832 results about "Chromaticity" patented technology

The invention provides a self-adaptive underwater image enhancement method and system based on a Retinex theory and Mama, and relates to the technical field of underwater image enhancement, and the method comprises the steps: taking an original underwater image and a corresponding illumination priori image as input, obtaining illumination factor mapping through feature splicing, a first convolution layer, a multi-scale channel aggregation module layer and a second convolution layer, and obtaining an illumination priori image; performing Hadamard product operation on the illumination factor mapping and an original image to obtain a basic enhanced image; the multi-scale degradation features of the basic enhanced image are extracted step by step through an encoder, the spatial resolution is recovered through jump connection of a decoder, the illumination-texture relevance is enhanced through selective state space modeling of a WLMama bottleneck module, and finally a high-quality underwater image enhancement result is generated. According to the method, the mathematical representation system of the optical characteristics of the water body is established, the model can adaptively adjust the chromaticity compensation intensity, and image restoration under the constraint of the physical characteristics of the underwater scene is realized.

The invention relates to the technical field of biomedical engineering and digital image processing, in particular to a digital image enhancement method based on cytopathology. The method comprises the following steps: eliminating noise and artifacts caused by non-uniform dyeing, section folding and dust factors; the problem of dyeing difference caused by different scanning devices is solved; key areas including cell nucleuses and cell membranes are highlighted, so that the visual effect of a low-contrast image is improved; segmenting an area, including cell nucleuses and cytoplasm, of the cells, and extracting morphological characteristics; the detail resolution of a low-resolution image, especially the definition of a small cell structure, is improved; the brightness component Y is corrected to compensate for the difference of different dyeing methods and dyeing qualities in brightness response, then the chromatic value is adjusted according to the color correction factor to make the colors of the images more consistent, and finally, the images which are acquired by different dyeing methods and dyeing qualities and subjected to brightness and chroma correction are fused to make the color of the images more consistent. And a final corrected image is obtained.

The invention relates to the technical field of machine vision and optical detection, in particular to a tea color automatic evaluation method and system based on machine vision. The method comprises the following steps: constructing a stable and repeatable optical measurement environment and calculating radiation baseline parameters; performing multi-band polarization sequence imaging on the tea sample to obtain original data with spectrum and structural characteristics; separating mirror surface and diffuse reflection components on the surfaces of the tea leaves, and performing spectrum consistency correction by combining a radiation baseline; performing multiband spectrum reconstruction and normalization processing on the basis of the corrected diffuse reflection image, mapping to a CIE Lab color space, extracting pixel-level brightness, chromaticity and color distribution statistical indexes, and generating a color feature vector in combination with spatial weighting matrix optimization; a final score is calculated by constructing a quantitative evaluation and grade judgment model and integrating perception color separation, drying calibration color separation and a penalty term, and a grade result is output. According to the method, objective, accurate and automatic evaluation of the color of the tea leaves is realized.

The invention provides a new energy battery coating thickness control method, which comprises the following steps: presetting a chromatic value range of a coating color, segmenting a stable image into a coating area and a non-coating area, carrying out binarization processing on the segmented coating area to obtain a coating binarization image, according to the segmented coating binarization image, carrying out closed operation on a coating area, filling small holes in the coating area, and removing noisy points outside the coating area through open operation to obtain a coating area image after morphological processing; a motion model of color change is established according to historical focusing data, the color change trend of a next frame of image is predicted, a predicted value is used as a feedforward control signal of a focusing system, and oscillation of the focusing system is suppressed.

The invention belongs to the technical field of display screens, and mainly provides a model construction method, a display screen uniformity correction method, a display screen uniformity correction device and display screen uniformity correction equipment. Inputting the brightness information and the chromaticity information of the display area to be corrected into a pre-established display screen RGB gain prediction model to obtain an estimated RGB gain output by the display screen RGB gain prediction model, and performing brightness and chromaticity uniformity correction on the display area to be corrected based on the estimated RGB gain. The brightness and chromaticity uniformity correction of the to-be-corrected display area is realized by one key based on the brightness information and chromaticity information of the to-be-corrected display area and the pre-established display screen RGB gain prediction model, and the problems that a traditional display screen brightness and chromaticity uniformity correction method is low in correction efficiency and needs to consume relatively high manpower and time are solved; and frequent implementation is difficult.

The invention relates to the technical field of image detection, in particular to a boiler scale detection method and device based on a multi-modal image, and the method comprises the steps: carrying out the panoramic scanning of the inner wall of a boiler through a dual-spectrum imaging system at a detection temperature, and synchronously collecting a visible light image and an infrared thermal imaging image; based on the scale chromaticity difference value in the visible light image, scale types are judged, and a scale classification graph is generated; based on a standard infrared thermal imaging image, obtained at the detection temperature, of the scale-free boiler, the infrared thermal imaging image is converted into a temperature anomaly distribution diagram with temperature anomaly blocks; inputting the temperature anomaly distribution diagram into a deep neural network to output a scale thickness distribution diagram, and aligning the scale classification diagram with the scale thickness distribution diagram through a spatial registration technology to ensure that pixels at the same spatial position contain scale types and thickness values at the same time; and generating a scale feature fusion graph of scale types presenting different color areas and corresponding thicknesses, thereby realizing accurate detection and quantification of the boiler scale.

The invention provides a consistency correction method and device based on a multi-source satellite inversion water color index, and the method comprises the steps: converting the surface reflectance of a multi-source satellite sensor of a target water area into remote sensing reflectance, and carrying out the water body range extraction of the multi-source satellite sensor, so as to determine the water body range, carrying out multi-class interference pixel removal on the image of the surface reflectance in the water body range to obtain effective remote sensing reflectance; calculating a chromaticity angle inverted by the multi-source satellite sensor based on the effective remote sensing reflectivity and a CIE chromaticity system; performing spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on a polynomial spectral correction model constructed by the water optical simulation data set; performing system deviation correction on the chromaticity angle of the multi-source satellite sensor after spectral response correction by a cross calibration correction model established based on the synchronous observation data of the multi-source satellite sensor; and determining the water color index of the target water area based on the chromaticity angle after system deviation correction.

The invention discloses a vehicle-mounted HUD optical performance comprehensive detection method, and aims to solve the problems that a traditional detection method depends on manual operation and is low in detection efficiency and large in error. Project loading and test starting, picture height position calibration, multi-parameter test, brightness and chromaticity test, ghosting test, static distortion test and binocular parallax test items are automatically implemented through detection equipment, and accurate control (such as application of camera pixel-millimeter ratio) of advanced image recognition algorithm and equipment in the test process is carried out. The vehicle machine controls the step number of the curved mirror motor, and the industrial personal computer controls the motion module, so that high-precision measurement of optical parameters is realized, and the test efficiency and the test accuracy are improved.

The invention provides a machine learning-based OLED display screen image defect detection method, which comprises the following steps of: acquiring brightness distribution data and chromaticity distribution data of an OLED display screen through an image acquisition device, calculating a spatial difference between the brightness data and a standard template to obtain an abnormal degree index, determining a geometric center coordinate of a defect area, and calculating the abnormal degree index according to the abnormal degree index. Judging whether the neighborhood contains normal pixels or not; if not, a defect feature texture map in the brightness data is extracted through the convolutional neural network, and after the defect feature texture map is matched with a preset defect type, defect positioning information and a quality early warning instruction are generated and sent to the detection terminal. According to the invention, high-precision automatic detection and classification of image defects of the OLED display screen can be realized, and the accuracy and detection efficiency of defect identification are effectively improved.

The invention relates to the technical field of soil image processing, in particular to a vegetation detection method and system for soil environment restoration. According to the method, color features and position distribution features of pixel points are analyzed, a vegetation coverage factor of each superpixel block is obtained, and vegetation coverage blocks are screened out; according to the position distribution of the pixel points in each vegetation coverage block and the chromaticity fluctuation characteristics, the artificial cultivation characteristic degree of each vegetation coverage block is obtained; according to the centroid position and the vegetation coverage factor of each superpixel block, obtaining a vegetation coverage class cluster and a non-vegetation coverage class cluster; and in combination with the relative position between each vegetation coverage block in each non-vegetation coverage class cluster and each vegetation coverage block in the adjacent vegetation coverage class cluster, obtaining the soil remediation vegetation significance of each super-pixel block, and further obtaining the soil remediation weighted significance of each super-pixel block. According to the method, the vegetation characteristics in the soil environment restoration process are accurately analyzed, so that the accuracy of soil environment restoration assessment is improved.

The invention discloses a method for dynamically monitoring the drying quality of fruits and vegetables by fusing chromatic aberration-shrinkage rate analysis. The method comprises the following steps: placing a fruit and vegetable sample on a rotatable objective table, drying according to a preset program, and carrying out multi-angle shooting by using a digital camera according to a timestamp to obtain an RGB color image; the method comprises the following steps: preprocessing by an image analysis system, converting an image into a Lab chromaticity space, calculating a chromatic aberration value delta E and a comprehensive shrinkage rate S, integrating data to generate a change curve, and constructing an appearance form model. In actual drying, real-time data and the model are compared, and early warning is carried out when the color difference value exceeds 3.5 or the shrinkage rate deviation exceeds 15% and image acquisition is abnormal. The drying equipment is provided with a high-precision electric rotary objective table, a specific annular light source array, a high-resolution camera and a standard calibration color card, and illumination and color measurement accuracy is ensured through calibration. Through multi-step operation, accurate and dynamic monitoring of fruit and vegetable drying quality is achieved, and drying process optimization is assisted.

The invention provides an exhaled gas alcohol content intelligent verification system based on image recognition, relates to the technical field of intelligent detection, and aims to solve the problems that a traditional sensor is prone to environmental interference, needs frequent calibration and is insufficient in dynamic response. Comprising a gas-sensitive developing unit, a multispectral dynamic imaging unit, a data processing unit and a closed-loop control unit, the gas-sensitive color development unit adopts a reversible gas-sensitive film and generates a color change signal through an alcohol specific color development reaction; the multispectral dynamic imaging unit is combined with an ambient light suppression and dynamic region positioning technology to capture spectral reflectivity time sequence data in a color development process; the data processing unit maps the chromaticity characteristic into an alcohol concentration value based on a Gaussian process regression model, and triggers near-infrared auxiliary verification through confidence evaluation; and the closed-loop control unit maintains a constant detection environment through the temperature control and airflow stabilization module. According to the scheme, non-intrusive high-precision detection of the alcohol concentration in a complex environment can be realized.

An end-to-end color imaging system chromaticity characterization method based on hyperspectral calibration belongs to the field of color imaging, color display and imaging chromaticity measurement, and comprises the following steps: firstly, acquiring or calling hyperspectral cube data, and measuring red, green and blue three-channel spectral sensitivity curves and calibration coefficients of imaging equipment; fixed weight linear mapping is adopted, a pixel-level RGB image and a corresponding XYZ tristimulus value image are generated, and paired calibration samples are formed. And an end-to-end deep network is constructed to map any RGB image into an XYZ image. The method has good prediction precision and robustness, and can be widely applied to scene chromaticity measurement, color imaging color management, display calibration, camera color recovery and other chromaticity consistency demand scenes.

The invention relates to the field of image processing, and discloses a figure image optimization method based on green screen image matting, which is used for carrying out optimization processing on a figure image. The figure image optimization method based on green screen image matting comprises the steps of performing fractional order differential transformation and other processing on an input green screen figure image, constructing a continuous chromaticity representation space, generating a chromaticity gradient field and multi-resolution chromaticity representation, establishing a geometric topology constraint model according to chromaticity information, generating an initial transparency field, and performing collaborative optimization. Optimizing a boundary curve, generating an accurate boundary contour, establishing a color mixing model, realizing natural transition between a foreground and a background, generating an optimized image, performing subsequent processing such as material optical attribute analysis and the like, and finally outputting an image completely fused with a background environment. According to the method, the foreground can be accurately separated, the edge can be optimized, natural transition and material simulation can be realized, and finally a high-quality figure image which is seamlessly fused with the background and has a physical reality sense is generated.

The present invention relates to an AR diffractive optical waveguide rainbow pattern testing device, which includes a Z-axis hollow rotary table, an A-axis rotary displacement table, a hollow waveguide carrier stage, an imaging brightness and chromaticity meter, and an optical generation module. By simulating the usage scenario of the diffractive optical waveguide in a hemispherical space, the present invention acquires and analyzes multi-dimensional information such as the brightness and chromaticity, azimuth angle, solid angle, and position of the rainbow pattern, and has the ability of calibration, achieving the effects of multi-dimensional and high-precision detection and analysis of the rainbow pattern and improving the repeated measurement accuracy.

The invention discloses a brightness and chrominance consistency correction method and system for an LED spliced screen, and the method comprises the steps: firstly recognizing the batch features of a module, dividing the batch features into correction partitions, and respectively collecting the original brightness and chrominance data of a high-gray linear region and a low-gray nonlinear region; and then, calculating the geometric intersection of the original color gamuts of all the partitions to determine the maximum common target color gamut, and eliminating the metamerism phenomenon caused by multi-batch mixed use from the physical level. Then, constructing a non-linear dual-order correction model, and generating a correction coefficient set containing a main linear conversion matrix and a low-gray-scale non-linear offset item; when the control system runs, the linear matrix is dynamically called according to the input gray level, and bias compensation is superposed. According to the invention, the problems of cross-batch visual angle chromatic aberration and color temperature drift and pocking marks under low gray level of the spliced screen are effectively solved, and the high-fidelity display consistency of the full gray level is realized.

The invention provides a low-gray-scale image imaging defect visual detection method and system for an OLED display screen. The method comprises the following steps: aiming at original image data of a display screen, extracting a gray scale picture with a pixel brightness mean value lower than a preset brightness threshold value as a first image, carrying out color characteristic analysis on the first image, and generating a second image marked with a chromaticity feature vector; identifying the chromaticity feature vector according to the second image, generating a feature vector matrix for representing chromaticity distribution of each pixel in the second image, and performing color jump analysis through the feature vector matrix to obtain a third image; extracting a jump region in the third image, and setting marks for distinguishing different dispersion distribution for chromaticity distribution dispersion distribution according to the jump region to obtain a fourth image; and identifying dispersion distribution according to the fourth image, combining with a chromaticity abnormal pixel region in the hopping region, performing pixel classification in the hopping region, and setting defect marks for pixels with imaging defects represented by a classification result to obtain a fifth image.

The invention discloses a multi-channel multi-color-temperature intelligent light source system for consistency calibration of a camera module and an optimization method, and the calibration system generates PWM signals through an STM32F103ZET6 main control unit of a light source controller to control the light output of each group of cold and warm LEDs so as to provide a stable and adjustable illumination environment; measuring reference brightness and chromaticity parameters of cold light and warm light of each group of LEDs, setting a target brightness value and a color temperature value, and converting the target brightness value and the color temperature value into target chromaticity coordinates; the initial PWM duty ratio of the cold and warm light LED is calculated, a piecewise polynomial nonlinear compensation algorithm is adopted to correct the duty ratio, and the LED is driven to output mixed light meeting the target brightness and color temperature; collecting a large amount of brightness and color temperature data by cyclically and gradually adjusting the duty ratio, and generating an error thermodynamic diagram; and the compensation model coefficient is optimized and updated according to error analysis, so that the light source output keeps high-precision consistency in a full range. Compared with the prior art, the method has the advantages that the brightness and color temperature consistency of the light source can be automatically calibrated and continuously optimized, the accuracy and stability of OTP burning calibration of the camera module are greatly improved, and the production test efficiency and the product imaging quality are improved.

The invention discloses a low-light image color restoration method and system based on multi-scale attention decomposition and adaptive chromaticity correction, and belongs to the technical field of image processing. The method comprises the following steps: extracting brightness information of a low-illumination image and performing exposure compensation to generate a reference image; inputting and constructing the low-illumination image and the reference image through a scale attention decomposition network to obtain a low-light reflection image, a low-light illumination image, a reference reflection image and a reference illumination image; inputting the obtained low-light reflection image into a self-adaptive chromaticity correction module to adjust the chromaticity to obtain an enhanced reflection image; obtaining an enhanced image through the low-light illumination image and the enhanced reflection image; and performing element-by-element multiplication on the low-illumination image and the enhanced image to obtain a final enhanced image. According to the method, image reflection and illumination information are separated, and balance between image details and the overall illumination effect is ensured through joint optimization training, so that the finally enhanced image is more natural and clear visually.

The invention relates to the technical field of image data processing, in particular to an injection molding part coating quality detection method and system based on machine vision. The method comprises the following steps: determining pixel points of a deep bright printing area and edge pixel points of the deep bright printing area according to a difference between a gray value of a pixel point in a surface gray image and a maximum gray value and a gray difference mean value between the pixel point and neighborhoods with different scales; according to the probability that the remaining pixel points are in the deep bright printing area, the difference between the brightness value and the standard value and the chromaticity distance between the remaining pixel points and the remaining pixel points corresponding to the standard value, the pixel points in the light bright printing area are obtained; the deep bright printing area pixel points and the corresponding edge pixel points in the surface image of the injection molding part, and the light bright printing area pixel points and the corresponding edge pixel points in the surface image of the injection molding part are trained after being enhanced, so that the coating quality detection of the injection molding part is realized, and the accuracy and the efficiency of the coating quality detection of the injection molding part are effectively improved.

The invention discloses a display device and a manufacturing method thereof. The display device comprises a display panel and a lens structure. The display panel comprises a substrate and a plurality of sub-pixels located on the substrate. The lens structure is located on the light emitting side of the display panel, and the lens surface, close to the display panel, of the lens structure is a curved surface. The display panel comprises a first area and a second area surrounding the first area, and the included angle between the light-emitting face of the sub-pixel located in the first area and the optical axis of the lens structure is larger than the included angle between the light-emitting face of the sub-pixel located in the second area and the optical axis. According to the display device provided by the invention, by adjusting the included angles between the light-emitting surfaces of the sub-pixels in different areas in the display panel and the optical axis of the lens structure, the main light emitting angle of the sub-pixels in the display panel can be matched with the main light emitting angle of the lens structure; the optical imaging design freedom degree is increased, the display image quality is improved, the display brightness or chromaticity uniformity is improved, stray light is reduced, and the contrast ratio is improved.

The invention discloses a partial discharge detection method and device based on discharge spectrum chromaticity, and belongs to the technical field of power systems, and the method comprises the steps: collecting a plurality of discharge optical signals of switch cabinet equipment; for each discharge optical signal, carrying out chromaticity analysis on the RGB monochromatic light signal, and respectively calculating color chaos degree, spectrum color gamut deviation degree, spectrum chromaticity dynamic coupling parameters and hue purity ratio; calculating a chromaticity difference weight based on the full-band optical signal and the RGB monochromatic light signal; performing weighted integration on the color chaos degree, the spectrum color gamut deviation degree, the spectrum chromaticity dynamic coupling parameter and the hue purity ratio based on the chromaticity difference weight to obtain a chromaticity comprehensive magnitude; calculating the mean value of the comprehensive chromaticity values of the plurality of discharge optical signals; and comparing the chromaticity comprehensive magnitude mean value with the plurality of judgment chromaticity ranges, and determining a discharge type detection result of the switch cabinet equipment. According to the invention, the problem of low accuracy of partial discharge detection in the prior art can be solved.

The invention provides a collectable walnut pairing method and device. The method comprises the following steps: classifying a plurality of collection walnuts based on a classification algorithm to obtain a first set; marking the amusement walnuts in the first set as first amusement walnuts; acquiring a first image of the target collection walnut, acquiring a second image of the first collection walnut, and acquiring texture complexity, contour variation and chromaticity dispersion of the second image; calculating texture similarity, color similarity and boundary similarity based on the first image and the second image; determining a texture similarity weight, a boundary similarity weight and a color similarity weight based on the texture complexity, the contour variability and the chromaticity dispersion; and based on the texture similarity, the texture similarity weight, the boundary similarity, the boundary similarity weight, the color similarity and the color similarity weight, determining a first collection walnut most similar to the target collection walnut. According to the invention, high matching between walnuts can be realized more accurately in a complex environment.

The invention relates to the technical field of industrial circuit board abnormity monitoring, and discloses an industrial circuit board abnormity monitoring method based on visual inspection, which comprises the following steps of: 1, acquiring image data and performance test data of all circuit boards, and classifying the image data and the performance test data to form an image data set and a performance data set; 2, evaluating the appearance defect of each circuit board according to the image data set, generating a corresponding bridging area proportion, chromaticity deviation and pin deviation, and constructing a three-dimensional defect scoring matrix by comprehensively applying technologies such as image processing, color analysis and geometric measurement; 3, according to the performance data set, evaluating the functional defect of each circuit board, generating a corresponding functional data set, and realizing high multi-dimensional monitoring precision; and 4, according to the image data set, the bridging area proportion, the chromaticity deviation, the pin deviation and the function data set, the defect type of each circuit board is divided into a connection defect, a scratch defect and an electrical defect, corresponding control instructions are output, and the intelligent repairing efficiency is high.

An image enhancement method, a vehicle, a computer device, a program product and a storage medium are characterized in that the method comprises the steps that an image of a YUV domain is obtained according to an original image of an RGB domain, and the image of the YUV domain comprises an original brightness diagram and an original chromaticity diagram; obtaining illumination information and relative brightness information of the original brightness map according to the original brightness map; obtaining an adjusted brightness map according to the illumination information, the relative brightness information and an original brightness map; obtaining an adjusted chromaticity diagram according to the illumination information, the relative brightness information and the original chromaticity diagram; and obtaining an adjusted RGB domain image according to the adjusted brightness diagram and chromaticity diagram. Through the method, the image quality can be improved to a great extent, and a better image enhancement effect is achieved.

The lighting device may be configured to perform black body curve fading. For example, the control circuit may be configured to control the drive circuit such that the light emitted by the lighting load is adjusted (e.g., faded) along a black body curve. The control circuit may be configured to determine whether to fade from an initial color to a destination color in a Correlated Color Temperature (CCT) chromaticity space or an XY chromaticity space. The control circuit may be configured to determine whether the initial color and / or the destination color are on the black body curve. When the initial color and the destination color are determined to be on the black body curve, the control circuit may be configured to control the drive circuit such that the light emitted by the lighting device is adjusted from the initial color to the destination color along the black body curve.

The invention provides a laser interference processing optimization method and system for improving the performance of a micro-nano structure, and relates to the technical field of laser micro-nano machining.The method comprises the steps that firstly, micro-nano structure sample processing parameters are extracted, a sensitive tracking model is constructed to analyze parameter sensitivity, and a lifting-retaining parameter set is obtained; according to the simulation modeling, processing and lifting a dynamic model diagram, analyzing the period consistency of the dynamic model diagram and a reference design diagram through foreground and background maximum flow cutting, and determining a structure period; if the local photoetching model diagram is of a periodic structure, projecting the actual local photoetching model diagram to a local photoetching model diagram of one type in a sine manner, analyzing the curve slope, and optimizing a periodic interference angle and a light beam wavelength; and if the structure is a non-periodic structure, interpolating a gradient processing chromaticity layout for the second-class local photoetching model diagram according to a chromaticity tradeoff criterion, and analyzing and optimizing a dynamic phase in real time. According to the method, high-precision optimization of the processing parameters of the micro-nano structure with consistency in different periods is realized, and the overall performance and stability of the micro-nano structure are effectively improved.

The present invention discloses an intelligent color deviation compensation method and system for a liquid crystal display screen, which relates to the technical field of color deviation compensation. The technical key points of this solution are as follows: Through the temperature distribution map of the liquid crystal display screen, the SLIC superpixel segmentation algorithm is used to divide the liquid crystal display screen into n pixel blocks. By the average brightness value and the target brightness value of the pixel blocks, the brightness correction coefficient of each pixel block is calculated. Through the pixel values of the R, G, and B channels of each pixel in the pixel block, the weights of the correction coefficients of the R, G, and B channels of each pixel block are obtained, and the chromaticity correction coefficients of the R, G, and B channels of each pixel block are calculated; The brightness correction coefficient of the pixel block and the chromaticity correction coefficients of the R, G, and B channels are fused to calculate the color deviation correction coefficient of each pixel block, and color deviation correction is performed on the pixel block. The color deviation is locally controlled through the temperature distribution of the liquid crystal display screen, so as to more accurately eliminate the color deviation and improve the accuracy of color correction.

The invention discloses an intelligent monitoring system for camellia oleifera processing based on artificial intelligence, and relates to the technical field of camellia oleifera fruit processing, and the system comprises a temperature monitoring module which comprises a multi-point temperature sensing array which is arranged along the axial direction of a presser and is installed at the front end, the middle part and an oil outlet of a barrel, the temperature sensor is used for collecting temperature and forming temperature distribution data in the barrel; the color detection module is arranged at an outlet of the oil outlet pipeline, obtains oil chromaticity parameters and a yellow-red ratio through spectral imaging, and extracts color feature data; and the volatile matter detection module is arranged above the discharged oil collection cavity and is used for detecting the concentration of volatile organic compounds through a gas sensing array and generating flavor characteristic data. By arranging the temperature monitoring module, the color detection module and the volatile matter detection module, real-time collection of multi-dimensional information in the squeezing process is achieved, and based on an asynchronous triggering and weighted fusion mechanism, space-time alignment and conjoint analysis are conducted, a joint feature vector is constructed, and a real-time prediction result is output.

The invention belongs to the technical field of LED driving, and particularly relates to a software compensation color correction method for LED lamp beads. By means of a light splitting test system in a lamp bead packaging link, key parameter values such as the position and the wavelength of a light-emitting wafer of each lamp bead and a lamp bead collection temperature value are comprehensively recorded, optical correction values are measured and calculated, and the optical correction values are stored in registers in the lamp beads; the application device is powered on to output the target brightness value, and the lamp bead calls the optical correction value in the register to achieve accurate control over the color of the lamp bead. The method provided by the invention thoroughly breaks through the limitation of a traditional Bin scheme, 96% of brightness / chromaticity deviation can be eliminated, lamp beads of different batches under different working conditions have completely consistent optical characteristics, and the method is particularly suitable for scenes such as Micro-LED display, medical endoscopes and the like which have extremely strict requirements on color precision.