Method and device for controlling moisture before tobacco shred drying, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a method and device for controlling moisture before tobacco shred drying, electronic equipment and a storage medium. The method comprises the steps of obtaining trademark information of a current batch of tobacco shreds, and determining a target prediction model; obtaining current environment parameters, current production parameters and current first tobacco shred water content, determining target second tobacco shred water content, and calculating expected tobacco shred water content of a moisture regaining outlet; determining the current tobacco shred water content of the moisture regaining outlet, calculating the tobacco shred water content difference value based on the current tobacco shred water content and the expected tobacco shred water content, and adjusting the water adding amount in the moisture regaining process based on the tobacco shred water content difference value. According to the embodiment of the specification, the water adding amount in the moisture regaining process can be adjusted, the moisture content of the tobacco shreds after moisture regaining can be continuously and timely adjusted, the moisture of the tobacco shreds before drying is kept at an expected value, the moisture standard requirement before drying is met, and the quality of final products is guaranteed.

Description

Technical Field

[0001] One or more embodiments of the present specification relate to tobacco processing technology, and more particularly to a method, device, electronic device and storage medium for controlling moisture in cut tobacco before drying. Background Art

[0002] In the tobacco heating process, the tobacco will be sent to the leaf storage cabinet for storage after adding water and rehydrating in the rehydration stage. The stored tobacco will be sent to the feeding cabinet in turn and enter the drying equipment for drying. After drying, it will pass through the discharging cabinet to wait for output. In this series of processes, the moisture control of the tobacco before drying is crucial to the quality of the final product. It is necessary to monitor the moisture of the tobacco before drying and keep it at the expected value. Traditional moisture monitoring methods mostly rely on manual detection and experience judgment, and have problems such as untimely response and insufficient accuracy. It is difficult to adapt to the standard requirements of moisture before drying in modern intelligent manufacturing, which affects the quality of the final product. Summary of the invention

[0003] In order to solve the above problems, one or more embodiments of this specification describe a method, device, electronic device and storage medium for controlling moisture in cut tobacco before drying.

[0004] According to a first aspect, a method for controlling moisture content of cut tobacco before drying is provided, the method comprising: receiving a moisture control instruction, responding to the moisture control instruction, obtaining brand information of a current batch of shredded tobacco, and determining a target prediction model that matches the brand information, wherein input data of the target prediction model includes environmental parameters, production parameters, and the moisture content of the first shredded tobacco at the entrance of the drying equipment, and output data of the target prediction model includes the moisture content of the second shredded tobacco before entering the leaf storage cabinet, wherein the environmental parameters include the ambient temperature and the ambient humidity, and the production parameters include the steam flow rate; Acquire current environmental parameters, current production parameters and current first shredded tobacco moisture content, determine target second shredded tobacco moisture content based on the target prediction model, and calculate expected shredded tobacco moisture content at the rehumidification outlet according to the first moisture content change mean value and the target second shredded tobacco moisture content, wherein the first moisture content change mean value includes the difference mean value between the historical second shredded tobacco moisture content and the historical shredded tobacco moisture content at the rehumidification outlet; The current moisture content of the cut tobacco at the rehumidification outlet is determined, the difference in moisture content of the cut tobacco is calculated based on the current moisture content of the cut tobacco and the expected moisture content of the cut tobacco, and the amount of water added during the rehumidification process is adjusted based on the difference in moisture content of the cut tobacco.

[0005] Preferably, before determining the target prediction model matching the brand information, the method further includes: For any brand information, historical data matching the brand information is obtained, and training data is obtained after feature correlation analysis and preprocessing of the historical data, wherein the preprocessing includes removing outliers and supplementing missing values; An initial model is constructed, and the initial model is trained based on the training data to obtain a prediction model corresponding to the brand information, wherein the initial model is an XGBoost model based on a gradient boosting decision tree algorithm.

[0006] Preferably, after performing feature correlation analysis and preprocessing on the historical data to obtain training data, the method further includes: Date information is obtained, season information is determined based on the date information, a moisture adjustment parameter corresponding to the season information is obtained, and the moisture content of the second shredded tobacco in the training data is adjusted based on the moisture adjustment parameter.

[0007] Preferably, the training data includes environmental parameters, production parameters, first shredded tobacco moisture content, second shredded tobacco moisture content, third shredded tobacco moisture content out of a leaf storage cabinet, cabinet number code, and moisture difference of leaves out of a leaf storage cabinet, the moisture difference of leaves out of a leaf storage cabinet is the average difference between the historical first shredded tobacco moisture content and the historical third shredded tobacco moisture content, and the initial model includes a first initial model and a second initial model; The step of training the initial model based on the training data to obtain a prediction model corresponding to the brand information includes: The first initial model is trained based on the training data to obtain a trained first model, wherein the first model is used to calculate the third tobacco shred moisture content according to the first tobacco shred moisture content, the difference in moisture between stored leaves and leaves out of the cabinet, and the standard deviation value, wherein the standard deviation value is the difference between the first tobacco shred moisture content and the set tobacco shred moisture content corresponding to the inlet of the drying device; The second initial model is trained based on the training data to obtain a trained second model, wherein the second model is used to calculate the moisture content of the second shredded tobacco according to the moisture content of the third shredded tobacco and the mean value of the second moisture content change, wherein the mean value of the second moisture content change is the mean value of the difference between the moisture content of the second shredded tobacco in each history and the moisture content of the third shredded tobacco in each history; The first model and the second model are integrated to obtain a prediction model corresponding to the brand information.

[0008] Preferably, the step of calculating the expected moisture content of the shredded tobacco at the rehumidification outlet according to the first moisture content change mean and the target second shredded tobacco moisture content comprises: The first moisture content change mean value corresponding to the brand information is obtained, and the difference between the target second shredded tobacco moisture content and the first moisture content change mean value is calculated to obtain the expected shredded tobacco moisture content at the rehumidification outlet.

[0009] Preferably, the method further comprises: When the accumulated weight of the moisture regain electronic scale reaches a preset weight, the accumulated weight is reset, and the production grade of the drying section is obtained; When the production brand matches the brand information, real-time collected data of the wire drying section is obtained, and the target prediction model is updated based on the real-time collected data; When the production brand does not match the brand information, a compensation value between the production brand and the brand information is obtained and determined, and the amount of water added is adjusted based on the compensation value.

[0010] Preferably, after adjusting the amount of water added in the moisture reconditioning process based on the moisture content difference of the shredded tobacco, the method further comprises: The variation interval of the moisture content of the first shredded tobacco is determined based on the moisture content difference of the shredded tobacco, the waiting time taken for the variation value of the moisture content of the first shredded tobacco to reach the variation interval is counted, and the waiting time is displayed.

[0011] According to a second aspect, a device for controlling moisture content of cut tobacco before drying is provided, the device comprising: a receiving module, configured to receive a moisture control instruction, respond to the moisture control instruction, obtain brand information of a current batch of shredded tobacco, and determine a target prediction model that matches the brand information, wherein input data of the target prediction model includes environmental parameters, production parameters, and the moisture content of the first shredded tobacco at the inlet of the drying equipment, and output data of the target prediction model includes the moisture content of the second shredded tobacco before entering the leaf storage cabinet, wherein the environmental parameters include the ambient temperature and the ambient humidity, and the production parameters include the steam flow rate; an acquisition module, used for acquiring current environmental parameters, current production parameters and current first shredded tobacco moisture content, determining a target second shredded tobacco moisture content based on the target prediction model, and calculating an expected shredded tobacco moisture content at a rehumidification outlet according to a first moisture content change mean value and the target second shredded tobacco moisture content, wherein the first moisture content change mean value includes a difference mean value between a historical second shredded tobacco moisture content and a historical shredded tobacco moisture content at a rehumidification outlet; The adjustment module is used to determine the current moisture content of the tobacco at the rehumidification outlet, calculate the difference in moisture content of the tobacco based on the current moisture content of the tobacco and the expected moisture content of the tobacco, and adjust the amount of water added in the rehumidification process based on the difference in moisture content of the tobacco.

[0012] According to a third aspect, there is provided an electronic device, comprising 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 provided in the first aspect or any possible implementation manner of the first aspect.

[0013] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a processor, cause the computer or the processor to execute the method provided in the first aspect or any possible implementation of the first aspect.

[0014] The method provided in the embodiments of this specification can determine the corresponding target prediction model according to the brand information, and process the environmental parameters, production parameters and the moisture content of the first tobacco according to the target prediction model to obtain the second tobacco moisture content, and determine the expected tobacco moisture content at the rehumidification outlet based on the second tobacco moisture content, and adjust the amount of water added during the rehumidification process. It can continuously and timely adjust the moisture content of the tobacco after rehumidification, so that the moisture content of the tobacco before drying is maintained at the expected value, meet the moisture standard requirements before drying, and ensure the quality of the final product. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a method for controlling moisture in tobacco before drying in one embodiment of the present specification.

[0017] Figure 2 It is a schematic diagram of the structure of a moisture control device before tobacco drying in one embodiment of the present specification.

[0018] Figure 3 It is a structural diagram of an electronic device in one embodiment of this specification. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0020] In the following introduction, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following introduction provides multiple embodiments of the present application, and 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 recorded. Therefore, 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 one or more of all other possible combinations of A, B, C, and D, although the embodiment may not be clearly recorded in the following text.

[0021] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present application. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described in some examples may be combined in other examples.

[0022] See also Figure 1 , Figure 1 : is a flow chart of a method for controlling moisture content of cut tobacco before drying provided in an embodiment of the present application. In an embodiment of the present application, the method comprises: S101, receiving a moisture control instruction, responding to the moisture control instruction, obtaining brand information of a current batch of shredded tobacco, and determining a target prediction model that matches the brand information.

[0023] Among them, the input data of the target prediction model includes environmental parameters, production parameters and the first tobacco moisture content at the inlet of the drying equipment, the output data of the target prediction model includes the second tobacco moisture content before entering the leaf storage cabinet, the environmental parameters include ambient temperature and ambient humidity, and the production parameters include steam flow.

[0024] The execution entity of this application can be a cloud server.

[0025] In an embodiment of the present specification, when the tobacco shreds begin the drying process and need to control the moisture content of the tobacco shreds before drying, the staff will generate a moisture control instruction and send it to the cloud server through operations on mobile phones, computers and other terminals. After the cloud server receives the moisture control instruction, it believes that it is necessary to control the moisture content of the tobacco shreds before drying, and will respond to the moisture control instruction, and determine the current batch of tobacco shreds that is currently executing the drying process based on the production information, and determine the corresponding brand information. Different varieties of tobacco have significant differences in moisture absorption and release characteristics, and different models need to be trained separately. Therefore, several prediction models are pre-trained and stored in the cloud server, and each prediction model can be used for the prediction process of tobacco shreds with a brand information. The cloud server will determine the matching target prediction model based on the brand information, and use the model for subsequent moisture prediction processes.

[0026] The tobacco drying process generally includes adding water to the tobacco in the rehumidification equipment for rehumidification, and then transporting it to the leaf storage cabinet for storage to balance the production process and ensure that the tobacco can maintain certain humidity and temperature conditions before entering the drying stage, thereby ensuring the consistency of the drying effect. Then, the tobacco in the leaf storage cabinet will be sent to the feed cabinet, and then sent from the feed cabinet to the drying equipment for drying. After the drying is completed, it will be sent to the discharge cabinet to wait for discharge. In this process, the moisture content of the tobacco will be fixedly measured once before being sent to the drying equipment for drying, that is, the first tobacco moisture content is measured. When it is found that the moisture content of the first tobacco does not match the set standard moisture content at this stage, it is difficult to adjust the moisture content of the tobacco. Therefore, the prediction model trained by this application needs to predict the moisture content of the second tobacco before entering the leaf storage cabinet based on the first tobacco moisture content, environmental parameters and production parameters, and then adjust the amount of water added to the tobacco in the rehumidification stage. Among them, the moisture content of the tobacco can be measured by an infrared rapid moisture meter, a microwave moisture meter, etc.

[0027] In one possible implementation, before determining the target prediction model matching the brand information, the method further includes: For any brand information, historical data matching the brand information is obtained, and training data is obtained after feature correlation analysis and preprocessing of the historical data, wherein the preprocessing includes removing outliers and supplementing missing values; An initial model is constructed, and the initial model is trained based on the training data to obtain a prediction model corresponding to the brand information, wherein the initial model is an XGBoost model based on a gradient boosting decision tree algorithm.

[0028] In the embodiments of this specification, for each brand information, a prediction model needs to be trained separately. First, the historical data corresponding to the brand information is obtained. Since there are many types of historical data, it is first necessary to perform feature correlation analysis on the historical data by means of Pearson correlation coefficient, principal component analysis, etc., to determine which features are redundant or highly relevant to the model. This process will focus on the relationship between environmental factors (such as ambient temperature, ambient humidity, etc.), production parameters (such as steam flow, etc.) and the moisture content of the second tobacco, to ensure that these parameters are incorporated into the model to improve the accuracy of the prediction. In addition, it is also necessary to pre-process the selected feature data with high correlation to eliminate outliers with obvious numerical abnormalities, and to supplement the missing values ​​according to the average value of adjacent data. After completing the above processing, the training data for training the model can be obtained. By training the initial model with training data, the prediction model corresponding to the brand information can be obtained.

[0029] The initial model used for training can adopt the XGBoost model, which has the characteristics of high precision, strong generalization ability and fast training. It can be based on the gradient boosting decision tree algorithm, improve the prediction accuracy by integrating multiple weak learners, and effectively prevent overfitting through the regularization mechanism, and adapt to complex nonlinear relationships and high-dimensional data. In addition, XGBoost can automatically handle missing values ​​and unbalanced data, reducing the complexity of data preprocessing, and has good computational efficiency, which is suitable for large-scale real-time data processing. In practical applications, XGBoost can also provide feature importance evaluation to help identify and analyze key factors, thereby further optimizing the production process.

[0030] In one possible implementation, after performing feature correlation analysis and preprocessing on the historical data to obtain training data, the process further includes: Date information is obtained, season information is determined based on the date information, a moisture adjustment parameter corresponding to the season information is obtained, and the moisture content of the second shredded tobacco in the training data is adjusted based on the moisture adjustment parameter.

[0031] In the embodiments of this specification, considering that seasonal changes may also have a certain impact on the accumulation and volatilization rate of moisture in tobacco, different moisture adjustment parameters are set for different seasons based on experience. The cloud server will determine the season information based on the current date information, and query the moisture adjustment parameter corresponding to the season information. After adjusting the moisture content of the second tobacco in the training data by the moisture adjustment parameter, the training data will be used to train the model, so as to eliminate the impact of season on moisture and further improve the prediction accuracy of the trained model.

[0032] In addition, in other embodiments, a manual intervention coefficient that reflects the impact of manual operations on moisture control during the production process may also be set in the training data.

[0033] In one embodiment, the training data includes environmental parameters, production parameters, first shredded tobacco moisture content, second shredded tobacco moisture content, third shredded tobacco moisture content out of a leaf storage cabinet, cabinet number code, and moisture difference of leaves out of a leaf storage cabinet, the moisture difference of leaves out of a leaf storage cabinet is the average difference between the historical first shredded tobacco moisture content and the historical third shredded tobacco moisture content, and the initial model includes a first initial model and a second initial model; The step of training the initial model based on the training data to obtain a prediction model corresponding to the brand information includes: The first initial model is trained based on the training data to obtain a trained first model, wherein the first model is used to calculate the third tobacco shred moisture content according to the first tobacco shred moisture content, the difference in moisture between stored leaves and leaves out of the cabinet, and the standard deviation value, wherein the standard deviation value is the difference between the first tobacco shred moisture content and the set tobacco shred moisture content corresponding to the inlet of the drying device; The second initial model is trained based on the training data to obtain a trained second model, wherein the second model is used to calculate the moisture content of the second shredded tobacco according to the moisture content of the third shredded tobacco and the mean value of the second moisture content change, wherein the mean value of the second moisture content change is the mean value of the difference between the moisture content of the second shredded tobacco in each history and the moisture content of the third shredded tobacco in each history; The first model and the second model are integrated to obtain a prediction model corresponding to the brand information.

[0034] In the embodiments of this specification, considering that in actual situations, different leaf storage cabinets, feed cabinets, etc. may have certain fluctuations in moisture content due to reasons such as service life and setting location, the cabinet number code of the corresponding cabinet will also be used as a feature in the training data. The initial model can be composed of a first initial model and a second initial model. The first initial model is mainly used to estimate the moisture content of the third tobacco shreds based on the moisture content of the first tobacco shreds, that is, the moisture content of the tobacco shreds after the leaf storage cabinet is out of the cabinet. The second initial model is mainly used to estimate the moisture content of the second tobacco shreds based on the moisture content of the third tobacco shreds, that is, the moisture content of the tobacco shreds before entering the leaf storage cabinet. Specifically, when other features such as environmental parameters, production parameters and cabinet number codes are fixed, the calculation formula of the third tobacco shreds moisture content of the trained first model can be that the moisture content of the third tobacco shreds is equal to the moisture content of the first tobacco shreds minus the standard deviation plus the moisture difference of the leaves out of the cabinet. The calculation formula of the second tobacco shreds moisture content of the trained second model can be that the moisture content of the second tobacco shreds is equal to the moisture content of the third tobacco shreds minus the mean value of the second moisture change.

[0035] S102, obtaining current environmental parameters, current production parameters and current first shredded tobacco moisture content, determining a target second shredded tobacco moisture content based on the target prediction model, and calculating an expected shredded tobacco moisture content at a rehumidification outlet according to a first moisture content change mean and the target second shredded tobacco moisture content.

[0036] The first mean value of moisture content change includes the mean value of the difference between the historical moisture content of the second tobacco shreds and the historical moisture content of the rehumidified outlet tobacco shreds.

[0037] In the embodiment of this specification, after determining the target prediction model, the current actual environmental parameters, production parameters and first tobacco moisture content will be obtained, and these data will be input into the target prediction model to obtain the target second tobacco moisture content output by the target prediction model. The target second tobacco moisture content is the moisture content of the tobacco before entering the leaf storage cabinet in order to meet the relevant standard requirements of moisture before drying. Considering that there will be a certain moisture change in the process of tobacco from the rehumidification outlet of the rehumidification equipment to the leaf storage cabinet, the first moisture change mean will be calculated based on historical data, and the expected tobacco moisture content at the rehumidification outlet will be calculated based on the first moisture change mean and the target second tobacco moisture content. The expected tobacco moisture content will be used as the moisture content that the tobacco should maintain during the rehumidification process.

[0038] In one embodiment, the step of calculating the expected moisture content of the cut tobacco at the rehumidification outlet according to the first moisture content change mean and the target second cut tobacco moisture content includes: The first moisture content change mean value corresponding to the brand information is obtained, and the difference between the target second shredded tobacco moisture content and the first moisture content change mean value is calculated to obtain the expected shredded tobacco moisture content at the rehumidification outlet.

[0039] In the embodiments of this specification, the moisture change rate of tobacco with different brand information may also be different, so it is necessary to pre-calculate the first moisture change mean value corresponding to each brand information. In actual situations, the first moisture change mean value of the current brand information is directly found, and the difference between the target second tobacco moisture content and the first moisture change mean value is calculated, and the difference is the expected tobacco moisture content.

[0040] S103, determining the current moisture content of the cut tobacco at the rehumidification outlet, calculating the difference in moisture content of the cut tobacco based on the current moisture content of the cut tobacco and the expected moisture content of the cut tobacco, and adjusting the amount of water added in the rehumidification process based on the difference in moisture content of the cut tobacco.

[0041] In the embodiment of the present specification, the rehumidification outlet can also detect the current moisture content of the tobacco. By calculating the difference between the current moisture content of the tobacco and the expected moisture content of the tobacco, it can be known how much water the current rehumidification equipment needs to increase or decrease to ensure that the moisture standard requirements are met during subsequent drying. The amount of water added during the rehumidification process can be adjusted accordingly to achieve control of the moisture content of the tobacco before drying.

[0042] In one embodiment, the method further comprises: When the accumulated weight of the moisture regain electronic scale reaches a preset weight, the accumulated weight is reset, and the production grade of the drying section is obtained; When the production brand matches the brand information, real-time collected data of the wire drying section is obtained, and the target prediction model is updated based on the real-time collected data; When the production brand does not match the brand information, a compensation value between the production brand and the brand information is obtained and determined, and the amount of water added is adjusted based on the compensation value.

[0043] In the embodiment of the present specification, a moisture recovery electronic scale is provided in the moisture recovery device, which records the total weight of the tobacco shreds that have undergone the moisture recovery process. A preset weight (e.g., 2000 kg) is set in the cloud server, and the preset weight is used as a data processing cycle. Whenever the cumulative weight reaches the preset weight again, the production information is used to determine the production grade of the tobacco shreds in the drying section, that is, the section where the drying equipment shreds the tobacco shreds, and compare it with the grade information. If the two match, it means that the tobacco shreds of the type corresponding to the grade information are still being dried at this time. At this time, the real-time collection data of the tobacco shreds in the drying section can be obtained. The real-time collection data may include the current moisture content, ambient temperature, ambient humidity, etc. of the tobacco shreds, so as to retrain the target prediction model through the real-time collection data, optimize and update the parameters of the model, so that the model can better meet the actual production conditions. If the production grade does not match the grade information, it means that the staff has forgotten to replace the new target prediction model, and the type of tobacco shreds has changed, and the amount of water added by the moisture recovery device may be inaccurate. The cloud server pre-stores the prediction result differences of each prediction model determined by the staff when testing the trained prediction models. The prediction result differences will be used as compensation values ​​for adjusting the amount of water added to improve the accuracy of the prediction of the moisture content of the second tobacco.

[0044] In one possible implementation, after adjusting the amount of water added during the rehumidification process based on the moisture content difference of the shredded tobacco, the method further includes: The variation interval of the moisture content of the first shredded tobacco is determined based on the difference in moisture content of the shredded tobacco, the waiting time taken for the variation value of the moisture content of the first shredded tobacco to reach the variation interval is counted, and the waiting time is displayed.

[0045] In the embodiment of the present specification, the target prediction model determines the expected moisture content of the tobacco shreds according to the moisture content of the first tobacco shreds, and then adjusts the amount of water added in the rehumidification process based on this, which requires a certain amount of time. During this period of time, a part of the tobacco shreds has been processed. If the quality requirements of the tobacco shreds for the current production batch are very high, this part of the tobacco shreds should be distinguished and processed separately. Therefore, the cloud server will determine the variation interval of the moisture content of the first tobacco shreds based on the difference in the moisture content of the tobacco shreds combined with the preset error fluctuation interval, and count the waiting time required for the change value of the moisture content of the first tobacco shreds to change to the variation interval. The tobacco shreds within the waiting time are the tobacco shreds that do not strictly meet the moisture requirements before baking. According to the production information, the real-time position of these dried tobacco shreds within the waiting time can be determined and marked so that the staff can quickly screen out these unqualified tobacco shreds.

[0046] The following will be combined with the attached Figure 2 , the tobacco moisture control device before drying provided by the embodiment of the present application is introduced in detail. It should be noted that the attached Figure 2 The tobacco moisture control device before drying is used to implement the present application Figure 1 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 1 The embodiment shown.

[0047] See also Figure 2 , Figure 2 Schematic diagram of a structure of a tobacco moisture control device before drying provided in an embodiment of the present application. Figure 2 As shown, the device comprises: A receiving module 201 is used to receive a moisture control instruction, respond to the moisture control instruction, obtain brand information of the current batch of shredded tobacco, and determine a target prediction model that matches the brand information, wherein input data of the target prediction model includes environmental parameters, production parameters, and the moisture content of the first shredded tobacco at the entrance of the drying equipment, and output data of the target prediction model includes the moisture content of the second shredded tobacco before entering the leaf storage cabinet, wherein the environmental parameters include the ambient temperature and the ambient humidity, and the production parameters include the steam flow rate; An acquisition module 202 is used to acquire current environmental parameters, current production parameters and current first shredded tobacco moisture content, determine a target second shredded tobacco moisture content based on the target prediction model, and calculate an expected shredded tobacco moisture content at a rehumidification outlet according to a first moisture content change mean value and the target second shredded tobacco moisture content, wherein the first moisture content change mean value includes a difference mean value between a historical second shredded tobacco moisture content and a historical shredded tobacco moisture content at a rehumidification outlet; The adjustment module 203 is used to determine the current moisture content of the tobacco at the rehumidification outlet, calculate the difference in moisture content of the tobacco based on the current moisture content of the tobacco and the expected moisture content of the tobacco, and adjust the amount of water added during the rehumidification process based on the difference in moisture content of the tobacco.

[0048] In one possible implementation, the receiving module 201 is further configured to: For any brand information, historical data matching the brand information is obtained, and training data is obtained after feature correlation analysis and preprocessing of the historical data, wherein the preprocessing includes removing outliers and supplementing missing values; An initial model is constructed, and the initial model is trained based on the training data to obtain a prediction model corresponding to the brand information, wherein the initial model is an XGBoost model based on a gradient boosting decision tree algorithm.

[0049] In one possible implementation, the receiving module 201 is further configured to: Date information is obtained, season information is determined based on the date information, a moisture adjustment parameter corresponding to the season information is obtained, and the moisture content of the second shredded tobacco in the training data is adjusted based on the moisture adjustment parameter.

[0050] In one embodiment, the training data includes environmental parameters, production parameters, first shredded tobacco moisture content, second shredded tobacco moisture content, third shredded tobacco moisture content out of a leaf storage cabinet, cabinet number code, and moisture difference of leaves out of a leaf storage cabinet, the moisture difference of leaves out of a leaf storage cabinet is the average difference between the historical first shredded tobacco moisture content and the historical third shredded tobacco moisture content, and the initial model includes a first initial model and a second initial model; The receiving module 201 is further specifically used for: The first initial model is trained based on the training data to obtain a trained first model, wherein the first model is used to calculate the third tobacco shred moisture content according to the first tobacco shred moisture content, the difference in moisture between stored leaves and leaves out of the cabinet, and the standard deviation value, wherein the standard deviation value is the difference between the first tobacco shred moisture content and the set tobacco shred moisture content corresponding to the inlet of the drying device; The second initial model is trained based on the training data to obtain a trained second model, wherein the second model is used to calculate the moisture content of the second shredded tobacco according to the moisture content of the third shredded tobacco and the mean value of the second moisture content change, wherein the mean value of the second moisture content change is the mean value of the difference between the moisture content of the second shredded tobacco in each history and the moisture content of the third shredded tobacco in each history; The first model and the second model are integrated to obtain a prediction model corresponding to the brand information.

[0051] In one possible implementation, the acquisition module 202 is specifically used for: The first moisture content change mean value corresponding to the brand information is obtained, and the difference between the target second shredded tobacco moisture content and the first moisture content change mean value is calculated to obtain the expected shredded tobacco moisture content at the rehumidification outlet.

[0052] In one possible implementation, the adjustment module 203 is further configured to: When the accumulated weight of the moisture regain electronic scale reaches a preset weight, the accumulated weight is reset, and the production grade of the drying section is obtained; When the production brand matches the brand information, real-time collected data of the wire drying section is obtained, and the target prediction model is updated based on the real-time collected data; When the production brand does not match the brand information, a compensation value between the production brand and the brand information is obtained and determined, and the amount of water added is adjusted based on the compensation value.

[0053] In one possible implementation, the adjustment module 203 is further configured to: The variation interval of the moisture content of the first shredded tobacco is determined based on the moisture content difference of the shredded tobacco, the waiting time taken for the variation value of the moisture content of the first shredded tobacco to reach the variation interval is counted, and the waiting time is displayed.

[0054] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" 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.

[0055] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0056] See also Figure 3 , which shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present application, the electronic device can be used to implement Figure 1 The method in the embodiment shown. Figure 3 As shown, the electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0057] The communication bus 302 is used to realize the connection and communication between these components.

[0058] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0059] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0060] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts of the entire electronic device 300, and executes various functions and processes data of the electronic device 300 by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics central processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0061] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0062] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call the tobacco pre-drying moisture control application stored in the memory 305, and specifically perform the following operations: receiving a moisture control instruction, responding to the moisture control instruction, obtaining brand information of a current batch of shredded tobacco, and determining a target prediction model that matches the brand information, wherein input data of the target prediction model includes environmental parameters, production parameters, and the moisture content of the first shredded tobacco at the entrance of the drying equipment, and output data of the target prediction model includes the moisture content of the second shredded tobacco before entering the leaf storage cabinet, wherein the environmental parameters include the ambient temperature and the ambient humidity, and the production parameters include the steam flow rate; Acquire current environmental parameters, current production parameters and current first shredded tobacco moisture content, determine target second shredded tobacco moisture content based on the target prediction model, and calculate expected shredded tobacco moisture content at the rehumidification outlet according to the first moisture content change mean value and the target second shredded tobacco moisture content, wherein the first moisture content change mean value includes the difference mean value between the historical second shredded tobacco moisture content and the historical shredded tobacco moisture content at the rehumidification outlet; The current moisture content of the cut tobacco at the rehumidification outlet is determined, the difference in moisture content of the cut tobacco is calculated based on the current moisture content of the cut tobacco and the expected moisture content of the cut tobacco, and the amount of water added during the rehumidification process is adjusted based on the difference in moisture content of the cut tobacco.

[0063] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0064] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0065] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0066] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0067] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0068] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0069] 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 the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0070] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct related hardware. The program may be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0071] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for controlling moisture content of cut tobacco before drying, characterized in that: The method comprises: receiving a moisture control instruction, responding to the moisture control instruction, obtaining brand information of a current batch of shredded tobacco, and determining a target prediction model that matches the brand information, wherein input data of the target prediction model includes environmental parameters, production parameters, and the moisture content of the first shredded tobacco at the entrance of the drying equipment, and output data of the target prediction model includes the moisture content of the second shredded tobacco before entering the leaf storage cabinet, wherein the environmental parameters include the ambient temperature and the ambient humidity, and the production parameters include the steam flow rate; Acquire current environmental parameters, current production parameters and current first shredded tobacco moisture content, determine target second shredded tobacco moisture content based on the target prediction model, and calculate expected shredded tobacco moisture content at the rehumidification outlet according to the first moisture content change mean value and the target second shredded tobacco moisture content, wherein the first moisture content change mean value includes the difference mean value between the historical second shredded tobacco moisture content and the historical shredded tobacco moisture content at the rehumidification outlet; The current moisture content of the cut tobacco at the rehumidification outlet is determined, the difference in moisture content of the cut tobacco is calculated based on the current moisture content of the cut tobacco and the expected moisture content of the cut tobacco, and the amount of water added during the rehumidification process is adjusted based on the difference in moisture content of the cut tobacco.

2. The method according to claim 1, characterized in that Before determining the target prediction model matching the brand information, the method further includes: For any brand information, historical data matching the brand information is obtained, and training data is obtained after feature correlation analysis and preprocessing of the historical data, wherein the preprocessing includes removing outliers and supplementing missing values; An initial model is constructed, and the initial model is trained based on the training data to obtain a prediction model corresponding to the brand information, wherein the initial model is an XGBoost model based on a gradient boosting decision tree algorithm.

3. The method according to claim 2, characterized in that After the historical data is subjected to feature correlation analysis and preprocessing to obtain training data, the method further includes: Date information is obtained, season information is determined based on the date information, a moisture adjustment parameter corresponding to the season information is obtained, and the moisture content of the second shredded tobacco in the training data is adjusted based on the moisture adjustment parameter.

4. The method according to claim 2, characterized in that: The training data includes environmental parameters, production parameters, first tobacco moisture content, second tobacco moisture content, third tobacco moisture content out of a leaf storage cabinet, cabinet number code, and moisture difference of leaves out of a leaf storage cabinet, wherein the moisture difference of leaves out of a leaf storage cabinet is the mean difference between the historical first tobacco moisture content and the historical third tobacco moisture content, and the initial model includes a first initial model and a second initial model; The step of training the initial model based on the training data to obtain a prediction model corresponding to the brand information includes: The first initial model is trained based on the training data to obtain a trained first model, wherein the first model is used to calculate the third tobacco shred moisture content according to the first tobacco shred moisture content, the difference in moisture between stored leaves and leaves out of the cabinet, and the standard deviation value, wherein the standard deviation value is the difference between the first tobacco shred moisture content and the set tobacco shred moisture content corresponding to the inlet of the drying device; The second initial model is trained based on the training data to obtain a trained second model, wherein the second model is used to calculate the moisture content of the second shredded tobacco according to the moisture content of the third shredded tobacco and the mean value of the second moisture content change, wherein the mean value of the second moisture content change is the mean value of the difference between the moisture content of the second shredded tobacco in each history and the moisture content of the third shredded tobacco in each history; The first model and the second model are integrated to obtain a prediction model corresponding to the brand information.

5. The method according to claim 1, characterized in that: The step of calculating the expected moisture content of the shredded tobacco at the rehumidification outlet according to the first moisture content change mean value and the target second shredded tobacco moisture content comprises: The first moisture content change mean value corresponding to the brand information is obtained, and the difference between the target second shredded tobacco moisture content and the first moisture content change mean value is calculated to obtain the expected shredded tobacco moisture content at the rehumidification outlet.

6. The method according to claim 1, characterized in that The method further comprises: When the accumulated weight of the moisture regain electronic scale reaches a preset weight, the accumulated weight is reset, and the production grade of the drying section is obtained; When the production brand matches the brand information, real-time collected data of the wire drying section is obtained, and the target prediction model is updated based on the real-time collected data; When the production brand does not match the brand information, a compensation value between the production brand and the brand information is obtained and determined, and the amount of water added is adjusted based on the compensation value.

7. The method according to claim 1, characterized in that After adjusting the amount of water added in the rehumidification process based on the moisture content difference of the shredded tobacco, the method further includes: The variation interval of the moisture content of the first shredded tobacco is determined based on the moisture content difference of the shredded tobacco, the waiting time taken for the variation value of the moisture content of the first shredded tobacco to reach the variation interval is counted, and the waiting time is displayed.

8. A device for controlling moisture in cut tobacco before drying, characterized in that: The device comprises: a receiving module, configured to receive a moisture control instruction, respond to the moisture control instruction, obtain brand information of a current batch of shredded tobacco, and determine a target prediction model that matches the brand information, wherein input data of the target prediction model includes environmental parameters, production parameters, and the moisture content of the first shredded tobacco at the inlet of the drying equipment, and output data of the target prediction model includes the moisture content of the second shredded tobacco before entering the leaf storage cabinet, wherein the environmental parameters include the ambient temperature and the ambient humidity, and the production parameters include the steam flow rate; an acquisition module, used for acquiring current environmental parameters, current production parameters and current first shredded tobacco moisture content, determining a target second shredded tobacco moisture content based on the target prediction model, and calculating an expected shredded tobacco moisture content at a rehumidification outlet according to a first moisture content change mean value and the target second shredded tobacco moisture content, wherein the first moisture content change mean value includes a difference mean value between a historical second shredded tobacco moisture content and a historical shredded tobacco moisture content at a rehumidification outlet; The adjustment module is used to determine the current moisture content of the tobacco at the rehumidification outlet, calculate the difference in moisture content of the tobacco based on the current moisture content of the tobacco and the expected moisture content of the tobacco, and adjust the amount of water added in the rehumidification process based on the difference in moisture content of the tobacco.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium has instructions stored therein, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 7.

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