Intelligent monitoring method, medium and system for bubble-shaped green spots in cigar tobacco airing

Abstract

The invention relates to the technical field of tobacco leaf monitoring, and discloses an intelligent monitoring method, medium and system for bubble-shaped green spots in cigar tobacco leaf air-curing, and the method comprises the steps: obtaining the image information of the bubble-shaped green spots in cigar tobacco leaf air-curing, and building a bubble-shaped green spot feature set according to the features of the image information; then, surface images of all cigar tobacco leaves in the airing area are obtained, and feature matching is carried out; and when the surface image features are matched with the bubble-shaped green spot features, determining that the tobacco leaves are sick tobacco leaves. And calculating an early warning score of the air-curing area based on the number of the patient tobacco leaves and the patient grade. Meanwhile, the illuminance of all the tobacco leaves and the environment temperature and humidity of the air-curing area are obtained so as to adjust the early warning score. And finally, according to the adjusted early warning score, determining the early warning grade of the airing area. Through combination of image analysis and environment monitoring, effective early warning and management of tobacco leaf diseases are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of tobacco leaf monitoring, and in particular to an intelligent monitoring method, medium and system for cigar tobacco leaf blister during air-drying. Background Art

[0002] High-quality cigar tobacco leaves are the basis for making high-quality cigars, especially the outermost layer of the cigar wrapper, which is the "face value" of a cigar. The value of a cigar can be basically determined by the color, veins, tightness, oiliness and taste of the cigar wrapper. At present, the cigar wrappers used in China are basically all imported, which is restricted by the supply of high-quality cigar wrapper raw materials. Although China has vigorously developed the production of cigar tobacco leaves in recent years, the bubble-like "blue spots" phenomenon often occurs during the cigar drying process, which has a great impact on the production of high-quality cigar wrappers.

[0003] At present, the occurrence of "blue spots" in the cigar drying process mainly relies on manual inspections of the drying room, observing the tobacco leaves in the drying room leaf by leaf, judging whether the phenotype of "blue spots" appears, and then adjusting the drying process in a targeted manner in order to reduce this phenomenon. However, this traditional manual detection method has many problems: for standard drying rooms, 5-7 layers of tobacco leaves are generally hung, more than 20 meters long and more than 10 meters wide, and the humidity, body temperature and air irritation in the drying room during the drying process are high. It takes at least 1-2 hours for one person to inspect a drying room, and the long-term "high-altitude" operation greatly increases the risk of falling. Secondly, when detecting blue spots, portable artificial light sources such as flashlights are relied on. In the early stage of blue spots, the phenotype is not obvious, and it is not easy for the human eye to make accurate judgments in weak light conditions. In addition, it is also required that the staff have rich experience in cigar tobacco leaf drying to improve the accuracy of detection.

[0004] Therefore, there is an urgent need to invent a technology for monitoring vesicular blue spots during the drying process of cigar tobacco leaves, which is used to solve the problem that the existing technology relies on manual inspections during the drying process of cigars, resulting in low efficiency, poor accuracy and safety risks in monitoring blue spots. Summary of the invention

[0005] In view of this, the present invention proposes an intelligent monitoring method, medium and system for bubbly blue spots during cigar tobacco leaf air-drying, aiming to solve the problem that the current technology relies on manual inspections during the cigar air-drying process, resulting in low efficiency, poor accuracy and safety risks in monitoring blue spots.

[0006] The present invention provides an intelligent monitoring method for cigar tobacco leaf blister during air-drying, comprising:

[0007] Acquire image information of blister-like spots during the drying of each cigar tobacco leaf, and establish a blister-like spot feature set according to the blister-like spot features in each of the image information;

[0008] The surface image of each cigar tobacco leaf in the drying area is obtained, and the vesicular stain features in the vesicular stain feature set are matched according to the surface image features, wherein:

[0009] When the surface image feature matches the livedo bullosa feature, it is determined that the cigar tobacco leaf corresponding to the surface image feature is a diseased tobacco leaf, and the area and number of the livedo bullosa feature in the diseased tobacco leaf are obtained, and the density of the livedo bullosa of the diseased tobacco leaf is determined according to the area and number of the livedo bullosa feature and the area of ​​the diseased tobacco leaf;

[0010] Determining the disease level of the diseased tobacco leaf according to the density of the livedo albosae, wherein the disease levels are a first disease level, a second disease level, and a third disease level in sequence;

[0011] Determining a warning score for the airing area according to the number of diseased tobacco leaves in the airing area and the disease level of each of the diseased tobacco leaves;

[0012] Obtaining the illumination of each cigar leaf in the drying area, the ambient temperature and the ambient humidity of the drying area, and adjusting the warning score according to the illumination of each cigar leaf, the ambient temperature and the ambient humidity of the drying area;

[0013] The adjusted warning score is obtained, and the warning level of the drying area is determined according to the relationship between the adjusted warning score and the pre-configured first preset warning score and the second preset warning score, wherein:

[0014] When the adjusted warning score is less than the first preset warning score, the warning level of the drying area is determined to be a low risk level;

[0015] When the adjusted warning score is greater than or equal to the first preset warning score, and the adjusted warning score is less than the second preset warning level, the warning level of the drying area is determined to be a medium risk level;

[0016] When the adjusted warning score is greater than or equal to the second preset warning score, it is determined that the warning level of the drying area is a high risk level.

[0017] Furthermore, according to the number of the diseased tobacco leaves in the drying area and the disease level of each of the diseased tobacco leaves, determining the early warning score of the drying area includes:

[0018] The number of tobacco leaves with patients of the first patient level, the number of tobacco leaves with patients of the second patient level, and the number of tobacco leaves with patients of the third patient level are obtained, and the number of tobacco leaves with patients of the first patient level, the number of tobacco leaves with patients of the second patient level, and the number of tobacco leaves with patients of the third patient level are substituted into formula I to determine the early warning score of the drying area, and formula I is as follows:

[0019] P = z1 × Q + z2 × K + z3 × J;

[0020] Among them, P is the warning score of the drying area, Q is the number of patient tobacco leaves of the first patient level, K is the number of patient tobacco leaves of the second patient level, J is the number of patient tobacco leaves of the third patient level, z1-z3 are weight coefficients, and the sum of z1-z3 is 1.

[0021] Furthermore, when the warning score is adjusted according to the illumination of each cigar leaf, the ambient temperature and humidity of the drying area, it includes:

[0022] Obtaining the illumination area of ​​each of the cigar leaves within a preset time period, and determining the illumination proportion of each of the cigar leaves according to the illumination area and the area of ​​the cigar leaves;

[0023] The average light intensity of the illuminated area of ​​the cigar leaves in the preset period of time is obtained, and the light proportion of each cigar leaf and the average light intensity in the preset period of time are substituted into Formula II to determine the comprehensive light score of the drying area. Formula II is as follows:

[0024]

[0025] Wherein, L is the comprehensive light score of the drying area, n is the number of cigar leaves, Ri is the light ratio of the cigar leaves, Ii is the average light intensity of the cigar leaves in the preset time period, Imax is the preset light intensity, W R and W I is the weight coefficient, and W R With W I The sum is 1;

[0026] Determining whether to adjust the early warning score according to the relationship between the comprehensive light score L of the drying area and a pre-configured preset comprehensive light score;

[0027] When the comprehensive illumination score L is greater than or equal to the preset comprehensive illumination score, it is determined that the early warning score is not adjusted;

[0028] When the comprehensive light score L is less than the preset comprehensive light score, an adjustment coefficient is determined according to the light score difference between the comprehensive light score L and the preset comprehensive light score, and the early warning score is adjusted according to the adjustment coefficient.

[0029] Furthermore, when determining the adjustment coefficient according to the illumination score difference between the comprehensive illumination score L and a preset comprehensive illumination score, it includes:

[0030] Comparing the illumination score difference with a pre-configured first preset illumination score difference and a pre-configured second preset illumination score difference, and determining the adjustment coefficient according to the comparison result;

[0031] When the illumination score difference is less than or equal to the first preset illumination score difference, the adjustment coefficient is determined to be X3;

[0032] When the illumination score difference is greater than the first preset illumination score difference, and the illumination score difference is less than or equal to the second preset illumination score difference, determining the adjustment coefficient to be X2;

[0033] When the illumination score difference is greater than the second preset illumination score difference, the adjustment coefficient is determined to be X1;

[0034] The first preset illumination score difference is smaller than the second preset illumination score difference, X1<X2<X3<1.

[0035] Further, when the adjustment coefficient is determined to be Xi, i=1, 2, 3, including:

[0036] Acquiring the real-time ambient temperature of the drying area, and determining whether to modify the adjustment coefficient Xi according to the relationship between the real-time ambient temperature and a pre-configured preset temperature;

[0037] When the real-time ambient temperature is greater than or equal to the preset temperature, it is determined that the adjustment coefficient Xi is not to be corrected;

[0038] When the real-time ambient temperature is lower than the preset temperature, a correction coefficient is determined according to the temperature difference between the real-time ambient temperature and the preset temperature, and the adjustment coefficient Xi is corrected according to the correction coefficient.

[0039] Furthermore, when determining the correction coefficient according to the temperature difference between the real-time ambient temperature and the preset temperature, it includes:

[0040] Determining the correction coefficient according to a relationship between the temperature difference and a preconfigured first preset temperature difference and a second preset temperature difference;

[0041] When the temperature difference is less than or equal to the first preset temperature difference, the correction coefficient is determined to be C3;

[0042] When the temperature difference is greater than the first preset temperature difference and the temperature difference is less than or equal to the second preset temperature difference, the correction coefficient is determined to be C2;

[0043] When the temperature difference is greater than the second preset temperature difference, the correction coefficient is determined to be C1;

[0044] The first preset temperature difference is smaller than the second preset temperature difference, and C1<C2<C3<1.

[0045] Further, when the correction coefficient is determined to be Ci, i=1, 2, 3, including:

[0046] Acquiring the real-time ambient humidity of the drying area, and determining whether to correct the correction coefficient Ci according to the relationship between the real-time ambient humidity and the pre-configured preset humidity;

[0047] When the real-time environmental humidity is less than the preset humidity, it is determined that the correction coefficient Ci is not corrected;

[0048] When the real-time ambient humidity is greater than or equal to the preset humidity, a correction coefficient is determined according to the humidity difference between the real-time ambient humidity and the preset humidity, and the correction coefficient Ci is corrected according to the correction coefficient.

[0049] Furthermore, when determining the correction coefficient according to the humidity difference between the real-time ambient humidity and the preset humidity, it includes:

[0050] Determining the correction coefficient according to the relationship between the humidity difference and the first preset humidity difference and the second preset humidity difference that are preconfigured;

[0051] When the humidity difference is less than or equal to the first preset humidity difference, the correction coefficient is determined to be B3;

[0052] When the humidity difference is greater than the first preset humidity difference, and the humidity difference is less than or equal to the second preset humidity difference, the correction coefficient is determined to be B2;

[0053] When the humidity difference is greater than the second preset humidity difference, the correction coefficient is determined to be B1;

[0054] The first preset humidity difference is smaller than the second preset humidity difference, and B1<B2<B3<1.

[0055] Another aspect of the present invention also discloses a computer-readable storage medium, in which program instructions are stored. When the program instructions are executed, they are used to execute an intelligent monitoring method for vesicular blue spots in cigar tobacco leaves during airing.

[0056] The last aspect of the present invention also discloses an intelligent monitoring system for blue vesicles during the drying of cigar tobacco leaves, which includes the above-mentioned computer-readable storage medium.

[0057] Compared with the prior art, the beneficial effect of the present invention is that by constructing a feature set of vesicular blue spots based on image recognition technology, rapid positioning and hierarchical management of diseased tobacco leaves are achieved. By acquiring and analyzing the characteristic area and number of vesicular blue spots of tobacco leaves, the density of blue spots is calculated, and then the disease grade of diseased tobacco leaves is automatically determined. The hierarchical management enables the system to clearly judge the distribution of tobacco leaves with different disease grades, providing reliable data support for subsequent process adjustments. The classification of patient grades also facilitates intelligent screening and hierarchical early warning, reducing the detection difficulty and risk of misjudgment caused by the unclear characteristics of blue spots. In addition, by introducing environmental parameters such as illumination, ambient temperature and humidity, the blue spot early warning score of the air-drying area is analyzed more comprehensively. Through real-time collection and dynamic adjustment, the addition of these environmental parameters makes the early warning score more accurate. Compared with the static scoring system, the dynamic scoring system adjusts the early warning level according to the real-time environmental conditions, thereby improving the accuracy of the early warning, helping managers to identify and prevent potential air-drying problems earlier, and optimizing the decision-making process of the air-drying process. Finally, by setting the first and second preset warning scoring standards and determining the warning level according to the difference between the score and the actual situation, the practicality and response speed of the system are further improved. The warning level classification mechanism can prompt and guide operators to take appropriate measures according to different levels of alarms, realizing an integrated management process from blue spot detection to environmental parameter adjustment and warning response, providing a more efficient, intelligent and reliable management method for cigar tobacco leaf drying. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0059] Figure 1 A flowchart of an intelligent monitoring method for cigar tobacco leaf blister during air-drying provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0061] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides an intelligent monitoring method for cigar tobacco leaf blister during drying, comprising:

[0062] Step S100, obtaining image information of blister blue spots during the drying of each cigar tobacco leaf, and establishing a blister blue spot feature set according to the blister blue spot features in each image information.

[0063] Step S200: Acquire the surface image of each cigar tobacco leaf in the drying area, and match the surface image features with the blue bubble features in the blue bubble feature set.

[0064] Specifically, matching the liveo-bubble features between the surface image features and the liveo-bubble feature set includes: when the surface image features match the liveo-bubble features, determining that the cigar tobacco leaf corresponding to the surface image features is a diseased tobacco leaf, obtaining the area and number of the liveo-bubble features in the diseased tobacco leaf, and determining the liveo-bubble density of the diseased tobacco leaf according to the area and number of the liveo-bubble features and the area of ​​the diseased tobacco leaf. Determining the patient level of the diseased tobacco leaf according to the liveo-bubble density, wherein the patient levels are the first patient level, the second patient level, and the third patient level.

[0065] It is understandable that the surface images of each cigar leaf in the drying area are obtained by high-resolution cameras. These images not only contain the overall appearance of the tobacco leaves, but also capture the subtle changes on their surface in detail. This process is the first step of intelligent monitoring and lays the foundation for subsequent image analysis. Next, by using image recognition algorithms, especially convolutional neural networks (CNNs) in deep learning, the acquired tobacco leaf surface images are feature extracted and matched. By comparing with the pre-established vesicular blue spot feature set, it is possible to quickly determine whether there is blue spot in the surface image. The key to this process lies in the training of the algorithm. The model needs to be trained on a large amount of labeled image data to improve its recognition accuracy of blue spot features. This intelligent matching technology effectively solves the problems of misjudgment and missed judgment caused by lack of experience in manual detection, thereby significantly improving the reliability and efficiency of detection. Once the presence of vesicular blue spots in tobacco leaves is confirmed, the characteristics of these blue spots, including their area and number, are further analyzed. By calculating the ratio of the area of ​​blue spots to the area of ​​the entire diseased tobacco leaf, the important indicator of blue spot density can be obtained. Blue spot density reflects the severity of the disease and is a key parameter for evaluating the health of tobacco leaves. The higher the density, the more serious the disease condition of the tobacco leaves. Managers can quickly take corresponding treatment measures based on this data to ensure the smooth progress of the air-drying process. Finally, according to the calculated green spot density, the diseased tobacco leaves are divided into the first, second and third disease levels. This hierarchical management not only facilitates the classification of tobacco leaf quality, but also provides managers with a clear disease risk assessment. By implementing different treatment strategies for tobacco leaves of different disease levels, the impact of diseases on the entire air-drying process can be more effectively reduced. In addition, the determination of the patient level also provides data support for the subsequent early warning mechanism, making the overall quality management more scientific and accurate.

[0066] Step S300: Determine a warning score for the drying area according to the number of diseased tobacco leaves in the drying area and the disease level of each diseased tobacco leaf.

[0067] Specifically, according to the number of sick tobacco leaves in the drying area and the patient level of each sick tobacco leaf, the early warning score of the drying area is determined, including: obtaining the number of sick tobacco leaves of the first patient level, the number of sick tobacco leaves of the second patient level, and the number of sick tobacco leaves of the third patient level, and substituting the number of sick tobacco leaves of the first patient level, the number of sick tobacco leaves of the second patient level, and the number of sick tobacco leaves of the third patient level into formula I to determine the early warning score of the drying area, and formula I is as follows: P = z1×Q+z2×K+z3×J. Among them, P is the early warning score of the drying area, Q is the number of sick tobacco leaves of the first patient level, K is the number of sick tobacco leaves of the second patient level, J is the number of sick tobacco leaves of the third patient level, z1-z3 are weight coefficients, and the sum of z1-z3 is 1.

[0068] It is understandable that by quantitatively analyzing the number of tobacco leaves with different levels of disease, managers can effectively evaluate the overall health status of the drying area. Specifically, the model uses the formula P = z1 × Q + z2 × K + z3 × J, where P represents the early warning score of the drying area, which can reflect the disease risk of tobacco leaves in the area to a certain extent. The calculation of this score takes into account the number of tobacco leaves with different levels of disease, and Q, K and J represent the number of diseased tobacco leaves of the first, second and third levels of disease, respectively. In this way, managers can clearly understand the role and influence of tobacco leaves of each level of disease in the overall evaluation. Secondly, in order to ensure the scientificity and rationality of the early warning score, weight coefficients z1, z2 and z3 are introduced into the model. The setting of these weight coefficients is not only based on the relative impact of each level of disease on the quality of tobacco leaves, but can also be dynamically adjusted according to actual conditions. For example, under specific climatic conditions or production links, a certain level of disease may have a more significant impact on the overall quality. At this time, managers can adjust the weight of the level accordingly to improve the prediction accuracy and adaptability of the model. In summary, the flexibility of weights makes the model highly practical and can better meet actual production needs. In addition, the calculated early warning score P not only provides managers with a quantitative risk assessment tool, but also helps them make scientific management decisions in actual operations. When the P value is low, it means that the tobacco leaves in the drying area are in good health, and managers can continue to maintain the existing production process; when the P value rises to a certain threshold, managers need to pay attention and may need to make corresponding adjustments to the drying conditions to prevent the spread of diseases. For example, if the number of tobacco leaves in the second patient level increases significantly, managers can consider reducing humidity or increasing ventilation to improve the drying environment, thereby reducing the risk of tobacco leaf damage. Finally, when managers monitor changes in the early warning score of the drying area, they can quickly formulate countermeasures through the preset response mechanism. By combining the actual situation of the drying area with the evaluation results, managers can more effectively optimize the production process and ensure the stable quality of the final product. This forward-looking management method not only reduces production risks, but also improves overall work efficiency, helping cigar production to develop in the direction of intelligence and refinement.

[0069] Step S400: Obtain the illumination of each cigar leaf in the drying area, the ambient temperature and humidity of the drying area, and adjust the warning score according to the illumination of each cigar leaf, the ambient temperature and humidity of the drying area.

[0070] Specifically, when adjusting the warning score according to the illumination of each cigar leaf, the ambient temperature and humidity of the drying area, it includes: obtaining the illumination area of ​​each cigar leaf during the preset period, and determining the illumination proportion of each cigar leaf based on the illumination area and the area of ​​the cigar leaf. Obtain the average illumination intensity of the illumination area of ​​the cigar leaf during the preset period, and substitute the illumination proportion of each cigar leaf and the average illumination intensity during the preset period into Formula II to determine the comprehensive illumination score of the drying area. Formula II is as follows:

[0071]

[0072] Where L is the comprehensive light score of the drying area, n is the number of cigar leaves, Ri is the light ratio of cigar leaves, Ii is the average light intensity of cigar leaves in the preset period, Imax is the preset light intensity, W R and W I is the weight coefficient, and W R With W I The sum is 1. According to the relationship between the comprehensive light score L of the drying area and the pre-configured preset comprehensive light score, it is determined whether to adjust the early warning score. When the comprehensive light score L is greater than or equal to the preset comprehensive light score, it is determined that the early warning score is not adjusted. When the comprehensive light score L is less than the preset comprehensive light score, the adjustment coefficient is determined according to the light score difference between the comprehensive light score L and the preset comprehensive light score, and the early warning score is adjusted according to the adjustment coefficient.

[0073] Specifically, when determining the adjustment coefficient according to the illumination score difference between the comprehensive illumination score L and the preset comprehensive illumination score, it includes: comparing the illumination score difference with the pre-configured first preset illumination score difference and the second preset illumination score difference, and determining the adjustment coefficient according to the comparison result: when the illumination score difference is less than or equal to the first preset illumination score difference, the adjustment coefficient is determined to be X3. When the illumination score difference is greater than the first preset illumination score difference, and the illumination score difference is less than or equal to the second preset illumination score difference, the adjustment coefficient is determined to be X2. When the illumination score difference is greater than the second preset illumination score difference, the adjustment coefficient is determined to be X1. Among them, the first preset illumination score difference is less than the second preset illumination score difference, and X1<X2<X3<1.

[0074] It can be understood that by obtaining the illuminated area of ​​each cigar leaf and calculating the illumination ratio with its total area, the degree of illumination received by each leaf in a specific period of time can be reflected. The calculation of the illumination ratio is an important step, which can intuitively show the relative intensity of illumination received by the tobacco leaf. The core of this process lies in how to accurately measure and calculate the illumination conditions for subsequent scoring analysis. In this way, the actual illumination conditions of each tobacco leaf can be obtained, thereby providing an accurate data basis for the subsequent comprehensive illumination scoring. Next, considering the illumination ratio of each tobacco leaf and its average illumination intensity, the comprehensive illumination score L of the drying area is calculated using Formula II. The calculation of this score integrates the illumination information and environmental conditions of multiple tobacco leaves, ensuring that the overall impact of illumination on the tobacco leaves is fully reflected. In this process, the average illumination intensity provides a benchmark for each tobacco leaf for comparison with other tobacco leaves. In addition, an appropriate weight coefficient W is set. R and W I The influence of light proportion and light intensity can be adjusted according to actual production needs to further improve the accuracy and practicality of the score. Furthermore, the comprehensive light score L will be used to compare with the preset standard light score to determine whether the current warning score needs to be adjusted. This dynamic feedback mechanism enables the monitoring system to respond to environmental changes in real time and adjust production strategies in a timely manner. When the comprehensive light score is lower than the preset value, it means that there may be a risk of insufficient light in the drying area, which may have a negative impact on the quality of cigar tobacco leaves. Therefore, by setting reasonable preset standards, it is possible to provide producers with necessary warnings and prompt them to take corresponding measures to ensure that the quality of tobacco leaves is not damaged. If the comprehensive light score is lower than the preset value, the adjustment coefficient is calculated based on the difference between the two. This adjustment coefficient is calculated by comparing with the pre-defined light score difference threshold to determine the specific degree of impact on the warning score. This refined adjustment mechanism enables the warning score to be dynamically adjusted according to the actual light conditions, thereby achieving more accurate risk management.

[0075] Specifically, when the adjustment coefficient is determined to be Xi, i=1, 2, 3, it includes: obtaining the real-time ambient temperature of the drying area, and determining whether to correct the adjustment coefficient Xi according to the relationship between the real-time ambient temperature and the pre-configured preset temperature: when the real-time ambient temperature is greater than or equal to the preset temperature, it is determined not to correct the adjustment coefficient Xi. When the real-time ambient temperature is less than the preset temperature, the correction coefficient is determined according to the temperature difference between the real-time ambient temperature and the preset temperature, and the adjustment coefficient Xi is corrected according to the correction coefficient.

[0076] Specifically, when determining the correction coefficient according to the temperature difference between the real-time ambient temperature and the preset temperature, it includes: determining the correction coefficient according to the relationship between the temperature difference and the pre-configured first preset temperature difference and the second preset temperature difference. When the temperature difference is less than or equal to the first preset temperature difference, the correction coefficient is determined to be C3. When the temperature difference is greater than the first preset temperature difference, and the temperature difference is less than or equal to the second preset temperature difference, the correction coefficient is determined to be C2. When the temperature difference is greater than the second preset temperature difference, the correction coefficient is determined to be C1. Among them, the first preset temperature difference is less than the second preset temperature difference, and C1<C2<C3<1.

[0077] Specifically, when the correction coefficient is determined to be Ci, i=1, 2, 3, it includes: obtaining the real-time ambient humidity of the drying area, and determining whether to correct the correction coefficient Ci according to the relationship between the real-time ambient humidity and the pre-configured preset humidity: when the real-time ambient humidity is less than the preset humidity, it is determined not to correct the correction coefficient Ci. When the real-time ambient humidity is greater than or equal to the preset humidity, the correction coefficient is determined according to the humidity difference between the real-time ambient humidity and the preset humidity, and the correction coefficient Ci is corrected according to the correction coefficient.

[0078] Specifically, when determining the correction coefficient according to the humidity difference between the real-time ambient humidity and the preset humidity, it includes: determining the correction coefficient according to the relationship between the humidity difference and the pre-configured first preset humidity difference and the second preset humidity difference. When the humidity difference is less than or equal to the first preset humidity difference, the correction coefficient is determined to be B3. When the humidity difference is greater than the first preset humidity difference, and the humidity difference is less than or equal to the second preset humidity difference, the correction coefficient is determined to be B2. When the humidity difference is greater than the second preset humidity difference, the correction coefficient is determined to be B1. Among them, the first preset humidity difference is less than the second preset humidity difference, and B1<B2<B3<1.

[0079] It is understandable that during the drying process of cigar tobacco leaves, environmental conditions such as temperature and humidity have a significant impact on the quality of tobacco leaves. Therefore, real-time monitoring of these factors and making corresponding adjustments are the key to ensuring product quality. Specifically, by obtaining the ambient temperature of the drying area in real time, it is possible to determine in real time whether the current temperature is within the ideal range. When the real-time ambient temperature is higher than or equal to the preset temperature, it means that the drying conditions are good, so there is no need to modify the adjustment coefficient. However, if the real-time temperature is lower than the preset temperature, the temperature difference will be calculated and the correction coefficient will be determined based on this difference. This mechanism ensures that active adjustments can be made under low temperature conditions to avoid affecting the drying effect of tobacco leaves due to too low temperature. At the same time, in the process of determining the correction coefficient Ci, the monitoring of ambient humidity also plays a vital role. Humidity has a direct impact on the drying speed of tobacco leaves and their final quality. When the real-time ambient humidity is lower than the preset humidity, it is judged that the environmental conditions are good, so no humidity correction is performed. When the humidity exceeds the preset value, the correction coefficient is calculated by using the humidity difference to further optimize the previous correction coefficient. The implementation of this process ensures that operating parameters can be adjusted in time in high humidity environments to cope with possible moisture damage to tobacco leaves. Finally, by combining the monitoring of temperature and humidity, this section builds a multi-level intelligent monitoring and adjustment mechanism. This mechanism can not only respond to environmental changes in real time and ensure the best drying conditions for tobacco leaves, but also improve producers' early warning capabilities for potential problems. Producers can respond quickly based on real-time data, thereby effectively reducing production losses caused by environmental factors. In addition, this dynamic adjustment capability also helps to reduce the frequency of manual intervention, reduce the risks and labor intensity of inspection work, and make the entire production process safer and more efficient.

[0080] Step S500: Obtain the adjusted warning score, and determine the warning level of the drying area according to the relationship between the adjusted warning score and the pre-configured first preset warning score and second preset warning score.

[0081] Specifically, when the adjusted warning score is less than the first preset warning score, the warning level of the drying area is determined to be a low risk level. When the adjusted warning score is greater than or equal to the first preset warning score, and the adjusted warning score is less than the second preset warning level, the warning level of the drying area is determined to be a medium risk level. When the adjusted warning score is greater than or equal to the second preset warning score, the warning level of the drying area is determined to be a high risk level.

[0082] It is understandable that by collecting a variety of data related to tobacco leaf quality, including the density of blue spots, ambient light intensity, temperature and humidity. These factors are considered to be important indicators affecting the quality of cigar tobacco leaves, and an adjusted early warning score is generated by comprehensive analysis of these data. This score reflects the overall health status of the current drying area and provides a quantitative basis for subsequent risk assessment. Next, the adjusted early warning score is compared with two pre-set key thresholds. The two preset scores represent different risk levels, corresponding to low-risk and high-risk areas respectively. By checking whether the adjusted score is lower than the first preset threshold. If so, the area is judged to be at a low risk level, indicating that the health of the tobacco leaves is good, and managers can continue to observe and maintain the existing drying process without additional intervention. This design not only reduces unnecessary operations, but also effectively reduces resource waste. If the adjusted early warning score is between the first and second thresholds, the area is judged to be at a medium risk level. This level of division means that there may be certain health risks in tobacco leaves, and managers should pay attention to it and consider making appropriate adjustments to the drying conditions, such as controlling humidity, improving ventilation or increasing light. Timely intervention measures can effectively reduce the risk of disease spread, thereby ensuring the quality of the final product. Finally, when the adjusted early warning score is higher than the second preset threshold, the area is marked as a high-risk level. This indicates that the health of the tobacco leaves has seriously deteriorated, and significant green spots or other diseases may have appeared. At this time, managers need to take immediate measures, including strengthening monitoring of the air-drying process, conducting comprehensive inspections, and even isolating the affected tobacco leaves to prevent the spread of the disease. Such a high-risk warning mechanism can help managers identify and respond to potential production problems in a timely manner, thereby ensuring the safety and stability of the entire cigar production process.

[0083] In the above embodiment, by constructing a feature set of vesicular blue spots based on image recognition technology, rapid positioning and hierarchical management of diseased tobacco leaves are achieved. By acquiring and analyzing the characteristic area and number of vesicular blue spots of tobacco leaves, the density of blue spots is calculated, and then the disease grade of diseased tobacco leaves is automatically determined. The hierarchical management enables the system to clearly judge the distribution of tobacco leaves with different disease grades, providing reliable data support for subsequent process adjustments. The classification of patient grades also facilitates intelligent screening and hierarchical early warning, reducing the difficulty of detection and the risk of misjudgment due to the unclear characteristics of blue spots. In addition, by introducing environmental parameters such as illumination, ambient temperature and humidity, the blue spot early warning score of the air-drying area is analyzed more comprehensively. Through real-time collection and dynamic adjustment, the addition of these environmental parameters makes the early warning score more accurate. Compared with the static scoring system, the dynamic scoring system adjusts the early warning level according to the real-time environmental conditions, thereby improving the accuracy of the early warning, helping managers to identify and prevent potential air-drying problems earlier, and optimizing the decision-making process of the air-drying process. Finally, by setting the first and second preset warning scoring standards and determining the warning level according to the difference between the score and the actual situation, the practicality and response speed of the system are further improved. The warning level classification mechanism can prompt and guide operators to take appropriate measures according to different levels of alarms, realizing an integrated management process from blue spot detection to environmental parameter adjustment and warning response, providing a more efficient, intelligent and reliable management method for cigar tobacco leaf drying.

[0084] In another preferred embodiment based on the above embodiment, this embodiment provides a computer-readable storage medium, wherein program instructions are stored in the computer-readable storage medium, and when the program instructions are executed, they are used to execute an intelligent monitoring method for bubbly blue spots in cigar tobacco leaves during drying.

[0085] In another preferred embodiment based on the above embodiment, the present embodiment further provides an intelligent monitoring system for blue blisters during the drying of cigar tobacco leaves, wherein the intelligent monitoring system for blue blisters during the drying of cigar tobacco leaves includes the above-mentioned computer-readable storage medium.

[0086] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0087] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent monitoring method for cigar tobacco leaf blister during airing, characterized in that: include: Acquire image information of blister-like spots during the drying of each cigar tobacco leaf, and establish a blister-like spot feature set according to the blister-like spots features in each of the image information; The surface image of each cigar tobacco leaf in the drying area is obtained, and the vesicular stain features in the vesicular stain feature set are matched according to the surface image features, wherein: When the surface image feature matches the livedo bullosa feature, it is determined that the cigar tobacco leaf corresponding to the surface image feature is a diseased tobacco leaf, and the area and number of the livedo bullosa feature in the diseased tobacco leaf are obtained, and the density of the livedo bullosa of the diseased tobacco leaf is determined according to the area and number of the livedo bullosa feature and the area of ​​the diseased tobacco leaf; Determining the disease level of the diseased tobacco leaf according to the density of the livedo albosae, wherein the disease levels are a first disease level, a second disease level, and a third disease level in sequence; Determining a warning score for the airing area according to the number of diseased tobacco leaves in the airing area and the disease level of each of the diseased tobacco leaves; Obtaining the illumination of each cigar leaf in the drying area, the ambient temperature and the ambient humidity of the drying area, and adjusting the warning score according to the illumination of each cigar leaf, the ambient temperature and the ambient humidity of the drying area; The adjusted warning score is obtained, and the warning level of the drying area is determined according to the relationship between the adjusted warning score and the pre-configured first preset warning score and the second preset warning score, wherein: When the adjusted warning score is less than the first preset warning score, the warning level of the drying area is determined to be a low risk level; When the adjusted warning score is greater than or equal to the first preset warning score, and the adjusted warning score is less than the second preset warning level, the warning level of the drying area is determined to be a medium risk level; When the adjusted warning score is greater than or equal to the second preset warning score, it is determined that the warning level of the drying area is a high risk level.

2. The intelligent monitoring method for cigar tobacco leaf blister during airing as claimed in claim 1, characterized in that: Determining the early warning score of the airing area according to the number of the diseased tobacco leaves in the airing area and the disease level of each of the diseased tobacco leaves includes: The number of tobacco leaves with patients of the first patient level, the number of tobacco leaves with patients of the second patient level, and the number of tobacco leaves with patients of the third patient level are obtained, and the number of tobacco leaves with patients of the first patient level, the number of tobacco leaves with patients of the second patient level, and the number of tobacco leaves with patients of the third patient level are substituted into formula I to determine the early warning score of the drying area, and formula I is as follows: P = z1 × Q + z2 × K + z3 × J; Among them, P is the warning score of the drying area, Q is the number of patient tobacco leaves of the first patient level, K is the number of patient tobacco leaves of the second patient level, J is the number of patient tobacco leaves of the third patient level, z1-z3 are weight coefficients, and the sum of z1-z3 is 1.

3. The intelligent monitoring method for cigar tobacco leaf blister during airing as claimed in claim 1, characterized in that: When the warning score is adjusted according to the illumination of each cigar leaf, the ambient temperature and humidity of the drying area, it includes: Obtaining the illumination area of ​​each of the cigar leaves within a preset time period, and determining the illumination proportion of each of the cigar leaves according to the illumination area and the area of ​​the cigar leaves; The average light intensity of the illuminated area of ​​the cigar leaves in the preset period of time is obtained, and the light proportion of each cigar leaf and the average light intensity in the preset period of time are substituted into Formula II to determine the comprehensive light score of the drying area. Formula II is as follows: Wherein, L is the comprehensive light score of the drying area, n is the number of cigar leaves, Ri is the light ratio of the cigar leaves, Ii is the average light intensity of the cigar leaves in the preset time period, Imax is the preset light intensity, W R and W I is the weight coefficient, and W R With W I The sum is 1; Determining whether to adjust the early warning score according to the relationship between the comprehensive light score L of the drying area and a pre-configured preset comprehensive light score; When the comprehensive illumination score L is greater than or equal to the preset comprehensive illumination score, it is determined that the early warning score is not adjusted; When the comprehensive light score L is less than the preset comprehensive light score, an adjustment coefficient is determined according to the light score difference between the comprehensive light score L and the preset comprehensive light score, and the early warning score is adjusted according to the adjustment coefficient.

4. The intelligent monitoring method for cigar tobacco leaf blister during airing as claimed in claim 3, characterized in that: When determining the adjustment coefficient according to the illumination score difference between the comprehensive illumination score L and the preset comprehensive illumination score, it includes: Comparing the illumination score difference with a pre-configured first preset illumination score difference and a pre-configured second preset illumination score difference, and determining the adjustment coefficient according to the comparison result; When the illumination score difference is less than or equal to the first preset illumination score difference, the adjustment coefficient is determined to be X3; When the illumination score difference is greater than the first preset illumination score difference, and the illumination score difference is less than or equal to the second preset illumination score difference, determining the adjustment coefficient to be X2; When the illumination score difference is greater than the second preset illumination score difference, the adjustment coefficient is determined to be X1; The first preset illumination score difference is smaller than the second preset illumination score difference, X1<X2<X3<1.

5. The intelligent monitoring method for cigar tobacco leaf blister during airing as claimed in claim 4, characterized in that: When the adjustment coefficient is determined to be Xi, i=1, 2, 3, including: Acquiring the real-time ambient temperature of the drying area, and determining whether to modify the adjustment coefficient Xi according to the relationship between the real-time ambient temperature and a pre-configured preset temperature; When the real-time ambient temperature is greater than or equal to the preset temperature, it is determined that the adjustment coefficient Xi is not to be corrected; When the real-time ambient temperature is lower than the preset temperature, a correction coefficient is determined according to the temperature difference between the real-time ambient temperature and the preset temperature, and the adjustment coefficient Xi is corrected according to the correction coefficient.

6. The intelligent monitoring method for cigar tobacco leaf blister during airing as claimed in claim 5, characterized in that: When determining the correction coefficient according to the temperature difference between the real-time ambient temperature and the preset temperature, it includes: Determining the correction coefficient according to a relationship between the temperature difference and a preconfigured first preset temperature difference and a second preset temperature difference; When the temperature difference is less than or equal to the first preset temperature difference, the correction coefficient is determined to be C3; When the temperature difference is greater than the first preset temperature difference and the temperature difference is less than or equal to the second preset temperature difference, the correction coefficient is determined to be C2; When the temperature difference is greater than the second preset temperature difference, the correction coefficient is determined to be C1; The first preset temperature difference is smaller than the second preset temperature difference, and C1<C2<C3<1.

7. The intelligent monitoring method for cigar tobacco leaf blister during airing as claimed in claim 6, characterized in that: When the correction coefficient is determined to be Ci, i=1, 2, 3, including: Acquiring the real-time ambient humidity of the drying area, and determining whether to correct the correction coefficient Ci according to the relationship between the real-time ambient humidity and the pre-configured preset humidity; When the real-time environmental humidity is less than the preset humidity, it is determined that the correction coefficient Ci is not corrected; When the real-time ambient humidity is greater than or equal to the preset humidity, a correction coefficient is determined according to the humidity difference between the real-time ambient humidity and the preset humidity, and the correction coefficient Ci is corrected according to the correction coefficient.

8. The intelligent monitoring method for cigar tobacco leaf blister during airing as claimed in claim 7, characterized in that: When determining the correction coefficient according to the humidity difference between the real-time ambient humidity and the preset humidity, it includes: Determining the correction coefficient according to the relationship between the humidity difference and the first preset humidity difference and the second preset humidity difference that are preconfigured; When the humidity difference is less than or equal to the first preset humidity difference, the correction coefficient is determined to be B3; When the humidity difference is greater than the first preset humidity difference, and the humidity difference is less than or equal to the second preset humidity difference, the correction coefficient is determined to be B2; When the humidity difference is greater than the second preset humidity difference, the correction coefficient is determined to be B1; The first preset humidity difference is smaller than the second preset humidity difference, and B1<B2<B3<1.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are executed, they are used to execute the intelligent monitoring method for vesicular blue spots in cigar tobacco leaves during airing as described in claims 1-8.

10. An intelligent monitoring system for cigar tobacco leaf blister spots during air-drying, characterized in that: Includes the computer-readable storage medium of claim 9.

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