Intelligent control method and system for drying materials and storage medium

Abstract

The invention provides an intelligent control method and system for drying materials and a storage medium. The intelligent control method comprises the following steps: acquiring an original material image containing a concerned material and a humidity parameter and a detection temperature of the concerned material, and obtaining a region of interest based on the original material image; obtaining feature parameters based on the image of the region of interest; under the condition of determining that the material state change meets the preset requirement based on the characteristic parameters, performing fuzzy logic control based on the characteristic parameters and the humidity parameters so as to obtain the predicted temperature of the concerned material; and adjusting working parameters of a drying device for drying the concerned material based on a comparison result of the detected temperature and the predicted temperature. Thus, the predicted temperature can better reflect the drying rule and the future temperature trend of the concerned material, pre-adjustment of the drying process of the concerned material is achieved, and the problem of excessive drying or insufficient drying is avoided.

Description

Technical Field

[0001] The present application relates to the field of intelligent control technology, and in particular to an intelligent control method, system and storage medium for drying materials. Background Art

[0002] With the continuous progress of dryer development, the intelligent development of dryers has become a trend. During the drying process, material dehydration mainly occurs in the slow heating and constant temperature stages. In the rapid heating stage, the drying rate of the material decreases but the temperature rises rapidly. Unreasonable drying parameters will cause the quality of the material to deteriorate or even carbonize. Material drying is a complex, dynamic, and highly nonlinear process. During the drying process, it is often necessary to control multiple indicators, such as moisture content, color, degree of deformation, antioxidant properties, and a variety of nutrients. The changes in moisture and nutrients in the material during drying are often unrelated, and a single mathematical model or kinetic model is difficult to accurately characterize the changes in multiple indicators.

[0003] At present, the drying technologies related to the materials include traditional natural drying, hot air drying, heat pump drying, solar drying, microwave drying, infrared drying, and combined drying. Most traditional drying technologies use PLC automation control technology, introduce fuzzy PID algorithm, and create material drying control algorithm to make the materials meet the drying requirements. However, the existing drying methods cannot detect the drying state of the materials in real time, and the detection speed is slow. In the process of drying the materials, it is easy to over-dry the materials, resulting in carbonization, or under-dry the materials, which reduces the quality of the materials. Summary of the invention

[0004] This application is proposed to solve the above technical problems existing in the prior art. This application aims to provide an intelligent control method, system and storage medium for drying materials, which can predict the future state changes of materials and adjust the working parameters of the drying device based on the prediction results to avoid over-drying or under-drying during the material drying process, thereby reducing the quality of the materials.

[0005] According to a first scheme of the present application, an intelligent control method for drying materials is provided, the intelligent control method comprising: obtaining, via a processor, an original material image containing a material of interest and humidity parameters and a detected temperature of the material of interest, and obtaining a region of interest based on the original material image; obtaining characteristic parameters for characterizing a state change of the material of interest based on the image of the region of interest; performing a material state change analysis on the image of the region of interest, and when it is determined based on the characteristic parameters that the material state change meets preset requirements, performing fuzzy logic control based on the characteristic parameters and the humidity parameters to obtain a predicted temperature of the material of interest; and adjusting, based on a comparison result of the detected temperature and the predicted temperature, an operating parameter of a drying device for drying the material of interest, so that a deviation between the predicted temperature obtained based on the current fuzzy logic control and the predicted temperature obtained by the previous fuzzy logic control meets a threshold range.

[0006] According to the second scheme of the present application, an intelligent control system for drying materials is provided, and the intelligent control system includes: a data acquisition module, which is configured to collect original material images of the material of interest, humidity parameters and detected temperature of the material of interest; a data processing module, which is configured to execute the intelligent control method for drying materials described in each embodiment of the present application; and a drying module, which is configured to dry the material of interest based on set working parameters.

[0007] According to a third solution of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the intelligent control method for drying materials described in each embodiment of the present application are implemented.

[0008] Compared with the prior art, the beneficial effects of the embodiments of the present application are:

[0009] The intelligent control method provided in the embodiment of the present application obtains the region of interest based on the original material image, and obtains the characteristic parameters based on the region of interest. When the material state change determined based on the characteristic parameters meets the preset requirements, fuzzy logic control is performed based on the characteristic parameters and humidity parameters to obtain the predicted temperature of the material of interest. In this way, by combining the humidity information and image information of the material of interest for fuzzy logic control, the predicted temperature obtained can reflect the future changes of the material of interest in advance.

[0010] In the embodiment of the present application, the working parameters of the drying device used to dry the material of interest are adjusted based on the comparison result of the detected temperature and the predicted temperature, so that the deviation between the predicted temperature obtained based on the current fuzzy logic control and the predicted temperature obtained by the previous fuzzy logic control meets the threshold range. In this way, intelligent control of the drying process of the material of interest is achieved, and the working parameters of the drying device are adjusted according to the comparison result of the predicted temperature and the detected temperature. The drying process can be pre-adjusted, and the drying process of the drying device can be more accurately controlled by adjusting the working parameters, avoiding the problem of over-drying or under-drying, which is conducive to ensuring that the material of interest maintains a high quality.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above description and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In the drawings, which are not necessarily drawn to scale, the same reference numerals may describe similar parts in different views. Similar reference numerals with letter suffixes or different letter suffixes may represent different examples of similar components. The accompanying drawings generally illustrate various embodiments by way of example and not by way of limitation, and together with the description and claims, are used to illustrate the disclosed embodiments. Such embodiments are illustrative and exemplary and are not intended to be exhaustive or exclusive embodiments of the present method, apparatus, system, or non-transitory computer-readable medium having instructions for implementing the method.

[0013] Figure 1 A flow chart of an intelligent control method for drying materials according to an embodiment of the present application is shown.

[0014] Figure 2 Another flow chart of the intelligent control method for drying materials according to the embodiment of the present application is shown.

[0015] Figure 3 A schematic diagram of a membership function according to an embodiment of the present application is shown.

[0016] Figure 4 An example of fuzzy rules according to an embodiment of the present application is shown.

[0017] Figure 5 A schematic diagram of an intelligent control system for drying materials according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solution of the present application, the present application is described in detail below in conjunction with the accompanying drawings and specific implementation examples. The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings and specific implementation examples, but are not intended to limit the present application.

[0019] The words "first", "second" and similar words used in this application do not indicate any order, quantity or importance, but are only used to distinguish. The words "include" or "comprises" and similar words used in this application mean that the elements before the word include the elements listed after the word, and do not exclude the possibility of covering other elements. In this application, the arrows shown in the figures of each step are only examples of the execution order, not limitations. The technical solution of this application is not limited to the execution order described in the embodiments. The various steps in the execution order can be combined, decomposed, or the order can be changed, as long as the logical relationship of the execution content is not affected.

[0020] All terms (including technical terms or scientific terms) used in this application have the same meaning as those understood by ordinary technicians in the field to which this application belongs, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined here. Techniques and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, the techniques and equipment should be considered as part of the specification.

[0021] In the present application, the arrows shown in the figures of each step are merely examples of the execution order, not limitations. The technical solution of the present application is not limited to the execution order described in the embodiments. The various steps in the execution order can be combined for execution, can be decomposed for execution, and can be reordered, as long as the logical relationship of the execution content is not affected.

[0022] Figure 1 A flow chart of an intelligent control method for drying materials according to an embodiment of the present application is shown. The intelligent control method executes the steps shown in step S101 to step S104 via a processor. In this embodiment, the arrows shown in the figure for each step are merely examples of the execution order, not limitations. The technical solution of the present application is not limited to the execution order described in the embodiment. The steps in the execution order can be combined, decomposed, or the order can be changed, as long as the logical relationship of the execution content is not affected.

[0023] In step S101, an original material image including a material of interest and a humidity parameter and a detected temperature of the material of interest are obtained, and a region of interest is obtained based on the original material image. Specifically, a camera device may be used to collect the original material image in real time, and a humidity sensor and a temperature sensor may be used to collect the humidity parameter and the detected temperature of the material of interest, respectively, wherein the humidity parameter may be the relative humidity of the material of interest, and the detected temperature is the temperature of the material of interest collected in real time.

[0024] Among them, the acquired original material image and the humidity parameters and detected temperature of the material of interest are also uploaded to the data storage space for subsequent processing and verification.

[0025] Specifically, the embodiments of the present application can be applied to drying multiple categories of herbs. For example, for mixed materials, the materials can be classified by type first.

[0026] In some other embodiments of the present application, a neural network model is used to classify the mixed materials to be dried to obtain various classes of materials of interest, wherein the materials of interest in each class of materials of interest have the same characteristic attributes. Taking the mixed materials including licorice, wolfberry, astragalus and lily as an example, the pre-trained VGG16 model can be used as the basic model in combination with the transfer learning method, and its classifier part can be fine-tuned to adapt to the herbal classification task.

[0027] For example, you can call the corresponding function in a deep learning framework (such as TensorFlow or PyTorch) to load a pre-trained VGG16 model. Taking PyTorch as an example, you can use torchvision.models.vgg16(pretrained=True) to load a pre-trained VGG16 model, which has been trained on large-scale image datasets (such as ImageNet) and has good image feature extraction capabilities. After loading, the parameters of the VGG16 model will be initialized. These parameters contain feature representation information learned from a large number of images, such as the weights of the convolution kernels.

[0028] By accurately classifying the mixed materials, the best drying-related working parameters can be set for herbs of different material categories to ensure that each herb can achieve the best drying effect. You can choose to freeze some layers of the VGG16 model, for example, freeze the parameters of the first 10 convolutional layers of the VGG16 model, because these layers have learned common image features such as texture, shape, etc., let the trained first 10 convolutional layers be used as feature extractors, and retain three layers for training, so that the model is more suitable for the data set generated based on the mixed materials. For example, in PyTorch, you can freeze the parameters of the corresponding layers by setting requires_grad = False, so that the parameters of these layers will not be updated during the back propagation process.

[0029] In this embodiment, for the classification of four mixed materials, the last linear layer sets the output 4096-dimensional features as four-dimensional features. For example, some parameter settings: batch_size = 32, Dropout (p = 0.5), Linear (4096, 4), epochs = 5, the classification results show that the average loss is 0.0047, the accuracy is 97.6%, and the VGG16 model migrated to four different herbs (licorice, lily, wolfberry, and astragalus) shows excellent material classification performance.

[0030] The VGG16 model obtained after training can be used as a prediction model for classifying mixed materials. The trained parameters are directly used in the prediction model to predict the mixed materials captured by the camera. First, image preprocessing is performed. The image is first center-cropped to 224x224 pixels, then converted to a tensor, and normalized to the range of [-1,1]. Next, the prediction model VGG16 is loaded and its classifier is replaced to adapt to the four-classification task. The new classifier contains three fully connected layers, each with 4096 nodes, and the ReLU activation function and Dropout regularization are applied. Then check whether there is an available GPU. If so, move the model to the GPU and set the model to evaluation mode to disable training-specific operations such as Dropout. Define four classification labels, corresponding to lily, licorice, wolfberry, and astragalus. Finally, the image is loaded and preprocessed, and then the prediction model is used for prediction. The prediction result is determined by taking the index of the maximum probability value and matching it with the predefined category label. Finally, the prediction result and the corresponding image are displayed, and the mixed material can be classified into four categories of interest: lily, licorice, wolfberry, and astragalus. In this way, a good classification accuracy can be achieved with only a small amount of herbal data and general computing power.

[0031] Among them, the materials of interest in each material category of interest have the same characteristic attributes, which include color, outline, texture, shape, etc. The materials of interest are distinguished based on the same characteristic attributes to classify the mixed materials into licorice, lily, wolfberry and astragalus, so that material state change analysis can be performed based on the material category of interest.

[0032] This is only an example and does not constitute a limitation on the specific solution. For example, the neural network model may also be LeNet5, AlexNet, or GoogLeNet.

[0033] Returning to the embodiment of the present application, the area where the material of interest is located can be extracted as the area of ​​interest based on the original material image. Specifically, the area of ​​interest can be extracted by using existing functions in OpenCV, such as threshold segmentation, edge detection, morphological operations and other different methods to extract the area of ​​interest, without limitation.

[0034] In step S102, characteristic parameters for characterizing the state change of the material of interest are obtained based on the image of the region of interest. The characteristic parameters may be, for example, color difference parameters, deformation parameters, viscosity parameters, browning index, etc., without limitation. Preferably, the characteristic parameters include color difference parameters and browning index.

[0035] Herein, the characteristic parameters are taken as color difference parameter and browning index as examples for explanation.

[0036] Specifically, color features of images of regions of interest can be extracted using methods such as color histograms and color moments, the color images can be converted into grayscale images, and then a binarization algorithm can be used to obtain a binary image to highlight the difference between the material of interest and the background in the image.

[0037] The binary image is masked on the image of the region of interest, so that the background interference and other related information of the material of interest are removed. The average values ​​of the three channels of R, G, and B are extracted based on the image of the region of interest after masking, and the color difference (ΔE) and browning index (BI) are obtained based on the average values ​​of the three channels of R, G, and B.

[0038] Taking CIELAB color space as an example, CIELAB color space is a three-dimensional color space composed of lightness coefficient (L * ), red-green axis (a * ) and the yellow-blue axis (b * ) three coordinates to describe the color. Among them, L * The range is 0-100 (0 means black, 100 means white), a * A positive value indicates the red direction, a negative value indicates the green direction, and b * A positive value indicates a yellow direction, and a negative value indicates a blue direction.

[0039] The CIE76 color difference formula is the most basic color difference calculation formula. Assume that there are two colors in the CIELAB color space, and their coordinates are divided into (L1 * , a1 * , b1 * ) and (L2 * , a2 * , b2 * ),

[0040]

[0041] Suppose, there is color A with coordinates (50,20,30) and color B with coordinates (60,25,35).

[0042] First calculate the brightness difference ΔL * =60-50=10;

[0043] Then calculate a * The difference Δa on the axis * =25-20=5;

[0044] Then calculate b * Difference Δb on the axis * =35-30=5;

[0045] Finally, substituting these values ​​into the formula, we get: ΔE ab * =12.25.

[0046] Browning index is an important indicator to measure the degree of color change (transformation to brown) of a substance. In CIELAB color space, it mainly uses the lightness coefficient (L * ), red-green axis (a * ) and the yellow-blue axis (b * ) are used to calculate these three parameters. A commonly used formula is: Here

[0047]

[0048] Assume that in the CIELAB color space, there is a color coordinate of (50, 10, 20). First, calculate X=0.42, and then substitute the X value into the browning index formula to calculate the value of BI, BI=64.71.

[0049] The above is only a method for calculating the color difference parameter and the browning index, which can be adjusted according to specific needs and is not limited to this.

[0050] That is to say, in some other embodiments of the present application, the color characteristics of the material of interest are extracted based on the image of the region of interest, and the image of the region of interest is converted into a grayscale image, and then a binary image is obtained based on the grayscale image; the binary image is masked on the image of the region of interest, and the average value of the three channels R, G, and B is obtained based on the color characteristics; the color difference parameter and browning index are obtained based on the average value of the three channels R, G, and B.

[0051] In step S103, a material state change analysis is performed based on the image of the region of interest. When it is determined based on the characteristic parameters that the material state change meets the preset requirements, fuzzy logic control is performed based on the characteristic parameters and the humidity parameters to obtain the predicted temperature of the material of interest. Specifically, during the drying process of the material of interest, the temperature, humidity, morphology, etc. of the material of interest will change. The characteristic parameters such as color difference parameters and browning index can be used to reflect the state change of the material of interest, such as whether the color difference change is too large or the browning is too large during the drying process.

[0052] Based on the characteristic parameters and humidity parameters, fuzzy logic control is performed to obtain the predicted temperature of the material of interest. The predicted temperature is obtained by performing fuzzy logic control on the humidity information and image information of the material of interest, and can reflect the future changes of the material of interest in advance.

[0053] Specifically, Figure 2 , taking the characteristic parameters as color difference parameters and browning index as examples for explanation. In step S201, the intelligent control system is started, and the computer vision processing program, sensors and other related equipment in the intelligent control system are all started, and the changes of the materials of interest are monitored based on computer vision technology, with good real-time performance and fast detection speed. In step S206, for example, humidity sensors and other equipment can be used to collect the relative humidity of the material, and in step S207, for example, temperature sensors can be used to collect the detected temperature T of the material, and the detected temperature T is the real-time temperature of the material of interest. In step S202, the acquired original material image is subjected to image processing, and the image processing includes identifying the region of interest, extracting color features of the image of the region of interest, and graying and image binarization processing, etc., which is not limited to this, for example, it can also include preprocessing of the image, etc., to remove interference background in the image except for the material of interest. In step S203, the color difference parameter and browning index are extracted based on the processed image. That is to say, for example, based on the image of the region of interest after masking, the average values ​​of the three channels R, G, and B can be extracted, and then the color difference and browning index can be obtained based on the average values ​​of the three channels R, G, and B.

[0054] In step S204, it is determined whether the color difference parameter and the browning index meet the preset requirements. Specifically, the color difference parameter can be compared with the preset color difference range, and the browning index can be compared with the preset browning range. When the color difference parameter is within the preset color difference range and the browning index is within the preset browning range, it means that the material change processing of the material of concern is within the normal range and the material state change meets the preset requirements.

[0055] There is no specific limitation on the preset color difference range and the preset browning range, and the user may set them according to the category of the material of concern or existing experience, or the processor may automatically perform a default configuration according to the category of the material of concern.

[0056] If the result of the judgment in step S204 is no, step S205 is executed to issue an alarm and stop the equipment in an emergency, thereby ensuring the safety of the drying process. That is to say, in other embodiments of the present application, when the material state change does not meet the preset requirements, a prompt message for indicating an abnormality is issued. The prompt message can be, for example, an alarm sound, text or a warning symbol, which is not limited to this, as long as it can be used to prompt the staff that a drying abnormality occurs.

[0057] In the case where the judgment result of step S204 is yes, step S208 is executed, and the color difference parameter, browning index and relative humidity are used as inputs, fuzzy logic control is performed, and the predicted temperature Ts is output. In some other embodiments of the present application, based on the values ​​of the color difference parameter, browning index and humidity parameter, the fuzzy sets to which the values ​​of the color difference parameter, browning index and humidity parameter belong respectively are determined by using a membership function, and the fuzzy sets include negative large (NB), negative small (NS), positive small (PS) and positive large (PB). In other words, the specific values ​​of the color difference parameter, browning index and humidity parameter are converted into fuzzy sets, and the membership function determines the relationship between the precise value of the input and the fuzzy variables after fuzzy transformation, which is a mapping from a precise value to a fuzzy set membership, and the membership represents the degree of a precise value in a fuzzy set (such as NB, NS, PS, PB).

[0058] Assuming that a triangular membership function is used to describe the fuzzy set of color difference, for the fuzzy set of negative large (NB), when the color difference is 2, its membership in negative large (NB) is 0.8, which means it is more inclined to belong to negative large (NB); when the color difference is 6, its membership in negative large (NB) is 0, and its membership in negative small (NS) is 1, which means it belongs to negative small (NS). This is only an exemplary description and does not constitute a limitation on the specific scheme.

[0059] In this embodiment, the color difference, browning index, and humidity range can be initially set to 0-18, 0.9-1.8, and 10-50, respectively, and can also be adjusted according to corresponding experiments of different materials of interest. The membership function has a variety of shapes, such as triangle, trapezoid, Gaussian, etc., which are not limited to this, as long as the membership function can be used to convert the exact value into a fuzzy set.

[0060] In some other embodiments of the present application, based on the fuzzy sets to which the values ​​of the color difference parameter, the browning index and the humidity parameter belong, temperature prediction is performed according to fuzzy rules to obtain the predicted temperature of the material of interest. Figure 3 A schematic diagram of a predicted temperature obtained by fuzzy logic control based on color difference, browning index and humidity is shown. Specifically, a fuzzy function editing tool can be used to design fuzzy sets and membership functions, wherein the input is color difference, browning index and humidity, and the output is the predicted temperature. According to the preset fuzzy rules, the predicted temperature membership function is determined based on color difference, browning index and humidity, and the predicted temperature can be obtained by converting the predicted temperature membership function into a numerical value.

[0061] Specifically, the fuzzy rules can be set by the user. For example, Figure 4 As shown, secha represents color difference, hebian represents browning index, H represents humidity, and T represents predicted temperature. Taking If(secha is NB)and(hebian is NB)and(H is NB)then(T is NB) as an example, it is explained that when the color difference is negative large, the browning index is negative large, and the humidity is negative large, the predicted temperature is negative large; Taking If(secha is NS)and(hebian is NB)and(H is NB)then(T is NB) as an example, it is explained that when the color difference is negative small, the browning index is negative large, and the humidity is negative large, the predicted temperature is negative large; Taking If(secha is PS)and(hebian is NB)and(H is NB)then(T is NS) as an example, it is explained that when the color difference is positive and small, the browning index is negative and large, and the humidity is negative and large, the predicted temperature is negative and small; when the color difference is positive and large, the browning index is negative and large, and the humidity is negative and large, the predicted temperature is positive and small; when the color difference is negative and large, the browning index is negative and large, and the humidity is negative and small, the predicted temperature is negative and small, and so on. All fuzzy rules will not be described one by one. For details, please refer to Figure 4 The 64 fuzzy rules are shown.

[0062] The fuzzy rules provided above are only exemplary and do not constitute a specific limitation on the fuzzy rules. Users can set fuzzy rules according to different materials of interest, so that the predicted temperature obtained based on the fuzzy rules can be used in advance to accurately characterize the state changes of the materials of interest. For the same material of interest, differences in fuzzy rules may lead to different prediction results, but as long as these prediction results are within a reasonable range, they are feasible. Different fuzzy rules make decisions based on different experiences or settings, and the differences between them reflect different processing strategies or different material characteristics. Due to the difference in predicted temperature, the adjustment of the working parameters of the drying device may also be different, but again, it is within a reasonable range.

[0063] The embodiment of the present application is capable of precise intelligent control, which is reflected in the fact that reasonable fuzzy rules have been set. In a dynamic and complex environment, the operating parameters of the drying device can be accurately adjusted according to the real-time feedback provided by the embodiment of the present application, thereby realizing intelligent adjustment of the drying temperature. In the embodiment of the present application, "precise" means that the operating parameters of the drying device are precisely adjusted under reasonable fuzzy rules. Different fuzzy rules will have different adjustment strategies, but ultimately they tend to focus on the appropriate drying temperature of the material, rather than producing the same determined parameters under different rules.

[0064] In some other embodiments of the present application, the material state change analysis is performed based on the original material image at a preset time interval to determine whether the current material state change meets the preset requirements. Specifically, for example, the image acquisition device caches the collected image information and the like into the data storage space, and then obtains the original material image at the current moment at a preset time interval (for example, every 30 seconds), and performs image processing based on the original material image obtained at the current moment, for example, including determining the region of interest, grayscale processing, binarization processing, and masking and other related image processing processes to remove interference such as the background of the image of the region of interest of the material of interest. And based on the processed image, characteristic parameters are obtained, and then the material state change analysis is continued. When the current change of the material of interest meets the preset requirements, fuzzy logic control is performed to obtain the predicted temperature. In this way, the material state change of the material of interest during the drying process can be quickly updated and monitored, and in particular, the predicted temperature of the material of interest can be obtained to obtain the future temperature of the material of interest during the drying process in advance.

[0065] Back to the embodiment of the present application, in step S104, based on the comparison result of the detected temperature and the predicted temperature, the working parameters of the drying device used to dry the material of interest are adjusted so that the deviation between the predicted temperature obtained based on the current fuzzy logic control and the predicted temperature obtained by the previous fuzzy logic control meets the threshold range. Wherein, the previous time refers to the last time relative to the current moment, for example, the processor obtains the latest original material image every 30s, and performs subsequent processing based on the latest acquired original material image. Assuming that the current moment is the 210th second, then the previous time refers to the 180th second, and based on the comparison result of the detected temperature and the predicted temperature, the working parameters of the drying device are adjusted, such as reducing the power of the drying device or increasing the power of the drying device, so that the predicted temperature obtained by analyzing the original material image collected at the 210th second and the predicted temperature obtained by analyzing the original material image collected at the 180th second meet the threshold range. If the deviation meets the threshold range, it means that the change state of the material of interest tends to be stable, and the drying can be stopped, thereby ensuring the high quality of the material of interest.

[0066] Specifically, the drying device can be, for example, a solar collector, a heat pump dryer, etc., without limitation thereto. The working parameters can be the power of the solar collector, the wind speed of the dryer, or other related working parameters that can be used to change the drying effect of the solar collector. Without specific limitation thereto, as long as the drying temperature of the drying device can be adjusted by adjusting the working parameters.

[0067] In some other embodiments of the present application, when the deviation between the detected temperature and the predicted temperature is greater than or equal to a threshold deviation, the operating parameters of the drying device are adjusted by lowering the temperature of the material of interest. The threshold deviation may be 0. Specifically, for example, the drying temperature of the material of interest may be lowered by lowering the power of the drying device, thereby lowering the detected temperature of the material of interest.

[0068] like Figure 2 In step S209, it is determined whether the difference between the material detection temperature T and the predicted temperature Ts is greater than or equal to 0. If the judgment result of step S209 is yes, it means that the current material detection temperature T is too high, and step S210 is continued to reduce the power of the collector, thereby reducing the temperature of the material of interest to avoid over-drying. If the judgment result of step S209 is no, it means that the current material detection temperature T is too low, and step S211 is continued to increase the power of the collector and thereby increase the drying temperature of the material of interest. In other words, when the deviation between the detection temperature and the predicted temperature is less than the threshold deviation, the working parameters of the drying device are adjusted by increasing the temperature of the material of interest.

[0069] In this embodiment, fuzzy logic control is used to predict the temperature of the material of interest at a future time based on color difference parameters, browning index and humidity parameters, and intelligent adjustment of the working parameters of the drying device is achieved based on the prediction results. This can effectively avoid the problem of over-drying of the material of interest or poor drying effect, thereby improving the drying quality of the dried material.

[0070] After step S210, step S212 is executed to determine whether the difference between the predicted temperature Ts obtained based on the current fuzzy logic control and the predicted temperature T0 obtained by the previous fuzzy logic control is less than or equal to 0.1. In this embodiment, the threshold range is 0-0.1, or 0-0.05, and there is no limitation on this. In other words, if Ts-T0≤0, it means that the material of interest has been dried to the optimal state, and the change of the material of interest after drying is small, then the drying can be terminated. In this way, intelligent drying control of the material of interest is achieved.

[0071] If the judgment result of step S212 is no, continue to collect the humidity parameters and detected temperature of the material of interest, perform material state change analysis on the material of interest, and adjust the working parameters of the drying device used to dry the material of interest based on the comparison result of the predicted temperature and the detected temperature, so that the deviation between the predicted temperature obtained based on the current fuzzy logic control and the predicted temperature obtained by the previous fuzzy logic control meets the threshold range.

[0072] In this way, intelligent control of the drying process of the material of concern is achieved, and the working parameters of the drying device are adjusted according to the comparison result of the predicted temperature and the detected temperature. The drying process can be pre-adjusted, and the drying process of the drying device can be more accurately controlled by adjusting the working parameters to avoid the problem of over-drying or insufficient drying, which is conducive to ensuring that the material of concern maintains a high quality.

[0073] Although the working parameters of the drying device can be adjusted directly based on the real-time temperature of the material detected by the sensor, this method lacks a comprehensive analysis of the complex dynamic characteristics of the material drying process. The real-time temperature can only reflect the current single temperature state of the material, and it is impossible to make intelligent drying adjustments based on the current single temperature state. For example, in the process of drying medicinal materials, the temperature sensor measures that the real-time temperature of the herbal medicine is low, but the humidity and image information of the herbal medicine at this time indicate that the material requires a higher drying temperature. In this case, there is no basis for adjusting the single real-time temperature, and it is impossible to predict the future change trend of the herbal medicine.

[0074] The intelligent control method of the embodiment of the present application can use fuzzy logic control to obtain the predicted temperature based on the characteristic parameters and humidity parameters. The predicted temperature is obtained based on fuzzy logic control, which can better reflect the drying law and future temperature trend of the material and realize advanced adjustment of the drying process of the material. By comparing the predicted temperature and the detected temperature, the working parameters of the drying device can be adjusted more accurately, thereby realizing advanced intelligent control, avoiding overdrying or underdrying, and improving the quality of the material.

[0075] In some other embodiments of the present application, the color difference parameter, browning index and humidity parameter, as well as the predicted temperature are stored in a storage space, so that users can query historical data, wherein the storage space can be in the cloud, so that users can log in to the cloud through a mobile phone or other portable smart device to realize the remote drying control process. For example, if an abnormal alarm prompt appears during the drying process, the user can call the relevant data from the storage space for tracing and analysis.

[0076] In some other embodiments of the present application, the original material image of the material of interest and the target material image after drying are displayed for comparison on the display interface so that the user can intuitively observe the state of the material after drying. In the drying process, the image acquisition equipment such as the camera collects the image information of the material of interest in real time. The image information of the material of interest obtained by the processor at a preset time interval each time is the original material image, and the image information obtained after the entire intelligent drying is the target material image. In addition, the initial material image of the material of interest can also be displayed on the display interface at the same time as the target material image, so as to observe the quality of the material of interest after drying by comparing the target material image and the initial material image.

[0077] Figure 4 An intelligent control system for drying materials is shown, including a data acquisition module 501, which is configured to collect the original material image of the material of interest, the humidity parameters of the material of interest, and the detected temperature. Specifically, the data acquisition module 501 may include a camera device (not shown), which uses the camera device to collect the initial image of the material of interest and the material image during the drying process in real time. It may also include a humidity sensor (not shown) and a temperature sensor (not shown) to respectively collect the humidity parameters and the detected temperature (real-time temperature of the material of interest) of the material of interest in real time. The data acquisition module 501 may also include other devices, which are not limited to this and can be set by the user according to the drying process.

[0078] The intelligent control system may also include a data storage module 504, and the image information data and the state information data of the material of interest (including the humidity and temperature of the material of interest, which are not limited thereto) acquired by the data acquisition module 501, as well as other data generated in the subsequent analysis process, may be stored in the data storage module 504. For example, in the intelligent drying process, once an abnormal alarm occurs, the user may suspend the drying and check the drying device or other related equipment, and may also retrieve relevant data parameters from the data storage module 504 to perform data verification, thereby finding out the cause of the problem. In this way, the troubleshooting effect of the user on the drying system may be improved.

[0079] The data processing module 502 is configured to execute the intelligent control method for drying materials described in each embodiment of the present application, and the drying module 503 is configured to dry the materials of interest based on the set working parameters. The drying module 503 includes a solar collector 505. After the user turns on the drying device, the suction fan draws external air into the solar collector 505, and the solar collector 505 converts solar energy into heat energy. The air temperature inside the solar collector 505 increases, and the hot air is blown into the drying chamber 506 to dry the materials of interest.

[0080] The power of the solar collector 505 is adjusted according to the comparison result between the predicted temperature and the detected temperature obtained by the data processing module 502, so as to realize the intelligent control of the drying process, avoid problems such as over-drying during the drying process, and ensure the quality of the material.

[0081] The processor may be a processing device including one or more general processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor running other instruction sets, or a processor running a combination of instruction sets. The processor may also be one or more special processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a system on chip (SoC), etc. The processor may cooperate with a near infrared brain function imaging device that collects the first near infrared data to perform relevant data analysis and processing.

[0082] This application describes various operations or functions that may be implemented as software codes or instructions or defined as software codes or instructions. Such content may be source code or differential code ("incremental" or "patch" code) that can be directly executed ("object" or "executable" form). Software codes or instructions may be stored in a computer-readable storage medium, and when executed, may cause a machine to perform the described functions or operations, and include any mechanism for storing information in a form accessible to a machine (e.g., a computing device, an electronic system, etc.), such as recordable or non-recordable media (e.g., read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, etc.).

[0083] The exemplary methods described in this application may be implemented at least in part by a machine or computer. In some embodiments, a computer-readable storage medium stores computer program instructions, which, when executed by a processor, cause the processor to execute the steps of the intelligent control method for drying materials described in each embodiment of this application. Each step of the intelligent control method for drying materials described in each embodiment above can be combined here and implemented by the processor running the computer program instructions, which will not be described in detail here.

[0084] The implementation of such a method may include software code, such as microcode, assembly language code, high-level language code, etc. Various software programming techniques may be used to create various programs or program modules. For example, a program portion or program module may be designed with or by means of Java, Python, C, C++, assembly language, or any known programming language. One or more of such software portions or modules may be integrated into a computer system and / or a computer-readable medium. Such software code may include computer-readable instructions for executing various methods. The software code may form a part of a computer program product or a computer program module. In addition, in an example, the software code may be tangibly stored on one or more volatile, non-temporary or non-volatile tangible computer-readable media, such as during execution or at other times. Examples of these tangible computer-readable media may include, but are not limited to, hard disks, removable disks, removable optical disks (such as optical disks and digital video disks), cassettes, memory cards or memory sticks, random access memories (RAMs), read-only memories (ROMs), etc.

[0085] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present application with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. The elements in the claims will be interpreted broadly based on the language adopted in the claims, and are not limited to the examples described in this specification or during the implementation of the application, and the examples will be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0086] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. For example, those of ordinary skill in the art can use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features can be grouped together to simplify the application. This should not be interpreted as an intention that a disclosed feature that does not require protection is necessary for any claim. On the contrary, the subject matter of the present application may be less than all the features of a specific disclosed embodiment. Thus, the claims are incorporated into the specific embodiments as examples or embodiments, wherein each claim is independently used as a separate embodiment, and it is considered that these embodiments can be combined with each other in various combinations or arrangements. The scope of the present application should be determined with reference to the full scope of the equivalent forms of the attached claims and these claims.

[0087] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. An intelligent control method for drying materials, characterized in that: The intelligent control method includes, via a processor, Acquire an original material image containing a material of interest and a humidity parameter and a detected temperature of the material of interest, and obtain a region of interest based on the original material image; Obtaining characteristic parameters for characterizing a state change of a material of interest based on the image of the region of interest; Performing material state change analysis based on the image of the region of interest, and when it is determined based on the characteristic parameters that the material state change meets preset requirements, performing fuzzy logic control based on the characteristic parameters and the humidity parameters to obtain a predicted temperature of the material of interest; Based on the comparison result of the detected temperature and the predicted temperature, the working parameters of the drying device used to dry the material of interest are adjusted so that the deviation between the predicted temperature obtained based on the current fuzzy logic control and the predicted temperature obtained by the previous fuzzy logic control meets the threshold range.

2. The intelligent control method according to claim 1, characterized in that: The characteristic parameters include color difference parameters and browning index; Determining whether the material state change meets the preset requirements based on the characteristic parameters specifically includes: The color difference parameter is compared with a preset color difference range, and the browning index is compared with a preset browning range. When the color difference parameter is within the preset color difference range and the browning index is within the preset browning range, the material state change meets the preset requirements.

3. The intelligent control method according to claim 1, characterized in that: Based on the comparison result of the detected temperature and the predicted temperature, the working parameters of the drying device for drying the material of interest are adjusted, specifically including: When the deviation between the detected temperature and the predicted temperature is greater than or equal to a threshold deviation, adjusting the operating parameters of the drying device in a manner of lowering the temperature of the material of interest; When the deviation between the detected temperature and the predicted temperature is less than a threshold deviation, the operating parameters of the drying device are adjusted in a manner of increasing the temperature of the material of interest.

4. The intelligent control method according to claim 2, characterized in that: Based on the image of the region of interest, characteristic parameters used to characterize the change in material state are obtained, including: Extracting the color features of the material of interest based on the image of the region of interest, converting the image of the region of interest into a grayscale image, and then obtaining a binary image based on the grayscale image; Masking the binary image on the image of the region of interest, and obtaining the average values ​​of the three channels of R, G, and B based on the color features; The color difference parameter and browning index are obtained based on the average values ​​of the three channels of R, G and B.

5. The intelligent control method according to claim 1 or 2, characterized in that: The intelligent control method further comprises: The material state change analysis is performed based on the original material image at preset time intervals to determine whether the current material state change meets the preset requirements.

6. The intelligent control method according to claim 1, characterized in that: The intelligent control method further includes: when the material state change does not meet the preset requirements, issuing a prompt message for indicating an abnormality.

7. The intelligent control method according to any one of claims 1 to 4, characterized in that: The intelligent control method further comprises: A neural network model is used to classify the mixed materials to be dried to obtain various categories of materials of interest, and a material state change analysis is performed based on each category of materials of interest, wherein the materials of interest in each category of materials of interest have the same characteristic attributes.

8. The intelligent control method according to claim 2, characterized in that: Fuzzy logic control is performed based on color difference parameters, browning index and humidity parameters to obtain the predicted temperature of the material of interest, including: Based on the values ​​of the color difference parameter, the browning index and the humidity parameter, the fuzzy sets to which the values ​​of the color difference parameter, the browning index and the humidity parameter belong are determined using a membership function, wherein the fuzzy sets include negative large, negative small, positive small and positive large; Based on the fuzzy sets to which the values ​​of the color difference parameter, the browning index and the humidity parameter belong respectively, temperature prediction is performed according to fuzzy rules to obtain the predicted temperature of the material of interest.

9. An intelligent control system for drying materials, characterized in that: The intelligent control system comprises: A data acquisition module configured to acquire an original material image of the material of interest, a humidity parameter of the material of interest, and a detected temperature; A data processing module configured to execute the intelligent control method for drying materials according to any one of claims 1 to 8; and A drying module is configured to dry the material of interest based on set working parameters.

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 intelligent control method for drying materials described in any one of claims 1 to 8 are implemented.

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