity curve data, and the local weighted regression scatter point smoothing technology is introduced to retain the characteristic information in the initial time-light intensity curve data, and the robust statistical method is further applied to automatically detect and remove outliers to obtain smoothed time-light intensity curve data; Based on the smoothed time-light intensity curve data, a five-point cubic smoothing algorithm is used to reduce noise interference, and a least squares fitting technique combined with an adaptive weight adjustment strategy is used to calculate the slope value corresponding to each time point, which is expressed as a preliminary melting rate; The preliminary melting rate is optimized using an unscented Kalman filter to obtain a melting rate.

[0012] Optionally, a median filtering method is used to remove noise and outliers from the initial time-light intensity curve data, and a local weighted regression scatter point smoothing technique is introduced to retain feature information in the initial time-light intensity curve data, and a robust statistical method is further applied to automatically detect and remove outliers to obtain smoothed time-light intensity curve data, including: Performing preliminary denoising on the initial time-light intensity curve data by using a median filtering method, reducing the influence of noise by replacing each data point with the median value in the neighborhood, and obtaining preliminary denoised data; Based on the data after preliminary denoising, a local weighted regression scatter point smoothing technique is introduced to smooth the curve by performing weighted least square fitting on local data points, retaining feature information and local change trends, and generating smoothed curve data; According to the smoothed curve data, a robust statistical method is applied to automatically detect and remove outliers to obtain a detection result; Based on the detection result, the data after preliminary denoising and the smoothed curve data, smoothed time-light intensity curve data are integrated.

[0013] In a second aspect, an embodiment of the present application provides a melting monitoring system for a solid colorant, comprising: A generating module, for placing a solid dye to be monitored in a transparent heating container, arranging at least one light source on one side of the transparent heating container, arranging at least one light sensor on the other side opposite thereto, selecting a wavelength combination according to an absorption spectrum of the solid dye, and using a multi-wavelength light source to make a light path pass through a maximum cross section of the solid dye, so as to generate an initial optical path; A denoising module, for recording the light intensity value transmitted through the solid dye and received by the light sensor at preset time intervals to form an initial time-light intensity curve, and performing denoising processing on the data of the initial optical path by a wavelet transform algorithm to obtain a time-light intensity curve; An indication module, for observing, based on the changing trend of the time-light intensity curve, using a preset support vector machine model, obtaining a turning point at which the slope of the light intensity curve changes, and indicating, based on the turning point, the moment when the solid dye begins to melt, so as to generate a melting start signal; An evaluation module, for calculating the melting rate using a Kalman filter based on the data of the time-light intensity curve of the melting start signal at a fixed time, predicting the stability of the solid dye under different environmental conditions in combination with a thermodynamic analysis algorithm, and generating a quality evaluation report; The monitoring module is used to automatically trigger the cooling program using a PID controller when it is detected that the melting rate reaches a preset threshold, predict and analyze the uniformity and purity of the solid colorant in combination with deep learning image analysis technology, obtain key parameters, integrate the key parameters and quality assessment results of the solid colorant during the melting process, and generate a melting monitoring report.

[0014] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a melting monitoring method for solid colorants as described in the first aspect above.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a melting monitoring method for a solid colorant as described in the first aspect.

[0016] In an embodiment of the present application, a solid dye to be monitored is placed in a transparent heating container, at least one light source is arranged on one side of the transparent heating container, at least one light sensor is arranged on the other opposite side, and a wavelength combination is selected according to the absorption spectrum of the solid dye, and a multi-wavelength light source is used to make the light path pass through the maximum cross-section of the solid dye to generate an initial optical path; the light intensity value received by the light sensor and transmitted through the solid dye is recorded according to a preset time interval to form an initial time-light intensity curve, and the wavelet transform algorithm is used to denoise the data of the initial optical path to obtain a time-light intensity curve; based on the change trend of the time-light intensity curve, a preset support vector machine model is used for observation to obtain The turning point where the slope of the light intensity curve changes indicates the moment when the solid colorant begins to melt based on the turning point, so as to generate a melting start signal; based on the data of the time-light intensity curve of the melting start signal within a fixed time, the melting rate is calculated using a Kalman filter, and the stability of the solid colorant under different environmental conditions is predicted in combination with a thermodynamic analysis algorithm to generate a quality assessment report; when it is detected that the melting rate reaches a preset threshold, the cooling program is automatically triggered by a PID controller, and the uniformity and purity of the solid colorant are predicted and analyzed in combination with image analysis technology based on deep learning to obtain key parameters, and the key parameters and quality assessment results of the solid colorant during the melting process are integrated to generate a melting monitoring report.

[0017] The technical solution of this application has the following beneficial effects: This application generates the initial optical path through a multi-wavelength light source and a light sensor, and applies wavelet transform denoising to obtain an accurate time-light intensity curve; uses a support vector machine model to accurately locate the start of melting, and uses a Kalman filter to calculate the melting rate, combined with a thermodynamic analysis algorithm to predict stability, ensuring accurate monitoring of the melting process. When the melting rate reaches a preset threshold, the cooling program is automatically triggered and the uniformity and purity of the dye are analyzed through deep learning technology, and a detailed melting monitoring report is finally generated. This method not only improves the monitoring accuracy, but also optimizes the production process and enhances the product quality control capability, with significant technical advantages and application value.

[0018] Furthermore, the present application provides a method for accurately identifying the moment when a solid dye begins to melt based on the changing trend of the time-light intensity curve. First, the time-light intensity curve is smoothed using a five-point cubic smoothing algorithm, and the slope value is calculated by combining the central difference method with the least squares fitting technique; then, the slope value is reduced in dimension using principal component analysis, and the slope value after dimensionality reduction is input into a pre-trained support vector machine model to classify and identify the initial turning point; then, based on the initial turning point, a dynamic time warping algorithm is used to match the curve within a specified time period, and an anomaly detection algorithm is applied to monitor the model output to predict and accurately locate the actual turning point; finally, a Kalman filter is used to optimize the estimation of the melting start time to generate a melting start signal.

[0019] Through the above method, a variety of advanced algorithms are integrated, including smoothing, slope calculation, dimensionality reduction, support vector machine classification, dynamic time warping, and Kalman filter optimization, to achieve high-precision identification of the melting start point of solid dyes. Compared with traditional methods, this technology not only improves the accuracy of monitoring, but also enhances the adaptability to complex nonlinear melting processes, can effectively reduce misjudgment and noise interference, and ensure accurate estimation of the melting start time, thereby improving product quality and production efficiency. In addition, this method provides reliable data support for subsequent automated control and further optimizes the production process.

[0020] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A flow chart of a melting monitoring method for a solid dye provided by the present application is shown; Figure 2 A schematic structural diagram of a melting monitoring system for a solid dye provided by the present application is shown; Figure 3 A schematic diagram of the structure of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0024] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0026] Figure 1 A flow chart of a method for monitoring the melting of a solid dye is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes: 101. Place a solid dye to be monitored in a transparent heating container, set at least one light source on one side of the transparent heating container, set at least one light sensor on the other side opposite thereto, select a wavelength combination according to the absorption spectrum of the solid dye, use a multi-wavelength light source to make the light path pass through the maximum cross section of the solid dye, and generate an initial optical path; Among them, light sensor: a device that can detect and quantify the intensity of light transmitted through an object; in this system, it is used to monitor the changes in the intensity of light transmitted through the solid dye, thereby providing data about the state of the dye; Absorption spectrum: indicates the degree of absorption of light of different wavelengths by a substance. By analyzing the absorption spectrum, the wavelength combination most suitable for monitoring a specific substance (such as solid dye) can be selected; Multi-wavelength light source: A light source that can emit multiple wavelengths of light. By selecting the appropriate wavelength combination, it can more accurately reflect the state changes of the dye and improve the monitoring accuracy; Initial optical path: The optical path formed by the multi-wavelength light source passing through the maximum cross-section of the solid dye. This path contains key information about the state of the dye and is the basis for subsequent analysis.

[0027] In actual operation, the solid dye to be monitored is placed in a transparent heating container, and a light source and a light sensor are installed on both sides of it. According to the absorption spectrum of the dye, a suitable wavelength combination is selected, and a multi-wavelength light source is used to make the light path pass through the maximum cross section of the dye to generate an initial optical path. This step provides basic data for the subsequent time-light intensity curve.

[0028] For example, in an automated production line, the system automatically identifies and loads a specified type of solid dye into a transparent heating container. Subsequently, the system configures a multi-wavelength light source according to a preset absorption spectrum and starts irradiating the dye with the light source. At the same time, the light sensor begins to collect the transmitted light intensity value in real time and preliminarily constructs the initial optical path. This process ensures the accuracy of data collection and lays the foundation for subsequent steps.

[0029] 102. Record the light intensity value transmitted through the solid dye received by the light sensor at preset time intervals to form an initial time-light intensity curve, and perform denoising on the data of the initial optical path using a wavelet transform algorithm to obtain a time-light intensity curve; Among them, the time-light intensity curve shows the light intensity value changing with time, reflecting the dynamic trend of the solid dye gradually melting during the heating process; Wavelet transform algorithm: A signal processing technique that can effectively remove noise interference while retaining the important features of the original signal. It is suitable for processing complex time series data.

[0030] In actual operation, the light intensity values ​​received by the light sensor are recorded at preset time intervals to form an initial time-light intensity curve. Then, the curve is denoised using a wavelet transform algorithm to obtain a smoother and more accurate time-light intensity curve for subsequent analysis.

[0031] Based on the above embodiment, the system records the light intensity value at a frequency of once per second to generate a time-light intensity curve. Then the wavelet transform algorithm is applied to denoise the curve to eliminate the influence of random noise. The processed data is stored and prepared for the next step of analysis, ensuring the quality and reliability of the data.

[0032] 103. Based on the change trend of the time-light intensity curve, a preset support vector machine model is used for observation to obtain a turning point where the slope of the light intensity curve changes, and based on the turning point, a moment when the solid dye begins to melt is indicated to generate a melting start signal; Among them, the support vector machine model: a machine learning model that is particularly suitable for classification tasks; in this application scenario, it is used to identify the turning point where the slope of the time-light intensity curve changes, that is, the moment when the solid dye begins to melt; Melting start signal: marks the precise moment when the solid dye begins to melt, providing a critical reference point for subsequent rate calculations and quality assessments.

[0033] In actual operation, based on the changing trend of the time-light intensity curve, a pre-trained support vector machine model is used to observe the slope change, determine the initial turning point, indicate the moment when the solid dye begins to melt, and generate a melting start signal.

[0034] Continuing with the above embodiment, the system uses a support vector machine model to analyze the denoised time-light intensity curve and identify the turning point where the slope changes significantly. Once this turning point is determined, the system immediately generates a melting start signal and transmits it to the control system to prepare for subsequent rate calculation and cooling procedures.

[0035] 104. Based on the data of the time-light intensity curve of the melting start signal at a fixed time, a Kalman filter is used to calculate the melting rate, and a thermodynamic analysis algorithm is combined to predict the stability of the solid dye under different environmental conditions, and a quality assessment report is generated; Among them, Kalman filter: a recursive filter used to estimate the state variables of a system from a series of incomplete and noisy measurement data; in this application, it is used to optimize the estimation of the melting rate; Thermodynamic analysis algorithm: Based on the principles of thermodynamics, it predicts the stability of solid dyes under different environmental conditions and helps evaluate product quality; Quality Assessment Report: A report generated after comprehensive analysis of factors such as melting rate and environmental conditions, used to evaluate the quality and stability of solid colorants.

[0036] In actual operation, based on the melting start signal, the melting rate is calculated using a Kalman filter on the data of the time-light intensity curve at a fixed moment. The stability of the solid dye under different environmental conditions is predicted by combining a thermodynamic analysis algorithm to generate a quality assessment report.

[0037] According to the above embodiment, after receiving the melting start signal, the system uses the Kalman filter to calculate the melting rate and combines the thermodynamic analysis algorithm to predict the stability of the dye under different environmental conditions. The system automatically generates a detailed quality assessment report, including key parameters such as melting rate and temperature change, for reference by production managers to optimize the production process.

[0038] 105. When it is detected that the melting rate reaches a preset threshold, the cooling program is automatically triggered by using a PID controller, and the uniformity and purity of the solid colorant are predicted and analyzed in combination with deep learning image analysis technology to obtain key parameters. The key parameters and quality assessment results of the solid colorant during the melting process are integrated to generate a melting monitoring report.

[0039] Among them, the PID controller: proportional integral derivative controller is used to automatically adjust system parameters, such as temperature or speed, to achieve a set target value; in this application, it is used to control the cooling process; Deep learning image analysis technology: Use deep neural networks to analyze images to identify and quantify the uniformity and purity of dyes; Melt Monitoring Report: Final report integrating all critical parameters and quality assessment results during the melting process for a comprehensive understanding of the entire melting process.

[0040] In actual operation, when the melting rate reaches the preset threshold, the PID controller automatically triggers the cooling process. The image analysis technology combined with deep learning predicts and analyzes the uniformity and purity of the solid dye to obtain key parameters. All key parameters and quality assessment results of the dye during the melting process are integrated to generate a melting monitoring report.

[0041] Continuing with the above example, when the system detects that the melting rate reaches a preset threshold, the PID controller automatically starts the cooling process to prevent product quality problems caused by overheating. At the same time, the system uses deep learning technology to analyze the uniformity and purity of the dye to ensure product quality. Finally, the system integrates all data and generates a detailed melting monitoring report for production personnel to further analyze and improve.

[0042] Through the implementation of steps 101 to 105, the method realizes high-precision monitoring and intelligent control of the solid dye melting process by using integrated optical sensors, multi-wavelength light sources, support vector machine models, Kalman filters, and deep learning image analysis technology. This method not only improves the accuracy of monitoring, but also enhances the adaptability to complex nonlinear melting processes, reduces misjudgment and noise interference, and ensures accurate estimation of the melting start time, rate, and end point. In addition, the method provides reliable data support for subsequent automated control, further optimizes the production process, and improves product consistency and production efficiency. In order to solve the problems of low precision and high misjudgment rate in the traditional method when identifying the melting start point of the solid dye, and further improve the accuracy and reliability of melting monitoring, in some embodiments, the changing trend of the time-light intensity curve in step 103 is observed by using a preset support vector machine model to obtain a turning point where the slope of the light intensity curve changes, and based on the turning point, the moment when the solid dye starts to melt is indicated to generate a melting start signal, including: The time-light intensity curve is smoothed by a five-point cubic smoothing algorithm to obtain a smooth curve, and the slope of the smooth curve is calculated according to the central difference method combined with the least squares fitting technology to obtain the slope value corresponding to each time point; the slope value is reduced in dimension by principal component analysis to obtain a reduced-dimensional slope value, and the reduced-dimensional slope value is input into a pre-trained support vector machine model to classify and identify the reduced-dimensional slope value to obtain an initial turning point where the slope of the light intensity curve changes; based on the initial turning point, a dynamic time warping algorithm is used to match the time-light intensity curve within a specified time period, and an anomaly detection algorithm is used to monitor the model output to predict and locate the turning point where the slope of the light intensity curve changes; based on the turning point, a Kalman filter is used to optimize the estimation of the melting start time to generate a melting start signal.

[0043] In this embodiment, the five-point cubic smoothing algorithm is a data smoothing technique that reduces the impact of noise by using the weighted average of five adjacent data points, and is suitable for smoothing time series data; the algorithm can effectively remove high-frequency noise while retaining the main trend of the data; Central difference method: a numerical differentiation method used to calculate the derivative (i.e. slope) of discrete data points; in this application scenario, it is used to estimate the slope of each point of the time-light intensity curve to help identify key change points in the melting process; Least squares fitting technique: A statistical method that determines the best-fitting line or curve by minimizing the sum of squared errors between observed data and model predictions; this method is often used to extract useful features from noisy data. Dynamic Time Warping (DTW): An algorithm used to measure the similarity between two time series, even if the series may have different speeds or lengths; in this scenario, DTW is used to match the time-light intensity curves within a specified time period to ensure the accuracy of turning point positioning; Anomaly detection algorithm: A technique used to identify data points in a dataset that do not conform to an expected pattern; in this application scenario, it is used to monitor and support the quality of the vector machine model output to improve the reliability of turning point location.

[0044] In the embodiment of the present application, first, the time-light intensity curve is smoothed using a five-point cubic smoothing algorithm to eliminate noise interference, and then the central difference method combined with the least squares fitting technique is applied to calculate the slope value corresponding to each time point.

[0045] Next, principal component analysis (PCA) is used to reduce the dimensionality of the slope value to obtain the reduced dimensionality slope value, which is then input into the pre-trained support vector machine model for classification and identification to find the preliminary turning point. Based on the preliminary turning point, the dynamic time warping algorithm is used to match the curve within the specified time period, and the anomaly detection algorithm is used to monitor the model output to accurately predict and locate the actual turning point.

[0046] Finally, based on the located turning point, the Kalman filter is used to optimize the estimation of the melting start time and generate a melting start signal.

[0047] Here is a specific example: For example, in an automated dye production line, the system first collects the time-light intensity curve data of the solid dye. In order to improve the data quality, the system applies a five-point cubic smoothing algorithm to smooth the original curve, significantly reducing the impact of random noise. Subsequently, the system uses the central difference method combined with the least squares fitting technique to calculate the slope value of each point and identify the key areas of slope change.

[0048] Next, the system inputs these slope values ​​into the pre-trained support vector machine model. After the principal component analysis and dimensionality reduction process, the model can more efficiently identify the initial turning point of the slope change. Based on this initial turning point, the system further applies the dynamic time warping algorithm to match the curve within the specified time period to ensure the accuracy of the turning point location. At the same time, the anomaly detection algorithm is used to monitor the model output to ensure the reliability of the recognition results.

[0049] Once the system determines the final turning point, that is, the moment when the solid dye begins to melt, the Kalman filter is used to optimize the estimate of this moment and generate an accurate melting start signal. This signal not only provides the basis for subsequent rate calculations, but also triggers a series of automatic control programs, such as automatically adjusting the heating power or starting the cooling program, thus achieving highly automated and intelligent management of the entire production process.

[0050] Through this method of comprehensively applying multiple advanced algorithms and technologies, the system not only improves the accuracy and reliability of melting monitoring, but also enhances the adaptability to complex nonlinear melting processes, significantly improving product quality and production efficiency.

[0051] In order to solve the complexity problem of processing and analyzing high-dimensional slope value matrices and further improve the accuracy and efficiency of support vector machine model classification, in some embodiments, principal component analysis is used to reduce the dimension of the slope values ​​to obtain reduced-dimensional slope values, including: The slope value is optimized by a robust regression algorithm to form a high-dimensional slope value matrix, and the features of the high-dimensional slope value matrix are standardized according to a Z-Score standardization method to generate a standardized slope value matrix, wherein the features include a zero mean and a unit variance; according to the standardized slope value matrix, a predefined covariance matrix based on Mahalanobis distance is calculated, and a random singular value decomposition algorithm is used to accelerate the decomposition process of the covariance matrix to obtain eigenvalues ​​and eigenvectors; based on the eigenvalues ​​and the eigenvectors, a cross-validation technique is used to evaluate the performance of the support vector machine model under different numbers of principal components, and N principal components that can retain maximum information and verify the performance of the support vector machine model are selected. When determining the value of N, the least absolute shrinkage and selection operator regression are used to screen out the principal components that are helpful for the classification task, and a principal component set is generated; the standardized slope value matrix is ​​preliminarily reduced in dimension by a t-distributed random neighborhood embedding algorithm to obtain a preliminary reduced-dimensionality slope value, and the preliminary reduced-dimensionality slope value is projected onto a new coordinate system defined by the principal component set to generate a reduced-dimensionality slope value.

[0052] In this embodiment, the robust regression algorithm is a regression analysis method for processing outliers in a data set; it ensures the stability and accuracy of the regression results by reducing the impact of outliers on the estimation of model parameters; in this application scenario, the robust regression algorithm is used to optimize the slope value to improve the reliability of subsequent analysis; High-dimensional slope value matrix: A data matrix consisting of slope values ​​corresponding to multiple time points, usually with a higher dimension; this matrix contains key information at different time points during the melting process of the solid dye; Z-Score normalization method: A data normalization technique that converts data into a form with zero mean and unit variance; this helps eliminate dimensional differences between different features, facilitating subsequent statistical analysis and machine learning model training; Mahalanobis distance: a measure of the similarity between data points that takes into account the covariance structure of the data set; in this application scenario, the covariance matrix is ​​calculated based on the Mahalanobis distance, which can more accurately capture the correlation between data points; Random Singular Value Decomposition (RSVD): An algorithm for accelerating the decomposition of large matrices. Through random sampling techniques, RSVD can significantly reduce the computational complexity while maintaining high accuracy, making it suitable for large-scale data processing. Cross-validation technique: A method for evaluating the performance of a machine learning model by dividing the dataset into multiple subsets for multiple training and testing to select the best model parameters or structure; in this application, it is used to determine the optimal value for the number of principal components; Least Absolute Shrinkage and Selection Operator (LASSO): A regularized regression method that introduces a penalty term to compress unimportant regression coefficients to zero, thereby achieving feature selection. In this scenario, LASSO is used to select principal components that are helpful for classification tasks. t-Distributed Stochastic Neighbor Embedding (t-SNE): A dimensionality reduction technique that is particularly suitable for visualization and cluster analysis of high-dimensional data; it works by simulating the probability distribution in a high-dimensional space and mapping it into a low-dimensional space, preserving the local structure.

[0053] In the embodiment of the present application, first, the slope value is optimized using a robust regression algorithm, and a high-dimensional slope value matrix is ​​organized. Then, the high-dimensional slope value matrix is ​​standardized using a Z-Score standardization method to generate a standardized slope value matrix. Then, the covariance matrix based on the Mahalanobis distance is calculated based on the standardized slope value matrix, and the random singular value decomposition algorithm is used to accelerate the decomposition process to obtain eigenvalues ​​and eigenvectors.

[0054] Next, the performance of the support vector machine model under different numbers of principal components is evaluated based on the cross-validation technology, and N principal components that can maximize the retention of information and verify the model performance are selected. At the same time, LASSO is used to screen out the principal components that are helpful for the classification task and generate a principal component set.

[0055] Finally, the t-SNE algorithm is used to perform preliminary dimensionality reduction on the standardized slope value matrix, and the preliminary dimensionality reduction slope values ​​are projected onto a new coordinate system defined by the principal component set to generate the final dimensionality reduction slope values.

[0056] Here is a specific example: According to the above embodiment, the system first collects the time-light intensity curve data during the melting process and calculates the slope value at each time point to form a high-dimensional slope value matrix. In order to improve the quality and stability of the data, the system uses a robust regression algorithm to optimize the slope value and reduce the impact of outliers.

[0057] Subsequently, the system normalizes the optimized slope values ​​to zero mean and unit variance to generate a normalized slope value matrix. Based on this normalized matrix, the system calculates the covariance matrix based on the Mahalanobis distance and uses the randomized singular value decomposition algorithm to quickly decompose the matrix and extract eigenvalues ​​and eigenvectors.

[0058] In order to determine the optimal number of principal components, the system uses cross-validation technology to evaluate the performance of the support vector machine model under different numbers of principal components, and uses LASSO regression to screen out the principal components that are most helpful for the classification task and generate a principal component set. This process not only improves the accuracy of the model, but also ensures the generalization ability of the model.

[0059] Next, the system uses the t-SNE algorithm to perform preliminary dimensionality reduction on the standardized slope value matrix, and projects the slope values ​​after preliminary dimensionality reduction onto a new coordinate system defined by the principal component set to generate dimensionality-reduced slope values. These dimensionality-reduced data not only reduce the computational complexity, but also retain the key features of the original data, providing high-quality input data for subsequent support vector machine model classification.

[0060] Through this method of comprehensive application of multiple advanced algorithms and technologies, the system not only improves the accuracy and efficiency of data processing, but also enhances the adaptability to complex nonlinear melting processes, significantly improving product quality and production efficiency. This method provides strong support for the intelligent management of automation control systems.

[0061] In order to solve the problem that the uniformity and purity of the solid colorant are difficult to accurately control during the melting process and further improve product quality and production efficiency, in some embodiments, when it is detected that the melting rate reaches a preset threshold in step 105, a PID controller is used to automatically trigger a cooling program, and the image analysis technology of deep learning is combined to predict and analyze the uniformity and purity of the solid colorant to obtain key parameters, and the key parameters and quality evaluation results of the solid colorant during the melting process are integrated to generate a melting monitoring report, including: When the melting rate reaches a preset threshold, the parameter setting of the PID controller is optimized by using fuzzy logic, and the cooling program is automatically triggered according to the preset temperature control strategy; during the cooling process, an initial image of the solid colorant is acquired by using a high-resolution camera, and the initial image is processed based on a multi-scale image fusion technique to obtain an optimized image; the optimized image is preprocessed according to a superpixel segmentation algorithm to obtain a processed image, and the color distribution, particle size and distribution and other features extracted from the processed image are analyzed by using an integrated learning method to obtain key parameters including the uniformity index, purity grade and impurity content of the solid colorant; based on the key parameters, external data in the melting process is collected, and the melting rate and environmental condition changes within a fixed time in the future are predicted based on time series analysis to obtain analysis results, wherein the external data include melting rate, ambient temperature and humidity; based on the analysis results, a melting monitoring report is automatically generated using natural language processing technology.

[0062] In this embodiment, fuzzy logic optimized PID controller: A fuzzy logic-based control strategy for optimizing the parameter settings of a PID (proportional-integral-derivative) controller; it handles uncertainty and imprecise information by mimicking the human decision-making process, thereby achieving more flexible and robust control; Multi-scale image fusion technology: a technology that combines images of multiple scales or resolutions into a single high-quality image; this method can improve the detail clarity and overall quality of the image, and is particularly suitable for complex scene images acquired by high-resolution cameras; Superpixel segmentation algorithm: An image segmentation technique that simplifies image representation by grouping pixels into superpixels; each superpixel consists of a group of adjacent pixels with similar color or texture characteristics, which facilitates subsequent feature extraction and analysis; Ensemble learning method: A machine learning strategy that improves overall performance by combining the predictions of multiple models; common ensemble learning methods include random forests and gradient boosted trees, which are used in this application to extract and analyze key features from processed images; Time series analysis: A statistical method used to analyze sequences of data that change over time to identify trends, periodicities, and other patterns; in this case, it is used to predict future melting rates and changes in environmental conditions. Natural Language Processing (NLP): A field of computer science that deals with techniques that enable computers to understand, interpret, and generate human language; in this case, NLP is used to automatically generate detailed melt monitoring reports.

[0063] In the embodiment of the present application, when the melting rate of the solid coloring agent is detected to reach a preset threshold, the system uses fuzzy logic to optimize the parameter setting of the PID controller, and automatically triggers the cooling program according to the pre-set temperature control strategy. During the cooling process, the initial image of the solid coloring agent is taken with a high-resolution camera, and the images are processed using multi-scale image fusion technology to obtain an optimized image. Then, the optimized image is pre-processed by applying a superpixel segmentation algorithm to generate a processed image. Then, the color distribution, particle size and distribution and other features are extracted from the processed image using an integrated learning method to calculate the key parameters such as the uniformity index, purity grade and impurity content of the solid coloring agent. Based on these key parameters, the system collects external data (such as melting rate, ambient temperature and humidity) during the melting process, and predicts the melting rate and environmental condition changes within a fixed time in the future through time series analysis. Finally, a melting monitoring report containing all key parameters and quality assessment results is automatically generated using natural language processing technology.

[0064] Here is a specific example: Continuing with the above embodiment, when the system detects that the melting rate reaches a preset threshold, the fuzzy logic optimized PID controller automatically adjusts its parameter settings and starts the cooling process. To ensure the cooling effect and product quality, the system uses a high-resolution camera to capture the state image of the solid dye in real time.

[0065] These initial images are first processed using multi-scale image fusion technology to enhance the details and clarity of the image and generate optimized images. Next, the system pre-processes the optimized images using a superpixel segmentation algorithm to decompose the image into multiple superpixel regions for subsequent feature extraction.

[0066] Based on the processed images, the system uses ensemble learning methods to analyze the features extracted from the images, such as color distribution, particle size and distribution, and calculates key parameters such as uniformity index, purity level and impurity content of solid dyes. For example, by analyzing the color differences in different areas, the uniformity of the dye can be quantified; by measuring the particle size and distribution, its purity and impurity content can be evaluated.

[0067] At the same time, the system collects external data during the melting process (such as melting rate, ambient temperature and humidity), and uses time series analysis methods to predict changes in melting rate and environmental conditions in the future. This step not only helps production personnel prepare in advance, but also provides important feedback information for optimizing the production process.

[0068] Finally, the system automatically generates a detailed melt monitoring report using natural language processing technology. The report contains all key parameters, quality assessment results, and forecasts for future trends, providing comprehensive support for production management and quality control. This comprehensive solution using multiple advanced technologies and methods has significantly improved the automation level and product quality of solid colorant production.

[0069] In order to solve the problem of low precision and loss of edge information in traditional image segmentation methods when processing solid dye images, and to further improve the accuracy of image segmentation and detail retention, in some embodiments, the optimized image is preprocessed according to a superpixel segmentation algorithm to obtain a processed image, including: The initial image of the solid dye acquired by a high-resolution camera is subjected to weighted average fusion processing by using a multi-view data set and a structural similarity index to generate an optimized image; based on the optimized image, a superpixel grid is initialized, and based on an improved simple linear iterative clustering algorithm combined with a spectral clustering technique, the boundary of the superpixel grid is iteratively optimized to obtain a preliminary segmentation result; based on the preliminary segmentation result, an active contour model and a graph cut algorithm are applied to refine and smooth the boundary of the superpixel grid, and shape regularity constraints such as ellipse fitting are introduced to generate an intermediate segmented image; based on the intermediate segmented image, a connected component labeling algorithm is used to remove isolated areas, and bilateral filtering and smoothing operations are applied to perform morphological operations to generate a processed image, wherein the morphological operations include opening operations and closing operations.

[0070] In this embodiment, a multi-view dataset is a dataset consisting of images taken from multiple different viewpoints, which can provide more comprehensive target information; in this application scenario, it is used to enhance the quality and details of the solid dye image; Structural Similarity Index (SSIM): A metric that measures the similarity between two images, taking into account differences in brightness, contrast, and structure; in this scenario, SSIM is used to guide the weighted average fusion process to produce a high-quality optimized image; Superpixel grid: An image is divided into a grid structure consisting of multiple superpixels. Each superpixel consists of a group of adjacent pixels with similar color or texture characteristics. Superpixel grids help simplify image representation and speed up subsequent processing. Simple Linear Iterative Clustering (SLIC): A commonly used superpixel segmentation algorithm that simplifies image representation by dividing the image into a fixed number of superpixels. The improved version of SLIC combined with spectral clustering technology can further improve segmentation accuracy. Spectral clustering technology: a clustering method based on graph theory, which performs cluster analysis by constructing a similarity matrix between image pixels; this technology can effectively capture complex structural features in images; Snake Model: An image segmentation method that minimizes an energy function to make a curve gradually approach the target boundary; in this application, it is used to refine the boundaries of the superpixel grid; Graph Cut: A graph-theory-based image segmentation method that segments an image by finding the minimum cut. This method is often used to refine and optimize segmentation results. Shape regularity constraints (such as ellipse fitting): a method used to constrain the shape of the segmentation result to ensure that the segmented area meets specific geometric shape requirements; in this scenario, ellipse fitting is used to smooth and adjust the segmentation boundary; Connected Component Labeling: An algorithm for identifying and labeling connected regions in an image; it can remove isolated small regions and improve the accuracy of segmentation results; Bilateral filtering: A nonlinear filtering method that can smooth image noise while preserving edges; in this application scenario, it is used for preprocessing before morphological operations; Morphological operations (opening and closing): A series of image processing operations based on set theory used to remove small objects or fill holes; opening operations are usually used to remove small noise points, while closing operations are used to fill holes.

[0071] In the embodiment of the present application, firstly, the initial image acquired by the high-resolution camera is subjected to weighted average fusion processing using the multi-view data set and the structural similarity index to generate an optimized image. Then, the superpixel grid is initialized according to the optimized image, and the boundary of the superpixel grid is iteratively optimized using the improved simple linear iterative clustering algorithm combined with the spectral clustering technology to obtain a preliminary segmentation result.

[0072] Next, based on the preliminary segmentation results, the active contour model and graph cut algorithm are applied to refine and smooth the boundaries of the superpixel grid, while introducing shape regularity constraints (such as ellipse fitting) to generate an intermediate segmented image. Finally, based on the intermediate segmented image, the connected component labeling algorithm is used to remove isolated areas, and bilateral filtering and smoothing operations are applied to perform morphological operations (including opening and closing operations) to generate the final processed image. The following is a specific example: Continuing with the above embodiment, the system uses a high-resolution camera to capture images of solid dyes from multiple angles to form a multi-view dataset. In order to generate high-quality optimized images, the system uses a structural similarity index to perform weighted average fusion processing on these images. This step not only improves the clarity of the image, but also enhances the detail information in the image.

[0073] Next, the system initializes the superpixel grid based on the optimized image and uses an improved simple linear iterative clustering algorithm combined with spectral clustering technology to iteratively optimize the boundaries of the superpixel grid. This method can accurately capture the subtle structural changes inside the dye and generate preliminary segmentation results.

[0074] To further refine and optimize the segmentation results, the system applies the active contour model and graph cut algorithm to adjust and smooth the boundaries of the superpixel grid and introduces shape regularity constraints (such as ellipse fitting). These steps ensure that the boundaries of the segmented area are smoother and more regular, generating an intermediate segmented image.

[0075] The system then uses a connected component labeling algorithm to remove isolated areas in the intermediate segmented image to prevent these small areas from interfering with subsequent analysis. To further improve image quality, the system applies bilateral filtering and smoothing operations to perform morphological operations (including opening and closing operations) to generate the final processed image.

[0076] Through this comprehensive application of multiple advanced image processing technologies and methods, the system not only improves the accuracy and reliability of image segmentation, but also enhances its adaptability to complex nonlinear melting processes, significantly improving product quality and production efficiency. This method provides strong support for the intelligent management and optimization of automated control systems.

[0077] In order to solve the problem of noise interference and outlier influence in the melting rate calculation process and further improve the accuracy and reliability of the melting rate calculation, in some embodiments, the melting rate is calculated using a Kalman filter based on the data of the time-light intensity curve of the melting start signal at a fixed time in step 104, including: The melting start signal is used in combination with an adaptive threshold algorithm to calculate the fixed time when the solid dye begins to melt. Based on the fixed time, a dynamic window selection method is used to automatically adjust the length of the time period according to the changing trend of the melting rate, thereby extracting the initial time-light intensity curve data starting from the fixed time; the median filtering method is used to remove the noise and outliers of the initial time-light intensity curve data, and the local weighted regression scatter point smoothing technology is introduced to retain the characteristic information in the initial time-light intensity curve data, and the robust statistical method is further applied to automatically detect and remove outliers to obtain smoothed time-light intensity curve data; based on the smoothed time-light intensity curve data, a five-point cubic smoothing algorithm is used to reduce noise interference, and the least squares fitting technology is used in combination with an adaptive weight adjustment strategy to calculate the slope value corresponding to each time point, which is expressed as a preliminary melting rate; the preliminary melting rate is optimized using an unscented Kalman filter to obtain a melting rate.

[0078] In this embodiment, the adaptive threshold algorithm: a method of dynamically adjusting the threshold, automatically adjusting the threshold level according to the real-time changes in the data; in this application scenario, it is used to determine the exact moment when the solid dye begins to melt; Dynamic window selection method: A flexible data processing technique that automatically adjusts the length of the analyzed time period according to the changing trend of the data to better capture the data characteristics; this helps to more accurately extract and analyze the time-light intensity curve data; Five-point cubic smoothing algorithm: A data smoothing technique based on weighted average that reduces noise interference by fitting a cubic polynomial to each data point and its four adjacent points; this method can effectively remove high-frequency noise while retaining the main trend of the data; Least squares fitting technique combined with adaptive weight adjustment strategy: A statistical method that determines the best fit line or curve by minimizing the sum of squared errors between observed data and model predictions, combined with an adaptive weight adjustment strategy to optimize the fit; this strategy allows the influence of different data points on the final fit to be adjusted according to their importance; Unscented Kalman Filter (UKF): An improved version of the Extended Kalman Filter that is suitable for nonlinear systems; the UKF improves the accuracy of state estimation by approximating the probability distribution of the system state using a set of carefully selected sample points (called Sigma points).

[0079] In the embodiment of the present application, firstly, the fixed time when the solid dye starts to melt is calculated by using the melting start signal in combination with the adaptive threshold algorithm. Based on this fixed time, the dynamic window selection method is used to automatically adjust the time period length according to the change trend of the melting rate, and the initial time-light intensity curve data starting from this time is extracted.

[0080] Next, the median filtering method is used to remove noise and outliers in these initial data. At the same time, the local weighted regression scatter point smoothing technique is introduced to retain the feature information. The robust statistical method is further applied to automatically detect and remove outliers to obtain smooth time-light intensity curve data.

[0081] Then, based on the smoothed data, a five-point cubic smoothing algorithm was used to further reduce noise interference, and the slope value corresponding to each time point was calculated using the least squares fitting technique combined with an adaptive weight adjustment strategy, which was expressed as the preliminary melting rate.

[0082] Finally, the unscented Kalman filter is used to optimize the preliminary melting rate to obtain a more accurate melting rate.

[0083] Here is a specific example: Continuing with the above embodiment, when the system detects a signal that the solid dye begins to melt, the adaptive threshold algorithm is first used to determine the exact start time of melting. Based on this moment, the system uses a dynamic window selection method to automatically adjust the analysis time period according to the change trend of the melting rate, and extracts the initial time-light intensity curve data from the moment of melting start.

[0084] To ensure the quality of the data, the system first uses the median filtering method to remove noise and outliers in the initial time-light intensity curve data. Next, the local weighted regression scatter point smoothing technique is applied to retain the key feature information in the data, and further robust statistical methods are used to automatically detect and remove any remaining outliers to generate smooth time-light intensity curve data.

[0085] After obtaining high-quality smoothed data, the system applies a five-point cubic smoothing algorithm to further reduce noise interference, and uses the least squares fitting technique combined with an adaptive weight adjustment strategy to calculate the slope value at each time point as the preliminary melting rate. This process not only improves the accuracy of the data, but also enhances the adaptability to complex nonlinear melting processes.

[0086] Finally, the system uses an unscented Kalman filter to optimize the initial melting rate to obtain a more accurate melting rate. The optimized melting rate not only provides more reliable monitoring results, but also provides an important basis for subsequent cooling procedures and other automated control operations. Through this comprehensive application of multiple advanced technologies and methods, the system significantly improves the accuracy and reliability of melting monitoring, further optimizes the production process, and improves product quality and production efficiency.

[0087] In order to solve the problem that the noise and outliers existing in the initial time-light intensity curve data affect the analysis accuracy, and further improve the effect of data smoothing and the retention of characteristic information, in some embodiments, a median filtering method is used to remove the noise and outliers of the initial time-light intensity curve data, and a local weighted regression scatter point smoothing technique is introduced to retain the characteristic information in the initial time-light intensity curve data, and a robust statistical method is further applied to automatically detect and remove outliers to obtain smoothed time-light intensity curve data, including: The initial time-light intensity curve data is subjected to preliminary denoising by adopting a median filtering method, and the noise influence is reduced by replacing each data point with the median in the neighborhood to obtain preliminary denoised data; based on the preliminary denoised data, a local weighted regression scatter point smoothing technique is introduced to smooth the curve by performing weighted least square fitting on local data points, retaining characteristic information and local variation trends, and generating smoothed curve data; based on the smoothed curve data, a robust statistical method is applied to automatically detect and remove outliers to obtain a detection result; based on the detection result, the preliminary denoised data and the smoothed curve data are integrated into smoothed time-light intensity curve data.

[0088] In this embodiment, median filtering method: a nonlinear digital filtering technique that reduces noise by replacing each data point with the median value of the data points in its neighborhood; this method is particularly suitable for removing impulse noise (also known as salt and pepper noise) while preserving the main features of the original signal as much as possible; Locally Weighted Regression Scatter Smoothing (LOESS): A nonparametric regression method that smoothes the curve by performing a weighted least squares fit on local data points; it can effectively capture local trends in the data while retaining important feature information; Robust statistical methods: refers to statistical techniques that can provide reliable results in the presence of outliers or noise; common robust statistical methods include RANSAC (random sampling consensus algorithm), M estimation, etc.; these methods can automatically detect and remove outliers in the data, thereby improving the accuracy of the analysis results.

[0089] In the embodiment of the present application, first, the initial time-light intensity curve data is subjected to preliminary denoising by using a median filtering method, and the noise effect is reduced by replacing each data point with the median value in its neighborhood to generate preliminary denoised data.

[0090] Next, based on the data after preliminary denoising, the local weighted regression scatter point smoothing technique (LOESS) was introduced to smooth the curve by performing weighted least squares fitting on local data points, retaining the characteristic information and local change trends, and generating smoothed curve data.

[0091] Then, a robust statistical method is applied to automatically detect and remove outliers in the smoothed curve data to obtain the detection results. Finally, based on the detection results, the preliminary denoised data and the smoothed curve data, the final smoothed time-light intensity curve data is integrated and generated.

[0092] Here is a specific example: Continuing with the above example, the system collects the time-light intensity curve data of the solid dye during the melting process. In order to ensure the quality of these data, the initial data is firstly subjected to preliminary denoising using the median filtering method. Specifically, for each data point, the system calculates the median value in its neighborhood and replaces the original data point with this median value. This can effectively remove random noise, especially impulse noise, while retaining the main trend information.

[0093] Next, the system introduces the local weighted regression scatter point smoothing technique (LOESS) based on the data after preliminary denoising. By assigning weights to the local area near each data point and performing weighted least squares fitting, the system is able to smooth the curve while retaining important local change trends and feature information. For example, during certain key time periods, the light intensity of the dye may change significantly, and these change trends need to be accurately captured and retained.

[0094] The system then applies robust statistical methods (such as RANSAC) to automatically detect and remove outliers in the smoothed curve data. These outliers may be caused by sensor failures or other external interferences, and their presence will affect the accuracy of subsequent analysis. Through robust statistical methods, the system is able to identify and exclude these outliers, ensuring the authenticity and reliability of the remaining data.

[0095] Finally, the system integrates the data after preliminary denoising, the smoothed curve data, and the detection results of the robust statistical method to generate the final smoothed time-light intensity curve data. These high-quality data not only provide a solid foundation for the subsequent melting rate calculation, but also enhance the automation control capability and intelligent management level of the entire production process. Through this comprehensive application of multiple advanced technologies and methods, the system significantly improves the accuracy and reliability of data processing, further optimizes the production process, and improves product quality and production efficiency.

[0096] Figure 2 A schematic diagram of a melting monitoring device (or system) for a solid dye is provided for an embodiment of the present application, such as Figure 2 As shown, the device comprises: A generating module 21 is used to place the solid dye to be monitored in a transparent heating container, set at least one light source on one side of the transparent heating container, set at least one light sensor on the other side opposite thereto, select a wavelength combination according to the absorption spectrum of the solid dye, use a multi-wavelength light source to make the light path pass through the maximum cross section of the solid dye, and generate an initial optical path; A denoising module 22 is used to record the light intensity value transmitted through the solid dye received by the light sensor according to a preset time interval to form an initial time-light intensity curve, and to perform denoising on the data of the initial optical path using a wavelet transform algorithm to obtain a time-light intensity curve; An indication module 23 is used to observe the change trend of the time-light intensity curve using a preset support vector machine model to obtain a turning point where the slope of the light intensity curve changes, and indicate the moment when the solid dye begins to melt based on the turning point to generate a melting start signal; An evaluation module 24 is used to calculate the melting rate based on the data of the time-light intensity curve of the melting start signal at a fixed time using a Kalman filter, predict the stability of the solid dye under different environmental conditions in combination with a thermodynamic analysis algorithm, and generate a quality evaluation report; The monitoring module 25 is used to automatically trigger the cooling program using the PID controller when it is detected that the melting rate reaches a preset threshold, and to predict and analyze the uniformity and purity of the solid colorant in combination with the image analysis technology of deep learning to obtain key parameters, integrate the key parameters and quality assessment results of the solid colorant during the melting process, and generate a melting monitoring report.

[0097] Figure 2 The melting monitoring device for solid dyeing agent can be performed Figure 1The implementation principle and technical effect of the melting monitoring method for solid dyes described in the embodiment are not described in detail. The specific manner in which each module and unit performs operations in the melting monitoring device for solid dyes in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0098] In one possible design, Figure 2 A melting monitoring device for a solid dye in the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0099] The processing component 32 is used for the above Figure 1 The embodiment provides a melting monitoring method for a solid colorant.

[0100] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0101] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0102] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0103] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0104] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0105] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0106] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 An XX method of the illustrated embodiment.

[0107] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0108] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these 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 application.

Claims

1. A method for monitoring the melting of a solid colorant, characterized in that: include: The solid dye to be monitored is placed in a transparent heating container, at least one light source is arranged on one side of the transparent heating container, at least one light sensor is arranged on the other side opposite thereto, a wavelength combination is selected according to the absorption spectrum of the solid dye, and a multi-wavelength light source is used to make the light path pass through the maximum cross section of the solid dye to generate an initial optical path; Recording the light intensity value transmitted through the solid dye received by the light sensor according to a preset time interval to form an initial time-light intensity curve, and performing denoising processing on the data of the initial optical path using a wavelet transform algorithm to obtain a time-light intensity curve; Based on the change trend of the time-light intensity curve, a preset support vector machine model is used for observation to obtain a turning point where the slope of the light intensity curve changes, and based on the turning point, the moment when the solid dye begins to melt is indicated to generate a melting start signal; Based on the data of the time-light intensity curve of the melting start signal at a fixed time, the melting rate is calculated using a Kalman filter, and the stability of the solid dye under different environmental conditions is predicted in combination with a thermodynamic analysis algorithm to generate a quality assessment report; When it is detected that the melting rate reaches a preset threshold, the cooling program is automatically triggered by the PID controller, and the uniformity and purity of the solid colorant are predicted and analyzed with the deep learning image analysis technology to obtain key parameters. The key parameters and quality assessment results of the solid colorant during the melting process are integrated to generate a melting monitoring report.

2. The method according to claim 1, characterized in that Based on the change trend of the time-light intensity curve, a preset support vector machine model is used for observation to obtain a turning point where the slope of the light intensity curve changes, and based on the turning point, the moment when the solid dye begins to melt is indicated to generate a melting start signal, including: The time-light intensity curve is smoothed by using a five-point cubic smoothing algorithm to obtain a smooth curve, and the slope of the smooth curve is calculated according to a central difference method combined with a least squares fitting technique to obtain the slope value corresponding to each time point; Performing dimensionality reduction processing on the slope value by using principal component analysis to obtain a dimensionality reduction slope value, inputting the dimensionality reduction slope value into a pre-trained support vector machine model, classifying and identifying the dimensionality reduction slope value, and obtaining an initial turning point where the slope of the light intensity curve changes; Based on the initial turning point, a dynamic time warping algorithm is used to match the time-light intensity curve within a specified time period, and an anomaly detection algorithm is used to monitor the model output to predict and locate the turning point where the slope of the light intensity curve changes; Based on the turning point, a Kalman filter is used to optimize the estimation of the melting start time to generate a melting start signal.

3. The method according to claim 2, characterized in that The slope value is subjected to dimensionality reduction processing by principal component analysis to obtain a dimensionality reduction slope value, including: Optimizing the slope value using a robust regression algorithm to form a high-dimensional slope value matrix, and standardizing the features of the high-dimensional slope value matrix according to a Z-Score standardization method to generate a standardized slope value matrix, wherein the features include a zero mean and a unit variance; According to the standardized slope value matrix, a predefined Mahalanobis distance-based covariance matrix is ​​calculated, and a random singular value decomposition algorithm is used to accelerate the decomposition process of the covariance matrix to obtain eigenvalues ​​and eigenvectors; Based on the eigenvalues ​​and the eigenvectors, a cross-validation technique is used to evaluate the performance of the support vector machine model under different numbers of principal components, and N principal components that can retain maximum information and verify the performance of the support vector machine model are selected. When determining the value of N, the least absolute shrinkage and selection operator regression is used to screen out the principal components that are helpful for the classification task, and a principal component set is generated; The standardized slope value matrix is ​​preliminarily reduced in dimension using a t-distributed random neighbor embedding algorithm to obtain preliminary reduced-dimensionality slope values, and the preliminary reduced-dimensionality slope values ​​are projected onto a new coordinate system defined by the principal component set to generate reduced-dimensionality slope values.

4. The method according to claim 1, characterized in that When it is detected that the melting rate reaches a preset threshold, the cooling program is automatically triggered by the PID controller, and the uniformity and purity of the solid dye are predicted and analyzed by the deep learning image analysis technology to obtain key parameters. The key parameters and quality assessment results of the solid dye during the melting process are integrated to generate a melting monitoring report, including: When the melting rate reaches a preset threshold, the parameter setting of the PID controller is optimized using fuzzy logic, and a cooling program is automatically triggered according to a preset temperature control strategy; During the cooling process, an initial image of the solid dye is obtained using a high-resolution camera, and the initial image is processed based on a multi-scale image fusion technology to obtain an optimized image; Preprocessing the optimized image according to a superpixel segmentation algorithm to obtain a processed image, and using an ensemble learning method to analyze the color distribution, particle size and distribution and other features extracted from the processed image to obtain key parameters including uniformity index, purity level and impurity content of the solid colorant; According to the key parameters, external data in the melting process are collected, and the melting rate and environmental condition changes in a fixed time in the future are predicted based on time series analysis to obtain analysis results, wherein the external data includes melting rate, environmental temperature and humidity; Based on the analysis results, natural language processing technology is used to automatically generate a melting monitoring report.

5. The method according to claim 4, characterized in that Preprocessing the optimized image according to a superpixel segmentation algorithm to obtain a processed image includes: Using a multi-view data set and a structural similarity index, a weighted average fusion process is performed on the initial image of the solid dye acquired by a high-resolution camera to generate an optimized image; Initializing a superpixel grid according to the optimized image, and iteratively optimizing the boundary of the superpixel grid based on an improved simple linear iterative clustering algorithm combined with a spectral clustering technique to obtain a preliminary segmentation result; Based on the preliminary segmentation result, an active contour model and a graph cut algorithm are applied to refine and smooth the boundaries of the superpixel grid, and shape regularity constraints such as ellipse fitting are introduced to generate an intermediate segmented image; According to the intermediate segmented image, a connected component labeling algorithm is used to remove isolated areas, and bilateral filtering and smoothing operations are applied to perform morphological operations to generate a processed image, wherein the morphological operations include opening operations and closing operations.

6. The method according to claim 1, characterized in that Based on the data of the time-light intensity curve of the melting start signal within a fixed time, the melting rate is calculated using a Kalman filter, including: The melting start signal is used in combination with an adaptive threshold algorithm to calculate a fixed time when the solid dye starts to melt, and based on the fixed time, a dynamic window selection method is used to automatically adjust the length of the time period according to the changing trend of the melting rate, thereby extracting initial time-light intensity curve data starting from the fixed time; The median filtering method is used to remove noise and outliers from the initial time-light intensity curve data, and the local weighted regression scatter point smoothing technology is introduced to retain the characteristic information in the initial time-light intensity curve data, and the robust statistical method is further applied to automatically detect and remove outliers to obtain smoothed time-light intensity curve data; Based on the smoothed time-light intensity curve data, a five-point cubic smoothing algorithm is used to reduce noise interference, and a least squares fitting technique combined with an adaptive weight adjustment strategy is used to calculate the slope value corresponding to each time point, which is expressed as a preliminary melting rate; The preliminary melting rate is optimized using an unscented Kalman filter to obtain a melting rate.

7. The method according to claim 6, characterized in that The median filtering method is used to remove the noise and outliers of the initial time-light intensity curve data, and the local weighted regression scatter point smoothing technology is introduced to retain the characteristic information in the initial time-light intensity curve data, and the robust statistical method is further applied to automatically detect and remove outliers to obtain smoothed time-light intensity curve data, including: Performing preliminary denoising on the initial time-light intensity curve data by using a median filtering method, reducing the influence of noise by replacing each data point with the median value in the neighborhood, and obtaining preliminary denoised data; Based on the data after preliminary denoising, a local weighted regression scatter point smoothing technique is introduced to smooth the curve by performing weighted least square fitting on local data points, retaining feature information and local change trends, and generating smoothed curve data; According to the smoothed curve data, a robust statistical method is applied to automatically detect and remove outliers to obtain a detection result; Based on the detection result, the data after preliminary denoising and the smoothed curve data, smoothed time-light intensity curve data are integrated.

8. A melting monitoring system for solid coloring agent, characterized in that: include: A generating module, for placing a solid dye to be monitored in a transparent heating container, arranging at least one light source on one side of the transparent heating container, arranging at least one light sensor on the other side opposite thereto, selecting a wavelength combination according to an absorption spectrum of the solid dye, and using a multi-wavelength light source to make a light path pass through a maximum cross section of the solid dye, so as to generate an initial optical path; A denoising module, for recording the light intensity value transmitted through the solid dye and received by the light sensor at preset time intervals to form an initial time-light intensity curve, and performing denoising processing on the data of the initial optical path by a wavelet transform algorithm to obtain a time-light intensity curve; An indication module, for observing, based on the changing trend of the time-light intensity curve, using a preset support vector machine model, obtaining a turning point at which the slope of the light intensity curve changes, and indicating, based on the turning point, the moment when the solid dye begins to melt, so as to generate a melting start signal; An evaluation module, for calculating the melting rate using a Kalman filter based on the data of the time-light intensity curve of the melting start signal at a fixed time, predicting the stability of the solid dye under different environmental conditions in combination with a thermodynamic analysis algorithm, and generating a quality evaluation report; The monitoring module is used to automatically trigger the cooling program using a PID controller when it is detected that the melting rate reaches a preset threshold, predict and analyze the uniformity and purity of the solid colorant in combination with deep learning image analysis technology, obtain key parameters, integrate the key parameters and quality assessment results of the solid colorant during the melting process, and generate a melting monitoring report.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a melting monitoring method for solid colorants as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a melting monitoring method for a solid colorant as described in any one of claims 1 to 7 is implemented.

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