Reconstituted tobacco sensory quality score determination method and system based on coating moisture

Abstract

The invention relates to the technical field of computer deep learning, in particular to a reconstituted tobacco sensory quality score determination method and system based on coating moisture. The method comprises the following steps: acquiring coating moisture of to-be-detected reconstituted tobacco; inputting the coating moisture into a trained sensory quality scoring model; and obtaining a sensory quality score prediction value which is output by the sensory quality score model and is calculated based on the coating moisture, and determining the sensory quality score prediction value as a sensory quality score value corresponding to the to-be-detected reconstituted tobacco. The invention aims to solve the problem of how to improve the product stability in the paper-making reconstituted tobacco production process.

Description

Technical Field

[0001] The present application relates to the field of computer deep learning technology, and in particular to a method and system for determining a sensory quality score of reconstituted tobacco leaves based on coating moisture. Background Art

[0002] Reconstituted tobacco is an important part of the tobacco industry, and its quality directly affects the quality of tobacco products. The national standard GB 5606.4-2005 "Cigarettes Part 4: Sensory Technical Requirements" stipulates several rules for judging the sensory quality of cigarettes, and evaluates the sensory quality of cigarettes from aspects such as the appearance, smoke, and aroma of tobacco leaves.

[0003] However, manual smoking evaluation usually involves sampling several cigarettes from a production batch, testing the parameters related to the sensory quality of the cigarettes using chemical experiments, and then combining the subjective judgment of professional smoking testers on the appearance, smoke, aroma and other aspects of the tobacco leaves to score the sensory quality of the cigarettes. If the score is unqualified, all cigarettes in the production batch will be judged as unqualified.

[0004] Related technical solution 1, Chinese patent publication number CN117422330A discloses a method for evaluating the quality of ordinary cigarette tobacco, which discloses the following indicators for evaluating the quality of tobacco: 1. Leaf quality indicators (including leaf moisture content, leaf filling value, leaf whole rate and leaf broken rate); 2. Cigarette physical indicators (including cigarette unit weight, cigarette unit weight standard deviation, cigarette draw resistance, cigarette draw resistance standard deviation, cigarette filter ventilation rate and cigarette filter ventilation rate standard deviation); 3. Smoke indicators (including tar release extremes, carbon monoxide release extremes and smoke nicotine release extremes); 4. Sensory indicators (including gloss, aroma, harmony, miscellaneous smell, irritation and aftertaste). The score of each indicator corresponds to a different weight. The final comprehensive evaluation score of tobacco is obtained by multiplying each indicator by its own weight and adding them together.

[0005] Related technical solution 2, Chinese patent publication number CN110458474A discloses a comprehensive evaluation method for the physical quality of cigarette rolling, which discloses the following 7 indicators for evaluating the quality of cut tobacco: support weight, draw resistance, hardness, ventilation rate, end cut tobacco, dust content and empty head rate. Through data collection, data standardization processing, and principal component analysis of standardized data, a batch of cigarette comprehensive physical quality evaluation model is established, and the cigarette rolling quality is quantitatively scored through the evaluation model.

[0006] However, the above-mentioned related technical solutions all focus on analyzing the indicators of tobacco itself, and do not involve the research on the indicators and / or process steps that are correlated with the sensory quality of reconstituted tobacco in the preparation process of cigarettes, especially the preparation process of reconstituted tobacco.

[0007] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0008] The main purpose of this application is to provide a method for determining the sensory quality score of reconstituted tobacco based on the moisture content at the coating stage, aiming to solve the problem of how to improve the product stability in the papermaking process of reconstituted tobacco production.

[0009] To achieve the above-mentioned purpose, the present application provides a method for determining the sensory quality score of reconstituted tobacco leaves based on coating moisture, the method comprising:

[0010] Obtain the coating moisture of the reconstituted tobacco leaves to be tested;

[0011] Inputting the coating moisture into the trained sensory quality scoring model;

[0012] The predicted sensory quality score value calculated based on the coating moisture output by the sensory quality score model is obtained, and the predicted sensory quality score value is determined as the sensory quality score value corresponding to the reconstituted tobacco leaf to be tested.

[0013] Optionally, the trained sensory quality scoring model includes a sensory quality scoring calculation module for coating moisture, and the training process of the sensory quality scoring model includes:

[0014] Obtain the coating moisture sample data set;

[0015] Preprocessing the coating moisture sample data set,

[0016] Based on the pre-processed coating moisture sample data set and the corresponding sensory quality score data set, constructing a correlation data table;

[0017] Input the associated data table into the model with lightGBM architecture for training, and calculate the AUC value of the model after each training is completed;

[0018] After the AUC value reaches the preset AUC threshold, the training is completed.

[0019] Optionally, the step of preprocessing the coating moisture sample data set includes:

[0020] Normalizing the coating moisture sample data set to accelerate the convergence speed of the gradient descent of the coating moisture data on the same scale;

[0021] Based on the normalized coating moisture sample data set and the corresponding sensory quality score data set, a correlation data table is constructed;

[0022] Input the associated data table into the model with lightGBM architecture for training.

[0023] Optionally, during the training process, at least one of the number of leaf nodes, learning rate, tree building feature selection ratio, tree building sample sampling ratio and bagging interval in each model is adjusted so that the AUC value of the model reaches a preset AUC threshold.

[0024] Optionally, after the step of obtaining the predicted sensory quality score value calculated based on the coating moisture output by the sensory quality score model, and determining the predicted sensory quality score value as the sensory quality score value corresponding to the reconstituted tobacco leaf to be tested, the step further includes:

[0025] During the process of executing the papermaking method reconstituted tobacco process, obtaining the sensory quality score value associated with the reconstituted tobacco in the current production line at every preset period;

[0026] Determine whether the sensory quality score is within a normal value range;

[0027] When it is detected that the sensory quality score value is not in the normal value range, determining whether the coating moisture in the current production line is in the standard coating moisture range;

[0028] If not, determine and output the abnormal target coating moisture process equipment parameters among the various coating moisture process equipment parameters.

[0029] Optionally, the abnormal target coating moisture process equipment parameters include at least one of sizing amount, vacuum degree of vacuum dehydration box, vacuum pressing pressure, Yankee hood hot air temperature, Yankee cylinder temperature and film base uniformity.

[0030] In addition, to achieve the above-mentioned objectives, the present application also provides a papermaking method reconstituted tobacco system, the papermaking method reconstituted tobacco system comprising: a memory, a processor, and a reconstituted tobacco sensory quality score determination program based on coating moisture stored in the memory and executable on the processor, wherein the reconstituted tobacco sensory quality score determination program based on coating moisture, when executed by the processor, implements the steps of the method for determining the sensory quality score of reconstituted tobacco based on coating moisture as described in any of the above items.

[0031] This application has at least the following beneficial effects:

[0032] 1. The process parameters of reconstituted tobacco with strong correlation with sensory quality scores in the production process of papermaking reconstituted tobacco were explored, as well as the equipment parameters associated with the process parameters of reconstituted tobacco, so as to achieve the correlation between the sensory quality scores and the process parameters of reconstituted tobacco;

[0033] 2. Convert the sensory quality score obtained by traditional manual smoking evaluation into a real-time calculation by the system based on process parameters, so as to realize the efficient application of sensory quality score in the industrial production of papermaking reconstituted tobacco leaves;

[0034] 3. Through the collection of reconstituted tobacco process parameters that have a strong correlation with the sensory quality score in the production process of papermaking reconstituted tobacco, the parameters are pre-processed and input into the sensory quality score model to simulate the predicted sensory quality score close to the actual manual smoking score, thereby realizing real-time simulation monitoring of the sensory quality score in the production process of reconstituted tobacco and ensuring the output stability of reconstituted tobacco in the production process of papermaking reconstituted tobacco. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the architecture of the hardware operating environment of the papermaking process reconstituted tobacco system involved in the embodiment of the present invention;

[0036] Figure 2 It is a flow chart of a first embodiment of a method for determining a sensory quality score of reconstituted tobacco leaves based on coating moisture of the present invention;

[0037] Figure 3 It is a schematic diagram of a fitting curve of coating moisture and sensory evaluation score involved in an embodiment of the present invention;

[0038] Figure 4 A schematic diagram of the association between the associated process parameters and the sensory quality scores of reconstituted tobacco leaves selected from the association table according to an embodiment of the present invention;

[0039] Figure 5 This is a flow chart of a second embodiment of the method for determining the sensory quality score of reconstituted tobacco leaves based on coating moisture content according to the present invention.

[0040] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0041] In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0042] As an implementation plan, Figure 1 This is a schematic diagram of the architecture of the hardware operating environment of the papermaking reconstituted tobacco system involved in the embodiment of the present application.

[0043] like Figure 1 As shown, the papermaking method reconstituted tobacco leaf system may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0044] Those skilled in the art will understand that Figure 1 The architecture of the reconstituted tobacco system for papermaking shown in the figure does not constitute a limitation on the reconstituted tobacco system for papermaking, and may include more or less components than those shown in the figure, or combine certain components, or arrange the components differently.

[0045] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a program for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the coating. The operating system is a program for managing and controlling the hardware and software resources of the papermaking process reconstituted tobacco leaf system, the program for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the coating, and the operation of other software or programs.

[0046] exist Figure 1 In the papermaking reconstituted tobacco system shown, the user interface 1003 is mainly used to connect to the terminal and communicate data with the terminal; the network interface 1004 is mainly used for the background server and communicates data with the background server; the processor 1001 can be used to call the reconstituted tobacco sensory quality score determination program based on the coating moisture stored in the memory 1005.

[0047] In this embodiment, the papermaking process reconstituted tobacco system includes: a memory 1005, a processor 1001, and a reconstituted tobacco sensory quality score determination program based on coating moisture stored in the memory and executable on the processor, wherein:

[0048] When the processor 1001 calls the reconstituted tobacco sensory quality score determination program based on coating moisture stored in the memory 1005, the following operations are performed:

[0049] Obtain the coating moisture of the reconstituted tobacco leaves to be tested;

[0050] Inputting the coating moisture into the trained sensory quality scoring model;

[0051] The predicted sensory quality score value calculated based on the coating moisture output by the sensory quality score model is obtained, and the predicted sensory quality score value is determined as the sensory quality score value corresponding to the reconstituted tobacco leaf to be tested.

[0052] When the processor 1001 calls the reconstituted tobacco sensory quality score determination program based on coating moisture stored in the memory 1005, the following operations are performed:

[0053] Obtain the coating moisture sample data set;

[0054] Preprocessing the coating moisture sample data set,

[0055] Based on the pre-processed coating moisture sample data set and the corresponding sensory quality score data set, constructing a correlation data table;

[0056] Input the associated data table into the model with lightGBM architecture for training, and calculate the AUC value of the model after each training is completed;

[0057] After the AUC value reaches the preset AUC threshold, the training is completed.

[0058] When the processor 1001 calls the reconstituted tobacco sensory quality score determination program based on coating moisture stored in the memory 1005, the following operations are performed:

[0059] Normalizing the coating moisture sample data set to accelerate the convergence speed of the gradient descent of the coating moisture data on the same scale;

[0060] Based on the normalized coating moisture sample data set and the corresponding sensory quality score data set, a correlation data table is constructed;

[0061] Input the associated data table into the model with lightGBM architecture for training.

[0062] When the processor 1001 calls the reconstituted tobacco sensory quality score determination program based on coating moisture stored in the memory 1005, the following operations are performed:

[0063] During the process of executing the papermaking method reconstituted tobacco process, obtaining the sensory quality score value associated with the reconstituted tobacco in the current production line at every preset period;

[0064] Determine whether the sensory quality score is within a normal value range;

[0065] When it is detected that the sensory quality score value is not in the normal value range, determining whether the coating moisture in the current production line is in the standard coating moisture range;

[0066] If not, determine and output the abnormal target coating moisture process equipment parameters among the various coating moisture process equipment parameters.

[0067] Based on the hardware architecture of the papermaking reconstituted tobacco system based on computer deep learning technology, an embodiment of the method for determining the sensory quality score of reconstituted tobacco based on the coating moisture content of the present application is proposed. Figure 2 In a first embodiment, the method for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the coating comprises the following steps:

[0068] Step S10, obtaining the moisture content of the reconstituted tobacco leaves to be tested before coating;

[0069] In this embodiment, the moisture content of the reconstituted tobacco leaves to be tested is first obtained;

[0070] It should be noted that the reconstituted tobacco to be tested refers to the reconstituted tobacco that has gone through the reconstituted tobacco process and is waiting to be tested to see if its quality meets the sensory quality standards. Optionally, the reconstituted tobacco to be tested can be selected from a product batch previously produced in the production batch as the reconstituted tobacco to be tested.

[0071] The traditional way to score the sensory quality of reconstituted tobacco is to manually smoke the cigarettes made from the reconstituted tobacco and then evaluate and score them. Among the process parameters of the reconstituted tobacco itself, the moisture content during coating that affects the sensory quality score is still unclear.

[0072] In this embodiment, in order to study which parameters are strongly correlated with the sensory quality score of papermaking reconstituted tobacco leaves, "water content in coating" was determined based on the existing production database Uniformence and the corresponding statistics were performed in combination with the actual sensory quality manual smoking scores, so as to determine the fitting curves in each process parameter.

[0073] Optionally, the moisture content of the coating is detected by an infrared moisture sensor installed inside the QCS. The principle is as follows: the change of the light signal is converted into a current signal through a circuit conversion amplifier, and the moisture current signal of the QCS client is read using OPCsmart, which is converted into a specific value and displayed on the DCS human-machine interface. The value is collected and sent to the background production database Uniformance for call;

[0074] In this embodiment, in order to study which parameters are strongly correlated with the sensory quality score of papermaking reconstituted tobacco leaves, "water content in coating" was determined based on the existing production database Uniformence and the corresponding statistics were performed in combination with the actual sensory quality manual smoking scores, so as to determine the fitting curves in each process parameter.

[0075] For example, see Figure 3 The following is a schematic diagram of the fitting curve of the coating moisture and sensory evaluation score. The following relationship can be obtained through the fitting curve:

[0076] Depend on Figure 3 It can be seen that the moisture content of the film base during coating and the sensory quality score (called the intrinsic sensory quality score in the figure) are positively correlated, and when the moisture content of the film base during coating is greater than 19%, the sensory quality score tends to be stable.

[0077] After determining the fitting curve, Minitab was further used to perform correlation analysis on the key process parameters involved, referring to the corresponding table between the correlation coefficient and the correlation strength shown in Table 1:

[0078] Table 1. Correspondence between correlation coefficient and correlation strength

[0079] Correlation coefficient Strength of association 0.8-10 Very strong correlation 0.6-0.8 Strong correlation 0.4-0.6 Moderately strong correlation 0.2-0.4 Weak correlation 0.0-0.2 Very weak or no correlation

[0080] Based on the relationship correspondence table given in Table 1, we screened out parameters with correlation coefficients close to 0.8 or above from various process parameters as process parameters with strong correlation.

[0081] Based on the relationship table given in Table 1, the correlation coefficient of the moisture content in the coating is 0.863.

[0082] Step S20, inputting the coating water into the trained sensory quality scoring model;

[0083] Furthermore, after determining that the associated process parameter is the coating moisture, during the papermaking reconstituted tobacco production process, the system collects the coating moisture in the reconstituted tobacco in real time, and inputs the coating moisture into the trained sensory quality score calculation model constructed based on the deep learning model. The sensory quality score calculation module based on the coating moisture in the sensory quality score calculation model simulates and predicts the sensory quality score value corresponding to the coating moisture.

[0084] In order to ensure that the sensory quality score calculated by the model is close enough to the sensory quality score after manual smoking, during the training process, we collected a large number of historical batches of reconstituted tobacco leaves with moisture content before coating, and scored these reconstituted tobacco leaves through manual smoking, thus obtaining a correlation table covering a large number of reconstituted tobacco leaves with moisture content before coating and sensory quality scores. Figure 4 This is a schematic diagram of the correlation between the moisture content of reconstituted tobacco leaves and the sensory quality score selected from the correlation table (the specific values ​​of the sensory quality score are omitted due to industrial secrets). Using the data in this correlation table, multiple deep learning training and algorithm optimization were carried out. After testing, the accuracy of the final model reached 94.8%.

[0085] Step S30, obtaining the predicted sensory quality score value calculated based on the coating moisture output by the sensory quality score model, and determining the predicted sensory quality score value as the sensory quality score value corresponding to the reconstituted tobacco leaf to be tested.

[0086] In this embodiment, a predicted sensory quality score that is as consistent as possible with the actual smoking score is calculated based on the moisture content before coating, and the predicted sensory quality score is determined as the sensory quality score corresponding to the reconstituted tobacco to be tested. Thus, by monitoring the sensory quality score, the moisture content before coating link that affects the sensory quality score in the production process of the reconstituted tobacco is monitored in real time. When the sensory quality score is abnormal, the moisture content before coating link that affects the sensory quality score is checked immediately.

[0087] Further and optionally, after obtaining the sensory quality score corresponding to the reconstituted tobacco leaves to be tested, it can be determined whether the reconstituted tobacco leaves to be tested are in a preset normal value range. When it is detected that the sensory quality score is not in the normal value range, it is then detected whether the coating moisture associated with the sensory quality score is abnormal.

[0088] In addition, it should be noted that in this embodiment, after we found the coating moisture that has a strong correlation with the sensory quality score, we did not directly monitor it based on the numerical value of the coating moisture. The reason is that there are many quality monitoring indicators in the production process of reconstructed tobacco leaves, but the general monitoring indicators are only for evaluating the output grade of reconstructed tobacco leaves, which does not affect whether the reconstructed tobacco leaves are qualified; and the sensory quality score is an indicator with a veto nature as to whether the production of reconstructed tobacco leaves is qualified.

[0089] In the actual papermaking process of reconstituted tobacco leaves, we allow a certain fluctuation in the value range of the moisture content of the coating. Therefore, even if the moisture content of the reconstituted tobacco leaves in a batch deviates from the preset value range, but does not cause the sensory quality score value monitored by the system to deviate from the normal value range, we still regard the output of this batch of reconstituted tobacco leaves as qualified, and do not regard the fluctuating moisture content of the coating as an abnormal process parameter. In other words, it is only meaningful to judge whether the moisture content of the coating that affects the sensory quality score is abnormal when the sensory quality score value is monitored to be abnormal.

[0090] In the technical solution provided in this embodiment, the coating moisture content that is strongly correlated with the sensory quality score in the process parameters of papermaking reconstituted tobacco is found, the coating moisture content is input into the trained sensory quality score model, and the sensory quality score prediction value is calculated based on the coating moisture content and used as the sensory quality score value corresponding to the reconstituted tobacco to be tested, thereby achieving efficient application of the sensory quality score in the industrial production of papermaking reconstituted tobacco and ensuring the output stability of reconstituted tobacco in the production process of papermaking reconstituted tobacco.

[0091] Second embodiment

[0092] Based on the first embodiment, in this embodiment, the trained sensory quality scoring model includes a sensory quality scoring calculation module for coating moisture, and the training process of the sensory quality scoring model includes:

[0093] Step S100, obtaining a coating moisture sample data set;

[0094] Step S200, pre-processing the coating moisture sample data set,

[0095] Step S300, inputting the pre-processed coating moisture sample data set into a model with a lightGBM architecture for training, and calculating the AUC value of the model after each training is completed;

[0096] Step S400: After the AUC value reaches the preset AUC threshold, the training is completed.

[0097] Wherein, the step S200 includes:

[0098] Step S201, normalizing the coating moisture sample data set to accelerate the convergence speed of the gradient descent of the coating moisture data on the same scale;

[0099] Step S202, constructing a correlation data table based on the normalized coating moisture sample data set and the corresponding sensory quality score data set;

[0100] Step S203, input the associated data table into the model with the lightGBM architecture for training.

[0101] During the training process of the sensory quality scoring model, at least one of the number of leaf nodes, learning rate, tree building feature selection ratio, tree building sample sampling ratio and bagging interval in each model is adjusted to make the AUC value of the model reach a preset AUC threshold.

[0102] In this embodiment, see Figure 3 Since the film base moisture content and the sensory quality score (called the intrinsic sensory quality score in the figure) are positively correlated, and when the film base moisture content is greater than 19%, the sensory quality score tends to be stable. Therefore, the collected sample data set of moisture content is normalized to scale the feature values ​​of the sample data set to a specific range, eliminating the impact of the dimension difference between the features, so that the model can better learn the feature relationship between the sample data set of moisture content and the sensory quality score.

[0103] After preprocessing the coating moisture samples, a correlation data table of the preprocessed coating moisture samples and the sensory quality score dataset is constructed, and the correlation data table is input into the model with the lightGBM architecture for training.

[0104] In this embodiment, the preprocessed sample data set is input into the corresponding model with lightGBM architecture in each module for training, and during the training process, at least one of the number of leaf nodes, learning rate, tree building feature selection ratio, tree building sample sampling ratio and bagging interval in each model is adjusted to make the AUC value of the model reach the preset AUC threshold.

[0105] In this embodiment, AUC (Area Under Curve, the area under the curve and the coordinate axis) is used to evaluate the performance index of the sensory quality scoring model. After each training is completed, the performance index of the current model, that is, the AUC value, can be calculated by calling the AUC function in Python. If the AUC value does not reach the preset AUC threshold after the training is completed, at least one of the number of leaf nodes, learning rate, tree building feature selection ratio, tree building sample sampling ratio and bagging interval in each model is adjusted to make the AUC value of the model reach the preset AUC threshold and complete the training.

[0106] When the AUC value of the deep learning model with the lightGBM architecture in the sensory quality score calculation module of the coating moisture reaches a preset AUC threshold, the training is terminated to obtain the trained sensory quality score calculation module of the coating moisture.

[0107] Third embodiment

[0108] Based on any embodiment, refer to Figure 5In this embodiment, after step S30, the following steps are further included:

[0109] Step S40, during the process of executing the papermaking method reconstituted tobacco process, obtaining the sensory quality score value associated with the reconstituted tobacco in the current production line at every preset period;

[0110] Step S50, determining whether the sensory quality score is within a normal value range;

[0111] Step S60, when it is detected that the sensory quality score value is not in the normal value range, determining whether the coating moisture in the current production line is in the standard coating moisture range;

[0112] Step S70: If not, determine and output the abnormal target coating moisture process equipment parameters among the various coating moisture process equipment parameters.

[0113] In this embodiment, after the sensory quality score value corresponding to the coating moisture is calculated by the deep learning model based on the first embodiment, during the execution of the papermaking process of reconstituted tobacco leaves, the sensory quality score value associated with the reconstituted tobacco leaves in the current production line is obtained every preset period.

[0114] Optionally, the preset period may be to obtain data once every 5 minutes.

[0115] After obtaining the sensory quality score value, determine whether the numerical interval of the sensory quality score value is in the normal value interval. When it is detected that the sensory quality score value is not in the normal value interval, it is determined that the coating moisture of the reconstituted tobacco to be tested is abnormal, and the coating moisture is used as the target tobacco process parameter to be tested. When it is determined that the coating moisture is not in its corresponding standard coating moisture interval, the abnormal target coating moisture process equipment parameters among the coating moisture process equipment parameters are determined and output.

[0116] Optionally, the normal value interval of the sensory quality score can be set to a fixed value of ±1; wherein the fixed value can be set according to the specific processing scenario of the reconstituted tobacco leaves.

[0117] Further and optionally, in this embodiment, when it is determined that the coating moisture is not within the normal value range, the coating moisture corresponds to one or more target reconstituted tobacco equipment parameters, and the target reconstituted tobacco equipment parameters refer to the parameters corresponding to the equipment that affects the coating moisture in the papermaking process of reconstituted tobacco. Specifically, it includes at least one of the sizing amount, the vacuum degree of the vacuum dehydration box, the vacuum pressing pressure, the hot air temperature of the Yankee hood, the temperature of the Yankee cylinder and the uniformity of the base.

[0118] The sizing amount refers to the slurry flow rate required to meet the quantitative requirements. Optionally, a flow meter can be set in the slurry pipeline in the coating process, and the system can collect the sizing amount by collecting the flow data on the flow meter. Among them, the sizing amount is positively correlated with the water content of the coating within a certain range, that is, within this range, the more sizing amount, the greater the water content of the coating.

[0119] The main function of the vacuum dehydration box is to remove excess moisture from the reconstituted tobacco slurry to increase the solid content of the slurry. The vacuum degree of the vacuum dehydration box can be directly collected by the system. The vacuum degree is negatively correlated with the moisture content of the coating, that is, the greater the vacuum degree, the smaller the moisture content of the coating.

[0120] The vacuum pressing pressure is the pressure value applied to the slurry by the vacuum dehydration box under a vacuum environment, thereby reducing the moisture content of the slurry. The vacuum pressing pressure can be directly collected by the system, among which the vacuum degree is negatively correlated with the moisture content of the coating, that is, the greater the vacuum pressing pressure, the lower the moisture content of the coating.

[0121] The hot air temperature of the Yankee hood refers to the problem of hot air blowing the paper through the Yankee hood. The Yankee hood is located above the dryer and helps accelerate the drying of the paper by spraying high-temperature hot air. The Yankee cylinder temperature refers to the temperature of the surface of the Yankee cylinder. The Yankee cylinder is heated by steam to evaporate the water when the paper contacts the cylinder surface. The hot air temperature of the Yankee hood can be collected by the system through the temperature sensor set on the Yankee hood, and the Yankee cylinder temperature can be collected by the system through the temperature sensor set on the surface of the Yankee cylinder. Among them, the hot air temperature of the Yankee hood and the temperature of the Yankee cylinder are negatively correlated with the moisture of the coating, that is, the higher the hot air temperature of the Yankee hood and the temperature of the Yankee cylinder, the lower the moisture of the coating.

[0122] The temperature of the small cylinder group refers to the surface temperature of the small diameter drying cylinder (also called the small cylinder group). The temperature of the small cylinder group can be collected by the system through a temperature sensor set on the surface of the small cylinder group. Among them, the vacuum degree is negatively correlated with the moisture of the incoming coating, that is, the greater the temperature pressure of the small cylinder group, the smaller the moisture of the incoming coating.

[0123] The uniformity of the sheet base refers to the quantitative value of the consistency of the sheet base material of the reconstituted tobacco leaf on the entire paper web. The uniformity of the sheet base is monitored in real time by a horizontal scanning frame and adjusted in linkage by the quality control system. Among them, the uniformity of the sheet base will affect the fluctuation of the moisture content of the coating. The better the uniformity, the smaller the fluctuation of the moisture content of the coating.

[0124] In the technical solution provided in this embodiment, after the sensory quality score value corresponding to the coating moisture is calculated through the deep learning model, when an abnormal sensory quality score value is detected during the execution of the papermaking reconstituted tobacco process, it is judged whether the coating moisture is abnormal, and when the coating moisture is abnormal, the abnormal equipment parameters related to the coating moisture are output, thereby realizing real-time monitoring of the key parameters affecting the sensory quality score in the papermaking reconstituted tobacco production process, and timely feedback when an abnormality occurs, thereby ensuring the product stability in the papermaking reconstituted tobacco production process.

[0125] In addition, it can be understood by a person skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the papermaking process reconstituted tobacco system to implement the process steps of the embodiment of the above method.

[0126] Therefore, the present application also provides a computer-readable storage medium, which stores a program for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the reconstituted tobacco leaves before coating. When the program for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the reconstituted tobacco leaves before coating is executed by a processor, the various steps of the method for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the reconstituted tobacco leaves before coating as described in the above embodiment are implemented.

[0127] The computer-readable storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which may store program codes.

[0128] It should be noted that since the storage medium provided in the embodiment of the present application is the storage medium used to implement the method of the embodiment of the present application, based on the method introduced in the embodiment of the present application, the person skilled in the art can understand the specific structure and deformation of the storage medium, so it is not repeated here. All storage media used in the method of the embodiment of the present application belong to the scope of protection of this application.

[0129] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0130] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0133] It should be noted that in the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present application may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0134] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0135] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for determining sensory quality score of reconstituted tobacco leaves based on coating moisture, characterized in that: The method comprises the following steps: Obtain the coating moisture of the reconstituted tobacco leaves to be tested; Inputting the coating moisture into the trained sensory quality scoring model; The predicted sensory quality score value calculated based on the coating moisture output by the sensory quality score model is obtained, and the predicted sensory quality score value is determined as the sensory quality score value corresponding to the reconstituted tobacco leaf to be tested.

2. The method according to claim 1, characterized in that The trained sensory quality scoring model includes a sensory quality scoring calculation module for coating moisture, and the training process of the sensory quality scoring model includes: Obtain the coating moisture sample data set; Preprocessing the coating moisture sample data set, Based on the pre-processed coating moisture sample data set and the corresponding sensory quality score data set, constructing a correlation data table; Input the associated data table into the model with lightGBM architecture for training, and calculate the AUC value of the model after each training is completed; After the AUC value reaches the preset AUC threshold, the training is completed.

3. The method according to claim 2, characterized in that The step of preprocessing the coating moisture sample data set comprises: Normalizing the coating moisture sample data set to accelerate the convergence speed of the gradient descent of the coating moisture data on the same scale; Based on the normalized coating moisture sample data set and the corresponding sensory quality score data set, a correlation data table is constructed; Input the associated data table into the model with lightGBM architecture for training.

4. The method according to claim 2, characterized in that During the training process, at least one of the number of leaf nodes, learning rate, tree building feature selection ratio, tree building sample sampling ratio and bagging interval in each model is adjusted to make the AUC value of the model reach the preset AUC threshold.

5. The method according to any one of claims 1 to 4, characterized in that: After the step of obtaining the sensory quality score prediction value calculated based on the coating moisture output by the sensory quality score model, and determining the sensory quality score prediction value as the sensory quality score value corresponding to the reconstituted tobacco to be tested, the method further includes: During the process of executing the papermaking method reconstituted tobacco process, obtaining the sensory quality score value associated with the reconstituted tobacco in the current production line at every preset period; Determine whether the sensory quality score is within a normal value range; When it is detected that the sensory quality score value is not in the normal value range, determining whether the coating moisture in the current production line is in the standard coating moisture range; If not, determine and output the abnormal target coating moisture process equipment parameters among the various coating moisture process equipment parameters.

6. The method according to claim 5, characterized in that The abnormal target coating moisture process equipment parameters include at least one of the sizing amount, the vacuum degree of the vacuum dehydration box, the vacuum pressing pressure, the hot air temperature of the Yankee hood, the temperature of the Yankee cylinder and the uniformity of the film base.

7. A papermaking process reconstituted tobacco system, characterized in that: The papermaking reconstituted tobacco system comprises: a memory, a processor, and a program for determining the sensory quality score of reconstituted tobacco based on coating moisture stored in the memory and executable on the processor, wherein the program for determining the sensory quality score of reconstituted tobacco based on coating moisture, when executed by the processor, implements the steps of the method for determining the sensory quality score of reconstituted tobacco based on coating moisture as described in any one of claims 1 to 6.

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