An identification and processing method and device for the stacking fermentation of cigar tobacco leaves

Abstract

The present application discloses a recognition and processing method and device for cigar tobacco leaf stacking fermentation. The method includes determining the number of layers of the tobacco leaf platform for out-stack and the target stacking number of each layer of the tobacco leaf platform for out-stack according to the fermentation weight of the tobacco leaves to be processed; when it is detected that the actual stacking number of each layer is consistent with the target stacking number, acquiring the detection parameters of each layer of the tobacco leaf platform for out-stack; performing regression analysis processing on the detection parameters of each layer of the tobacco leaf platform for out-stack to obtain state characterization parameters, and performing stack turning processing according to the state characterization parameters. By performing hierarchical shelving processing on the tobacco leaf stacks and combining the detection parameters collected by a variety of sensors to judge the state of each layer of tobacco leaf stacks in real time, so as to perform automatic stack turning processing on each layer of tobacco leaf stacks according to this state, effectively solving the problem of over-fermentation or under-fermentation caused by unbalanced local fermentation during the stacking fermentation process, and eliminating the need for manual experience judgment, thereby improving the overall work efficiency.

Description

Technical Field

[0001] This application belongs to the technical field of tobacco leaf processing, and particularly relates to a recognition and processing method and device for cigar tobacco leaf stacking fermentation. Background Art

[0002] Tobacco leaf stacking fermentation is to stack tobacco leaf raw materials with a certain moisture content into a tobacco stack with a certain volume and place it in a fermentation workshop with constant temperature and humidity. Microorganisms in the stack grow and metabolize according to the substances contained in the tobacco leaves and generate heat, further promoting the biochemical changes in the tobacco leaves to achieve the purpose of improving the processing quality of tobacco leaves.

[0003] Due to the large volume of the stack, there is obvious unevenness in the fermentation degree during the fermentation process of the tobacco leaves in the stack, and it is necessary to turn the stack multiple times during the fermentation process of the tobacco leaves. However, this processing method is mainly based on manual experience judgment. The fermentation degree and quality of the tobacco leaves need to be judged by artificially smelling the smell of the tobacco leaves combined with sensory evaluation, and the subjectivity is relatively strong. Secondly, it is impossible to effectively avoid over-fermentation or under-fermentation of the tobacco leaves, that is, the fermentation duration of the stack is uncertain, which greatly affects the work efficiency. Summary of the Invention

[0004] To solve the above-mentioned technical defects that the fermentation degree and quality of tobacco leaves need to be judged by artificially smelling the smell of tobacco leaves combined with sensory evaluation, with relatively strong subjectivity, secondly, it is impossible to effectively avoid over-fermentation or under-fermentation of tobacco leaves, that is, the fermentation duration of the stack is uncertain, which greatly affects the work efficiency, etc., this application proposes a recognition and processing method and device for cigar tobacco leaf stacking fermentation, including:

[0005] In a first aspect, an embodiment of this application provides a recognition and processing method for cigar tobacco leaf stacking fermentation, including:

[0006] Determine the number of layers of the tobacco stack platform for out-of-stack tobacco leaves and the target number of stacks for each layer of the tobacco stack platform according to the fermentation weight of the tobacco leaves to be processed;

[0007] When it is detected that the actual number of stacks on each layer of the tobacco stack platform is consistent with the corresponding target number of stacks, obtain the detection parameters of each layer of the tobacco stack platform at a preset time interval;

[0008] Perform regression analysis on the detection parameters of each layer of the tobacco stack platform to obtain a state characterization parameter, and perform stack turning processing on the tobacco stack platform according to the state characterization parameter.

[0009] In an optional solution of the first aspect, the types of detection parameters include temperature parameters and gas concentration parameters;

[0010] Performing regression analysis on the detection parameters of each layer of the tobacco stack platform to obtain a state characterization parameter, including:

[0011] Construct a temperature change function based on the temperature parameters corresponding to each layer of the stacked tobacco leaf platform at at least two time intervals, and perform a derivative operation on the temperature change function to obtain a temperature derivative function;

[0012] Construct a concentration change function based on the gas concentration parameters corresponding to each layer of the stacked tobacco leaf platform at at least two time intervals, and perform a derivative operation on the concentration change function to obtain a concentration derivative function;

[0013] Input the temperature derivative function and the concentration derivative function into a preset regression learning model to obtain state characterization parameters.

[0014] In another alternative solution of the first aspect, performing a turning operation on the stacked tobacco leaf platform according to the state characterization parameters includes:

[0015] When the state characterization parameter is in a preset first interval, determine the vertical distance between the stacked tobacco leaf platform and the adjacent stacked tobacco leaf platform;

[0016] When the vertical distance is in a preset distance interval, perform a first turning operation on the stacked tobacco leaf platform according to a preset first turning path.

[0017] In another alternative solution of the first aspect, performing a turning operation on the stacked tobacco leaf platform according to the state characterization parameters further includes:

[0018] When the state characterization parameter is in a preset second interval, determine whether the temperature change differences corresponding to any two adjacent time intervals are consistent;

[0019] When the temperature change differences corresponding to any two adjacent time intervals are consistent, perform an nth turning operation on the stacked tobacco leaf platform according to a preset second turning path; where n is a positive integer greater than or equal to 2.

[0020] In another alternative solution of the first aspect, after performing a turning operation on the stacked tobacco leaf platform according to the state characterization parameters, it further includes:

[0021] When the number of turning operations on the stacked tobacco leaf platform exceeds a preset number threshold, determine whether the highest temperature corresponding to any three consecutive turning operations satisfies a preset change trend;

[0022] When the highest temperature corresponding to any three consecutive turning operations satisfies a preset change trend, calculate the temperature mean value corresponding to each turning operation in sequence;

[0023] Determine whether the temperature mean values corresponding to any three consecutive turning operations satisfy a preset change trend;

[0024] When the average temperature corresponding to any three consecutive stacking treatments satisfies a preset change trend, it is determined that the fermentation treatment of the tobacco leaf platform of the stack stops.

[0025] In another alternative of the first aspect, after the average temperature corresponding to any three consecutive stacking treatments satisfies a preset change trend and before it is determined that the fermentation treatment of the tobacco leaf platform of the stack stops, it further includes:

[0026] Judge whether the maximum gas concentrations corresponding to the first two stacking treatments are the same among the maximum gas concentrations corresponding to any three consecutive stacking treatments, and whether the maximum gas concentrations corresponding to the first two stacking treatments and the maximum gas concentration corresponding to the last stacking treatment satisfy a preset change trend;

[0027] Determining that the fermentation treatment of the tobacco leaf platform of the stack stops includes:

[0028] When the maximum gas concentrations corresponding to the first two stacking treatments are the same and the maximum gas concentrations corresponding to the first two stacking treatments and the maximum gas concentration corresponding to the last stacking treatment satisfy a preset change trend, it is determined that the fermentation treatment of the tobacco leaf platform of the stack stops.

[0029] In another alternative of the first aspect, before determining that the fermentation treatment of the tobacco leaf platform of the stack stops, it further includes:

[0030] Judge whether the humidity change difference corresponding to any one of the stacking treatments is within a preset difference interval among the humidity parameters corresponding to any three consecutive stacking treatments;

[0031] Determining that the fermentation treatment of the tobacco leaf platform of the stack stops includes:

[0032] When the humidity change difference corresponding to any one of the stacking treatments is within the preset difference interval, it is determined that the fermentation treatment of the tobacco leaf platform of the stack stops.

[0033] In a second aspect, an identification processing device for cigar tobacco leaf stacking fermentation provided by an embodiment of the present application includes:

[0034] A first processing module, configured to determine the number of layers of the tobacco leaf platform of the stack and the target stacking number of each layer of the tobacco leaf platform of the stack according to the fermentation weight of the tobacco leaves to be processed;

[0035] A second processing module, configured to obtain the detection parameters of each layer of the tobacco leaf platform of the stack at a preset time interval when it is detected that the actual stacking number of each layer of the tobacco leaf platform of the stack is the same as the corresponding target stacking number;

[0036] A third processing module is configured to perform regression analysis on the detection parameters of each layer of the stack of tobacco leaves platform, obtain state characterization parameters, and perform stack turning processing on the stack of tobacco leaves platform according to the state characterization parameters.

[0037] In a third aspect, an identification and processing device for cigar tobacco leaf stacking and fermentation provided by an embodiment of the present application includes a processor and a memory;

[0038] The processor is connected to the memory;

[0039] The memory is used to store executable program codes;

[0040] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the identification and processing method for cigar tobacco leaf stacking and fermentation provided by the first aspect or any one implementation manner of the first aspect of the embodiment of the present application.

[0041] In a fourth aspect, an embodiment of the present application provides a computer storage medium. The computer storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the identification and processing method for cigar tobacco leaf stacking and fermentation provided by the first aspect or any one implementation manner of the first aspect of the embodiment of the present application can be implemented.

[0042] In the embodiment of the present application, during the fermentation process of the cigar tobacco leaf stack, the number of layers of the stack of tobacco leaves platform and the target number of stacks of each layer of the stack of tobacco leaves platform can be determined according to the fermentation weight of the tobacco leaves to be processed; when it is detected that the actual number of stacks of each layer of the stack of tobacco leaves platform is consistent with the corresponding target number of stacks, the detection parameters of each layer of the stack of tobacco leaves platform are obtained at a preset time interval; regression analysis is performed on the detection parameters of each layer of the stack of tobacco leaves platform to obtain state characterization parameters, and stack turning processing is performed on the stack of tobacco leaves platform according to the state characterization parameters. By performing hierarchical shelving processing on the tobacco leaf stack and combining the detection parameters collected by a variety of sensors to judge the state of each layer of tobacco leaf stack in real time, so as to perform automatic stack turning processing on each layer of tobacco leaf stack according to this state, effectively solving the problem of over-fermentation or under-fermentation caused by unbalanced local fermentation during the stacking and fermentation process, and eliminating the need for manual experience judgment, thereby improving the overall work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 The overall flowchart of a recognition and processing method for cigar tobacco leaf stacking fermentation provided by an embodiment of the present application;

[0045] Figure 2 The schematic structural diagram of a system for cigar tobacco leaf stacking fermentation provided by an embodiment of the present application;

[0046] Figure 3 The schematic structural diagram of an identification and processing device for cigar tobacco leaf stacking fermentation provided by an embodiment of the present application;

[0047] Figure 4 The schematic structural diagram of another identification and processing device for cigar tobacco leaf stacking fermentation provided by an embodiment of the present application. Detailed implementation manners

[0048] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application.

[0049] In the following introduction, the terms "first" and "second" are only for the purpose of description and cannot be construed as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application. Different embodiments can be replaced or combined, so the present application can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present application should also be considered to include embodiments containing all other possible combinations of A, B, C, and D, although such embodiments may not be explicitly described in the following content.

[0050] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes can be made to the functions and arrangements of the described elements without departing from the scope of the content of the present application. Each example can omit, substitute, or add various processes or components as appropriate. For example, the described method can be executed in a different order than the described order, and various steps can be added, omitted, or combined. In addition, the features described in some examples can be combined into other examples.

[0051] Please refer to Figure 1 , Figure 1 which shows the overall flowchart of a recognition and processing method for cigar tobacco leaf stacking fermentation provided by an embodiment of the present application.

[0052] As Figure 1 shown, the recognition and processing method for cigar tobacco leaf stacking fermentation can at least include the following steps:

[0053] Step 102: Determine the number of layers of the stack tobacco leaf platform and the target number of stacks on each layer of the stack tobacco leaf platform according to the fermentation weight of the tobacco leaves to be processed.

[0054] In the embodiment of the present application, the identification processing method for cigar tobacco leaf stacking fermentation can be but is not limited to being applied in a tobacco leaf stacking fermentation system. The tobacco leaf stacking fermentation system may include at least two layers of stack tobacco leaf platforms, a motor and a guide rail for controlling the up and down movement of each layer of stack tobacco leaf platform, and an integrated sensor for collecting data of the stacks on each layer of stack tobacco leaf platform. Among them, each layer of stack tobacco leaf platform can be used to place stacks distributed in an array. The distance and weight between each stack can be but are not limited to being kept consistent. And as the motor controls each layer of stack tobacco leaf platform to move according to a preset movement mode, the stacks on the stack tobacco leaf platform can be automatically turned over; the integrated sensor can be but is not limited to being arranged directly above the center of each layer of stack tobacco leaf platform to obtain data such as temperature data and gas concentration data generated by the stacks on each layer of stack tobacco leaf platform during the fermentation process.

[0055] It can be understood that the tobacco leaf stacking fermentation system can analyze and process the sensor data collected during the fermentation process of the stacks placed on each layer of stack tobacco leaf platform to obtain the state characterization parameters corresponding to the stacks on each layer of stack tobacco leaf platform, and judge whether it is necessary to turn over the stacks on each layer of stack tobacco leaf platform according to the state characterization parameters, which can effectively solve the problems of over-fermentation or under-fermentation caused by unbalanced local fermentation during the stacking fermentation process and does not require manual experience judgment, thereby improving the overall work efficiency.

[0056] Specifically, during the process of fermenting the cigar tobacco leaf stacks, first determine the number of layers of the stack tobacco leaf platforms for placing the tobacco leaves to be processed according to the fermentation weight of the tobacco leaves to be processed and the fermentation weight corresponding to each layer of stack tobacco leaf platform in the ideal state. The number of layers of the stack tobacco leaf platform can be but is not limited to being obtained according to the ratio between the fermentation weight of the tobacco leaves to be processed and the fermentation weight corresponding to each layer of stack tobacco leaf platform in the ideal state, so as to ensure that the fermentation effect of the tobacco leaf stacks on each layer of stack tobacco leaf platform is more ideal.

[0057] Further, after obtaining the number of layers of the stack tobacco leaf platform for placing the tobacco leaves to be processed, the quality of the tobacco leaves to be processed and the number of layers of the stack tobacco leaf platform can be used to determine the quality of the tobacco leaves to be processed corresponding to each layer of the stack tobacco leaf platform. Combining the number of stack positions set for each layer of the stack tobacco leaf platform, the required target stack number for each layer of the stack tobacco leaf platform and the target position corresponding to each stack can be obtained. Preferably, the required target stack number for each layer of the stack tobacco leaf platform and the target position corresponding to each stack can be kept consistent. Here, in order to ensure that the data collected by the integrated sensor is more accurate, the areas directly below the integrated sensors of each layer of the stack tobacco leaf platform can be preferentially used as the target positions, that is, the stacks divided from the tobacco leaves to be processed are preferentially placed at these target positions, and the quality of the stacks placed at each target position can be kept consistent. It can be understood that if the quality of the stack placed at the target position exceeds the preset quality threshold, a plurality of alternative positions can be determined around the target position, and the quality of the stack corresponding to each position can be determined according to the number of the target position and the alternative positions. This is not limited in the embodiments of the present application.

[0058] Step 104: When it is detected that the actual stack number of each layer of the stack tobacco leaf platform is consistent with the corresponding target stack number, obtain the detection parameters of each layer of the stack tobacco leaf platform at preset time intervals.

[0059] Specifically, after determining the number of layers of the stack tobacco leaf platform and the target stack number of each layer of the stack tobacco leaf platform, the number of layers of the stack tobacco leaf platform and the target stack number of each layer of the stack tobacco leaf platform can be, but not limited to, fed back to the operators in the workshop, so that the operators can divide the corresponding stacks from the tobacco leaves to be processed and place them on each layer of the stack tobacco leaf platform. Of course, in combination with a preset automatic control program, according to the number of layers of the stack tobacco leaf platform and the target stack number of each layer of the stack tobacco leaf platform, the corresponding stacks can be automatically divided from the tobacco leaves to be processed and automatically placed at the target positions on each layer of the stack tobacco leaf platform.

[0060] Further, in the process of placing the stacks divided from the tobacco leaves to be processed on each layer of the stack tobacco leaf platform by manual or automatic control program, the stack images on each layer of the stack tobacco leaf platform can be, but not limited to, obtained by a photographing device, and the actual stack number of each layer of the stack tobacco leaf platform can be recognized in the stack images through algorithms such as image recognition. It can be understood that when it is detected that the actual stack number of each layer of the stack tobacco leaf platform is consistent with the corresponding target stack number, it indicates that all the stacks have been placed at the designated positions at this time. Further, the data of the stacks on each layer of the stack tobacco leaf platform can be, but not limited to, collected by an integrated sensor to collect detection parameters such as temperature parameters and gas concentration parameters corresponding to the stacks during the fermentation process.

[0061] Here, the integrated sensor can, but is not limited to, be integrated with a temperature sensor and a gas concentration detection sensor to collect the temperature and gas concentration generated by the stack of tobacco leaves on each layer of the stack platform during the fermentation process, and the collection method can be, at preset time intervals, to periodically analyze the changes in the temperature and gas concentration generated by the stack of tobacco leaves on each layer of the stack platform during the fermentation process.

[0062] Step 106: Perform regression analysis on the detection parameters of each layer of the stack of tobacco leaves platform to obtain state characterization parameters, and perform stack turning on the stack of tobacco leaves platform according to the state characterization parameters.

[0063] Specifically, after obtaining the detection parameters of each layer of the stack of tobacco leaves platform, it can, but is not limited to, analyze and process the detection parameters of each layer of the stack of tobacco leaves platform to obtain state characterization parameters for characterizing the fermentation state. The state characterization parameters can be obtained by statistical analysis of the historical fermentation records of the tobacco leaf stack, so as to quickly judge the current fermentation state of the tobacco leaves. Here, the method of analyzing and processing the detection parameters of each layer of the stack of tobacco leaves platform can be, but is not limited to, performing regression analysis on the temperature parameters and gas concentration parameters of each layer of the stack of tobacco leaves platform to predict the state characterization parameters corresponding to the temperature parameters and gas concentration parameters. For example, the temperature parameters and gas concentration parameters can be input into a preset regression learning model, and the result output by the regression learning model can be used as the state characterization parameter.

[0064] As an option in the embodiment of the present application, performing regression analysis on the detection parameters of each layer of the stack of tobacco leaves platform to obtain state characterization parameters includes:

[0065] Construct a temperature change function according to the temperature parameters corresponding to each layer of the stack of tobacco leaves platform at at least two time intervals, and perform a derivative operation on the temperature change function to obtain a temperature derivative function;

[0066] Construct a concentration change function according to the gas concentration parameters corresponding to each layer of the stack of tobacco leaves platform at at least two time intervals, and perform a derivative operation on the concentration change function to obtain a concentration derivative function;

[0067] Input the temperature derivative function and the concentration derivative function into a preset regression learning model to obtain state characterization parameters.

[0068] Specifically, in the process of obtaining the state characterization parameters, it can, but is not limited to, construct a temperature change function according to the change law corresponding to the temperature parameters of each layer of the stack of tobacco leaves platform at at least two time intervals. The independent variable of the temperature change function can be the moment corresponding to each time interval, and the dependent variable can be the temperature parameter corresponding to each moment, and each moment and the corresponding temperature parameter can satisfy or be close to satisfying the temperature change function.

[0069] Next, after obtaining the temperature change function, the temperature change function can be differentiated to use the processed temperature derivative function as an input variable of the regression learning model.

[0070] Next, it is also possible but not limited to construct a concentration change function according to the change rule corresponding to the gas concentration parameters of each layer of stacked tobacco leaf platforms at at least two time intervals. The independent variable of the concentration change function can be the moment corresponding to each time interval, the dependent variable can be the gas concentration parameter corresponding to each moment, and each moment and the corresponding gas concentration parameter can satisfy or approximately satisfy the concentration change function.

[0071] Next, after obtaining the concentration change function, the concentration change function can be differentiated to use the processed concentration derivative function as another input variable of the regression learning model.

[0072] Next, after obtaining the temperature derivative function and the concentration derivative function respectively, the temperature derivative function and the concentration derivative function can be input into a preset regression learning model to predict the state characterization parameter. It can be understood that the preset regression learning model can be trained by various temperature derivative functions, concentration derivative functions and corresponding state characterization parameters, and the state characterization parameter can be defined manually according to the historical fermentation records of the tobacco leaf stack, but it is not limited to this here.

[0073] As another option of the embodiment of the present application, turning the stacked tobacco leaf platform according to the state characterization parameter includes:

[0074] When the state characterization parameter is in a preset first interval, determine the vertical distance between the stacked tobacco leaf platform and the adjacent stacked tobacco leaf platform;

[0075] When the vertical distance is in a preset distance interval, perform a first turning process on the stacked tobacco leaf platform according to a preset first turning path.

[0076] Specifically, in the process of turning the stacked tobacco leaf platform according to the state characterization parameter, it is possible but not limited to judge whether to perform the first turning process or the nth turning process (n is a positive integer greater than or equal to 2) on the stack on the stacked tobacco leaf platform according to the interval where the state characterization parameter is located. The turning paths corresponding to the first turning process and the nth turning process may be different here, and in order to ensure better turning effect and fermentation effect of the stack, the turning degree corresponding to the first turning process can be greater than the turning degree corresponding to the nth turning process.

[0077] It can be understood that when the state characterization parameter is within a preset first interval, it indicates that the stack of tobacco leaves on the stack tobacco leaf platform is in the initial stage of fermentation treatment. To ensure a better fermentation effect, the vertical distance between the stack tobacco leaf platform and the adjacent stack tobacco leaf platform can be determined to judge whether the stack tobacco leaf platform meets the turning condition for the first turning treatment. When it is detected that the vertical distance is within the preset distance interval, it indicates that the vertical distance between the stack tobacco leaf platform and the adjacent stack tobacco leaf platform is relatively far. Then, the stack on the stack tobacco leaf platform can be turned according to the preset first turning path.

[0078] Here, the preset first turning path can, but is not limited to, first moving the stack tobacco leaf platform vertically upward along the guide rail by a certain distance, and then quickly moving vertically downward by a certain distance, so that during the rapid upward movement of the stack tobacco leaf platform, the stack is separated from the stack tobacco leaf platform, and the stack falls back onto the stack tobacco leaf platform that has stopped descending under the action of its own gravity. At this time, the stack has undergone a certain degree of flipping change compared to its initial state. Moreover, the faster the stack tobacco leaf platform moves vertically upward, the greater the flipping change amplitude of the stack compared to its initial state. In other words, the acceleration (which can also be speed or distance) that controls the stack tobacco leaf platform to move vertically upward along the guide rail in the first turning path corresponding to the first turning treatment is greater than the acceleration (which can also be speed or distance) that controls the stack tobacco leaf platform to move vertically upward along the guide rail in the second turning path corresponding to the nth turning treatment.

[0079] As another option of the embodiment of the present application, turning the stack tobacco leaf platform according to the state characterization parameter further includes:

[0080] When the state characterization parameter is within a preset second interval, judge whether the temperature change differences corresponding to any two adjacent time intervals are the same;

[0081] When the temperature change differences corresponding to any two adjacent time intervals are the same, perform the nth turning treatment on the stack tobacco leaf platform according to the preset second turning path; where n is a positive integer greater than or equal to 2.

[0082] Specifically, during the process of turning the stack tobacco leaf platform according to the state characterization parameter, when the state characterization parameter is within the preset second interval, it indicates that the stack on the stack tobacco leaf platform is in the middle or late stage of fermentation treatment. To ensure a better fermentation effect, after determining that the vertical distance between the stack tobacco leaf platform and the adjacent stack tobacco leaf platform is relatively far, it can be judged whether the temperature change differences corresponding to any two adjacent time intervals are the same at this time. Here, the temperature change difference can be understood as the difference between the temperature corresponding to the end moment and the temperature corresponding to the start moment within the time interval.

[0083] It can be understood that when the temperature change differences corresponding to any two adjacent time intervals are consistent, the nth turning operation can be performed on the stack of tobacco leaves on the stack platform of the tobacco leaves according to the preset second turning path, and after the nth turning operation, the temperature parameters and gas concentration parameters generated by the stack of tobacco leaves on the stack platform can be obtained again at the preset time interval. To determine whether it is necessary to perform another turning operation on the stack of tobacco leaves on the stack platform, or whether it is necessary to stop the turning operation on the stack of tobacco leaves on the stack platform. Among them, the preset second turning path can be referred to the above embodiments and will not be elaborated here too much.

[0084] As another option of the embodiment of the present application, after performing the turning operation on the stack platform of the tobacco leaves according to the state characterization parameter, it further includes:

[0085] When the number of turning operations on the stack platform of the tobacco leaves exceeds the preset number threshold, it is determined whether the highest temperature corresponding to any three consecutive turning operations satisfies the preset change trend;

[0086] When the highest temperature corresponding to any three consecutive turning operations satisfies the preset change trend, the temperature average value corresponding to each turning operation is calculated in sequence;

[0087] It is determined whether the temperature average value corresponding to any three consecutive turning operations satisfies the preset change trend;

[0088] When the temperature average value corresponding to any three consecutive turning operations satisfies the preset change trend, it is determined that the fermentation treatment of the stack platform of the tobacco leaves is stopped.

[0089] Specifically, after performing the turning operation on the stack platform of the tobacco leaves according to the state characterization parameter, the number of turning operations corresponding to the stack platform of the tobacco leaves can be counted in real time, and when the number of turning operations exceeds the preset number threshold, it is necessary to combine the currently collected temperature parameters to determine whether it is necessary to stop the turning operation on the stack platform of the tobacco leaves.

[0090] It can be understood that when the highest temperature corresponding to any three consecutive turning operations satisfies the preset change trend, the preset change trend can be, but is not limited to, a gradually decreasing trend, that is, when the highest temperature corresponding to three consecutive turning operations is gradually decreasing, the temperature average value corresponding to each turning operation can be calculated again to determine whether it is necessary to stop the turning operation on the stack platform of the tobacco leaves according to the temperature average value.

[0091] Next, when it is detected that the average temperature corresponding to any three consecutive stack - turning processes is also gradually decreasing, it indicates that the stack on the stack - pile tobacco leaf platform is approaching the end of fermentation. Subsequently, the stack - turning process for this stack - pile tobacco leaf platform can be stopped, and the fermented stack can be transferred to the next process manually or through an automatic control program to promote the subsequent processing of tobacco leaves.

[0092] As another option in the embodiment of the present application, after the average temperature corresponding to any three consecutive stack - turning processes meets the preset change trend, before determining to stop the fermentation process of the stack - pile tobacco leaf platform, it further includes:

[0093] Judging whether the maximum gas concentrations corresponding to the first two stack - turning processes among the maximum gas concentrations corresponding to any three consecutive stack - turning processes are the same, and whether the maximum gas concentrations corresponding to the first two stack - turning processes and the maximum gas concentration corresponding to the last stack - turning process meet the preset change trend;

[0094] Determining to stop the fermentation process of the stack - pile tobacco leaf platform includes:

[0095] When the maximum gas concentrations corresponding to the first two stack - turning processes are the same, and the maximum gas concentrations corresponding to the first two stack - turning processes and the maximum gas concentration corresponding to the last stack - turning process meet the preset change trend, it is determined to stop the fermentation process of the stack - pile tobacco leaf platform.

[0096] Specifically, after the average temperature corresponding to any three consecutive stack - turning processes meets the preset change trend, it is also possible, but not limited to, combining gas concentration parameters to further determine whether to stop the stack - turning process for the stack - pile tobacco leaf platform, so as to effectively ensure the accuracy of the determination.

[0097] It can be understood that when the maximum gas concentrations corresponding to any three consecutive stack - turning processes, the maximum gas concentrations corresponding to the first two stack - turning processes are the same, and the maximum gas concentrations corresponding to the first two stack - turning processes and the maximum gas concentration corresponding to the last stack - turning process meet the preset change trend, that is, the maximum gas concentrations corresponding to the first two stack - turning processes are the same and greater than the maximum gas concentration corresponding to the third stack - turning process, it indicates that the stack on the stack - pile tobacco leaf platform is approaching the end of fermentation. Subsequently, the stack - turning process for this stack - pile tobacco leaf platform can be stopped, and the fermented stack can be transferred to the next process manually or through an automatic control program to promote the subsequent processing of tobacco leaves.

[0098] Here, it is also possible that when the maximum gas concentration corresponding to any three consecutive piling treatments satisfies the preset change trend, that is, when the maximum gas concentration corresponding to three consecutive piling treatments gradually decreases, it can also indicate that the stack on the stack tobacco leaf platform is approaching the end of fermentation. Furthermore, the piling treatment of the stack tobacco leaf platform can be stopped, and the fermented stack can be transferred to the next process by manual or automatic control program to promote the subsequent processing process of tobacco leaves.

[0099] As another option of the embodiment of the present application, before determining the stop of the fermentation treatment of the stack tobacco leaf platform, it further includes:

[0100] Judging whether the humidity change difference corresponding to any one piling treatment among the humidity parameters corresponding to any three consecutive piling treatments is within a preset difference interval;

[0101] The determination of the stop of the fermentation treatment of the stack tobacco leaf platform includes:

[0102] When the humidity change difference corresponding to any one piling treatment is within the preset difference interval, it is determined that the fermentation treatment of the stack tobacco leaf platform stops.

[0103] Specifically, before determining the stop of the fermentation treatment of the stack tobacco leaf platform, it is also possible but not limited to combine the humidity parameters corresponding to three consecutive piling treatments to further judge whether the stack on the stack tobacco leaf platform is approaching the end of fermentation. It can be understood that when the humidity change difference corresponding to any one piling treatment is within the preset difference interval, it indicates that the stack on the stack tobacco leaf platform is approaching the end of fermentation. Furthermore, the fermented stack can be transferred to the next process by manual or automatic control program to promote the subsequent processing process of tobacco leaves.

[0104] Please refer to Figure 2 , Figure 2 which shows a schematic structural diagram of a system for cigar tobacco leaf stacking fermentation provided by an embodiment of the present application. As Figure 2 shown, the tobacco leaf stacking fermentation system may include a motor 1, a motor buffer pad 2, a fixed bracket 3, a power distribution box 4, a pulley 5, a buffer pad 6, a steel cable 7, a top plate 8, an integrated sensor 9, a stack tobacco leaf sub-platform 10, a guide rail column 11, a control panel 12, a rubber foot pad 13, a pipeline integration 14, and a central column 15. The motor 1 can be used to control the up and down movement of each layer of the stack tobacco leaf sub-platform 10 along the guide rail column 11 to realize the piling treatment of the stack on each layer of the stack tobacco leaf platform 10, and a position for placing the stack can be set on each layer of the stack tobacco leaf sub-platform 10, which can be but not limited to obtaining the number of layers of the stack tobacco leaf platform and the number of target stacks on each layer of the stack tobacco leaf platform according to the fermentation quality of the tobacco leaves to be processed input by the user on the control panel 12.

[0105] Please refer to Figure 3 , Figure 3 which shows a schematic structural diagram of an identification processing device for cigar tobacco stacking fermentation provided by an embodiment of the present application.

[0106] As Figure 3 shown, the identification processing device for cigar tobacco stacking fermentation may at least include a first processing module 301, a second processing module 302, and a third processing module 303, where:

[0107] The first processing module 301 is configured to determine the number of layers of the out-stack tobacco platform and the target stacking number of each layer of the stacking tobacco platform according to the fermentation weight of the tobacco to be processed;

[0108] The second processing module 302 is configured to obtain the detection parameters of each layer of the stacking tobacco platform at a preset time interval when it is detected that the actual stacking number of each layer of the stacking tobacco platform is consistent with the corresponding target stacking number;

[0109] The third processing module 303 is configured to perform regression analysis processing on the detection parameters of each layer of the stacking tobacco platform to obtain a state characterization parameter, and perform a turning-over processing on the stacking tobacco platform according to the state characterization parameter.

[0110] In some possible embodiments, the types of the detection parameters include temperature parameters and gas concentration parameters;

[0111] Performing regression analysis processing on the detection parameters of each layer of the stacking tobacco platform to obtain a state characterization parameter includes:

[0112] Constructing a temperature change function according to the temperature parameters corresponding to each layer of the stacking tobacco platform at at least two time intervals, and performing a derivative processing on the temperature change function to obtain a temperature derivative function;

[0113] Constructing a concentration change function according to the gas concentration parameters corresponding to each layer of the stacking tobacco platform at at least two time intervals, and performing a derivative processing on the concentration change function to obtain a concentration derivative function;

[0114] Inputting the temperature derivative function and the concentration derivative function into a preset regression learning model to obtain a state characterization parameter.

[0115] In some possible embodiments, performing a turning-over processing on the stacking tobacco platform according to the state characterization parameter includes:

[0116] When the state characterization parameter is in a preset first interval, determining the vertical distance between the stacking tobacco platform and the adjacent stacking tobacco platform;

[0117] When the vertical distance is within a preset distance range, the first stacking turnover process is performed on the stacked tobacco leaf platform according to a preset first stacking turnover path.

[0118] In some possible embodiments, performing the stacking turnover process on the stacked tobacco leaf platform according to the state characterization parameter further includes:

[0119] When the state characterization parameter is within a preset second range, determine whether the temperature change differences corresponding to any two adjacent time intervals are consistent;

[0120] When the temperature change differences corresponding to any two adjacent time intervals are consistent, perform the nth stacking turnover process on the stacked tobacco leaf platform according to a preset second stacking turnover path; where n is a positive integer greater than or equal to 2.

[0121] In some possible embodiments, after performing the stacking turnover process on the stacked tobacco leaf platform according to the state characterization parameter, it further includes:

[0122] When the number of stacking turnover processes of the stacked tobacco leaf platform exceeds a preset number threshold, determine whether the highest temperature corresponding to any three consecutive stacking turnover processes satisfies a preset change trend;

[0123] When the highest temperature corresponding to any three consecutive stacking turnover processes satisfies a preset change trend, calculate the temperature mean value corresponding to each stacking turnover process in sequence;

[0124] Determine whether the temperature mean values corresponding to any three consecutive stacking turnover processes satisfy a preset change trend;

[0125] When the temperature mean values corresponding to any three consecutive stacking turnover processes satisfy a preset change trend, determine that the fermentation process of the stacked tobacco leaf platform stops.

[0126] In some possible embodiments, after the temperature mean values corresponding to any three consecutive stacking turnover processes satisfy a preset change trend and before determining that the fermentation process of the stacked tobacco leaf platform stops, it further includes:

[0127] Determine whether the maximum gas concentrations corresponding to the first two stacking turnover processes among the maximum gas concentrations corresponding to any three consecutive stacking turnover processes are consistent, and whether the maximum gas concentrations corresponding to the first two stacking turnover processes and the maximum gas concentration corresponding to the last stacking turnover process satisfy a preset change trend;

[0128] Determining that the fermentation process of the stacked tobacco leaf platform stops includes:

[0129] When the maximum values of the gas concentrations corresponding to the previous two piling - turning treatments are the same, and the maximum values of the gas concentrations corresponding to the previous two piling - turning treatments and the maximum value of the gas concentration corresponding to the last piling - turning treatment satisfy a preset change trend, it is determined that the fermentation treatment of the tobacco leaf platform in the stack stops.

[0130] In some possible embodiments, before determining that the fermentation treatment of the tobacco leaf platform in the stack stops, it further includes:

[0131] Judging whether the humidity change difference corresponding to any one of the piling - turning treatments among the humidity parameters corresponding to any three consecutive piling - turning treatments is within a preset difference interval;

[0132] Determining that the fermentation treatment of the tobacco leaf platform in the stack stops includes:

[0133] When the humidity change difference corresponding to any one of the piling - turning treatments is within the preset difference interval, it is determined that the fermentation treatment of the tobacco leaf platform in the stack stops.

[0134] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented by means of software and / or hardware. The "units" and "modules" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a Field - Programmable Gate Array (FPGA), an Integrated Circuit (IC), etc.

[0135] Please refer to Figure 4 , Figure 4 which shows a schematic structural diagram of another recognition processing device for cigar tobacco leaf stacking fermentation provided by the embodiments of the present application.

[0136] As Figure 4 shown, the recognition processing device 400 for cigar tobacco leaf stacking fermentation may include at least one processor 401, at least one network interface 404, a user interface 403, a memory 405, and at least one communication bus 402.

[0137] Among them, the communication bus 402 can be used to realize the connection and communication of the above - mentioned various components.

[0138] Among them, the user interface 403 may include keys, and the optional user interface may further include a standard wired interface and a wireless interface.

[0139] Among them, the network interface 404 can include, but is not limited to, a Bluetooth module, an NFC module, a Wi - Fi module, etc.

[0140] Among them, the processor 401 may include one or more processing cores. The processor 401 is connected to various parts within the recognition processing device 400 for cigar tobacco stacking fermentation through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 405, and by calling the data stored in the memory 405, the processor 401 executes various functions of the recognition processing device 400 for cigar tobacco stacking fermentation and processes data. Optionally, the processor 401 may be implemented in at least one hardware form of DSP, FPGA, or PLA. The processor 401 may integrate one or a combination of several of CPU, GPU, and modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 401 and may be implemented separately by a single chip.

[0141] Among them, the memory 405 may include RAM and may also include ROM. Optionally, the memory 405 includes a non-transitory computer-readable medium. The memory 405 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. Optionally, the memory 405 may also be at least one storage device located far from the aforementioned processor 401. As Figure 4 shown, the memory 405, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an identification processing application program for cigar tobacco stacking fermentation.

[0142] Specifically, the processor 401 may be used to call the identification processing application program for cigar tobacco stacking fermentation stored in the memory 405 and specifically perform the following operations:

[0143] According to the fermentation weight of the tobacco leaves to be processed, determine the number of layers of the out-stack tobacco leaf platform and the target stack number of each layer of the stack tobacco leaf platform;

[0144] When it is detected that the actual stack number of each layer of the stack tobacco leaf platform is consistent with the corresponding target stack number, obtain the detection parameters of each layer of the stack tobacco leaf platform at a preset time interval;

[0145] Perform regression analysis processing on the detection parameters of each layer of the stack tobacco leaf platform to obtain state characterization parameters, and perform turning stack processing on the stack tobacco leaf platform according to the state characterization parameters.

[0146] In some possible embodiments, the types of detection parameters include temperature parameters and gas concentration parameters;

[0147] Perform regression analysis on the detection parameters of each layer of the stack of tobacco leaves platform to obtain state characterization parameters, including:

[0148] Construct a temperature change function based on the temperature parameters corresponding to each layer of the stack of tobacco leaves platform at at least two time intervals, and perform a derivative operation on the temperature change function to obtain a temperature derivative function;

[0149] Construct a concentration change function based on the gas concentration parameters corresponding to each layer of the stack of tobacco leaves platform at at least two time intervals, and perform a derivative operation on the concentration change function to obtain a concentration derivative function;

[0150] Input the temperature derivative function and the concentration derivative function into a preset regression learning model to obtain state characterization parameters.

[0151] In some possible embodiments, performing a turning operation on the stack of tobacco leaves platform according to the state characterization parameters includes:

[0152] When the state characterization parameter is within a preset first interval, determine the vertical distance between the stack of tobacco leaves platform and the adjacent stack of tobacco leaves platform;

[0153] When the vertical distance is within a preset distance interval, perform a first turning operation on the stack of tobacco leaves platform according to a preset first turning path.

[0154] In some possible embodiments, performing a turning operation on the stack of tobacco leaves platform according to the state characterization parameters further includes:

[0155] When the state characterization parameter is within a preset second interval, determine whether the temperature change differences corresponding to any two adjacent time intervals are consistent;

[0156] When the temperature change differences corresponding to any two adjacent time intervals are consistent, perform an nth turning operation on the stack of tobacco leaves platform according to a preset second turning path; where n is a positive integer greater than or equal to 2.

[0157] In some possible embodiments, after performing a turning operation on the stack of tobacco leaves platform according to the state characterization parameters, it further includes:

[0158] When the number of turning operations on the stack of tobacco leaves platform exceeds a preset number threshold, determine whether the highest temperature corresponding to any three consecutive turning operations satisfies a preset change trend;

[0159] When the highest temperature corresponding to any three consecutive stacking treatments satisfies a preset change trend, calculate the temperature average value corresponding to each stacking treatment in turn;

[0160] Judge whether the temperature average value corresponding to any three consecutive stacking treatments satisfies a preset change trend;

[0161] When the temperature average value corresponding to any three consecutive stacking treatments satisfies a preset change trend, determine that the fermentation treatment of the tobacco leaf platform of the stack is stopped.

[0162] In some possible embodiments, after the temperature average value corresponding to any three consecutive stacking treatments satisfies a preset change trend and before determining that the fermentation treatment of the tobacco leaf platform of the stack is stopped, it further includes:

[0163] Judge whether the maximum gas concentration values corresponding to the first two stacking treatments among the maximum gas concentration values corresponding to any three consecutive stacking treatments are the same, and whether the maximum gas concentration values corresponding to the first two stacking treatments and the maximum gas concentration value corresponding to the last stacking treatment satisfy a preset change trend;

[0164] Determining that the fermentation treatment of the tobacco leaf platform of the stack is stopped includes:

[0165] When the maximum gas concentration values corresponding to the first two stacking treatments are the same, and the maximum gas concentration values corresponding to the first two stacking treatments and the maximum gas concentration value corresponding to the last stacking treatment satisfy a preset change trend, determine that the fermentation treatment of the tobacco leaf platform of the stack is stopped.

[0166] In some possible embodiments, before determining that the fermentation treatment of the tobacco leaf platform of the stack is stopped, it further includes:

[0167] Judge whether the humidity change difference corresponding to any one of the humidity parameters corresponding to any three consecutive stacking treatments is within a preset difference interval;

[0168] Determining that the fermentation treatment of the tobacco leaf platform of the stack is stopped includes:

[0169] When the humidity change difference corresponding to any one of the stacking treatments is within the preset difference interval, determine that the fermentation treatment of the tobacco leaf platform of the stack is stopped.

[0170] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the above method are implemented. Among them, the computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0171] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0172] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0173] In the several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0174] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0175] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0176] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of this application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, read-only memories (ROM), random access memories (RAM), external hard drives, magnetic disks, or optical discs.

Claims

1. An identification and processing method for the stacking fermentation of cigar tobacco leaves, characterized in that, Including: Determine the number of layers of the tobacco stack platform and the target number of stacks for each layer of the tobacco stack platform according to the fermentation weight of the tobacco leaves to be processed; When it is detected that the actual number of stacks for each layer of the tobacco stack platform is consistent with the corresponding target number of stacks, obtain the detection parameters for each layer of the tobacco stack platform at a preset time interval; the types of the detection parameters include temperature parameters and gas concentration parameters; Perform regression analysis on the detection parameters for each layer of the tobacco stack platform to obtain a state characterization parameter, and perform stack turning processing on the tobacco stack platform according to the state characterization parameter; Among them, the performing regression analysis on the detection parameters for each layer of the tobacco stack platform to obtain a state characterization parameter includes: Construct a temperature change function according to the temperature parameters corresponding to each layer of the tobacco stack platform at at least two time intervals, and perform a derivative operation on the temperature change function to obtain a temperature derivative function; Construct a concentration change function according to the gas concentration parameters corresponding to each layer of the tobacco stack platform at at least two time intervals, and perform a derivative operation on the concentration change function to obtain a concentration derivative function; Input the temperature derivative function and the concentration derivative function into a preset regression learning model to obtain a state characterization parameter; The performing stack turning processing on the tobacco stack platform according to the state characterization parameter includes: When the state characterization parameter is in a preset first interval, determine the vertical distance between the tobacco stack platform and the adjacent tobacco stack platform; When the vertical distance is in a preset distance interval, perform the first stack turning processing on the tobacco stack platform according to a preset first stack turning path; The performing stack turning processing on the tobacco stack platform according to the state characterization parameter further includes: When the state characterization parameter is in a preset second interval, determine whether the temperature change differences corresponding to any two adjacent time intervals are consistent; When the temperature change differences corresponding to any two adjacent time intervals are consistent, perform the nth stack turning processing on the tobacco stack platform according to a preset second stack turning path; where n is a positive integer greater than or equal to 2.

2. The method according to claim 1, wherein After the performing stack turning processing on the tobacco stack platform according to the state characterization parameter, it further includes: When the number of stack turning processing times of the tobacco stack platform exceeds a preset number threshold, determine whether the highest temperatures corresponding to any three consecutive stack turning processings satisfy a preset change trend; When the highest temperatures corresponding to any three consecutive stack turning processings satisfy the preset change trend, calculate the temperature mean value corresponding to each stack turning processing in turn; Determine whether the temperature mean values corresponding to any three consecutive stack turning processings satisfy the preset change trend; When the temperature mean values corresponding to any three consecutive stack turning processings satisfy the preset change trend, determine that the fermentation processing of the tobacco stack platform stops.

3. The method according to claim 2, wherein After the temperature mean values corresponding to any three consecutive stack turning processings satisfy the preset change trend and before determining that the fermentation processing of the tobacco stack platform stops, it further includes: Determine whether the maximum gas concentrations corresponding to the first two of any three consecutive piling-turning treatments are the same among the maximum gas concentrations corresponding to any three consecutive piling-turning treatments, and whether the maximum gas concentrations corresponding to the first two piling-turning treatments and the maximum gas concentration corresponding to the last piling-turning treatment satisfy the preset change trend; The determination of stopping the fermentation treatment of the tobacco leaf platform of the stack includes: When the maximum gas concentrations corresponding to the first two piling-turning treatments are the same, and the maximum gas concentrations corresponding to the first two piling-turning treatments and the maximum gas concentration corresponding to the last piling-turning treatment satisfy the preset change trend, determine to stop the fermentation treatment of the tobacco leaf platform of the stack.

4. The method according to claim 3, wherein Before the determination of stopping the fermentation treatment of the tobacco leaf platform of the stack, it further includes: Judge whether the humidity change difference corresponding to any one of the piling-turning treatments is within a preset difference interval among the humidity parameters corresponding to any three consecutive piling-turning treatments; The determination of stopping the fermentation treatment of the tobacco leaf platform of the stack includes: When the humidity change difference corresponding to any one of the piling-turning treatments is within the preset difference interval, determine to stop the fermentation treatment of the tobacco leaf platform of the stack.

5. An identification and processing device for the stacking and fermentation of cigar tobacco leaves, characterized in that, The device is applied to the recognition processing method for cigar tobacco leaf stacking fermentation according to any one of claims 1-4, and the device includes: A first processing module, configured to determine the number of layers of the tobacco leaf platform of the stack and the target stack number of each layer of the tobacco leaf platform of the stack according to the fermentation weight of the tobacco leaves to be processed; A second processing module, configured to obtain the detection parameters of each layer of the tobacco leaf platform of the stack at a preset time interval when it is detected that the actual stack number of each layer of the tobacco leaf platform of the stack is the same as the corresponding target stack number; A third processing module, configured to perform regression analysis processing on the detection parameters of each layer of the tobacco leaf platform of the stack to obtain a state characterization parameter, and perform a piling-turning treatment on the tobacco leaf platform of the stack according to the state characterization parameter.

6. An identification and processing device for stacking and fermenting cigar tobacco leaves, characterized in that, Including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method according to any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, Instructions are stored in the computer-readable storage medium, and when the instructions run on a computer or a processor, the computer or the processor is made to execute the steps of the method according to any one of claims 1-4.

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