Graphene infrared blanching and vacuum pulse drying integrated intelligent equipment

Abstract

The invention discloses graphene infrared blanching and vacuum pulse drying integrated intelligent equipment which comprises a drying box main body, a heating system, a material rack system, a vacuum system and a control system, the drying box main body is used for blanching and drying agricultural products, fruits and vegetables and providing a vacuum environment; the heating system is used for providing a heat source for the drying box body in the agricultural product blanching and drying process. The material frame system is placed in the drying box body and used for supporting the heating plate and transferring materials. The vacuum system is used for providing a vacuum environment for the agricultural product fruit and vegetable processing process in the drying box body to reduce the water evaporation boiling point and isolate oxygen. The control system is used for controlling the automatic operation of the drying and blanching stages of the heating system in the drying box main body and feeding back drying data in real time; the control system controls on-off of the heating system and switching between the vacuum state and the normal pressure state. According to the invention, green additive-free drying of the product is realized. And the problems of serious loss of water-soluble substances and serious environmental pollution in the material processing process are effectively solved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent equipment technology for agricultural product processing, specifically relating to an integrated intelligent equipment for graphene infrared scalding and vacuum pulse drying. Background Technology

[0002] During agricultural product processing, mechanical operations such as peeling and slicing damage the material's structure, leading to the loss of the boundaries between PPO and POD oxidases and their substrate polyphenols. Upon contact with the substrate, the enzymes rapidly trigger enzymatic browning, catalyzing the oxidation of polyphenols into quinones, causing fresh-cut tissues to brown and turn black within minutes. Traditional processing methods rely on chemical additives to inhibit enzyme activity, but these methods suffer from problems such as chemical residues, excessive use, environmental pollution, and high costs.

[0003] Blanching, a chemical-free treatment method, rapidly deactivates key enzymes at high temperatures, effectively inhibiting enzymatic browning, protecting the color and quality of fruits and vegetables, and providing sterilization. It is often used as a pretreatment before drying. However, traditional hot water blanching leads to the loss of water-soluble nutrients and a reduction in dry weight.

[0004] Blanching and drying are closely linked production processes, but in traditional agricultural product processing, these two processes often rely on separate equipment. When switching processes, materials need to be transferred and re-laid, which increases labor intensity, reduces overall production efficiency, and may also lead to material loss and the risk of secondary contamination of raw materials.

[0005] During the drying process, as moisture evaporates, the bound water in the processed material gradually becomes difficult to remove. Under continuous high-temperature drying conditions, this not only easily leads to the decomposition of nutrients but may also cause adverse phenomena such as browning; while continuous low-temperature drying can effectively protect nutrients, it significantly prolongs the drying cycle, thereby reducing drying efficiency. Summary of the Invention

[0006] In order to overcome the problems existing in the prior art, the purpose of this invention is to provide an integrated intelligent equipment for graphene infrared scalding and vacuum pulse drying. This equipment, by adopting graphene infrared combined with vacuum pulse technology, can achieve green and additive-free drying of products and effectively solve the problems of serious loss of water-soluble substances, large environmental pollution and low efficiency in the material processing process.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An integrated intelligent device for graphene infrared hot bleaching and vacuum pulse drying includes a drying oven body, a heating system, a material rack system, a vacuum system, and a control system. The main body of the drying chamber is used for blanching and drying agricultural products and fruits and vegetables, and provides a vacuum environment; The heating system is used to provide a heat source for the blanching and drying process of agricultural products in the main body of the drying box. The heating system is supported by the material rack system. The material rack system is placed inside the main body of the drying chamber and is used to support the heating plate and transfer materials; The vacuum system is used to provide a vacuum environment inside the drying oven for the processing of agricultural products and fruits and vegetables, so as to reduce the boiling point of water evaporation and isolate oxygen. The control system is used to control the automatic operation of the drying and scalding stages of the heating system inside the drying oven and to provide real-time feedback of drying data; the control system also controls the on / off state of the heating system and the switching between vacuum and normal pressure states.

[0008] The heating system employs a multi-heat source system, including a graphene infrared plate and a cast aluminum plate. The graphene infrared plate has a surface temperature of up to 400°C, which can effectively achieve enzyme inactivation and sterilization, while reducing the loss of water-soluble nutrients. The cast aluminum plate is mainly used as a comparative reference in infrared technology experiments.

[0009] The material rack system has a total of nine layers, with each of the nine layers supported by four columns at its four corners, allowing for the processing of multiple batches of materials at once. The first, third, fifth, and seventh layers are graphene infrared plates, while the second, fourth, and sixth layers serve as material layers under the infrared heating system. The eighth and ninth layers are cast aluminum plates. The material trays are placed directly on the cast aluminum plates for heating. The three-layer infrared material is placed between two graphene infrared plates, and the two-layer cast aluminum material is placed directly on the cast aluminum plate. The first material layer of the second layer in the material rack system is designed with an automatic weighing device. The measurement process relies on four collar-type load cells, which are respectively installed on the four columns of the material rack. The collar-type load cells are pre-tightened longitudinally by wing studs and wrapped with heat insulation material on all sides. The material layer is directly pressed on the load cells.

[0010] The graphene infrared plate and the cast aluminum plate are used as heating plates and are placed on a support frame. Each heating plate is equipped with a temperature probe to detect the surface temperature of the heating plate. Temperature probes are left on the material layer to detect the internal temperature changes of the material. The data collected by the temperature probes are transmitted to the PLC system. Considering that graphene infrared panels are easily deformed by heat, a 1cm wide crossbeam is used as a support in the middle of the support frame below the graphene infrared panels without affecting their radiation function; the materials are placed on the material tray between the graphene infrared panels.

[0011] The support frame under the cast aluminum plate has multiple crossbeams in both the horizontal and vertical directions, and also has crossbeams around the perimeter to ensure that the cast aluminum plate is stable on the support frame and does not slip. The support frame and the material rack are connected to the column by threads. The minimum diameter of the bolts is designed to be 2mm based on the condition that the heaviest cast aluminum plate can achieve relative non-slippage after pre-tightening. The support frame and material rack are fastened to the column by wing studs. The cast aluminum plate, graphene infrared plate, support frame and column work together to form an integral structure and are placed in the internal chamber of the drying oven. The support frame and the material rack columns are connected by bolts and nuts, allowing each layer to be adjusted independently up and down.

[0012] The vacuum system includes a vacuum port reserved inside the drying oven body, which is connected to a vacuum pump located on the outside of the drying oven body via a vacuum pipe; The drying state inside the drying chamber can be captured in real time by an external industrial camera. The actuators, weighing sensors, and temperature probes inside the drying chamber are connected to the control system, which is a control cabinet connected to a computer. The control cabinet and the computer are used to complete the intelligent control of the drying process. The actuator includes a vacuum pump, a vacuum solenoid valve, and a venting valve; the vacuum pump is connected to the venting valve through the vacuum solenoid valve, and the venting valve is located on the rear side of the drying oven body.

[0013] The main body of the drying oven has an internal cavity size of 600mm×560mm×640mm and is made of 304 stainless steel, which has good durability and structural strength. The outer layer is covered with a 2mm thick 304 stainless steel skin, and asbestos is filled between the two for good heat preservation.

[0014] The drying oven body has a front observation window on the door and a right observation window on the side of the body. The front and right observation windows are made of tempered glass with high light transmittance and the glass surface is coated with a hydrophobic material. The bottom of the drying oven body is equipped with three aviation plugs for connecting the electrical components inside the oven; The top of the drying chamber is equipped with a vacuum pressure transmitter for real-time feedback of pressure changes inside the chamber. The drying chamber is equipped with a temperature and humidity sensor to detect changes in humidity inside the drying chamber. The drying oven is equipped with high-temperature resistant and explosion-proof LED lights on both sides of its main body cavity. An industrial camera is located on the outside of the main body of the drying oven, and the industrial camera is fixed by a camera bracket. The vacuum pressure transmitter, temperature and humidity sensor, and industrial camera transmit the collected signals to the computer. The industrial camera is connected to the computer via a USB interface, and the drying status of the material can be monitored in real time through the front observation window.

[0015] The operation of the control system specifically includes the following steps; S100: Collects real-time drying data using an industrial camera mounted on a camera mount. S200: The vision system relies on an industrial camera to automatically capture images of dried materials every minute and update them to the computer desktop, ensuring the timeliness and accuracy of image data; S300: Based on the OpenCV framework, it performs image processing operations on the material image to remove the influence of irrelevant factors in the image, aiming to obtain a high-quality image focused on the material. S400: Calculates the image parameters of the dried material, including color changes and shrinkage, displays them in real time on the human-machine interface, records data every 2 minutes, and automatically saves the data to the desktop in .csv format every 30 minutes. S500: A fuzzy logic controller is built using image parameters as input and decision temperature as output.

[0016] Specifically, S300 is: Image processing includes image denoising, image segmentation, and morphological operations; Image processing is performed in the PyCharm environment based on the OpenCV library. The image denoising method uses the medianBlur method to perform median filtering on the image to remove noise such as water vapor in the material image acquired by the industrial camera from the image analysis. After filtering, the image contour becomes clearer, which facilitates image segmentation in subsequent stages. The image segmentation process requires first removing background interference from the image, using the cvtColor method to convert the image to a grayscale image, and then using the threshold method to perform threshold segmentation on the grayscale image to obtain a binary image.

[0017] The morphological operations involve using erode and dilate methods to erode and dilate the binary image to further optimize the image quality after thresholding. Finally, the binary image generated by image processing is multiplied with the original image to eliminate background interference in image analysis.

[0018] Specifically, S400 is: The shrinkage rate is calculated based on the analysis of the binary image generated during image processing. The formula for calculating the shrinkage rate is as follows: (1) In the formula: SR The material surface shrinkage rate, S 1 represents the total number of white pixels before drying. S 2 represents the total number of white pixels at the current drying moment; Before calculating the total color change, the RGB color values ​​need to be converted to the CIE Lab color space for color change analysis. Using the rgb2lab method in the skimage library, the RGB color values ​​are converted to the CIE Lab color space. Finally, the total color change is calculated according to the following formula: (2) In the formula: For the overall color change, L* , a* , b* The average color at a specific drying time, while , , This represents the initial color average. L* The value ranges from 0 (black) to +100 (white). a* The value ranges from -100 (green) to +100 (red). b* The value ranges from -100 (blue) to +100 (yellow); Specifically, S500 is: Temperature changes during the drying process directly affect SR and MR, established a system based on A fuzzy logic controller with SR and MR as inputs and T as output; The design of the fuzzy controller requires first fuzzifying the data. The fuzzy set is defined as: {S, M, L, VL} = {"slight color change", "medium color change", "significant color change", "very large color change"}; the SR fuzzy set is defined as: {S, M, L, VL} = {"slight shrinkage", "medium shrinkage", "significant shrinkage", "very large shrinkage"}; the MR fuzzy set is defined as: {S, M, L, VL} = {"low moisture", "medium moisture", "high moisture", "extremely high moisture"}; T is divided into four fuzzy sets: {S, M, L, VL} = {"low temperature", "medium temperature", "high temperature", "extremely high temperature"}; The fuzzy controller uses triangular membership functions and is designed with a fuzzy rule base based on the accumulated knowledge of experts and practical experience in the drying process. In the fuzzy rule base, fuzzy rules are represented by the IF-THEN statement: if S is S, SR is S and MR is S, T outputs VL; if S is S, SR is M and MR is S, T outputs L; if S is S, SR is L and MR is S, T outputs L; if S is S, SR is VL and MR is S, T outputs M; The temperature data output by the fuzzy controller is transmitted back to the PLC via the Modbus TCP protocol. The PLC uses a PID algorithm to control the temperature of the graphene infrared plate by rapidly switching on and off a solid-state relay based on the temperature value output by the fuzzy controller, thereby realizing intelligent temperature regulation based on the drying state of the material during the drying process.

[0019] The beneficial effects of this invention are: This invention utilizes graphene infrared technology to perform rapid high-temperature anhydrous blanching treatment on materials in the early stages of drying using a graphene infrared plate, thereby quickly achieving enzyme inactivation and sterilization effects in a short time, and achieving the desired color without adding any chemical color-protecting agents. During the drying stage, the equipment combines graphene infrared technology with vacuum pulse technology to ensure uniform heating of the material and effectively improve drying efficiency. The equipment is equipped with an adjustable material rack, which provides experimental conditions for the later research and development of processing technology applicable to more materials. The equipment adopts a multi-heat source system, which provides experimental conditions for the research and development of processing technology applicable to more materials in the future; The equipment enables real-time communication between the PLC and the computer via Ethernet, automatically collecting and recording data changes, replacing manual data collection and facilitating detailed analysis of the drying process later. The host computer uses a computer-based human-machine interface design, providing conditions for developing more intelligent control of the drying process. Computer vision combined with fuzzy logic control can improve the drying rate while ensuring material quality. Attached Figure Description

[0020] Figure 1 This is a front view schematic diagram of the overall structure of the present invention.

[0021] Figure 2 This is a right-side view of the overall structure of the present invention.

[0022] Figure 3 This is a schematic diagram of the material rack structure of the present invention.

[0023] Figure 4 This is a schematic diagram of the intelligent control process of the present invention.

[0024] Figure 5 This is the fuzzy rule diagram of the present invention.

[0025] Figure label: 1. Computer, 2. Control cabinet, 3. Vacuum pump, 4. Vacuum solenoid valve, 5. Air venting valve, 6. Casters, 7. Material rack, 8. Sealing ring, 9. Vacuum pressure sensor, 10. LED light, 11. Front observation window, 12. Camera bracket, 13. Industrial camera, 14. Right observation window, 15. Cast aluminum plate, 16. Material rack, 17. Graphene infrared plate, 18. Support frame, 19. Column, 20. Weighing sensor, 21. Wing stud. Detailed Implementation

[0026] The present invention will now be described in further detail with reference to the accompanying drawings.

[0027] like Figure 1 As shown, a graphene infrared hot bleaching and vacuum pulse drying integrated processing equipment includes a drying oven body, a heating system, a material rack system, a vacuum system and a control system; The heating system employs a multi-heat source system, including a graphene infrared plate 17 and a cast aluminum plate 15. The graphene infrared plate 17, with a surface temperature reaching up to 400℃, effectively achieves enzyme inactivation and sterilization while simultaneously reducing the loss of water-soluble nutrients. The cast aluminum plate 15 primarily serves as a comparative reference in infrared technology experiments.

[0028] The material rack system has a nine-layer structure, allowing for the processing of multiple batches of materials at once. Layers one, three, five, and seven are graphene infrared heating layers (17), while layers two, four, and six serve as material layers (16) under the infrared heating system. Layers eight and nine are cast aluminum heating layers, allowing the material trays to be placed directly on the cast aluminum plate (15) for heating.

[0029] The graphene infrared plate 17 and the cast aluminum plate 15 are placed on the support frame 18. Considering that the infrared plate is easily deformed by heat, a 1cm wide crossbeam is used in the middle of the support frame for support without affecting its radiation function. The materials are placed on the material tray between the infrared plates. For the cast aluminum plate 15, the support frame 18 has multiple crossbeams in both the horizontal and vertical directions, and also has crossbeams around its perimeter to ensure that the cast aluminum plate 15 is stable on the support frame 18 and does not slip. The support frame 18 and the material rack 16 are connected to the column 21 by threads. The minimum diameter of the bolts is designed to be 2mm to ensure that the heaviest cast aluminum plate 15 can achieve a relatively non-slip condition after pre-tightening.

[0030] This equipment integrates a single processing flow for material blanching and drying, reducing material transfer and fabric application times, lowering labor intensity, and improving overall production efficiency. During the blanching stage, the control system manipulates the graphene infrared plate 17 to quickly achieve anhydrous blanching before drying. Once the preset blanching time is reached, the control system automatically switches to drying mode. During the drying stage, the graphene infrared plate 17 evenly provides heat to the material through thermal radiation. Simultaneously, the control system controls the vacuum pump 3 and the venting valve 5 to alternate between vacuum and atmospheric pressure conditions in the drying chamber, disrupting the balance between the material surface and the water vapor partial pressure within the drying chamber, thereby significantly accelerating the efficient removal of moisture. The real-time status within the drying chamber can be directly observed and monitored through the observation window 11.

[0031] In the vacuum system, when vacuuming is required, vacuum pump 3 reaches the preset vacuum state of the chamber through the vacuum pipeline and then stops. The normally open vacuum solenoid valve 4 on the vacuum pipeline is then energized. After the vacuum holding time is reached, the normally closed venting valve 5 is energized. Vacuum pressure sensor 9 displays the current vacuum level inside the chamber in real time.

[0032] The control system is control cabinet 2, which uses a Leadcon LK3U PLC as the controller. Control cabinet 2 is responsible for collecting data from the sensor network inside the drying oven and controlling the start and stop of vacuum pump 3, vacuum solenoid valve 4, venting valve 5, cast aluminum plate 15, venting solenoid valve 5, and graphene infrared plate 17 through a feedback mechanism. The sensor network includes a temperature sensing system, a temperature and humidity sensing system, a vacuum pressure sensing system, and a weight sensing system. Each sensing system uses a PLC as the master station and temperature transmitters and weighing transmitters as slave stations. They communicate via RS485 bus using the Modbus RTU protocol. Vacuum pressure and material weight are acquired through analog signals, and the acquired signals are transmitted to the PLC. The PLC is equipped with an Ethernet port, which enables hardware connection between computer 1 and control cabinet 2, and enables real-time communication between the PLC and computer based on the Modbus TCP protocol.

[0033] The temperature sensing system needs to sense a total of nine temperature data points on the heating plate and the material rack. The temperature of the heating plate is measured using a patch-type PT100 sensor, which is directly bonded to the graphene infrared plate 17 with heat-resistant silicone. The temperature of the material layer 16 is monitored by inserting a probe-type PT100 sensor into the material. All temperature signals are acquired by a ZS-PT100-3W temperature transmitter.

[0034] The temperature and humidity sensing system inside the drying oven is collected by an SHT30 temperature and humidity sensor. The pressure sensing system inside the drying oven body collects data through a pre-drilled hole in the upper part of the body by a PT128 vacuum pressure sensor 9, which is installed on the top of the drying oven body. The weight sensing system is fixed to the column 19 by the JHHM-4 type weighing sensor 21 with side screws. It can sense the changes in material weight in real time. The changes in material weight can indirectly reflect the changes in the internal moisture of the material. The weighing sensor is connected to the LZ-808 type transmitter, which has been designed and manufactured with temperature compensation.

[0035] A human-computer interaction system is designed on computer 1. A network cable is used to realize the hardware connection between computer 1 and PLC. The human-computer interaction system includes an online control system, an online monitoring system, and a communication connection system. The communication connection system requires the user to accurately enter the PLC's IP address and port number; when the input is correct and the connection is successfully established, the system will immediately pop up a prompt box to notify the user that the connection has been successful. The online control system includes two operating modes: manual and intelligent. The manual mode is used to verify the reliability of each component. Users can control the opening of the graphene infrared plate 17, cast aluminum plate 15, vacuum pump 3 and venting solenoid valve 5 by inputting keypad inputs. It plays an important role in troubleshooting circuit faults. In intelligent mode, the system first sets the drying parameters (drying time, atmospheric pressure holding time, and vacuum holding time), and controls the working state of the corresponding heating plate according to the selected heating layer. At the same time, it automatically executes vacuum pulse cycle and intelligently adjusts the drying temperature according to the real-time drying status.

[0036] The online monitoring system allows users to automatically read the register data of six PLCs and automatically save this data in table format on the desktop at set intervals for subsequent data analysis and plotting.

[0037] The PLC's register data includes temperature, humidity, and pressure values ​​stored in the data register; The online monitoring system is equipped with the function of displaying real-time images of dried materials. The system automatically updates the latest drying images to the computer desktop at set intervals to ensure the timeliness and accuracy of the image data. The human-computer interaction system integrates visual algorithms. It removes water vapor noise from material images acquired by industrial cameras using the `medianBlur` method in the OpenCV framework; obtains a binary image through image processing operations; quantizes the material shrinkage rate based on the binary image according to Formula 1; eliminates background interference by multiplying the binary image with the original image; converts RGB color values ​​to the CIE Lab color space using the `rgb2lab` method in the `skimage` library; and finally, quantizes the total color change according to Formula 2. These results are then fed back to the online monitoring system in real time.

[0038] (1) (2) The human-computer interaction system integrates a set of total color changes ( A fuzzy logic control algorithm that takes shrinkage rate (SR) and moisture content (MR) as model inputs and drying temperature (T) as output; The model employs fuzzification through the design of triangular membership functions; it then uses fuzzy control rules and performs inference based on the Mamdani fuzzy inference model; finally, it converts the aggregated membership degrees into specific values ​​using a weighted average method. This enables intelligent adjustment of the drying temperature based on real-time material conditions, thereby achieving adaptive process control based on material characteristics.

[0039] This invention also provides a control system for an integrated intelligent device combining graphene infrared hot bleaching and vacuum pulse drying. The control system, applied to the device described above, includes the following steps: S100: Real-time drying status is collected by an industrial camera 13 mounted on a camera bracket 12; It should be noted that the real-time captured images need to be displayed on the human-machine interface of the host computer. Therefore, the human-machine interface needs to be designed before the control system is implemented.

[0040] The human-machine interface includes a communication connection interface between the PLC and the computer, an online monitoring interface, and an online control interface.

[0041] The human-machine interface was designed on the host computer based on the PyQt5 framework. The communication connection interface requires the user to accurately input the IP address and port number of the PLC. When the input is correct and the connection is successfully established, the system will immediately pop up a prompt box to notify the user that the connection has been successful. In the PyCharm integrated development environment of the host computer, the HslCommunication library is used to call the ModbusTcpNet class to implement the real-time communication function between the PLC and the host computer based on the Modbus TCP protocol.

[0042] The online control interface is divided into two operation modes: manual and automatic. Before the equipment runs, the user needs to set the necessary parameters, which are written into the PLC's data register by calling the WriteInt method. After the parameters are set, when the user presses the "Set Confirm" button, the system will pop up a prompt box for confirmation. In automatic mode, the equipment can run autonomously according to the preset parameters. The manual operation button is designed to be associated with the internal auxiliary register by calling the WriteBool method. The manual mode allows users to perform more precise control and adjustment of the equipment according to actual needs.

[0043] The online monitoring interface can display the temperature data of the five material layers and the vacuum pressure value inside the chamber in real time by calling the ReadInt method.

[0044] Furthermore, the online monitoring interface supports users reading data from six predefined registers. By using the NetworkDeviceBase class and calling the ReadInt method, the data from these six registers can be automatically read at set intervals, and the data can be automatically saved to the desktop in .csv format at set intervals, thus realizing real-time automatic data collection and recording.

[0045] The monitoring interface can display the material drying status in real time. The machine vision system captures material images in real time by reading industrial cameras and crops the images to display only material information.

[0046] S200: The vision system relies on an industrial camera to automatically capture images of dried materials every minute and update them to the computer desktop, ensuring the timeliness and accuracy of image data; S300: Based on the OpenCV framework, it performs image processing operations on the material image to remove the influence of irrelevant factors in the image, aiming to obtain a high-quality image focused on the material. The image processing includes image denoising, image segmentation and morphological operations. Image processing is performed in the PyCharm environment based on the OpenCV library. The image denoising method uses the medianBlur method to perform median filtering on the image to remove noise such as water vapor in the material image acquired by the industrial camera from the image analysis. After filtering, the image contour becomes clearer, which facilitates image segmentation in subsequent stages. The image segmentation process requires first removing background interference from the image, using the cvtColor method to convert the image to a grayscale image, and then using the threshold method to perform threshold segmentation on the grayscale image to obtain a binary image.

[0047] The morphological operation involves using the erode and dilate methods to erode and dilate the binary image to further optimize the image quality after threshold segmentation. Finally, the binary image generated by the image processing is multiplied with the original image to eliminate background interference in the image analysis. S400: Calculates the image parameters of the dried material, including color changes and shrinkage, displays them in real time on the human-machine interface, records data every 2 minutes, and automatically saves the data to the desktop in .csv format every 30 minutes. The shrinkage rate calculation is based on the analysis of the binary image generated during image processing. The shrinkage rate calculation formula is as follows: (1) In the formula: SR The material surface shrinkage rate, S 1 represents the total number of white pixels before drying. S 2 represents the total number of white pixels at the current drying moment; Before calculating the total color change, the RGB color values ​​need to be converted to the CIE Lab color space for color change analysis. Using the rgb2lab method in the skimage library, the RGB color values ​​are converted to the CIE Lab color space. Finally, the total color change is calculated according to the following formula: (2) In the formula: For the overall color change, L* , a* , b* The average color at a specific drying time, while , , This represents the initial color average. L* The value ranges from 0 (black) to +100 (white). a* The value ranges from -100 (green) to +100 (red). b* The value ranges from -100 (blue) to +100 (yellow); S500: A fuzzy logic controller is built using image parameters as input and decision temperature as output. Temperature changes during the drying process directly affect SR and MR, therefore this invention establishes a system based on A fuzzy logic controller with SR and MR as inputs and T as output; The design of the fuzzy controller requires first fuzzifying the data. The fuzzy set is defined as: {S, M, L, VL} = {"slight color change", "medium color change", "significant color change", "very large color change"}; the SR fuzzy set is defined as: {S, M, L, VL} = {"slight shrinkage", "medium shrinkage", "significant shrinkage", "very large shrinkage"}; the MR fuzzy set is defined as: {S, M, L, VL} = {"low moisture", "medium moisture", "high moisture", "extremely high moisture"}; T is divided into four fuzzy sets: {S, M, L, VL} = {"low temperature", "medium temperature", "high temperature", "extremely high temperature"}.

[0048] like Figure 5 As shown, the fuzzy controller uses triangular membership functions and designs a rule base based on the accumulated knowledge of experts and practical experience in the drying process.

[0049] The rule base of the fuzzy controller is designed based on the accumulated knowledge of experts and practical experience in the drying process; the constructed fuzzy controller contains 3 input variables ( The fuzzy controller integrates 64 fuzzy control rules (SR and MR) and one output variable (T). After multiple tests and iterative optimizations, the fuzzy rules were finally determined.

[0050] The design of the fuzzy rule base is shown in Table 1. Fuzzy rules are represented by IF-THEN statements: if S is S, SR is S and MR is S, T outputs VL; if S is S, SR is M and MR is S, T outputs L; if S is S, SR is L and MR is S, T outputs L; if If the input is S, SR is VL and MR is S, then T outputs M, etc. When the input is other, the output rules are as follows: Figure 5 As shown.

[0051] Table 1 Fuzzy Control Rule Table

[0052] The fuzzy controller is ultimately based on the Mamdani fuzzy inference model, which includes rule matching, rule triggering, rule premise inference, rule conclusion inference, rule aggregation, and fuzzy output.

[0053] The rule matching involves substituting the input data into the corresponding membership function to calculate the membership degree. The rule triggering is based on membership degree to determine which fuzzy rules (IF-THEN statements) are activated; The rule premise reasoning is conducted within the activated rule, where each premise is associated with another premise through a logical AND operation, and the membership degree of the rule conclusion is determined according to the principle of least membership. The rule-based conclusion reasoning derives the membership degree of the conclusion part based on the premise membership degree of the activation rule. The rule aggregation is to aggregate the membership degrees of the conclusions of all activated rules; The fuzzy output uses a weighted average method to convert the aggregated membership degrees into specific values, which are then used as the output of the fuzzy system.

[0054] The temperature data finally output by the fuzzy controller is transmitted back to the PLC via the Modbus TCP protocol. The PLC controls the temperature of the graphene infrared plate 17 by controlling the solid-state relay to quickly switch on and off based on the temperature value output by the fuzzy controller using a PID algorithm, so as to realize the intelligent temperature regulation process according to the drying state of the material during the drying process.

[0055] It should be noted that the combination of machine vision and fuzzy logic controllers can intelligently and dynamically adjust the drying process, effectively preventing browning caused by high temperatures and avoiding excessively long drying cycles due to low temperatures. A fuzzy controller is established using the material's drying image parameters as input and the drying temperature as output. In the early stages of drying, when the material's color and shrinkage rate do not change significantly, the drying temperature is appropriately increased to accelerate the removal of free water; conversely, when the material's color and shrinkage rate change significantly, the temperature is appropriately decreased to protect the material's quality during the drying process.

[0056] In this invention, infrared radiation technology can directly penetrate the interior of materials and form a more uniform heat field distribution compared to traditional contact heating. Furthermore, the novel graphene infrared technology exhibits higher electrothermal conversion efficiency, faster thermal response speed, and better safety performance compared to traditional infrared heating methods.

[0057] Vacuum pulse drying creates a well-developed pore network inside the material, providing ideal conditions for deep infrared radiation penetration. This not only enhances the penetration depth of infrared radiation but also accelerates the migration of internal moisture.

Claims

1. An integrated intelligent device for graphene infrared hot bleaching and vacuum pulsed drying, characterized in that, It includes the main body of the drying oven, the heating system, the material rack system, the vacuum system, and the control system; The main body of the drying chamber is used for blanching and drying agricultural products and fruits and vegetables, and provides a vacuum environment; The heating system is used to provide a heat source for the blanching and drying process of agricultural products in the main body of the drying box. The heating system is supported by the material rack system. The material rack system is placed inside the main body of the drying chamber and is used to support the heating plate and transfer materials; The vacuum system is used to provide a vacuum environment inside the drying oven for the processing of agricultural products and fruits and vegetables, so as to reduce the boiling point of water evaporation and isolate oxygen. The control system is used to control the automatic operation of the drying and scalding stages of the heating system inside the drying oven and to provide real-time feedback of drying data; the control system also controls the on / off state of the heating system and the switching between vacuum and normal pressure states.

2. The integrated intelligent equipment for graphene infrared hot rinsing and vacuum pulse drying according to claim 1, characterized in that, The heating system adopts a multi-heat source system, including a graphene infrared plate (17) and a cast aluminum plate (15). The material rack system includes a nine-layer structure, with each of the four corners supported by four columns (19). The first, third, fifth, and seventh layers are graphene infrared plates (17), and the second, fourth, and sixth layers between the graphene infrared plates (17) serve as material layers (16) under the infrared heating system. The eighth and ninth layers are cast aluminum plates (15). The material tray is placed on the cast aluminum plate (15) for heating. An automatic weighing device is provided on the first material layer (16) of the second layer in the material rack system. The automatic weighing device consists of four ring-type weighing sensors (20). The ring-type weighing sensors (20) are respectively installed on the four columns (19) of the material rack. The ring-type weighing sensors (20) are pre-tightened longitudinally by wing studs (21) and wrapped with heat insulation material on all sides. The material layer (16) is directly pressed on the weighing sensors (20).

3. The integrated intelligent equipment for graphene infrared hot rinsing and vacuum pulse drying according to claim 2, characterized in that, The graphene infrared plate (17) and the cast aluminum plate (15) are heating plates, which are placed on the support frame (18). Each heating plate is attached with a temperature probe to detect the surface temperature of the heating plate. The material layer (16) is left with a temperature probe to detect the internal temperature change of the material. The data collected by the temperature probe is transmitted to the PLC system. The support frame (18) below the graphene infrared plate (17) uses a 1cm wide crossbeam as support; the material is placed on the material tray between the graphene infrared plates (17); The support frame (18) under the cast aluminum plate (15) is provided with multiple crossbeams in both the horizontal and vertical directions, and also with crossbeams around the perimeter, to ensure that the cast aluminum plate (15) is stable on the support frame (18) and does not slip. The support frame (18) and the material rack (16) are connected to the column (19) by threads. The support frame (18) and the material rack (16) are fastened to the column (19) by the wing stud (21). The cast aluminum plate (15), the graphene infrared plate (17), the support frame (18) and the column (19) work together to form an integral structure and are placed in the internal cavity of the drying oven body.

4. The integrated intelligent equipment for graphene infrared hot bleaching and vacuum pulse drying according to claim 3, characterized in that, The vacuum system includes a vacuum port reserved inside the drying oven body, which is connected to a vacuum pump (3) located on the outside of the drying oven body via a vacuum pipe; The drying state inside the drying chamber can be captured in real time by an external industrial camera (13). The actuator, weighing sensor (20), and temperature probe inside the drying chamber are connected to the control system. The control system is a control cabinet (2). The control cabinet (2) is connected to a computer (1). The control cabinet (2) and the computer (1) are used to complete the intelligent control of the drying process. The actuator includes a vacuum pump (3), a vacuum solenoid valve (4), and a venting valve (5); the vacuum pump (3) is connected to the venting valve (5) through the vacuum solenoid valve (4), and the venting valve (5) is located on the rear side of the drying oven body.

5. The integrated intelligent equipment for graphene infrared hot blotting and vacuum pulse drying according to claim 4, characterized in that, The drying oven body has a front observation window (11) on the door and a right observation window (14) on the side of the drying oven body. The front observation window (11) and the right observation window (14) are made of tempered glass with high light transmittance and the surface of the tempered glass is coated with a hydrophobic material. The bottom of the drying oven body is equipped with three aviation plugs for connecting the electrical components inside the oven; The top of the drying chamber is equipped with a vacuum pressure transmitter (9) for real-time feedback of pressure changes inside the chamber. The drying chamber is equipped with a temperature and humidity sensor to detect changes in humidity inside the drying chamber. The drying oven is equipped with high-temperature resistant and explosion-proof LED lights (10) on both sides of the main body cavity. An industrial camera (13) is located on the outside of the main body of the drying oven, and the industrial camera (13) is fixed by a camera bracket (12); The vacuum pressure transmitter (9), temperature and humidity sensor, and industrial camera (13) transmit the collected signals to the control cabinet (2). The industrial camera (13) is connected to the computer (1) via USB interface and can monitor the drying status of the material in real time through the front observation window (11).

6. The integrated intelligent equipment for graphene infrared hot blotting and vacuum pulse drying according to claim 5, characterized in that, The operation of the control system specifically includes the following steps; S100: Real-time drying status is collected by an industrial camera (13) mounted on a camera bracket (12); S200: The vision system relies on an industrial camera to automatically capture images of the dried material every minute and update them to the computer (1) to ensure the timeliness and accuracy of the image data; S300: Based on the OpenCV framework, it performs image processing operations on the material image to remove the influence of irrelevant factors in the image, aiming to obtain a high-quality image focused on the material. S400: Calculates the image parameters of the dried material, including color changes and shrinkage, and displays them in real time on the computer (1) human-computer interaction interface. It records the data every 2 minutes and automatically saves the data to the desktop in .csv format every 30 minutes. S500: A fuzzy logic controller is built using image parameters as input and decision temperature as output.

7. The integrated intelligent equipment for graphene infrared hot bleaching and vacuum pulse drying according to claim 6, characterized in that, Specifically, S300 is: Image processing includes image denoising, image segmentation, and morphological operations; Image processing is performed in the PyCharm environment based on the OpenCV library. The image denoising method is to use the medianBlur method to perform median filtering on the image acquired by the industrial camera (13) to remove the influence of water vapor noise in the material image acquired by the industrial camera (13) on image analysis; The image segmentation process first removes background interference from the image, then uses the cvtColor method to convert the image into a grayscale image, and uses the threshold method to perform threshold segmentation on the grayscale image to obtain a binary image. The morphological operation involves using erode and dilate methods to erode and dilate the binary image, optimizing the image quality after threshold segmentation. Finally, the binary image generated by image processing is multiplied with the original image to eliminate background interference in image analysis.

8. The integrated intelligent equipment for graphene infrared hot bleaching and vacuum pulse drying according to claim 7, characterized in that, Specifically, S400 is: The shrinkage rate is calculated based on the analysis of the binary image generated during image processing. The formula for calculating the shrinkage rate is as follows: （1） In the formula: SR The material surface shrinkage rate, S 1 represents the total number of white pixels before drying. S 2 represents the total number of white pixels at the current drying moment; Before calculating the total color change, the RGB color values ​​need to be converted to the CIE Lab color space. This is done using the rgb2lab method in the skimage library. Finally, the total color change is calculated using the following formula: （2） In the formula: For the overall color change, L* , a* , b* The average color at a specific drying time, while , , The initial color average; where, L* The value ranges from 0 to +100. a* The value ranges from -100 to +100. b* The value ranges from -100 to +100.

9. The integrated intelligent equipment for graphene infrared hot bleaching and vacuum pulse drying according to claim 7, characterized in that, Specifically, S500 is: During the drying process, establish a system based on A fuzzy logic controller with SR and MR as inputs and T as output; The fuzzy controller first performs fuzzification processing on the data, The fuzzy set is defined as: {S, M, L, VL} = {"slight color change", "medium color change", "significant color change", "very large color change"}; the SR fuzzy set is defined as: {S, M, L, VL} = {"slight shrinkage", "medium shrinkage", "significant shrinkage", "very large shrinkage"}; the MR fuzzy set is defined as: {S, M, L, VL} = {"low moisture", "medium moisture", "high moisture", "extremely high moisture"}; T is divided into four fuzzy sets: {S, M, L, VL} = {"low temperature", "medium temperature", "high temperature", "extremely high temperature"}; The fuzzy controller uses triangular membership functions and is designed with a fuzzy rule base based on the accumulated knowledge of experts and practical experience in the drying process. In the fuzzy rule base, fuzzy rules are represented by the IF-THEN statement: if S is S, SR is S and MR is S, T outputs VL; if S is S, SR is M and MR is S, T outputs L; if S is S, SR is L and MR is S, T outputs L; if S is S, SR is VL and MR is S, T outputs M; The temperature data finally output by the fuzzy controller is transmitted back to the PLC via the Modbus TCP protocol. The PLC controls the temperature of the graphene infrared plate (17) by controlling the solid-state relay to quickly switch on and off based on the temperature value output by the fuzzy controller using the PID algorithm, so as to realize the intelligent temperature regulation process according to the drying state of the material during the drying process.

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