A system and method for detecting the whiteness of a sintered potash feldspar ceramic

Abstract

The application discloses a kind of potash feldspar ceramic sintering whiteness detection system and method, it is related to ceramic detection technical field, including: in ceramic sintering furnace, construct space coordinate system, and utilize multiple high-temperature industrial video cameras to periodically capture ceramic image, convert image pixel coordinates into three-dimensional coordinates by preprocessing, and then screen out ceramic pixel points;Refer to historical sintering data, draw the time series graph of ceramic surface whiteness with time change, and determine the standard whiteness value of each monitoring period, while fitting the correlation function of temperature in furnace and sintering time;For the ceramic image data of current monitoring period, extract RGB color data and convert to whiteness value, establish real-time monitoring whiteness time series graph;By comparing actual whiteness and standard whiteness, classify ceramic pixel points, identify abnormal pixels, intelligently judge whether temperature needs to be adjusted or model anomaly is checked according to the number and type of abnormal pixels, to ensure the stability and consistency of ceramic sintering quality.

Description

Technical Field

[0001] The present invention relates to the technical field of ceramic detection, in particular to a system and method for detecting the sintered whiteness of potassium feldspar ceramics. Background Art

[0002] Potassium feldspar, an important natural mineral, is widely used in architecture, ceramics, glass, and enamel due to its unique chemical composition and physical properties. When melted at high temperatures, potassium feldspar can dissolve some of the decomposition products of quartz and kaolin, making it an indispensable flux in ceramic manufacturing. However, the quality of potassium feldspar ceramics, particularly their whiteness, directly affects the aesthetics and market value of the final product.

[0003] In the ceramic production process, whiteness is an important indicator of ceramic material quality. Whiteness not only reflects the material's ability to reflect light but is also closely related to its composition, structure, and sintering process. Testing the whiteness of potassium feldspar ceramics has traditionally relied on manual visual inspection or simple measurement using a colorimeter. However, these methods are subject to high subjectivity, low efficiency, and insufficient precision. With the development of computer vision and image processing technologies, machine vision-based whiteness detection technology has gradually become a research hotspot.

[0004] Traditional methods for detecting the whiteness of sintered potassium feldspar ceramics are often unable to monitor the whiteness changes during the ceramic sintering process in real time. If serious abnormalities have already occurred during the ceramic sintering process, continuing to wait for the completion of the ceramic sintering will result in a waste of time and equipment resources. In addition, traditional methods for detecting the whiteness of sintered potassium feldspar ceramics often only monitor the whiteness of certain areas. The whiteness distribution of potassium feldspar ceramics after sintering may not be uniform. Monitoring only certain areas cannot fully reflect the overall whiteness status. Such incomplete detection results may lead to misjudgment of the sintering quality of potassium feldspar ceramics. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the technical problems in the background technology, the present invention proposes a potassium feldspar ceramic sintering whiteness detection system and method. Based on computer vision technology, by setting a monitoring cycle and detecting the whiteness value of each ceramic pixel at the end of each monitoring cycle, it can achieve timely and accurate identification of whiteness anomalies during the ceramic sintering process, thereby solving the problems raised in the background technology.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A method for detecting the sintered whiteness of potassium feldspar ceramics, comprising:

[0010] A spatial rectangular coordinate system is established in the ceramic sintering furnace; multiple high-temperature industrial cameras are arranged at designated locations in the ceramic sintering furnace to periodically acquire images of potassium feldspar ceramics in corresponding directions; after preprocessing the acquired images, the pixel coordinates in the images are converted into three-dimensional coordinates in the spatial rectangular coordinate system, thereby screening out ceramic pixels in the images;

[0011] Historical sintering data is obtained from the ceramic preparation library. Based on the obtained data, a time series graph is plotted showing the whiteness of each pixel on the ceramic surface changing over time at different sintering temperatures. The standard whiteness value of each ceramic pixel at the end of each monitoring period is determined. Based on the historical sintering duration and temperature data, a correlation function between the furnace temperature and the total sintering time is fitted.

[0012] For the image data of the ceramic surface obtained in each monitoring cycle, the RGB color data of all ceramic pixels in the image are extracted, the color data is converted into whiteness values ​​using the Hunter white value algorithm, and a whiteness monitoring time series graph is established; the whiteness value of each ceramic pixel is compared with the standard whiteness value under the corresponding sintering time, and all ceramic pixels in each cycle are classified; based on the number and type of abnormal pixels, it is determined whether a temperature adjustment instruction or a model abnormality instruction needs to be issued.

[0013] Specifically, a spatial rectangular coordinate system is established with the center of the bottom surface of the ceramic sintering furnace as the origin, the east direction as the positive direction of the X axis, the north direction as the positive direction of the Y axis, and the upward direction as the positive direction of the Z axis;

[0014] The specific determination of the designated position is as follows: obtaining the height h1 of the ceramic model and the height h2 of the bottom of the ceramic in the ceramic sintering furnace, obtaining the height h1 of the ceramic sintering furnace, and obtaining the height h2 of the ceramic sintering furnace. A high-temperature industrial camera is placed at each of the four intersections of the side wall at the height and the X-axis plane and the Y-axis plane, and the lenses of the high-temperature industrial cameras face the ceramic model in the east, west, north and south directions respectively; a high-temperature industrial camera is placed at the center of the established spatial rectangular coordinate system, and faces the ceramic model directly above the Z axis.

[0015] Specifically, a monitoring cycle is set, and whenever the monitoring cycle ends, a shooting signal is sent to all high-temperature industrial cameras in the ceramic sintering furnace to obtain images in the corresponding direction;

[0016] The pixel coordinates of each pixel in the image are converted into three-dimensional coordinates in the camera coordinate system, and the direction of the camera coordinate system is adjusted to be consistent with the direction of the established spatial rectangular coordinate system;

[0017] Based on the rotation matrix R and translation vector s of each high-temperature industrial camera, the coordinates of each pixel in the image taken by each high-temperature industrial camera in the camera coordinate system are converted to the coordinates in the established spatial rectangular coordinate system. For the high-temperature industrial camera facing due east, the rotation matrix is ​​the unit matrix and the translation vector is For a high-temperature industrial camera facing due east, it faces due west and rotates 180° counterclockwise around the Y axis relative to the due east. At this time, the rotation matrix is ​​obtained by rotating 180° around the Y axis, and the translation vector is For a high-temperature industrial camera facing due south, it is rotated 270° counterclockwise around the Z axis relative to the due east direction. At this time, the rotation matrix is ​​obtained by rotating 270° around the Z axis, and the translation vector is For a high-temperature industrial camera facing due south, it faces due north and rotates 90° counterclockwise around the Z axis relative to the due east. At this time, the rotation matrix is ​​obtained by rotating 90° around the Z axis, and the translation vector is For the high-temperature industrial camera directly below, it faces upward. At this time, the rotation matrix is ​​the unit matrix and the translation vector is (0,0,0). T .

[0018] Specifically, historical sintering data of the corresponding potassium feldspar ceramic is obtained from the ceramic preparation library, including the temperature data during sintering and the whiteness monitoring value of each ceramic pixel; each ceramic pixel in the historical sintering data is matched one-to-one with the ceramic pixel of the ceramic to be sintered based on the three-dimensional coordinates;

[0019] Several historical sintering data of potassium feldspar ceramics that passed the whiteness test after sintering were screened from the acquired historical data, and a time series graph of the whiteness value Wh and sintering time t of each ceramic pixel point on the surface of the potassium feldspar ceramic at different sintering temperatures was plotted, where the abscissa of the data series graph is the total sintering time, and the ordinate is the standard whiteness value of the ceramic pixel point; for multiple potassium feldspar ceramic sintering data at the same temperature, the multiple whiteness values ​​of the ceramic pixel points at the same coordinate position and at the same monitoring time point and without whiteness abnormality were averaged, and the result was used as the standard whiteness value data for the corresponding ceramic pixel point position and the corresponding sintering time;

[0020] After the drawing is completed, a time series diagram of the standard whiteness values ​​of all ceramic pixels at several different temperatures is obtained.

[0021] Specifically, based on the sintering duration data of potassium feldspar ceramics at various temperatures, the temperature T in the ceramic sintering furnace and the total sintering time t are plotted. total The time series diagram is used to fit the correlation function t between the furnace temperature T and the total sintering time ttotal based on the data in the time series diagram.total (T), determine the corresponding relationship between the furnace temperature and the total sintering time from the function graph.

[0022] Specifically, at the end of each monitoring cycle, the image data captured by each high-temperature industrial camera is obtained, and the R, G, and B values ​​of each ceramic pixel in the image are obtained using GIMP image processing software;

[0023] Normalize the R, G, and B values ​​of each ceramic pixel; convert the normalized R, G, and B values ​​of each ceramic pixel into X, Y, and Z tristimulus values ​​based on the sRGB conversion matrix; calculate the relative values ​​of the X, Y, and Z tristimulus values ​​at each ceramic pixel relative to the X, Y, and Z tristimulus values ​​under the D65 light source; convert the relative values ​​in the XYZ color space at each ceramic pixel into Lab values; calculate the whiteness value of each ceramic pixel based on the L, a, and b values ​​of each ceramic pixel in the Lab color space. The expression is: The above conversion method is encapsulated into a whiteness conversion function. When an image of a ceramic surface is received, the whiteness conversion function is called to convert the whiteness value of each pixel on the ceramic surface.

[0024] Furthermore, based on the whiteness value of each ceramic pixel at the end of each monitoring cycle, a whiteness monitoring time series graph of each ceramic pixel is established, where the horizontal axis is the duration of the sintering operation and the vertical axis is the whiteness value of each ceramic pixel;

[0025] Obtain the temperature value set for the potassium feldspar ceramic sintering operation and obtain a time series graph of the standard whiteness values ​​of all ceramic pixels at the corresponding temperature;

[0026] At the end of each monitoring cycle, the whiteness value at each ceramic pixel is calculated and the whiteness value Wh at each ceramic pixel is calculated. i The standard whiteness value of the pixel at the corresponding sintering time In comparison, if Then the pixel is recorded as an insufficient abnormal pixel;

[0027] like Then the pixel point is recorded as a qualified pixel point;

[0028] like The pixel is recorded as an excessive abnormal pixel.

[0029] Furthermore, at the end of each monitoring cycle, all ceramic pixels on the ceramic surface are divided into three categories based on the size of the whiteness value, and the number of ceramic pixels in the three categories is counted respectively, with the number of insufficient abnormal pixels being recorded as n1, the number of qualified pixels being recorded as n2, and the number of excessive abnormal pixels being recorded as n3;

[0030] like No adjustment instructions will be triggered;

[0031] like The adjustment instruction is judged based on the quantitative relationship between the two types of abnormal pixels;

[0032] like The current monitoring cycle is marked as an abnormal cycle. If two consecutive monitoring cycles are marked as abnormal cycles, a model abnormality instruction is sent.

[0033] Specifically, if The judgment of the adjustment instruction is based on the quantitative relationship between the two types of abnormal pixels, specifically:

[0034] If n3 < n1, based on the corresponding relationship between the furnace temperature and the total sintering time, if the higher the furnace temperature, the shorter the total sintering time, then the furnace temperature needs to be lowered. Specifically, obtain the insufficient abnormal pixel points closest to the standard whiteness value in turn, calculate the difference between the whiteness value of these abnormal pixel points in the current monitoring period and the standard whiteness value, and add it to the absolute value TWh1 of the difference between the standard whiteness value of the abnormal pixel points corresponding to the current monitoring period and the standard whiteness value of the abnormal pixel points corresponding to the next monitoring period to obtain the whiteness value TWh2 that needs to be adjusted; obtain the total sintering time Tt1 at the current temperature, based on the proportional formula Calculate the adjusted total sintering time Tt2, and based on the function ttotal(T) of the furnace temperature and the total sintering time, calculate the temperature value T when the total sintering time is Tt2. ad ; At this time, calculate in sequence when the temperature is adjusted to T ad After that, the whiteness value of each qualified pixel in the next monitoring cycle is calculated in descending order until the whiteness value is still within The number of qualified pixels that have been calculated is n5, and then the number of abnormal pixels in the next monitoring cycle is calculated. next , specific n next =n1-n4+n5-1+n3, where n4 represents the n4th abnormal pixel closest to the standard whiteness value; calculate the number of abnormal pixels in the next monitoring cycle after temperature adjustment of all abnormal pixels one by one next If there are insufficient abnormal pixels to adjust the temperature, the number of abnormal pixels in the next monitoring cycle If the temperature is adjusted, select n next The minimum temperature adjustment strategy is used, and the temperature in the ceramic sintering furnace is adjusted to this temperature; if there is no temperature adjustment for insufficient abnormal pixels, the number of abnormal pixels in the next monitoring cycle is If the current monitoring cycle is marked as an abnormal cycle, if two consecutive monitoring cycles are marked as abnormal cycles, a model abnormality instruction is sent;

[0035] If n3≥n1, the same analysis method as when n3<n1 is used to determine whether to adjust the temperature in the ceramic sintering furnace.

[0036] A potassium feldspar ceramic sintering whiteness detection system, comprising:

[0037] The image data acquisition and processing module establishes a spatial rectangular coordinate system in the ceramic sintering furnace. Multiple high-temperature industrial cameras are arranged at designated locations in the ceramic sintering furnace to periodically acquire images of potassium feldspar ceramics in corresponding directions. After preprocessing the acquired images, the pixel coordinates in the images are converted into three-dimensional coordinates in the spatial rectangular coordinate system, thereby screening out the ceramic pixels in the images.

[0038] The historical data acquisition and processing module obtains historical sintering data from the ceramic preparation library, plots a time series graph of the whiteness of each pixel on the ceramic surface at different sintering temperatures, and determines the standard whiteness value of each ceramic pixel at the end of each monitoring cycle; based on the historical sintering duration and temperature data, fits the correlation function between the furnace temperature and the total sintering time;

[0039] The whiteness conversion and monitoring module extracts the RGB color data of all ceramic pixels in the image data of the ceramic surface obtained in each monitoring cycle, converts the color data into whiteness values ​​using the Hunter white value algorithm, and establishes a whiteness monitoring time series graph;

[0040] The whiteness analysis and processing module compares the whiteness value of each ceramic pixel with the standard whiteness value under the corresponding sintering time, and classifies all ceramic pixels in each cycle; based on the number and type of abnormal pixels, it determines whether a temperature adjustment instruction or a model abnormality instruction needs to be issued.

[0041] (3) Beneficial effects

[0042] The present invention provides a system and method for detecting the sintered whiteness of potassium feldspar ceramics, which has the following beneficial effects:

[0043] 1. By establishing a spatial rectangular coordinate system and deploying multiple high-temperature industrial cameras in the ceramic sintering furnace, the system achieves comprehensive, multi-angle monitoring of the potassium feldspar ceramic sintering process. A depth sensor acquires depth information for each pixel. Combined with the camera's intrinsic parameter matrix, optical center coordinates, and other parameters, the system achieves precise conversion of image pixel coordinates to three-dimensional coordinates in a spatial rectangular coordinate system, facilitating subsequent pixel matching with historical data. Image recognition technology is used to identify ceramic areas and select ceramic pixels, providing robust data support for subsequent whiteness calculation and analysis.

[0044] 2. By obtaining historical sintering data of potassium feldspar ceramics from a ceramic preparation library and plotting time series graphs of the whiteness of each pixel on the ceramic surface at different sintering temperatures, this method can comprehensively and accurately reflect the whiteness changes of potassium feldspar ceramics during the sintering process. By determining the standard whiteness value of each ceramic pixel at the end of each monitoring cycle, this method provides a reliable reference standard for subsequent whiteness testing. By fitting the correlation function between furnace temperature and total sintering time, the intrinsic relationship between furnace temperature and total sintering time is further revealed, providing a theoretical basis for subsequent furnace temperature adjustment.

[0045] 3. The Hunter White Value algorithm is used to accurately convert color data into whiteness values, thereby establishing a detailed whiteness monitoring time series diagram and improving the accuracy of whiteness conversion. By carefully comparing the whiteness values ​​of all ceramic pixels with the standard whiteness values ​​under the corresponding sintering time, and accurately identifying and classifying all ceramic pixels in each cycle, this provides a solid data foundation for subsequent temperature adjustment strategies, making adjustments more accurate and efficient.

[0046] 4. Based on the number and type of abnormal pixels, it intelligently determines whether a temperature adjustment instruction or a model abnormality instruction needs to be issued. This intelligent decision-making mechanism not only avoids unnecessary adjustment operations, but also ensures the stability and continuity of the sintering process, thereby significantly improving the sintering quality and production efficiency of potassium feldspar ceramics. The above-mentioned whiteness detection method is performed at the end of each monitoring cycle, which improves the timeliness of whiteness detection and avoids unnecessary waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flowchart of the steps of a method for detecting the whiteness of sintered potassium feldspar ceramics provided by the present invention;

[0048] Figure 2 This is a structural schematic diagram of a potassium feldspar ceramic sintering whiteness detection system provided by the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] refer to Figure 1 The present invention provides a method for detecting the sintered whiteness of potassium feldspar ceramics, comprising:

[0051] Step 1: Establish a spatial rectangular coordinate system in the ceramic sintering furnace; arrange multiple high-temperature industrial cameras at designated locations in the ceramic sintering furnace to periodically acquire images of potassium feldspar ceramics in corresponding directions; after preprocessing the acquired images, convert the pixel coordinates in the images into three-dimensional coordinates in the spatial rectangular coordinate system, and then screen out the ceramic pixels in the images;

[0052] The step one includes the following steps:

[0053] Step 101: Place the potassium feldspar ceramic model in a ceramic sintering furnace, establish a spatial rectangular coordinate system with the center of the circle on the bottom of the ceramic sintering furnace as the origin, the east direction as the positive direction of the X axis, the north direction as the positive direction of the Y axis, and the upward direction as the positive direction of the Z axis; fix the potassium feldspar ceramic model directly above the bottom of the ceramic sintering furnace, that is, the center of the circle formed by its bottom edge is in the positive direction of the Z axis;

[0054] Step 102: Place several high-temperature industrial cameras in the ceramic sintering furnace to obtain surface images of the potassium feldspar ceramic model during the sintering process of the potassium feldspar ceramic. The method for determining the placement position of the high-temperature industrial cameras is as follows: obtain the height h1 of the ceramic model and the height h2 of the bottom of the ceramic in the ceramic sintering furnace, and obtain the height of the ceramic sintering furnace. Place a high-temperature industrial camera at each of the four intersections of the side wall at the height with the X-axis plane and the Y-axis plane, and the lenses of the high-temperature industrial cameras face the ceramic model in the east, west, north, and south directions respectively; place a high-temperature industrial camera at the center of the spatial rectangular coordinate system established in step 101, and face the ceramic model directly above the Z axis;

[0055] Step 103: Set a monitoring cycle. Whenever a monitoring cycle ends, send a shooting signal to all high-temperature industrial cameras in the ceramic sintering furnace to acquire images in the corresponding direction. Perform preprocessing on the acquired images, specifically image denoising, image correction, and image enhancement. Image denoising is used to remove noise caused by interference factors such as high temperature and smoke in the sintering furnace. Image correction is used to correct deformation or distortion caused by factors such as the camera's installation position, angle, and lens distortion. Image enhancement is used to highlight key information in the image, which is the highlighting of each pixel in the image.

[0056] Step 104: The high temperature industrial camera is integrated with a depth sensor, that is, the image captured by the high temperature industrial camera contains the depth information of each pixel; obtain the internal parameter matrix K and the optical center coordinates (c u ,c v ), the focal length f on the x-axis and y-axis of the image u and f v , and the pixel coordinates (u, v) and depth value d of each pixel in the image captured by each high-temperature industrial camera, where the optical center coordinates are the coordinates at the center of the image captured by the camera; converting the pixel coordinates of each pixel in the image into three-dimensional coordinates in the camera coordinate system, and adjusting the direction of the camera coordinate system to be consistent with the direction of the spatial rectangular coordinate system established in step 101;

[0057] For high-temperature industrial cameras facing due east and due west, the conversion expression is:

[0058]

[0059] For high-temperature industrial cameras facing due north and due south, the conversion expression is:

[0060]

[0061] For a high-temperature industrial camera facing upward, the conversion expression is:

[0062]

[0063] Based on the rotation matrix R and translation vector s of the position of each high-temperature industrial camera, the coordinates of each pixel in the image taken by each high-temperature industrial camera in the camera coordinate system are converted to the coordinates of the spatial rectangular coordinate system established in step 101. The conversion expression is:

[0064]

[0065] Among them, for the high-temperature industrial camera in the west, it faces the east direction. At this time, the rotation matrix is ​​the unit matrix and the translation vector is in() T The symbol represents the transposition operation of the vector; for the high-temperature industrial camera in the east, it faces the west direction and rotates 180° counterclockwise around the Y axis relative to the east direction. At this time, the rotation matrix is ​​obtained by rotating 180° around the Y axis, and the translation vector is For a high-temperature industrial camera facing due south, it is rotated 270° counterclockwise around the Z axis relative to the due east direction. At this time, the rotation matrix is ​​obtained by rotating 270° around the Z axis, and the translation vector is For a high-temperature industrial camera facing due south, it faces due north and rotates 90° counterclockwise around the Z axis relative to the due east. At this time, the rotation matrix is ​​obtained by rotating 90° around the Z axis, and the translation vector is For the high-temperature industrial camera directly below, it faces upward. At this time, the rotation matrix is ​​the unit matrix and the translation vector is (0,0,0). T ;

[0066] Step 105: Based on the five images acquired at the end of each cycle and the three-dimensional coordinates of each pixel in the image, the ceramic area in each image is identified based on image recognition technology, and all pixels in the ceramic area are recorded as ceramic pixels.

[0067] When using, combine the contents in steps 101 to 105:

[0068] By establishing a spatial rectangular coordinate system and deploying multiple high-temperature industrial cameras in the ceramic sintering furnace, the system achieves comprehensive, multi-angle monitoring of the potassium feldspar ceramic sintering process. A depth sensor acquires depth information for each pixel. Combined with the camera's intrinsic parameter matrix, optical center coordinates, and other parameters, the system accurately converts image pixel coordinates into three-dimensional coordinates in a spatial rectangular coordinate system, facilitating subsequent pixel matching with historical data. Image recognition technology is used to identify ceramic areas and select ceramic pixels, providing robust data support for subsequent whiteness calculation and analysis.

[0069] Step 2: Obtain historical sintering data from the ceramic preparation library. Based on the acquired data, plot a time series graph of the whiteness of each pixel on the ceramic surface changing over time at different sintering temperatures, and determine the standard whiteness value of each ceramic pixel at the end of each monitoring cycle. Based on the historical sintering duration and temperature data, fit the correlation function between the furnace temperature and the total sintering time.

[0070] The step 2 includes the following steps:

[0071] Step 201: Acquire historical sintering data corresponding to potassium feldspar ceramics from a ceramic preparation library, including temperature data during sintering and a whiteness monitoring value for each ceramic pixel. In the historical sintering data, the ceramic pixels are also established and acquired according to the method in step 1. The placement and orientation of the ceramic surfaces are the same during each sintering, resulting in a plurality of ceramic pixels at the same coordinate position. Then, based on the three-dimensional coordinates, each ceramic pixel in the historical sintering data is associated with a ceramic pixel of the ceramic to be sintered.

[0072] From the data obtained above, several historical sintering data of potassium feldspar ceramics that have passed the whiteness test after sintering are screened out. Based on the historical sintering data of multiple potassium feldspar ceramics (including the changes in the whiteness monitoring value of each ceramic pixel point), a time series diagram of the whiteness value Wh of each ceramic pixel point on the surface of the potassium feldspar ceramic and the sintering time t at different sintering temperatures is plotted, wherein the horizontal axis of the data series diagram is the total sintering time, and the vertical axis is the standard whiteness value of the ceramic pixel point; the standard whiteness value represents the whiteness value of each ceramic pixel point monitored at a certain monitoring time point in the historical sintering data of the potassium feldspar ceramic when no abnormality is found, as determined in the subsequent step 303; for multiple potassium feldspar ceramics at the same temperature, the whiteness value Wh of each ceramic pixel point on the surface of the potassium feldspar ceramics is plotted against the sintering time t. For the sintering data of potassium feldspar ceramics, the whiteness values ​​of the ceramic pixels at the same coordinate position and at the same monitoring time point without whiteness abnormality are averaged to obtain the standard whiteness value data corresponding to the ceramic pixel position and the corresponding monitoring time, i.e., the sintering time in the time series diagram drawn above; for example, if there are three sets of historical sintering data of potassium feldspar ceramics at the same specified temperature in the historical sintering data of potassium feldspar ceramics, and in these data, at the end of the fifth monitoring cycle, the whiteness value monitored at a specified pixel point on the ceramic surface is abnormal once and normal twice, then the average of the two whiteness values ​​without abnormality is used as the standard whiteness value at the end of the fifth monitoring cycle at the specified temperature;

[0073] Based on the above operation, after the drawing is completed, a time series diagram of the standard whiteness values ​​of all ceramic pixels at different temperatures is obtained;

[0074] Step 202: Based on the sintering duration data of potassium feldspar ceramics at various temperatures, plot the temperature T in the ceramic sintering furnace and the total sintering time t. total The time series diagram is used to fit the correlation function t between the furnace temperature T and the total sintering time ttotal based on the data in the time series diagram. total (T), the corresponding relationship between the furnace temperature and the total sintering time can be obtained from the function graph, that is, the higher the furnace temperature, the longer or shorter the total sintering time.

[0075] When using, combine the contents in steps 201 to 201:

[0076] By obtaining historical sintering data for potassium feldspar ceramics from a ceramic preparation library and plotting time series graphs of the whiteness of each pixel on the ceramic surface at different sintering temperatures, this method can comprehensively and accurately reflect the whiteness changes of potassium feldspar ceramics during the sintering process. By determining the standard whiteness value of each ceramic pixel at the end of each monitoring cycle, this method provides a reliable reference standard for subsequent whiteness testing. By fitting the correlation function between furnace temperature and total sintering time, the intrinsic relationship between furnace temperature and total sintering time is further revealed, providing a theoretical basis for subsequent furnace temperature adjustment.

[0077] Step 3: For the image data of the ceramic surface obtained in each monitoring cycle, extract the RGB color data of all ceramic pixels in the image, convert the color data into whiteness values ​​using the Hunter white value algorithm, and establish a whiteness monitoring time series graph; compare the whiteness value of each ceramic pixel with the standard whiteness value under the corresponding sintering time, and classify all ceramic pixels in each cycle; determine whether to issue a temperature adjustment instruction or a model abnormality instruction based on the number and type of abnormal pixels;

[0078] The step three includes the following steps:

[0079] Step 301: After starting the sintering operation of the potassium feldspar ceramic, based on the set monitoring cycle length, at the end of each monitoring cycle, obtain image data captured by each high-temperature industrial camera, filter the ceramic pixels in step 105 on the image data, and use GIMP image processing software to obtain the R, G, and B values ​​at each ceramic pixel;

[0080] Step 302: Based on the Hunter white value algorithm, the R, G, and B values ​​of each ceramic pixel are converted into a specific whiteness value. The specific conversion method is as follows:

[0081] Step 3021: Normalize the R, G, and B values ​​of each ceramic pixel. The expression is:

[0082]

[0083] Step 3022: Convert the normalized R, G, and B values ​​of each ceramic pixel into X, Y, and Z tristimulus values ​​based on the sRGB conversion matrix. The expression is:

[0084]

[0085] Where H represents the sRGB conversion matrix, and

[0086] Step 3023: Calculate the relative values ​​of the X, Y, and Z tristimulus values ​​at each ceramic pixel relative to the X, Y, and Z tristimulus values ​​under the D65 light source. The expression is:

[0087]

[0088] Among them, X n 、Y n , Z n They represent the X, Y, and Z tristimulus values ​​under the D65 illuminant, a standard light source that represents natural daylight (average daylight). It can well simulate the color display under a daylight environment. Therefore, in many fields, the D65 illuminant is widely used as a standard light source for color evaluation and comparison.

[0089] Step 3024: Convert the relative value in the XYZ color space at each ceramic pixel into a Lab value. The Lab value is derived based on the Lab color space and is used to accurately describe and quantify color. The Lab value includes an L value, an a value, and a b value. The L value represents the brightness of the color, or lightness; the a value represents the position of the color on the red-green axis; and the b value represents the position of the color on the yellow-blue axis. The specific conversion formula is:

[0090]

[0091] where function f(x) represents a nonlinear conversion function that converts relative values ​​in the XYZ color space (relative to the D65 illuminant) to L, a, and b values ​​in the Lab color space, and

[0092] Step 3025: Calculate the whiteness value (Hunter whiteness value) of each ceramic pixel based on the L, a, and b values ​​of each ceramic pixel in the Lab color space. The expression is: In the historical sintering data stored in the ceramic preparation library, the whiteness value of each pixel is also calculated according to step 302. The conversion method in step 302 is encapsulated into a whiteness conversion function. When an image of the ceramic surface is received, the whiteness conversion function is called to convert the whiteness value of each pixel on the ceramic surface.

[0093] Step 303: At the end of each monitoring cycle, the whiteness value of each ceramic pixel is calculated based on the image captured by the high-temperature industrial camera; and a whiteness monitoring time series graph of each ceramic pixel is established, with the horizontal axis representing the duration of the sintering operation and the vertical axis representing the whiteness value of each ceramic pixel.

[0094] Obtain the temperature value set for the potassium feldspar ceramic sintering operation and obtain a time series graph of the standard whiteness values ​​of all ceramic pixels at the corresponding temperature;

[0095] Starting from the sintering operation of potassium feldspar ceramics, the whiteness value of each ceramic pixel is calculated at the end of each monitoring cycle, and the whiteness value Wh at each ceramic pixel is calculated. i The standard whiteness value of the pixel at the corresponding sintering time In comparison, if It means that at the end of the current monitoring cycle, the whiteness value Wh at the i-th ceramic pixel is i Less than the standard whiteness value under the corresponding sintering time That is, the whiteness at this point on the surface of the potassium feldspar ceramic is insufficient, and the pixel is recorded as an insufficient abnormal pixel;

[0096] like It means that at the end of the current monitoring cycle, the whiteness value Wh at the i-th ceramic pixel is i Standard whiteness value at the corresponding sintering time If the error range is within, that is, the whiteness of the point on the surface of the potassium feldspar ceramic is qualified, the pixel point is recorded as a qualified pixel point, and also as a ceramic pixel point without abnormality;

[0097] like It means that at the end of the current monitoring cycle, the whiteness value Wh at the i-th ceramic pixel is i Greater than the standard whiteness value under the corresponding sintering time That is, the whiteness at this point on the surface of the potassium feldspar ceramic is excessive, and the pixel is recorded as an excessive abnormal pixel;

[0098] Step 304: At the end of each monitoring cycle, all ceramic pixels on the ceramic surface are divided into three categories according to step 303; the number of ceramic pixels in each of the three categories is counted, and the number of insufficient abnormal pixels is recorded as n1, the number of qualified pixels is recorded as n2, and the number of excessive abnormal pixels is recorded as n3;

[0099] Determine the relationship between n1, n2 and n3, if This means that at the end of the current monitoring cycle, the number of abnormal pixels is within the normal range and no adjustment instructions are triggered;

[0100] like This means that at the end of the current monitoring cycle, the number of abnormal pixels is not within the normal range, but the number of abnormal pixels can be adjusted to the normal range by adjusting the temperature. At this time, the adjustment instruction is determined based on the relationship between the numbers of the two types of abnormal pixels, specifically:

[0101] If n3<n1, it means that at the end of the current monitoring cycle, the number of insufficient abnormal pixels is greater than the number of excessive abnormal pixels. At this time, it is necessary to determine whether the temperature in the ceramic sintering furnace can be adjusted; based on the correspondence between the furnace temperature and the total sintering time in step 203, if the higher the furnace temperature, the shorter the total sintering time, then the furnace temperature needs to be lowered to slow down the rate of decrease in the whiteness of the ceramic surface, and then adjust these insufficient abnormal pixels. Specifically, obtain the insufficient abnormal pixel points closest to the standard whiteness value in turn, calculate the difference between the whiteness value of the abnormal pixel point in the current monitoring cycle and the standard whiteness value, and add it to the absolute value TWh1 of the difference between the standard whiteness value of the abnormal pixel point in the current monitoring cycle and the standard whiteness value of the abnormal pixel point in the next monitoring cycle to obtain the whiteness value TWh2 to be adjusted; obtain the total sintering time Tt1 at the current temperature, based on the proportional formula Calculate the adjusted total sintering time Tt2, and based on the function ttotal(T) of the furnace temperature and the total sintering time, calculate the temperature value T when the total sintering time is Tt2. ad ; At this time, calculate the qualified pixels in sequence and adjust the temperature to T ad After that, the whiteness value of each qualified pixel in the next monitoring cycle is calculated in descending order until the whiteness value is still within The number of qualified pixels that have been calculated is n5, and then the number of abnormal pixels in the next monitoring cycle is calculated. next , specific n next =n1-n4+n5-1+n3, where n4 represents the n4th abnormal pixel closest to the standard whiteness value; calculate the number of abnormal pixels in the next monitoring cycle after temperature adjustment of all abnormal pixels one by one next If there are insufficient abnormal pixels to adjust the temperature, the number of abnormal pixels in the next monitoring cycle If the temperature is adjusted, select n next The minimum temperature adjustment strategy is used, and the temperature in the ceramic sintering furnace is adjusted to this temperature; if there is no temperature adjustment for insufficient abnormal pixels, the number of abnormal pixels in the next monitoring cycle is If the current monitoring period is marked as an abnormal period, if two consecutive monitoring periods are marked as abnormal periods, a model abnormality instruction is sent and the potassium feldspar model needs to be rebuilt;

[0102] If n3≥n1, it means that at the end of the current monitoring period, the number of excess abnormal pixels is not less than the number of excess abnormal pixels. At this time, according to the above analysis method, that is, the same analysis method as when n3<n1, it is judged whether the temperature in the ceramic sintering furnace can be adjusted. The specific method is: based on the correspondence between the temperature in the furnace and the total sintering time, if the temperature in the furnace is higher, the total sintering time is shorter, then the temperature in the furnace needs to be increased at this time, specifically: obtain the excess abnormal pixels closest to the standard whiteness value in turn, calculate the difference between the whiteness value of these abnormal pixels in the current monitoring period and the standard whiteness value, and add the absolute value TWh1 of the difference between the standard whiteness value of the abnormal pixel corresponding to the current monitoring period and the standard whiteness value of the abnormal pixel corresponding to the next monitoring period to obtain the whiteness value TWh2 that needs to be adjusted; obtain the total sintering time Tt1 at the current temperature, based on the proportional formula Calculate the adjusted total sintering time Tt2, and based on the function ttotal(T) of the furnace temperature and the total sintering time, calculate the temperature value T when the total sintering time is Tt2. ad ; At this time, calculate in sequence when the temperature is adjusted to T ad After that, the whiteness value of each qualified pixel in the next monitoring cycle is calculated in ascending order until the whiteness value is still within the range of The number of qualified pixels that have been calculated is n5, and then the number of abnormal pixels in the next monitoring cycle is calculated. next , specific n next =n1+n5-1+n3-n4, where n4 represents the n4th excessive abnormal pixel closest to the standard whiteness value; calculate the number of abnormal pixels in the next monitoring cycle after temperature adjustment of all excessive abnormal pixels one by one next If there are excessive abnormal pixels, after temperature adjustment, the number of abnormal pixels in the next monitoring cycle will be If the temperature is adjusted, select n next The minimum temperature adjustment strategy is used, and the temperature in the ceramic sintering furnace is adjusted to this temperature; if there is no temperature adjustment for excessive abnormal pixels, the number of abnormal pixels in the next monitoring cycle If the current monitoring cycle is marked as an abnormal cycle, if two consecutive monitoring cycles are marked as abnormal cycles, a model abnormality instruction is sent;

[0103] like This means that at the end of the current monitoring cycle, the number of abnormal pixels is not within the normal range and the temperature cannot be adjusted to bring the number of abnormal pixels within the normal range. The current monitoring cycle is marked as an abnormal cycle. If two consecutive monitoring cycles are marked as abnormal cycles, a model abnormality instruction is sent and the potassium feldspar model needs to be rebuilt.

[0104] When using, combine the contents in steps 301 to 304:

[0105] The Hunter White Value algorithm is used to accurately convert color data into whiteness values, thereby establishing a detailed whiteness monitoring time series diagram and improving the accuracy of whiteness conversion. By carefully comparing the whiteness values ​​of all ceramic pixels with the standard whiteness values ​​at the corresponding sintering time, and accurately identifying and classifying all ceramic pixels within each cycle, this provides a solid data foundation for subsequent temperature adjustment strategies, making adjustments more precise and efficient.

[0106] Based on the number and type of abnormal pixels, an intelligent determination is made as to whether a temperature adjustment instruction or a model abnormality instruction needs to be issued. This intelligent decision-making mechanism not only avoids unnecessary adjustments but also ensures the stability and continuity of the sintering process, thereby significantly improving the sintering quality and production efficiency of potassium feldspar ceramics. The aforementioned whiteness detection method is performed at the end of each monitoring cycle, improving the timeliness of whiteness detection and avoiding unnecessary waste of resources.

[0107] refer to Figure 2 The present invention also provides a potassium feldspar ceramic sintering whiteness detection system, comprising:

[0108] The image data acquisition and processing module establishes a spatial rectangular coordinate system in the ceramic sintering furnace. Multiple high-temperature industrial cameras are arranged at designated locations in the ceramic sintering furnace to periodically acquire images of potassium feldspar ceramics in corresponding directions. After preprocessing the acquired images, the pixel coordinates in the images are converted into three-dimensional coordinates in the spatial rectangular coordinate system, thereby screening out the ceramic pixels in the images.

[0109] The historical data acquisition and processing module obtains historical sintering data from the ceramic preparation library, plots a time series graph of the whiteness of each pixel on the ceramic surface at different sintering temperatures, and determines the standard whiteness value of each ceramic pixel at the end of each monitoring cycle; based on the historical sintering duration and temperature data, fits the correlation function between the furnace temperature and the total sintering time;

[0110] The whiteness conversion and monitoring module extracts the RGB color data of all ceramic pixels in the image data of the ceramic surface obtained in each monitoring cycle, converts the color data into whiteness values ​​using the Hunter white value algorithm, and establishes a whiteness monitoring time series graph;

[0111] The whiteness analysis and processing module compares the whiteness value of each ceramic pixel with the standard whiteness value under the corresponding sintering time, and classifies all ceramic pixels in each cycle; based on the number and type of abnormal pixels, it determines whether a temperature adjustment instruction or a model abnormality instruction needs to be issued.

[0112] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer storage medium or transmitted via a computer storage medium.

[0113] Computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. Computer storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0114] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for detecting the whiteness of sintered potassium feldspar ceramics, characterized by: include: A spatial rectangular coordinate system is established in the ceramic sintering furnace; multiple high-temperature industrial cameras are arranged at designated locations in the ceramic sintering furnace to periodically acquire images of potassium feldspar ceramics in corresponding directions; after preprocessing the acquired images, the pixel coordinates in the images are converted into three-dimensional coordinates in the spatial rectangular coordinate system, thereby screening out ceramic pixels in the images; Historical sintering data is obtained from the ceramic preparation library. Based on the obtained data, a time series graph is plotted showing the whiteness of each pixel on the ceramic surface changing over time at different sintering temperatures. The standard whiteness value of each ceramic pixel at the end of each monitoring period is determined. Based on the historical sintering duration and temperature data, a correlation function between the furnace temperature and the total sintering time is fitted. For the image data of the ceramic surface obtained in each monitoring cycle, the RGB color data of all ceramic pixels in the image are extracted, the color data is converted into whiteness values ​​using the Hunter white value algorithm, and a whiteness monitoring time series graph is established; the whiteness value of each ceramic pixel is compared with the standard whiteness value under the corresponding sintering time, and all ceramic pixels in each cycle are classified; based on the number and type of abnormal pixels, it is determined whether a temperature adjustment instruction or a model abnormality instruction needs to be issued.

2. The method for detecting the sintered whiteness of potassium feldspar ceramics according to claim 1, wherein: Establish a spatial rectangular coordinate system with the center of the bottom of the ceramic sintering furnace as the origin, the east direction as the positive direction of the X axis, the north direction as the positive direction of the Y axis, and the upward direction as the positive direction of the Z axis; The specific determination of the designated position is as follows: obtaining the height h1 of the ceramic model and the height h2 of the bottom of the ceramic in the ceramic sintering furnace, obtaining the height of the ceramic sintering furnace A high-temperature industrial camera is placed at each of the four intersections of the side wall at the height and the X-axis plane and the Y-axis plane, and the lenses of the high-temperature industrial cameras face the ceramic model in the east, west, north and south directions respectively; a high-temperature industrial camera is placed at the center of the established spatial rectangular coordinate system, and faces the ceramic model directly above the Z axis.

3. A method for detecting the sintered whiteness of potassium feldspar ceramics according to claim 2, characterized in that: Set a monitoring cycle. When the monitoring cycle ends, send a shooting signal to all high-temperature industrial cameras in the ceramic sintering furnace to obtain images in the corresponding direction. The pixel coordinates of each pixel in the image are converted into three-dimensional coordinates in the camera coordinate system, and the direction of the camera coordinate system is adjusted to be consistent with the direction of the established spatial rectangular coordinate system; Based on the rotation matrix R and translation vector s of each high-temperature industrial camera, the coordinates of each pixel in the image taken by each high-temperature industrial camera in the camera coordinate system are converted to the coordinates in the established spatial rectangular coordinate system. For the high-temperature industrial camera facing due east, the rotation matrix is ​​the unit matrix and the translation vector is For a high-temperature industrial camera facing due east, it faces due west and rotates 180° counterclockwise around the Y axis relative to the due east. At this time, the rotation matrix is ​​obtained by rotating 180° around the Y axis, and the translation vector is For a high-temperature industrial camera facing due south, it is rotated 270° counterclockwise around the Z axis relative to the due east direction. At this time, the rotation matrix is ​​obtained by rotating 270° around the Z axis, and the translation vector is For a high-temperature industrial camera facing due south, it faces due north and rotates 90° counterclockwise around the Z axis relative to the due east. At this time, the rotation matrix is ​​obtained by rotating 90° around the Z axis, and the translation vector is For the high-temperature industrial camera directly below, it faces upward. At this time, the rotation matrix is ​​the unit matrix and the translation vector is (0,0,0). T .

4. A method for detecting the sintered whiteness of potassium feldspar ceramics according to claim 3, characterized in that: Obtain historical sintering data of the corresponding potassium feldspar ceramic from the ceramic preparation library, including the temperature data during sintering and the whiteness monitoring value of each ceramic pixel; and match each ceramic pixel in the historical sintering data with the ceramic pixel of the current ceramic to be sintered based on the three-dimensional coordinates. Several historical sintering data of potassium feldspar ceramics that passed the whiteness test after sintering were screened from the acquired historical data, and a time series graph of the whiteness value Wh and sintering time t of each ceramic pixel point on the surface of the potassium feldspar ceramic at different sintering temperatures was plotted, where the abscissa of the data series graph is the total sintering time, and the ordinate is the standard whiteness value of the ceramic pixel point; for multiple potassium feldspar ceramic sintering data at the same temperature, the multiple whiteness values ​​of the ceramic pixel points at the same coordinate position and at the same monitoring time point and without whiteness abnormality were averaged, and the result was used as the standard whiteness value data for the corresponding ceramic pixel point position and the corresponding sintering time; After the drawing is completed, a time series diagram of the standard whiteness values ​​of all ceramic pixels at several different temperatures is obtained.

5. A method for detecting the sintered whiteness of potassium feldspar ceramics according to claim 4, characterized in that: Based on the sintering duration data of potassium feldspar ceramics at various temperatures, the temperature T in the ceramic sintering furnace and the total sintering time t are plotted. total The time series diagram is used to fit the furnace temperature T and the total sintering time t based on the data in the time series diagram. total The correlation function t total (T), determine the corresponding relationship between the furnace temperature and the total sintering time from the function graph.

6. A method for detecting the sintered whiteness of potassium feldspar ceramics according to claim 5, characterized in that: At the end of each monitoring cycle, the image data captured by each high-temperature industrial camera was obtained, and the R, G, and B values ​​of each ceramic pixel in the image were obtained using GIMP image processing software; Normalize the R, G, and B values ​​of each ceramic pixel; convert the normalized R, G, and B values ​​of each ceramic pixel into X, Y, and Z tristimulus values ​​based on the sRGB conversion matrix; calculate the relative values ​​of the X, Y, and Z tristimulus values ​​at each ceramic pixel relative to the X, Y, and Z tristimulus values ​​under the D65 light source; convert the relative values ​​in the XYZ color space at each ceramic pixel into Lab values; calculate the whiteness value of each ceramic pixel based on the L, a, and b values ​​of each ceramic pixel in the Lab color space. The expression is: The conversion method of converting the R, G, and B values ​​of each ceramic pixel into a specific whiteness value based on the Hunter white value algorithm is encapsulated into a whiteness conversion function. When an image of the ceramic surface is received, the whiteness conversion function is called to convert the whiteness value of each pixel on the ceramic surface.

7. A method for detecting the sintered whiteness of potassium feldspar ceramics according to claim 6, characterized in that: Based on the whiteness value of each ceramic pixel at the end of each monitoring cycle, a whiteness monitoring time series diagram of each ceramic pixel is established, with the horizontal axis representing the duration of the sintering operation and the vertical axis representing the whiteness value of each ceramic pixel; Obtain the temperature value set for the potassium feldspar ceramic sintering operation and obtain a time series graph of the standard whiteness values ​​of all ceramic pixels at the corresponding temperature; At the end of each monitoring cycle, the whiteness value at each ceramic pixel is calculated and the whiteness value Wh at each ceramic pixel is calculated. i The standard whiteness value of the pixel at the corresponding sintering time In comparison, if Then the pixel is recorded as an insufficient abnormal pixel; like Then the pixel point is recorded as a qualified pixel point; like The pixel is recorded as an excessive abnormal pixel.

8. A method for detecting the sintered whiteness of potassium feldspar ceramics according to claim 7, characterized in that: At the end of each monitoring cycle, all ceramic pixels on the ceramic surface are divided into three categories based on the size of the whiteness value. The number of ceramic pixels in the three categories is counted respectively. The number of insufficient abnormal pixels is recorded as n1, the number of qualified pixels is recorded as n2, and the number of excessive abnormal pixels is recorded as n3. like No adjustment instructions will be triggered; like The adjustment instruction is judged based on the quantitative relationship between the two types of abnormal pixels; like The current monitoring cycle is marked as an abnormal cycle. If two consecutive monitoring cycles are marked as abnormal cycles, a model abnormality instruction is sent.

9. A method for detecting the sintered whiteness of potassium feldspar ceramics according to claim 8, characterized in that: like The judgment of the adjustment instruction is based on the quantitative relationship between the two types of abnormal pixels, specifically: If n3 < n1, based on the corresponding relationship between the furnace temperature and the total sintering time, if the higher the furnace temperature, the shorter the total sintering time, then the furnace temperature needs to be lowered. Specifically, obtain the insufficient abnormal pixel points closest to the standard whiteness value in turn, calculate the difference between the whiteness value of these abnormal pixel points in the current monitoring period and the standard whiteness value, and add it to the absolute value TWh1 of the difference between the standard whiteness value of the abnormal pixel points corresponding to the current monitoring period and the standard whiteness value of the abnormal pixel points corresponding to the next monitoring period to obtain the whiteness value TWh2 that needs to be adjusted; obtain the total sintering time Tt1 at the current temperature, based on the proportional formula Calculate the adjusted total sintering time Tt2 based on the function of furnace temperature and total sintering time t total (T), calculate the temperature value T when the total sintering time is Tt2 ad ; At this time, calculate in sequence when the temperature is adjusted to T ad After that, the whiteness value of each qualified pixel in the next monitoring cycle is calculated in descending order until the whiteness value is still within The number of qualified pixels that have been calculated is n5, and then the number of abnormal pixels in the next monitoring cycle is calculated. next , specifically: n next =n1-n4+n5-1+n3, where n4 represents the n4th abnormal pixel closest to the standard whiteness value; calculate the number of abnormal pixels in the next monitoring cycle after temperature adjustment of all abnormal pixels one by one next If there are insufficient abnormal pixels to adjust the temperature, the number of abnormal pixels in the next monitoring cycle If the temperature is adjusted, select n next The minimum temperature adjustment strategy is used, and the temperature in the ceramic sintering furnace is adjusted to this temperature; if there is no temperature adjustment for insufficient abnormal pixels, the number of abnormal pixels in the next monitoring cycle is If the current monitoring cycle is marked as an abnormal cycle, if two consecutive monitoring cycles are marked as abnormal cycles, a model abnormality instruction is sent; If n3≥n1, the same analysis method as when n3<n1 is used to determine whether to adjust the temperature in the ceramic sintering furnace.

10. A potassium feldspar ceramic sintering whiteness detection system, characterized in that: include: The image data acquisition and processing module establishes a spatial rectangular coordinate system in the ceramic sintering furnace. Multiple high-temperature industrial cameras are arranged at designated locations in the ceramic sintering furnace to periodically acquire images of potassium feldspar ceramics in corresponding directions. After preprocessing the acquired images, the pixel coordinates in the images are converted into three-dimensional coordinates in the spatial rectangular coordinate system, thereby screening out the ceramic pixels in the images. The historical data acquisition and processing module obtains historical sintering data from the ceramic preparation library, plots a time series graph of the whiteness of each pixel on the ceramic surface at different sintering temperatures, and determines the standard whiteness value of each ceramic pixel at the end of each monitoring cycle; based on the historical sintering duration and temperature data, fits the correlation function between the furnace temperature and the total sintering time; The whiteness conversion and monitoring module extracts the RGB color data of all ceramic pixels in the image data of the ceramic surface obtained in each monitoring cycle, converts the color data into whiteness values ​​using the Hunter white value algorithm, and establishes a whiteness monitoring time series graph; The whiteness analysis and processing module compares the whiteness value of each ceramic pixel with the standard whiteness value under the corresponding sintering time, and classifies all ceramic pixels in each cycle; based on the number and type of abnormal pixels, it determines whether a temperature adjustment instruction or a model abnormality instruction needs to be issued.

Images