CN113951533A - Tobacco processing method, system, equipment and storage medium - Google Patents
Tobacco processing method, system, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a tobacco leaf processing method, a system, equipment and a storage medium, wherein the method comprises the following steps: dividing the tobacco leaf processing procedure into a plurality of sub-stages; predicting the water content of the tobacco leaves after the current sub-stage is finished based on a tobacco leaf water content prediction model to obtain the predicted water content of the tobacco leaves; if the predicted water content of the tobacco leaves is not within the preset error range after the current sub-stage is finished, adjusting the steam water content of the current sub-stage based on the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves; the adjusted steam water content of the current sub-stage is utilized to carry out processing treatment on the tobacco leaves at the current sub-stage again until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and then processing treatment at the next sub-stage is carried out; the method solves the problem of low qualified rate of the water content of the tobacco leaves, improves the qualified rate of the water content of the tobacco leaves and ensures the production quality of the tobacco leaves.
Description
Technical Field
The invention relates to the field of big data and data prediction, in particular to a tobacco processing method, a system, equipment and a storage medium.
Background
The effects of the tobacco leaf moistening and feeding process are mainly divided into three major parts, including rebalancing of the temperature and the water content of the tobacco leaves and applying of sugar materials, and under the combined action of the influence of the three parts, the sensory quality of the tobacco leaves is finally improved, and the internal quality of products is improved. In the production process, the control effect of the water content in the tobacco shred has important influence on the inherent quality of the tobacco shred in the next process. The research shows that the influence of the added feed liquid and steam on the influence of different formula modules in the feeding process of moistening the tobacco leaves is the product of fixed coefficient and flow, the type of the tobacco leaves is not refined, the requirement that the water supply of the tobacco leaves cannot reach the water content of the tobacco leaves by the actual water adding flow is caused, and then the system continuously improves the theoretical water adding flow according to the water content of the outlet, so that the conditions that the water content fluctuation of the tobacco leaves at the outlet of the moistening feeding is large, the control is disordered, the stability of the water content of the moistening feeding is influenced, and the qualified rate of the tobacco leaves is low.
Disclosure of Invention
In view of this, embodiments of the present application provide a method, a system, a device and a storage medium for processing tobacco leaves, and aim to solve the problem of low qualified rate of moisture content of tobacco leaves.
The embodiment of the application provides a tobacco leaf processing method, which comprises the following steps:
dividing the tobacco leaf processing procedure into a plurality of sub-stages;
predicting the water content of the tobacco leaves after the current sub-stage is finished based on a tobacco leaf water content prediction model to obtain the predicted water content of the tobacco leaves;
if the predicted water content of the tobacco leaves is not within the preset error range after the current sub-stage is finished, adjusting the steam water content of the current sub-stage based on the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves;
and performing processing treatment on the tobacco leaves in the current sub-stage again by using the adjusted steam water content of the current sub-stage until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and performing processing treatment in the next sub-stage.
In an embodiment, the step of predicting the moisture content of the tobacco leaves after the current sub-stage is ended based on the tobacco leaf moisture content prediction model comprises:
and constructing the tobacco leaf water content prediction model.
In an embodiment, the constructing the tobacco leaf moisture content prediction model includes:
constructing a tobacco leaf water content prediction training set;
inputting the tobacco leaf water content prediction training set into a mathematical model for training to generate a tobacco leaf water content prediction model to be optimized;
and optimizing the tobacco leaf water content prediction model to be optimized to generate the tobacco leaf water content prediction model.
In an embodiment, the optimizing the tobacco leaf moisture content prediction model to be optimized to generate the tobacco leaf moisture content prediction model includes:
inputting the tobacco leaf water content prediction test set into the tobacco leaf water content prediction model to be optimized to obtain the predicted water content of the tobacco leaf;
calculating an error value of the predicted water content of the tobacco leaves and the standard water content of the tobacco leaves in the tobacco leaf water content prediction test set, reversely transmitting the error value to the tobacco leaf water content prediction model to be optimized, updating parameters of the tobacco leaf water content prediction model to be optimized and the weight of the parameters until the error value is smaller than a preset error threshold value, and obtaining the tobacco leaf water content prediction model.
In an embodiment, the constructing the tobacco leaf moisture content prediction training set includes:
acquiring images of tobacco leaves after different sub-stages in the tobacco leaf processing procedure are finished;
acquiring the water content of the tobacco leaves corresponding to the image;
and establishing a corresponding relation between the image of the tobacco leaves and the moisture content of the tobacco leaves corresponding to the image.
In an embodiment, the predicting the moisture content of the tobacco leaves after the current sub-stage is finished based on the tobacco leaf moisture content prediction model to obtain the predicted moisture content of the tobacco leaves includes:
acquiring an image of the tobacco leaves after the current sub-stage is finished;
and inputting the image of the tobacco leaf into the tobacco leaf water content prediction model, and outputting the predicted water content of the tobacco leaf.
In one embodiment, the tobacco processing step is divided into a plurality of sub-stages, including:
the tobacco leaf processing procedure is at least divided into a loosening and moisture regaining stage, a leaf moistening and feeding stage and a hot air leaf moistening stage.
To achieve the above object, there is also provided a tobacco processing system, the system comprising:
the decomposition module is used for dividing the tobacco leaf processing procedure into a plurality of sub-stages;
the prediction module is used for predicting the moisture content of the tobacco leaves after the current sub-stage is finished based on the tobacco leaf moisture content prediction model to obtain the predicted moisture content of the tobacco leaves;
the adjustment optimization module is used for adjusting the steam water content of the current sub-stage based on the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves if the predicted water content of the tobacco leaves after the current sub-stage is finished is not within the preset error range; and performing processing treatment on the tobacco leaves in the current sub-stage again by using the adjusted steam water content of the current sub-stage until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and performing processing treatment in the next sub-stage.
To achieve the above object, there is also provided a computer storage medium having a tobacco processing method program stored thereon, which when executed by a processor, implements any of the steps of the tobacco processing method described above.
In order to achieve the above object, there is also provided a tobacco processing apparatus, including a memory, a processor, and a tobacco processing method program stored in the memory and operable on the processor, wherein the processor implements any of the steps of the tobacco processing method when executing the tobacco processing method program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: dividing the tobacco leaf processing procedure into a plurality of sub-stages; by dividing the tobacco leaf processing procedure into a plurality of sub-stages, the water content of the tobacco leaves can be more carefully adjusted in the plurality of sub-stages, and the qualification rate of the water content of the tobacco leaves is improved.
Predicting the water content of the tobacco leaves after the current sub-stage is finished based on a tobacco leaf water content prediction model to obtain the predicted water content of the tobacco leaves; by utilizing the tobacco leaf water content prediction model, the accurate predicted water content of the tobacco leaves is obtained, and the accuracy of the predicted water content of the tobacco leaves is ensured.
If the predicted water content of the tobacco leaves is not within the preset error range after the current sub-stage is finished, adjusting the steam water content of the current sub-stage based on the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves; and performing processing treatment on the tobacco leaves in the current sub-stage again by using the adjusted steam water content of the current sub-stage until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and performing processing treatment in the next sub-stage. Through adjusting the steam water content of the current sub-stage, and processing the tobacco leaves again according to the adjusted steam water content, the predicted water content of the tobacco leaves of the current sub-stage is guaranteed to be within a preset error range, so that the qualification rate of the water content of the tobacco leaves is improved, and the production quality of the tobacco leaves is guaranteed.
Drawings
FIG. 1 is a schematic flow diagram of a first embodiment of a tobacco processing method according to the present application;
FIG. 2 is a schematic flow chart of a second embodiment of the tobacco processing method of the present application;
FIG. 3 is a schematic flow chart illustrating a specific implementation step of step S220 in a second embodiment of the tobacco leaf processing method according to the present application;
FIG. 4 is a schematic flow chart illustrating an embodiment of step S224 in the tobacco processing method of the present application;
FIG. 5 is a schematic flow chart illustrating the specific implementation step of step S221 in the tobacco leaf processing method of the present application;
FIG. 6 is a schematic flow chart illustrating a specific implementation step of step S120 in the first embodiment of the tobacco leaf processing method according to the present application;
FIG. 7 is a schematic view of a tobacco processing system according to the present application;
fig. 8 is a schematic view of a tobacco processing apparatus according to the present application.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: dividing the tobacco leaf processing procedure into a plurality of sub-stages; predicting the water content of the tobacco leaves after the current sub-stage is finished based on a tobacco leaf water content prediction model to obtain the predicted water content of the tobacco leaves; if the predicted water content of the tobacco leaves is not within the preset error range after the current sub-stage is finished, adjusting the steam water content of the current sub-stage based on the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves; the adjusted steam water content of the current sub-stage is utilized to carry out processing treatment on the tobacco leaves at the current sub-stage again until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and then processing treatment at the next sub-stage is carried out; the method solves the problem of low qualified rate of the water content of the tobacco leaves, improves the qualified rate of the water content of the tobacco leaves and ensures the production quality of the tobacco leaves.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Referring to fig. 1, fig. 1 is a first embodiment of a tobacco processing method of the present application, the method comprising:
step S110: the tobacco leaf processing procedure is divided into a plurality of sub-stages.
Specifically, in one embodiment, the tobacco processing procedure is divided into a plurality of sub-stages, including:
the tobacco leaf processing procedure is at least divided into a loosening and moisture regaining stage, a leaf moistening and feeding stage and a hot air leaf moistening stage.
It should be noted that the tobacco processing procedure can be divided into different sub-stages according to different tobacco production processes, and is not limited to the three sub-stages. In addition, the decomposition can be carried out according to the steam adding treatment process in the tobacco leaf processing procedure.
Specifically, the more sub-stages of the decomposition of the tobacco leaf processing procedure, the better the deviation rectifying effect on the moisture content of the tobacco leaf is, so that the qualification rate of the moisture content of the tobacco leaf can be improved.
Step S120: and predicting the water content of the tobacco leaves after the current sub-stage is finished based on the tobacco leaf water content prediction model to obtain the predicted water content of the tobacco leaves.
Specifically, the tobacco moisture content prediction model may be a mathematical model having a prediction function.
Step S130: and if the predicted water content of the tobacco leaves is not within the preset error range after the current sub-stage is finished, adjusting the steam water content of the current sub-stage based on the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves.
Specifically, the preset error range may be adjusted and set according to production requirements of different types of tobacco leaves, and is not limited herein.
Specifically, there may be one moisture meter in each sub-stage production facility for measuring the moisture content measurements within the current sub-stage production facility. And adjusting the steam water content of the current sub-stage according to the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves.
Step S140: and performing processing treatment on the tobacco leaves in the current sub-stage again by using the adjusted steam water content of the current sub-stage until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and performing processing treatment in the next sub-stage.
Specifically, if the predicted water content of the tobacco leaves is lower than the minimum value of the preset error range, the steam water content of the current sub-stage is increased; and if the predicted water content of the tobacco leaves is higher than the maximum value of the preset error range, adjusting the steam water content of the current sub-stage to be zero, and drying the current sub-stage to reduce the water content of the tobacco leaves and ensure the production quality of the tobacco leaves.
In the above embodiment, there are advantageous effects of: dividing the tobacco leaf processing procedure into a plurality of sub-stages; by dividing the tobacco leaf processing procedure into a plurality of sub-stages, the water content of the tobacco leaves can be more carefully adjusted in the plurality of sub-stages, and the qualification rate of the water content of the tobacco leaves is improved.
Predicting the water content of the tobacco leaves after the current sub-stage is finished based on a tobacco leaf water content prediction model to obtain the predicted water content of the tobacco leaves; by utilizing the tobacco leaf water content prediction model, the accurate predicted water content of the tobacco leaves is obtained, and the accuracy of the predicted water content of the tobacco leaves is ensured.
If the predicted water content of the tobacco leaves is not within the preset error range after the current sub-stage is finished, adjusting the steam water content of the current sub-stage based on the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves; and performing processing treatment on the tobacco leaves in the current sub-stage again by using the adjusted steam water content of the current sub-stage until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and performing processing treatment in the next sub-stage. Through adjusting the steam water content of the current sub-stage, and processing the tobacco leaves again according to the adjusted steam water content, the predicted water content of the tobacco leaves of the current sub-stage is guaranteed to be within a preset error range, so that the qualification rate of the water content of the tobacco leaves is improved, and the production quality of the tobacco leaves is guaranteed.
Referring to fig. 2, fig. 2 is a second embodiment of the tobacco processing method according to the present application, where before the step of predicting the moisture content of the tobacco leaf after the current sub-stage is finished based on the tobacco leaf moisture content prediction model, the method includes:
step S210: the tobacco leaf processing procedure is divided into a plurality of sub-stages.
Step S220: and constructing the tobacco leaf water content prediction model.
Specifically, the tobacco water content prediction model may be a mathematical model with a prediction function, specifically, a neural network model, or a naive bayes model, or a logistic regression model, or a random forest model, or a combination model of the above models, and the models are combined through ensemble learning, or a combination of two or more of the two models, so as to improve the prediction accuracy of the tobacco water content. Specifically, a neural network model, a naive Bayes model, a logistic regression model and a random forest model are integrated into a strong learner by adopting a combined strategy of a voting method. The combination strategy at least comprises an averaging method, a voting method and a learning method.
Step S230: and predicting the water content of the tobacco leaves after the current sub-stage is finished based on the tobacco leaf water content prediction model to obtain the predicted water content of the tobacco leaves.
Step S240: and if the predicted water content of the tobacco leaves is not within the preset error range after the current sub-stage is finished, adjusting the steam water content of the current sub-stage based on the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves.
Step S250: and performing processing treatment on the tobacco leaves in the current sub-stage again by using the adjusted steam water content of the current sub-stage until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and performing processing treatment in the next sub-stage.
Compared with the first embodiment, the second embodiment includes step S220, and other steps have already been described in the first embodiment, and are not described herein again.
In the above embodiment, there are advantageous effects of: and the correct prediction of the moisture content of the tobacco leaves is ensured by constructing a tobacco leaf moisture content prediction model.
Referring to fig. 3, fig. 3 is a specific implementation step of step S220 in the second embodiment of the tobacco processing method of the present application, including:
step S221: and constructing a tobacco leaf water content prediction training set.
Specifically, in the tobacco moisture content prediction training data, 90% is extracted as a tobacco moisture content prediction training set, and the remaining 10% is used as a tobacco moisture content prediction testing set. And the data formats of the tobacco leaf water content prediction training set and the tobacco leaf water content prediction testing set are the same.
Specifically, the tobacco leaf moisture content prediction training set is constructed, which may be a correspondence relationship between tobacco leaf images and corresponding moisture contents thereof.
Step S222: and inputting the tobacco leaf water content prediction training set into a mathematical model for training to generate a tobacco leaf water content prediction model to be optimized.
Specifically, a supervised training mode is adopted, and the tobacco water content prediction training set is input into the mathematical model so as to train the mathematical model. The specific mathematical model is defined with reference to the implementation of step S220, and will not be described herein again.
Step S223: and optimizing the tobacco leaf water content prediction model to be optimized to generate the tobacco leaf water content prediction model.
Specifically, the prediction effect of the generated tobacco water content prediction model is improved by performing optimization operation on the tobacco water content prediction model to be optimized.
In the above embodiment, there are advantageous effects of: through optimization processing, the prediction effect of the generated tobacco water content prediction model is further improved, and the accuracy of tobacco water content prediction is ensured, so that the quality of tobacco production is ensured.
Referring to fig. 4, fig. 4 is a specific implementation step of step S223 in the tobacco processing method of the present application, where the optimizing the tobacco moisture content prediction model to be optimized to generate the tobacco moisture content prediction model includes:
step S2231: and inputting the tobacco leaf water content prediction test set into the tobacco leaf water content prediction model to be optimized to obtain the predicted water content of the tobacco leaf.
Specifically, the tobacco leaf water content prediction model to be optimized is an intermediate model in the training process, wherein each parameter of the tobacco leaf water content prediction model to be optimized and the weight of the parameter can be updated according to the optimization operation, so as to further improve the accuracy of the tobacco leaf water content prediction.
Step S2232: calculating an error value of the predicted water content of the tobacco leaves and the standard water content of the tobacco leaves in the tobacco leaf water content prediction test set, reversely transmitting the error value to the tobacco leaf water content prediction model to be optimized, updating parameters of the tobacco leaf water content prediction model to be optimized and the weight of the parameters until the error value is smaller than a preset error threshold value, and obtaining the tobacco leaf water content prediction model.
Specifically, the back propagation algorithm, BP algorithm for short, the learning process of BP algorithm is composed of a forward propagation process and a back propagation process. In the forward propagation process, input information passes through the hidden layer through the input layer, is processed layer by layer and is transmitted to the output layer. If the expected output value cannot be obtained in the output layer, taking the square sum of the output and the expected error as an objective function, turning into reverse propagation, calculating the partial derivative of the objective function to the weight of each neuron layer by layer to form the gradient of the objective function to the weight vector, and finishing the learning of the network in the weight modifying process as the basis for modifying the weight. And when the error reaches the expected value, namely the error value is smaller than the preset error threshold value, stopping updating the parameters and the weights of the parameters, and generating the tobacco water content prediction model.
In the above embodiment, there are advantageous effects of: and optimizing the tobacco leaf water content prediction model to be optimized through back propagation, and updating the parameters of the tobacco leaf water content prediction model to be optimized and the weights of the parameters so as to improve the prediction effect of the tobacco leaf water content prediction model.
Referring to fig. 5, fig. 5 is a specific implementation step of step S221 in the tobacco processing method of the present application, where the building of the tobacco moisture content prediction training set includes:
step S2211: and acquiring images of the tobacco leaves after different sub-stages in the tobacco leaf processing procedure.
Specifically, the moisture content of the tobacco leaves is different, and the tobacco leaves have different differences in appearance, color and form. In this embodiment, therefore, images of the same type of tobacco leaves after the different sub-stages of the processing procedure are completed are obtained.
Step S2212: and acquiring the water content of the tobacco leaves corresponding to the image.
Specifically, the moisture content of the tobacco leaves can be detected or measured by a tobacco leaf moisture content testing instrument.
Step S2213: and establishing a corresponding relation between the image of the tobacco leaves and the moisture content of the tobacco leaves corresponding to the image.
Specifically, the moisture content of the tobacco leaves is used as a label of the tobacco leaf image, and the corresponding relation between the tobacco leaf image and the moisture content of the tobacco leaves corresponding to the image is established.
It should be further noted that the tobacco leaf moisture content prediction test set and the tobacco leaf moisture content prediction training set are the same in construction method, and are not described herein again, and refer to the specific implementation of the foregoing embodiment.
In the above embodiment, there are advantageous effects of: by constructing a high-quality tobacco leaf water content prediction training set, the prediction effect of a tobacco leaf water content prediction model is ensured, and the accuracy of the tobacco leaf water content prediction is improved.
Referring to fig. 6, fig. 6 is a specific implementation step of step S120 in the first embodiment of the tobacco processing method of the present application, where the predicting, based on the tobacco moisture content prediction model, the moisture content of the tobacco leaves after the current sub-stage is finished to obtain the predicted moisture content of the tobacco leaves includes:
step S121: and acquiring an image of the tobacco leaves after the current sub-stage is finished.
Specifically, if the current sub-stage processing is finished, an image of the processed tobacco leaves is obtained.
Step S122: and inputting the image of the tobacco leaf into the tobacco leaf water content prediction model, and outputting the predicted water content of the tobacco leaf.
Specifically, the current tobacco leaf image is used as input data and put into a tobacco leaf moisture content prediction model for prediction, and the predicted moisture content of the tobacco leaves is used as output data through calculation of the tobacco leaf moisture content prediction model.
In the above embodiment, there are advantageous effects of: by accurately predicting the water content of the image of the tobacco leaves after the current sub-stage is finished, the qualification rate of the water content of the tobacco leaves in the tobacco leaf processing procedure is improved, and the production quality of the tobacco leaves is ensured.
The present application further provides a tobacco processing system 20, the system comprising:
the decomposition module 21 is used for dividing the tobacco processing procedure into a plurality of sub-stages;
the prediction module 22 is used for predicting the moisture content of the tobacco leaves after the current sub-stage is finished based on the tobacco leaf moisture content prediction model to obtain the predicted moisture content of the tobacco leaves;
the adjusting and optimizing module 23 is configured to adjust the steam water content of the current sub-stage based on the measured value of the current sub-stage moisture meter and the predicted water content of the tobacco leaf if the predicted water content of the tobacco leaf after the current sub-stage is ended is not within the preset error range; and performing processing treatment on the tobacco leaves in the current sub-stage again by using the adjusted steam water content of the current sub-stage until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and performing processing treatment in the next sub-stage.
The tobacco processing system 20 shown in fig. 7 comprises a decomposition module 21, a prediction module 22, and an adjustment optimization module 23, which can execute the method of the embodiment shown in fig. 1 to 6, and the detailed description of the embodiment can refer to the related description of the embodiment shown in fig. 1 to 6. The implementation process and technical effect of the technical solution refer to the descriptions in the embodiments shown in fig. 1 to fig. 6, and are not described herein again.
The present application further provides a computer storage medium having a tobacco processing method program stored thereon, which when executed by a processor, implements any of the steps of the tobacco processing method described above.
The application also provides tobacco processing equipment which comprises a memory, a processor and a tobacco processing method program which is stored on the memory and can run on the processor, wherein the processor realizes any one of the steps of the tobacco processing method when executing the tobacco processing method program.
The present application relates to a tobacco processing apparatus 10 comprising as shown in figure 8: at least one processor 12, a memory 11.
The processor 12 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 12. The processor 12 described above may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 11, and the processor 12 reads the information in the memory 11 and completes the steps of the method in combination with the hardware thereof.
It will be appreciated that memory 11 in embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double data rate Synchronous Dynamic random access memory (ddr DRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 11 of the systems and methods described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method of tobacco processing, the method comprising:
dividing the tobacco leaf processing procedure into a plurality of sub-stages;
predicting the water content of the tobacco leaves after the current sub-stage is finished based on a tobacco leaf water content prediction model to obtain the predicted water content of the tobacco leaves;
if the predicted water content of the tobacco leaves is not within the preset error range after the current sub-stage is finished, adjusting the steam water content of the current sub-stage based on the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves;
and performing processing treatment on the tobacco leaves in the current sub-stage again by using the adjusted steam water content of the current sub-stage until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and performing processing treatment in the next sub-stage.
2. The tobacco processing method according to claim 1, wherein the step of predicting the moisture content of the tobacco leaves after the current sub-phase is finished based on the tobacco moisture content prediction model comprises:
and constructing the tobacco leaf water content prediction model.
3. The tobacco processing method of claim 2, wherein said constructing the tobacco moisture content prediction model comprises:
constructing a tobacco leaf water content prediction training set;
inputting the tobacco leaf water content prediction training set into a mathematical model for training to generate a tobacco leaf water content prediction model to be optimized;
and optimizing the tobacco leaf water content prediction model to be optimized to generate the tobacco leaf water content prediction model.
4. The tobacco processing method of claim 3, wherein the optimizing the tobacco moisture prediction model to be optimized to generate the tobacco moisture prediction model comprises:
inputting the tobacco leaf water content prediction test set into the tobacco leaf water content prediction model to be optimized to obtain the predicted water content of the tobacco leaf;
calculating an error value of the predicted water content of the tobacco leaves and the standard water content of the tobacco leaves in the tobacco leaf water content prediction test set, reversely transmitting the error value to the tobacco leaf water content prediction model to be optimized, updating parameters of the tobacco leaf water content prediction model to be optimized and the weight of the parameters until the error value is smaller than a preset error threshold value, and obtaining the tobacco leaf water content prediction model.
5. The tobacco processing method of claim 3, wherein the constructing a training set of tobacco moisture content predictions comprises:
acquiring images of tobacco leaves after different sub-stages in the tobacco leaf processing procedure are finished;
acquiring the water content of the tobacco leaves corresponding to the image;
and establishing a corresponding relation between the image of the tobacco leaves and the moisture content of the tobacco leaves corresponding to the image.
6. The tobacco processing method of claim 1, wherein the predicting the moisture content of the tobacco leaf after the current sub-phase is finished based on the tobacco leaf moisture content prediction model to obtain the predicted moisture content of the tobacco leaf comprises:
acquiring an image of the tobacco leaves after the current sub-stage is finished;
and inputting the image of the tobacco leaf into the tobacco leaf water content prediction model, and outputting the predicted water content of the tobacco leaf.
7. The tobacco processing method of claim 1, wherein the dividing of the tobacco processing sequence into a plurality of sub-stages comprises:
the tobacco leaf processing procedure is at least divided into a loosening and moisture regaining stage, a leaf moistening and feeding stage and a hot air leaf moistening stage.
8. A tobacco processing system, characterized in that the system comprises:
the decomposition module is used for dividing the tobacco leaf processing procedure into a plurality of sub-stages;
the prediction module is used for predicting the moisture content of the tobacco leaves after the current sub-stage is finished based on the tobacco leaf moisture content prediction model to obtain the predicted moisture content of the tobacco leaves;
the adjustment optimization module is used for adjusting the steam water content of the current sub-stage based on the measured value of the moisture meter of the current sub-stage and the predicted water content of the tobacco leaves if the predicted water content of the tobacco leaves after the current sub-stage is finished is not within the preset error range; and performing processing treatment on the tobacco leaves in the current sub-stage again by using the adjusted steam water content of the current sub-stage until the predicted water content of the tobacco leaves is within a preset error range after the current sub-stage is finished, and performing processing treatment in the next sub-stage.
9. A computer storage medium, characterized in that the computer storage medium has stored thereon a tobacco processing method program which, when executed by a processor, implements the steps of the tobacco processing method according to any one of claims 1 to 7.
10. A tobacco processing apparatus comprising a memory, a processor and a tobacco processing method program stored on the memory and executable on the processor, the processor implementing the steps of the tobacco processing method according to any one of claims 1 to 7 when executing the tobacco processing method program.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150347815A1 (en) * | 2012-11-19 | 2015-12-03 | Altria Client Services Inc. | Hyperspectral imaging system for monitoring agricultural products during processing and manufacturing |
| CN105520183A (en) * | 2015-12-31 | 2016-04-27 | 山东中烟工业有限责任公司 | Method for improving moisture stability of outlet of loosening and conditioning machine |
| CN110646425A (en) * | 2019-09-12 | 2020-01-03 | 厦门中软海晟信息技术有限公司 | Tobacco leaf online auxiliary grading method and system |
| CN112257948A (en) * | 2020-10-30 | 2021-01-22 | 红云红河烟草(集团)有限责任公司 | Method, device and equipment for predicting moisture content of moistening leaf feeding outlet |
| CN112273695A (en) * | 2020-10-30 | 2021-01-29 | 红云红河烟草(集团)有限责任公司 | Method, device and equipment for predicting water content of loose moisture regain outlet |
| CN113128764A (en) * | 2021-04-19 | 2021-07-16 | 广西中烟工业有限责任公司 | Generation of tobacco dryer outlet water content prediction model and regulation and control method based on same |
-
2021
- 2021-09-15 CN CN202111083606.2A patent/CN113951533B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150347815A1 (en) * | 2012-11-19 | 2015-12-03 | Altria Client Services Inc. | Hyperspectral imaging system for monitoring agricultural products during processing and manufacturing |
| CN105520183A (en) * | 2015-12-31 | 2016-04-27 | 山东中烟工业有限责任公司 | Method for improving moisture stability of outlet of loosening and conditioning machine |
| CN110646425A (en) * | 2019-09-12 | 2020-01-03 | 厦门中软海晟信息技术有限公司 | Tobacco leaf online auxiliary grading method and system |
| CN112257948A (en) * | 2020-10-30 | 2021-01-22 | 红云红河烟草(集团)有限责任公司 | Method, device and equipment for predicting moisture content of moistening leaf feeding outlet |
| CN112273695A (en) * | 2020-10-30 | 2021-01-29 | 红云红河烟草(集团)有限责任公司 | Method, device and equipment for predicting water content of loose moisture regain outlet |
| CN113128764A (en) * | 2021-04-19 | 2021-07-16 | 广西中烟工业有限责任公司 | Generation of tobacco dryer outlet water content prediction model and regulation and control method based on same |
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