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

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 liquid temperature. The method comprises the steps that the temperature of coating liquid of reconstituted tobacco to be detected is obtained; inputting the temperature of the coating liquid 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 temperature of the coating liquid, 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 liquid temperature. 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 the present application is to provide a method for determining the sensory quality score of reconstituted tobacco based on the coating liquid temperature, aiming to solve the problem of how to improve the product stability in the papermaking process of reconstituted tobacco.

[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 the temperature of the coating liquid, the method comprising:

[0010] Obtaining the coating liquid temperature of the reconstituted tobacco leaves to be tested;

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

[0012] The predicted sensory quality score value calculated based on the coating liquid temperature and 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 the coating liquid temperature, and the training process of the sensory quality scoring model includes:

[0014] Obtain a coating liquid temperature sample data set;

[0015] Preprocessing the coating liquid temperature sample data set,

[0016] Constructing a correlation data table based on the preprocessed coating liquid temperature sample data set and the corresponding sensory quality score data set;

[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 liquid temperature sample data set includes:

[0020] Dividing the difference between each coating liquid temperature sample and the minimum coating liquid temperature by the difference between the maximum coating liquid temperature and the minimum coating liquid temperature in the coating liquid temperature sample data set to obtain a scaled coating liquid temperature sample;

[0021] The scaled coating liquid temperature samples are segmented according to a target ratio to obtain target scaled coating liquid temperature samples as a training set to complete preprocessing.

[0022] Optionally, during the training process, at least one of the number of leaf nodes in the model, the learning rate, the tree building feature selection ratio, the tree building sample sampling ratio and the bagging interval is adjusted so that the AUC value of the model reaches a preset AUC threshold.

[0023] Optionally, after the step of obtaining the predicted sensory quality score value calculated based on the coating liquid temperature and 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:

[0024] 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;

[0025] Determine whether the sensory quality score is within a normal value range;

[0026] When it is detected that the sensory quality score value is not in the normal value range, determining whether the coating liquid temperature in the current production line is in the standard coating liquid temperature range;

[0027] If not, the abnormal target coating liquid temperature process equipment parameters among the coating liquid temperature process equipment parameters are determined and output.

[0028] Optionally, the abnormal target coating liquid temperature process equipment parameters include steam usage and / or coating liquid heating time.

[0029] In addition, to achieve the above-mentioned purpose, the present application also provides a papermaking method reconstituted tobacco system, characterized in that the papermaking method reconstituted tobacco system includes: a memory, a processor, and a reconstituted tobacco sensory quality score determination program based on the coating liquid temperature stored in the memory and executable on the processor, wherein the reconstituted tobacco sensory quality score determination program based on the coating liquid temperature, when executed by the processor, implements the steps of the reconstituted tobacco sensory quality score determination method based on the coating liquid temperature as described in any one of claims 1 to 6.

[0030] This application has at least the following beneficial effects:

[0031] 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;

[0032] 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;

[0033] 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

[0034] 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;

[0035] 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 liquid temperature according to the present invention;

[0036] Figure 3 It is a schematic diagram of a fitting curve of the coating liquid temperature and the sensory evaluation score involved in an embodiment of the present invention;

[0037] 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;

[0038] Figure 5 It is a flow chart of the second embodiment of the method for determining the sensory quality score of reconstituted tobacco leaves based on the coating liquid temperature of the present invention.

[0039] 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

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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 of the reconstituted tobacco system for papermaking, and may include more or less components than those shown in the figure, or a combination of certain components, or a different arrangement of components.

[0044] 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 coating liquid temperature. 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 coating liquid temperature, and the operation of other software or programs.

[0045] exist Figure 1In 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 liquid temperature stored in the memory 1005.

[0046] In this embodiment, the papermaking method reconstituted tobacco system includes: a memory 1005, a processor 1001, and a reconstituted tobacco sensory quality score determination program based on the coating liquid temperature stored in the memory and executable on the processor, wherein:

[0047] When the processor 1001 calls the reconstituted tobacco sensory quality score determination program based on the coating liquid temperature stored in the memory 1005, the following operations are performed:

[0048] Obtaining the coating liquid temperature of the reconstituted tobacco leaves to be tested;

[0049] Inputting the coating liquid temperature into the trained sensory quality scoring model;

[0050] The predicted sensory quality score value calculated based on the coating liquid temperature and 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.

[0051] When the processor 1001 calls the reconstituted tobacco sensory quality score determination program based on the coating liquid temperature stored in the memory 1005, the following operations are performed:

[0052] Obtain a coating liquid temperature sample data set;

[0053] Preprocessing the coating liquid temperature sample data set,

[0054] Constructing a correlation data table based on the preprocessed coating liquid temperature sample data set and the corresponding sensory quality score data set;

[0055] 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;

[0056] After the AUC value reaches the preset AUC threshold, the training is completed.

[0057] When the processor 1001 calls the reconstituted tobacco sensory quality score determination program based on the coating liquid temperature stored in the memory 1005, the following operations are performed:

[0058] Dividing the difference between each coating liquid temperature sample and the minimum coating liquid temperature by the difference between the maximum coating liquid temperature and the minimum coating liquid temperature in the coating liquid temperature sample data set to obtain a scaled coating liquid temperature sample;

[0059] The scaled coating liquid temperature samples are segmented according to a target ratio to obtain target scaled coating liquid temperature samples as a training set to complete preprocessing.

[0060] When the processor 1001 calls the reconstituted tobacco sensory quality score determination program based on the coating liquid temperature stored in the memory 1005, the following operations are performed:

[0061] 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;

[0062] Determine whether the sensory quality score is within a normal value range;

[0063] When it is detected that the sensory quality score value is not in the normal value range, determining whether the coating liquid temperature in the current production line is in the standard coating liquid temperature range;

[0064] If not, the abnormal target coating liquid temperature process equipment parameters among the coating liquid temperature process equipment parameters are determined and output.

[0065] Based on the hardware architecture of the above-mentioned 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 liquid temperature of the present application is proposed.

[0066] Reference Figure 2 In a first embodiment, the method for determining the sensory quality score of reconstituted tobacco leaves based on the coating liquid temperature comprises the following steps:

[0067] Step S10, obtaining the coating liquid temperature of the reconstituted tobacco leaf to be tested;

[0068] In this embodiment, the temperature of the coating liquid of the reconstituted tobacco leaf to be tested is firstly obtained;

[0069] 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.

[0070] 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 coating liquid temperature that affects the sensory quality score is still unclear.

[0071] Optionally, the coating liquid temperature can be detected by a temperature sensor installed on the pipeline. The front end of the temperature sensor is made of two different metals. Since temperature is proportional to resistance, if the temperature changes, the resistance value will change. The resistance value is converted into a 4 to 20 mA current signal through a temperature transmitter. The current signal is connected to the AI ​​module of the DCS, and the configuration is converted into a specific value to be displayed on the DCS human-machine interface and the value is collected to the background production database Uniformance for call;

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

[0073] For example, see Figure 3 The schematic diagram of the fitting curve of the coating liquid temperature and the sensory evaluation score is shown. The following relationship can be obtained through the fitting curve:

[0074] When the coating liquid temperature is less than 51°C, the coating liquid temperature and the sensory quality score are positively correlated, and when the coating liquid concentration is greater than 51°C, the coating liquid temperature and the sensory quality score are negatively correlated.

[0075] After determining the fitting curve, Minitab is 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:

[0076] Table 1. Correspondence between correlation coefficient and correlation strength

[0077] 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

[0078] 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.

[0079] Based on the relationship correspondence table given in Table 1, the correlation coefficient of the coating liquid temperature is 0.881.

[0080] Step S20, inputting the coating liquid temperature into the trained sensory quality scoring model;

[0081] Furthermore, after determining that the associated process parameter with a strong correlation with the sensory quality score value is the coating liquid temperature, during the papermaking reconstituted tobacco production process, the system collects the coating liquid temperature in the reconstituted tobacco in real time, and inputs the coating liquid temperature 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 liquid temperature in the sensory quality score calculation model simulates and predicts the sensory quality score value corresponding to the coating liquid temperature.

[0082] 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 the coating liquid temperatures of a large number of reconstituted tobacco leaves produced in historical batches, and scored these reconstituted tobacco leaves through manual smoking, and obtained a correlation table covering the coating liquid temperature and sensory quality score of a large number of reconstituted tobacco leaves. Figure 4 This is a schematic diagram of the correlation between the coating liquid temperature and the sensory quality score of reconstituted tobacco leaves extracted 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 92.3%.

[0083] Step S30, obtaining the predicted sensory quality score value calculated based on the coating liquid temperature and 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.

[0084] In this embodiment, a sensory quality score prediction value that is as consistent as possible with the actual smoking score is calculated based on the coating liquid temperature, and the sensory quality score prediction value is determined as the sensory quality score value corresponding to the reconstituted tobacco to be tested. Thus, by monitoring the sensory quality score value, the coating liquid temperature link that affects the sensory quality score in the reconstituted tobacco production process is monitored in real time. When the sensory quality score is abnormal, the coating liquid temperature link that affects the sensory quality score is checked immediately.

[0085] 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 within a preset normal value range. When it is detected that the sensory quality score is not within the normal value range, it is then detected whether the coating liquid temperature associated with the sensory quality score is abnormal.

[0086] In addition, it should be noted that, in this embodiment, after we find the coating liquid temperature that has a strong correlation with the sensory quality score, we do not directly monitor it based on the numerical value of the coating liquid temperature. The reason is that there are many quality monitoring indicators in the production process of reconstructed tobacco leaves, but general monitoring indicators are only used to assess 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.

[0087] In the actual papermaking process of reconstituted tobacco leaves, we allow a certain fluctuation in the numerical range of the coating liquid temperature. Therefore, even if the coating liquid temperature of the reconstituted tobacco leaves in a certain batch deviates from the preset numerical 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 coating liquid temperature as an abnormal process parameter. In other words, it is only meaningful to judge whether the coating liquid temperature that affects the sensory quality score value is abnormal when the sensory quality score value is monitored to be abnormal.

[0088] In the technical solution provided in this embodiment, the coating liquid temperature that is strongly correlated with the sensory quality score in the process parameters of papermaking reconstituted tobacco is found, the coating liquid temperature is input into the trained sensory quality score model, and the sensory quality score prediction value is calculated based on the coating liquid temperature 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.

[0089] Second embodiment

[0090] Based on the first embodiment, in this embodiment, the trained sensory quality scoring model includes a sensory quality scoring calculation module for the coating liquid temperature, and the training process of the sensory quality scoring model includes:

[0091] Step S100, obtaining a coating liquid temperature sample data set;

[0092] Step S200, preprocessing the coating liquid temperature sample data set,

[0093] Step S300, constructing a correlation data table based on the preprocessed coating liquid temperature sample data set and the corresponding sensory quality score data set;

[0094] Step S400, input the associated data table into a model with a lightGBM architecture for training, and calculate the AUC value of the model after each training is completed;

[0095] Step S500: After the AUC value reaches the preset AUC threshold, the training is completed.

[0096] Wherein, the step S200 includes:

[0097] Step S201, dividing the difference between each coating liquid temperature sample and the minimum coating liquid temperature by the difference between the maximum coating liquid temperature and the minimum coating liquid temperature in the coating liquid temperature sample data set to obtain a scaled coating liquid temperature sample;

[0098] Step S202 , dividing the scaled coating liquid temperature samples according to a target ratio to obtain target scaled coating liquid temperature samples as a training set to complete preprocessing.

[0099] During the training process, at least one of the number of leaf nodes, the learning rate, the tree building feature selection ratio, the tree building sample sampling ratio and the bagging interval in the model is adjusted so that the AUC value of the model reaches a preset AUC threshold.

[0100] In this embodiment, see Figure 3 , Figure 3 The distribution of the coating liquid temperature data in the experiment presents a parabola, and the values ​​are between [0,100]. Therefore, feature scaling is used to preprocess the coating liquid temperature data. That is, the difference between each coating liquid temperature sample and the minimum coating liquid temperature is divided by the difference between the maximum coating liquid temperature and the minimum coating liquid temperature in the coating liquid temperature sample data set to obtain the scaled coating liquid temperature sample; the scaled coating liquid temperature sample is further divided according to the target ratio to reduce the redundant data in the coating liquid temperature data, and the target scaled coating liquid temperature sample is obtained as the training set to complete the local processing of the coating liquid temperature sample.

[0101] After preprocessing the coating liquid concentration samples, a correlation data table of the preprocessed coating liquid concentration 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.

[0102] In this embodiment, the preprocessed sample data set is input into the corresponding model with lightGBM architecture in the 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.

[0103] 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.

[0104] When the AUC value of the deep learning model with the lightGBM architecture in the sensory quality score calculation module of the coating liquid temperature reaches a preset AUC threshold, the training is terminated to obtain the trained sensory quality score calculation module of the coating liquid temperature.

[0105] Third embodiment

[0106] Based on any embodiment, refer to Figure 5 In this embodiment, after step S30, the following steps are further included:

[0107] 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;

[0108] Step S50, determining whether the sensory quality score is within a normal value range;

[0109] Step S60, when it is detected that the sensory quality score value is not in the normal value range, determining whether the coating liquid temperature in the current production line is in the standard coating liquid temperature range;

[0110] Step S70: if not, determine and output the abnormal target coating liquid temperature process equipment parameters among the coating liquid temperature process equipment parameters.

[0111] In this embodiment, after the sensory quality score value corresponding to the coating liquid temperature is calculated by the deep learning model based on the first embodiment, during the execution of the papermaking method reconstituted tobacco process, the sensory quality score value associated with the reconstituted tobacco in the current production line is obtained every preset period.

[0112] Optionally, the preset period may be to obtain data once every 5 minutes.

[0113] After obtaining the sensory quality score value, determine whether the numerical range of the sensory quality score value is in the normal value range. When it is detected that the sensory quality score value is not in the normal value range, it is determined that the coating liquid temperature of the reconstituted tobacco to be tested is abnormal, and the coating liquid temperature is used as the target tobacco process parameter to be tested for detection. When it is determined that the coating liquid temperature is not in its corresponding standard coating liquid temperature range, the abnormal target coating liquid temperature process equipment parameter among the coating liquid temperature process equipment parameters is determined and output.

[0114] 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.

[0115] Further and optionally, in the present embodiment, when it is determined that the coating liquid temperature is not within the normal value range, the coating liquid temperature corresponds to one or several target reconstituted tobacco equipment parameters, and the target reconstituted tobacco equipment parameters refer to the parameters corresponding to the equipment that affects the coating liquid temperature in the papermaking reconstituted tobacco process.

[0116] The target coating liquid temperature process equipment parameters specifically include steam usage and / or coating liquid heating time.

[0117] The steam consumption of the heat exchanger refers to the amount of steam required by the heat exchanger used to heat the coating liquid. The steam consumption data can be directly collected through the built-in steam flow monitoring module. The steam consumption is positively correlated with the coating liquid temperature.

[0118] The coating liquid heating time refers to the time required to heat the coating liquid to the temperature required by the process. This operating parameter is a collected value. The coating liquid heating time is positively correlated with the coating liquid temperature.

[0119] When an abnormal value of the coating liquid temperature is detected, based on the above-mentioned equipment parameters and the positive / negative correlation between these operating parameters and the coating liquid temperature, it is detected whether the above-mentioned target coating liquid temperature process equipment parameters are too large / too small, and when it is found that the target coating liquid temperature process equipment parameters are too large / too small, the parameters are quickly adjusted to regulate the coating liquid temperature of papermaking reconstituted tobacco leaves.

[0120] In the technical solution provided in this embodiment, after the sensory quality score value corresponding to the coating liquid temperature is calculated by the deep learning model, when an abnormal sensory quality score value is detected during the execution of the papermaking reconstituted tobacco process, it is determined whether the coating liquid temperature is abnormal, and when the coating liquid temperature is abnormal, the abnormal equipment parameters related to the coating liquid temperature 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 product stability in the papermaking reconstituted tobacco production process.

[0121] 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.

[0122] 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 coating liquid temperature. When the program for determining the sensory quality score of reconstituted tobacco leaves based on the coating liquid temperature is executed by a processor, the various steps of the method for determining the sensory quality score of reconstituted tobacco leaves based on the coating liquid temperature as described in the above embodiment are implemented.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 A step that specifies a function in one or more boxes.

[0129] 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.

[0130] 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.

[0131] 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 liquid temperature, characterized in that: The method comprises the following steps: Obtaining the coating liquid temperature of the reconstituted tobacco leaves to be tested; Inputting the coating liquid temperature into the trained sensory quality scoring model; The predicted sensory quality score value calculated based on the coating liquid temperature and 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 the coating liquid temperature, and the training process of the sensory quality scoring model includes: Obtain a coating liquid temperature sample data set; Preprocessing the coating liquid temperature sample data set, Constructing a correlation data table based on the preprocessed coating liquid temperature sample data set and the corresponding sensory quality score data set; 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 liquid temperature sample data set comprises: Dividing the difference between each coating liquid temperature sample and the minimum coating liquid temperature by the difference between the maximum coating liquid temperature and the minimum coating liquid temperature in the coating liquid temperature sample data set to obtain a scaled coating liquid temperature sample; The scaled coating liquid temperature samples are segmented according to a target ratio to obtain target scaled coating liquid temperature samples as a training set to complete preprocessing.

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 the 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 predicted sensory quality score value calculated based on the coating liquid temperature 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 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 liquid temperature in the current production line is in the standard coating liquid temperature range; If not, the abnormal target coating liquid temperature process equipment parameters among the coating liquid temperature process equipment parameters are determined and output.

6. The method according to claim 5, characterized in that The abnormal target coating liquid temperature process equipment parameters include steam usage and / or coating liquid heating time.

7. A papermaking process reconstituted tobacco system, characterized in that: The papermaking reconstituted tobacco system includes: a memory, a processor, and a program for determining the sensory quality score of reconstituted tobacco based on the coating liquid temperature, which is stored in the memory and can be run on the processor. When the program for determining the sensory quality score of reconstituted tobacco based on the coating liquid temperature is executed by the processor, the steps of the method for determining the sensory quality score of reconstituted tobacco based on the coating liquid temperature as described in any one of claims 1 to 6 are implemented.

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