Method for determining applicable control range of key process quality indexes based on tobacco plasticity

Abstract

The invention discloses a method for determining a key process quality index applicable control range based on tobacco plasticity. The method comprises the following steps: screening evaluation indexes by taking a processing process quality index, a finished product quality index and an economic index as basic indexes; dividing the weight of the basic index, and determining the weight of the evaluation index; according to the existing threshing and redrying processing data distribution condition, determining the weight and the index scoring scale of each evaluation index; and according to the weight and the index scoring scale of each evaluation index, screening out batches with relatively good tobacco leaf finished product quality and economic indexes with different plasticity, carrying out statistical analysis on the process quality indexes, and according to the statistical analysis result, obtaining the applicable control range of the tobacco leaf plasticity in the key process quality indexes. According to the method, the problems of large tobacco leaf raw material processing quality fluctuation range in batches and high threshing loss caused by inaccurate plasticity judgment in the threshing and redrying processing process are solved, and the threshing and redrying processing process control capability is improved.

Description

A method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves Technical Field

[0001] This invention belongs to the field of tobacco processing and relates to a method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves. Background Technology

[0002] In recent years, based on the effective guarantee of cigarette processing quality and production efficiency by "large-scale technology," the trend of moving the tobacco processing to the leaf threshing and re-drying stage has become increasingly obvious, and the cigarette industry has imposed increasingly stringent quality requirements on re-drying enterprises. Currently, the determination of key processing quality indicators in leaf threshing and re-drying mainly relies on the experience of operators to judge the plasticity of tobacco raw materials. Due to the large differences in experience among personnel, the weak correlation between tobacco appearance and sensory quality indicators and tobacco plasticity, and the inconsistency in the scale of plasticity judgment among different personnel, the leaf threshing and re-drying process suffers from problems such as large fluctuations in the processing quality of tobacco raw materials within batches and high leaf threshing losses due to inaccurate plasticity judgment.

[0003] Therefore, it is necessary to study a method for determining the appropriate control range of tobacco processing quality indicators in the key process of threshing and re-drying, to objectively determine the plasticity of tobacco raw materials, and to improve the stability of processing quality. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves. This method solves the problems of large fluctuations in the processing quality of tobacco raw materials within batches and high losses due to inaccurate judgment of plasticity during the threshing and re-drying process, thereby improving the process control capability of threshing and re-drying.

[0005] To achieve the above-mentioned technical effects, the present invention adopts the following technical solution: The present invention provides a method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves. The method includes: screening evaluation indicators based on processing quality indicators, finished product quality indicators, and economic indicators as basic indicators; dividing the weights of the basic indicators and determining the weights of the evaluation indicators; determining the weights and scoring scales of each evaluation indicator based on the distribution of existing leaf threshing and re-drying processing data; screening tobacco batches with different plasticity of finished product quality and economic indicators based on the weights and scoring scales of each evaluation indicator; statistically analyzing the process quality indicators of the screened tobacco batches; and obtaining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves based on the results of the statistical analysis.

[0006] As a preferred technical solution of the present invention, the method for screening evaluation indicators includes: selecting different evaluators to rate the indicators included in each basic indicator, and assigning scores to the indicators according to the ratings; for the same indicator, the scores corresponding to the ratings of different evaluators are multiplied to obtain the final score of the indicator, and the evaluation indicator is selected based on the final score.

[0007] As a preferred technical solution of the present invention, the evaluation indicators selected for the quality indicators of the processing process include leaf moisture content, standard deviation of leaf moisture content, large leaf rate before roasting, broken leaf rate before roasting, stem content in leaves before roasting, uniformity of leaf shape structure, moisture content in the cold room, and standard deviation of moisture content in the cold room; the evaluation indicators selected for the quality indicators of the finished product include finished tobacco leaf moisture content and CV value of finished tobacco leaf moisture content; the evaluation indicators selected for the economic indicators include the broken leaf rate below 2.36mm and the yield of finished product.

[0008] As a preferred technical solution of the present invention, with a total weight value of 100, the weight of the processing quality index is 50~55, the weight of the finished product quality index is 25~35, and the weight of the economic index is 15~25.

[0009] As a preferred technical solution of the present invention, the weights of the evaluation indicators included in the finished product quality indicators and economic indicators are determined based on the final score.

[0010] As a preferred technical solution of the present invention, the method for determining the weights of the evaluation indicators included in the quality indicators of the processing process includes the analytic hierarchy process (AHP).

[0011] As a preferred technical solution of the present invention, the analytic hierarchy process includes: constructing an importance comparison matrix of the evaluation indicators included in the quality indicators of the processing process; performing normalization processing on the importance comparison matrix and obtaining the matrix eigenvectors; and performing normalization processing on the eigenvectors to obtain the weights of the evaluation indicators included in the quality indicators of the processing process.

[0012] As a preferred technical solution of the present invention, the consistency of the importance comparison matrix is ​​checked.

[0013] As a preferred technical solution of the present invention, a reference value determination method is used to determine the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves.

[0014] As a preferred technical solution of the present invention, for tobacco raw materials that do not conform to the assumption of normal distribution with existing data on leaf re-drying and processing, and for which the amount of representative grade data selected from different plasticity screenings is insufficient, exploratory data analysis is used to determine the corresponding control range based on critical values.

[0015] Compared with the prior art, the present invention has at least the following beneficial effects: The present invention provides a method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves. This method determines the appropriate control range of processing quality indicators for tobacco leaves with different plasticities by analyzing the distribution of processing data of threshing and re-drying over the years and using a reference value range determination method. This solves the problems of large fluctuation range of processing quality of tobacco raw materials within batches and high threshing loss due to inaccurate plasticity judgment in the threshing and re-drying process, and improves the process control capability of threshing and re-drying. Detailed Implementation

[0016] The technical solution of this application will be further described below through specific embodiments.

[0017] This invention provides a method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves. The method includes: screening evaluation indicators using processing quality indicators, finished product quality indicators, and economic indicators as basic indicators; assigning weights to the basic indicators and determining the weights of the evaluation indicators; determining the weights and scoring scales of each evaluation indicator based on the distribution of existing tobacco leaf threshing and re-drying processing data; selecting batches of tobacco leaves with relatively good finished product quality and economic indicators based on the weights and scoring scales of each evaluation indicator, and statistically analyzing their process quality indicators; and obtaining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves based on the statistical analysis results.

[0018] In this invention, by rationally selecting evaluation indicators and using weighting, the impact of each evaluation indicator on processing quality can be accurately assessed. Based on the analysis of past processing data, the control range of the most suitable evaluation indicators for different plastic tobacco raw materials can be determined, thereby guiding production and improving the stability of processing quality.

[0019] In one specific embodiment of the present invention, the method for screening evaluation indicators includes: selecting different evaluators to rate the indicators included in each basic indicator, and assigning scores to the indicators according to the ratings; for the same indicator, the scores corresponding to the ratings of different evaluators are multiplied to obtain the final score of the indicator, and the evaluation indicator is selected based on the final score.

[0020] In one specific embodiment of the present invention, as an example, each indicator is set with three evaluation levels: Level I, Level II, and Level III, with corresponding standard scores of 3, 2, and 1. Then, different evaluators assess the evaluation level of each indicator and multiply the standard scores of each indicator's evaluation level, selecting the indicator that reaches a certain product value as the evaluation indicator.

[0021] In one specific embodiment of the present invention, the evaluator can be any relevant enterprise, practitioner or scholar in the tobacco processing field, and the specific evaluator and the number of evaluators are not further limited here.

[0022] In one specific embodiment of the present invention, taking 4 evaluators as an example, indicators with product values ​​of 81 and 54 are selected as evaluation indicators; that is, only when all 4 units consider the indicator to be Level I, or 3 units consider the indicator to be Level I and 1 unit considers the indicator to be Level II, can it be determined as an evaluation indicator.

[0023] In one specific embodiment of the present invention, the evaluation indicators selected from the quality indicators of the processing process include leaf moisture content, standard deviation of leaf moisture content, large leaf rate before roasting, broken leaf rate before roasting, stem content in leaves before roasting, uniformity of leaf shape structure, moisture content in the cold room, and standard deviation of moisture content in the cold room.

[0024] In one specific embodiment of the present invention, the evaluation indicators selected from the finished product quality indicators include the moisture content of the finished tobacco sheet and the CV value of the moisture content of the finished tobacco sheet.

[0025] In one specific embodiment of the present invention, the evaluation indicators selected from the economic indicators include the fragmentation rate below 2.36mm and the yield of finished products.

[0026] In one specific embodiment of the present invention, with a total weight value of 100, the weight of the processing quality indicators is 50-55, the weight of the finished product quality indicators is 25-35, and the weight of the economic indicators is 15-25. Preferably, with a total weight value of 100, the weight of the processing quality indicators is 50, the weight of the finished product quality indicators is 30, and the weight of the economic indicators is 20.

[0027] In one specific embodiment of the present invention, the weights of the evaluation indicators included in the finished product quality indicators and economic indicators are determined based on the final score.

[0028] In one specific embodiment of the present invention, the weight of the dominant evaluation indicators in the finished product quality indicators accounts for 60% or more of the total weight of the finished product quality indicators, while the weight of the secondary indicators accounts for 40% or less of the total weight of the finished product quality indicators. Taking a finished product quality indicator weight of 30 as an example, the weight of the moisture content of the finished tobacco sheet is 18, and the weight of the CV value of the moisture content of the finished tobacco sheet is 12.

[0029] In one specific embodiment of the present invention, the weight of the dominant evaluation indicator in the economic indicators accounts for 60% or more of the total weight of the economic indicators, while the weight of the secondary indicators accounts for 40% or less of the total weight of the economic indicators. Taking an economic indicator weight of 20 as an example, the weight of the fragmentation rate below 2.36mm is 12, and the weight of the finished product yield is 8.

[0030] In one specific embodiment of the present invention, the method for determining the weights of the evaluation indicators included in the quality indicators of the processing process includes the analytic hierarchy process (AHP).

[0031] In one specific embodiment of the present invention, the analytic hierarchy process (AHP) includes: constructing an importance comparison matrix of the evaluation indicators included in the quality indicators of the processing process; normalizing the importance comparison matrix and obtaining the matrix eigenvectors; and normalizing the eigenvectors to obtain the weights of the evaluation indicators included in the quality indicators of the processing process.

[0032] In one specific embodiment of the present invention, the method of constructing the importance comparison matrix is ​​shown in Table 1.

[0033] Table 1 In one specific embodiment of the present invention, the data in each column of the importance comparison matrix is ​​normalized according to Equation 1, and the matrix eigenvector is obtained. Normalizing the eigenvector yields the weight of each indicator. In Equation 1, A ij Let B be the element in the i-th row and j-th column of the matrix, representing the importance of the i-th indicator relative to the j-th indicator. ij This represents the normalized matrix elements.

[0034] In one specific embodiment of the present invention, when constructing the comparison matrix of each indicator, the maximum eigenvalue of the matrix is ​​calculated using Equation 2. In Equation 2, λ max Let A represent the largest eigenvalue, and W represent the judgment matrix. i This represents the weight vector, where n represents the number of indicators.

[0035] In one specific embodiment of the present invention, the consistency index CI is calculated according to the formula (Equation 3) of the consistency index. In one specific embodiment of the present invention, Equation 3 states that the consistency index of the matrix is ​​the random consistency ratio of the matrix, as tested according to Equation 4. When CR < 0.1, the consistency test is satisfied. RI is the average random consistency index, which is a constant and can be looked up in Table 2 according to its order. Formula 4 Table 2 In one specific embodiment of the present invention, the weights of the indicators, the matrix consistency test results, and the final weights of the indicators are calculated based on the constructed importance comparison matrix of each indicator, as shown in Table 3.

[0036] Table 3 In one specific embodiment of the present invention, a reference value determination method is used to determine the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves.

[0037] In one specific embodiment of the present invention, based on Table 3, processing batches with relatively good processing quality and economic benefits of tobacco leaves with different plasticity are selected, and the appropriate control range of quality indicators such as leaf structure and moisture content of tobacco leaves of different plasticity types is determined by the reference value range determination method.

[0038] In one specific embodiment of the present invention, for tobacco raw materials that do not conform to the normal distribution of the data from the leaf threshing and re-drying processing and for which the representative grade data from different plasticity screenings are relatively small, exploratory data analysis (EDA) is used to determine the corresponding technical requirements using critical values. The calculation method is as follows: Lower critical value XL: XL = min{xi: xi ≥ FL - 1.5dF} Upper critical value XU: XU = max{xi: xi ≤ FU + 1.5dF} Where: FL is the lower quartile, FU is the upper quartile, and dF is the tetrad distribution.

[0039] In one specific embodiment of the present invention, the method for determining the plasticity of tobacco leaf raw material after threshing and re-drying includes: detecting the plasticity characterization index of tobacco leaves, preprocessing the detection results to obtain standardized data; determining the weight of each plasticity characterization index of tobacco leaves based on the standardized data; calculating the comprehensive score of tobacco leaves based on the weights; and determining the plasticity level of the tobacco leaves based on the comprehensive score.

[0040] In one specific embodiment of the present invention, the required indicators for characterizing the plasticity of tobacco leaves can be screened according to actual production needs and the type of tobacco leaves. Preferred indicators for characterizing the plasticity of tobacco leaves include shear strength, penetration strength, tensile strength, elongation, midrib bonding strength, branch vein bonding strength, and adhesion strength.

[0041] In one specific embodiment of the present invention, the shear strength, penetration strength, tensile strength, elongation, main vein bonding force, branch vein bonding force, and adhesion force of tobacco raw materials are all tested using conventional testing methods in the tobacco processing field (such as methods specified in national standards), and therefore no further limitations are made here.

[0042] In one specific embodiment of the present invention, the preprocessing of the detection results includes unifying the direction of the indicators and performing inverse processing on the inverse indicators. For example, taking the above 7 indicators as an example, if 5 of the indicators are positively correlated with plasticity, while the remaining 2 indicators are negatively correlated with plasticity, then performing inverse processing on the two indicators can make the two indicators positively correlated with plasticity, so as to facilitate the subsequent weight calculation.

[0043] In one specific embodiment of the present invention, the preprocessing of the detection results further includes standardization, and the standardization method includes range standardization. The purpose of range standardization is to eliminate the influence of data dimensions and linearly transform the original data to a specific range. The specific calculation formula is: (current value - minimum value) / (maximum value - minimum value). Through this transformation, the original data is compressed or stretched to a new standard range, preserving the relationships between the original data.

[0044] In one specific embodiment of the present invention, the range standardization is to map the detection result to a specified interval [0,1] through linear transformation.

[0045] In one specific embodiment of the present invention, the standardized data is smoothed to avoid errors in logarithmic calculation caused by the presence of zero values ​​in the data. The smoothing process can employ methods such as simple moving average, exponential smoothing, and kernel smoothing. These smoothing methods are all conventional methods in the field of numerical computation, and therefore are not further limited herein.

[0046] In one specific embodiment of the present invention, the method for determining the weights includes a first weight determination method and a second weight determination method, and a comprehensive weight is calculated based on the results of the first weight determination method and the second weight determination method.

[0047] In one specific embodiment of the present invention, the method for determining the first weight includes: calculating the standard deviation of each tobacco leaf plasticity characterization index; calculating the correlation coefficient between each tobacco leaf plasticity characterization index; calculating the conflict between each tobacco leaf plasticity characterization index; calculating the information content between each tobacco leaf plasticity characterization index; applying enhanced weights to highlight the weight differences of important indicators; and normalizing the information content between each tobacco leaf plasticity characterization index to obtain the first weight.

[0048] In one specific embodiment of the present invention, the method for determining the second weight includes: calculating the proportion of each observed value under each tobacco leaf plasticity characterization index; calculating the information entropy based on the proportion; calculating the information utility value based on the information entropy; enhancing the information utility value to highlight the weight differences of important indicators; and calculating the second weight based on the enhanced information utility value.

[0049] In one specific embodiment of the present invention, the squared enhancement weight of the information utility value can be adopted.

[0050] In one specific embodiment of the present invention, the method for calculating the comprehensive weight includes: assuming the combined weight is: Based on game theory, the goal is to minimize the combined weights. The objective function is the deviation from the two basic weights ω1 and ω2. Solve for the optimal combination coefficients a and b.

[0051] In this invention, different weight calculation methods each have their limitations. Subjective methods may be influenced by expert preferences, while objective methods rely excessively on the data itself. Combining the two methods allows for mutual verification and constraint, effectively balancing subjective intentions with objective facts, reducing the potential bias or extreme results of a single method, and making the weight allocation more reasonable and reliable.

[0052] In one specific embodiment of the present invention, the plasticity grade of tobacco raw material for threshing and re-drying is divided according to the comprehensive score range of all tobacco leaf samples; the plasticity grade is 3 to 10, such as grade 3, grade 4, grade 5, grade 6, grade 7, grade 8, grade 9 or grade 10, but is not limited to the listed values, and other unlisted values ​​within this range are also applicable.

[0053] In one specific embodiment of the present invention, the grading method for determining the plasticity grade of the tobacco leaves may include the natural breakpoint method, the equal interval method, the quantile method, etc., with the natural breakpoint method being preferred.

[0054] In one specific embodiment of the present invention, the method for classifying plasticity levels includes the natural breakpoint method. The natural breakpoint method minimizes intra-group differences and maximizes inter-group differences by identifying natural inflection points in the data distribution itself. The natural breakpoint method determines the classification boundary entirely by the distribution pattern of the data itself, and can better reflect the inherent structure and natural grouping of the data.

[0055] To better illustrate the present invention and facilitate understanding of its technical solutions, typical but non-limiting embodiments of the present invention are as follows: Embodiment: In this embodiment, the tobacco raw materials are 221 batches from re-drying companies in Yunnan, Guizhou, Fujian, Hunan, and Huahuan during the 2020 and 2021 curing seasons. The quality of the re-drying process, the quality of the finished product, and economic indicators were evaluated. The appropriate control range for quality indicators of tobacco leaves with different plasticity was determined according to the technical means of this invention.

[0056] (1) Based on the statistical analysis results of the distribution of data on leaf re-drying processing (Table 4), the weights of evaluation indicators and the scoring scale of indicators covering the quality of leaf re-drying processing, finished product quality and economic indicators were determined (Table 5).

[0057] Table 4 Table 5 (2) Statistical analysis of process quality indicators of tobacco leaves with different plasticity According to the scoring scale of the evaluation indicators determined by this invention, batches of tobacco leaves with relatively good finished product quality and economic indicators of different plasticity types were selected and their process quality indicators were statistically analyzed. The results are shown in Tables 6-10 (Tables 6-10 correspond to the plasticity of tobacco raw material threshing and re-drying processing, grades I-V respectively).

[0058] Table 6 Table 7 Table 8 Table 9 Table 10 The appropriate control range of key process quality indicators for threshing and re-drying of different tobacco raw materials, ranging from Class I to Class V, was obtained from the statistical analysis of process quality indicators. See Table 11.

[0059] Table 11 The criteria for judging the plasticity grading of tobacco raw material after threshing and re-drying are shown in Table 12, where S represents the comprehensive score of tobacco raw material.

[0060] Table 12 The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0061] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0062] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves, characterized in that, The method includes: selecting evaluation indicators based on processing quality indicators, finished product quality indicators, and economic indicators; assigning weights to the basic indicators and determining the weights of the evaluation indicators; determining the weights and scoring scales of each evaluation indicator based on the existing distribution of leaf threshing and re-drying processing data; selecting tobacco batches with different plasticity of finished product quality and economic indicators based on the weights and scoring scales of each evaluation indicator; statistically analyzing the process quality indicators of the selected tobacco batches; and obtaining the applicable control range of quality indicators for key processes based on the plasticity of tobacco leaves based on the results of the statistical analysis.

2. The method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves according to claim 1, characterized in that, The method for selecting evaluation indicators includes: selecting different evaluators to rate the indicators included in each basic indicator, and assigning scores to the indicators according to the ratings; for the same indicator, the scores corresponding to the ratings of different evaluators are multiplied to obtain the final score of the indicator, and the evaluation indicators are selected based on the final scores.

3. The method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves according to claim 1, characterized in that, The quality indicators selected for the processing process include leaf moisture content, standard deviation of leaf moisture content, pre-roasting large leaf rate, pre-roasting broken leaf rate, pre-roasting stem content, leaf shape uniformity, cold room moisture content, and cold room moisture content standard deviation; the quality indicators selected for the finished product include finished tobacco leaf moisture content and finished tobacco leaf moisture content CV value; the economic indicators selected include the broken leaf rate below 2.36mm and the finished product yield.

4. The method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves according to claim 3, characterized in that, With a total weight of 100, the weight of the processing quality indicators is 50-55, the weight of the finished product quality indicators is 25-35, and the weight of the economic indicators is 15-25.

5. The method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves according to claim 4, characterized in that, The weights of the evaluation indicators included in the finished product quality indicators and economic indicators are determined based on the final score.

6. The method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves according to claim 4, characterized in that, The weighting method for the evaluation indicators included in the quality indicators of the processing process includes the analytic hierarchy process (AHP).

7. The method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves according to claim 6, characterized in that, The analytic hierarchy process includes: constructing an importance comparison matrix of the evaluation indicators included in the quality indicators of the processing process; normalizing the importance comparison matrix and obtaining the matrix eigenvectors; and normalizing the eigenvectors to obtain the weights of the evaluation indicators included in the quality indicators of the processing process.

8. The method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves according to claim 7, characterized in that, The consistency of the importance comparison matrix is ​​checked.

9. The method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves according to claim 1, characterized in that, The reference value determination method was used to determine the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves.

10. The method for determining the applicable control range of quality indicators in key processes based on the plasticity of tobacco leaves according to claim 1, characterized in that, For tobacco raw materials that do not conform to the assumption of a normal distribution with existing data on leaf re-drying and processing, and for which the amount of representative grade data selected from different plasticity screenings is insufficient, exploratory data analysis is used to determine the corresponding control range based on critical values.

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