Entropy weight TOPSIS-based thick paste method tobacco sheet raw material ratio evaluation method and application thereof

Abstract

The invention provides a thick paste method tobacco sheet raw material ratio evaluation method based on entropy weight TOPSIS, and the evaluation method comprises the steps: (1) obtaining evaluation index data of tobacco sheets with different raw material ratios, and dividing the evaluation index data into extremely large index data, intermediate index data and interval index data according to the direction of the evaluation index data influencing a final ranking result; constructing an original matrix X based on the evaluation index data; standardizing the original matrix X to obtain a matrix Z; (2) calculating the matrix Z by adopting an entropy weight method to obtain entropy values of various evaluation indexes of the tobacco sheets, and normalizing the entropy values to obtain weight values wj of the evaluation indexes; and (3) calculating a comprehensive score of the tobacco sheets with different raw material ratios by adopting a TOPSIS method, and evaluating and ranking the tobacco sheets with different raw material ratios according to the comprehensive score to obtain an optimal raw material ratio scheme.

Description

Technical Field

[0001] The invention belongs to the technical field of cigarette production monitoring, and in particular relates to a method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS and an application thereof. Background Art

[0002] Tobacco flakes are also called reconstituted tobacco leaves, and their raw materials are tobacco leaf fragments, tobacco dust and tobacco stems, etc. The main methods for making tobacco flakes are rolling method, thick pulp method and papermaking method.

[0003] Thick pulp tobacco flakes are made by drying tobacco fragments, tobacco stems and other raw materials, grinding them into tobacco powder, and then mixing them with adhesives, moisturizers and wood pulp fibers. After sufficient stirring and homogenization, thick slurry is made, and the thick slurry is evenly spread on a stainless steel belt, dried and cut. The ratio of raw materials such as tobacco powder, adhesives, and wood pulp fibers will affect its physical indicators, mechanical indicators, and flavor loading capacity.

[0004] CN111436636A discloses a method for preparing reconstituted tobacco using a thick slurry method, wherein the reconstituted tobacco slurry is coated on a substrate using the thick slurry method, the coated slurry is dried to control the moisture content therein to obtain tobacco flakes, and then tobacco flakes are stacked again on the basis of the tobacco flakes, and the above steps are repeated at least once to obtain reconstituted tobacco with multiple layers of tobacco flakes stacked on top of each other.

[0005] At present, the raw material ratio, the diversification of production processes, and the multidimensionality of evaluation indicators have brought difficulties to the quality evaluation of tobacco flakes. Previous studies mainly studied certain aspects of the characteristics of tobacco flakes, and rarely involved comprehensive, multi-level, and refined evaluation of system solutions. Therefore, how to provide a method for evaluating and ranking tobacco flake raw material ratio schemes and guide the decision-making of tobacco flake raw material ratios is a problem that technicians in this field need to solve. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention aims to provide a method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS and its application. The evaluation method is used to solve the problem that the evaluation of the ratio of raw materials of tobacco sheets lacks hierarchy and accuracy, and provides a scientific basis for formulating effective production and optimization plans for the ratio of raw materials of tobacco sheets.

[0007] In order to achieve the purpose of the invention, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS, the evaluation method comprising:

[0009] (1) Obtain the evaluation index data of reconstituted tobacco with different raw material ratios. According to the direction in which the evaluation index data affects the final ranking result, it is divided into extremely large type index data, intermediate type index data, and interval type index data; construct the original matrix X based on the evaluation index data; perform standardization processing on the original matrix X to obtain the matrix Z;

[0010] (2) Use the entropy weight method to calculate the matrix Z, obtain the entropy values of various evaluation indexes of reconstituted tobacco and normalize them to obtain the weight values w of various evaluation indexes j ;

[0011] (3) Use the TOPSIS method to calculate the comprehensive scores of reconstituted tobacco with different raw material ratios, evaluate and rank the reconstituted tobacco with different raw material ratios according to the comprehensive scores, and obtain the optimal raw material ratio scheme.

[0012] Preferably, in step (1), the evaluation indexes include: tensile strength, basis weight, thickness, bulk density, and the ability to carry different flavor substances.

[0013] In the present invention, the flavor substances include any one or a combination of at least two of: D-limonene, hydroxyacetone, amyl butyrate, propylene glycol, butyric acid, eugenol, or vanillin.

[0014] Preferably, the extremely large type index data includes: bulk density and the ability to carry different flavor substances.

[0015] Preferably, the intermediate type index data includes: basis weight.

[0016] Preferably, the interval type index data includes: thickness and tensile strength.

[0017] In the present invention, an extremely large index refers to an index where the larger the data, the better. During the evaluation process, the larger the value of this index, the better the performance of the target formulation of tobacco sheet raw materials participating in the evaluation in terms of this index. For example, when the bulk density and the ability to carry different flavor substances are larger, the amount of flavoring agents required is less. While meeting the sensory requirements of the product, the raw material cost can be reduced, the benefit is greater, and the scoring ranking is higher. An intermediate index is an evaluation index with an optimal value in the middle of the data. The smaller the deviation from the optimal value, the better the performance of the target formulation of tobacco sheet raw materials being evaluated in terms of this index, and the higher the scoring ranking. The greater the deviation from the optimal value, the worse the performance of the target formulation of tobacco sheet raw materials being evaluated in terms of this index, and the lower the scoring ranking. For example, when the basis weight is relatively high, it will not only lead to an increase in the cigarette filling weight, an increase in the quality, and an increase in the cost, but also easily result in poor sensory quality of the smoke in the first few puffs during smoking. When the basis weight is relatively low, the filling weight is small and cannot meet the requirements of the sensory quality of the smoke. An interval index is an evaluation index with an optimal interval in the middle of the data. The index value is optimal within the interval range. Outside the optimal interval, the smaller the gap between the data of this type of index and the interval boundary, the better the performance of the target formulation of tobacco sheet raw materials being evaluated in terms of this index, and the higher the scoring ranking. The greater the gap from the interval boundary, the worse the performance of the target formulation of tobacco sheet raw materials being evaluated in terms of this index, and the lower the scoring ranking. For example, when the tensile strength is small, it will break during subsequent production, unable to meet the requirements of continuous production, resulting in waste of raw materials and relatively high production costs.

[0018] Preferably, in step (1), the original matrix X is as shown in Equation 1.

[0019]

[0020] In Equation 1, for the intermediate index data and the interval index data, x ij represents the value after positive normalization of the j-th evaluation index of the i-th evaluation object; for the extremely large index data, x ij represents the value of the j-th evaluation index of the i-th evaluation object.

[0021] In the present invention, in step (1), the intermediate index data and the interval index data in the original matrix X are data after positive normalization processing.

[0022] Preferably, the formula for positive normalization processing of the intermediate index data is as shown in Equation 2;

[0023] M = max{|x i - x best |}

[0024]

[0025] In formula 2, x i is the data of a certain index of the i-th tobacco sheet, and x best is the optimal value of the data of this index of the tobacco sheet. M is the value with the farthest distance from x among the data of this index in all tobacco sheets. best x is the data of this index of the i-th tobacco sheet after being positive-oriented.

[0026] Preferably, the formula for positive-orienting the interval-type index data is as shown in formula 3;

[0027] M = max{a - min{x i}, max{x i} - b}

[0028]

[0029] In formula 3, the interval of [a, b] is the optimal interval of the index data. M is the value with the farthest distance from the interval among all tobacco sheets in this index. x i is the data of a certain index of the i-th tobacco sheet; x is the data of this index of the i-th tobacco sheet after being positive-oriented.

[0030] Preferably, in step (1), the original matrix X is standardized to obtain matrix Z. The calculation formula for each element in matrix Z is as shown in formula 4;

[0031]

[0032] In formula 4, z ij is the value after standardization of the j-th evaluation index of the i-th evaluation object, which is an element in matrix Z. x ij are the elements of the original matrix X.

[0033] Preferably, the constructed matrix Z is as shown in formula 5;

[0034]

[0035] In formula 5, z ij represents the value after standardization of the j-th evaluation index of the i-th evaluation object.

[0036] Preferably, in step (2), the calculation formula for the entropy value of each evaluation index is as shown in formula 6;

[0037]

[0038] In formula 6, e j is the entropy value, 1 / ln(n) > 0, and it satisfies e j > 0; p ij is each element z ijThe proportion probability p in the same index j ij 。

[0039] Preferably, the p ij is calculated according to Formula 7;

[0040]

[0041] Preferably, in step (2), the weight value w of each evaluation index j is calculated according to Formula 8;

[0042] d j = 1 - e j

[0043]

[0044] In Formula 8, d j is the utility value of the data information of each index of the reconstituted tobacco.

[0045] Preferably, in step (3), the calculation steps of the comprehensive score include:

[0046] (A) Determine the optimal solution and the worst solution of the data of each evaluation index of the reconstituted tobacco, and calculate the optimal plan and the worst plan based on the optimal solution and the worst solution;

[0047] (B) Calculate the positive ideal solution distance D of the data of each evaluation index of the reconstituted tobacco with different raw material ratios from the optimal plan + , and the negative ideal solution distance D from the worst plan - ;

[0048] (C) Calculate the comprehensive score C of the reconstituted tobacco with different raw material ratios i 。

[0049] Preferably, in step (A), the optimal solution z + is composed of the maximum value of each column element in the matrix Z; the worst solution z - is composed of the minimum value of each column element in the matrix Z.

[0050] Preferably, the calculation formula of the optimal solution is as shown in Formula 9;

[0051]

[0052] In Formula 9, z + is the optimal solution, z 1 + is the optimal solution of the data of the first index of the reconstituted tobacco, z 2 + is the optimal solution of the data of the second index of the reconstituted tobacco, z j +is the optimal solution for the j-th index data of the tobacco sheet, and so on for the rest;

[0053] Preferably, the calculation formula for the worst solution is shown in Equation 10;

[0054]

[0055] In Equation 10, z - is the worst solution, z 1 - is the worst solution for the first index data of the tobacco sheet, z 2 - is the worst solution for the second index data of the tobacco sheet, z j - is the worst solution for the j-th index data of the tobacco sheet, and so on for the rest.

[0056] Preferably, in step (B), the distance D + from the positive ideal solution is calculated as shown in Equation 11;

[0057]

[0058] In Equation 11, D i + is the distance between the i-th tobacco sheet and the positive ideal solution, z j + is the optimal solution for the j-th index data of the tobacco sheet, z ij is the value of the j-th evaluation index of the i-th evaluation object after standardization, w j is the weight value of the j-th evaluation index.

[0059] Preferably, in step (B), the distance D - from the negative ideal solution is calculated as shown in Equation 12;

[0060]

[0061] In Equation 12, D i - is the distance between the i-th tobacco sheet and the negative ideal solution, z j - is the worst solution for the j-th index data of the tobacco sheet, z ij is the value of the j-th evaluation index of the i-th evaluation object after standardization, z j is the weight value of the j-th evaluation index.

[0062] In step (C), the comprehensive score C i is calculated as shown in Equation 13;

[0063]

[0064] In a second aspect, the present invention provides an application of the evaluation method for the raw material ratio of the thick slurry method tobacco sheet based on entropy weight TOPSIS described in the first aspect in reconstituted tobacco leaves.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The evaluation method for the raw material ratio of the thick slurry method tobacco sheet based on entropy weight TOPSIS proposed by the present invention avoids evaluating the product production plan by relying on a single index traditionally. Starting from data, a multi-level, multi-dimensional, and refined evaluation method for the raw material ratio plan of the thick slurry method tobacco sheet is constructed through entropy weight method weighting and TOPSIS analysis. This combination helps to quickly identify the optimal or relatively optimal plan and provides technical support for various types of decision-making problems. Specific embodiments

[0067] The technical solution of the present invention will be further described below through specific embodiments. Those skilled in the art should understand that the embodiments are only for helping to understand the present invention and should not be regarded as specific limitations on the present invention.

[0068] For those not specifying specific techniques or conditions in the embodiments, they shall be carried out according to the techniques or conditions described in the literature in this field or according to the product specifications. For those reagents or instruments not specifying the manufacturer, they are all conventional products that can be obtained through regular channels.

[0069] Comparative Example 1

[0070] This comparative example provides an evaluation method for the raw material ratio of the thick slurry method tobacco sheet.

[0071] 1. Prepare thick slurry method tobacco sheets with different raw material ratios

[0072] First, dissolve the adhesive, then mix the adhesive, glycerol, tobacco powder, pulp, and water, and stir with an electric stirrer at a speed of 400 r / min for 30 min to prepare a uniform thick slurry. The thick slurry is placed on a special experimental platform for reconstituted tobacco leaves by the thick slurry method, and the casting thickness is 1.2 mm ± 0.1 mm. Different thick slurry sheets are prepared by changing the glycerol addition amount, fiber dry content, tobacco powder content, and adhesive content. The preparation plan for the tobacco core sheet is shown in Table 1. The obtained tobacco core sheets are baked at 60 °C for 1 h for sheet forming and removing excess moisture.

[0073] Table 1

[0074]

[0075] 2. Detect various evaluation indexes of the tobacco sheet

[0076] After the thin sheet is formed, its tensile strength, basis weight, thickness, and the ability to carry different flavor substances are measured. Table 2 shows the index data of tobacco thin sheets with different raw material ratios.

[0077] Table 2

[0078]

[0079] 3. Set the thresholds of each evaluation index

[0080] The conventional evaluation method is that the tobacco thin sheet produced to meet the product process index needs to have a certain tensile strength and elongation at break to meet the subsequent processing procedures and continuous production. The current process production standard only emphasizes that the tensile strength of the thin sheet shall not be lower than 440 N / m.

[0081] 4. Evaluation results

[0082] Based on the above test results and threshold ranges, the qualified raw material ratio schemes include S1, S3, S18 - 21 (shown in Table 3). The single evaluation index gives 6 suitable tobacco thin sheet raw material ratio schemes, and the optimal tobacco thin sheet raw material ratio scheme cannot be obtained.

[0083] Table 3

[0084]

[0085] Example 1

[0086] This example provides an evaluation method for the raw material ratio of tobacco thin sheets by thick slurry method based on entropy weight TOPSIS. The raw material ratio schemes for evaluation and the test results are the same as those in Comparative Example 1.

[0087] 1. Obtain the evaluation index data of tobacco thin sheets with different raw material ratios, and divide them into extremely large - type index data, intermediate - type index data, and interval - type index data according to the direction of the evaluation index data affecting the final ranking result; construct the original matrix X based on the evaluation index data; perform standardization processing on the original matrix X to obtain the matrix Z.

[0088] 1.1 Divide them into extremely large - type index data, intermediate - type index data, and interval - type index data according to the different directions of the evaluation index data affecting the final ranking result. Take the bulk density and the ability to carry different flavor substances as extremely large - type indexes; take the basis weight as an intermediate - type index; take the thickness and tensile strength as interval - type indexes.

[0089] 1.2 Positive - direction processing

[0090] The extremely large - type index data does not need to be positively - direction processed.

[0091] Perform positive - direction processing on the intermediate - type index data, and the formula for the positive - direction processing is shown in Equation 2.

[0092] M = max{|x i - x best |}

[0093]

[0094] In Equation 2, x i is the data of a certain index of the i-th tobacco sheet, x best is the optimal value of the data of this index of the tobacco sheet, M is the value of the data of this index that is farthest from x best among all tobacco sheets, and x is the data of this index of the i-th tobacco sheet after being made positive.

[0095] Perform positive processing on the interval-type index data, and the formula for the positive processing is as shown in Equation 3.

[0096] M = max{a - min{x i}, max{x i}- b}

[0097]

[0098] In Equation 3, the interval of [a, b] is the optimal interval of the index data, M is the value that is farthest from the interval in this index among all tobacco sheets, x i is the data of a certain index of the i-th tobacco sheet; x is the data of this index of the i-th tobacco sheet after being made positive.

[0099] 1.3 Construct the original matrix X

[0100] Combine all the intermediate-type index data and interval-type index data after positive processing, and the extremely large-type index data to form the original matrix X, as follows:

[0101]

[0102] In Equation 14, x ij represents the value of the j-th evaluation index of the i-th evaluation object, where i = 1, 2, 3... 21 and j = 1, 2, 3... 11.

[0103] 1.4 Construct the matrix Z

[0104] Perform standardization processing on the original matrix X to obtain the matrix Z, and the calculation formula for each element in the matrix Z is as shown in Equation 4.

[0105]

[0106] In Equation 4, z ij is the value after standardization of the j-th evaluation index of the i-th evaluation object, which is an element in the matrix Z, x ijThe value of the j-th evaluation index of the i-th evaluation object is each element of the original matrix X; where i = 1, 2, 3... 21 and j = 1, 2, 3... 11.

[0107] The constructed matrix Z is shown in Equation 15:

[0108]

[0109] In Equation 15, z ij represents the value after standardization of the j-th evaluation index of the i-th evaluation object, where i = 1, 2, 3... 21 and j = 1, 2, 3... 11.

[0110] 2. Use the entropy weight method to calculate the matrix Z, obtain the entropy values of each evaluation index of the tobacco sheet and normalize them to obtain the weight values w j .

[0111] 2.1 Calculate the entropy value

[0112] The calculation formula for the entropy value of each evaluation index is shown in Equation 6.

[0113]

[0114] In Equation 6, e j is the entropy value, 1 / ln(n) > 0, satisfying e j > 0; p ij is the proportion probability p ij of each element z ij in the same index j.

[0115] The calculation formula for the said p ij is shown in Equation 7.

[0116]

[0117] 2.2 Calculate the weight value w j

[0118] The calculation formula for the weight value w j of each evaluation index is shown in Equation 8.

[0119] d j = 1 - e j

[0120]

[0121] In Equation 8, d j is the utility value of the data information of each index of the tobacco sheet.

[0122] The summary of the weight calculation results by the entropy value method is shown in Table 4.

[0123] Table 4

[0124] Item Information entropy value e Information utility value d Weight coefficient w Quantitative 0.9521 0.0479 37.44％ Thickness 0.9869 0.0131 10.24％ Bulkiness 0.9993 0.0007 0.52％ Tensile strength 0.9640 0.0360 28.14％ D-limonene 0.9935 0.0065 5.04％ Hydroxyacetone 0.9977 0.0023 1.83％ Amyl butyrate 0.9959 0.0041 3.23％ Propylene glycol 0.9965 0.0035 2.75％ Butyric acid 0.9976 0.0024 1.84％ Eugenol 0.9935 0.0065 5.09％ Vanillin 0.9950 0.0050 3.88％

[0125] 3. Calculate the comprehensive scores of reconstituted tobacco with different raw material ratios by the TOPSIS method, evaluate and rank the reconstituted tobacco with different raw material ratios according to the comprehensive scores, and obtain the optimal raw material ratio plan.

[0126] 3.1 Calculate the optimal and worst plans

[0127] Determine the optimal and worst solutions of the data of each evaluation index of reconstituted tobacco, and calculate the optimal and worst plans based on the optimal and worst solutions.

[0128] The optimal solution z + is composed of the maximum values of each column element in the matrix Z; the worst solution z - is composed of the minimum values of each column element in the matrix Z.

[0129] The calculation formula of the optimal solution is as shown in Equation 16;

[0130]

[0131] In Equation 16, z + is the optimal solution, z 1 + is the optimal solution of the first index data of reconstituted tobacco, z 2 + is the optimal solution of the second index data of reconstituted tobacco, z j + is the optimal solution of the j-th index data of reconstituted tobacco, and so on; where i = 1, 2, 3... 21, j = 1, 2, 3... 11.

[0132] Preferably, the calculation formula of the worst solution is as shown in Equation 17;

[0133]

[0134] In Equation 17, z - is the worst solution, z 1 - is the worst solution of the first index data of reconstituted tobacco, z 2 - is the worst solution of the second index data of reconstituted tobacco, z j - is the worst solution of the j-th index data of reconstituted tobacco, and so on. Where i = 1, 2, 3... 21, j = 1, 2, 3... 11.

[0135] 3.2 Calculate the distance D to the positive ideal solution +Distance D from the negative ideal solution -

[0136] Calculate the distance D from the positive ideal solution of the evaluation index data of the reconstituted tobacco with different raw material ratios to the optimal solution + , and the distance D from the negative ideal solution of the worst solution - .

[0137] The distance D from the positive ideal solution + The calculation formula is shown in Equation 11;

[0138]

[0139] In Equation 11, D i + is the distance from the i-th reconstituted tobacco to the positive ideal solution, z j + is the optimal solution of the j-th index data of the reconstituted tobacco, z ij is the value after standardization of the j-th evaluation index of the i-th evaluation object, w j is the weight value of the j-th evaluation index.

[0140] The distance D from the negative ideal solution - The calculation formula is shown in Equation 12;

[0141]

[0142] In Equation 12, D i - is the distance from the i-th reconstituted tobacco to the negative ideal solution, z j - is the worst solution of the j-th index data of the reconstituted tobacco, z ij is the value after standardization of the j-th evaluation index of the i-th evaluation object, w j is the weight value of the j-th evaluation index.

[0143] 3.3 Calculate the comprehensive score C of the reconstituted tobacco with different raw material ratios i

[0144] The comprehensive score C i The calculation formula is shown in Equation 13;

[0145]

[0146] Evaluate and rank the reconstituted tobacco with different raw material ratios according to the comprehensive score to obtain the optimal raw material ratio plan. The TOPSIS evaluation calculation results are shown in Table 5.

[0147] Table 5

[0148]

[0149]

[0150] In Table 5, the tobacco sheet No. 3 has the highest ranking, indicating that its raw material ratio scheme is better.

[0151] From the comparison between Comparative Example 1 and Example 1, it can be seen that the raw material ratio schemes of S19, S20, and S21 given in the comparative example have poor rankings, and the raw material ratio schemes of S3 and S18 rank first and second respectively. The results show that the screening scheme in Comparative Example 1 cannot screen out the optimal scheme, and the results given in Comparative Example 1 are not conducive to subsequent research and development.

[0152] In summary, the present invention provides an evaluation method for the raw material ratio of tobacco sheet by thick slurry method based on entropy weight TOPSIS. The method can screen out the optimal raw material ratio scheme from numerous schemes, providing a scientific basis for formulating effective production and optimization schemes for the raw material ratio of tobacco sheet.

[0153] The applicant declares that the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed by the present invention fall within the protection scope and the disclosure scope of the present invention.

Claims

1. A method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS, characterized in that: The evaluation method includes: (1) Obtaining evaluation index data of tobacco sheets with different raw material ratios, and dividing the evaluation index data into extremely large index data, intermediate index data, and interval index data according to the direction in which the evaluation index data affects the final ranking result; constructing an original matrix X based on the evaluation index data; and performing standardization processing on the original matrix X to obtain a matrix Z; (2) The entropy weight method is used to calculate the matrix Z, obtain the entropy value of each evaluation index of the tobacco sheet and normalize it to obtain the weight value w of each evaluation index. j ; (3) The TOPSIS method was used to calculate the comprehensive scores of tobacco sheets with different raw material ratios. The tobacco sheets with different raw material ratios were evaluated and ranked according to the comprehensive scores to obtain the optimal raw material ratio scheme.

2. The method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS according to claim 1, characterized in that: In step (1), the evaluation indexes include: tensile strength, basis weight, thickness, bulk and the ability to load different flavor substances; Preferably, the extremely large index data include: bulkiness and the ability to load different flavor substances; Preferably, the intermediate indicator data includes: quantitative; Preferably, the interval-type indicator data include: thickness and tensile strength.

3. The method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS according to claim 1 or 2, characterized in that: In step (1), the original matrix X is as shown in Formula 1; In formula 1, for intermediate indicator data and interval indicator data, x ij Represents the positive value of the jth evaluation indicator of the i-th evaluation object; For extremely large index data, x ij Represents the value of the jth evaluation indicator of the i-th evaluation object; Preferably, the formula for performing positive processing on the intermediate indicator data is shown in Formula 2; M=max{|x i -x best |} In formula 2, x i is the index data of the ith tobacco sheet, x best is the optimal value of the indicator data of tobacco slices, and M is the distance x of the indicator data of all tobacco slices best The farthest value, x is the index data of the positive transformation of the ith tobacco sheet; Preferably, the formula for performing positive processing on the interval-type indicator data is as shown in Formula 3; M=max{a-min{x i },max{x i }-b} In formula 3, the interval [a, b] is the optimal interval of the indicator data, M is the value of all tobacco sheets that is farthest from the interval in this indicator, and x i is a certain indicator data of the ith tobacco sheet; x is the positive indicator data of the ith tobacco sheet.

4. The method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS according to any one of claims 1 to 3, characterized in that: In step (1), the original matrix X is normalized to obtain a matrix Z, and the calculation formula of each element in the matrix Z is shown in Formula 4; In formula 4, z ij is the standardized value of the jth evaluation index of the ith evaluation object, which is the element in the matrix Z, x ij are the elements of the original matrix X; Preferably, the constructed matrix Z is as shown in Formula 5; In formula 5, z ij Represents the standardized value of the jth evaluation indicator of the i-th evaluation object.

5. The method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS according to any one of claims 1 to 4, characterized in that: In step (2), the calculation formula of the entropy value of each evaluation index is shown in Formula 6; In formula 6, e j is the entropy value, 1 / ln(n)>0, satisfying e j >0; p ij For each element z in the matrix Z ij The probability p of the same indicator j ij ; Preferably, the p ij The calculation formula is shown in Formula 7; Preferably, in step (2), the weight value w of each evaluation index is j The calculation formula is shown in formula 8; In formula 8, d j It is the utility value of various index data of tobacco flakes.

6. The method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS according to any one of claims 1 to 5, characterized in that: In step (3), the step of calculating the comprehensive score includes: (A) determining the optimal solution and the worst solution of each evaluation index data of the tobacco sheet, and calculating the optimal solution and the worst solution based on the optimal solution and the worst solution; (B) Calculate the distance D between the evaluation index data of tobacco sheets with different raw material ratios and the positive ideal solution of the optimal solution + , and the negative ideal solution distance D from the worst solution - ; (C) Calculate the comprehensive score C of tobacco sheets with different raw material ratios i .

7. The method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS according to claim 6, characterized in that: In step (A), the optimal solution z + The worst solution z is composed of the maximum value of each column element in the matrix Z; - It is composed of the minimum value of each column element in the matrix Z; Preferably, the calculation formula of the optimal solution is as shown in Formula 9; In formula 9, z + is the optimal solution, z1 + is the optimal solution for the first indicator data of tobacco sheets, z2 + is the optimal solution for the second indicator data of tobacco sheets, z j + is the optimal solution for the jth index data of tobacco slices, and the rest are similar; Preferably, the calculation formula of the worst solution is as shown in Formula 10; In formula 10, z - is the worst solution, z1 - is the worst solution for the first indicator data of tobacco flakes, z2 - is the worst solution for the second indicator data of tobacco sheets, z j - is the worst solution for the j-th index data of tobacco sheets, and the rest are similar.

8. The method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS according to claim 6 or 7, characterized in that: In step (B), the positive ideal solution distance D + The calculation formula is shown in formula 11; In formula 11, D i + is the distance between the ith tobacco sheet and the positive ideal solution, z j + is the optimal solution for the jth index data of tobacco sheets, z ij is the standardized value of the jth evaluation index of the i-th evaluation object, w j is the weight value of the jth evaluation index; Preferably, in step (B), the negative ideal solution distance D - The calculation formula is shown in formula 12; In formula 12, D i - is the distance between the ith tobacco sheet and the negative ideal solution, z j - is the worst solution for the jth index data of tobacco sheets, z ij is the standardized value of the jth evaluation index of the i-th evaluation object, w j is the weight value of the jth evaluation index.

9. The method for evaluating the ratio of raw materials of thick pulp tobacco sheets based on entropy weight TOPSIS according to any one of claims 6 to 8, characterized in that: In step (C), the comprehensive score C i The calculation formula is shown in formula 13; 10. Application of the thick pulp tobacco sheet raw material ratio evaluation method based on entropy weight TOPSIS according to any one of claims 1 to 9 in reconstituted tobacco.

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