Reconstituted tobacco sensory quality score determination method and system based on finished product 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 finished product moisture. The method comprises the following steps: obtaining finished product moisture of to-be-detected reconstituted tobacco; inputting the moisture of the finished product 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 finished product 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 the moisture content of the finished product. 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 invention is to provide a method for determining the sensory quality score of reconstituted tobacco based on the moisture content of the finished product, aiming to solve the problem of how to improve the product stability in the production process of reconstituted tobacco by papermaking method.

[0009] To achieve the above object, the present invention provides a method for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the finished product, the method comprising the following steps:

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

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

[0012] The predicted sensory quality score value calculated based on the moisture content of the finished product 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 finished product moisture, and the training process of the sensory quality scoring model includes:

[0014] Get the finished product moisture sample data set;

[0015] Preprocessing the finished product moisture sample data set,

[0016] Based on the preprocessed finished product moisture sample data set and the corresponding sensory quality score data set, construct a related 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 finished product moisture sample data set includes:

[0020] Calculating the variance of each finished product moisture sample in the finished product moisture sample data set;

[0021] Redundant finished product moisture samples whose variance is less than a preset variance threshold in the finished product moisture sample data set are eliminated.

[0022] Optionally, before the step of inputting the associated data table into a model with a lightGBM architecture for training and calculating the AUC value of the model after each training is completed, the step further includes:

[0023] Calculating the observed frequency and expected frequency between the finished product moisture data and the sensory quality score in the associated data table, and calculating the chi-square statistic based on the observed frequency and the expected frequency;

[0024] According to the chi-square statistic and the degree of freedom associated with the preset finished product moisture data set, the chi-square distribution table is searched, the p-value is calculated, and the finished product moisture with a p-value less than the significance level threshold is determined as the target finished product moisture;

[0025] The step of inputting the associated data table into a model with a lightGBM architecture for training, and calculating the AUC value of the model after each training is completed includes:

[0026] The associated data table of the moisture content of the target finished product is filtered out and input into the model with the lightGBM architecture for training, and the AUC value of the model is calculated after each training is completed.

[0027] 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.

[0028] Optionally, it is characterized in that after the step of obtaining the sensory quality score prediction value calculated based on the finished product 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, it also includes:

[0029] 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;

[0030] Determine whether the sensory quality score is within a normal value range;

[0031] When it is detected that the sensory quality score value is not in the normal value range, determining whether the moisture content of the finished product in the current production line is in the standard finished product moisture content range;

[0032] If not, determine and output the abnormal target finished product moisture process equipment parameters among the finished product moisture process equipment parameters.

[0033] Optionally, the abnormal target finished product moisture process equipment parameter includes at least one of a drying temperature, a rotary dryer temperature and a paper machine speed.

[0034] In addition, to achieve the above-mentioned objectives, the present invention also provides a papermaking method reconstituted tobacco system, the papermaking method reconstituted tobacco system comprising: a memory, a processor, and a program for determining the sensory quality score of reconstituted tobacco based on the moisture content of a finished product, which is stored in the memory and can be run on the processor, and the program for determining the sensory quality score of reconstituted tobacco based on the moisture content of a finished product, when executed by the processor, implements the steps of the method for determining the sensory quality score of reconstituted tobacco based on the moisture content of a finished product as described in any of the above items.

[0035] This application has at least the following beneficial effects:

[0036] 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;

[0037] 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;

[0038] 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

[0039] Figure 1 This 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 application;

[0040] Figure 2 This is a flow chart of a first embodiment of a method for determining a sensory quality score of reconstituted tobacco leaves based on finished product moisture content in the present application;

[0041] Figure 3 It is a schematic diagram of the fitting curve of the moisture content and sensory evaluation score of the finished product involved in the embodiment of the present application;

[0042] Figure 4 This is 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 involved in the embodiments of the present application;

[0043] 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 the moisture content of the finished product of the present application.

[0044] 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

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] like Figure 1As 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 finished product. 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 finished product, and the operation of other software or programs.

[0050] 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 sensory quality score determination program of the reconstituted tobacco based on the moisture content of the finished product stored in the memory 1005.

[0051] In this embodiment, the papermaking process reconstituted tobacco system includes: a memory 1005, a processor 1001, and a sensory quality score determination program for reconstituted tobacco based on finished product moisture content stored in the memory and executable on the processor, wherein:

[0052] When the processor 1001 calls the program for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the finished product stored in the memory 1005, the following operations are performed:

[0053] Obtain the finished product moisture of the reconstituted tobacco leaves to be tested;

[0054] Inputting the finished product moisture into the trained sensory quality scoring model;

[0055] The predicted sensory quality score value calculated based on the moisture content of the finished product 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.

[0056] When the processor 1001 calls the program for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the finished product stored in the memory 1005, the following operations are performed:

[0057] Get the finished product moisture sample data set;

[0058] Preprocessing the finished product moisture sample data set,

[0059] Based on the preprocessed finished product moisture sample data set and the corresponding sensory quality score data set, construct a related data table;

[0060] 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;

[0061] After the AUC value reaches the preset AUC threshold, the training is completed.

[0062] When the processor 1001 calls the program for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the finished product stored in the memory 1005, the following operations are performed:

[0063] Calculating the variance of each finished product moisture sample in the finished product moisture sample data set;

[0064] Redundant finished product moisture samples whose variance is less than a preset variance threshold in the finished product moisture sample data set are eliminated.

[0065] When the processor 1001 calls the program for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the finished product stored in the memory 1005, the following operations are performed:

[0066] Calculating the observed frequency and expected frequency between the finished product moisture data and the sensory quality score in the associated data table, and calculating the chi-square statistic based on the observed frequency and the expected frequency;

[0067] According to the chi-square statistic and the degree of freedom associated with the preset finished product moisture data set, the chi-square distribution table is searched, the p-value is calculated, and the finished product moisture with a p-value less than the significance level threshold is determined as the target finished product moisture;

[0068] The step of inputting the associated data table into a model with a lightGBM architecture for training, and calculating the AUC value of the model after each training is completed includes:

[0069] The associated data table of the moisture content of the target finished product is filtered out and input into the model with the lightGBM architecture for training, and the AUC value of the model is calculated after each training is completed.

[0070] When the processor 1001 calls the program for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the finished product stored in the memory 1005, the following operations are performed:

[0071] 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;

[0072] Determine whether the sensory quality score is within a normal value range;

[0073] When it is detected that the sensory quality score value is not in the normal value range, determining whether the moisture content of the finished product in the current production line is in the standard finished product moisture content range;

[0074] If not, determine and output the abnormal target finished product moisture process equipment parameters among the finished product moisture process equipment parameters.

[0075] 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 moisture content of the finished product of the present application is proposed.

[0076] Reference 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 finished product comprises the following steps:

[0077] Step S10, obtaining the finished product moisture of the reconstituted tobacco leaves to be tested;

[0078] In this embodiment, the moisture content of the finished product of the reconstituted tobacco leaves to be tested is firstly obtained;

[0079] 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.

[0080] 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 process parameters that affect the sensory quality score are still unclear.

[0081] 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.

[0082] For example, see Figure 3 The figure shows the fitting curve diagram of the finished product moisture and sensory evaluation score. The following relationship can be obtained through the fitting curve:

[0083] The moisture content of the finished product is positively correlated with the sensory quality score. When the moisture content of the finished product exceeds 10.7%, the effect of the moisture content on the sensory quality score gradually decreases, and after 11%, it gradually shows a negative correlation with the sensory quality score.

[0084] 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:

[0085] Table 1. Correspondence between correlation coefficient and correlation strength

[0086] 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

[0087] 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.

[0088] Based on the relationship correspondence table given in Table 1, the correlation coefficient of the finished product moisture is 0.861.

[0089] Optionally, the moisture content of the finished product is detected by an infrared online moisture meter installed at the end of the conveyor belt. The principle is that the infrared online moisture meter emits infrared light through a halogen light source to pass through a filter to form a series of narrow-band light pulse sequences, at least one of which is related to the band absorbed by the object being measured. These specific bands of light are irradiated on the object being measured to form scattered light and collected by the temperature-controlled detector. The detector converts the light signal into a current signal output, and the processor performs fixed algorithm analysis and processing to obtain the specific content in the object being measured. 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.

[0090] Step S20, inputting the moisture content of the finished product into the trained sensory quality scoring model;

[0091] Furthermore, after determining that the associated process parameter with a strong correlation with the sensory quality score value is the finished product moisture content, during the papermaking reconstituted tobacco production process, the system collects the finished product moisture content in the reconstituted tobacco leaves in real time, and inputs the finished product moisture content into the trained sensory quality score calculation model constructed based on the deep learning model. The sensory quality score calculation module based on the finished product moisture content in the sensory quality score calculation model simulates and predicts the sensory quality score value corresponding to the finished product moisture content.

[0092] 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 finished product moisture values ​​of reconstituted tobacco leaves produced in historical batches, and scored these reconstituted tobacco leaves through manual smoking, and obtained a correlation table covering a large number of finished product moisture values ​​of reconstituted tobacco leaves 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 97.4%.

[0093] Step S30, obtaining the predicted sensory quality score value calculated based on the moisture content of the finished product 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.

[0094] 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 finished product moisture content, 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 finished product moisture 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 finished product moisture link that affects the sensory quality score is checked immediately.

[0095] 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 moisture content of the finished product associated with the sensory quality score is abnormal.

[0096] In addition, it should be noted that, in this embodiment, after we find the finished product moisture content that is strongly correlated with the sensory quality score value, we do not directly monitor it based on the numerical value of the finished product moisture content. 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 value is an indicator with a veto nature as to whether the production of reconstructed tobacco leaves is qualified.

[0097] In the actual papermaking process of reconstituted tobacco leaves, we allow a certain fluctuation in the numerical range of the moisture content of the finished product. Therefore, even if the moisture content of the finished reconstituted tobacco leaves in a 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 moisture content of the finished product as an abnormal process parameter. In other words, it is only meaningful to judge whether the moisture content of the finished product that affects the sensory quality score value is abnormal when the sensory quality score value is monitored to be abnormal.

[0098] In the technical solution provided in this embodiment, the finished product moisture content that is strongly correlated with the sensory quality score in the process parameters of papermaking reconstituted tobacco is found, the finished product moisture content is input into the trained sensory quality score model, and the sensory quality score prediction value is calculated based on the finished product 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.

[0099] Second embodiment

[0100] Based on the first embodiment, in this embodiment, the trained sensory quality scoring model includes a sensory quality scoring calculation module for finished product moisture, and the training process of the sensory quality scoring model includes:

[0101] Step S100, obtaining a finished product moisture sample data set;

[0102] Step S200, pre-processing the finished product moisture sample data set,

[0103] Step S300, constructing a correlation data table based on the preprocessed finished product moisture sample data set and the corresponding sensory quality score data set;

[0104] 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;

[0105] Step S500: After the AUC value reaches the preset AUC threshold, the training is completed.

[0106] In this embodiment, the finished product moisture sample dataset is preprocessed, and an associated data table is constructed based on the preprocessed finished product moisture sample dataset and the corresponding sensory quality score dataset. The associated data table is input into a model with a lightGBM architecture for training, and the AUC value of the model is calculated after each training is completed.

[0107] In this embodiment, AUC (Area Under Curve) 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, the hyperparameters in each model are adjusted and the training continues.

[0108] When the AUC value of the deep learning model with the lightGBM architecture in the sensory quality score calculation module of the finished product moisture reaches a preset AUC threshold, the training is terminated to obtain the trained sensory quality score calculation module of the finished product moisture.

[0109] Furthermore, in this embodiment, step S200 includes:

[0110] Step S201, calculating the variance of each finished product moisture sample in the finished product moisture sample data set;

[0111] Step S202, removing redundant finished product moisture samples whose variance is less than a preset variance threshold in the finished product moisture sample data set.

[0112] In this embodiment, Figure 3It can be seen from the data pattern that when the moisture content of the finished product is above 10.8%, the increase in the moisture content of the finished product gradually reduces the impact of the sensory quality score. In order to reduce computing power overhead, in this embodiment, the samples with less impact on the sensory quality score in the finished product moisture sample data set are eliminated.

[0113] Specifically, first calculate the variance of each finished product moisture sample in the finished product moisture sample data set. The calculation formula for the survey is:

[0114]

[0115] Among them, X i is the characteristic value of the i-th sample in the finished product moisture sample data set, μ is the mean of the characteristic, and N is the number of samples;

[0116] Next, the redundant finished product moisture samples whose variance is less than the preset variance threshold in the finished product moisture sample data set are eliminated, thereby eliminating the samples with less influence on the sensory quality score in the finished product moisture sample data set, and constructing an associated data table.

[0117] Furthermore, in this embodiment, before step S400, the following steps are further included:

[0118] Step S600, calculating the observed frequency and expected frequency between the finished product moisture data and the sensory quality score in the associated data table, and calculating the chi-square statistic according to the observed frequency and the expected frequency;

[0119] Step S700, according to the chi-square statistic and the degrees of freedom associated with the preset finished product moisture data set, the chi-square distribution table is searched, the p-value is calculated, and the finished product moisture with a p-value less than the significance level threshold is determined as the target finished product moisture;

[0120] The step S400 includes:

[0121] Step S401, the associated data table of the moisture content of the target finished product is screened out and input into the model with the lightGBM architecture for training, and the AUC value of the model is calculated after each training is completed.

[0122] In this embodiment, data selection is performed again on the associated data table, and the redundant finished product moisture sample data in the associated data table is screened again. Finally, the associated data table after screening the redundant finished product moisture sample data is input into the model with the lightGBM architecture for training. When the AUC value of the deep learning model with the lightGBM architecture in the sensory quality score calculation module of the finished product moisture reaches the preset AUC threshold, the training is terminated to obtain the trained sensory quality score calculation module for the finished product moisture.

[0123] First, the observed frequency and expected frequency between each finished product moisture value and the sensory quality score in the finished product moisture data set are calculated, and the chi-square statistic is calculated based on the observed frequency and the expected frequency. The calculation formula of the chi-square statistic is as follows:

[0124]

[0125] In the formula, O i is the observed frequency, is the collected value, E i is the expected frequency, is the calculated value;

[0126] Among them, E i The calculation formula is:

[0127]

[0128] Furthermore, according to the chi-square statistic and the degrees of freedom associated with the preset absolute dry quantitative data set before coating, the chi-square distribution table is searched to obtain the p-value.

[0129] Optionally, the degrees of freedom is the total number of eigenvalues ​​in the Product Moisture dataset minus 1.

[0130] Finally, the finished product moisture value with a p-value less than the significance level threshold is determined as the target finished product moisture value, and the values ​​in the data set other than the target finished product moisture value are removed as redundant data.

[0131] Optionally, the significance level threshold is set to 0.05.

[0132] Furthermore, in this embodiment, 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 during the training process so that the AUC value of the model reaches a preset AUC threshold.

[0133] Third embodiment

[0134] Based on any embodiment, refer to Figure 5 In this embodiment, after step S30, the following steps are further included:

[0135] 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;

[0136] Step S50, determining whether the sensory quality score is within a normal value range;

[0137] Step S60, when it is detected that the sensory quality score value is not in the normal value range, determining whether the moisture content of the finished product in the current production line is in the standard finished product moisture content range;

[0138] Step S70: If not, determine and output the abnormal target finished product moisture process equipment parameters among the finished product moisture process equipment parameters.

[0139] In this embodiment, after the sensory quality score value corresponding to the moisture content of the finished product 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.

[0140] Optionally, the preset period may be to obtain data once every 5 minutes.

[0141] 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 finished product moisture content of the reconstituted tobacco leaves to be tested is abnormal. The finished product moisture content is used as the target tobacco leaf process parameter to be tested. When it is determined that the finished product moisture content is not in its corresponding standard finished product moisture content interval, the abnormal target finished product moisture process equipment parameters among the finished product moisture process equipment parameters are determined and output.

[0142] 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.

[0143] Further and feasible, in this embodiment, when it is determined that the finished product moisture is not within the normal value range, the finished product moisture corresponds to one or more target finished product moisture process equipment parameters.

[0144] Specifically, the target finished product moisture process equipment parameter includes at least one of a drying temperature, a rotary dryer temperature, and a paper machine speed.

[0145] The drying temperature is negatively correlated with the moisture content of the finished product. The target reconstituted tobacco equipment parameters that affect the drying temperature can also affect the moisture content of the final reconstituted tobacco product.

[0146] Specifically, the target reconstituted tobacco equipment parameters that affect the drying temperature include at least one of slope spray flow, hot air usage, condensate discharge, dehumidification fan power, tunnel usage number, and paper machine speed.

[0147] The slope spray flow rate refers to the amount of water sprayed by the slope spray equipment during the spraying process, which can be directly collected through the system. The slope spray equipment forms water mist after high pressure and acts on the coated product to facilitate the penetration of the coating liquid. The principle is to stabilize the water entering the tunnel and drive the penetration of the coating liquid through the permeability of water. The amount of water will lower the temperature of the product entering the tunnel, thereby affecting the drying temperature. Therefore, the slope spray flow rate and the drying temperature are negatively correlated.

[0148] Among them, when the tunnel dryer has multiple sections, the slope spray flow rate affects the drying temperature in the first section of the tunnel.

[0149] Hot air usage refers to the hot air usage of both tunnel dryer and rotary dryer, which can be directly collected through the system. There is a positive correlation between hot air usage and drying temperature.

[0150] The condensate discharge situation refers to the condensate discharge situation in the condensate pipe of the tunnel dryer, including but not limited to the condensate discharge volume, condensate temperature, discharge frequency, condensate pipe pressure and total discharge volume. Poor discharge will directly affect the temperature control of the tunnel dryer. The system collects at least one of the condensate discharge volume, condensate temperature, discharge frequency, condensate pipe pressure and total discharge volume and obtains a calculated amount by weighting. The calculated amount falls into different numerical ranges, corresponding to different condensate discharge situations. The larger the calculated amount, the better the condensate discharge situation.

[0151] The dehumidification fan power refers to the operating power of the dehumidification fan, which can be directly collected through the system. There is a positive correlation between the dehumidification fan power and the drying temperature.

[0152] The number of tunnel sections used refers to the number of tunnel sections opened and used in the tunnel dryer. There is a positive correlation between the number of tunnel sections used and the drying temperature.

[0153] Paper machine speed refers to the running speed of the paper machine, which is determined by the driving roller of the paper machine's fourdrinier wire and can be directly collected by the system. The paper machine speed is negatively correlated with the drying temperature, and positively correlated with the moisture content of the finished product.

[0154] The rotary dryer temperature can be directly collected by the system and is negatively correlated with the moisture content of the finished product.

[0155] When an abnormality is detected in the value of the moisture content of the finished product, based on the above-mentioned equipment parameters and the positive / negative correlation between these operating parameters and the drying temperature, it is detected whether the above-mentioned target finished product moisture process equipment parameters are too large / too small, and when it is found that the target finished product moisture process equipment parameters are too large / too small, the parameters are quickly adjusted to regulate the moisture content of the finished product of papermaking reconstituted tobacco leaves.

[0156] 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.

[0157] 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 finished product. When the program for determining the sensory quality score of reconstituted tobacco leaves based on the moisture content of the finished product 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 finished product as described in the above embodiment are implemented.

[0158] 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.

[0159] 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.

[0160] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take 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.) containing computer-usable program code.

[0161] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the 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.

[0162] 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 including 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.

[0163] 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.

[0164] 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 claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention 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.

[0165] Although the preferred embodiments of the present invention 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 that fall within the scope of the present invention.

[0166] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for determining sensory quality score of reconstituted tobacco leaves based on finished product moisture, characterized in that: The method comprises the following steps: Obtain the finished product moisture of the reconstituted tobacco leaves to be tested; Inputting the finished product moisture into the trained sensory quality scoring model; The predicted sensory quality score value calculated based on the moisture content of the finished product 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 finished product moisture, and the training process of the sensory quality scoring model includes: Get the finished product moisture sample data set; Preprocessing the finished product moisture sample data set, Based on the preprocessed finished product moisture sample data set and the corresponding sensory quality score data set, construct a related 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 finished product moisture sample data set includes: Calculating the variance of each finished product moisture sample in the finished product moisture sample data set; Redundant finished product moisture samples whose variance is less than a preset variance threshold in the finished product moisture sample data set are eliminated.

4. The method according to claim 2, characterized in that Before the step of inputting the associated data table into the model with the lightGBM architecture for training and calculating the AUC value of the model after each training is completed, the method further includes: Calculating the observed frequency and expected frequency between the finished product moisture data and the sensory quality score in the associated data table, and calculating the chi-square statistic based on the observed frequency and the expected frequency; According to the chi-square statistic and the degree of freedom associated with the preset finished product moisture data set, the chi-square distribution table is searched, the p-value is calculated, and the finished product moisture with a p-value less than the significance level threshold is determined as the target finished product moisture; The step of inputting the associated data table into a model with a lightGBM architecture for training, and calculating the AUC value of the model after each training is completed includes: The associated data table of the moisture content of the target finished product is filtered out and input into the model with the lightGBM architecture for training, and the AUC value of the model is calculated after each training is completed.

5. 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.

6. The method according to any one of claims 1 to 5, characterized in that: After the step of obtaining the predicted sensory quality score value calculated based on the moisture content of the finished product 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 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 moisture content of the finished product in the current production line is in the standard finished product moisture content range; If not, determine and output the abnormal target finished product moisture process equipment parameters among the finished product moisture process equipment parameters.

7. The method according to claim 6, characterized in that The abnormal target finished product moisture process equipment parameter includes at least one of a drying temperature, a rotary dryer temperature and a paper machine speed.

8. 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 the moisture content of a finished product, 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 moisture content of a finished product is executed by the processor, the steps of the method for determining the sensory quality score of reconstituted tobacco based on the moisture content of a finished product as described in any one of claims 1 to 7 are implemented.

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