Online estimation method and device for density of cut tobacco in cut tobacco dryer and storage medium

Abstract

The invention discloses an online estimation method and device for the density of cut tobacco in a cut tobacco dryer and a storage medium, and belongs to the technical field of tobacco detection. The method comprises the following steps: acquiring visible light and infrared images acquired by a multispectral sensor system; extracting brightness information of the visible light image, and inputting obtained tobacco shred brightness data into a brightness-density mapping model to obtain first tobacco shred density; on the basis of an optical flow algorithm, tobacco shred motion state calculation is conducted on the visible light image, primary density correction is conducted on the first tobacco shred density in combination with obtained tobacco shred flow rate data, and second tobacco shred density is obtained; and calculating the tobacco shred surface temperature of the infrared image, and performing secondary density correction on the second tobacco shred density in combination with the obtained tobacco shred temperature data to obtain the final target tobacco shred density. According to the method, the flow velocity, the temperature and the brightness data are combined, so that the density of the tobacco shreds is accurately estimated in real time, and reliable data support is provided for automatic regulation and control of the tobacco shred drying process.

Description

Technical Field

[0001] The invention relates to an online estimation method, device and storage medium for density of shredded tobacco in a tobacco shred drying machine, belonging to the technical field of tobacco detection. Background Art

[0002] Tobacco shreds drying is one of the key processes in tobacco processing. Its purpose is to optimize the aroma, combustibility and taste of tobacco by controlling the temperature and airflow to make the tobacco shreds reach an appropriate drying state. In the process of shreds drying, the density of the tobacco shreds is an important factor affecting the drying effect. Uneven density distribution will lead to uneven heating during the drying process, which will affect the final quality of the tobacco. Since the traditional shreds drying control method relies on fixed parameter settings and simple temperature control, it is difficult for this method to cope with the dynamic changes of the tobacco shreds during the drying process, which leads to the instability of the drying effect. Therefore, in order to further improve the drying effect, it is very important to monitor the density changes of the tobacco shreds online in real time and give targeted dynamic feedback control.

[0003] In the existing technology, the online detection technology for tobacco density is relatively backward, and it is difficult to achieve real-time and accurate density estimation. Traditional density measurement methods mostly rely on offline sampling and laboratory analysis, which cannot meet the demand for real-time data feedback in modern tobacco processing. The most critical thing is that since the density change is not only affected by the physical properties of the tobacco itself, but also closely related to factors such as flow rate and temperature, in other words, a single measurement method often ignores the influencing factors such as flow rate and temperature of the tobacco during the drying process, resulting in insufficient accuracy of density estimation, and thus it is difficult to form effective control feedback. Summary of the invention

[0004] The purpose of the present invention is to provide an online estimation method, device and storage medium for the density of shredded tobacco in a tobacco drying machine. By adopting an online multi-spectral sensor and integrating multi-parameter correction technology, the real-time and accuracy of online monitoring of shredded tobacco density are significantly improved, thereby providing reliable data support for the automated control of the shredded tobacco drying process.

[0005] In order to achieve the above objectives / solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0006] In one aspect, the present invention provides an online estimation method for density of shredded tobacco in a tobacco drying machine, comprising: Acquire visible light images and infrared images collected by the multispectral sensor system; Extracting brightness information from the visible light image, and inputting the corresponding tobacco shred brightness value into a brightness-density mapping model to obtain a first tobacco shred density; Based on the optical flow algorithm, the movement state of the tobacco shreds is calculated for the visible light image, and a density correction is performed on the first tobacco shreds density in combination with the corresponding obtained tobacco shreds flow rate value to obtain a second tobacco shreds density; The surface temperature of the tobacco shreds is calculated for the infrared image, and a secondary density correction is performed on the density of the second tobacco shreds in combination with the corresponding acquired tobacco shreds temperature value to obtain a final target tobacco shreds density.

[0007] The online estimation method of tobacco shred density in a tobacco shred drying machine provided by the present invention realizes accurate estimation of tobacco shred density by combining multispectral data of visible light images and infrared images. The method not only considers the brightness information of tobacco shreds, but also considers the movement state and surface temperature of tobacco shreds, thereby improving the accuracy and reliability of estimation. This online estimation method is of great significance for quality control and efficiency improvement in the tobacco production process.

[0008] Optionally, extracting brightness information from the visible light image to obtain a brightness value of the tobacco shreds includes: Perform grayscale conversion on the collected visible light image to obtain a corresponding grayscale image; By manual selection or automatic segmentation method, the region containing the tobacco in the grayscale image is determined as the region of interest; The pixel values ​​in the region of interest are counted to obtain the tobacco brightness value, which may be an average value, a median value, a maximum value or a minimum value of the pixel values ​​in the region of interest.

[0009] Optionally, the collected visible light image is converted into grayscale to obtain a corresponding grayscale image, which is expressed as: ; In the formula, , and Represent the pixel values ​​of the red, green and blue channels respectively, Represents the brightness value of a pixel in a grayscale image.

[0010] Optionally, the obtained tobacco shred brightness value is input into a brightness-density mapping model to obtain a first tobacco shred density, which is expressed as: ; In the formula, Indicated in coordinates and time The first tobacco density at represents the brightness value at the corresponding position and time, represents the brightness-density function obtained by experimental fitting.

[0011] Optionally, based on an optical flow algorithm, the movement state of the tobacco is calculated for the visible light image to obtain a tobacco flow rate value, including: Perform grayscale conversion on the collected visible light image sequence to obtain a corresponding grayscale image; Extract key points from the grayscale image using a feature point detection algorithm; Based on the Lucas-Kanade optical flow algorithm, the extracted key points are tracked by optical flow, and the displacement vectors of the feature points between adjacent frames are calculated; By iterative solution, the optical flow velocity vector corresponding to each key point is obtained; The tobacco flow velocity value on the image plane is calculated based on the optical flow velocity vector.

[0012] Optionally, the tobacco flow velocity value on the image plane is calculated according to the optical flow velocity vector, which is expressed as: ; In the formula, Indicates the tobacco flow rate value, Indicates that the pixel is The speed of movement on the axis, Indicates that the pixel is The speed of movement on the axis.

[0013] Optionally, according to a density-flow rate correction model, a density correction is performed on the first shredded tobacco density by using the shredded tobacco flow rate value to obtain a second shredded tobacco density, which is expressed as: ; In the formula, It indicates the second tobacco density after the primary density correction. Indicates the flow rate correction factor.

[0014] Optionally, according to Planck's radiation law, the surface temperature of the tobacco is calculated for the infrared image in combination with the wavelength band of the infrared sensor to obtain a tobacco temperature value, wherein the relationship between the tobacco temperature value and the infrared radiation intensity is as follows: ; In the formula, Indicates wavelength The corresponding infrared radiation intensity is: Indicates the tobacco temperature value. is Planck's constant, represents the speed of light, represents the Boltzmann constant.

[0015] Optionally, according to the temperature correction model, a secondary density correction is performed on the second tobacco shred density by using the tobacco shred temperature value to obtain a final target tobacco shred density, which is expressed as: ; In the formula, It indicates the target tobacco density after secondary density correction. represents the temperature correction factor, Indicates the tobacco temperature value detected by the infrared sensor. Indicates the reference temperature.

[0016] Optionally, the visible light image and the infrared image are simultaneously captured through a synchronization trigger, so that each pair of visible light images and infrared images has the same time stamp.

[0017] In a second aspect, the present invention further provides an online estimation device for the density of shredded tobacco in a tobacco shred drying machine, comprising: An image acquisition module is configured to acquire visible light images and infrared images collected by the multispectral sensor system; a first tobacco shred density determination module configured to extract brightness information from the visible light image and input the corresponding tobacco shred brightness value obtained into a brightness-density mapping model to obtain a first tobacco shred density; a second tobacco shred density determination module configured to calculate the tobacco shred motion state of the visible light image based on an optical flow algorithm, and perform a density correction on the first tobacco shred density in combination with a corresponding tobacco shred flow rate value to obtain a second tobacco shred density; And, the target tobacco density determination module is configured to calculate the tobacco surface temperature of the infrared image, and perform secondary density correction on the second tobacco density in combination with the corresponding tobacco temperature value obtained to obtain the final target tobacco density.

[0018] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for online estimation of tobacco density in a tobacco drying machine as described in the first aspect.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention integrates multi-source data (visible light image, infrared temperature data) and advanced algorithms (brightness-density mapping, optical flow algorithm, temperature correction model) to achieve real-time and accurate estimation of tobacco density. First, by processing tobacco brightness data, the present invention combines the brightness-density mapping relationship established by the experiment to accurately convert the image brightness value collected by the visible light sensor into the initial tobacco density. This process provides a solid foundation for density estimation and significantly improves the accuracy of estimation. Secondly, considering the influence of tobacco flow rate on density, the present invention also uses a density-flow rate correction model to finely adjust the preliminary estimated density. This process ensures that the density estimation result is highly consistent with the actual tobacco state, further improving the accuracy of estimation. In addition, in order to further ensure the accuracy and stability of density estimation, the present invention also combines infrared temperature sensor data to further optimize the density estimation result through a temperature correction model. In summary, the method proposed in the present invention not only overcomes the defects of poor real-time performance and insufficient precision of traditional methods, but also brings a more intelligent and accurate control strategy to the modern tobacco processing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Shown is a flow chart of an embodiment of an online estimation method for shredded tobacco density in a tobacco shred drying machine according to the present invention. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0022] The term "and / or" is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the associated objects are in an "or" relationship.

[0023] Example 1 like Figure 1 As shown, this embodiment introduces an online estimation method for the density of shredded tobacco in a tobacco shred drying machine, which includes: Acquire visible light images and infrared images collected by the multispectral sensor system; Extracting brightness information from the visible light image, and inputting the corresponding tobacco shred brightness value into a brightness-density mapping model to obtain a first tobacco shred density; Based on the optical flow algorithm, the movement state of the tobacco shreds is calculated for the visible light image, and a density correction is performed on the first tobacco shreds density in combination with the corresponding obtained tobacco shreds flow rate value to obtain a second tobacco shreds density; The surface temperature of the tobacco shreds is calculated for the infrared image, and a secondary density correction is performed on the density of the second tobacco shreds in combination with the corresponding acquired tobacco shreds temperature value to obtain a final target tobacco shreds density.

[0024] In practical application, the present embodiment first uses a multispectral sensor system to simultaneously collect visible light images and infrared images in a tobacco drying machine. These two images provide information on the optical and thermal properties of tobacco, respectively, and provide basic data for subsequent processing. Secondly, brightness information is extracted from the visible light image, and the extracted brightness value is input into a pre-established brightness-density mapping model. This model is trained based on experimental data or historical data, and can convert the brightness value into a corresponding tobacco density value, thereby obtaining a first tobacco density estimation result. Thirdly, the optical flow algorithm is applied to process the visible light image to calculate the motion state of the tobacco. Combined with the calculated tobacco flow rate value, the first tobacco density is corrected once. This step considers the influence of the dynamic change of tobacco on the density measurement, and improves the accuracy of the estimation. Finally, the infrared image is processed to calculate the surface temperature of the tobacco. Combined with the calculated tobacco temperature value, the second tobacco density after the first correction is corrected twice. This step further considers the influence of the thermal state of tobacco on the density, thereby obtaining a more accurate target tobacco density.

[0025] In general, this embodiment achieves online accurate estimation of tobacco density in a tobacco drying machine by combining multispectral information of visible light images and infrared images, as well as optical flow algorithm and temperature calculation. This method not only improves the accuracy of density measurement, but also provides strong support for quality control in the tobacco processing process.

[0026] Example 2 On the basis of Example 1, this example introduces the specific implementation process of online estimation of tobacco density in a tobacco drying machine in combination with corresponding brightness data, flow rate data and temperature data. The relevant steps are described as follows.

[0027] 1. Acquisition and preprocessing of visible light images and infrared images The visible light image and infrared image are obtained by the visible light sensor and infrared sensor in the multispectral sensor system respectively. Among them, the visible light sensor is used to collect the flow form and movement trajectory information of the tobacco, and the infrared sensor is used to measure the temperature information of the tobacco and the cavity.

[0028] The visible light sensor usually uses a camera with a wavelength of 400-700nm to regularly collect images of tobacco in the drying chamber. The visible light sensor collects each frame of image regularly through an internal or external clock to generate an image sequence. Each frame of image can be represented as a matrix ,in, and represents the pixel coordinates of the image, Represents the time dimension.

[0029] The infrared camera corresponding to the infrared sensor uses dual bands of 3-5μm and 8-14μm for acquisition, which are used to correspond to the two temperature regions of high temperature environment (such as within 550℃) and low temperature environment (such as 20℃ to 100℃). These corresponding bands provide sufficient sensitivity to monitor tobacco and cavity surfaces with a wide temperature range.

[0030] In addition, according to the speed of tobacco movement, the frame rate of visible light sensor and infrared sensor also needs to be adjusted dynamically. Generally speaking, the visible light sensor needs a higher frame rate (such as 120 frames per second) to capture the details of high-speed moving tobacco; while the frame rate of infrared sensor can be lower (30-60 frames per second) for temperature monitoring.

[0031] It is worth noting that in order to ensure that the visible light image and the infrared light image are captured at the same time, this embodiment is also equipped with a synchronization trigger in the actual application process to control the two sensors to collect images at the same time. By using the timestamp technology, it is ensured that each pair of visible light images and infrared images can have the same timestamp, so as to facilitate the subsequent fusion and data processing of related images.

[0032] Furthermore, this embodiment also performs a series of preprocessing operations on the acquired image, as described in detail below.

[0033] Image alignment and registration: Due to the different optical properties of visible light sensors and infrared sensors, there may be differences in field of view and resolution. Through image registration technology, visible light images and infrared images can be geometrically aligned to ensure that the two data can accurately correspond to the same spatial position.

[0034] Extraction and matching of feature points: Select multiple feature points (such as the edges or key nodes of tobacco) in visible light images and infrared images, and align them using image registration algorithms (such as SIFT, ORB, etc.) to ensure that the spatial positions of the images are consistent.

[0035] Image filtering and enhancement: The collected images are filtered to reduce the impact of noise and enhance the image contrast. Specifically, for visible light images, due to the complex internal environment of the device, the image may be affected by noise. Image filtering techniques (such as Gaussian filtering or median filtering) can be used to reduce the interference of noise on brightness data to achieve denoising. Image enhancement techniques (such as histogram equalization) can be used to improve the image contrast to make the brightness information more obvious, or edge detection can be used to enhance the motion contour of tobacco to achieve image enhancement. To correct the brightness deviation caused by the sensor position or illumination changes, the sensor's automatic white balance and automatic brightness adjustment functions can be used to ensure the accuracy of the image brightness and achieve image correction. Pseudo-color processing is performed on infrared images to convert temperature information into visual images. At the same time, non-uniformity correction (NUC) is used to improve the quality of infrared images to effectively reduce the error of temperature measurement.

[0036] 2. Extraction of brightness information and determination of initial tobacco density First of all, it is worth explaining that since brightness information is closely related to density estimation, the collected visible light images are usually converted into grayscale images, that is, each collected image will be processed accordingly to generate a two-dimensional grayscale image, and the brightness information is stored in each pixel of the image accordingly. The pixel value range of a grayscale image is usually 0-255, indicating the grayscale level from black to white. In actual application, the grayscale image can be corrected according to actual needs through linear transformation, nonlinear transformation and other methods to ensure the accuracy of the grayscale value.

[0037] For color images, this can be done by Convert the pixel values ​​of the three channels to grayscale values , the conversion formula is as follows: ; In the formula, , and Represent the pixel values ​​of the red, green and blue channels respectively, Represents the brightness value of a pixel in a grayscale image.

[0038] The preprocessed grayscale image contains the brightness information of each pixel. The process of extracting the brightness value of tobacco includes the following steps: Pixel value reading: For each image at each moment, read the brightness value of each pixel. These brightness values ​​represent the light reflection intensity of the tobacco at that position, which is related to the density of the tobacco.

[0039] Regional average of brightness value: In order to reduce the impact of local noise, it is necessary to calculate the brightness average of the local area for each frame of the image. Specifically, the brightness in a small area (for example, 3×3 pixels) can be averaged to obtain a smoother brightness distribution map.

[0040] Visible light image brightness data :Get the image of tobacco from the visible light sensor and extract the brightness information of each pixel through the image processing algorithm. Specifically, in the grayscale image, use manual selection, automatic segmentation and other methods to select the area containing tobacco as the region of interest (ROI); count the pixel values ​​in the ROI and calculate the brightness value, which can be the average, median, maximum or minimum value of the pixel values ​​in the ROI; if necessary, the calculated brightness value can be corrected using calibration curves, calibration tables and other methods to eliminate the influence of ambient light, sensor characteristics and other factors on the brightness value. Tobacco brightness data is the basis for tobacco density estimation.

[0041] Based on tobacco brightness data , using the brightness-density mapping model to convert the brightness value into the initial density value of the tobacco , which is the first tobacco density. The brightness-density mapping model is obtained through experimental fitting, specifically: ; In the formula, Indicated in coordinates and time The first tobacco density at represents the brightness value at the corresponding position and time, represents the brightness-density function obtained by experimental fitting.

[0042] The relationship between brightness and density is established by polynomial fitting. and density The relationship between them is: ; In the formula, , ,…, represents the polynomial coefficients obtained by fitting the experimental data, Indicates the polynomial order of the fitting. In practical applications, the corresponding model coefficients can be determined by performing least square fitting on the brightness and density data collected in the experiment.

[0043] In addition, if the relationship between brightness and density is more complex, machine learning methods can be used for mapping modeling. Specifically, by collecting a large amount of brightness-density pairing data, supervised learning methods are used to train models, such as neural network models or support vector regression models, to better predict tobacco density.

[0044] 3. Acquisition of flow rate data and primary density correction of tobacco density First, a sequence of visible light images should be obtained. These images should contain the motion information of the tobacco. The images are grayed to simplify the calculation process. In the preprocessed images, feature point detection algorithms (such as Shi-Tomasi corner detector, etc.) are used to extract key points for optical flow tracking. These key points are usually points with large changes in grayscale values ​​in the image, such as edges, corners, etc. Select a suitable optical flow algorithm, perform optical flow tracking on the extracted feature points, and calculate the displacement vector of the feature points between adjacent frames. Through iterative solution, the optical flow velocity vector of each feature point is obtained. . The flow velocity of the tobacco on the image plane is calculated based on the optical flow velocity vector of the feature point. This usually requires averaging or weighted averaging the flow velocities of multiple feature points to obtain a more accurate flow velocity value. It should be noted that since the tobacco in the image may present different shapes and motion states, it is necessary to calculate and analyze the tobacco in different areas separately.

[0045] Specifically, the calculation method of the tobacco flow rate in this embodiment is mainly based on the optical flow method (Optical Flow), which is a calculation method for estimating the movement speed of an object by analyzing the pixel movement in continuous image frames. The relevant mathematical model and specific implementation steps are described as follows.

[0046] The optical flow method estimates the speed of an object in an image by analyzing the brightness changes caused by the object's movement in the image. The pixel brightness value is ,in, Represents pixel coordinates, assuming that the object is at time A displacement has occurred, moving to a new position , then the brightness value It also changes accordingly.

[0047] According to the basic assumption of optical flow, if the brightness of an object remains unchanged in a short period of time, the corresponding expression can be shown as follows: ; Furthermore, by performing Taylor expansion on the above expression and ignoring the higher-order terms, the following optical flow constraint equation can be obtained: ; In the formula, , indicating that the pixel is The velocity component in the direction, , indicating that the pixel is The velocity component in the direction, and Respectively represent the images in and Directional brightness gradient, Indicates the rate of change of image brightness over time.

[0048] It can be noted that the above equation contains two unknowns, namely the velocity components and , but there is only one equation, so additional constraints need to be introduced to solve it.

[0049] Therefore, this embodiment uses the Lucas-Kanade optical flow algorithm to solve the corresponding optical flow field. The proposed method assumes that the movement of objects is consistent within a small local window (such as a 3×3 pixel area). Based on this assumption, the system can use multiple pairs of equations to solve the optical flow.

[0050] Specifically, assuming that within a local window Ω, the motion of all pixels is the same, an optical flow constraint equation can be generated for each pixel. N pixels, we have N Equations: ; In the formula, for The matrix represents the image brightness gradient, and the specific form is: ; is the optical flow vector to be solved, expressed in and The component of velocity in the direction.

[0051] yes The column vector represents the rate of change of brightness over time, and the specific form is: ; because In the equation usually , so the optical flow vector can be solved by using the least squares method . Minimize the error of the following equation: ; Through the above equation, the velocity component of the tobacco in the local window can be obtained: and .

[0052] Furthermore, according to the calculated flow velocity vector, the flow velocity of each pixel is calculated using the following formula: ; In the formula, Indicates the flow velocity of each pixel. and Respectively represent the flow rate in and Directional velocity component.

[0053] This embodiment uses an optical flow algorithm on continuous visible light image frames to calculate the flow velocity vector field of the tobacco shreds and obtain the flow velocity at each point. Since the flow velocity of the tobacco shreds affects the density measurement, this embodiment makes a correction to the initially estimated tobacco shreds density based on the flow velocity data. The density-flow velocity correction model formula is as follows: ; in, is the second tobacco density after the primary density correction, is the initial first tobacco density calculated based on the brightness value, Indicates the flow rate of tobacco. Represents the flow rate correction factor, which is used to correct the density according to the flow rate. This correction formula is used to compensate for the effect of flow rate on density estimation to ensure the accuracy of density estimation.

[0054] 4. Acquisition of temperature data and secondary density correction of tobacco density The image obtained by the infrared sensor measures the temperature of the tobacco and the cavity through the law of spectral radiation. The intensity of infrared radiation energy is directly related to the temperature of the object. According to Planck's radiation law, combined with the band of the infrared sensor, the surface temperature distribution of the tobacco corresponding to each position in the cavity can be calculated to provide temperature field data .temperature and infrared radiation intensity The relationship can be expressed by the following formula: ; In the formula, Indicates wavelength The radiation intensity under the condition can be directly measured by infrared sensor. Indicates the surface temperature of tobacco. is Planck's constant, represents the speed of light, represents the Boltzmann constant.

[0055] Since the temperature change also affects the estimation of tobacco density, this embodiment also introduces a temperature correction model to perform a secondary correction on the density to obtain the final target tobacco density. The temperature correction model is based on the following formula: ; in, Indicates the target tobacco density after temperature correction. represents the temperature correction factor, Indicates the temperature detected by the infrared sensor, corresponding to the tobacco temperature value mentioned above. Represents the preset reference temperature. Through this formula, the influence of temperature changes is taken into account to ensure the accuracy of tobacco density estimation under different temperature conditions.

[0056] Furthermore, in order to reduce the impact of short-term fluctuations in the real-time estimation process, the system also performs time series smoothing on the density estimation values ​​of multiple frames of images. Specifically, the sliding average method is used to average the density estimation values ​​of consecutive time points after secondary correction. The formula is as follows: ; In the formula, Indicates the density of tobacco after smoothing. Represents the density estimation value of a frame of image. Indicates the size of the smoothing window, usually 3 to 5 frames of data.

[0057] In actual application, this embodiment visualizes the tobacco density distribution data obtained by real-time calculation, and generates a two-dimensional or three-dimensional density distribution map to accurately display the tobacco density status at various positions in the cavity. The visualization interface can help operators monitor the density changes during the tobacco drying process in real time, and can adjust the working parameters of the tobacco drying equipment accordingly.

[0058] At the same time, this embodiment can also generate feedback suggestions based on the density estimation results, combined with parameters such as flow rate and temperature. Through the corresponding feedback control module, key parameters such as wind speed, heating power and drying time of the tobacco drying equipment are automatically adjusted to optimize the drying effect and ensure uniform drying of tobacco. The specific feedback control steps are as follows: If it is detected that the density in a local area is too high, the system will automatically increase the wind speed to promote the flow of tobacco and prevent over-drying; if it is detected that the density is too low or the flow rate is too fast, the system will reduce the wind speed or reduce the heating power to avoid insufficient drying of the tobacco.

[0059] Secondly, the system corresponding to this embodiment is also connected to the cloud platform through the Internet of Things (IoT) technology, and all density estimation data and adjustment suggestions can be uploaded to the cloud for long-term data storage and analysis. The cloud platform can perform trend analysis based on historical data and provide process optimization suggestions to further improve the quality consistency of tobacco drying.

[0060] In addition, this embodiment also conducted relevant experiments to verify the effectiveness of the proposed method. The specific experimental process is as follows.

[0061] Experimental equipment: industrial tofu drying machine, equipped with visible light sensor and infrared sensor.

[0062] Sensor parameters, including visible light sensor setting parameters and infrared sensor setting parameters: Visible light sensor: resolution 1080p, frame rate 120fps; Infrared sensor: temperature measurement range 0-600℃, accuracy ±2℃.

[0063] Optical flow algorithm: Use Lucas-Kanade algorithm to calculate flow velocity.

[0064] Experimental conditions: By combining different wind speeds and heating powers, the flow rate and density changes of tobacco under different working conditions are simulated.

[0065] Working condition 1: higher wind speed, moderate heating power; Working condition 2: moderate wind speed, high heating power; Working condition 3: lower wind speed and moderate heating power.

[0066] The experiment collected the relationship between flow rate and density under different working conditions. The following is an experimental data table showing the flow rate, density and temperature data under different working conditions:

[0067] Working condition 1 (higher wind speed, moderate heating power) Flow velocity data: The average flow velocity is 1.5m / s, and the flow velocity variance is small, indicating that the tobacco flow is relatively uniform and has small fluctuations.

[0068] Initial density: The initial density obtained by the brightness-density mapping model is 0.30 g / cm³.

[0069] Corrected density: Due to the fast flow rate, the corrected density is 0.28g / cm³ after calculation using the density-flow rate correction model.

[0070] Temperature: The infrared sensor measured a temperature of 80°C.

[0071] Working condition 2 (moderate wind speed, higher heating power) Flow velocity data: The average flow velocity is 1.2 m / s, and the variance is slightly large, indicating that there are flow velocity fluctuations in local areas.

[0072] Initial density: The initial density obtained by the brightness-density mapping model is 0.32 g / cm³.

[0073] Corrected density: The flow rate has little effect, and the density is corrected to 0.31g / cm³.

[0074] Temperature: The infrared sensor measured a temperature of 90°C, indicating that the temperature inside the cavity is high.

[0075] Working condition 3 (lower wind speed, moderate heating power) Flow rate data: The average flow rate is 0.8m / s, with a large variance, obvious flow rate fluctuations, and slow tobacco flow in some areas.

[0076] Initial density: The initial density obtained by the brightness-density mapping model is 0.35 g / cm³.

[0077] Corrected density: The flow rate is low, which has little effect on the density correction. The corrected density is 0.34g / cm³.

[0078] Temperature: The infrared sensor measured a temperature of 95°C.

[0079] Experimental conclusion Effect of flow rate on density: The experimental results show that the flow rate is closely related to the correction of density. Under the condition of high flow rate (such as condition 1), the corrected density drops significantly, indicating that high flow rate will dilute the density. Under the condition of low flow rate (such as condition 3), the correction of density is small.

[0080] Flow rate stability: The condition with small flow rate variance (such as condition 1) indicates that the flow uniformity of tobacco is good and suitable for uniform density distribution. However, the condition with large flow rate fluctuation (such as condition 3) will cause uneven heating in local areas, thus affecting the density distribution.

[0081] Effectiveness of the density-flow rate correction model: The corrected density calculated by the density correction formula is more accurate and can reflect the dynamic changes of actual tobacco. The corrected density value is more consistent with the actual working conditions than the initial estimate under various working conditions.

[0082] Example 3 Based on the same inventive concept as that of Embodiment 1 or 2, this embodiment introduces an online estimation device for the density of shredded tobacco in a tobacco drying machine, which comprises: An image acquisition module is configured to acquire visible light images and infrared images collected by the multispectral sensor system; a first tobacco shred density determination module configured to extract brightness information from the visible light image and input the corresponding tobacco shred brightness value obtained into a brightness-density mapping model to obtain a first tobacco shred density; a second tobacco shred density determination module configured to calculate the tobacco shred motion state of the visible light image based on an optical flow algorithm, and perform a density correction on the first tobacco shred density in combination with a corresponding tobacco shred flow rate value to obtain a second tobacco shred density; And, the target tobacco density determination module is configured to calculate the tobacco surface temperature of the infrared image, and perform secondary density correction on the second tobacco density in combination with the corresponding tobacco temperature value obtained to obtain the final target tobacco density.

[0083] refer to Figure 1 The online estimation device for the density of shredded tobacco in a tobacco drying machine can execute the online estimation method for the density of shredded tobacco in a tobacco drying machine introduced in the above-mentioned embodiment 1 or 2, and the specific functional implementation of each functional module is not repeated here.

[0084] Example 4 This embodiment introduces a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the online estimation method of tobacco density in a tobacco drying machine as described in Embodiment 1 or 2 are implemented.

[0085] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0089] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An online estimation method for the density of shredded tobacco in a tobacco drying machine, characterized in that: include: Acquire visible light images and infrared images collected by the multispectral sensor system; Extracting brightness information from the visible light image, and inputting the corresponding tobacco shred brightness value into a brightness-density mapping model to obtain a first tobacco shred density; Based on the optical flow algorithm, the movement state of the tobacco shreds is calculated for the visible light image, and a density correction is performed on the first tobacco shreds density in combination with the corresponding obtained tobacco shreds flow rate value to obtain a second tobacco shreds density; The surface temperature of the tobacco shreds is calculated for the infrared image, and a secondary density correction is performed on the density of the second tobacco shreds in combination with the corresponding acquired tobacco shreds temperature value to obtain a final target tobacco shreds density.

2. The method for online estimating density of shredded tobacco in a tobacco drying machine according to claim 1, characterized in that: Extracting brightness information from the visible light image to obtain a brightness value of the tobacco, including: Perform grayscale conversion on the collected visible light image to obtain a corresponding grayscale image; By manual selection or automatic segmentation method, the region containing the tobacco in the grayscale image is determined as the region of interest; Counting the pixel values ​​in the region of interest to obtain the brightness value of the tobacco; Among them, the collected visible light image is converted into grayscale to obtain the corresponding grayscale image, which is expressed as: ； In the formula, , and Represent the pixel values ​​of the red, green and blue channels respectively, Represents the brightness value of a pixel in a grayscale image.

3. The method for online estimating density of shredded tobacco in a tobacco drying machine according to claim 2, characterized in that: The corresponding tobacco brightness value is input into the brightness-density mapping model to obtain the first tobacco density, which is expressed as: ； In the formula, Indicated in coordinates and time The first tobacco density at represents the brightness value at the corresponding position and time, represents the brightness-density function obtained by experimental fitting.

4. The method for online estimating density of shredded tobacco in a tobacco drying machine according to claim 3, characterized in that: Based on the optical flow algorithm, the movement state of the tobacco is calculated for the visible light image to obtain the tobacco flow rate value, including: Perform grayscale conversion on the collected visible light image sequence to obtain a corresponding grayscale image; Extract key points from the grayscale image using a feature point detection algorithm; Based on the Lucas-Kanade optical flow algorithm, the extracted key points are tracked by optical flow, and the displacement vectors of the feature points between adjacent frames are calculated; By iterative solution, the optical flow velocity vector corresponding to each key point is obtained; According to the optical flow velocity vector, the tobacco flow velocity value on the image plane is calculated and expressed as: ； In the formula, Indicates the tobacco flow rate value, Indicates that the pixel is The speed of movement on the axis, Indicates that the pixel is The speed of movement on the axis.

5. The method for online estimating density of shredded tobacco in a tobacco drying machine according to claim 4, characterized in that: According to the density-flow rate correction model, the first tobacco density is corrected by the tobacco flow rate value to obtain the second tobacco density, which is expressed as: ； In the formula, It indicates the second tobacco density after the primary density correction. Indicates the flow rate correction factor.

6. The method for online estimating density of shredded tobacco in a tobacco drying machine according to claim 5, characterized in that: According to Planck's radiation law, the surface temperature of the tobacco is calculated for the infrared image in combination with the wavelength of the infrared sensor to obtain the tobacco temperature value, wherein the relationship between the tobacco temperature value and the infrared radiation intensity is as follows: ； In the formula, Indicates wavelength The corresponding infrared radiation intensity is: Indicates the tobacco temperature value. is Planck's constant, represents the speed of light, represents the Boltzmann constant.

7. The method for online estimating density of shredded tobacco in a tobacco drying machine according to claim 6, characterized in that: According to the temperature correction model, the second tobacco density is corrected twice by the tobacco temperature value to obtain the final target tobacco density, which is expressed as: ； In the formula, It indicates the target tobacco density after secondary density correction. represents the temperature correction factor, Indicates the tobacco temperature value detected by the infrared sensor. Indicates the reference temperature.

8. The method for online estimating density of shredded tobacco in a tobacco drying machine according to claim 7, characterized in that: The visible light image and the infrared image are collected simultaneously through a synchronization trigger, so that each pair of the visible light image and the infrared image has the same time stamp.

9. An online estimation device for tobacco shred density in a tobacco shred drying machine, characterized in that: include: An image acquisition module is configured to acquire visible light images and infrared images collected by the multispectral sensor system; a first tobacco shred density determination module configured to extract brightness information from the visible light image and input the corresponding tobacco shred brightness value obtained into a brightness-density mapping model to obtain a first tobacco shred density; a second tobacco shred density determination module configured to calculate the tobacco shred motion state of the visible light image based on an optical flow algorithm, and perform a density correction on the first tobacco shred density in combination with a corresponding tobacco shred flow rate value to obtain a second tobacco shred density; And, the target tobacco density determination module is configured to calculate the tobacco surface temperature of the infrared image, and perform secondary density correction on the second tobacco density in combination with the corresponding tobacco temperature value obtained to obtain the final target tobacco density.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for online estimation of the density of shredded tobacco in a tobacco drying machine according to any one of claims 1 to 8 are implemented.

Images