Method, device and equipment for determining substitute leaf group in cigarette formula and medium

Abstract

The invention discloses a method, a device and equipment for determining a substitute leaf group in a cigarette formula and a medium. The method comprises the steps that a second tobacco leaf group is determined based on tobacco leaf information of at least two to-be-replaced tobacco leaves in a first tobacco leaf group needing to be replaced in a to-be-processed cigarette formula; determining an error attribute between the first tobacco leaf group and the second tobacco leaf group based on the to-be-adjusted proportion attribute of each to-be-used tobacco leaf in the second tobacco leaf group, a first thermogravimetric curve prediction model corresponding to the first tobacco leaf group and a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group; taking the minimum error attribute as a target, determining a target proportion attribute of each to-be-used tobacco leaf in a target tobacco leaf group based on the to-be-adjusted proportion attribute of each to-be-used tobacco leaf when the target is reached, and replacing a first tobacco leaf group in the to-be-processed cigarette formula based on the target tobacco leaf group. While the determination accuracy of the weight ratio of the replaced tobacco leaves is improved, the tobacco leaf replacement efficiency and accuracy are improved, and the quality of a cigarette formula is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of computer processing technology, and in particular to a method, device, equipment and medium for determining a replacement leaf group in a cigarette formula. Background Art

[0002] The formula of a cigarette refers to the composition of the tobacco leaves in a cigarette and the weight percentage of each tobacco leaf. The formula of a cigarette directly determines the sensory quality and combustion performance of the cigarette and is a crucial factor in the cigarette production process. In some cases, it is necessary to replace some tobacco leaves on the basis of the original cigarette formula to maintain the cigarette formula.

[0003] At present, the maintenance method of cigarette formula usually relies on the personal experience of formula personnel, and replaces the tobacco to be replaced in the cigarette formula with replacement tobacco leaves that are similar to the production area and flavor of the tobacco leaves to be replaced from the tobacco leaf database, or compares the tobacco leaves to be replaced with similar tobacco leaves one by one. The above two tobacco leaf replacement methods not only have the problems of low replacement efficiency and low accuracy, but also cannot accurately measure the weight proportion of the replacement tobacco leaves, thus affecting the quality of cigarettes. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for determining a replacement leaf group in a cigarette formula, so as to improve the accuracy of determining the weight proportion of the replacement tobacco leaves while improving the efficiency and accuracy of tobacco leaf replacement, thereby ensuring the quality of the cigarette formula and ensuring the quality stability of the cigarette products.

[0005] According to one aspect of the present invention, a method for determining a replacement leaf group in a cigarette formulation is provided, the method comprising:

[0006] Determine a first tobacco leaf group to be replaced in the cigarette formula to be processed, and determine a second tobacco leaf group based on tobacco leaf information of at least two tobacco leaves to be replaced in the first tobacco leaf group, wherein the first tobacco leaf group includes tobacco leaves to be used that match each of the tobacco leaves to be replaced;

[0007] Determining a proportion attribute to be adjusted of each of the tobacco leaves to be used in the second tobacco leaf group;

[0008] Determine the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the to-be-adjusted proportion attribute of each of the tobacco leaves to be used, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group; wherein the first thermogravimetric curve prediction model is used to describe the tobacco leaf quality attributes of the first tobacco leaf group at different thermogravimetric analysis times;

[0009] With the goal of minimizing the error attribute, based on the adjusted proportion attribute of each of the tobacco leaves to be used when the target is reached, the target proportion attribute of each of the tobacco leaves to be used in the target tobacco leaf group is determined, and the first tobacco leaf group in the cigarette formula to be processed is replaced based on the target tobacco leaf group.

[0010] According to another aspect of the present invention, there is provided a device for determining a replacement leaf group in a cigarette formula, the device comprising:

[0011] A second tobacco leaf group determination module is used to determine a first tobacco leaf group to be replaced in a to-be-processed cigarette formula, and determine a second tobacco leaf group based on tobacco leaf information of at least two to-be-replaced tobacco leaves in the first tobacco leaf group, wherein the first tobacco leaf group includes to-be-used tobacco leaves that match each of the to-be-replaced tobacco leaves;

[0012] A module for determining the attribute of the proportion to be adjusted, used for determining the attribute of the proportion to be adjusted of each of the tobacco leaves to be used in the second tobacco leaf group;

[0013] an error attribute determination module, for determining the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the to-be-adjusted proportion attribute of each of the tobacco leaves to be used, a first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group; wherein the first thermogravimetric curve prediction model is used to describe the tobacco leaf quality attributes of the first tobacco leaf group at different thermogravimetric analysis times;

[0014] An error attribute minimization module is used to determine the target proportion attribute of each of the tobacco leaves to be used in the target tobacco leaf group based on the adjusted proportion attribute of each of the tobacco leaves to be used when the target is reached, with the goal of minimizing the error attribute, and replace the first tobacco leaf group in the cigarette formula to be processed based on the target tobacco leaf group.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor; and a memory in communication with the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for determining a replacement leaf group in a cigarette recipe according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions for enabling a processor to implement the method for determining a replacement leaf group in a cigarette recipe as described in any embodiment of the present invention when the computer instructions are executed.

[0019] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the method for determining a replacement leaf group in a cigarette recipe as described in any embodiment of the present invention.

[0020] The technical solution of the embodiment of the present invention is to determine the first tobacco leaf group to be replaced in the formula of the cigarette to be processed, and determine the second tobacco leaf group based on the tobacco leaf information of at least two tobacco leaves to be replaced in the first tobacco leaf group, wherein the first tobacco leaf group includes tobacco leaves to be used that match each tobacco leaf to be replaced; determine the to-be-adjusted proportion attribute of each tobacco leaf to be used in the second tobacco leaf group; determine the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the to-be-adjusted proportion attribute of each tobacco leaf to be used, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group; wherein the first thermogravimetric curve prediction model is used to describe the tobacco quality attributes of the first tobacco leaf group at different thermogravimetric analysis times; with the goal of minimizing the error attribute, determine the error attribute of each tobacco leaf to be used in the target tobacco leaf based on the to-be-adjusted proportion attribute of each tobacco leaf to be used when the target is reached. The target proportion attribute of the group is determined, and the first tobacco group in the formula of the cigarette to be processed is replaced based on the target tobacco group, which solves the problem that the weight proportion of the replacement tobacco leaves cannot be accurately measured in the prior art, and realizes that the error attribute between the first tobacco leaf group and the second tobacco leaf group is determined based on the to-be-used tobacco leaf in the second tobacco leaf group The proportion attribute to be adjusted, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group, and the proportion attribute to be adjusted of each tobacco leaf to be used in the second tobacco leaf group is adjusted with the goal of minimizing the error attribute. When the goal of minimizing the error attribute is reached, the target proportion attribute of each tobacco leaf to be used in the target tobacco leaf group is obtained, which improves the accuracy of determining the weight proportion of the replacement tobacco leaves. At the same time, the efficiency and accuracy of tobacco leaf replacement are improved, and the quality of the cigarette formula is guaranteed, thereby ensuring the quality stability of cigarette products.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a method for determining a replacement leaf group in a cigarette formula provided in Example 1 of the present invention;

[0024] Figure 2 is a schematic diagram for characterizing a thermogravimetric curve according to Embodiment 1 of the present invention;

[0025] Figure 3 is a schematic diagram of the structure of a device for determining a replacement leaf group in a cigarette formula provided in Embodiment 3 of the present invention;

[0026] Figure 4 It is a schematic diagram of the structure of an electronic device for implementing the method for determining a replacement leaf group in a cigarette recipe according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1This is a flow chart of a method for determining a replacement leaf group in a cigarette formula according to the first embodiment of the present invention. This embodiment is applicable to the case of determining a replacement leaf group in a cigarette formula and its mass occupancy ratio. The method can be executed by a device for determining a replacement leaf group in a cigarette formula. The device for determining a replacement leaf group in a cigarette formula can be implemented in the form of hardware and / or software. The device for determining a replacement leaf group in a cigarette formula can be configured in a computing device. Figure 1 As shown, the method includes:

[0031] S110, determining a first tobacco leaf group to be replaced in the cigarette formula to be processed, and determining a second tobacco leaf group based on tobacco leaf information of at least two tobacco leaves to be replaced in the first tobacco leaf group, wherein the first tobacco leaf group includes tobacco leaves to be used that match each tobacco leaf to be replaced.

[0032] Among them, the cigarette formula to be processed can be the leaf group formula of the heated cigarette that needs maintenance, or it can be the leaf group formula of the cigarette determined based on the maintenance needs. For example, based on the discontinuation of production, reduction of production, reduction of quality, insufficient inventory or increase in cost of certain tobacco leaves in the cigarette formula, the cigarette formula containing the above tobacco leaves is determined as the cigarette formula to be processed. The tobacco leaves to be replaced refer to the tobacco leaves that need to be replaced. For example, the tobacco leaves to be replaced are those in the cigarette formula to be processed that have been discontinued, reduced in production, reduced in quality, insufficient in inventory or increased in cost. Heated cigarettes refer to cigarettes that heat the tobacco core through a special heat source, which produces smoke for the object to inhale in a non-combustion state, such as heating a heated cigarette containing glycerin to a certain temperature with a heating device, and releasing smoke in a non-combustion state. The tobacco leaves to be used may be tobacco leaves that can replace the tobacco leaves to be replaced.

[0033] In this embodiment, the method for determining the first tobacco leaf group to be replaced in the cigarette formula to be processed may be: for each cigarette formula to be processed, if the tobacco leaf property of a certain tobacco leaf in the cigarette formula to be processed reaches the preset replacement condition, then all tobacco leaves in the cigarette formula to be processed that reach the preset replacement condition (i.e., tobacco leaves to be replaced) are determined as the first tobacco leaf group. It may also be: for each tobacco leaf to be processed, if the tobacco leaf property of the tobacco leaf to be processed reaches the preset replacement condition, then at least one cigarette formula to be processed to which the tobacco leaf to be processed that reaches the preset replacement condition belongs is determined, and for each cigarette formula to be processed, all tobacco leaves in the cigarette formula to be processed that reach the preset replacement condition are determined as the first tobacco leaf group. Optionally, the preset replacement conditions include but are not limited to suspension of production, reduction of production, reduction of quality, insufficient inventory, and increase in cost.

[0034] Furthermore, according to the tobacco leaf information of each tobacco leaf to be replaced in the first tobacco leaf group (such as sensory quality, weight proportion in the cigarette formula, year range, origin, inventory, grade, etc.), tobacco leaves matching the tobacco leaves to be replaced can be found from other tobacco leaves as the tobacco leaves to be used. All the tobacco leaves to be used constitute the second tobacco leaf group.

[0035] In this embodiment, based on the tobacco leaf information of at least two tobacco leaves to be replaced in the first tobacco leaf group, the second tobacco leaf group is determined, including: for each tobacco leaf to be replaced in the first tobacco leaf group, based on the tobacco leaf information of the tobacco leaf to be replaced, determining the tobacco leaf to be used that matches the tobacco leaf to be replaced from a preset tobacco leaf library to be selected; based on each tobacco leaf to be used, the second tobacco leaf group is obtained.

[0036] Among them, tobacco leaf information refers to characteristic information related to tobacco leaves. Tobacco leaf information at least includes tobacco leaf attributes such as tobacco leaf origin, tobacco leaf variety, tobacco leaf type, growth position, tobacco leaf grade, chemical composition and sensory evaluation attributes. Tobacco leaf origin refers to the specific geographical location where tobacco leaves are planted. Tobacco leaf variety refers to the plant variety of tobacco leaves. Tobacco leaf type can be a classification result based on various conditions such as the processing method and purpose of tobacco leaves. Growth position refers to the growth position of tobacco leaves on tobacco plants, such as upper leaves, middle leaves and lower leaves. Tobacco leaf grade refers to the grouping of tobacco leaves according to their appearance. For example, tobacco leaves of the same variety from the same origin are divided into different groups according to growth position and color, and each group corresponds to a different grade. Sensory evaluation attributes include but are not limited to appearance, aroma, smoke taste, etc. Chemical composition includes but is not limited to nicotine content and reducing sugar content. Reducing sugar content refers to the content of reducing sugars (such as glucose, fructose, etc.) in tobacco leaves.

[0037] In this embodiment, the similarity between each tobacco leaf to be selected and the tobacco leaf to be replaced in the preset tobacco leaf library under different tobacco leaf attributes can be determined according to the tobacco leaf information of the tobacco leaf to be replaced. According to the weights of different tobacco leaf attributes, the similarity between the same tobacco leaf to be selected and the tobacco leaf to be replaced under different tobacco leaf attributes can be processed by weighted mean, and the weighted average value is obtained as the target similarity between the tobacco leaf to be selected and the tobacco leaf to be replaced. Accordingly, the target similarity between each tobacco leaf to be selected and the tobacco leaf to be replaced can be obtained, and the tobacco leaf to be selected corresponding to the maximum target similarity can be used as the tobacco leaf to be used that matches the tobacco leaf to be replaced. Accordingly, the tobacco leaf to be used that matches each tobacco leaf to be replaced can be obtained, and then, all the tobacco leaves to be used are used as a second tobacco leaf group. It should be noted that the weights of different tobacco leaf attributes can be the same or different, and can be determined according to the importance of different tobacco leaf attributes of the tobacco leaf to be replaced, or according to the importance of different tobacco leaf attributes of the cigarette to be processed. The higher the importance, the higher the weight. For example, for Class A heated cigarettes, the higher the importance of the tobacco leaf attributes of aroma and smoke flavor, the higher the corresponding weight. For Class B heated cigarettes, the higher the importance of the tobacco leaf attributes of variety and type, the higher the corresponding weight. Alternatively, according to the priority of different tobacco leaf attributes, tobacco leaves with different tobacco leaf attributes that are completely consistent or highly similar to the tobacco leaves to be replaced can be searched in sequence from the preset tobacco leaf library to be selected as tobacco leaves to be used that match the tobacco leaves to be replaced. For example, first screen tobacco leaves that are completely consistent or highly similar to the variety and type of the tobacco leaves to be replaced, and then select tobacco leaves with the same growth part as the tobacco leaves to be replaced from the screened tobacco leaves until the most similar tobacco leaves are found as the tobacco leaves to be used. Alternatively, if tobacco leaves that are completely consistent with a certain tobacco leaf attribute cannot be found, the range of differences in tobacco leaf attributes can be appropriately relaxed, and the matching requirements for other tobacco leaf attributes can be improved at the same time.

[0038] In this way, the accuracy and efficiency of selecting replacement tobacco leaves can be improved, while ensuring that the quality and taste of the replaced cigarettes are not affected.

[0039] It should be noted that the methods for determining the tobacco leaves to be used that match each tobacco leaf to be replaced can be the same or different. The advantage of using different methods is that the tobacco leaf characteristics of the tobacco leaf to be replaced can be combined to select an appropriate method, which can further improve the accuracy of selecting the replacement tobacco leaf and ensure the quality of the cigarette formula.

[0040] S120, determining the proportion attribute to be adjusted of each tobacco leaf to be used in the second tobacco leaf group.

[0041] The proportion attribute to be adjusted can be used to characterize the weight proportion of the tobacco leaves to be used in the second tobacco leaf group. For example, if the proportion attribute to be adjusted of tobacco leaves A to be used is 20%, it means that tobacco leaves A to be used account for 20% of the total weight of the second tobacco leaf group.

[0042] In this embodiment, when determining the to-be-adjusted proportion attribute of each tobacco leaf to be used in the second tobacco leaf group for the first time, the to-be-adjusted proportion attribute of each tobacco leaf to be used in the second tobacco leaf group may be a random value or a preset value that satisfies a preset proportion constraint. Wherein, the preset proportion constraint refers to the restriction condition that needs to be satisfied during the leaf group proportion adjustment process. For example, the proportion attribute of a certain tobacco leaf satisfies the minimum and maximum proportion ranges (such as 10% to 30%); or, the total proportion of each tobacco leaf to be used in the entire second tobacco leaf group must be 100%; or, the proportion relationship of a specific tobacco leaf combination (such as the ratio of tobacco leaf A and tobacco leaf B must be 1:2), etc. Optionally, the preset proportion constraint condition includes the sum of the to-be-adjusted proportion attribute of each tobacco leaf to be used being a preset threshold value; the preset threshold value may be 100%. When determining the to-be-adjusted proportion attribute of each tobacco leaf to be used in the second tobacco leaf group each time after the first time, the to-be-adjusted proportion attribute of each tobacco leaf to be used in the second tobacco leaf group may be adjusted based on the preset proportion constraint condition to obtain the adjusted proportion attribute of each tobacco leaf to be used. Exemplarily, an initial ratio can be assigned to each tobacco leaf to be used based on historical data or empirical values, as the initial ratio attribute to be adjusted of each tobacco leaf to be used in the second tobacco leaf group, and these ratio attributes to be adjusted meet the preset ratio constraint conditions (such as the total ratio is a preset threshold value). For example, assuming that there are three types of tobacco leaves A, B, and C to be used, the initial ratios can be: A=30%, B=40%, and C=30%. In the process of determining the ratio attribute to be adjusted of each tobacco leaf to be used in the second tobacco leaf group, the ratio attribute to be adjusted of each tobacco leaf to be used can be adjusted according to the preset ratio constraint conditions.

[0043] S130, determining the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the to-be-adjusted proportion attribute of each tobacco leaf to be used, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group.

[0044] Among them, the first thermogravimetric curve prediction model is used to describe the tobacco quality attributes of the first tobacco leaf group at different thermogravimetric analysis times. The second thermogravimetric curve prediction model is used to describe the tobacco quality attributes of the second tobacco leaf group at different thermogravimetric analysis times. The tobacco leaf quality attribute is used to characterize the mass percentage of the tobacco leaf group, that is, the weight proportion of the tobacco leaf group. In other words, the thermogravimetric curve prediction model is used to describe the mass change of the tobacco leaf group during the heating process. The error attribute can be used to characterize the difference between the first tobacco leaf group and the second tobacco leaf group in thermogravimetric analysis. The error attribute can be represented by the area difference between the first thermogravimetric curve prediction model and the second thermogravimetric curve prediction model.

[0045] In this embodiment, a first thermogravimetric curve prediction model of the mass percentage of the first tobacco leaf group at the time of thermogravimetric analysis and a second thermogravimetric curve prediction model of the mass percentage of the second tobacco leaf group at the time of thermogravimetric analysis can be determined. Furthermore, the first thermogravimetric curve prediction model and the second thermogravimetric curve prediction model can be compared to evaluate the performance difference between the two models during the heating process to obtain the error attribute. Alternatively, the adjusted proportion attribute of each tobacco leaf to be used can be input into the second thermogravimetric curve prediction model, the second thermogravimetric curve prediction model can be updated, and the first thermogravimetric curve prediction model and the updated second thermogravimetric curve prediction model can be compared to obtain the error attribute. For example, see Figure 2 , the first thermogravimetric curve prediction model and the second thermogravimetric curve prediction model can be thermogravimetric curves, the horizontal coordinates of the two thermogravimetric curves are both thermogravimetric analysis moments, and the vertical coordinates are both tobacco leaf quality attributes. For the two thermogravimetric curves, the areas formed by the thermogravimetric curves, the horizontal coordinates and the vertical coordinates can be calculated respectively, and accordingly, the areas of the two thermogravimetric curves are obtained, that is, the areas of the first thermogravimetric curve prediction model and the second thermogravimetric curve prediction model are obtained respectively. The difference between the two areas can be used as the error attribute between the first tobacco leaf group and the second tobacco leaf group. Alternatively, the error between the tobacco leaf quality attributes of the two thermogravimetric curves at the same thermogravimetric analysis moment can be calculated point by point, and the sum of all errors or the mean of the sum of errors can be used as the error attribute between the first tobacco leaf group and the second tobacco leaf group.

[0046] In this way, the error attributes between the first tobacco leaf group and the second tobacco leaf group can be accurately determined, so as to optimize and adjust the adjusted proportion attributes of each tobacco leaf to be used in the second tobacco leaf group according to the error attributes to meet the expected thermogravimetric performance requirements.

[0047] In this embodiment, before determining the error attributes between the first tobacco leaf group and the second tobacco leaf group, for each tobacco leaf to be used, a thermogravimetric surface prediction function corresponding to the tobacco leaf to be used can be determined; based on the working temperature of the heating device that heats the cigarettes to be processed, the thermogravimetric surface prediction function is reduced in dimension to obtain a thermogravimetric curve prediction function corresponding to the tobacco leaf to be used; based on each tobacco leaf to be used and its corresponding thermogravimetric curve prediction function, a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group is determined.

[0048] Among them, the heating device is used to heat the cigarettes to be processed, and its category is not limited. The thermogravimetric surface prediction function is used to describe the tobacco quality attributes of the tobacco leaves to be used at different thermogravimetric analysis times and different thermogravimetric analysis temperatures. The thermogravimetric curve prediction function is used to describe the tobacco quality attributes of the tobacco leaves to be used at different thermogravimetric analysis times. It should be noted that due to the different tobacco properties of different tobacco leaves to be used, their thermogravimetric analysis results are also different. Correspondingly, the thermogravimetric surface prediction functions of the tobacco leaves to be used with different tobacco properties are different, and the thermogravimetric curve prediction functions of the tobacco leaves to be used with different tobacco properties are also different. It should also be noted that the method of determining the thermogravimetric curve prediction function corresponding to each tobacco leaf to be used is the same, and it can be introduced by taking the determination of the thermogravimetric curve prediction function corresponding to any one of the tobacco leaves to be used as an example.

[0049] In this embodiment, the tobacco quality attributes of the tobacco to be used at different thermogravimetric analysis times and different thermogravimetric analysis temperatures can be analyzed based on the experimental data of the tobacco to be used, and a thermogravimetric surface prediction function corresponding to the tobacco to be used can be obtained. Furthermore, the operating temperature of the heating device for heating the cigarettes to be processed can be substituted into the thermogravimetric analysis temperature parameter in the thermogravimetric surface prediction function, and the dimension can be reduced to obtain a thermogravimetric curve prediction function corresponding to the tobacco to be used. Furthermore, the proportional attribute of the tobacco to be used can be used as a variable parameter, and the variable parameter can be multiplied with the thermogravimetric curve prediction function corresponding to the tobacco to be used to obtain the curve function to be used corresponding to the tobacco to be used. Accordingly, the curve function to be used corresponding to each tobacco to be used can be obtained, and then all the curve functions to be used are added to obtain a second thermogravimetric curve prediction model corresponding to the second tobacco group.

[0050] Exemplarily, a thermogravimetric surface prediction function of the tobacco leaves to be used is established, taking the tobacco quality attributes of the tobacco leaves to be used as dependent variables, and taking the thermogravimetric analysis time and the thermogravimetric analysis temperature as independent variables.

[0051] The thermogravimetric surface prediction function can be expressed as:

[0052]

[0053] H(x,y) is the quality attribute of the tobacco leaves to be used at the thermogravimetric analysis time x and thermogravimetric analysis temperature y; k 1 -k 14 are all fitting parameters in the thermogravimetric surface prediction function; x is the thermogravimetric analysis time; y is the thermogravimetric analysis temperature. Then, the operating temperature w of the heating device of the cigarette to be processed is substituted into y in H(x,y), and the thermogravimetric surface prediction function is reduced in dimension to obtain the thermogravimetric curve prediction function corresponding to the tobacco leaf to be used.

[0054] The thermogravimetric curve prediction function can be expressed as:

[0055]

[0056] is the tobacco quality attribute of the tobacco to be used at the thermogravimetric analysis time x. At this time, the tobacco quality attribute is the dependent variable and the thermogravimetric analysis time is the independent variable. Further, the proportion attribute of each tobacco leaf to be used can be used as a variable parameter, and the variable parameter can be multiplied by the corresponding thermogravimetric curve prediction function to obtain a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group.

[0057] The second thermogravimetric curve prediction model can be expressed as: F(x) represents the tobacco quality attributes of the second tobacco leaf group at different thermogravimetric analysis times; c i represents the proportion attribute parameter of the i-th tobacco leaf to be used; n represents the number of tobacco leaves to be used; G i (x) represents the prediction function of the thermogravimetric curve of the i-th tobacco leaf to be used.

[0058] The technical solution provided in this embodiment processes the thermogravimetric surface prediction function by considering the working temperature of the heating device for heating the cigarettes to be processed, and obtains a thermogravimetric curve prediction function for describing the quality attributes of the tobacco leaves to be used at different thermogravimetric analysis moments. Then, by combining each tobacco leaf to be used and its corresponding thermogravimetric curve prediction function, a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group is determined to ensure that the second thermogravimetric curve prediction model can accurately describe the mass changes of the second tobacco leaf group in the thermogravimetric analysis, thereby facilitating the subsequent optimization of the tobacco leaf group formula and improving the accuracy of leaf group replacement.

[0059] It should be noted that the method of determining the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group is similar to the method of determining the thermogravimetric curve prediction function corresponding to the tobacco leaves to be used, which will not be elaborated here.

[0060] In this embodiment, based on the to-be-adjusted proportion attribute of each tobacco leaf to be used, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group, the error attribute between the first tobacco leaf group and the second tobacco leaf group is determined, including: determining the first tobacco leaf quality attribute of the first tobacco leaf group at multiple thermogravimetric analysis moments based on the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group; determining the second tobacco leaf quality attribute of the second tobacco leaf group at multiple thermogravimetric analysis moments based on the to-be-adjusted proportion attribute of each tobacco leaf to be used and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group; determining the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the first tobacco leaf quality attribute and the second tobacco leaf quality attribute at multiple thermogravimetric analysis moments.

[0061] The first tobacco leaf quality attribute of the first tobacco leaf group at multiple thermogravimetric analysis moments can be analyzed by the first thermogravimetric curve prediction model. The proportion attribute to be adjusted of each tobacco leaf to be used is input into the proportion attribute parameter of the corresponding tobacco leaf to be used in the second thermogravimetric curve prediction model, and the second thermogravimetric curve prediction model is updated. Based on the updated second thermogravimetric curve prediction model, the second tobacco leaf quality attribute of the second tobacco leaf group at multiple thermogravimetric analysis moments is analyzed. Furthermore, the error value between the first tobacco leaf quality attribute and the second tobacco leaf quality attribute at the same thermogravimetric analysis moment can be calculated, and the error values ​​at all thermogravimetric analysis moments are summed to obtain the error attribute.

[0062] Exemplarily, the absolute error between the first thermogravimetric curve prediction model G1(x) of the first tobacco leaf group and the second thermogravimetric curve prediction model F(x) corresponding to the second tobacco leaf group can be calculated based on formula (1), and the absolute error is trapezoidally integrated over the time interval of the thermogravimetric analysis time to obtain the thermogravimetric prediction curve error area M between the first tobacco leaf group and the second tobacco leaf group as the error attribute. The expression of formula (1) can be expressed as: M = trapz(x, abs(F(x)-G1(x))); wherein x is the thermogravimetric analysis time, indicating the value of the independent variable; abs indicates the absolute value function; the trapz function is a numerical integration function used to calculate the integral based on the provided x and abs(F(x)-G1(x)) values; and M indicates the error attribute.

[0063] In this embodiment, the method for determining the second tobacco leaf quality attribute at multiple thermogravimetric analysis moments can be: for each thermogravimetric analysis moment, the adjusted proportion attribute of each tobacco leaf to be used and the current thermogravimetric analysis moment are input into the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group, and the second tobacco leaf quality attribute of the second tobacco leaf group at the current thermogravimetric analysis moment is obtained.

[0064] For each thermogravimetric analysis moment, the to-be-adjusted proportion attribute of each tobacco leaf to be used can be input into the proportion attribute parameter of the corresponding tobacco leaf to be used in the second thermogravimetric curve prediction model, and the current thermogravimetric analysis moment can be input into the parameter corresponding to the thermogravimetric analysis moment in the second thermogravimetric curve prediction model, and the second tobacco leaf quality attribute of the second tobacco leaf group at the current thermogravimetric analysis moment can be output. In this way, the second tobacco leaf quality attribute of the second tobacco leaf group at each thermogravimetric analysis moment can be determined.

[0065] S140, with the goal of minimizing the error attribute, determine the target proportion attribute of each tobacco leaf to be used in the target tobacco leaf group based on the proportion attribute to be adjusted of each tobacco leaf to be used when the target is reached, and replace the first tobacco leaf group in the cigarette formula to be processed based on the target tobacco leaf group.

[0066] Among them, the target proportion attribute refers to the weight proportion of the tobacco leaves to be used in the target tobacco leaf group.

[0067] In this embodiment, after the error attribute between the first tobacco leaf group and the second tobacco leaf group is determined, the proportion attribute to be adjusted of each tobacco leaf to be used in the second tobacco leaf group can be continuously adjusted with the goal of minimizing the error attribute, so that the error attribute changes until the error attribute reaches the minimum, and it is considered that the goal is achieved. The proportion attribute to be adjusted of each tobacco leaf to be used when the goal is reached can be used as the target proportion attribute of the corresponding tobacco leaf to be used. Each tobacco leaf to be used and its corresponding target proportion attribute constitute a target tobacco leaf group, and then, the target tobacco leaf group can be used to replace the first tobacco leaf group in the formula of the cigarette to be processed, so that after the replacement, the quality performance of the formula of the cigarette to be processed reaches the best. Furthermore, the replaced cigarette formula to be processed can be used to design and produce the leaf group formula of the heated cigarette product to ensure the performance of the cigarette product.

[0068] In order to enable users to more clearly and intuitively compare the differences in tobacco leaf composition before and after the replacement of the to-be-processed cigarette formula, the second weight proportion of each to-be-replaced tobacco leaf in the first tobacco leaf group can also be determined according to the first weight proportion of each to-be-replaced tobacco leaf in the to-be-processed cigarette formula; the second weight proportion of each to-be-replaced tobacco leaf in the first tobacco leaf group and the target proportion attribute of each to-be-used tobacco leaf in the target tobacco leaf group are displayed separately. The first tobacco leaf group is a formula combination of one or more to-be-replaced tobacco leaves.

[0069] Specifically, for each tobacco leaf to be replaced, the first weight proportion of the tobacco leaf to be replaced in the cigarette formula to be processed can be processed based on formula (1) to obtain the second weight proportion of the tobacco leaf to be replaced in the first tobacco leaf group. The expression of formula (1) can be expressed as: Among them, A i is the second weight proportion of the xth tobacco leaf to be replaced in the first tobacco leaf group; B x is the first weight proportion of the xth tobacco leaf to be replaced in the cigarette formula to be processed; B i is the first weight proportion of the i-th tobacco leaf to be replaced in the cigarette formula to be processed; i is the number of tobacco leaves to be replaced; ∑· represents a summation function. Further, the second weight proportion of each tobacco leaf to be replaced in the first tobacco leaf group and the target proportion attribute of each tobacco leaf to be used in the target tobacco leaf group can be displayed differently according to a preset display method, so that the user can intuitively know which tobacco leaves have changed in proportion and the magnitude of the change.

[0070] In this embodiment, with the goal of minimizing the error attribute, the target proportion attribute of each tobacco leaf to be used in the target tobacco leaf group is determined based on the to-be-adjusted proportion attribute of each tobacco leaf to be used when the target is reached, including: if the error attribute does not reach the minimization target, then re-execute the steps of determining the to-be-adjusted proportion attribute of each tobacco leaf to be used in the second tobacco leaf group, the error attribute, and judging whether the target is reached; if the error attribute reaches the minimization target, then the to-be-adjusted proportion attribute of each tobacco leaf to be used when the target is reached is used as the target proportion attribute in the target tobacco leaf group.

[0071] Specifically, it can be determined whether the error attribute reaches the minimum. If it is the minimum, it is considered that the target is reached, and the to-be-adjusted ratio attribute of each tobacco leaf to be used when the target is reached at this time is used as the target ratio attribute of the tobacco leaf to be used in the target tobacco leaf group. If it is not the minimum, it is considered that the target is not reached, and the step S120 is returned to update the to-be-adjusted ratio attribute of each tobacco leaf to be used in the second tobacco leaf group, and based on the updated to-be-adjusted ratio attribute of each tobacco leaf to be used, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group, the error attribute between the first tobacco leaf group and the second tobacco leaf group is determined, and it is further determined whether the error attribute is the minimum, so as to obtain the target ratio attribute of each tobacco leaf to be used in the target tobacco leaf group when the error attribute is the minimum. Exemplarily, the minimum value of the error attribute between the error attributes between the first tobacco leaf group and the second tobacco leaf group is obtained, and the to-be-adjusted ratio attribute of each tobacco leaf to be used corresponding to the minimum value of the error attribute is obtained as the target ratio attribute, so as to perform leaf group formula maintenance of heated cigarette products.

[0072] In this embodiment, the method for determining the proportion attribute to be adjusted of each tobacco leaf to be used in the second tobacco leaf group is: based on the preset proportion constraint condition, adjust the proportion attribute to be adjusted of each tobacco leaf to be used in the second tobacco leaf group to obtain the adjusted proportion attribute to be adjusted of each tobacco leaf to be used.

[0073] Specifically, a step-by-step adjustment method can be adopted to gradually adjust the adjusted proportion attributes of each tobacco leaf to be used in the second tobacco leaf group according to a preset proportion constraint condition and a preset step size, so as to determine the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the adjusted adjusted proportion attributes of each tobacco leaf to be used, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group.

[0074] For example, in the process of determining the proportion attribute to be adjusted of each tobacco leaf to be used in the second tobacco leaf group, the proportion attribute of each tobacco leaf to be used can be set as a variable, such as xA, xB, and xC respectively representing the proportion attributes of tobacco leaves A, B, and C. A proportion attribute to be adjusted of a tobacco leaf to be used that needs to be adjusted (such as xA) can be selected, and its proportion can be gradually increased or decreased according to the preset proportion constraint condition, while adjusting the proportions of other tobacco leaves to keep the total proportion at 100%.

[0075] It should be noted that the objective function can also be defined according to actual needs, so as to adjust the proportion attribute to be adjusted of each tobacco leaf to be used in the second tobacco leaf group according to the objective function and the preset proportion constraint. For example, the objective function can be to minimize the proportion change of a certain tobacco leaf, or to maximize the content of a certain component, or to maximize one or more tobacco leaf attributes. For example, the objective function can be: minimize the total change of the proportion attribute adjustment: min|xA-xA0|+|xB-xB0|+|xC-xC0|(where xA0, xB0, xC0 are the initial proportion attributes). Alternatively, the objective function can be: maximize a certain tobacco leaf attribute: max|a×xA+b×xB+c×xC|(where a, b, c are the tobacco leaf attribute values ​​of each tobacco leaf to be used). The preset proportion constraint is xA+xB+xC=100%. It is also possible to set a preset adjustment ratio range for each tobacco leaf to be used, such as 10% ≤ xA ≤ 30%, 20% ≤ xB ≤ 40%, and 15% ≤ xC ≤ 35%. The advantage of this setting is that it can improve the accuracy of determining the target ratio attribute of each tobacco leaf to be used in the target tobacco leaf group, while ensuring that the adjusted ratio attribute can achieve the expected formula effect, optimize the tobacco leaf group formula, and thus improve the quality of cigarette products.

[0076] The technical solution provided by the embodiment of the present invention determines a first tobacco leaf group to be replaced in a to-be-processed cigarette formula, and determines a second tobacco leaf group based on tobacco leaf information of at least two to-be-replaced tobacco leaves in the first tobacco leaf group, wherein the first tobacco leaf group includes to-be-used tobacco leaves that match each to-be-replaced tobacco leaf; determines the to-be-adjusted proportion attribute of each to-be-used tobacco leaf in the second tobacco leaf group; determines the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the to-be-adjusted proportion attribute of each to-be-used tobacco leaf, a first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group; wherein the first thermogravimetric curve prediction model is used to describe the tobacco quality attributes of the first tobacco leaf group at different thermogravimetric analysis times; with the goal of minimizing the error attribute, determines the to-be-adjusted proportion attribute of each to-be-used tobacco leaf in the target tobacco leaf group based on the to-be-adjusted proportion attribute of each to-be-used tobacco leaf when the target is reached. The target proportion attribute in the leaf group is determined, and the first tobacco leaf group in the cigarette formula to be processed is replaced based on the target tobacco leaf group, which solves the problem in the prior art that the weight proportion of the replacement tobacco leaves cannot be accurately measured. The error attribute between the first tobacco leaf group and the second tobacco leaf group is determined based on the to-be-used tobacco leaf in the second tobacco leaf group The proportion attribute to be adjusted, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group, and the proportion attribute to be adjusted for each tobacco leaf to be used in the second tobacco leaf group is adjusted with the goal of minimizing the error attribute. When the goal of minimizing the error attribute is reached, the target proportion attribute of each tobacco leaf to be used in the target tobacco leaf group is obtained, which improves the accuracy of determining the weight proportion of the replacement tobacco leaves. At the same time, the efficiency and accuracy of tobacco leaf replacement are improved, and the quality of the cigarette formula is guaranteed, thereby ensuring the quality stability of cigarette products.

[0077] Embodiment 2

[0078] As an optional embodiment of the above embodiment, in order to make those skilled in the art further understand the technical solution of the embodiment of the present invention, a specific application scenario example is given. For details, please refer to the following specific content.

[0079] Assume that the first tobacco leaf group consists of two tobacco leaves A and B to be replaced in 2022, and the mass percentages of A and B in the tobacco leaves to be replaced (i.e., the second weight proportion) are 60% and 40% respectively; based on the two tobacco leaves A and B to be replaced, select two tobacco leaves C and D to be used in 2023 according to the same tobacco leaf attributes such as origin, variety, type, and part. Then, the thermogravimetric surface prediction functions H(x, y) corresponding to the first tobacco leaf group AB, the tobacco leaves C to be used, and the tobacco leaves D to be used are respectively established, where x is the thermogravimetric analysis time, and the analysis time range is 3min to 10min; y is the thermogravimetric analysis temperature, and the analysis temperature range is 200℃ to 350℃. The working temperature of the heating device for heating cigarette products (i.e., cigarettes to be processed) is 290℃. According to the working temperature, the dimension of each thermogravimetric surface prediction function is reduced to obtain the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group AB, and the thermogravimetric curve prediction functions corresponding to the tobacco leaves C to be used and the tobacco leaves D to be used are respectively obtained: G(x) AB 、G(x) C 、G(x) D .in,

[0080] G(x) AB =0.2199×x 3 +0.170415×x 2 -14.482943×x+100.02652;

[0081] G(x) C =0.1432×x 3 +0.38413×x 2 -14.555845×x+99.996024;

[0082] G(x) D =0.3305×x 3 -0.224592×x 2 -13.960952×x+99.908698;

[0083] Further, the tobacco leaves to be used are combined into a second tobacco leaf group, C 1 , C 2 are the proportion attributes of the tobacco leaves C and D to be used in the second tobacco leaf group to be adjusted, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group is expressed as: F(x) CD =C 1 ×G(x) C +C 2 ×G(x) D Calculate the first thermogravimetric curve prediction model G(x) of the first tobacco leaf group AB The second thermogravimetric curve prediction model F(x) of the second tobacco leaf group CDThe absolute error between the two groups is calculated by trapezoidal integration of the absolute error over the analysis time range at the time of thermogravimetric analysis, thereby obtaining the error attribute M between the first tobacco leaf group and the second tobacco leaf group: M = trapz(x, abs(F(x) CD -G(x) AB )) Obtain the minimum error attribute value M between the first tobacco leaf group and the second tobacco leaf group min It is 0.29945. The corresponding minimum value is 62.6% for the proportion attribute of tobacco leaf C to be used, and 37.4% for the proportion attribute of tobacco leaf D to be used. The first tobacco leaf group AB is replaced by the second tobacco leaf group CD, and the leaf group formula design and production are carried out.

[0084] To give another example, suppose that the first tobacco leaf group consists of three tobacco leaves E, F, and G to be replaced in 2022. Based on the three tobacco leaves to be replaced, three tobacco leaves H, I, and J to be used in 2023 are selected according to the tobacco leaf properties such as the same origin, variety, type, and part. Furthermore, the thermogravimetric surface prediction functions H(x, y) of the first tobacco leaf group EFG, tobacco leaves H to be used, tobacco leaves I to be used, and tobacco leaves J to be used are respectively established, where x is the thermogravimetric analysis time, and the analysis time range is 3min to 10min; y is the thermogravimetric analysis temperature, and the analysis temperature range is 200℃ to 350℃. The operating temperature of the heating device for heating cigarette products (i.e., cigarettes to be processed) is 260℃. According to the operating temperature, the dimension of each thermogravimetric surface prediction function is reduced, and the thermogravimetric curve prediction functions of the tobacco mass percentage fraction of the first tobacco leaf group EFG, tobacco leaves H to be used, tobacco leaves I to be used, and tobacco leaves J to be used at the thermogravimetric analysis time are obtained: G(x) EFG 、G(x) H 、G(x) I 、G(x) J .in,

[0085] G(x) EFG =-0.1391×x 3 +1.8145×x 2 -16.5097×x+99.79779;

[0086] G(x) H =-0.155×x 3 +1.81512×x 2 -16.5094×x+99.89763;

[0087] G(x) I =-0.03488×x 3 +1.3332×x 2 -15.984×x+99.7083;

[0088] G(x) J =-0.1248×x 3 +1.61676×x 2 -15.9254×x+99.79464;

[0089] Further, the tobacco leaves to be used are combined into a second tobacco leaf group, C 1 , C 2 , C 3 are the properties of the tobacco leaf H to be used, the tobacco leaf J to be used and the proportion of the tobacco leaf J to be used in the second tobacco leaf group to be adjusted. The second thermogravimetric curve prediction model corresponding to the second tobacco leaf group is expressed as:

[0090] F(x) HIJ =C 1 G(x) H +C 2 G(x) I +C 3 G(x) J Calculate the first thermogravimetric curve prediction model G(x) of the first tobacco leaf group EFG Thermogravimetric prediction curve F(x) of the second tobacco leaf group HIJ The absolute error between the first and second tobacco leaf groups is calculated by performing trapezoidal integration on the analysis time range of the thermogravimetric analysis to obtain the error attribute M between the first and second tobacco leaf groups:

[0091] M=trapz(x,abs(F(x) HIJ -G(x) EFG )) Obtain the minimum error attribute value M between the first tobacco leaf group and the second tobacco leaf group min It is 0.42768. The corresponding minimum value is 30.5% for the proportion attribute of tobacco leaf H to be used, 25.5% for tobacco leaf I to be used, and 44.0% for tobacco leaf J to be used. The second tobacco leaf group EFG replaces the first tobacco leaf group HIJ, and the leaf group formula is designed and produced.

[0092] To give another example, suppose that the first tobacco leaf group consists of four tobacco leaves K, L, M, and N to be replaced in 2022. Based on the four tobacco leaves to be replaced, four tobacco leaves P, Q, R, and S to be used in 2023 are selected according to the tobacco leaf attributes such as the same origin, variety, type, and part. Then, the thermogravimetric surface prediction functions H(x, y) of the first tobacco leaf group KLMN, tobacco leaves P to be used, tobacco leaves Q to be used, tobacco leaves R to be used, and tobacco leaves S to be used are established respectively, where x is the thermogravimetric analysis time, and the analysis time range is 3min to 10min; y is the thermogravimetric analysis temperature, and the analysis temperature range is 200℃ to 350℃. The working temperature of the heating device for heating cigarette products (i.e., cigarettes to be processed) is 230℃. According to the working temperature, the dimension of each thermogravimetric surface prediction function is reduced to obtain the thermogravimetric curve prediction functions of the first tobacco leaf group KLMN, tobacco leaves P to be used, tobacco leaves Q to be used, tobacco leaves R to be used, and tobacco leaves S to be used: G(x) KLMN 、G(x) P 、G(x) Q 、G(x) R 、G(x) S .in,

[0093] G(x) KLMN =-0.1576×x 3 +3.432463×x 2 -20.9476×x+99.7904;

[0094] G(x) P =-0.09269×x 3 +3.375525×x 2 -21.04605×x+99.80494;

[0095] G(x) Q =-0.1887×x 3 +3.38821×x 2 -20.7143×x+99.67705;

[0096] G(x) R =-0.2183×x 3 +3.41466×x 2 -20.7848×x+99.838;

[0097] G(x) S =-0.09477×x 3 +3.37229×x 2 -20.8609×x+99.5989;

[0098] Further, the tobacco leaves to be used are combined into a second tobacco leaf group, C1 , C 2 , C 3 , C 4 are the proportion attributes to be adjusted of the tobacco leaves P, Q, R and S in the second tobacco leaf group, respectively. The second thermogravimetric curve prediction model corresponding to the second tobacco leaf group is expressed as: F(x) PQRS =C 1 G(x) P +C 2 G(x) Q +C 3 G(x) R +C 4 G(x) S Calculate the first thermogravimetric curve prediction model G(x) of the first tobacco leaf group KLMN The second thermogravimetric curve prediction model F(x) of the second tobacco leaf group PQRS The absolute error between the two groups is calculated by trapezoidal integration of the absolute error over the analysis time range at the time of thermogravimetric analysis, thereby obtaining the error attribute M between the first tobacco leaf group and the second tobacco leaf group: M = trapz(x, abs(F(x) KLMN -G(x) PQRS )) Obtain the minimum error attribute value M between the first tobacco leaf group and the second tobacco leaf group min It is 0.027918. The corresponding minimum value is 33.6% for the proportion attribute of tobacco leaves P to be used, 16.3% for tobacco leaves Q to be used, 33.6% for tobacco leaves R to be used, and 16.5% for tobacco leaves S to be used. The first tobacco leaf group KLMN is replaced by the second tobacco leaf group PQRS, and the leaf group formula is designed and produced.

[0099] The technical solution of this embodiment determines the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the attribute of the to-be-used proportion in the second tobacco leaf group to be adjusted, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group, and adjusts the attribute of the to-be-used proportion in the second tobacco leaf group to minimize the error attribute until the goal of minimizing the error attribute is reached, thereby obtaining the target proportion attribute of each tobacco leaf to be used in the target tobacco leaf group, thereby improving the accuracy of determining the weight proportion of the replacement tobacco leaves, and at the same time, improving the efficiency and accuracy of tobacco leaf replacement, ensuring the quality of the cigarette formula, and thus ensuring the quality stability of the cigarette products.

[0100] Embodiment 3

[0101] Figure 3 1 is a schematic diagram of the structure of a device for determining a replacement leaf group in a cigarette formula according to Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a second tobacco leaf group determination module 210, a to-be-adjusted ratio attribute determination module 220, an error attribute determination module 230 and an error attribute minimization module 240.

[0102] Among them, the second tobacco leaf group determination module 210 is used to determine the first tobacco leaf group to be replaced in the to-be-processed cigarette formula, and determine the second tobacco leaf group based on the tobacco leaf information of at least two to-be-replaced tobacco leaves in the first tobacco leaf group, wherein the first tobacco leaf group includes to-be-used tobacco leaves that match each of the to-be-replaced tobacco leaves; the to-be-adjusted proportion attribute determination module 220 is used to determine the to-be-adjusted proportion attribute of each of the to-be-used tobacco leaves in the second tobacco leaf group; the error attribute determination module 230 is used to determine the to-be-adjusted proportion attribute of each of the to-be-used tobacco leaves in the second tobacco leaf group based on the to-be-adjusted proportion attribute of each of the to-be-used tobacco leaves, the first thermogravimetric curve prediction module corresponding to the first tobacco leaf group, and the error attribute determination module 231. model and a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group, to determine the error attribute between the first tobacco leaf group and the second tobacco leaf group; wherein the first thermogravimetric curve prediction model is used to describe the tobacco quality attributes of the first tobacco leaf group at different thermogravimetric analysis moments; an error attribute minimization module 240 is used to determine the target proportion attribute of each of the tobacco leaves to be used in the target tobacco leaf group based on the proportion attribute to be adjusted of each of the tobacco leaves to be used when the target is reached, with the goal of minimizing the error attribute, and replace the first tobacco leaf group in the cigarette formula to be processed based on the target tobacco leaf group.

[0103] The technical solution of this embodiment is to determine the first tobacco leaf group to be replaced in the formula of the cigarette to be processed, and determine the second tobacco leaf group based on the tobacco leaf information of at least two tobacco leaves to be replaced in the first tobacco leaf group, wherein the first tobacco leaf group includes tobacco leaves to be used that match each tobacco leaf to be replaced; determine the to-be-adjusted proportion attribute of each tobacco leaf to be used in the second tobacco leaf group; determine the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the to-be-adjusted proportion attribute of each tobacco leaf to be used, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group; wherein the first thermogravimetric curve prediction model is used to describe the tobacco quality attributes of the first tobacco leaf group at different thermogravimetric analysis times; with the goal of minimizing the error attribute, determine the to-be-adjusted proportion attribute of each tobacco leaf to be used in the target tobacco leaf group based on the to-be-adjusted proportion attribute of each tobacco leaf to be used when the goal is reached. The target proportion attribute in the target tobacco group is replaced by the first tobacco group in the cigarette formula to be processed, which solves the problem that the weight proportion of the replacement tobacco leaves cannot be accurately measured in the prior art, and realizes that the error attribute between the first tobacco leaf group and the second tobacco leaf group is determined based on the to-be-adjusted proportion attribute of each tobacco leaf to be used in the second tobacco leaf group, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group. The to-be-adjusted proportion attribute of each tobacco leaf to be used in the second tobacco leaf group is adjusted with the goal of minimizing the error attribute, until the goal of minimizing the error attribute is reached, and the target proportion attribute of each tobacco leaf to be used in the target tobacco leaf group is obtained, which improves the accuracy of determining the weight proportion of the replacement tobacco leaves, and at the same time, improves the efficiency and accuracy of tobacco leaf replacement, ensures the quality of the cigarette formula, and thus ensures the quality stability of cigarette products.

[0104] On the basis of the above device, optionally, the second tobacco leaf group determination module 210 includes:

[0105] a tobacco leaf determination unit for determining, for each tobacco leaf to be replaced in the first tobacco leaf group, a tobacco leaf to be used that matches the tobacco leaf to be replaced from a preset tobacco leaf library based on tobacco leaf information of the tobacco leaf to be replaced;

[0106] The second tobacco leaf group determination unit is used to obtain the second tobacco leaf group based on each of the tobacco leaves to be used; wherein the tobacco leaf information at least includes the tobacco leaf origin, tobacco leaf variety, tobacco leaf type, growing position, tobacco leaf grade, chemical composition and sensory evaluation attributes.

[0107] On the basis of the above device, optionally, the device further includes:

[0108] A thermogravimetric surface prediction function determination unit is used to determine, for each of the tobacco leaves to be used, a thermogravimetric surface prediction function corresponding to the tobacco leaves to be used; wherein the thermogravimetric surface prediction function is used to describe the tobacco quality attributes of the tobacco leaves to be used at different thermogravimetric analysis times and different thermogravimetric analysis temperatures;

[0109] A thermogravimetric curve prediction function determination unit, used to reduce the dimension of the thermogravimetric surface prediction function based on the working temperature of the heating device for heating the cigarette to be processed, so as to obtain a thermogravimetric curve prediction function corresponding to the tobacco to be used; wherein the thermogravimetric curve prediction function is used to describe the tobacco quality attributes of the tobacco to be used at different thermogravimetric analysis times;

[0110] The second thermogravimetric curve prediction model determining unit is used to determine a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group based on each of the tobacco leaves to be used and the corresponding thermogravimetric curve prediction function.

[0111] Based on the above device, optionally, the error attribute determination module 230 includes:

[0112] A first tobacco leaf quality attribute determination unit, configured to determine a first tobacco leaf quality attribute of the first tobacco leaf group at a plurality of thermogravimetric analysis moments based on a first thermogravimetric curve prediction model corresponding to the first tobacco leaf group;

[0113] A second tobacco leaf quality attribute determination unit, configured to determine the second tobacco leaf quality attribute of the second tobacco leaf group at a plurality of the thermogravimetric analysis moments based on the to-be-adjusted proportion attribute of each of the to-be-used tobacco leaves and a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group;

[0114] An error attribute determination unit is used to determine the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the first tobacco leaf quality attribute and the second tobacco leaf quality attribute at multiple thermogravimetric analysis moments.

[0115] On the basis of the above-mentioned device, optionally, a second tobacco leaf quality attribute determination unit is used to input the adjusted proportion attribute of each of the tobacco leaves to be used and the current thermogravimetric analysis moment into a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group for each of the thermogravimetric analysis moments, so as to obtain the second tobacco leaf quality attribute of the second tobacco leaf group at the current thermogravimetric analysis moment.

[0116] Based on the above device, optionally, the error attribute minimization module 240 includes:

[0117] A first minimization unit, configured to re-execute the steps of determining the to-be-adjusted proportion attribute and the error attribute of each of the to-be-used tobacco leaves in the second tobacco leaf group and judging whether the target is reached if the error attribute does not reach the minimization target;

[0118] The second minimization unit is used to use the to-be-adjusted proportion attribute of each of the to-be-used tobacco leaves when the target is reached as the target proportion attribute in the target tobacco leaf group if the error attribute reaches the minimization target.

[0119] On the basis of the above-mentioned device, optionally, a module 220 for determining the attribute to be adjusted ratio is used to adjust the attribute to be adjusted ratio of each of the tobacco leaves to be used in the second tobacco leaf group based on a preset ratio constraint condition to obtain the adjusted attribute to be adjusted ratio of each of the tobacco leaves to be used after adjustment; wherein the preset ratio constraint condition includes the sum of the attribute to be adjusted ratio of each of the tobacco leaves to be used being a preset threshold value.

[0120] The device for determining a replacement leaf group in a cigarette formula provided by an embodiment of the present invention can execute the method for determining a replacement leaf group in a cigarette formula provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0121] Embodiment 4

[0122] Figure 4 1 is a schematic diagram of the structure of an electronic device for implementing a method for determining a replacement leaf group in a cigarette recipe of an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0123] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0124] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0125] The processor 11 may be a variety of general and / or dedicated processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining a replacement leaf group in a cigarette recipe.

[0126] In some embodiments, the method for determining a replacement leaf group in a cigarette recipe may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining a replacement leaf group in a cigarette recipe described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method for determining a replacement leaf group in a cigarette recipe in any other appropriate manner (e.g., by means of firmware).

[0127] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0129] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0131] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0132] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0133] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for determining a replacement leaf group in a cigarette recipe as provided in any embodiment of the present invention.

[0134] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, 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 can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0135] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0136] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining a replacement leaf group in a cigarette formulation, characterized in that: include: Determine a first tobacco leaf group to be replaced in the cigarette formula to be processed, and determine a second tobacco leaf group based on tobacco leaf information of at least two tobacco leaves to be replaced in the first tobacco leaf group, wherein the first tobacco leaf group includes tobacco leaves to be used that match each of the tobacco leaves to be replaced; Determining a proportion attribute to be adjusted of each of the tobacco leaves to be used in the second tobacco leaf group; Determine the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the to-be-adjusted proportion attribute of each of the tobacco leaves to be used, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group; wherein the first thermogravimetric curve prediction model is used to describe the tobacco leaf quality attributes of the first tobacco leaf group at different thermogravimetric analysis times; With the goal of minimizing the error attribute, based on the adjusted proportion attribute of each of the tobacco leaves to be used when the target is reached, the target proportion attribute of each of the tobacco leaves to be used in the target tobacco leaf group is determined, and the first tobacco leaf group in the cigarette formula to be processed is replaced based on the target tobacco leaf group.

2. The method according to claim 1, characterized in that The determining of the second tobacco leaf group based on tobacco leaf information of at least two tobacco leaves to be replaced in the first tobacco leaf group includes: For each tobacco leaf to be replaced in the first tobacco leaf group, based on the tobacco leaf information of the tobacco leaf to be replaced, determining a tobacco leaf to be used that matches the tobacco leaf to be replaced from a preset tobacco leaf library to be selected; Based on each of the tobacco leaves to be used, a second tobacco leaf group is obtained; The tobacco leaf information includes at least the tobacco leaf origin, tobacco leaf variety, tobacco leaf type, growing part, tobacco leaf grade, chemical composition and sensory evaluation attributes.

3. The method according to claim 1, characterized in that Before determining the error attribute between the first tobacco leaf group and the second tobacco leaf group, the method further includes: For each of the tobacco leaves to be used, determining a thermogravimetric surface prediction function corresponding to the tobacco leaves to be used; wherein the thermogravimetric surface prediction function is used to describe the tobacco quality attributes of the tobacco leaves to be used at different thermogravimetric analysis times and different thermogravimetric analysis temperatures; Based on the working temperature of the heating device for heating the cigarette to be processed, the thermogravimetric surface prediction function is reduced in dimension to obtain a thermogravimetric curve prediction function corresponding to the tobacco to be used; wherein the thermogravimetric curve prediction function is used to describe the tobacco quality attributes of the tobacco to be used at different thermogravimetric analysis times; Based on each of the tobacco leaves to be used and the corresponding thermogravimetric curve prediction function, a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group is determined.

4. The method according to claim 1, characterized in that: The step of determining the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the to-be-used tobacco leaf's to-be-adjusted proportion attribute, the first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group comprises: determining first tobacco leaf quality attributes of the first tobacco leaf group at multiple thermogravimetric analysis moments based on a first thermogravimetric curve prediction model corresponding to the first tobacco leaf group; Determining the second tobacco leaf quality attribute of the second tobacco leaf group at a plurality of the thermogravimetric analysis moments based on the to-be-adjusted proportion attribute of each of the to-be-used tobacco leaves and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group; Based on the first tobacco leaf quality attributes and the second tobacco leaf quality attributes at multiple thermogravimetric analysis moments, an error attribute between the first tobacco leaf group and the second tobacco leaf group is determined.

5. The method according to claim 4, characterized in that The determining of the second tobacco leaf quality attributes of the second tobacco leaf group at a plurality of thermogravimetric analysis moments based on the to-be-adjusted proportion attribute of each of the to-be-used tobacco leaves and the second thermogravimetric curve prediction model corresponding to the second tobacco leaf group comprises: For each of the thermogravimetric analysis moments, the adjusted proportion attributes of each of the tobacco leaves to be used and the current thermogravimetric analysis moment are input into a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group to obtain the second tobacco leaf quality attributes of the second tobacco leaf group at the current thermogravimetric analysis moment.

6. The method according to claim 1, characterized in that The method of minimizing the error attribute as a goal and determining a target proportion attribute of each of the tobacco leaves to be used in the target tobacco leaf group based on the proportion attribute to be adjusted of each of the tobacco leaves to be used when the target is reached includes: If the error attribute does not reach the minimization target, re-execute the steps of determining the to-be-adjusted proportion attribute and the error attribute of each of the to-be-used tobacco leaves in the second tobacco leaf group and judging whether the target is reached; If the error attribute reaches the minimization target, the to-be-adjusted proportion attribute of each of the to-be-used tobacco leaves when the target is reached is used as the target proportion attribute in the target tobacco leaf group.

7. The method according to claim 1 or 6, characterized in that: The determining of the to-be-adjusted proportion attribute of each of the to-be-used tobacco leaves in the second tobacco leaf group includes: Based on a preset proportion constraint condition, adjusting the proportion attribute to be adjusted of each of the tobacco leaves to be used in the second tobacco leaf group to obtain an adjusted proportion attribute to be adjusted of each of the tobacco leaves to be used; The preset proportion constraint condition includes that the sum of the proportion attributes to be adjusted of each of the tobacco leaves to be used is a preset threshold.

8. A device for determining a replacement leaf group in a cigarette formula, characterized in that: include: A second tobacco leaf group determination module is used to determine a first tobacco leaf group to be replaced in a to-be-processed cigarette formula, and determine a second tobacco leaf group based on tobacco leaf information of at least two to-be-replaced tobacco leaves in the first tobacco leaf group, wherein the first tobacco leaf group includes to-be-used tobacco leaves that match each of the to-be-replaced tobacco leaves; A module for determining the attribute of the proportion to be adjusted, used for determining the attribute of the proportion to be adjusted of each of the tobacco leaves to be used in the second tobacco leaf group; an error attribute determination module, for determining the error attribute between the first tobacco leaf group and the second tobacco leaf group based on the to-be-adjusted proportion attribute of each of the tobacco leaves to be used, a first thermogravimetric curve prediction model corresponding to the first tobacco leaf group, and a second thermogravimetric curve prediction model corresponding to the second tobacco leaf group; wherein the first thermogravimetric curve prediction model is used to describe the tobacco leaf quality attributes of the first tobacco leaf group at different thermogravimetric analysis times; An error attribute minimization module is used to determine the target proportion attribute of each of the tobacco leaves to be used in the target tobacco leaf group based on the adjusted proportion attribute of each of the tobacco leaves to be used when the target is reached, with the goal of minimizing the error attribute, and replace the first tobacco leaf group in the cigarette formula to be processed based on the target tobacco leaf group.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory in communication with the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, wherein the computer program is executed by the at least one processor so as to enable the at least one processor to perform the method for determining a replacement leaf group in a cigarette formulation according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining a replacement leaf group in a cigarette recipe according to any one of claims 1 to 7 when executed.

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