Tobacco product processing method and device and medium

Abstract

The invention relates to a tobacco product processing method and device and a medium, and relates to the technical field of tobacco production. The method comprises the following steps: determining a first processing path corresponding to each tobacco product raw material according to first thermogravimetric analysis data of each tobacco product raw material in a plurality of tobacco product raw materials for preparing a target tobacco product; according to the historical probability that each tobacco product raw material is processed by adopting the first processing path in the historical processing process of the target tobacco product, determining a first tobacco product raw material of which the processing path needs to be replaced in the multiple tobacco product raw materials; and distributing the first tobacco product raw material to a second processing path different from the first processing path for processing, and distributing a second tobacco product raw material except the first tobacco product raw material in the plurality of tobacco product raw materials to the first processing path for processing to prepare the target tobacco product. According to the technical scheme, the sensory quality of the prepared tobacco product can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of tobacco production technology, and in particular to a method, apparatus and computer-readable storage medium for processing tobacco products. Background Technology

[0002] The processing of tobacco products is a complex, multi-step industrial process aimed at transforming tobacco raw materials (such as tobacco leaves) into various tobacco products (such as cigarettes) for consumer use. The grouping and processing of tobacco raw materials is a technological process.

[0003] Based on the different characteristics of the raw materials used in the preparation of tobacco products, the technology of dividing tobacco product raw materials into different groups for targeted processing is called group processing of tobacco product raw materials. Through scientific and accurate group processing, it can be ensured that each tobacco product raw material has predictable and controllable sensory properties before entering the formulation, thus laying the foundation for the precise blending and style construction of tobacco products.

[0004] Therefore, in the production process of tobacco products, how to efficiently and accurately group and process the raw materials used to prepare tobacco products is of great significance for improving the sensory quality of tobacco products. Summary of the Invention

[0005] According to some embodiments of this disclosure, a method for processing tobacco products is provided, comprising: determining a first processing path corresponding to each tobacco product raw material based on first thermogravimetric analysis data of each of a plurality of tobacco product raw materials used to prepare a target tobacco product, wherein the first thermogravimetric analysis data indicates the relationship between the mass change rate of each tobacco product raw material and temperature during pyrolysis; determining a first tobacco product raw material among the plurality of tobacco product raw materials that needs to have its processing path replaced based on the historical probability of each tobacco product raw material being processed using the first processing path in the historical processing of the target tobacco product; assigning the first tobacco product raw material to a second processing path different from the first processing path for processing, and assigning a second tobacco product raw material among the plurality of tobacco product raw materials other than the first tobacco product raw material to the first processing path for processing, so as to prepare the target tobacco product.

[0006] In some embodiments, determining the first tobacco product raw material whose processing path needs to be replaced among the plurality of tobacco product raw materials based on the historical probability of each tobacco product raw material being processed using the first processing path in the historical processing of the target tobacco product includes: sorting the plurality of tobacco product raw materials in ascending order of the historical probability to obtain a sorting result; selecting each tobacco product raw material among the plurality of tobacco product raw materials as candidate tobacco product raw materials according to the sorting result, and performing processing path replacement processing on the candidate tobacco product raw materials, the replacement processing including replacing the processing path of the candidate tobacco product raw materials from the first processing path to the second processing path; after each replacement processing, determining whether to stop the replacement processing based on the second thermogravimetric analysis data of the candidate tobacco product raw materials processed through the second processing path; and determining the candidate tobacco product raw materials whose processing paths have been replaced before stopping the replacement processing as the first tobacco product raw material.

[0007] In some embodiments, determining whether to stop the replacement process includes: determining whether to stop the replacement process based on a first difference between the second thermogravimetric analysis data and the third thermogravimetric analysis data of other tobacco product raw materials (excluding the candidate tobacco product raw material) processed through the first processing path.

[0008] In some embodiments, determining whether to stop the replacement process includes stopping the replacement process in response to a first difference between the second thermogravimetric analysis data and the third thermogravimetric analysis data satisfying a preset range corresponding to the size category to which the target tobacco product belongs.

[0009] In some embodiments, the method further includes: acquiring sample thermogravimetric analysis data for each of a plurality of tobacco product samples, each tobacco product sample being made from a first tobacco product raw material sample processed via the first processing path and a second tobacco product raw material sample processed via the second processing path, the sample thermogravimetric analysis data being determined based on a second difference between the first sample raw material thermogravimetric analysis data of the first tobacco product raw material sample and the second sample raw material thermogravimetric analysis data of the second tobacco product raw material sample; processing the sample thermogravimetric analysis data using a trained machine learning model to classify the plurality of tobacco product samples into a plurality of size categories, wherein tobacco product samples in the same size category have the same size, and tobacco product samples in different size categories have different sizes; and determining a preset range corresponding to each size category based on the second difference corresponding to the sample thermogravimetric analysis data of the tobacco product samples in each of the plurality of size categories.

[0010] In some embodiments, the lower limit of the preset range corresponding to each size category is the minimum value of the second difference corresponding to the sample thermogravimetric analysis data of the tobacco product samples in each size category, and the upper limit of the preset range corresponding to each size category is the maximum value of the second difference corresponding to the sample thermogravimetric analysis data of the tobacco product samples in each size category.

[0011] In some embodiments, the number of the first tobacco product raw material samples is the same as the number of the second tobacco product raw material samples.

[0012] In some embodiments, the plurality of size categories include standard support, medium support, and fine support.

[0013] In some embodiments, the first thermogravimetric analysis data includes multiple pyrolysis temperatures of each tobacco product raw material during the pyrolysis process and the mass change rate of each tobacco product raw material corresponding to each of the multiple pyrolysis temperatures, wherein the multiple pyrolysis temperatures form multiple temperature ranges. Determining the first processing path corresponding to each tobacco product raw material based on the first thermogravimetric analysis data of each of the multiple tobacco product raw materials used to prepare the target tobacco product includes: determining a characteristic temperature range from the multiple temperature ranges according to the quality grade of the target tobacco product; determining the mass change of each tobacco product raw material within the characteristic temperature range based on characteristic data corresponding to the characteristic temperature range, wherein the characteristic data includes a characteristic temperature within the characteristic temperature range and a mass change rate corresponding to the characteristic temperature; and determining the first processing path corresponding to each tobacco product raw material based on the quality grade and the mass change.

[0014] In some embodiments, determining a characteristic temperature range from the plurality of temperature ranges based on the quality grade of the target tobacco product includes: determining a first temperature range from the plurality of temperature ranges as the characteristic temperature range in response to the quality grade being a first quality grade, the first temperature range being associated with the combustion characteristics of each tobacco product raw material; and determining a second temperature range from the plurality of temperature ranges as the characteristic temperature range in response to the quality grade being a second quality grade lower than the first quality grade, the second temperature range being associated with the aroma volatility characteristics of each tobacco product raw material.

[0015] In some embodiments, the thermogravimetric curve formed from the first thermogravimetric analysis data of each tobacco product raw material includes a peak associated with combustion characteristics and a peak associated with aroma volatility characteristics, wherein the first temperature range covers the peak associated with combustion characteristics and the second temperature range covers the peak associated with aroma volatility characteristics.

[0016] In some embodiments, determining the mass change of each tobacco product raw material within the characteristic temperature range based on the characteristic data corresponding to the characteristic temperature range includes: determining the mass change of each tobacco product raw material within the characteristic temperature range based on the area of ​​the thermogravimetric analysis curve formed by the characteristic data.

[0017] In some embodiments, the processing intensity of the first processing path and the processing intensity of the second processing path are different.

[0018] In some embodiments, the first processing path includes one of airflow machining and roller machining, and the second processing path includes the other of the airflow machining and roller machining.

[0019] According to some embodiments of this disclosure, a tobacco product processing apparatus is provided, comprising: a first determining module configured to determine a first processing path corresponding to each tobacco product raw material based on first thermogravimetric analysis data of each of a plurality of tobacco product raw materials used to prepare a target tobacco product, wherein the first thermogravimetric analysis data indicates the relationship between the mass change rate of each tobacco product raw material and temperature during pyrolysis; a second determining module configured to determine a first tobacco product raw material among the plurality of tobacco product raw materials that needs to have its processing path replaced based on the historical probability of each tobacco product raw material being processed using the first processing path in the historical processing of the target tobacco product; and a processing module configured to assign the first tobacco product raw material to a second processing path different from the first processing path for processing, and assign the second tobacco product raw material other than the first tobacco product raw material among the plurality of tobacco product raw materials to the first processing path for processing, so as to prepare the target tobacco product.

[0020] According to further embodiments of the present disclosure, a tobacco product processing apparatus is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute the tobacco product processing method of any of the above embodiments based on instructions stored in the memory device.

[0021] According to further embodiments of the present disclosure, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the tobacco product processing method of any of the above embodiments.

[0022] According to further embodiments of this disclosure, a computer program product is also provided, including instructions that, when executed by a processor, cause the processor to perform a method for processing tobacco products according to any of the foregoing embodiments.

[0023] In the above embodiments, the corresponding processing path is initially determined using thermogravimetric analysis data of tobacco product raw materials. Based on this, and combined with the historical probability of using this processing path in the historical processing of tobacco product raw materials in the target tobacco product, the initially determined processing path is dynamically optimized and adjusted. In this way, by combining the pyrolysis characteristics of tobacco product raw materials with experience from historical processing, the scientific nature and accuracy of processing path selection are improved, thereby enhancing the sensory quality of the finished tobacco product. Attached Figure Description

[0024] The accompanying drawings, which form part of this specification, illustrate embodiments of this disclosure and, together with the specification, serve to explain the principles of this disclosure.

[0025] This disclosure will become clearer with reference to the accompanying drawings and the following detailed description, wherein:

[0026] Figure 1 A flowchart illustrating a method according to some embodiments of the present disclosure is shown;

[0027] Figure 2 A flowchart illustrating how step 120 is implemented according to some embodiments of the present disclosure;

[0028] Figure 3 A flowchart illustrating a method for determining a preset range according to some embodiments of the present disclosure;

[0029] Figure 4 A flowchart illustrating a method for determining a first processing path in step 110 according to some embodiments of the present disclosure;

[0030] Figure 5 A block diagram showing an apparatus for processing tobacco products according to some embodiments of the present disclosure;

[0031] Figure 6 A block diagram illustrating a processing apparatus for tobacco articles according to other embodiments of the present disclosure;

[0032] Figure 7 A block diagram of a processing apparatus for tobacco products according to some embodiments of the present disclosure is shown. Detailed Implementation

[0033] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0034] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0035] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0036] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0037] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0038] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0039] In related technologies, the main approach is to combine the sensory evaluation results of formulators to select suitable processing paths for each tobacco product raw material used in the preparation of tobacco products, and to combine the processed tobacco product raw materials with similar or related characteristics to prepare tobacco products.

[0040] In this approach, on the one hand, organizing formulators to conduct sensory evaluations of tobacco product raw materials requires a significant amount of time, leading to low efficiency in group processing; on the other hand, the sensory evaluation results of formulators are highly subjective, resulting in low reliability of group processing. Consequently, tobacco product production efficiency is low, and the quality of the final product is difficult to guarantee.

[0041] The inventors of this disclosure have discovered through research that tobacco product raw materials of different qualities exhibit different pyrolysis characteristics during the pyrolysis process, which makes the focus of their processing different.

[0042] Pyrolysis is one of the main chemical reactions that occur when tobacco product raw materials (such as tobacco leaves) are heated further within a temperature range exceeding the pyrolysis threshold temperature. The pyrolysis characteristics of tobacco product raw materials can be characterized by collecting data such as temperature and mass change rate during the reaction process. Therefore, appropriate processing paths can be selected for each tobacco product raw material based on its pyrolysis characteristics.

[0043] However, relying solely on the pyrolysis characteristics of tobacco raw materials to select processing paths only considers the physicochemical properties of the raw materials and ignores the empirical adjustments made by formulators in actual production to balance the style of tobacco products. This results in the grouping processing results deviating from actual production needs and having low consistency with the results of manual grouping processing verified through practice, thus leading to lower sensory quality of the produced tobacco products.

[0044] In view of this, this disclosure proposes a method for processing tobacco products. The method initially determines the corresponding processing path based on the pyrolysis characteristics of the tobacco product raw materials. On this basis, the method dynamically optimizes and adjusts the initially determined processing path by combining the historical probability of using the processing path in the historical processing of the tobacco product raw materials in the target tobacco product.

[0045] In this way, by taking the pyrolysis characteristics of tobacco product raw materials as an objective scientific basis and incorporating empirical data from historical processing, the processing path is selected. This avoids the subjective bias caused by relying solely on human experience to select the processing path, and overcomes the limitation of relying solely on pyrolysis characteristics to select the processing path, which cannot effectively meet the actual production needs. This improves the scientificity and accuracy of the group processing path selection, thereby improving the sensory quality of the produced tobacco products.

[0046] Figure 1 A flowchart illustrating a method for processing tobacco products according to some embodiments of the present disclosure is shown.

[0047] like Figure 1 As shown, in step 110, the first processing path corresponding to each tobacco product raw material is determined based on the first thermogravimetric analysis data of each of the multiple tobacco product raw materials used to prepare the target tobacco product.

[0048] Here, the first thermogravimetric analysis data indicates the relationship between the rate of mass change of each tobacco product raw material and temperature during the pyrolysis process.

[0049] In this disclosure, tobacco products are products made wholly or partially from tobacco leaves and intended for smoking, chewing, snorting, or other use. Tobacco products may include, but are not limited to, cigarettes, cigars, pipe tobacco, or hookah. Cigarettes may be classified, based on the tobacco leaf formulation and processing method, including but not limited to flue-cured tobacco, burley tobacco, aromatic tobacco, or sun-cured tobacco. Tobacco products release chemical substances such as nicotine (a common term for "nicotine") through heating (e.g., heated tobacco products) and / or combustion (e.g., cigarettes, cigars, etc.). Tobacco products may or may not have a wrapper and may or may not have a filter. In this disclosure, for ease of description, cigarettes are sometimes used as examples of tobacco products. However, it should be understood that the various features or limitations described herein regarding cigarettes also apply to other types of tobacco products.

[0050] Tobacco product raw materials may include tobacco leaves in various forms, such as tobacco leaves or tobacco shreds, tobacco sheets, tobacco powder, or tobacco blocks processed from tobacco leaves. In some embodiments, tobacco product raw materials may be tobacco leaves from a specific origin. Here, multiple tobacco product raw materials may be tobacco leaves from different origins, or tobacco leaves from different growing parts of the same origin (e.g., classified as "upper," "middle," and "lower" according to their growing part on the tobacco plant), or tobacco leaves from different batches from the same growing part of the same origin.

[0051] In some embodiments, the first thermogravimetric analysis data for each tobacco product raw material includes multiple pyrolysis temperatures of the pyrolysis process of each tobacco product raw material and the rate of mass change of each tobacco product raw material corresponding to each of the multiple pyrolysis temperatures. For example, the multiple pyrolysis temperatures may form multiple temperature ranges.

[0052] For example, thermogravimetric analysis data for tobacco product raw materials can be obtained by conducting pyrolysis experiments on the raw materials and recording multiple pyrolysis temperatures and the corresponding rate of mass change of the raw materials at each pyrolysis temperature. The rate of mass change can be determined by calculating the derivative of the mass change of the raw materials with respect to temperature during pyrolysis.

[0053] The following example uses a certain type of tobacco leaf as the raw material for tobacco products to illustrate the process of conducting a pyrolysis experiment on the raw material for tobacco products.

[0054] First, a tobacco leaf is placed in a constant temperature and humidity chamber for a period of time to equilibrate. Then, the tobacco leaf is ground, and the ground tobacco powder is filtered through a sieve and thoroughly mixed. For example, 10 mg of the tobacco powder is weighed as a sample. In a thermal analyzer, the sample is heated to a certain temperature (e.g., 373 Kelvin) at a constant rate (e.g., 10 Kelvin / min) under a nitrogen atmosphere (e.g., a nitrogen flow rate of 50 mL / min, i.e., a carrier gas flow rate of 50 mL / min) and held at this temperature for a period of time to eliminate the influence of varying moisture content in the tobacco leaf. Then, the sample is heated to an even higher temperature (e.g., 630 Kelvin) at the same rate. The relationship between the sample's mass and temperature during the heating process is recorded, thus obtaining the Thermo-Gravimetry (TG) curve. To make the pyrolysis behavior of tobacco more apparent, the derivative of the TG curve is calculated to obtain the Derivative Thermo-Gravimetry (DTG) curve (hereinafter referred to as the "thermogravimetry analysis curve"). This curve reflects the relationship between the rate of mass change and temperature, which is the "thermogravimetry analysis data" discussed in this paper. If the thermogravimetry analysis data is displayed as a graph (i.e., the DTG curve), the horizontal axis represents temperature (i.e., the "pyrolysis temperature" discussed in this paper), in Kelvin (K), and the vertical axis represents the rate of mass change of the tobacco leaf during pyrolysis, in percentage / Kelvin (% / K). This curve is also known as a thermal analysis spectrum.

[0055] It can be understood that the pyrolysis temperature in the thermogravimetric analysis data (i.e., the abscissa of the DTG curve) reflects the temperature change range of tobacco leaves during the pyrolysis process, and different pyrolysis temperatures may correspond to different pyrolysis stages in the pyrolysis reaction. The rate of mass change in the thermogravimetric analysis data (i.e., the ordinate of the DTG curve) reflects the intensity of the reaction of tobacco leaves during the pyrolysis process, thus revealing the kinetic characteristics of the pyrolysis reaction of tobacco leaves.

[0056] The multiple pyrolysis temperatures in the thermogravimetric analysis data can be uniformly or non-uniformly distributed. To more accurately characterize the pyrolysis properties of tobacco leaves, the temperature difference between adjacent pyrolysis temperatures can be as small as possible, for example, 10K, 5K, 1K, 0.5K, or other values. For example, with a step size of 0.5K, a total of 801 data points between 401.138 K and 801.138 K can be used as the thermogravimetric analysis data for this tobacco leaf, and displayed in the form of a curve.

[0057] In step 120, based on the historical probability that each tobacco product raw material is processed using the first processing path in the historical processing of the target tobacco product, the first tobacco product raw material among multiple tobacco product raw materials that needs to have its processing path replaced is determined.

[0058] In this disclosure, the processing path initially determined based on the first thermogravimetric analysis data of each tobacco product raw material is referred to as the "first processing path," and the replaced processing path is referred to as the "second processing path." The first processing paths for different tobacco product raw materials can be different. For example, the processing intensity of the first processing path for tobacco leaf A can be greater than that of the first processing path for tobacco leaf B. For instance, the first processing path for tobacco leaf A includes an airflow processing technology (also known as an airflow line), while the first processing path for tobacco leaf B includes a drum processing technology (also known as a drum line).

[0059] In some embodiments, based on the grade of each tobacco product raw material, the historical probability of processing that raw material using a first processing path in the historical processing of the target tobacco product is determined. For example, the historical probability corresponding to each tobacco product raw material can reflect the empirical confidence level that the raw material was assigned to the preliminarily determined first processing path in the historical formulation.

[0060] For example, by obtaining the number of times different brands of tobacco product raw materials were processed using different processing paths in the historical processing process, the percentage of each processing path's occurrences in the total number of processing attempts for each brand of tobacco product raw material is taken as the historical probability of that brand of tobacco product raw material being processed using that processing path. For instance, taking a certain brand of tobacco product raw material as an example, assuming that the total number of times this brand of tobacco product raw material was processed in the historical processing process was 10, with 5 times using a roller conveyor and 5 times using an airflow conveyor, then the probability of roller conveyor processing is 50%, and the probability of airflow conveyor processing is 50%.

[0061] For example, taking tobacco leaves as the raw material for tobacco products, we can count the number of times each tobacco leaf was processed on the production line using various processing paths (such as roller line or airflow line) during the historical processing process, in order to calculate the proportion of the number of times corresponding to each processing path in the total number of times, so as to construct a "tobacco leaf-historical probability (also known as production line affiliation probability) library", as shown in Table 1.

[0062] Table 1 schematically illustrates the historical probability of different brands of tobacco leaves being processed using corresponding processing paths according to some embodiments of this disclosure.

[0063] Table 1

[0064] As shown in Table 1, Table 1 schematically illustrates the historical probability of 15 different brands of tobacco being processed using different processing paths. For example, the historical probability of tobacco leaf 1 being processed using a drum line is 0, while the historical probability of it being processed using an airflow line is 100%. This indicates that tobacco leaf of this brand has never been processed using a drum line in its historical processing, but has always been processed using an airflow line.

[0065] Suppose that for a certain sheet of tobacco X, the processing path initially determined based on the first thermogravimetric analysis data of sheet of tobacco X is a roller mill line. If a historical sheet of tobacco with the same grade as sheet of tobacco X is found in the "Sheet of Tobacco – Historical Probability Database", for example, sheet of tobacco 12, and the historical probability of sheet of tobacco 12 being processed by a roller mill line is 100%, then this probability can be taken as the historical probability of sheet of tobacco X being processed by a roller mill line (i.e., the first processing path), which is 100%.

[0066] In some embodiments, multiple tobacco product raw materials can be sorted according to the order of their historical probabilities, and the tobacco product raw materials with historical probabilities less than a threshold can be identified as the first tobacco product raw materials whose processing paths need to be replaced.

[0067] In step 130, the first tobacco product raw material is assigned to a second processing path different from the first processing path for processing, and the second tobacco product raw material other than the first tobacco product raw material is assigned to the first processing path for processing, so as to prepare the target tobacco product.

[0068] In some embodiments, the processing intensity of the first processing path and the processing intensity of the second processing path are different. For example, the first processing path includes one of airflow machining and roller machining, and the second processing path includes the other of airflow machining and roller machining. For example, if the first processing path can be airflow machining, then the second processing path can be roller machining.

[0069] In some embodiments, there may be multiple quantities of the first tobacco product raw material and the second tobacco product raw material. For example, each first tobacco product raw material may be assigned to a second processing path that is different from the first processing path corresponding to that first tobacco product raw material for processing. Each second tobacco product raw material may be assigned to the first processing path corresponding to that second tobacco product raw material for processing.

[0070] In the above embodiments, a first processing path is initially determined for each tobacco product raw material based on thermogravimetric analysis data used to prepare the target tobacco product. For the first tobacco product raw material that requires a replacement processing path based on historical probability, a second processing path different from the initially determined first processing path is used for processing. For the second tobacco product raw material that does not require a replacement processing path, the first processing path initially determined based on the pyrolysis characteristics of the second tobacco product raw material is used for processing.

[0071] This approach, by introducing historical probability as a correction factor, allows for the adjustment of the processing path initially determined based on the pyrolysis characteristics of tobacco product raw materials using empirical data from historical processing. This ensures that the selection of the processing path not only considers the physicochemical properties of each tobacco product raw material but also fully takes into account the successful practices of similar raw materials in past large-scale production. This improves the scientific rigor and accuracy of the processing path selection, thereby enhancing the sensory quality of the resulting tobacco products.

[0072] The implementation of step 120 is illustrated below with reference to some embodiments.

[0073] Figure 2 A flowchart illustrating an implementation of step 120 according to some embodiments of the present disclosure is shown. For example, as Figure 2 As shown, step 120 may include steps 210 to 213.

[0074] In step 210, multiple tobacco product raw materials are sorted in ascending order of historical probability to obtain the sorting result.

[0075] In some embodiments, the historical probability that each tobacco product raw material was processed using a first processing path in its historical processing reflects the degree of consistency between the processing path determined by thermogravimetric analysis data and the processing path verified in practice.

[0076] For example, for each tobacco product raw material, a higher historical probability of using the first processing path in past processing indicates a higher frequency of that raw material being assigned to the first processing path in past production practices. This suggests a higher consistency between the processing path initially determined based on the thermogravimetric analysis data of the tobacco product raw material and the processing path proven effective in actual production. In other words, processing the tobacco product raw material using the first processing path demonstrates good process adaptability and quality stability.

[0077] Conversely, if the historical probability of the tobacco product raw material being processed using the first processing path is low, it indicates that the raw material has been assigned to the first processing path less frequently in past production practices (e.g., it may have been assigned to other processing paths due to poor process compatibility). In this case, even if the thermogravimetric analysis data of the tobacco product raw material determines that it needs to be processed using the first processing path, the consistency between the determined first processing path and the processing path that has been verified as effective in actual production may be low. In other words, using the first processing path to process the tobacco product raw material may have certain technological limitations, which may adversely affect the sensory quality of the final product.

[0078] In this case, processing using the processing path determined based on pyrolysis characteristics may not be sufficient to meet the actual production needs of the raw material. The processing path needs to be modified based on historical experience to avoid adverse effects on the sensory quality of the final product due to improper path selection.

[0079] In step 211, based on the sorting results, each tobacco product raw material among multiple tobacco product raw materials is selected as a candidate tobacco product raw material, and the processing path of the candidate tobacco product raw material is replaced.

[0080] Here, the replacement process includes replacing the processing path of the candidate tobacco product raw material from the first processing path to the second processing path.

[0081] For example, several tobacco product raw materials include tobacco leaves Q1, Q2, Q3, Q4, and Q5. These five raw materials are sorted in ascending order of their historical probability of being processed using their respective first processing path, resulting in the following order: tobacco leaves Q2, Q3, Q1, Q4, and Q5. The processing path replacement process is then performed sequentially, starting with tobacco leaves Q2, which has the lowest historical probability.

[0082] In step 212, after each replacement process, it is determined whether to stop the replacement process based on the second thermogravimetric analysis data of the candidate tobacco product raw materials processed through the second processing path.

[0083] In some embodiments, the sensory differences between multiple tobacco product raw materials are determined based on second thermogravimetric analysis data of the candidate tobacco product raw materials processed through the second processing path, thereby determining whether to stop the replacement process. For example, the sensory differences between multiple tobacco product raw materials refer to the degree of difference in sensory experience (e.g., aroma, taste, irritation, aftertaste, etc.) after multiple tobacco product raw materials are formulated into a target tobacco product according to a combined formulation.

[0084] For example, if the sensory differences among multiple tobacco product raw materials meet the standard, indicating that the process adaptability and quality stability exhibited after processing according to the replaced processing path meet production requirements, then the replacement process is stopped. Otherwise, the replacement process continues. For example, this standard is related to the variety of the target tobacco product, and the standard corresponding to each variety of tobacco product can reflect the tolerance of that variety of tobacco product to sensory differences. Different varieties of tobacco products may have different standards.

[0085] Following the example above, after performing a replacement process on tobacco leaf Q2, the sensory differences between multiple tobacco product raw materials are determined based on the second thermogravimetric analysis data of tobacco leaf Q2 processed using the replaced second processing path. If the sensory differences between multiple tobacco product raw materials are determined to meet the standard, the replacement process is stopped. Otherwise, the replacement process continues on tobacco leaf Q3.

[0086] In some embodiments, a combined formulation of multiple tobacco product ingredients may include the mass percentage of each of the multiple tobacco product ingredients in the corresponding tobacco product. The mass percentage can be presented as an absolute value or a relative value. In the absolute value form, the combined formulation may include, for example, the mass of each tobacco product ingredient (e.g., in grams, milligrams, micrograms, etc.), thereby indirectly reflecting the mass percentage. In the relative value form, the combined formulation may include, for example, the ratio obtained by dividing the mass of each tobacco product ingredient by the mass of the tobacco product (e.g., in percentage form), thereby directly reflecting the mass percentage.

[0087] In step 213, the candidate tobacco product raw materials whose processing paths have been replaced before the replacement process is stopped are identified as the first tobacco product raw materials.

[0088] Following the example above, assuming that the candidate tobacco product raw materials that have undergone replacement processing before the replacement process is stopped include tobacco leaf Q2, tobacco leaf Q3, and tobacco leaf Q1, then tobacco leaf Q2, tobacco leaf Q3, and tobacco leaf Q1 are the first tobacco product raw materials that need to have their processing paths replaced, while the other tobacco product raw materials among the multiple tobacco product raw materials, namely tobacco leaf Q4 and tobacco leaf Q5, are the second tobacco product raw materials that do not need to have their processing paths replaced.

[0089] In the above embodiments, the first processing path for tobacco product raw materials with a historically low probability of replacement is prioritized, and the replacement process is stopped after each replacement based on the thermogravimetric analysis data of the tobacco product raw materials from the replaced processing paths. This enables efficient optimization of the processing paths, improves the consistency between the final selected processing path and the processing path proven effective in actual production, and thus enhances the sensory quality of the produced tobacco products.

[0090] In some embodiments, when there are multiple candidate tobacco product raw materials, the second thermogravimetric analysis data can be determined based on the weighted average of the thermogravimetric analysis data of the multiple candidate tobacco product raw materials processed through the second processing path.

[0091] For example, the mass percentage of each candidate tobacco product raw material in the combined formulation of the target tobacco product after processing through the second processing path can be used as the weight of the thermogravimetric analysis data of each candidate tobacco product raw material, so as to calculate the weighted average of the thermogravimetric analysis data of multiple candidate tobacco product raw materials.

[0092] Following the example above, after the third replacement process, the multiple candidate tobacco product raw materials processed through the second processing path include tobacco leaves Q2, Q3, and Q1. The mass percentage (e.g., 'a') of tobacco leaf Q2, the mass percentage of tobacco leaf Q3, and the mass percentage of tobacco leaf Q1 are all 'c'. Using 'a' as the weight of the thermogravimetric analysis data S1 of tobacco leaf Q2, 'b' as the weight of the thermogravimetric analysis data S2 of tobacco leaf Q3, and 'c' as the weight of the thermogravimetric analysis data S3 of tobacco leaf Q1, the second thermogravimetric analysis data P1 can be calculated as P1 = a × S1 + b × S2 + c × S3.

[0093] In this way, by using the mass percentage of each candidate tobacco ingredient in the combined formulation as a weighted sum for calculation, it can be ensured that the impact of the pyrolysis characteristics of each candidate tobacco ingredient on the overall pyrolysis characteristics of the subsequently produced tobacco product is fully considered. This quantitative calculation method allows the second thermogravimetric analysis data to more accurately reflect the actual pyrolysis characteristics of the multiple candidate tobacco ingredients after processing through the second processing path. This helps to more accurately determine whether to stop the replacement process, thereby improving the accuracy of processing path optimization and ultimately enhancing the sensory quality of the produced tobacco product.

[0094] In some embodiments, a determination is made as to whether to stop the replacement process based on a first difference between second thermogravimetric analysis data and third thermogravimetric analysis data of other tobacco product raw materials (excluding candidate tobacco product raw materials) processed through a first processing path.

[0095] In some embodiments, when there are multiple other tobacco product raw materials, the third thermogravimetric analysis data can be determined based on the weighted average of the thermogravimetric analysis data of multiple other tobacco product raw materials processed through the first processing path.

[0096] For example, the weighted average of the thermogravimetric analysis data of multiple other tobacco product raw materials (i.e., tobacco product raw materials without replacing the processing path) can be calculated by using the mass percentage of each other tobacco product raw material in the combined formulation as the weight of the thermogravimetric analysis data of each other tobacco product raw material.

[0097] Following the example above, after the third replacement process, the other tobacco product raw materials processed through the first processing path include tobacco leaf Q4 and tobacco leaf Q5. The mass percentage (e.g., d) of tobacco leaf Q4 and the mass percentage of tobacco leaf Q5 are respectively e. Using d as the weight of the thermogravimetric analysis data S4 for tobacco leaf Q4 and e as the weight of the thermogravimetric analysis data S5 for tobacco leaf Q5, the third thermogravimetric analysis data P2 can be calculated as: P2 = d × S4 + e × S5.

[0098] In this way, by using the mass percentage of each other tobacco product raw material in the combined formulation as a weighted sum, it can be ensured that the impact of the pyrolysis characteristics of each other tobacco product raw material on the overall pyrolysis characteristics of the subsequent tobacco product is fully considered.

[0099] This quantitative calculation method allows the third thermogravimetric analysis data to more accurately reflect the actual pyrolysis characteristics of multiple other tobacco product raw materials after processing through the first processing path. It helps to more accurately determine whether to stop the replacement process based on the difference between the second and third thermogravimetric analysis data, thereby improving the accuracy of processing path optimization and ultimately improving the sensory quality of the produced tobacco products.

[0100] In some embodiments, a first difference between the second and third thermogravimetric analysis data can be determined based on the difference between the two.

[0101] In some embodiments, the first difference between the second and third thermogravimetric analysis data can be determined based on the ratio of the difference between the two to the sum of the two (e.g., expressed as a percentage).

[0102] In some embodiments, the amount of data in the second thermogravimetric analysis data and the third thermogravimetric analysis data is equal.

[0103] Continuing with the example above, if the thermogravimetric analysis data of tobacco leaves Q2, Q3, and Q1 processed through the second processing path are all represented as curves, then for each of these three tobacco leaves, a total of 801 data points between 401.138 K and 801.138 K can be uniformly selected to form the corresponding DTG curve. Then, the mass percentage corresponding to each tobacco leaf is used as the weight of each data point in the DTG curve of each tobacco leaf, and 801 weighted sum data points are calculated accordingly to form the DTG curve corresponding to the second thermogravimetric analysis data.

[0104] If the thermogravimetric analysis data of tobacco leaves Q4 and Q5 processed through the first processing path are both represented as curves, then for each of these two tobacco leaves, a total of 801 data points between 401.138 K and 801.138 K can be uniformly selected to form the corresponding DTG curve. Then, the mass percentage of each tobacco leaf is used as the weight of each data point in the DTG curve of each tobacco leaf, and 801 weighted sum data points are calculated accordingly to form the DTG curve corresponding to the third thermogravimetric analysis data.

[0105] In the above embodiments, the second thermogravimetric analysis data can accurately reflect the actual pyrolysis characteristics of multiple candidate tobacco product raw materials after processing through the second processing path, and the third thermogravimetric analysis data can accurately reflect the actual pyrolysis characteristics of multiple other tobacco product raw materials after processing through the first processing path. Therefore, the first difference between the two (also known as the thermogravimetric difference) can accurately reflect the sensory difference between multiple tobacco product raw materials after each replacement treatment.

[0106] In this way, by using the differences between thermogravimetric analysis data as an objective basis, the sensory differences of different tobacco product raw materials after processing can be effectively reflected, thus providing scientific support for the optimization of processing paths and helping to improve the sensory quality of the final product.

[0107] In some embodiments, the replacement process is stopped in response to a first difference between the second and third thermogravimetric analysis data meeting a preset range corresponding to the size category of the target tobacco product. The replacement process continues in response to a first difference between the second and third thermogravimetric analysis data not meeting the preset range corresponding to the size category of the target tobacco product.

[0108] For example, if the first difference is greater than or equal to the lower limit of a preset range corresponding to the size category of the target tobacco product, and less than or equal to the upper limit of that preset range, then the first difference can be confirmed to meet the preset range. If the first difference is less than the lower limit of the preset range corresponding to the size category of the target tobacco product, or greater than the upper limit of that preset range, then the first difference can be confirmed to not meet the preset range.

[0109] For example, tobacco products of different size categories have different dimensions (e.g., circumference), and each size category corresponds to a different preset range. For instance, the circumference of a regular tobacco product is larger than that of a medium-sized tobacco product, and the circumference of a medium-sized tobacco product is larger than that of a slim tobacco product. The size category of the target tobacco product can be one of regular, medium, or slim.

[0110] In the above embodiments, considering that tobacco products of different sizes are sensitive to differences in the sensory characteristics of raw materials, the processing path can be optimized in a targeted manner according to the product type based on the differences in pyrolysis characteristics revealed by thermogravimetric analysis and combined with product specification characteristics. This allows for precise control and overall improvement of sensory quality for tobacco products of different product types.

[0111] The following examples illustrate how to determine the preset range corresponding to different size categories.

[0112] Figure 3 A flowchart illustrating a method for determining a preset range according to some embodiments of the present disclosure is provided.

[0113] like Figure 3 As shown, in step 310, sample thermogravimetric analysis data of each tobacco product sample in multiple tobacco product samples are obtained.

[0114] Each tobacco product sample is made from a first tobacco product raw material sample processed via a first processing path and a second tobacco product raw material sample processed via a second processing path.

[0115] In some embodiments, when there are multiple first tobacco product raw material samples, the thermogravimetric analysis data of the first sample raw material can be determined based on the weighted average of the thermogravimetric analysis data of multiple first tobacco product raw material samples.

[0116] For example, for each tobacco product sample, when there are multiple first tobacco product raw material samples, the mass percentage of each first tobacco product raw material sample in the combined formulation of the tobacco product sample can be used as the weight of the thermogravimetric analysis data of each first tobacco product raw material sample, so as to calculate the weighted average of the thermogravimetric analysis data of multiple first tobacco product raw material samples.

[0117] In some embodiments, when there are multiple second tobacco product raw material samples, the thermogravimetric analysis data of the second sample raw material can be determined based on the weighted average of the thermogravimetric analysis data of the multiple second tobacco product raw material samples.

[0118] For example, for each tobacco product sample, if there are multiple second tobacco product raw material samples, the mass percentage of each second tobacco product raw material sample in the combined formulation of the tobacco product sample can be used as the weight of the thermogravimetric analysis data of each second tobacco product raw material sample, so as to calculate the weighted average of the thermogravimetric analysis data of multiple second tobacco product raw material samples.

[0119] The sample thermogravimetric analysis data for each tobacco product sample were determined based on the second difference between the first sample raw material thermogravimetric analysis data of the first tobacco product raw material sample and the second sample raw material thermogravimetric analysis data of the second tobacco product raw material sample.

[0120] For example, a second difference between the thermogravimetric analysis data of the first sample and the thermogravimetric analysis data of the second sample can be determined based on the difference between the two. Alternatively, a second difference between the thermogravimetric analysis data of the first sample and the thermogravimetric analysis data of the second sample can be determined based on the ratio of the difference between the two to their sum.

[0121] It should be noted that the method for calculating the weighted average of the thermogravimetric analysis data of multiple first / second tobacco product raw material samples is similar to the method for calculating the weighted average of the thermogravimetric analysis data of multiple candidate tobacco product raw materials described above. The method for calculating the second difference is similar to the method for calculating the first difference; the relevant implementation methods can be found in the descriptions of the relevant embodiments above, and will not be repeated here.

[0122] In step 320, the sample thermogravimetric analysis data are processed using a trained machine learning model to classify multiple tobacco product samples into multiple size categories.

[0123] For example, tobacco product samples of the same size category have the same dimensions (e.g., circumference), while tobacco product samples of different size categories have different dimensions. For example, multiple size categories may include regular, medium, and slim cigarettes.

[0124] In some embodiments, the trained machine learning model can be an unsupervised model. For example, the machine learning model can be trained using thermogravimetric analysis data of multiple tobacco products of known size categories as input and the size category of the tobacco products as output, until a training termination condition is met to obtain a trained machine learning model. For example, the training termination condition may be that the number of training iterations reaches a specified number and / or the classification accuracy reaches a specified threshold.

[0125] In step 330, a preset range corresponding to each size category is determined based on the second difference corresponding to the sample thermogravimetric analysis data of tobacco product samples in each of the multiple size categories.

[0126] Each size category includes multiple tobacco product samples, and the second difference corresponding to each tobacco product sample may be the same or different.

[0127] In some embodiments, two different values ​​of the second difference corresponding to the sample thermogravimetric analysis data of tobacco product samples in each size category may be selected as the upper and lower limits of a preset range to determine the preset range corresponding to each size category.

[0128] In the above embodiments, the machine learning model is used to intelligently classify various tobacco product samples. It can accurately identify and define the preset range of thermogravimetric differences that need to be met for different size categories to determine whether to suspend or replace the processing path. This provides a scientific basis for optimizing the processing path and helps to improve the sensory quality of the final product.

[0129] In some embodiments, the number of first tobacco product raw material samples used to make each tobacco product is the same as the number of second tobacco product raw material samples. For example, each tobacco product sample is made from 10 tobacco product raw material samples, of which 5 tobacco product raw material samples are processed by a first processing path, and the remaining 5 tobacco product raw material samples are processed by a second processing path.

[0130] In this way, by preparing each tobacco product sample in equal quantities from a first tobacco product raw material sample processed by the first processing path and a second tobacco product raw material sample processed by the second processing path, and characterizing the overall pyrolysis characteristics of each tobacco product sample based on the difference between their thermogravimetric analysis data (i.e., the second difference), the contribution of different processing paths to the sensory attributes of the final product can be effectively decoupled.

[0131] This equal-quantity design ensures the consistency and comparability of the comparison conditions, which helps to improve the reliability of the determined preset range, thereby facilitating more reliable optimization of the processing path and enhancing the sensory quality of the final product.

[0132] In some embodiments, a preset range corresponding to each size category can be determined based on the minimum and maximum values ​​of the second difference corresponding to the sample thermogravimetric analysis data of tobacco product samples in each size category.

[0133] For example, the lower limit of the preset range corresponding to each size category is the minimum value of the second difference corresponding to the sample thermogravimetric analysis data of tobacco product samples in each size category.

[0134] The upper limit of the preset range corresponding to each size category is the maximum value of the second difference corresponding to the sample thermogravimetric analysis data of tobacco product samples in each size category.

[0135] Taking cigarettes as an example of tobacco product samples, Table 2 schematically shows the preset ranges corresponding to different size categories of cigarettes according to some embodiments of this disclosure.

[0136] Table 2

[0137] As shown in Table 2, Table 2 illustrates the preset ranges for different size categories of cigarettes when the second difference is expressed as a ratio. For example, the preset range for regular cigarettes is 6% to 8%. Assuming the target tobacco product is a cigarette and its size category is regular, if the first difference is within the range of 6% to 8% after a replacement process, the replacement process is stopped; otherwise, if the first difference is outside the range of 6% to 8%, the replacement process continues according to the historical probability order.

[0138] In the above embodiments, a targeted threshold range is constructed by using the minimum and maximum values ​​of the second difference between the thermogravimetric analysis data of raw materials in each size category as boundaries. This objectively quantifies the sensitivity of products of different size categories to the differences in the pyrolysis characteristics of tobacco product raw materials, thereby helping to optimize the processing path more reliably and improving the sensory quality of the final product.

[0139] The following examples illustrate how to determine the first processing path.

[0140] The quality grade of tobacco products is affected by the quality of the raw materials used and the processing methods of those raw materials. Different qualities of raw materials exhibit different pyrolysis characteristics during the pyrolysis process, which leads to different focuses in their processing.

[0141] For example, high-quality tobacco raw materials are typically rich in volatile aroma components. These components release a rich, delicate, and layered aroma during pyrolysis, meaning they have strong aroma volatility. Therefore, they are often used to produce higher-quality tobacco products. In contrast, low-quality tobacco raw materials have a lower content of volatile aroma components, and the aroma produced during pyrolysis is weaker and lacks richness and complexity. Therefore, they are often used to produce lower-quality tobacco products.

[0142] During the pyrolysis of tobacco product raw materials, the pyrolysis behavior in different temperature ranges can reflect the different pyrolysis characteristics of the raw materials. For example, the pyrolysis behavior in some temperature ranges reflects the combustion characteristics of the tobacco product raw materials, while the pyrolysis behavior in other temperature ranges reflects the aroma volatility characteristics of the raw materials.

[0143] Therefore, based on the pyrolysis characteristics of each tobacco product raw material in different temperature ranges and the quality grade of the target tobacco product to be prepared, a suitable processing path can be selected for each tobacco product raw material.

[0144] Figure 4 A flowchart illustrating a method for determining a first processing path in step 110 according to some embodiments of the present disclosure is shown.

[0145] In step 110, the first thermogravimetric analysis data for each tobacco product raw material includes multiple pyrolysis temperatures of the pyrolysis process of each tobacco product raw material and the mass change rate of each tobacco product raw material corresponding to each of the multiple pyrolysis temperatures, wherein the multiple pyrolysis temperatures form multiple temperature ranges. For example... Figure 4 As shown, step 110 may include steps 111 to 113.

[0146] In step 111, a characteristic temperature range is determined from multiple temperature ranges based on the quality grade of the target tobacco product.

[0147] Quality grades are used to reflect the quality of tobacco products. A higher quality grade indicates a better quality tobacco product. In some embodiments, the quality grade of a target tobacco product can be reflected by its selling price. Therefore, its quality grade can be determined based on the selling price of the target tobacco product. For example, the quality grade of a target tobacco product can be positively correlated with its selling price. The higher the selling price, the higher the quality grade of the target tobacco product.

[0148] In some embodiments, different quality grades may correspond to different characteristic temperature ranges. For example, the characteristic temperature range corresponding to a quality grade that exceeds (or is higher than) a specified grade may be different from the characteristic temperature range corresponding to a quality grade that does not exceed the specified grade.

[0149] Building upon this, in further embodiments, different quality grades can correspond to the same characteristic temperature range. For example, there may be multiple quality grades exceeding a specified level, and these multiple quality grades can correspond to the same characteristic temperature range. Alternatively, there may be multiple quality grades not exceeding a specified level, and these multiple quality grades can correspond to the same characteristic temperature range.

[0150] Taking the five quality grades of tobacco products as an example, assuming the quality grades from highest to lowest are R1, R2, R3, R4, and R5. Quality grades R1 and R2 correspond to characteristic temperature range W1, while R3, R4, and R5 correspond to characteristic temperature range W2. For example, characteristic temperature ranges W1 and W2 can be different. In other words, tobacco products can be grouped according to their quality grades; tobacco products within the same group correspond to the same characteristic temperature range, while tobacco products in different groups correspond to different characteristic temperature ranges.

[0151] In step 112, the mass change of each tobacco product raw material within the characteristic temperature range is determined based on the characteristic data corresponding to the characteristic temperature range.

[0152] Here, the characteristic data includes the characteristic temperature within the characteristic temperature range and the corresponding rate of mass change at the characteristic temperature. For example, the thermogravimetric analysis data of each tobacco product raw material can form a DTG curve, where the characteristic data corresponding to the characteristic temperature range forms a portion of the DTG curve.

[0153] In some embodiments, the mass change of each tobacco product raw material within a characteristic temperature range can be determined based on the area of ​​the thermogravimetric analysis curve formed by the characteristic data. For example, the area of ​​the thermogravimetric analysis curve formed by the characteristic data can be accurately calculated by integrating the rate of mass change over the characteristic temperature range.

[0154] Taking the DTG curve of a certain tobacco leaf A as an example, assuming the temperature range [400K, 500K] is the determined characteristic temperature range, the area of ​​the DTG curve formed by the characteristic data corresponding to the temperature range [400K, 500K] is the area enclosed by a portion of the DTG curve of tobacco leaf A between the horizontal axis [400K, 500K] and the horizontal axis (the horizontal axis passes through the point on the vertical axis where the rate of mass change is 0).

[0155] In a further embodiment, the area of ​​the thermogravimetric analysis curve formed by the characteristic data of each tobacco product raw material is positively correlated with the amount of mass change of the tobacco product raw material within the characteristic temperature range. For example, the larger the area of ​​the thermogravimetric analysis curve formed by the characteristic data of each tobacco product raw material, the greater the amount of mass change of the tobacco product raw material within the characteristic temperature range, which means that the tobacco product raw material exhibits stronger characteristics within the characteristic temperature range. Conversely, the smaller the area, the weaker the characteristics.

[0156] In step 113, the first processing path corresponding to each tobacco product raw material is determined based on the quality grade and the amount of quality change.

[0157] In some embodiments, the first processing path corresponding to each tobacco product raw material includes one of an airflow processing process with higher processing intensity and a roller processing process with lower processing intensity.

[0158] In some embodiments, for target tobacco products of different quality grades, a first processing path corresponding to each tobacco product raw material can be determined based on the magnitude of the mass change of each tobacco product raw material used within a characteristic temperature range.

[0159] In the above embodiments, the quality grade of the target tobacco product to be prepared is taken into account in the grouping and processing design of the tobacco product raw materials. Based on the quality grade of the target tobacco product, characteristic data corresponding to the characteristic temperature range is extracted from the thermogravimetric analysis data of each tobacco product raw material. Then, combined with the characteristic data and the quality grade of the target tobacco product, a corresponding processing path is initially assigned to each tobacco product raw material.

[0160] In this approach, the thermogravimetric analysis data of each tobacco product raw material is combined with the quality grade of the target tobacco product for objective analysis. This allows for the initial selection of the corresponding processing path for each tobacco product raw material, reducing subjective errors and improving the reliability of the initially determined processing path. This, in turn, helps to improve the reliability of subsequent optimized processing paths, and ultimately contributes to the sensory quality of the final product.

[0161] In some embodiments, in response to a quality level of a first quality level, a first temperature range among a plurality of temperature ranges is determined as a characteristic temperature range; in response to a quality level of a second quality level lower than the first quality level, a second temperature range among a plurality of temperature ranges is determined as the characteristic temperature range.

[0162] Here, the first temperature range is associated with the combustion characteristics (also known as "combustion features") of each tobacco product raw material, and the second temperature range is associated with the aroma volatility characteristics (also known as "main features") of each tobacco product raw material.

[0163] In other words, if the target tobacco product has a high quality grade, a corresponding first processing path can be determined for each tobacco product raw material based on the characteristic data corresponding to the characteristic temperature range associated with the combustion characteristics of each raw material. If the target tobacco product has a low quality grade, a corresponding first processing path can be determined for each tobacco product raw material based on the characteristic data corresponding to the characteristic temperature range associated with the aroma volatility characteristics of each raw material.

[0164] In this approach, considering that each tobacco product raw material used in high-quality target tobacco products is of good quality and has strong and relatively small differences in aroma volatility, the characteristic data corresponding to the characteristic temperature range associated with the combustion characteristics of each tobacco product raw material are used as the basis to select the first processing path corresponding to each tobacco product raw material, so as to further enhance the combustion potential of each tobacco product raw material through processing.

[0165] Meanwhile, considering that the quality of each tobacco product raw material used in the target tobacco products of lower quality grades is poor, and the corresponding aroma volatility characteristics are weak and vary greatly, the characteristic data corresponding to the characteristic temperature range associated with the aroma volatility characteristics of each tobacco product raw material are used as the basis to select the first processing path corresponding to each tobacco product raw material, so as to retain the limited aroma of each tobacco product raw material as much as possible through processing.

[0166] This further improves the reliability of the initially determined processing path, which in turn helps to improve the quality of the final product.

[0167] In some embodiments, the thermogravimetric analysis data from the first thermogravimetric analysis of each tobacco product raw material forms a thermogravimetric curve including peaks associated with combustion characteristics and peaks associated with aroma volatility characteristics. A first temperature range covers the peaks associated with combustion characteristics, and a second temperature range covers the peaks associated with aroma volatility characteristics. For example, the number of peaks associated with combustion characteristics can be one or more. The number of peaks associated with aroma volatility characteristics can be one or more.

[0168] For example, the second temperature range could be [400K, 500K], covering the peaks associated with aroma volatility characteristics. The first temperature range could be [500K, 630K], covering the peaks associated with combustion characteristics.

[0169] In this approach, considering that different peaks in the thermogravimetric analysis curve usually correspond to different reaction stages in which tobacco product raw materials undergo significant quality changes, for example, peaks associated with combustion characteristics correspond to the combustion process of tobacco product raw materials at higher temperatures, mainly involving the pyrolysis of cellulose, hemicellulose, and lignin; while peaks associated with aroma volatility characteristics correspond to the thermal decomposition process of tobacco product raw materials at lower temperatures, mainly involving the pyrolysis of sugars, nicotine, pectin, and some other volatile substances.

[0170] Therefore, by dividing temperature ranges according to the peak types of the thermogravimetric analysis curves, the first temperature range covers peaks associated with combustion characteristics. Analyzing the characteristic temperatures and mass changes of these combustion-related peaks allows for a more accurate analysis of the combustion behavior of tobacco product raw materials at higher temperatures. Conversely, the second temperature range covers peaks associated with aroma volatility. Analyzing the characteristic temperatures and mass changes of these aroma-related peaks allows for a more accurate analysis of the pyrolysis behavior of tobacco product raw materials at lower temperatures, further enhancing their aroma volatility. This enables a more precise identification of the characteristics of each reaction stage in the thermogravimetric process, improving the reliability of the initially determined first processing path and ultimately enhancing the sensory quality of the final product.

[0171] The following examples illustrate how to adopt the processing technology solutions disclosed herein and the corresponding effects.

[0172] For example, the processing technology disclosed herein can be used to process a target tobacco product F (e.g., a Class II conventional cigarette) that is of a higher quality grade and belongs to the category of regular cigarettes.

[0173] First, based on the first thermogravimetric analysis data of each of the multiple tobacco product raw materials used to prepare the target tobacco product F, the corresponding first processing path is initially determined for each tobacco product raw material.

[0174] For example, for each tobacco product raw material, the area of ​​the DTG curve within a first temperature range (e.g., [500K, 630K]) is calculated as the mass change of the tobacco product raw material within the first temperature range, and the area of ​​the DTG curve within a second temperature range ([400K, 500K]) is calculated as the mass change of the tobacco product raw material within the second temperature range. Note that in this example, calculating the area of ​​the DTG curve within the second temperature range as the mass change of the tobacco product raw material within the second temperature range is not necessary; this result is for reference only.

[0175] Since the target tobacco product F is a high-quality tobacco product, the first temperature range is determined as the characteristic temperature range, and the corresponding first processing path is determined for each tobacco product raw material based on the magnitude of the mass change within the first temperature range.

[0176] Then, based on the historical probability of each tobacco product raw material being processed using the first processing path in the historical processing of the target tobacco product, the first tobacco product raw material among multiple tobacco product raw materials that needs to have its processing path replaced is determined.

[0177] For example, based on the grade of each tobacco product raw material, determine the historical probability of using the first processing path to process the tobacco product raw material in the historical processing of the target tobacco product.

[0178] Table 3 schematically illustrates the historical probability of each tobacco product raw material being processed using the first processing path after the processing path was initially determined.

[0179] Table 3

[0180] As shown in Table 3, taking tobacco leaves as an example, Table 3 schematically shows the historical probability of each of the 30 tobacco product raw materials used to prepare the target tobacco product F being processed using the corresponding first processing path.

[0181] As one approach, multiple tobacco product raw materials are sorted in ascending order of historical probability, and each of these raw materials is selected as a candidate tobacco product raw material. The processing path of the candidate tobacco product raw materials is then replaced.

[0182] After each replacement process, a decision is made on whether to stop the replacement process based on the first difference between the second thermogravimetric analysis data of the candidate tobacco product raw material processed through the second processing path and the third thermogravimetric analysis data of other tobacco product raw materials processed through the first processing path, excluding the candidate tobacco product raw material.

[0183] Candidate tobacco product raw materials whose processing paths have been replaced before the replacement process is stopped are identified as the first tobacco product raw materials.

[0184] For example, in the example shown in Table 3, the processing paths of tobacco leaves 19, 21, 30, 31, 42, and 43 are replaced sequentially. Since the target tobacco product belongs to the size category of "regular size," as shown in the example in Table 2, the preset range for "regular size" is 6% to 8%. After each replacement process, the first difference is calculated, and it is determined whether it falls within this preset range; if not, the next replacement process continues until the first difference is within the range of 6% to 8%.

[0185] Subsequently, the first tobacco product raw material is assigned to a second processing path different from the first processing path for processing, and the second tobacco product raw material other than the first tobacco product raw material is assigned to the first processing path for processing to prepare the target tobacco product.

[0186] Table 4 shows the thermogravimetric differences and consistency rates of the processing paths determined by the grouping processing method according to this disclosure (also known as the "two-stage grouping processing method", hereinafter referred to as "two-stage grouping"), the manual grouping method, and the grouping method based solely on thermogravimetric analysis data (hereinafter referred to as "thermogravimetric grouping"), with the results of the manual grouping as the benchmark.

[0187] Table 4

[0188] As shown in Table 4, the grouping results of tobacco flakes obtained by the two-stage intelligent grouping method are compared with the results of manual grouping. The consistency rate reaches 83%, which is significantly better than the method based solely on thermogravimetric analysis data (whose consistency rate is 57%).

[0189] This result demonstrates that the processing path optimization logic of the two-stage intelligent grouping strategy disclosed herein effectively enhances the consistency between the grouping processing results and the manually grouped processing results verified in practice, improves the scientificity and accuracy of the grouping processing path selection, and thus improves the sensory quality of the produced tobacco products.

[0190] To verify the impact of different grouping methods on the quality of the final tobacco product, multiple batches of tobacco raw materials were processed using the following three grouping methods: (1) grouping based solely on human experience; (2) grouping based solely on thermogravimetric analysis data (hereinafter referred to as "thermogravimetric grouping"); and (3) the two-stage intelligent grouping method proposed in this disclosure (hereinafter referred to as "two-stage grouping"). Subsequently, sensory evaluations were conducted on the target tobacco product F obtained by the three methods, and the results are shown in Table 5.

[0191] Table 5 shows the comparative results of sensory evaluation of tobacco product F prepared by three methods: two-stage grouping, artificial grouping, and thermogravimetric grouping, with the results of artificial grouping as the benchmark.

[0192] Table 5

[0193] As shown in Table 5, tobacco product F prepared using the two-stage grouping processing scheme proposed in this disclosure achieved the highest total score in sensory evaluation. In contrast, the grouping method relying solely on human experience did not quantitatively consider the pyrolysis characteristics of tobacco raw materials, resulting in a lack of scientific basis for process configuration; while the grouping method based solely on thermogravimetric analysis data (i.e., "thermogravimetric grouping"), although considering the pyrolysis behavior of tobacco product raw materials, often resulted in differences in the thermogravimetric loss of raw materials obtained under different processing paths, leading to differences in the sensory experience of the final tobacco product (such as uneven aroma and taste).

[0194] The above results demonstrate that this disclosure, by combining thermogravimetric data with historical experience, effectively overcomes the limitations of related technologies during the processing, and improves the consistency and stability of the sensory quality of the final product.

[0195] Figure 5 A block diagram of a processing apparatus for tobacco products according to some embodiments of the present disclosure is shown.

[0196] like Figure 5 As shown, the tobacco product processing apparatus 500 includes a first determining module 501, a second determining module 502, and a processing module 503.

[0197] The first determining module 501 is configured to determine a first processing path corresponding to each tobacco product raw material based on the first thermogravimetric analysis data of each of the multiple tobacco product raw materials used to prepare the target tobacco product, wherein the first thermogravimetric analysis data indicates the relationship between the rate of mass change of each tobacco product raw material and temperature during the pyrolysis process.

[0198] The second determining module 502 is configured to determine, based on the historical probability that each tobacco product raw material is processed using the first processing path in the historical processing of the target tobacco product, the first tobacco product raw material that needs to have its processing path replaced among multiple tobacco product raw materials.

[0199] The processing module 503 is configured to allocate the first tobacco product raw material to a second processing path different from the first processing path for processing, and to allocate the second tobacco product raw material other than the first tobacco product raw material to the first processing path for processing, so as to prepare the target tobacco product.

[0200] In some embodiments, the second determining module 502 is configured to sort a plurality of tobacco product raw materials in ascending order of historical probability to obtain a sorting result; according to the sorting result, each tobacco product raw material among the plurality of tobacco product raw materials is selected as a candidate tobacco product raw material in sequence, and the candidate tobacco product raw material is subjected to a processing path replacement process, the replacement process including replacing the processing path of the candidate tobacco product raw material from a first processing path to a second processing path; after each replacement process, based on the second thermogravimetric analysis data of the candidate tobacco product raw material processed through the second processing path, it is determined whether to stop the replacement process; the candidate tobacco product raw material whose processing path has been replaced before the replacement process is stopped is determined as the first tobacco product raw material.

[0201] In some embodiments, the second determining module 502 is configured to determine whether to stop the replacement process based on a first difference between second thermogravimetric analysis data and third thermogravimetric analysis data of other tobacco product raw materials (excluding candidate tobacco product raw materials) processed through the first processing path.

[0202] In some embodiments, the second determining module 502 is configured to stop the replacement process in response to a first difference between the second and third thermogravimetric analysis data satisfying a preset range corresponding to the size category to which the target tobacco product belongs.

[0203] In some embodiments, the tobacco product processing apparatus 500 further includes an acquisition module, a sorting module, and a third determining module. Figure 5 (Not shown).

[0204] The acquisition module is configured to acquire sample thermogravimetric analysis data for each of a plurality of tobacco product samples. Each tobacco product sample is made from a first tobacco product raw material sample processed by a first processing path and a second tobacco product raw material sample processed by a second processing path. The sample thermogravimetric analysis data is determined based on a second difference between the first sample raw material thermogravimetric analysis data of the first tobacco product raw material sample and the second sample raw material thermogravimetric analysis data of the second tobacco product raw material sample.

[0205] The classification module is configured to process sample thermogravimetric analysis data using a trained machine learning model to classify multiple tobacco product samples into multiple size categories. Tobacco product samples in the same size category have the same size, while tobacco product samples in different size categories have different sizes.

[0206] The third determining module is configured to determine a preset range corresponding to each size category based on the second difference corresponding to the sample thermogravimetric analysis data of tobacco product samples in each of the multiple size categories.

[0207] In some embodiments, the lower limit of the preset range corresponding to each size category is the minimum value of the second difference corresponding to the sample thermogravimetric analysis data of tobacco product samples in each size category, and the upper limit of the preset range corresponding to each size category is the maximum value of the second difference corresponding to the sample thermogravimetric analysis data of tobacco product samples in each size category.

[0208] In some embodiments, the number of the first tobacco product raw material samples is the same as the number of the second tobacco product raw material samples.

[0209] In some embodiments, the multiple size categories include standard support, medium support, and fine support.

[0210] In some embodiments, the first thermogravimetric analysis data includes multiple pyrolysis temperatures of the pyrolysis process of each tobacco product raw material and the rate of mass change of each tobacco product raw material corresponding to each of the multiple pyrolysis temperatures, wherein the multiple pyrolysis temperatures form multiple temperature ranges.

[0211] In some embodiments, the first determining module 501 may be configured to determine a characteristic temperature range from multiple temperature ranges based on the quality grade of the target tobacco product; determine the mass change of each tobacco product raw material within the characteristic temperature range based on the characteristic data corresponding to the characteristic temperature range, wherein the characteristic data includes the characteristic temperature within the characteristic temperature range and the mass change rate corresponding to the characteristic temperature; and determine a first processing path corresponding to each tobacco product raw material based on the quality grade and the mass change.

[0212] In some embodiments, the first determining module 501 may be configured to determine a first temperature range among a plurality of temperature ranges as a characteristic temperature range in response to a quality grade of a first quality grade, the first temperature range being associated with the combustion characteristics of each tobacco product raw material; and to determine a second temperature range among a plurality of temperature ranges as a characteristic temperature range in response to a quality grade of a second quality grade lower than the first quality grade, the second temperature range being associated with the aroma volatility characteristics of each tobacco product raw material.

[0213] In some embodiments, the thermogravimetric curve formed from the first thermogravimetric analysis data of each tobacco product raw material includes a peak associated with combustion characteristics and a peak associated with aroma volatility characteristics, wherein the first temperature range covers the peak associated with combustion characteristics and the second temperature range covers the peak associated with aroma volatility characteristics.

[0214] In some embodiments, the first determining module 501 may be configured to determine the mass change of each tobacco product raw material within a characteristic temperature range based on the area of ​​the thermogravimetric analysis curve formed by the characteristic data.

[0215] In some embodiments, the processing intensity of the first processing path and the processing intensity of the second processing path are different.

[0216] In some embodiments, the first processing path includes one of airflow machining and roller machining, and the second processing path includes another of airflow machining and roller machining.

[0217] Figure 6 A block diagram of a processing apparatus for tobacco articles according to other embodiments of the present disclosure is shown.

[0218] like Figure 6 As shown, the tobacco product processing apparatus 600 of this embodiment includes: a memory 601 and a processor 602 coupled to the memory 601. The processor 602 is configured to execute the processing method of any embodiment of this disclosure based on instructions stored in the memory 601.

[0219] The memory 601 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory may store, for example, an operating system, application programs, a boot loader, a database, and other programs.

[0220] Figure 7 A block diagram of a processing apparatus for tobacco products according to some embodiments of the present disclosure is shown.

[0221] like Figure 7 As shown, the tobacco product processing apparatus 700 of this embodiment includes: a memory 701 and a processor 702 coupled to the memory 701. The processor 702 is configured to execute the processing method of any of the foregoing embodiments based on instructions stored in the memory 701.

[0222] The memory 701 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory stores, for example, the operating system, application programs, boot loader, and other programs.

[0223] The tobacco product processing apparatus 700 may further include an input / output interface 703, a network interface 704, and a storage interface 705. These interfaces 703, 704, and 705, as well as the memory 701 and processor 702, can be connected, for example, via a bus 706. The input / output interface 703 provides a connection interface for input / output devices such as displays, mice, keyboards, touchscreens, microphones, and speakers. The network interface 704 provides a connection interface for various networked devices. The storage interface 705 provides a connection interface for external storage devices such as SD cards and USB flash drives.

[0224] This disclosure also provides a computer-readable storage medium including computer program instructions that, when executed by a processor, implement the processing method of any of the above embodiments.

[0225] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the processing method of any of the above embodiments.

[0226] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0227] The processing technology for tobacco products according to this disclosure has now been described in detail. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art can fully understand how to implement the disclosed technology based on the above description.

[0228] The methods and systems of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the specific order described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0229] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A method for processing tobacco products, comprising: Based on the first thermogravimetric analysis data of each of the multiple tobacco product raw materials used to prepare the target tobacco product, a first processing path is determined for each tobacco product raw material, wherein the first thermogravimetric analysis data indicates the relationship between the mass change rate of each tobacco product raw material and temperature during the pyrolysis process. Based on the historical probability of each tobacco product raw material being processed using the first processing path in the historical processing of the target tobacco product, the first tobacco product raw material among the plurality of tobacco product raw materials that needs to have its processing path replaced is determined. The first tobacco product raw material is assigned to a second processing path different from the first processing path for processing, and the second tobacco product raw material other than the first tobacco product raw material is assigned to the first processing path for processing to prepare the target tobacco product.

2. The processing method according to claim 1, wherein, The step of determining the first tobacco product raw material that needs its processing path replaced among the plurality of tobacco product raw materials based on the historical probability of each tobacco product raw material being processed using the first processing path in the historical processing of the target tobacco product includes: The multiple tobacco product raw materials are sorted in ascending order of their historical probabilities to obtain the sorting result; Based on the sorting results, each tobacco product raw material among the plurality of tobacco product raw materials is selected as a candidate tobacco product raw material in sequence, and the processing path of the candidate tobacco product raw material is replaced. The replacement process includes replacing the processing path of the candidate tobacco product raw material from the first processing path to the second processing path. After each replacement process, a determination is made as to whether to stop the replacement process based on the second thermogravimetric analysis data of the candidate tobacco product raw material processed through the second processing path. The candidate tobacco product raw material whose processing path has been replaced before the replacement process is stopped is identified as the first tobacco product raw material.

3. The processing method according to claim 2, wherein, The step of determining whether to stop the replacement process includes: Based on the first difference between the second thermogravimetric analysis data and the third thermogravimetric analysis data of other tobacco product raw materials (excluding the candidate tobacco product raw materials) processed through the first processing path, it is determined whether to stop the replacement process.

4. The processing method according to claim 3, wherein, The step of determining whether to stop the replacement process includes: The replacement process is stopped when a first difference between the second and third thermogravimetric analysis data meets a preset range corresponding to the size category of the target tobacco product.

5. The processing method according to claim 4 further includes: Thermogravimetric analysis data of each tobacco product sample among multiple tobacco product samples are obtained. Each tobacco product sample is made from a first tobacco product raw material sample processed by the first processing path and a second tobacco product raw material sample processed by the second processing path. The sample thermogravimetric analysis data is determined based on a second difference between the first sample raw material thermogravimetric analysis data of the first tobacco product raw material sample and the second sample raw material thermogravimetric analysis data of the second tobacco product raw material sample. The thermogravimetric analysis data of the samples are processed using a trained machine learning model to classify the multiple tobacco product samples into multiple size categories. Tobacco product samples in the same size category have the same size, while tobacco product samples in different size categories have different sizes. Based on the second difference corresponding to the sample thermogravimetric analysis data of tobacco product samples in each of the plurality of size categories, a preset range corresponding to each size category is determined.

6. The processing method according to claim 5, wherein, The lower limit of the preset range corresponding to each size category is the minimum value of the second difference corresponding to the sample thermogravimetric analysis data of the tobacco product samples in each size category, and the upper limit of the preset range corresponding to each size category is the maximum value of the second difference corresponding to the sample thermogravimetric analysis data of the tobacco product samples in each size category.

7. The processing method according to claim 5, wherein, The number of the first tobacco product raw material samples is the same as the number of the second tobacco product raw material samples.

8. The processing method according to claim 5, wherein, The various size categories include standard support, medium support, and fine support.

9. The processing method according to any one of claims 1-8, wherein, The first thermogravimetric analysis data includes multiple pyrolysis temperatures of each tobacco product raw material and the mass change rate of each tobacco product raw material corresponding to each of the multiple pyrolysis temperatures, wherein the multiple pyrolysis temperatures form multiple temperature ranges. The step of determining the first processing path corresponding to each tobacco product raw material based on the first thermogravimetric analysis data of each of the multiple tobacco product raw materials used to prepare the target tobacco product includes: Based on the quality grade of the target tobacco product, a characteristic temperature range is determined from the plurality of temperature ranges; Based on the characteristic data corresponding to the characteristic temperature range, the mass change of each tobacco product raw material within the characteristic temperature range is determined, wherein the characteristic data includes the characteristic temperature within the characteristic temperature range and the mass change rate corresponding to the characteristic temperature. Based on the quality grade and the amount of quality change, a first processing path is determined for each tobacco product raw material.

10. The processing method according to claim 9, wherein, The step of determining the characteristic temperature range from the plurality of temperature ranges based on the quality grade of the target tobacco product includes: In response to the quality grade being a first quality grade, a first temperature range among the plurality of temperature ranges is determined as the characteristic temperature range, the first temperature range being associated with the combustion characteristics of each tobacco product raw material; and In response to a quality grade that is a second quality grade lower than the first quality grade, a second temperature range among the plurality of temperature ranges is determined as the characteristic temperature range, the second temperature range being associated with the aroma volatility characteristics of each tobacco product raw material.

11. The processing method according to claim 10, wherein, The thermogravimetric curve formed from the first thermogravimetric analysis data of each tobacco product raw material includes peaks associated with combustion characteristics and peaks associated with aroma volatility characteristics, wherein the first temperature range covers the peaks associated with combustion characteristics and the second temperature range covers the peaks associated with aroma volatility characteristics.

12. The processing method according to claim 9, wherein, The step of determining the mass change of each tobacco product raw material within the characteristic temperature range based on the characteristic data corresponding to the characteristic temperature range includes: The area of ​​the thermogravimetric analysis curve formed by the characteristic data is used to determine the mass change of each tobacco product raw material within the characteristic temperature range.

13. The processing method according to any one of claims 1-8, wherein, The processing intensity of the first processing path is different from that of the second processing path.

14. The processing method according to claim 13, wherein, The first processing path includes one of airflow processing and roller processing, and the second processing path includes the other of the airflow processing and roller processing.

15. A tobacco product processing apparatus, comprising: The first determining module is configured to determine a first processing path corresponding to each tobacco product raw material based on first thermogravimetric analysis data of each of a plurality of tobacco product raw materials used to prepare the target tobacco product, wherein the first thermogravimetric analysis data indicates the relationship between the mass change rate of each tobacco product raw material and temperature during the pyrolysis process. The second determining module is configured to determine, based on the historical probability that each tobacco product raw material is processed using the first processing path in the historical processing of the target tobacco product, the first tobacco product raw material among the plurality of tobacco product raw materials that needs to have its processing path replaced; The processing module is configured to allocate the first tobacco product raw material to a second processing path different from the first processing path for processing, and to allocate the second tobacco product raw material other than the first tobacco product raw material to the first processing path for processing, so as to prepare the target tobacco product.

16. A tobacco product processing apparatus, comprising: Memory; and A processor coupled to the memory, the processor being configured to perform the processing method of any one of claims 1-14 based on instructions stored in the memory.

17. A computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, implement the processing method according to any one of claims 1-14.

18. A computer program product comprising instructions that, when executed by a processor, cause the processor to perform the processing method according to any one of claims 1-14.

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