Raw material sampling calculation method and device for reconstituted tobacco

Abstract

The invention relates to a raw material sampling calculation method and device for reconstituted tobacco. The method comprises the following steps: determining a chemical component content historical variance of each formula module corresponding to the reconstituted tobacco based on a raw material database of the reconstituted tobacco; and obtaining the proportion of each grade of raw materials in the corresponding formula module, and calculating the maximum variance of each grade of raw materials based on the proportion of each grade of raw materials and the chemical component content historical variance of each formula module. And based on the maximum variance of each grade of raw material and the historical variance of the chemical component content of the corresponding grade of raw material, obtaining the sampling amount of each grade of raw material. Through the mode, the maximum variance of the raw materials of each grade in the corresponding formula module can be scientifically and reliably determined in combination with a statistical principle, and the sampling amount of the raw materials of each grade is determined from the aspect of chemical component stability, so that the stability of chemical components of the reconstituted tobacco is scientifically guaranteed; the sampling amount meets the actual production requirement of the reconstituted tobacco, and the production efficiency and cost of the reconstituted tobacco are prevented from being affected.

Description

TECHNICAL FIELD

[0001] The embodiment of the present specification belongs to the technical field of reconstituted tobacco processing, and particularly relates to a raw material sampling calculation method and device for reconstituted tobacco. BACKGROUND

[0002] Paper-making process reconstituted tobacco technology is a kind of sheet-shaped tobacco product which is reprocessed by using waste materials such as tobacco stems, tobacco dust and tobacco stems produced in the tobacco processing process as the main raw materials through specific physical and chemical treatment, with paper-making process as the core process. Since the raw materials of reconstituted tobacco are mainly from tobacco stems, tobacco stems and tobacco dust produced in the tobacco processing process, the mixing quality of different types of raw materials directly affects the stability and consistency of reconstituted tobacco products, therefore, raw material homogenization is an important way to ensure the stability of the chemical composition of paper-making process reconstituted tobacco products.

[0003] In the raw material formula design process of paper-making process reconstituted tobacco, raw material homogenization is generally carried out by high, medium and low collocation according to the chemical composition content of the raw materials. At present, the raw material sampling method of reconstituted tobacco is mainly random sampling according to the actual inventory of the raw materials in a certain proportion, however, this sampling method has problems such as excessive sampling or insufficient sampling, which affects the production efficiency and cost of reconstituted tobacco. SUMMARY

[0004] The embodiment of the present disclosure provides a raw material sampling calculation method and device for reconstituted tobacco.

[0005] In the first aspect of the present disclosure, a raw material sampling calculation method for reconstituted tobacco is provided. The method comprises determining the chemical composition content historical variance of each formula module corresponding to the reconstituted tobacco based on the raw material database of the reconstituted tobacco, the reconstituted tobacco having a plurality of formula modules, and each formula module comprising a plurality of grade raw materials. The method further comprises obtaining the proportion of each grade raw material in the corresponding formula module, and calculating the maximum variance of each grade raw material in the corresponding formula module based on the proportion of each grade raw material in the corresponding formula module and the chemical composition content historical variance of each formula module. In addition, the method further comprises obtaining the sampling amount of each grade raw material in the corresponding formula module based on the maximum variance of each grade raw material in the corresponding formula module and the chemical composition content historical variance of the corresponding grade raw material.

[0006] In a second aspect of the present disclosure, a raw material sampling calculation device for reconstituted tobacco is provided. The device comprises a historical data determination module configured to determine a historical variance of chemical component content of each formula module corresponding to reconstituted tobacco based on a raw material database of reconstituted tobacco, the reconstituted tobacco having a plurality of formula modules, each formula module comprising a plurality of grade raw materials. The device further comprises a maximum variance calculation module configured to obtain a proportion of each grade raw material in the corresponding formula module, and calculate a maximum variance of each grade raw material in the corresponding formula module based on the proportion of each grade raw material in the corresponding formula module and the historical variance of chemical component content of each formula module. In addition, the device further comprises a sampling amount calculation module configured to obtain a sampling amount of each grade raw material in the corresponding formula module based on the maximum variance of each grade raw material in the corresponding formula module and the historical variance of chemical component content of the corresponding grade raw material.

[0007] In a third aspect of the present disclosure, a computer program product is provided, comprising a computer program executable by a processor to implement the method according to the first aspect.

[0008] In a fourth aspect of the present disclosure, a machine readable storage medium is provided. The machine readable storage medium has stored thereon machine executable instructions, wherein the machine executable instructions are executable by a processor to implement the method provided by the first aspect of the present disclosure.

[0009] It should be understood that the description in the summary is not intended to identify key or essential features of embodiments of the present disclosure or to limit the scope of the present disclosure. Other features of the present disclosure will be apparent from review of the description below. BRIEF DESCRIPTION OF DRAWINGS

[0010] The above and other features, aspects and advantages of embodiments of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings. In the drawings similar elements are denoted by similar reference numerals, and:

[0011] Figure 1 a schematic diagram showing an example environment in which some embodiments of the present disclosure can be implemented;

[0012] Figure 2 a flowchart showing a raw material sampling calculation method for reconstituted tobacco according to some embodiments of the present disclosure;

[0013] Figure 3 a raw material database diagram of reconstituted tobacco according to some embodiments of the present disclosure;

[0014] Figure 4 a block diagram showing a raw material sampling calculation device for reconstituted tobacco according to some embodiments of the present disclosure; and

[0015] Figure 5 A block diagram of an electronic device in which a plurality of embodiments of the present disclosure can be implemented is shown. DETAILED DESCRIPTION

[0016] For the purposes of the present application, the technical solutions and advantages thereof will be more clearly apparent from the following description of embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0017] The terms "comprise", "comprising", "include", "including", "have" and "having" in the specification and claims and above and below drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a list of steps or units is not limited to the listed steps or units, but can optionally further include other steps or units not listed, or can optionally further include other steps or units inherent to such a process, method, product or device. Depending on the context, the word "if" as used herein can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting".

[0018] As described above, the current raw material sampling method for reconstituted tobacco is mainly random sampling according to the actual inventory of the raw material at a certain proportion. This sampling method is relatively extensive, and cannot guarantee the rationality and representativeness of the sampling. In addition, it may cause over-sampling or insufficient sampling due to the difference in the actual inventory of different raw materials, and may also cause instability of the chemical composition of the reconstituted tobacco, thereby affecting the overall production efficiency and cost of the reconstituted tobacco.

[0019] Therefore, an embodiment of the present disclosure provides a raw material sampling calculation method for reconstituted tobacco. The method includes determining the chemical composition content historical variance of each formula module corresponding to the reconstituted tobacco based on a raw material database of the reconstituted tobacco, the reconstituted tobacco having a plurality of formula modules, and each formula module including a plurality of grade raw materials. The method further includes obtaining the proportion of each grade raw material in the corresponding formula module, and calculating the maximum variance of each grade raw material in the corresponding formula module based on the proportion of each grade raw material in the corresponding formula module and the chemical composition content historical variance of each formula module. In addition, the method further includes obtaining the sampling amount of each grade raw material in the corresponding formula module based on the maximum variance of each grade raw material in the corresponding formula module and the chemical composition content historical variance of the corresponding grade raw material.

[0020] In this way, based on the historical variance of the chemical composition content of each formulation module and the required proportion of each grade of raw materials in the corresponding formulation module, combined with statistical principles, the maximum variance of each grade of raw materials in the corresponding formulation module can be scientifically and reliably determined. Based on the maximum variance of each grade of raw materials in the corresponding formulation module and the historical variance of the chemical composition content, the sampling amount of each grade of raw materials in the corresponding formulation module can be determined from the perspective of chemical composition stability. This not only scientifically ensures the stability of the chemical composition of reconstituted tobacco, but also ensures that the sampling amount of each grade of raw materials in the corresponding formulation module meets the actual production needs of reconstituted tobacco, so as to avoid affecting the production efficiency and cost of reconstituted tobacco, and thus provide technical support for the homogenized production of reconstituted tobacco.

[0021] Figure 1 Schematic diagrams are shown illustrating example environments in which some embodiments of this disclosure can be implemented. For example... Figure 1 As shown, the example environment 100 may include a chemical composition detection device 101, which is used to test the chemical composition content of raw materials of different grades in different formulation modules of reconstituted tobacco according to standard specifications, so as to obtain the chemical composition content of each grade of raw materials. In some embodiments, the chemical composition detection device 101 includes a sugar detection device, a nitrogen-containing compound detection device, an organic acid detection device, and a mineral detection device, so as to obtain the sugar content, nitrogen-containing compound content, organic acid content, and mineral content of each grade of raw materials, respectively. Of course, other types of chemical composition detection devices can also be used to obtain the content of other types of chemical components of each grade of raw materials, and it is not limited to this. Here, reconstituted tobacco can be divided into multiple types according to manufacturing process, physical form, or functional use. Each type of reconstituted tobacco has multiple formulation modules, and the multiple grades of raw materials contained in each formulation module can form the raw materials required for the preparation of the corresponding reconstituted tobacco. In one example, the multiple grades of raw materials contained in each formulation module can be at least two of the following types: tobacco stems, fragments, tobacco dust, and low-grade tobacco leaves.

[0022] Example environment 100 also includes a processing terminal 102, which establishes a communication connection with a chemical composition detection device 101 to obtain the chemical composition content of each grade of raw materials in each formulation module of each reconstituted tobacco leaf obtained by the chemical composition detection device 101 at different time periods. Furthermore, the processing terminal 102 is also used to obtain the proportion of each grade of raw materials corresponding to each formulation module of each reconstituted tobacco leaf at different time periods, and can summarize and process the chemical composition content and proportion of each grade of raw materials corresponding to each formulation module of each reconstituted tobacco leaf at different time periods to obtain a raw material database for reconstituted tobacco leaves.

[0023] In addition, the processing terminal 102 can also determine the historical variance of the chemical component content of each formula module corresponding to the reconstituted tobacco according to the chemical component stability requirements of the reconstituted tobacco and the required proportion of each grade raw material in the corresponding formula module in the raw material database of the reconstituted tobacco, and calculate the maximum variance of each grade raw material in the corresponding formula module based on the required proportion of each grade raw material in the corresponding formula module and the historical variance of the chemical component content of each formula module. Then, the processing terminal 102 can also obtain the sampling amount of each grade raw material in the corresponding formula module based on the maximum variance of each grade raw material in the corresponding formula module and the historical variance of the chemical component content of the corresponding grade raw material.

[0024] The processing terminal 102 involved in some embodiments of the present disclosure can be a smart phone, a tablet computer, a desktop computer, a laptop computer, a notebook computer, an Ultra-mobile Personal Computer (UMPC), a handheld computer, a PC device, a Personal Digital Assistant (PDA), a routing device, a virtual reality device, etc., and of course can also be a hardware server, a virtual server, a cloud server, a routing device, a gateway device, etc.

[0025] In this way, the maximum variance of each grade raw material in the corresponding formula module can be scientifically and reliably determined based on the historical variance of the chemical component content of each formula module and the required proportion of each grade raw material in the corresponding formula module in combination with statistical principles, and the sampling amount of each grade raw material in the corresponding formula module can be determined from the perspective of chemical component stability based on the maximum variance and the historical variance of the chemical component content of each grade raw material in the corresponding formula module, which not only scientifically guarantees the stability of the chemical components of the reconstituted tobacco, but also makes the sampling amount of each grade raw material in the corresponding formula module meet the actual production requirements of the reconstituted tobacco, so as to avoid affecting the production efficiency and cost of the reconstituted tobacco, and thus provides technical support for the homogenization production of the reconstituted tobacco.

[0026] It should be understood that the architecture and functions in the example environment 100 are described for the purpose of illustration only, and do not imply any limitation on the scope of the present disclosure. Embodiments of the present disclosure can also be applied to other environments with different structures and / or functions.

[0027] Figure 2 A flowchart of a raw material sampling calculation method for reconstituted tobacco according to some embodiments of the present disclosure is shown. The method 200 may, for example, be performed by the processing terminal 102 in the example environment 100 shown. Figure 1 The processing terminal 102 in the example environment 100 shown. As Figure 2As shown in box 202, method 200 can determine the historical variance of chemical component content for each formulation module corresponding to reconstituted tobacco based on the raw material database of reconstituted tobacco. Here, the raw material database of reconstituted tobacco includes each formulation module corresponding to each type of reconstituted tobacco. Each formulation module includes multiple sets of historical raw material data over multiple historical periods. Each set of historical raw material data includes the historical proportion of each grade of raw material in the corresponding formulation module and the historical content of chemical components. It is understood that the processing terminal can filter out all historical raw material data corresponding to the type of reconstituted tobacco and the chemical component stability requirements from the raw material database of reconstituted tobacco. In one example, taking the chemical component stability requirement of papermaking reconstituted tobacco as the water-soluble sugar stability requirement, the processing terminal can filter out each set of historical raw material data corresponding to each formulation module of papermaking reconstituted tobacco from the raw material database of reconstituted tobacco, and then filter out the historical proportion of each grade of raw material and the historical content of water-soluble sugar from each set of historical raw material data. Subsequently, the processing terminal can process all the selected historical raw material data by combining statistical variance calculation formulas, and then obtain the historical variance of chemical composition of each formula module corresponding to reconstituted tobacco.

[0028] Figure 3 A schematic diagram of a raw material database for reconstituted tobacco leaves according to some embodiments of the present disclosure is shown. For example... Figure 3 As shown, the raw material database 300 of reconstituted tobacco leaf illustrates that reconstituted tobacco leaf A has corresponding formula module A1 and formula module A2. Formula module A1 has three grades of raw materials: A11 grade, A12 grade, and A13 grade. Formula module A2 has three grades of raw materials: A21 grade, A22 grade, and A23 grade. Each grade of raw material has a different proportion (i.e., historical proportion) and chemical component content (i.e., historical chemical component content) under different seasons. The chemical component content includes the content of three chemical components: chemical component a, chemical component b, and chemical component c.

[0029] For example, the historical raw material data of the formula module A1 in the first quarter includes the proportion A1, the chemical component content a11, the chemical component content b11 and the chemical component content c11 corresponding to the A11 grade raw material, the proportion B1, the chemical component content a21, the chemical component content b21 and the chemical component content c21 corresponding to the A12 grade raw material, the proportion C1, the chemical component content a31, the chemical component content b31 and the chemical component content c31 corresponding to the A13 grade raw material, and the sum of the proportion A1, the proportion B1 and the proportion C1 is 1. For example, the historical raw material data of the formula module A2 in the first quarter includes the proportion D1, the chemical component content a41, the chemical component content b41 and the chemical component content c41 corresponding to the A21 grade raw material, the proportion E1, the chemical component content a51, the chemical component content b51 and the chemical component content c51 corresponding to the A22 grade raw material, the proportion F1, the chemical component content a61, the chemical component content b61 and the chemical component content c61 corresponding to the A23 grade raw material, and the sum of the proportion D1, the proportion E1 and the proportion F1 is 1.

[0030] In some embodiments, based on the raw material database of the reconstituted tobacco leaf, the chemical component content historical variance of each formula module corresponding to the reconstituted tobacco leaf is determined, including:

[0031] Based on the historical proportion of each grade raw material in the corresponding formula module and the chemical component historical content in each set of historical raw material data, the chemical component historical content corresponding to each set of historical raw material data is determined; and

[0032] Based on all the chemical component historical contents corresponding to each formula module, the chemical component content historical variance of each formula module is determined.

[0033] Here, after the processing terminal screens all the historical raw material data corresponding to the reconstituted tobacco leaf type and the chemical component stability requirement in the raw material database of the reconstituted tobacco leaf, the historical proportion of each grade raw material in the corresponding formula module and the chemical component historical content in each set of historical raw material data are substituted into the chemical component content weighting formula to obtain the chemical component historical content corresponding to each set of historical raw material data. In an example, the reconstituted tobacco leaf has a formula module g and a formula module p, the formula module g has n grade raw materials corresponding to the historical proportion and the chemical component historical content, and the formula module p has m grade raw materials corresponding to the historical proportion and the chemical component historical content. For example, the chemical component historical content corresponding to a set of historical raw material data of the formula module g can be expressed as the following formula (1) in combination with the chemical component content weighting formula:

[0034] (1)

[0035] In the above formula, Yg can be the historical chemical composition content corresponding to the certain set of historical raw material data of the formulation module g, an can be the historical proportion corresponding to the nth grade raw material in the certain set of historical raw material data (for example, a1 can be the historical proportion corresponding to the 1st grade raw material in the certain set of historical raw material data), and Tgn can be the historical chemical composition content corresponding to the nth grade raw material in the certain set of historical raw material data (for example, Tg1 can be the historical chemical composition content corresponding to the 1st grade raw material in the certain set of historical raw material data).

[0036] The historical chemical composition content corresponding to the certain set of historical raw material data of the formulation module p can be expressed as formula (2) in combination with the chemical composition content weighting formula:

[0037] (2)

[0038] In the above formula, Yp can be the historical chemical composition content corresponding to the certain set of historical raw material data of the formulation module p, bm can be the historical proportion corresponding to the mth grade raw material in the certain set of historical raw material data (for example, b1 can be the historical proportion corresponding to the 1st grade raw material in the certain set of historical raw material data), and Tpm can be the historical chemical composition content corresponding to the mth grade raw material in the certain set of historical raw material data (for example, Tp1 can be the historical chemical composition content corresponding to the 1st grade raw material in the certain set of historical raw material data).

[0039] Subsequently, the processing terminal can calculate all the historical chemical composition contents corresponding to each formulation module by using a variance calculation formula in statistics to determine the historical variance of the chemical composition content of each formulation module. Here, the variance calculation formula can be a technical means known in the art, and further description is omitted herein.

[0040] At block 204, the method 200 can obtain the proportion of each grade raw material in the corresponding formula module, and calculate the maximum variance of each grade raw material in the corresponding formula module based on the proportion of each grade raw material in the corresponding formula module and the historical variance of the chemical component content of each formula module. In some embodiments, the processing terminal can obtain the demand proportion of each grade raw material in the corresponding formula module based on the interactive interface, and substitute the demand proportion of each grade raw material in the corresponding formula module and the historical variance of the chemical component content of each formula module into the maximum variance expression set of each grade raw material in the corresponding formula module, and then calculate the maximum variance of each grade raw material in the corresponding formula module. Here, the maximum variance expression set of each grade raw material in the corresponding formula module can be derived using the variance formula of statistics, which is composed of the chemical component content variance expression corresponding to each formula module of the reconstituted tobacco and the chemical component content variance expression of the reconstituted tobacco. The chemical component content variance expression corresponding to any formula module includes the demand proportion of each grade raw material in the corresponding formula module and the historical variance of the chemical component content of the corresponding formula module, the chemical component content variance expression corresponding to other formula modules includes the historical variance of the chemical component content of the corresponding formula module, and the chemical component content variance expression of the reconstituted tobacco includes the chemical component content variance expression corresponding to each formula module.

[0041] At block 206, the method 200 can obtain the sampling amount of each grade raw material in the corresponding formula module based on the maximum variance of each grade raw material in the corresponding formula module and the historical variance of the chemical component content of the corresponding grade raw material. In some embodiments, the processing terminal can determine the allowable error of each grade raw material based on the maximum variance of each grade raw material in the corresponding formula module and a preset second parameter. Here, the preset second parameter includes a normal distribution critical value determined based on a confidence level value and a preset correction parameter, and the normal distribution critical value has a corresponding relationship with the confidence level value (which can be obtained by looking up a table). For example, when the confidence level value is set to 90%, the normal distribution critical value is 1.645; when the confidence level value is set to 95%, the normal distribution critical value is 1.96; and when the confidence level value is set to 99%, the normal distribution critical value is 2.576.

[0042] In an example, taking the reconstituted tobacco having a formula module g and a formula module p, the formula module g having n grade raw materials, the formula module p having m grade raw materials, and the maximum variance of the first grade raw material in the formula module g as an example, the processing terminal can substitute the maximum variance of the first grade raw material in the formula module g and the preset second parameter into the allowable error expression to obtain the allowable error of the first grade raw material in the formula module g. The allowable error expression can be expressed as the following formula (3):

[0043] (3)

[0044] In the above formula, E can be the allowable error of the first grade raw material in the formula module g, can be the critical value of the normal distribution in the preset second parameter, can be the maximum variance of the first grade raw material in the formula module g, and R can be the preset correction parameter in the preset second parameter.

[0045] Then, the processing terminal can obtain the sampling amount of each grade raw material in the corresponding formula module based on the allowable error of each grade raw material, the preset second parameter, and the historical variance of the chemical component content of the corresponding grade raw material. Here, the historical variance of the chemical component content of each grade raw material can be obtained based on the raw material database of the reconstituted tobacco mentioned above, for example, the historical chemical component content of each grade raw material in the corresponding set of historical raw material data can be screened in the raw material database of the reconstituted tobacco, and the historical variance of the chemical component content of each grade raw material can be calculated using the variance calculation formula in statistics.

[0046] In an example, taking the reconstituted tobacco having formula module g and formula module p, the formula module g having n grade raw materials, the formula module p having m grade raw materials, and the allowable error of the first grade raw material in the formula module g as an example, the processing terminal can substitute the allowable error of the first grade raw material in the formula module g, the preset second parameter, and the historical variance of the chemical component content of the corresponding grade raw material into the sampling amount expression to obtain the actual sampling amount of the first grade raw material in the formula module g. The sampling amount expression can be expressed as the following formula (4):

[0047] (4)

[0048] In the above formula, N can be the actual sampling amount of the first grade raw material in the formula module g, can be the critical value of the normal distribution in the preset second parameter, can be the historical variance of the chemical component content of the first grade raw material in the formula module g, and E can be the allowable error of the first grade raw material in the formula module g.

[0049] In this way, the maximum variance of each grade raw material in the corresponding formula module can be scientifically and reliably determined based on the historical variance of the chemical component content of each formula module and the demand proportion of each grade raw material in the corresponding formula module in combination with statistical principles, and the sampling amount of each grade raw material in the corresponding formula module can be determined from the perspective of chemical component stability based on the maximum variance of each grade raw material in the corresponding formula module and the historical variance of the chemical component content, so as to not only scientifically guarantee the stability of the chemical components of the reconstituted tobacco, but also make the sampling amount of each grade raw material in the corresponding formula module meet the actual production needs of the reconstituted tobacco, so as to avoid affecting the production efficiency and cost of the reconstituted tobacco, and thus provide technical support for the homogenization production of the reconstituted tobacco.

[0050] In some embodiments, the reconstituted tobacco has a chemical component content setting range, and

[0051] Based on the proportion of each grade raw material in the corresponding formula module and the historical variance of the chemical component content of each formula module, the maximum variance of each grade raw material in the corresponding formula module is calculated, including:

[0052] Based on the chemical component content setting range, the maximum variance of the chemical component content is determined; and

[0053] Based on the proportion of each grade raw material in the corresponding formula module, the historical variance of the chemical component content of each formula module, and the maximum variance of the chemical component content, the maximum variance of each grade raw material in the corresponding formula module is calculated.

[0054] Here, the chemical component content setting range of the reconstituted tobacco can be understood as the set range that each chemical component content in the reconstituted tobacco can allow, for example, including any one of a sugar content setting range, a nitrogen compound content setting range, an organic acid content setting range, and a mineral content setting range.

[0055] It can be understood that, in order to meet the chemical component stability requirement of the reconstituted tobacco, the processing terminal can determine the maximum variance of the corresponding chemical component content based on the chemical component content setting range corresponding to the chemical component stability requirement. In an example, the processing terminal can determine the maximum value, the minimum value, and the intermediate value of the chemical component content setting range based on the chemical component content setting range. Here, the intermediate value can be the mean value corresponding to the chemical component content setting range, which can be obtained according to the above-mentioned raw material database of the reconstituted tobacco, for example, the chemical component historical content of each grade raw material in the corresponding each group of historical raw material data can be screened out in the raw material database of the reconstituted tobacco (and the chemical component historical content not within the chemical component content setting range can also be excluded), and the mean value is calculated. Of course, the intermediate value can also be the median of the chemical component content setting range, for example, the mean value between the maximum value and the minimum value.

[0056] Afterwards, the processing terminal can determine the maximum variance of the chemical component content based on the maximum value, the minimum value and the intermediate value of the chemical component content setting range. In an example, taking the intermediate value as the mean value corresponding to the chemical component content setting range as an example, the processing terminal can substitute the maximum value, the minimum value and the intermediate value of the chemical component content setting range into the mean maximum variance expression to obtain the maximum variance of the chemical component content, which can be expressed as the following formula (5):

[0057] (5)

[0058] In the above formula, may be the allowable error of each grade raw material, may be the mean value corresponding to the chemical component content setting range, may be the minimum value of the chemical component content setting range, may be the minimum value of the chemical component content setting range.

[0059] In another example, taking the median value of the intermediate value as the chemical component content setting range, the processing terminal can substitute the maximum value and the minimum value of the chemical component content setting range into the median maximum variance expression to obtain the maximum variance of the chemical component content, which can be expressed as the following formula (6):

[0060] (6)

[0061] In the above formula, may be the allowable error of each grade raw material, may be the minimum value of the chemical component content setting range, may be the minimum value of the chemical component content setting range.

[0062] After determining the maximum variance of the chemical component content, the processing terminal can further substitute the proportion of each grade raw material in the corresponding formula module, the historical variance of the chemical component content of each formula module, and the maximum variance of the chemical component content into a maximum variance expression set of each grade raw material in the corresponding formula module to calculate the maximum variance of each grade raw material in the corresponding formula module. Here, the maximum variance expression set of each grade raw material in the corresponding formula module can be derived using a variance property formula of statistics, and is composed of a chemical component content variance expression corresponding to each formula module of the reconstituted tobacco and a chemical component content variance expression of the reconstituted tobacco. The chemical component content variance expression corresponding to any formula module includes the required proportion of each grade raw material in the corresponding formula module and the historical variance of the chemical component content of the corresponding formula module, the chemical component content variance expressions corresponding to other formula modules include the historical variance of the chemical component content of the corresponding formula module, and the chemical component content variance expression of the reconstituted tobacco includes the chemical component content variance expressions corresponding to each formula module, and the maximum value of the chemical component content variance expression of the reconstituted tobacco represents the maximum variance of the chemical component content.

[0063] In some embodiments, based on the proportion of each grade raw material in the corresponding formula module, the historical variance of the chemical component content of each formula module, and the maximum variance of the chemical component content, calculating the maximum variance of each grade raw material in the corresponding formula module further includes:

[0064] constructing a chemical component content variance expression of each formula module based on the proportion of each grade raw material in the corresponding formula module;

[0065] constructing a chemical component content variance expression of the reconstituted tobacco based on the chemical component content variance expression of each formula module and a preset first parameter; and

[0066] performing conversion processing on the chemical component content variance expression of each formula module and the chemical component content variance expression of the reconstituted tobacco to obtain a maximum variance expression set of each grade raw material in the corresponding formula module.

[0067] Here, the processing terminal can construct a chemical component content variance expression of each formula module using a variance property formula in statistics based on the required proportion of each grade raw material in the corresponding formula module. In an example, taking the reconstituted tobacco having a formula module g and a formula module p, the formula module g having n grade raw materials, and the formula module p having m grade raw materials as an example, the chemical component content variance expression of the formula module g can be represented by the following formula (7):

[0068] (7)

[0069] In the above formula, SYg can represent the chemical component content variance of the formula module g, an can represent the demand proportion of the nth grade raw material corresponding to the formula module g (for example, a1 can be the demand proportion of the 1st grade raw material corresponding to the formula module g), and Sgn can represent the chemical component content variance of the nth grade raw material corresponding to the formula module g (for example, Sg1 can be the chemical component content variance of the 1st grade raw material corresponding to the formula module g, and the chemical component content variances of various grade raw materials are unknown data).

[0070] The chemical component content variance of the formula module p can be represented by the following formula (8):

[0071] (8)

[0072] In the above formula, SYg can represent the chemical component content variance of the formula module g, an can represent the demand proportion of the nth grade raw material corresponding to the formula module g (for example, a1 can be the demand proportion of the 1st grade raw material corresponding to the formula module g), and Sgn can represent the chemical component content variance of the nth grade raw material corresponding to the formula module g (for example, Sg1 can be the chemical component content variance of the 1st grade raw material corresponding to the formula module g, and the chemical component content variances of various grade raw materials are unknown data).

[0073] Then, the processing terminal can construct a chemical component content variance expression of the reconstituted tobacco based on the chemical component content variance expressions of various formula modules and preset first parameters. Here, the preset first parameters include preset constants corresponding to various formula modules of the reconstituted tobacco. In an example, in combination with the chemical component content variance expression of the formula module g represented by the above formula (7) and the chemical component content variance expression of the formula module p represented by the above formula (8), the chemical component content variance expression of the reconstituted tobacco can be represented by the following formula (9):

[0074] (9)

[0075] In the above formula, SYg can represent the chemical component content variance of the formula module g, an can represent the demand proportion of the nth grade raw material corresponding to the formula module g (for example, a1 can be the demand proportion of the 1st grade raw material corresponding to the formula module g), and Sgn can represent the chemical component content variance of the nth grade raw material corresponding to the formula module g (for example, Sg1 can be the chemical component content variance of the 1st grade raw material corresponding to the formula module g, and the chemical component content variances of various grade raw materials are unknown data).

[0076] Afterwards, the processing terminal can perform conversion processing on the chemical component content variance expression of each recipe module and the chemical component content variance expression of the reconstituted tobacco to obtain a maximum variance expression set of each grade raw material in the corresponding recipe module. Here, when performing conversion processing on the chemical component content variance expression of each recipe module, the processing terminal can approximate the chemical component content variance corresponding to all other grade raw materials except the grade raw material in the chemical component content variance expression of the corresponding recipe module to the historical chemical component content variance of the corresponding recipe module based on the demand proportion of any grade raw material in the corresponding recipe module, and can approximate the value of the chemical component content variance expression of other recipe modules to the historical chemical component content variance of the corresponding recipe module. In an example, taking the reconstituted tobacco having a recipe module g and a recipe module p, the recipe module g having n grade raw materials, and the recipe module p having m grade raw materials as an example, when the demand proportion corresponding to the first grade raw material in the recipe module g is obtained, the above-mentioned chemical component content variance expression (7) of the recipe module g can be converted to the formula (10) shown as follows:

[0077] (10)

[0078] In the above formula, SYg can represent the chemical component content variance of the recipe module g, a1 can be the demand proportion corresponding to the first grade raw material in the recipe module g, can represent the maximum variance (as data to be solved) corresponding to the first grade raw material in the recipe module g, can represent the historical chemical component content variance of the recipe module g.

[0079] and the above-mentioned chemical component content variance expression (8) of the recipe module p can also be converted to the formula (11) shown as follows:

[0080] (11)

[0081] In the above formula, SYp can represent the chemical component content variance of the recipe module p, can represent the historical chemical component content variance of the recipe module p.

[0082] Further, the processing terminal can approximate the maximum value of the chemical component content variance of the reconstituted tobacco to the maximum variance of the chemical component content when performing the conversion processing on the chemical component content variance expression of the reconstituted tobacco. In an example, the chemical component content variance expression (9) of the reconstituted tobacco mentioned above can be converted to the formula (12) shown below, for example, when the reconstituted tobacco has a formula module g with n grades of raw materials and a formula module p with m grades of raw materials:

[0083] (12)

[0084] In the above formula, SZmax can represent the maximum value of the chemical component content variance of the reconstituted tobacco (i.e., the maximum variance of the chemical component content), SYg can represent the chemical component content variance of the formula module g, SYp can represent the chemical component content variance of the formula module p, c1 can be a preset constant corresponding to the formula module g, and c2 can be a preset constant corresponding to the formula module p.

[0085] It can be understood that after obtaining the demand proportion corresponding to the first grade of raw materials in the formula module g, the processing terminal can substitute the demand proportion corresponding to the first grade of raw materials in the formula module g, the historical chemical component content variance of the formula module g, the historical chemical component content variance of the formula module p, and the maximum variance of the chemical component content into the above-mentioned formulas (10), (11), and (12), to obtain the maximum variance of the first grade of raw materials in the formula module g.

[0086] In some embodiments of the present disclosure, for example, when the reconstituted tobacco has a formula module g with 4 grades of raw materials and a formula module p with 6 grades of raw materials, and the demand proportion corresponding to the first grade of raw materials in the formula module g is 30% and the chemical component stability demand of the reconstituted tobacco is the water-soluble sugar stability demand, the chemical component content variance expressions of the formula modules and the chemical component content variance expression of the reconstituted tobacco can be represented by the formulas (13), (14), and (15) shown below, respectively:

[0087] (13)

[0088] In the above formula, SYg can represent the chemical component content variance of the formula module g, a2 can represent the demand proportion corresponding to the second grade of raw materials in the formula module g, and Sg2 can represent the chemical component content variance corresponding to the second grade of raw materials in the formula module g (the chemical component content variances corresponding to each grade of raw materials are unknown data).

[0089] (14)

[0090] In the above formula, SYp can represent the variance of the chemical component content of the formula module p, b1 can represent the demand proportion corresponding to the first grade raw material in the formula module p, and Sp1 can represent the variance of the chemical component content corresponding to the first grade raw material in the formula module p (the variance of the chemical component content corresponding to each grade raw material is unknown data).

[0091] (15)

[0092] In the above formula, SZ can represent the variance of the chemical component content of the reconstituted tobacco, SYg can represent the variance of the chemical component content of the formula module g, SYp can represent the variance of the chemical component content of the formula module p, 0.26 can be a preset constant corresponding to the formula module g, and 0.2 can be a preset constant corresponding to the formula module p.

[0093] Afterwards, the variance expression of the chemical component content of each formula module and the variance expression of the chemical component content of the reconstituted tobacco can be converted according to the above embodiments, to obtain a maximum variance expression set corresponding to the first grade raw material in the formula module g, and the historical variance 8.67 of the water-soluble sugar of the formula module g, the historical variance 5.76 of the water-soluble sugar of the formula module p, and the maximum variance 2 of the water-soluble sugar are substituted into the maximum variance expression set corresponding to the first grade raw material in the formula module g, to obtain the maximum variance of the first grade raw material in the formula module g. Here, the maximum variance expression set corresponding to the first grade raw material in the formula module g can be represented by the following formulas (16), (17) and (18) shown as follows:

[0094] (16)

[0095] In the above formula, SYg can represent the variance of the chemical component content of the formula module g, can represent the maximum variance corresponding to the first grade raw material in the formula module g.

[0096] (17)

[0097] In the above formula, SYp can represent the variance of the chemical component content of the formula module p.

[0098] (18)

[0099] According to the above formulas (16), (17) and (18), the maximum variance of the first grade raw material in the formula module g is 161.

[0100] Then, in conjunction with the above embodiments, the maximum variance (i.e., 161) of the first grade raw material in the formulation module g and the preset second parameter are substituted into the allowable error expression (see the above formula (3)) to obtain the allowable error of the first grade raw material in the formulation module g. Here, taking the normal distribution critical value in the preset second parameter as 1.645 corresponding to the confidence level value set to 90%, and the preset correction parameter as 100 as an example, the allowable error of the first grade raw material in the formulation module g is obtained as 2.09.

[0101] Then, in conjunction with the above embodiments, the allowable variance of the first grade raw material in the formulation module g (i.e., 2.09), the normal distribution critical value in the preset second parameter, and the historical variance of the water-soluble sugar of the first grade raw material in the formulation module g are substituted into the sampling quantity expression (see the above formula (4)) to obtain the actual sampling quantity of the first grade raw material in the formulation module g. Here, taking the normal distribution critical value in the preset second parameter as 1.645 corresponding to the confidence level value set to 90%, and the historical variance of the water-soluble sugar of the first grade raw material in the formulation module g as 9.73, the actual sampling quantity of the first grade raw material in the formulation module g is obtained as 9.

[0102] Figure 4 A block diagram of a raw material sampling calculation device for reconstituted tobacco leaves, according to some embodiments of the present disclosure, is shown. Figure 4 As shown, the raw material sampling calculation device 400 for reconstituted tobacco includes a historical data determination module 402, configured to determine the historical variance of the chemical composition content of each formulation module corresponding to the reconstituted tobacco based on the raw material database of the reconstituted tobacco. The reconstituted tobacco has multiple formulation modules, and each formulation module includes multiple grades of raw materials. The raw material sampling calculation device 400 also includes a maximum variance calculation module 404, configured to obtain the proportion of each grade of raw material in the corresponding formulation module, and calculate the maximum variance of each grade of raw material in the corresponding formulation module based on the proportion of each grade of raw material in the corresponding formulation module and the historical variance of the chemical composition content of each formulation module. In addition, the raw material sampling calculation device 400 also includes a sampling quantity calculation module 406, configured to obtain the sampling quantity of each grade of raw material in the corresponding formulation module based on the maximum variance of each grade of raw material in the corresponding formulation module and the historical variance of the chemical composition content of the corresponding grade of raw material.

[0103] Figure 5 Block diagrams of electronic devices that can implement various embodiments of the present disclosure are shown. For example... Figure 5As shown, the electronic device 500 includes a processor 501 that can execute computer program instructions loaded in a random access memory (RAM) 503 from a read only memory (ROM) 502 to perform various suitable actions and processes. Various programs and data used by the electronic device 500, in addition to the computer program instructions, can also be stored in the RAM 503. The processor 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0104] The various processes and processes described above, such as the method 200, can be performed by the processor 501. For example, in some embodiments, the method 200 can be implemented as a software program tangibly embodied in a machine readable medium. In some embodiments, part or all of the software program can be loaded and / or installed on the electronic device 500 via the ROM 502. When the software program is loaded into the RAM 503 and executed by the processor 501, one or more actions of the method 200 described above can be performed.

[0105] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on a chip systems (SOCs), complex programmable logic devices (CPLDs), etc.

[0106] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0107] The present disclosure can be a method, apparatus, system, and / or program product. Program products can include machine-readable storage media on which machine-readable program instructions are stored. Machine-readable program instructions described herein can be downloaded to various computing / processing devices from a machine-readable storage medium or to external devices or external storage devices from a network, for example, the Internet, a local area network, a wide area network, and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives machine-readable program instructions from the network and forwards the machine-readable program instructions to a machine-readable storage medium within the respective computing / processing device for execution by the machine-readable program instructions by the machine.

[0108] Machine program instructions for performing operations of the present disclosure can be assembly instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The machine-readable program instructions can be executed entirely on a user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on a remote computer or server. In situations involving remote computers, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, the electronic circuitry, for example, programmable logic circuitry, field programmable gate arrays (FPGA), or programmable logic arrays (PLA), which is personalized with the state information of the machine-readable program instructions, can execute the machine-readable program instructions to implement aspects of the present disclosure.

[0109] In the context of this disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing. Further, while operations are depicted in a particular, chronological sequence, this should not be understood as requiring such order or sequence of operations, or that all illustrated operations be performed to achieve desirable results. In certain circumstances, multitasking and parallel processing can be advantageous. Likewise, while specific implementations are discussed herein, the scope of the present disclosure is not limited to the specific details and representations herein. Certain features described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination.

[0110] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A method for calculating raw material sampling for reconstituted tobacco, characterized in that, include: Based on the raw material database of reconstituted tobacco, the historical variance of the chemical composition content of each formulation module corresponding to the reconstituted tobacco is determined. The reconstituted tobacco has multiple formulation modules, and each formulation module includes multiple grades of raw materials. Obtain the proportion of each grade of raw material in the corresponding formulation module, and calculate the maximum variance of each grade of raw material in the corresponding formulation module based on the proportion of each grade of raw material in the corresponding formulation module and the historical variance of the chemical composition content of each formulation module; as well as Based on the maximum variance of each grade of raw material in the corresponding formulation module and the historical variance of the chemical composition content of the corresponding grade of raw material, the sampling amount of each grade of raw material in the corresponding formulation module is obtained.

2. The method according to claim 1, characterized in that, The reconstituted tobacco leaves have a set range for the content of chemical components, and The calculation of the maximum variance of each grade of raw material in the corresponding formulation module, based on the proportion of each grade of raw material in the corresponding formulation module and the historical variance of the chemical composition content of each formulation module, includes: Based on the set range of chemical component content, determine the maximum variance of the chemical component content; and Based on the proportion of each grade of raw material in the corresponding formulation module, the historical variance of the chemical component content in each formulation module, and the maximum variance of the chemical component content, the maximum variance of each grade of raw material in the corresponding formulation module is calculated.

3. The method according to claim 2, characterized in that, The step of determining the maximum variance of the chemical component content based on the set range of the chemical component content includes: Based on the set range of chemical component content, determine the maximum, minimum, and median values ​​of the set range; and The maximum variance of the chemical component content is determined based on the maximum, minimum, and median values ​​of the set range of the chemical component content.

4. The method according to claim 2, characterized in that, The calculation of the maximum variance of each grade of raw material in the corresponding formulation module, based on the proportion of each grade of raw material in the corresponding formulation module, the historical variance of the chemical component content in each formulation module, and the maximum variance of the chemical component content, includes: The proportion of each grade of raw material in the corresponding formulation module, the historical variance of the chemical component content of each formulation module, and the maximum variance of the chemical component content are substituted into the set of maximum variance expressions for the corresponding grade of raw material in the corresponding formulation module to calculate the maximum variance of each grade of raw material in the corresponding formulation module.

5. The method according to claim 4, characterized in that, The step of calculating the maximum variance of each grade of raw material in the corresponding formulation module based on the proportion of each grade of raw material in the corresponding formulation module, the historical variance of the chemical component content of each formulation module, and the maximum variance of the chemical component content, further includes: Based on the proportion of each grade of raw material in the corresponding formulation module, construct the variance expression of the chemical composition content of each formulation module; Based on the variance expressions of the chemical component content of each of the formulation modules and the preset first parameter, a variance expression of the chemical component content of the reconstituted tobacco leaf is constructed; and The variance expressions of chemical component content in each formulation module and the variance expressions of chemical component content in the reconstituted tobacco leaf are transformed to obtain the set of maximum variance expressions of each grade of raw material in the corresponding formulation module.

6. The method according to any one of claims 1-5, characterized in that, The method of obtaining the sampling amount of each grade of raw material in the corresponding formulation module based on the maximum variance of each grade of raw material in the corresponding formulation module and the historical variance of the chemical composition content of the corresponding grade of raw material includes: Based on the maximum variance of each grade of raw material in the corresponding formulation module and a preset second parameter, the allowable error of each grade of raw material is determined; and Based on the allowable error of each grade of raw material, the preset second parameter, and the historical variance of the chemical composition content of the corresponding grade of raw material, the sampling amount of each grade of raw material in the corresponding formulation module is obtained.

7. The method according to claim 1, characterized in that, The raw material database for reconstituted tobacco includes multiple sets of historical raw material data for each formulation module corresponding to the reconstituted tobacco. Each set of historical raw material data includes the historical proportion of each grade of raw material in the corresponding formulation module and the historical content of its chemical components. The raw material database based on reconstituted tobacco leaves determines the historical variance of the chemical component content of each formulation module corresponding to the reconstituted tobacco leaves, including: Based on the historical proportions and historical chemical content of each grade of raw material in the corresponding formulation module of each group of historical raw material data, determine the historical chemical content corresponding to each group of historical raw material data; and Based on the historical content of all the chemical components corresponding to each of the formulation modules, the historical variance of the chemical component content of each formulation module is determined.

8. A raw material sampling and calculation device for reconstituted tobacco, characterized in that, include: The historical data determination module is configured to determine the historical variance of the chemical composition content of each formulation module corresponding to the reconstituted tobacco leaf based on the raw material database of the reconstituted tobacco leaf. The reconstituted tobacco leaf has multiple formulation modules, and each formulation module includes multiple grades of raw materials. The maximum variance calculation module is configured to obtain the proportion of each grade of raw material in the corresponding formulation module, and calculate the maximum variance of each grade of raw material in the corresponding formulation module based on the proportion of each grade of raw material in the corresponding formulation module and the historical variance of the chemical composition content of each formulation module. as well as The sampling quantity calculation module is configured to obtain the sampling quantity of each grade of raw material in the corresponding formulation module based on the maximum variance of each grade of raw material in the corresponding formulation module and the historical variance of the chemical composition content of the corresponding grade of raw material.

9. A computer-readable storage medium having a computer program stored thereon, the computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the steps of the method as claimed in any one of claims 1-7.

10. An electronic device, characterized in that, include: One or more processors, and A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1-7.

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