Intelligent control method and device for loosening and regaining process

Abstract

The present disclosure provides an intelligent control method and device for a loosening and rehumidification process, which relate to the technical field of tobacco manufacturing. The method comprises: obtaining a first parameter, wherein the first parameter comprises an inlet moisture content, storage time, production flow rate and target outlet moisture content of tobacco leaves; inputting the first parameter into a first model to obtain a first water addition amount expected to meet the target outlet moisture content of the tobacco leaves; and performing a first operation to add water to the tobacco leaves according to the first water addition amount.

Description

Technical Field

[0001] The present disclosure relates to the technical field of tobacco manufacturing, and in particular to an intelligent control method and device for a loosening and rehumidification process. Background Art

[0002] The loosening and rehumidification process is a key process in cigarette processing. By adding a certain amount of water to the tobacco leaves, the tobacco leaves can reach the target outlet moisture content and improve the processing resistance of the tobacco leaves. In related technologies, proportional integral differential control (PID control) can be used to determine the required amount of water. Summary of the invention

[0003] The inventors noticed that the loosening and rehumidification process in the related art was inefficient. After analysis, the inventors found that the amount of water required in the related art was inaccurate, and frequent and repeated water addition was required to make the moisture content of the tobacco leaves meet the requirements, resulting in low efficiency of the loosening and rehumidification process.

[0004] In order to solve the above problems, the embodiments of the present disclosure propose the following solutions.

[0005] According to one aspect of an embodiment of the present disclosure, there is provided an intelligent control method for a loosening and conditioning process, comprising obtaining a first parameter, the first parameter including an inlet moisture content, storage time, production flow rate and target outlet moisture content of tobacco leaves; inputting the first parameter into a first model to obtain a first water addition amount expected to meet the target outlet moisture content of the tobacco leaves; and performing a first operation to add water to the tobacco leaves according to the first water addition amount.

[0006] In some embodiments, the actual outlet moisture content of the tobacco leaves after the first operation is obtained; if the actual outlet moisture content of the tobacco leaves is less than the target outlet moisture content of the tobacco leaves, a second parameter is input into a second model different from the first model to obtain a second water addition amount expected to meet the target outlet moisture content of the tobacco leaves, the second parameter including the actual outlet moisture content of the tobacco leaves, the target outlet moisture content and the production flow rate; a second operation is performed to add water to the tobacco leaves according to the second water addition amount.

[0007] In some embodiments, the first parameter also includes a first time-varying parameter; the method further includes: adjusting the first time-varying parameter according to the actual outlet moisture content of the tobacco leaf.

[0008] In some embodiments, adjusting the first time-varying parameter according to the actual outlet moisture content of the tobacco leaves includes: if the actual outlet moisture content of the tobacco leaves is higher than the target outlet moisture content of the tobacco leaves, reducing the first time-varying parameter; if the actual outlet moisture content of the tobacco leaves is lower than the target outlet moisture content of the tobacco leaves, increasing the first time-varying parameter.

[0009] In some embodiments, the first model is trained in the following manner: obtaining first parameters of sample tobacco leaves, the first parameters including the inlet moisture content, storage time, production flow rate and target outlet moisture content of the sample tobacco leaves; using the first parameters of the sample tobacco leaves as input and the sample water addition amount that meets the target outlet moisture content of the sample tobacco leaves as output, the first model is trained.

[0010] In some embodiments, the first parameter also includes at least one of a first group of parameters, a second group of parameters and a third group of parameters, wherein: the first group of parameters includes the origin, grade and type of the tobacco leaves; the second group of parameters includes the transportation time of the tobacco leaves; the third group of parameters includes the average temperature and humidity during at least part of the year, and the indoor temperature and humidity when the tobacco leaves are stored.

[0011] In some embodiments, the first parameters include multiple groups of a first group of parameters, a second group of parameters, and a third group of parameters.

[0012] In some embodiments, the second parameter further comprises a second time-varying parameter.

[0013] In some embodiments, different production lines correspond to different first models.

[0014] According to another aspect of an embodiment of the present disclosure, there is provided an intelligent control device for a loosening and conditioning process, comprising: an acquisition module, configured to acquire a first parameter, wherein the first parameter comprises an inlet moisture content, storage time, production flow rate and target outlet moisture content of tobacco leaves; an input module, configured to input the first parameter into a first model to obtain a first water addition amount expected to meet the target outlet moisture content of the tobacco leaves; and an execution module, configured to execute a first operation to add water to the tobacco leaves according to the first water addition amount.

[0015] According to another aspect of an embodiment of the present disclosure, there is provided an intelligent control device for a loosening and rehumidification process, comprising: a memory; and a processor coupled to the memory, configured to execute the method described in any one of the above embodiments based on instructions stored in the memory.

[0016] According to another aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, comprising computer program instructions, wherein when the computer program instructions are executed by a processor, the method described in any one of the above embodiments is implemented.

[0017] According to another aspect of the embodiments of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the method described in any one of the above embodiments is implemented.

[0018] In the disclosed embodiment, in the process of using the model to obtain the first amount of water added to meet the target outlet moisture content of the tobacco leaves, in addition to considering the inlet moisture content, production flow rate and target outlet moisture content of the tobacco leaves, the storage time of the tobacco leaves is also considered, and the impact of the storage process on the characteristics of the tobacco leaves themselves is fully considered, thereby improving the accuracy of the amount of water added in the water adding link of the loosening and conditioning process, thereby improving the efficiency of the loosening and conditioning process.

[0019] The technical solution of the present disclosure is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 is a flow chart of an intelligent control method for a loosening and moisture regaining process according to some embodiments of the present disclosure;

[0022] Figure 2 is a flow chart of an intelligent control method for a loosening and moisture regaining process according to some other embodiments of the present disclosure;

[0023] Figure 3 is a structural schematic diagram of an intelligent control device for a loosening and moisture regaining process according to some embodiments of the present disclosure;

[0024] Figure 4 It is a structural schematic diagram of an intelligent control device for a loosening and moisture regaining process according to other embodiments of the present disclosure. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0026] Unless specifically stated otherwise, the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0027] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0028] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0029] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0030] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0031] The inventor studied multiple factors that affect the amount of water added, and ultimately found that the storage time of tobacco leaves before the loosening and rehumidification process may affect the water absorption characteristics of the tobacco leaves, thereby affecting whether the amount of water is accurate. Based on this, the present application proposes the following technical solution.

[0032] Figure 1 It is a flow chart of an intelligent control method for a loosening and moistening process according to some embodiments of the present disclosure.

[0033] In step 102, first parameters are obtained, which include the inlet moisture content, storage time, production flow rate and target outlet moisture content of the tobacco leaves.

[0034] In some embodiments, the inlet moisture content of the tobacco leaves refers to the moisture content of the tobacco leaves before the loosening and conditioning process; the storage time refers to the storage time of the tobacco leaves in the warehouse before the loosening and conditioning process; the production flow rate refers to the mass of the tobacco leaves transported per unit time (for example, the unit is kg / h); the target outlet moisture content refers to the moisture content that the tobacco leaves are expected to reach after completing the loosening and conditioning process.

[0035] In step 104, the first parameter is input into the first model to obtain a first water addition amount that is expected to meet the target outlet moisture content of the tobacco leaves.

[0036] For example, the first model is a machine learning model such as clustering, adaptive neural network, Gaussian process regression model, vector regression model, polynomial curve fitting model or principal component analysis model.

[0037] In step 106, a first operation is performed to add water to the tobacco leaves according to a first water addition amount.

[0038] In some embodiments, the first model may be used in conjunction with proportional-integral-derivative control.

[0039] In the above embodiment, in the process of using the model to obtain the first amount of water added to meet the target outlet moisture content of the tobacco leaves, in addition to considering the inlet moisture content, production flow rate and target outlet moisture content of the tobacco leaves, the storage time of the tobacco leaves is also considered, and the impact of the storage process on the characteristics of the tobacco leaves themselves is fully considered, thereby improving the accuracy of the amount of water added in the water adding link of the loosening and conditioning process, thereby improving the efficiency of the loosening and conditioning process.

[0040] Figure 2 It is a flow chart of an intelligent control method for a loosening and moistening process according to some other embodiments of the present disclosure.

[0041] In step 202, the actual outlet moisture content of the tobacco leaves after the first operation is performed in step 106 is obtained.

[0042] In step 204, it is determined whether the actual outlet moisture content of the tobacco leaves is less than the target outlet moisture content of the tobacco leaves. If so, step 206 is executed. If not, watering the tobacco leaves may not be continued.

[0043] In step 206, the second parameter is input into a second model different from the first model to obtain a second water addition amount to meet the target outlet moisture content of the tobacco leaves. The second parameter includes the actual outlet moisture content of the tobacco leaves, the target outlet moisture content and the production flow rate.

[0044] The second model is different from the first model. For example, the first model is a certain machine learning model, and the second model is another machine learning model.

[0045] As some implementations, the second model is used to obtain the second water addition amount to meet the target outlet moisture content of tobacco leaves as desired, as follows:

[0046] (1) According to the physical relationship between the second parameters in the loosening and moisture regaining process, a corresponding mathematical model is established, for example, X×(T×Q+W)=M×T×Q+W.

[0047] Wherein, W is the second water addition amount, Q is the tobacco leaf production flow rate, M is the tobacco leaf inlet moisture content, T is a parameter, and X is the tobacco leaf outlet moisture content.

[0048] (2) Determine whether the second parameter is within a normal range. If so, predict a second water addition amount that satisfies the target outlet moisture content of the tobacco leaves based on the mathematical model and the second parameter obtained in real time.

[0049] In step 208, a second operation is performed to add water to the tobacco leaves according to a second water addition amount.

[0050] In some embodiments, steps 204 to 208 are executed in a loop until the actual outlet moisture content of the tobacco leaves meets the target outlet moisture content.

[0051] In the above embodiment, after the tobacco leaves are watered according to the first water addition amount obtained by the first model, if the actual outlet moisture content of the tobacco leaves is less than the target outlet moisture content of the tobacco leaves, the tobacco leaves are watered according to the second water addition amount obtained by the second model different from the first model. In this way, the accuracy of the water addition amount applied in the water addition link of the loosening and rehumidification process is further improved, thereby further improving the efficiency of the loosening and rehumidification process.

[0052] In some embodiments, the total number of parameters in the first parameters used as input to the first model is greater than the total number of parameters in the second parameters used as input to the second model, so that the amount of water added in the water adding link of the loosening and moisturizing process can be more accurately controlled.

[0053] In some embodiments, the first parameter includes the inlet moisture content of the tobacco leaves, storage time, production flow, target outlet moisture content and the first time-varying parameter. In this case, the first time-varying parameter can also be adjusted according to the actual outlet moisture content of the tobacco leaves.

[0054] As some implementations, if the actual outlet moisture content of the tobacco leaves is higher than the target outlet moisture content of the tobacco leaves, the first time-varying parameter is reduced. If the actual outlet moisture content of the tobacco leaves is lower than the target outlet moisture content of the tobacco leaves, the first time-varying parameter is increased.

[0055] In the above embodiment, by adjusting the first time-varying parameter according to the actual outlet moisture content of the tobacco leaves, the uncertainties that may occur in the loosening and rehumidification process are fully taken into account. By timely adjusting the first water addition amount through the first time-varying parameter, the accuracy of the water addition amount applied in the water addition link of the loosening and rehumidification process is further improved, thereby further improving the efficiency of the loosening and rehumidification process.

[0056] In some embodiments, the first model is trained by:

[0057] (1) Obtaining first parameters of the sample tobacco leaves, where the first parameters include an inlet moisture content, storage time, production flow rate, and target outlet moisture content of the sample tobacco leaves.

[0058] (2) The first model is trained by taking the first parameter of the sample tobacco leaf as input and the sample water addition amount that satisfies the target outlet moisture content of the sample tobacco leaf as output.

[0059] In the above embodiment, the first parameter of the sample tobacco leaf is obtained, and the first parameter of the sample tobacco leaf is used as input, and the sample water addition amount that meets the target outlet moisture content of the sample tobacco leaf is used as output to train the first model, thereby improving the accuracy of the first model in predicting the first water addition amount.

[0060] In some embodiments, the first parameter further includes at least one of a first group of parameters, a second group of parameters, and a third group of parameters. The first group of parameters includes the origin, grade, and type of tobacco leaves; the second group of parameters includes the transportation time of tobacco leaves; and the third group of parameters includes the average temperature and humidity during at least part of the year, and the indoor temperature and humidity of the tobacco leaves when they are stored.

[0061] In the above embodiment, through at least one of the first group of parameters, the second group of parameters and the third group of parameters, the influence of the characteristics of the tobacco leaves themselves on the water absorption characteristics of the tobacco leaves is more fully considered, and the accuracy of the amount of water added in the water adding link of the loosening and conditioning process is further improved, thereby further improving the efficiency of the loosening and conditioning process.

[0062] As some implementations, the tobacco leaf transportation time is the time it takes for the tobacco leaves to be transported from a storage location to a production location where the loosening and conditioning process is performed.

[0063] In some embodiments, the first parameters further include a plurality of a first set of parameters, a second set of parameters, and a third set of parameters.

[0064] In the above embodiment, through multiple groups of the first group of parameters, the second group of parameters and the third group of parameters, the influence of the characteristics of the tobacco leaves themselves on the water absorption characteristics of the tobacco leaves is more fully considered, and the accuracy of the amount of water added in the water adding link of the loosening and conditioning process is further improved, thereby further improving the efficiency of the loosening and conditioning process.

[0065] In some embodiments, the first parameter also includes the temperature of the production site where the loosening and moisture restoration process is performed, the humidity of the production site where the loosening and moisture restoration process is performed, and the preset time of the loosening and moisture restoration process.

[0066] In some embodiments, the second parameter includes the actual outlet moisture content of the tobacco leaves, the target outlet moisture content production flow rate, and a second time-varying parameter.

[0067] As some implementations, the second time-varying parameter is adjusted according to the actual outlet moisture content of the tobacco leaves after the second operation is performed.

[0068] In the above embodiment, by adjusting the second time-varying parameter according to the actual outlet moisture content of the tobacco leaves after executing the second operation, the uncertainties that may occur in the loosening and rehumidification process are fully taken into account. By timely adjusting the first water addition amount through the second time-varying parameter, the precise control of the water addition link in the loosening and rehumidification process is further achieved, and the efficiency of the loosening and rehumidification process is further improved.

[0069] In some embodiments, different production lines correspond to different first models.

[0070] As some implementations, the second models corresponding to different production lines are the same.

[0071] As some implementation methods, using a transfer learning algorithm to train the corresponding second models of different production lines can avoid repeated training and improve the training efficiency of the second model.

[0072] In the above embodiment, different production lines correspond to different first models, which fully considers the influence of different production lines on the execution of the loosening and moisturizing process, improves the accuracy of the first model, and further improves the efficiency of the loosening and moisturizing process.

[0073] In some embodiments, tobacco leaves from different origins correspond to different first models.

[0074] In the above embodiment, different first models are corresponding to tobacco leaves from different origins, which fully considers the influence of different production lines on the execution of the loosening and conditioning process, improves the accuracy of the first model, and further improves the efficiency of the loosening and conditioning process.

[0075] In some embodiments, the Simulink system simulation module in Matlab software is used to simulate the first model and the second model, and the first model and the second model are verified and optimized.

[0076] As some implementation methods, the first model and the second model communicate with the loosening and rehumidification production line through a data communication method (such as the OPC data acquisition protocol), thereby obtaining the first or second parameter for predicting the first water addition amount or the second water addition amount, thereby achieving control of the water addition amount in the loosening and rehumidification process.

[0077] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0078] Figure 3 It is a structural schematic diagram of an intelligent control device for a loosening and moistening process according to some embodiments of the present disclosure.

[0079] like Figure 3 As shown, the intelligent control device for the loosening and moisture regaining process includes an acquisition module 301 , an input module 302 and an execution module 303 .

[0080] The acquisition module 301 is configured to acquire first parameters, which include the inlet moisture content, storage time, production flow rate and target outlet moisture content of the tobacco leaves.

[0081] The input module 302 is configured to input the first parameter into the first model to obtain a first water addition amount that is expected to meet the target outlet moisture content of the tobacco leaves.

[0082] The execution module 303 is configured to execute a first operation to add water to the tobacco leaves according to a first water addition amount.

[0083] In some embodiments, the intelligent control device for the loosening and moisture regaining process further includes other modules to execute the intelligent control method for the loosening and moisture regaining process of any one of the above embodiments.

[0084] Figure 4 It is a structural schematic diagram of an intelligent control device for a loosening and moisture regaining process according to some other embodiments of the present disclosure.

[0085] like Figure 4 As shown, the intelligent control device 400 for the loosening and rehumidification process includes a memory 401 and a processor 402 coupled to the memory 401 , and the processor 402 is configured to execute the method of any one of the aforementioned embodiments based on the instructions stored in the memory 401 .

[0086] The memory 401 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, an application program, a boot loader, and other programs.

[0087] The intelligent control device 400 for the loosening and rehumidification process may also include an input / output interface 403, a network interface 404, a storage interface 405, etc. These interfaces 403, 404, 405, and the memory 401 and the processor 402 may be connected, for example, via a bus 406. The input / output interface 403 provides a connection interface for input / output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface 404 provides a connection interface for various networked devices. The storage interface 405 provides a connection interface for external storage devices such as an SD card and a USB flash drive.

[0088] The embodiments of the present disclosure further provide a computer-readable storage medium, comprising computer program instructions, which implement the method of any one of the above embodiments when executed by a processor.

[0089] The embodiments of the present disclosure further provide a computer program product, including a computer program, which implements the method of any one of the above embodiments when executed by a processor.

[0090] So far, various embodiments of the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0091] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. It should be understood that the functions specified in one or more processes in the flowchart and / or one or more blocks in the block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the functions specified in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0095] Although some specific embodiments of the present disclosure have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified or some technical features may be replaced by equivalents without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. An intelligent control method for a loosening and regaining process, comprising: Obtaining first parameters, the first parameters including inlet moisture content of tobacco leaves, storage time, production flow, target outlet moisture content, tobacco leaf transportation time, average temperature and humidity in at least part of the year, indoor temperature and humidity of tobacco leaves when stored, temperature of a production site where a loosening and rehydration process is performed, humidity of the production site where a loosening and rehydration process is performed, and a preset time of the loosening and rehydration process; Inputting the first parameter into a first model to obtain a first water addition amount expected to meet a target outlet moisture content of the tobacco leaves; Performing a first operation to add water to the tobacco leaves according to the first water addition amount; Obtaining the actual outlet moisture content of the tobacco leaves after the first operation; If the actual outlet moisture content of the tobacco leaves is less than the target outlet moisture content of the tobacco leaves, inputting a second parameter into a second model different from the first model to obtain a second water addition amount expected to meet the target outlet moisture content of the tobacco leaves, wherein the second parameter includes the actual outlet moisture content of the tobacco leaves, the target outlet moisture content and the production flow rate; Performing a second operation to add water to the tobacco leaves according to the second water addition amount; Among them, different production lines correspond to different first models, and tobacco leaves from different origins correspond to different first models; the second models corresponding to different production lines are the same, and the transfer learning algorithm is used to train the second models corresponding to different production lines.

2. The method according to claim 1, wherein: The first parameter also includes a first time-varying parameter; and the method further includes: The first time-varying parameter is adjusted according to the actual outlet moisture content of the tobacco leaves.

3. The method according to claim 2, wherein: Adjusting the first time-varying parameter according to the actual outlet moisture content of the tobacco leaves includes: If the actual outlet moisture content of the tobacco leaves is higher than the target outlet moisture content of the tobacco leaves, reducing the first time-varying parameter; If the actual outlet moisture content of the tobacco leaves is lower than the target outlet moisture content of the tobacco leaves, the first time-varying parameter is increased.

4. The method according to claim 1, wherein: The first model is trained in the following way: Acquiring first parameters of the sample tobacco leaves, the first parameters including inlet moisture content, storage time, production flow rate and target outlet moisture content of the sample tobacco leaves; The first model is trained by taking the first parameter of the sample tobacco leaf as input and the sample water addition amount that satisfies the target outlet moisture content of the sample tobacco leaf as output.

5. The method according to any one of claims 1 to 4, wherein: The first parameter also includes a first group of parameters, which includes the origin, grade and type of the tobacco leaves.

6. The method according to any one of claims 1 to 4, wherein: The second parameter also includes a second time-varying parameter.

7. An intelligent control device for a loosening and moisture regaining process, comprising: an acquisition module, configured to acquire first parameters, wherein the first parameters include an inlet moisture content of tobacco leaves, storage time, production flow, target outlet moisture content, tobacco leaf transportation time, average temperature and humidity in at least part of the year, indoor temperature and humidity of tobacco leaves when stored, temperature of a production site where a loosening and rehydration process is performed, humidity of the production site where a loosening and rehydration process is performed, and a preset time of the loosening and rehydration process; An input module configured to input the first parameter into a first model to obtain a first water addition amount expected to meet a target outlet moisture content of the tobacco leaves; an execution module, configured to execute a first operation to add water to the tobacco leaves according to the first water addition amount; The intelligent control device is also configured as: Obtaining the actual outlet moisture content of the tobacco leaves after the first operation; If the actual outlet moisture content of the tobacco leaves is less than the target outlet moisture content of the tobacco leaves, inputting a second parameter into a second model different from the first model to obtain a second water addition amount expected to meet the target outlet moisture content of the tobacco leaves, wherein the second parameter includes the actual outlet moisture content of the tobacco leaves, the target outlet moisture content and the production flow rate; Performing a second operation to add water to the tobacco leaves according to the second water addition amount; Among them, different production lines correspond to different first models, and tobacco leaves from different origins correspond to different first models; the second models corresponding to different production lines are the same, and the transfer learning algorithm is used to train the second models corresponding to different production lines.

8. An intelligent control device for a loosening and moisture regaining process, comprising: Memory; as well as A processor coupled to the memory, configured to execute the method according to any one of claims 1 to 6 based on instructions stored in the memory.

9. A computer-readable storage medium comprising computer program instructions, wherein: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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