Method and device for predicting growth part of tobacco product raw material and model training method

Abstract

The invention relates to a method and a device for predicting a growth part of a tobacco product raw material and a training method of a prediction model, and relates to the technical field of tobacco production. The method comprises the steps that thermogravimetric analysis data and chemical component data of tobacco product raw materials are obtained, and the thermogravimetric analysis data comprise a plurality of pyrolysis temperatures in the pyrolysis process of the tobacco product raw materials and the mass change rate corresponding to each pyrolysis temperature; the chemical component data comprises the content of each chemical component in various chemical components in the tobacco product raw materials; determining characteristic data comprising a characteristic temperature in the plurality of pyrolysis temperatures and a mass change rate corresponding to the characteristic temperature from the thermogravimetric analysis data; and based on at least one parameter in the characteristic temperature and the quality change rate corresponding to the characteristic temperature and the chemical component data, utilizing a trained prediction model to predict the growth part of the tobacco product raw material. According to the technical scheme, the prediction accuracy of the growth part of the tobacco product raw material can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of tobacco production, and particularly to a method, a device for predicting the growth position of tobacco product raw materials, and a training method for a prediction model. Background Art

[0002] In the manufacturing process of tobacco products, the growth position characteristics of tobacco product raw materials are an important basis for the formulation design of tobacco products. By reasonably matching tobacco product raw materials from different growth positions, the flavor and quality requirements of different tobacco products can be met. Taking tobacco product raw materials as tobacco leaves as an example, tobacco leaves can be divided into upper tobacco leaves, middle tobacco leaves, and lower tobacco leaves according to their growth positions on the tobacco plant. There are significant differences in chemical composition, physical properties, and sensory qualities among tobacco leaves from different growth positions, and these differences determine their different uses in the formulation of tobacco products. Therefore, the accurate distinction of the growth position of tobacco product raw materials plays an important role in the subsequent industrial use of tobacco product raw materials (such as the design of tobacco product formulations).

[0003] In the related art, the near-infrared spectrum information of tobacco product raw materials is used to predict the growth position of tobacco product raw materials. Summary of the Invention

[0004] The following problems exist in the above-mentioned related art: The accuracy of predicting the growth position of tobacco product raw materials is relatively low.

[0005] To solve the above problems, the embodiments of the present disclosure provide the following solutions.

[0006] According to some embodiments of the present disclosure, there is provided a method for predicting the growth position of tobacco product raw materials, including: obtaining thermogravimetric analysis data and chemical composition data of the tobacco product raw materials, where the thermogravimetric analysis data includes multiple pyrolysis temperatures of the pyrolysis process of the tobacco product raw materials and the mass change rate of the tobacco product raw materials corresponding to each pyrolysis temperature among the multiple pyrolysis temperatures, and the chemical composition data includes the content of each chemical component among multiple chemical components in the tobacco product raw materials; determining characteristic data from the thermogravimetric analysis data, where the characteristic data includes characteristic temperatures among the multiple pyrolysis temperatures and the mass change rate corresponding to the characteristic temperatures; and predicting the growth position of the tobacco product raw materials by using a trained prediction model based on at least one parameter among the characteristic temperatures and the mass change rate corresponding to the characteristic temperatures and the chemical composition data.

[0007] In some embodiments, the multiple pyrolysis temperatures form a temperature range, and the mass change rate corresponding to the characteristic temperature includes one or more extreme values of the mass change rate of the tobacco product raw materials within the temperature range.

[0008] In some embodiments, the plurality of pyrolysis temperatures form adjacent first and second temperature ranges, and the mass change rate corresponding to the characteristic temperature includes an intermediate value between a first extreme value of the mass change rate of the tobacco product raw material within the first temperature range and a second extreme value of the mass change rate of the tobacco product raw material within the second temperature range. The characteristic temperature includes a first temperature corresponding to the intermediate value in the thermogravimetric analysis data. The temperature corresponding to the first extreme value is less than the temperature corresponding to the second extreme value, and the first temperature is greater than the temperature corresponding to the first extreme value and less than the temperature corresponding to the second extreme value.

[0009] In some embodiments, the characteristic temperature includes a second temperature corresponding to the intermediate value in the thermogravimetric analysis data, and the second temperature is less than the temperature corresponding to the first extreme value or greater than the temperature corresponding to the second extreme value.

[0010] In some embodiments, the characteristic temperature includes a starting temperature or an ending temperature among the plurality of pyrolysis temperatures.

[0011] In some embodiments, the chemical composition data further includes a ratio of the contents of different chemical components among the multiple chemical components in the tobacco product raw material.

[0012] In some embodiments, the trained prediction model is obtained by the following method: Obtain the thermogravimetric analysis data and chemical composition data of each tobacco product raw material sample from multiple tobacco product raw material samples from multiple different growth parts. The thermogravimetric analysis data of each tobacco product raw material sample includes a plurality of sample pyrolysis temperatures of the pyrolysis process of each tobacco product raw material sample and the mass change rate corresponding to each sample pyrolysis temperature among the plurality of sample pyrolysis temperatures. The chemical composition data of each tobacco product raw material sample includes the content of each chemical component among the multiple chemical components in each tobacco product raw material sample. Determine the characteristic data of each tobacco product raw material sample from the thermogravimetric analysis data of each tobacco product raw material sample. The characteristic data of each tobacco product raw material sample includes a sample characteristic temperature among the plurality of sample pyrolysis temperatures and the mass change rate corresponding to the sample characteristic temperature. Use at least one parameter among the sample characteristic temperature and the mass change rate corresponding to the sample characteristic temperature and the chemical composition data of each tobacco product raw material sample as inputs, and the predicted growth part to which each tobacco product raw material sample belongs as an output, and train the prediction model until the training end condition is met.

[0013] In some embodiments, the trained prediction model includes a random forest model.

[0014] In some embodiments, the multiple chemical components include at least one of total nitrogen, nicotine, total sugar, reducing sugar, potassium, and chlorine.

[0015] According to some other embodiments of the present disclosure, there is provided a method for training a prediction model for predicting the growth location to which a tobacco product raw material belongs. The training method includes: obtaining, for each tobacco product raw material sample among a plurality of tobacco product raw material samples from multiple different growth locations, thermogravimetric analysis data and chemical composition data. The thermogravimetric analysis data for each tobacco product raw material sample includes multiple sample pyrolysis temperatures of the pyrolysis process of each tobacco product raw material sample and the mass change rate corresponding to each sample pyrolysis temperature among the multiple sample pyrolysis temperatures. The chemical composition data for each tobacco product raw material sample includes the content of each chemical component among the multiple chemical components in each tobacco product raw material sample; determining, from the thermogravimetric analysis data of each tobacco product raw material sample, the characteristic data of each tobacco product raw material sample. The characteristic data of each tobacco product raw material sample includes the sample characteristic temperature among the multiple sample pyrolysis temperatures and the mass change rate corresponding to the sample characteristic temperature; using at least one parameter among the sample characteristic temperature and the mass change rate corresponding to the sample characteristic temperature and the chemical composition data of each tobacco product raw material sample as inputs and the predicted growth location to which each tobacco product raw material sample belongs as an output to train the prediction model until the training end condition is satisfied.

[0016] According to some further embodiments of the present disclosure, there is provided a device for predicting the growth location to which a tobacco product raw material belongs, including: an obtaining module configured to obtain thermogravimetric analysis data and chemical composition data of the tobacco product raw material. The thermogravimetric analysis data includes multiple pyrolysis temperatures of the pyrolysis process of the tobacco product raw material and the mass change rate of the tobacco product raw material corresponding to each pyrolysis temperature among the multiple pyrolysis temperatures. The chemical composition data includes the content of each chemical component among the multiple chemical components in the tobacco product raw material; a determining module configured to determine characteristic data from the thermogravimetric analysis data. The characteristic data includes the characteristic temperature among the multiple pyrolysis temperatures and the mass change rate corresponding to the characteristic temperature; a prediction module configured to predict the growth location to which the tobacco product raw material belongs by using the trained prediction model based on at least one parameter among the characteristic temperature and the mass change rate corresponding to the characteristic temperature and the chemical composition data.

[0017] According to some still further embodiments of the present disclosure, there is provided an electronic device, including: a memory; and a processor coupled to the memory. The processor is configured to execute the method for predicting the growth location to which a tobacco product raw material belongs or the method for training a prediction model in any one of the above embodiments based on instructions stored in the memory device.

[0018] According to some further embodiments of the present disclosure, there is provided a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the method for predicting the growth part to which a tobacco product raw material belongs or the training method of the prediction model in any one of the above embodiments is implemented.

[0019] According to some further embodiments of the present disclosure, there is also provided a computer program product, including instructions, and when the instructions are executed by a processor, the processor is caused to execute the method for predicting the growth part to which a tobacco product raw material belongs or the training method of the prediction model according to any one of the above embodiments.

[0020] In the above embodiments, the characteristic data determined from the thermogravimetric analysis data of the tobacco product raw material includes two parameters, namely the characteristic temperature and the mass change rate corresponding to the characteristic temperature, which can characterize the pyrolysis characteristics of the tobacco product raw material. The trained prediction model is used to perform a fusion process on at least one of the two parameters that can characterize the pyrolysis characteristics of the tobacco product raw material and the chemical composition data of the tobacco product raw material, so as to predict the growth part to which the tobacco product raw material belongs. In this way, in the process of predicting the growth part to which the tobacco product raw material belongs, the pyrolysis characteristics of the tobacco product raw material and the contents of various chemical components are considered together, thereby achieving an accurate prediction of the growth part to which the tobacco product raw material belongs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings forming a part of the specification depict embodiments of the present disclosure and, together with the specification, are used to explain the principles of the present disclosure.

[0022] Referring to the drawings, the present disclosure can be more clearly understood according to the following detailed description, wherein:

[0023] Figure 1 A flowchart showing some embodiments of the method for predicting the growth part to which a tobacco product raw material belongs according to the present disclosure;

[0024] Figure 2 A schematic diagram showing some embodiments of the DTG curve according to the present disclosure;

[0025] Figure 3 A schematic diagram showing some other embodiments of the DTG curve according to the present disclosure;

[0026] Figure 4 A flowchart showing some embodiments of the training method of the prediction model according to the present disclosure;

[0027] Figure 5 A flowchart showing some other embodiments of the training method of the prediction model according to the present disclosure;

[0028] Figure 6 Block diagram showing some embodiments of an apparatus for predicting the growth location of tobacco product raw materials according to the present disclosure;

[0029] Figure 7 Block diagram showing some embodiments of a training apparatus for a prediction model according to the present disclosure;

[0030] Figure 8 Block diagram showing some embodiments of an electronic device according to the present disclosure;

[0031] Figure 9 Block diagram showing some other embodiments of an electronic device according to the present disclosure. Detailed embodiments

[0032] 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 arrangements of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure.

[0033] At the same time, it should be understood that, for the sake of convenience of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationships.

[0034] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way a limitation on the present disclosure or its application or use.

[0035] Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be regarded as part of the specification.

[0036] In all the examples shown and discussed herein, any specific values should be construed as merely exemplary and not as a limitation. Thus, other examples of the exemplary embodiments may have different values.

[0037] It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0038] As mentioned above, the usage ratios and collocation methods of tobacco leaves from different growth locations in tobacco product formulations directly affect the quality and style of tobacco products. In related technologies, the near-infrared spectral information of tobacco product raw materials is usually used to predict the growth location of tobacco product raw materials. However, in the way of predicting the growth location of tobacco product raw materials by only using a single-dimensional index of the same type, the obtained prediction results of the growth location of tobacco product raw materials are relatively one-sided, resulting in a low accuracy in predicting the growth location of tobacco product raw materials.

[0039] The inventors of the present disclosure have found through research that the formation of tobacco product smoke is mainly affected by the chemical composition of the tobacco product raw materials and the pyrolysis environment, and the latter is closely related to the pyrolysis characteristics of the tobacco product raw materials themselves. There are obvious differences in the chemical composition and pyrolysis characteristics of tobacco product raw materials from different growth positions. For example, the nicotine content in the upper tobacco leaves is relatively high, and the smoke produced during pyrolysis is relatively strong; the chemical composition of the middle tobacco leaves is relatively balanced, and the smoke produced during pyrolysis is soft and has less irritation; the nicotine content in the lower tobacco leaves is low, and the smoke produced during pyrolysis is relatively light.

[0040] Pyrolysis reaction is one of the main chemical reactions that occur when the tobacco product raw materials are continuously heated in the temperature range above the pyrolysis threshold temperature. The pyrolysis reaction can occur in the heat-not-burn stage or in the combustion stage, and the temperature in the former stage is generally lower than that in the latter stage. Due to the different chemical compositions of tobacco product raw materials from different growth positions, the relevant indicators (such as pyrolysis temperature and mass change rate) shown in the pyrolysis reaction also have their own characteristics.

[0041] Therefore, in the distinction of the growth position of the tobacco product raw materials, multiple dimensions of indicators including chemical composition and pyrolysis characteristics can be used in combination to achieve accurate prediction of the growth position of the tobacco product raw materials.

[0042] In view of this, the embodiments of the present disclosure propose a technical solution for predicting the growth position of tobacco product raw materials, which can fuse and process two types of indicators, namely the thermogravimetric analysis data and chemical composition data of the tobacco product raw materials, by using a trained prediction model to achieve the prediction of the growth position of the tobacco product raw materials. Compared with the method of predicting the growth position of tobacco product raw materials by only using a single dimension of indicators of the same type, the prediction accuracy of the growth position of tobacco product raw materials is effectively improved.

[0043] Figure 1 The flowchart showing some embodiments of the method for predicting the growth position of tobacco product raw materials according to the present disclosure is as follows. As Figure 1 shown, for example, the method for predicting the growth position of tobacco product raw materials may include step 110 to step 130.

[0044] In step 110, obtain the thermogravimetric analysis data and chemical composition data of the tobacco product raw materials.

[0045] In the present disclosure, a tobacco product is a product made wholly or partly from tobacco leaves as raw materials and is used for smoking, chewing, nasal inhalation, or other uses. Tobacco products may include, but are not limited to, cigarettes, cigars, pipe tobacco, or hookah, etc. From the perspective of tobacco leaf formulation and processing technology, cigarettes may include, but are not limited to, flue-cured tobacco, burley tobacco, oriental tobacco, or sun-cured tobacco, etc. Tobacco products release chemical substances such as nicotine by heating (e.g., heat-not-burn tobacco products) and / or burning (e.g., cigarettes, cigars, etc.). Tobacco products may or may not have a cigarette paper wrapper and may or may not have a filter tip. In the present disclosure, for the convenience of description, in some cases, cigarettes are taken as examples of tobacco products for illustration. However, it should be recognized that the various features or limitations described herein regarding cigarettes also apply to other types of tobacco products.

[0046] The raw materials of tobacco products may include tobacco leaves in various forms. For example, they can be tobacco leaves or cut tobacco, tobacco flakes, tobacco powder, or tobacco blocks formed by processing tobacco leaves.

[0047] The chemical composition data of the raw materials of tobacco products may include the content of each chemical component among various chemical components in the raw materials of tobacco products. For example, the content of each chemical component among various chemical components in the raw materials of tobacco products can be obtained by using a continuous flow method.

[0048] In some embodiments, the various chemical components may include at least one of total nitrogen, nicotine, total sugar, reducing sugar, potassium, and chlorine.

[0049] The thermogravimetric analysis data of the raw materials of tobacco products includes multiple pyrolysis temperatures in the pyrolysis process of the raw materials of tobacco products and the mass change rate of the raw materials of tobacco products corresponding to each pyrolysis temperature among the multiple pyrolysis temperatures.

[0050] For example, the thermogravimetric analysis data of the raw materials of tobacco products can be obtained by conducting a pyrolysis experiment on the raw materials of tobacco products and recording the multiple pyrolysis temperatures in the pyrolysis process and the mass change rate of the raw materials of tobacco products corresponding to each pyrolysis temperature. The mass change rate can be determined by taking the derivative of the mass of the raw materials of tobacco products with respect to temperature during the pyrolysis process.

[0051] Taking the raw materials of tobacco products as a certain tobacco leaf as an example, the process of conducting a pyrolysis experiment on the raw materials of tobacco products is introduced exemplarily below.

[0052] First, place a certain tobacco leaf in a thermostatic and humidostatic chamber (for example, at a temperature of (22 ± 1) °C and a relative humidity of (60 ± 2)%) for a certain period of time (for example, 48 h) to equilibriate. Then, grind the tobacco leaf and filter and fully mix the ground tobacco powder through a sieve (for example, a sieve with a mesh number in the range of 80 to 100 or a sieve with a sieve hole diameter in the range of 150 micrometers (μm) to 180 micrometers). Weigh, for example, 20 mg of the tobacco powder as a sample. In a thermal analyzer, heat the sample in a nitrogen atmosphere (for example, the flow rate of nitrogen is 100 milliliters per minute (i.e., the carrier gas flow rate is 100 mL / min)) at a certain rate (for example, 10 Kelvin per minute (10 K / min)) to a certain temperature (for example, 373 Kelvin (K)), and keep it at a constant temperature for a certain period of time to remove the influence caused by different moisture contents of the tobacco leaf. Then, continue to heat the sample to a higher temperature (for example, 873 Kelvin (K)) at the same rate. Record the relationship between the mass and temperature of the sample during the heating process to obtain a weight loss (TG, Thermo-Gravimetry) curve. To make the pyrolysis behavior of the tobacco more obvious, take the derivative of the TG curve to obtain a derivative weight loss (DTG, Derivative Thermo-Gravimetry) curve, which reflects the relationship between the mass change rate and temperature, that is, the "thermal weight loss analysis data" described in this article. If the thermal weight loss analysis data is presented in the form of a curve graph (i.e., the DTG curve), its abscissa represents the temperature (i.e., the "pyrolysis temperature" described in this article), with the unit of Kelvin (K), and the ordinate represents the mass change rate of the tobacco product raw material during pyrolysis, with the unit of percentage / Kelvin (% / K). This curve can also be called a thermal analysis spectrum.

[0053] Figure 2 Schematic diagrams showing some embodiments of the DTG curves of the present disclosure.

[0054] For example, in the above example, data points can be taken at intervals of 0.5 K, and a total of 801 data points between 401.138 K and 801.138 K can be used as the thermal weight loss analysis data of the tobacco leaf, and presented in the form of a curve to obtain a DTG curve as shown by the black solid line in Figure 2 Figure [].

[0055] It can be understood here that the pyrolysis temperature in the thermogravimetric analysis data (i.e., the abscissa of the DTG curve) can reflect the temperature change range of the tobacco product raw material during the pyrolysis process, and different pyrolysis temperatures may correspond to different pyrolysis stages in the pyrolysis reaction. The mass change rate in the thermogravimetric analysis data (i.e., the ordinate of the DTG curve) can reflect the intensity of the reaction during the pyrolysis process of the tobacco product raw material, thereby revealing the kinetic characteristics of the pyrolysis reaction of the tobacco product raw material. That is to say, the pyrolysis temperature and the mass change rate in the thermogravimetric analysis data of the tobacco product raw material can reflect the pyrolysis characteristics of the tobacco product raw material from two different dimensions.

[0056] The multiple pyrolysis temperatures in the thermogravimetric analysis data can be evenly distributed or unevenly distributed. In order to more accurately characterize the pyrolysis characteristics of the tobacco product raw material, the temperature difference between adjacent pyrolysis temperatures among the multiple pyrolysis temperatures can be as small as possible, for example, it can be 10K, 5K, 1K, 0.5K or other values.

[0057] In step 120, characteristic data is determined from the thermogravimetric analysis data. The characteristic data can include the characteristic temperature among the multiple pyrolysis temperatures and the mass change rate corresponding to the characteristic temperature. That is, the characteristic data includes two different types of parameters: the characteristic temperature and the mass change rate corresponding to the characteristic temperature.

[0058] In some embodiments, a part of characteristic points can be selected from the DTG curve corresponding to the thermogravimetric analysis data. The value of the abscissa of this part of the characteristic points in the DTG curve is determined as the characteristic temperature in the characteristic data, and the value of the ordinate of this part of the characteristic points in the DTG curve is determined as the mass change rate corresponding to the characteristic temperature in the characteristic data.

[0059] In step 130, based on at least one parameter among the characteristic temperature and the mass change rate corresponding to the characteristic temperature and the chemical composition data, the growth part to which the tobacco product raw material belongs is predicted by using the trained prediction model.

[0060] In some embodiments, at least one parameter among the characteristic temperature and the mass change rate corresponding to the characteristic temperature and the chemical composition data can be jointly used as the input of the trained prediction model to obtain the prediction result of the growth part to which the tobacco product raw material belongs output by the prediction model.

[0061] For example, at least one parameter among the characteristic temperature and the mass change rate corresponding to the characteristic temperature, as well as the chemical composition data, can be jointly used as the input of the trained prediction model to obtain the prediction label output by the prediction model, and the growth part to which the tobacco product raw material belongs can be determined based on this prediction label. For example, different growth parts to which it belongs can correspond to different prediction labels. For example, the number 1 is used to represent that the growth part to which it belongs is the upper part of the tobacco leaf (i.e., the tobacco product raw material is the upper tobacco leaf), the number 2 is used to represent that the growth part to which it belongs is the lower part of the tobacco leaf (i.e., the tobacco product raw material is the lower tobacco leaf), and the number 3 is used to represent that the growth part is the middle part of the tobacco leaf (i.e., the tobacco product raw material is the middle tobacco leaf). In response to the prediction label output by the prediction model being 1, it can be determined that the growth part to which the tobacco product raw material belongs is the upper part of the tobacco leaf.

[0062] Here, it can be understood that the number of characteristic temperatures can be one or more. Correspondingly, the number of mass change rates corresponding to the characteristic temperatures can also be one or more. The characteristic temperature and the mass change rate corresponding to the characteristic temperature are two types of parameters in different dimensions. That is to say, in step 130, based on the chemical composition data, any one type of parameter among the characteristic temperature and the mass change rate corresponding to the characteristic temperature, or both types of parameters, namely the characteristic temperature and the mass change rate corresponding to the characteristic temperature, can be combined, and the trained prediction model can be used to predict the growth part to which the tobacco product raw material belongs.

[0063] For example, the characteristic data can include multiple characteristic temperatures and the mass change rate corresponding to each characteristic temperature among the multiple characteristic temperatures.

[0064] For example, multiple characteristic temperatures and the chemical composition data can be jointly input into the trained prediction model to predict the growth part to which the tobacco product raw material belongs; or the chemical composition data and the mass change rate corresponding to each characteristic temperature among the multiple characteristic temperatures can be jointly input into the trained prediction model to predict the growth part to which the tobacco product raw material belongs; or multiple characteristic temperatures, the mass change rate corresponding to each characteristic temperature among the multiple characteristic temperatures, and the chemical composition data can be jointly input into the trained prediction model to predict the growth part to which the tobacco product raw material belongs.

[0065] In the above embodiments, the characteristic data determined from the thermogravimetric analysis data of the tobacco product raw material includes two parameters that can characterize the pyrolysis characteristics of the tobacco product raw material, namely, the characteristic temperature and the mass change rate corresponding to the characteristic temperature. The trained prediction model is used to perform fusion processing on at least one of these two parameters that can characterize the pyrolysis characteristics of the tobacco product raw material and the chemical composition data of the tobacco product raw material, so as to predict the growth location to which the tobacco product raw material belongs. In this way, in the process of predicting the growth location to which the tobacco product raw material belongs, not only the pyrolysis characteristics of the tobacco product raw material are considered, but also the contents of various chemical components in the tobacco product raw material are considered, thereby achieving an accurate prediction of the growth location to which the tobacco product raw material belongs.

[0066] In some embodiments, based on the characteristic temperature, the mass change rate corresponding to the characteristic temperature, and the chemical composition data, the trained prediction model can be used to predict the growth location to which the tobacco product raw material belongs. For example, the characteristic temperature, the mass change rate corresponding to the characteristic temperature, and the chemical composition data can be jointly used as the input of the trained prediction model to obtain the prediction label output by the prediction model, and the growth location to which the tobacco product raw material belongs can be determined based on this prediction label.

[0067] In this way, since the combination of the pyrolysis temperature and the mass change rate can more accurately reveal the pyrolysis characteristics of the tobacco product raw material, on the basis of the chemical composition data, further jointly inputting the characteristic temperature and the mass change rate corresponding to the characteristic temperature included in the characteristic data into the trained prediction model can more accurately predict the growth location to which the tobacco product raw material belongs.

[0068] The following uses some embodiments to give an exemplary illustration of the characteristic data in step 120.

[0069] In some embodiments, the characteristic temperature may include the starting temperature and / or the ending temperature among multiple pyrolysis temperatures.

[0070] That is to say, the characteristic data determined from the thermogravimetric analysis data may include the starting temperature among multiple pyrolysis temperatures and the mass change rate corresponding to the starting temperature, and / or the characteristic data determined from the thermogravimetric analysis data may include the ending temperature among multiple pyrolysis temperatures and the mass change rate corresponding to the ending temperature.

[0071] In some embodiments, the starting temperature among the multiple pyrolysis temperatures can be the temperature at the start of the pyrolysis process, i.e., the temperature corresponding to the moment when the tobacco product raw material begins to undergo thermal decomposition. In other embodiments, the starting temperature among the multiple pyrolysis temperatures can be a temperature before or after the start moment of the pyrolysis process. Similarly, in some embodiments, the ending temperature among the multiple pyrolysis temperatures can be the temperature at the end of the pyrolysis process, i.e., the temperature corresponding to the moment when the tobacco product raw material no longer undergoes thermal decomposition (or when the thermal decomposition is complete). In other embodiments, the starting temperature among the multiple pyrolysis temperatures can be a temperature before or after the end moment of the pyrolysis process.

[0072] For example, continuing to refer to Figure 2 , as described above, taking 801 data points between 401.138 K and 801.138 K at a step size of 0.5 K as the thermogravimetric analysis data of the tobacco leaf to obtain the DTG curve shown by the Figure 2 black solid line. That is, in the example shown in Figure 2 , the starting temperature among the multiple pyrolysis temperatures is 401.138 K and the ending temperature is 801.138 K.

[0073] Select the starting point A1 and the ending point A2 from the DTG curve as characteristic points. The abscissa value of the starting point A1 is the starting temperature TA1, and the ordinate value of the starting point A1 is the mass change rate WA1 corresponding to the starting temperature. The abscissa value of the ending point A2 is the ending temperature TA2, and the ordinate value of the ending point A2 is the mass change rate WA2 corresponding to the ending temperature.

[0074] For example, any set of data among (TA1, TA2), (WA1, WA2), (TA1, TA2, WA1, WA2) and the chemical composition data of the tobacco leaf can be input into the trained prediction model to predict the growth location to which the tobacco leaf belongs.

[0075] In this way, since the starting temperature marks the beginning of thermal decomposition of the tobacco product raw material and the ending temperature marks the completion of thermal decomposition of the tobacco product raw material, including the starting temperature and the mass change rate corresponding to the starting temperature, the ending temperature and the mass change rate corresponding to the ending temperature in the characteristic data can effectively utilize the thermal stability characteristics of the tobacco product raw material for growth location prediction. Thus, the accuracy of predicting the growth location of the tobacco product raw material can be further improved.

[0076] In some embodiments, multiple pyrolysis temperatures form a temperature range. As a non-limiting example, the lower limit of the temperature range can be the minimum pyrolysis temperature among the multiple pyrolysis temperatures, and the upper limit of the temperature range can be the maximum pyrolysis temperature among the multiple pyrolysis temperatures. The mass change rate corresponding to the characteristic temperature includes one or more extreme values of the mass change rate of the tobacco product raw material within the temperature range. That is to say, the characteristic data determined from the thermogravimetric analysis data can include at least one extreme value of the mass change rate of the tobacco product raw material and the temperature corresponding to this at least one extreme value. The at least one extreme value can include a maximum value and / or a minimum value. Here, if the mass change rate is differentiated with respect to temperature, the extreme value is the mass change rate corresponding to when the derivative is 0. Or, if the pyrolysis temperature and the mass change rate are plotted as a curve, the extreme value corresponds to the peak or valley on the curve.

[0077] For example, as Figure 2 shown, the lower limit of the temperature range formed by the multiple pyrolysis temperatures is 401.138 K, and the upper limit is 801.138 K. Multiple points (B1~B5) with a slope of 0 on the DTG curve are selected as characteristic points. Among them, B1 and B4 are the valley points on the DTG curve, and B2, B3, and B5 are the peak points on the DTG curve.

[0078] The ordinate values (WB1~WB5) of these characteristic points are the extreme values of the mass change rate of the tobacco leaf within the temperature range of 401.138 K to 801.138 K, and the abscissa values (TB1~TB5) of these characteristic points are the temperatures corresponding to the extreme values (i.e., the characteristic temperatures).

[0079] For example, any one set of data among (TB1~TB5), (WB1~WB5), ((TB1, WB1)~(TB5, WB5)) and the chemical composition data of the tobacco leaf can be input into the trained prediction model to predict the growth location to which the tobacco leaf belongs.

[0080] In this way, since the peak points and valley points in the curve corresponding to the thermogravimetric analysis data can reflect the main thermal decomposition stages of the tobacco product raw material, making the characteristic data include one or more extreme values of the mass change rate of the tobacco product raw material within the temperature range during the pyrolysis process and the corresponding characteristic temperatures can effectively utilize the characteristics of the tobacco product raw material in each thermal decomposition stage for predicting the growth location to which it belongs. Thus, the prediction accuracy of the growth location to which the tobacco product raw material belongs can be further improved.

[0081] In some embodiments, the characteristic data determined from the thermogravimetric analysis data may include: the onset temperature and the mass change rate corresponding to the onset temperature, the termination temperature and the mass change rate corresponding to the termination temperature, at least one extreme value of the mass change rate of the tobacco product raw material, and the temperature corresponding to the at least one extreme value.

[0082] For example, the starting point A1, the ending point A2, and the characteristic points B1 - B5 can be selected together from the DTG curve to obtain the characteristic data for predicting the growth part to which the tobacco product raw material belongs.

[0083] Continuing to refer to Figure 2 , the curve obtained after curve fitting based on these 7 characteristic points A1, A2, and B1 to B5 is as shown by the dashed line in Figure 2 . It can be seen that the curve fitted based on these 7 characteristic points can more accurately reflect the trend of the actual DTG curve. This indicates that the characteristic data obtained using these 7 characteristic points can more accurately reflect the pyrolysis characteristics of the tobacco product raw material during the specific decomposition process. Therefore, using these characteristic data can help accurately predict the growth part to which the tobacco product raw material belongs.

[0084] In some embodiments, multiple pyrolysis temperatures form adjacent first and second temperature intervals.

[0085] The mass change rate corresponding to the characteristic temperature includes the intermediate value between the first extreme value of the mass change rate of the tobacco product raw material in the first temperature interval and the second extreme value of the mass change rate of the tobacco product raw material in the second temperature interval. In other words, the mass change rate corresponding to the characteristic temperature can include the intermediate value between the two extreme values of the mass change rate of the tobacco product raw material in two adjacent temperature intervals.

[0086] In some embodiments, the characteristic temperature corresponding to the intermediate value includes the first temperature corresponding to the intermediate value in the thermogravimetric analysis data. The temperature corresponding to the first extreme value is less than the temperature corresponding to the second extreme value. The first temperature is greater than the temperature corresponding to the first extreme value and less than the temperature corresponding to the second extreme value. In other words, the characteristic temperature corresponding to the intermediate value can include the first temperature between the temperature corresponding to the first extreme value and the temperature corresponding to the second extreme value.

[0087] In this way, further enabling the characteristic data to include the intermediate value between the two extreme values of the mass change rate of the tobacco product raw material in two adjacent temperature intervals and the first temperature corresponding to the intermediate value can further effectively utilize the characteristics of the tobacco product raw material in each pyrolysis stage for predicting the growth part. Thus, the prediction accuracy of the growth part to which the tobacco product raw material belongs can be further improved.

[0088] In some embodiments, the characteristic temperature corresponding to the intermediate value includes a second temperature corresponding to the intermediate value in the thermogravimetric analysis data. The second temperature is less than the temperature corresponding to the first extreme value or greater than the temperature corresponding to the second extreme value. In other words, the characteristic temperature corresponding to the intermediate value may include a second temperature less than the temperature corresponding to the first extreme value or a second temperature greater than the temperature corresponding to the second extreme value.

[0089] Thus, considering that the intermediate value may correspond to multiple different temperatures in the thermogravimetric analysis data, further making the characteristic data include the intermediate value and the second temperature different from the first temperature corresponding to the intermediate value can more effectively utilize the characteristics of the tobacco product raw material in each thermal decomposition stage for predicting the growth location to which it belongs. Therefore, the prediction accuracy of the growth location to which the tobacco product raw material belongs can be further improved.

[0090] The following Figure 3 further illustrates the case where the characteristic data includes the intermediate value of the two extreme values of the mass change rate of the tobacco product raw material in two adjacent temperature intervals and the temperature corresponding to the intermediate value.

[0091] Figure 3 Schematic diagrams showing other embodiments of the DTG curve of the present disclosure.

[0092] Figure 3 Continuing Figure 2 the example shown, the DTG curve of the example shown above is also shown as a solid black line. As Figure 3 shown, A1 and A2 are respectively the starting point and the ending point on the DTG curve. B1 and B4 are the trough points on the DTG curve, and B2, B3, and B5 are the peak points on the DTG curve.

[0093] For example, B1 and B2 are the extreme values in two adjacent temperature intervals. The value of the ordinate of the characteristic point B1 can be taken as the first extreme value, the value of the ordinate of the characteristic point B2 can be taken as the second extreme value, and the points C1 and D1 corresponding to the value of the ordinate of the characteristic point B1 and the value of the ordinate of the characteristic point B2 on the DTG curve can be selected as characteristic points. Among them, the value of the abscissa of the characteristic point C1 is between the values of the abscissas corresponding to the characteristic points B1 and B2, that is, the value of the abscissa of the characteristic point C1 is the first temperature TC1 corresponding to the intermediate value WC1; the value of the abscissa of the characteristic point D1 is less than the value of the abscissa corresponding to the characteristic point B1, that is, the value of the abscissa of the characteristic point D1 is the second temperature TD1 corresponding to the intermediate value WD1. And so on.

[0094] It can be understood here that the midpoint value between the ordinate value of feature point B1 and the ordinate value of feature point B2 can also be selected, and the corresponding point D1' on the DTG curve is used as a feature point. The abscissa value of feature point D1' is greater than the abscissa value corresponding to feature point B2, that is, the abscissa value of feature point D1' is the second temperature TD1' corresponding to the midpoint value WD1'.

[0095] Thus, Figure 3 Eight feature points (also known as "half-peak points") C1 to C4 and D1 to D4 that are selected are schematically shown. Any combination of one or two types of data among the temperatures and mass change rates of these eight feature points can be used as the characteristic temperature and / or the corresponding mass change rate and input into the trained prediction model to predict the growth location to which the tobacco leaf belongs.

[0096] For example, any set of data among (TC1~TC4), (WC1~WC4), ((TC1, WC1)~(TC4, WC4)) and the chemical composition data of the tobacco leaf can be input into the trained prediction model to predict the growth location to which the tobacco leaf belongs.

[0097] For example, any set of data among (TD1~TD4), (WD1~WD4), (TD1, WD1)~(TD4, WD4) and the chemical composition data of the tobacco leaf can be input into the trained prediction model to predict the growth location to which the tobacco leaf belongs.

[0098] For example, any set of data among (TC1~TC4 and TD1~TD4), (WC1~WC4 and WD1~WD4), ((TC1, WC1)~(TC4, WC4), (TD1, WD1)~(TD4, WD4)) and the chemical composition data of the tobacco leaf can be input into the trained prediction model to predict the growth location to which the tobacco leaf belongs.

[0099] In some embodiments, the characteristic data determined from the thermogravimetric analysis data may include: the starting temperature and the mass change rate corresponding to the starting temperature, the termination temperature and the mass change rate corresponding to the termination temperature, at least one extreme value of the mass change rate of the tobacco product raw material and the temperature corresponding to this at least one extreme value, the midpoint value between two extreme values of the mass change rate of the tobacco product raw material in two adjacent temperature intervals and the first temperature corresponding to the midpoint value, the midpoint value between two extreme values of the mass change rate of the tobacco product raw material in two adjacent temperature intervals and the second temperature corresponding to the midpoint value.

[0100] For example, the starting point A1, the ending point A2, the characteristic points B1 - B5 (also known as "inflection points"), the "half - peak points" C1 - C4, and D1 - D4 can be selected from the DTG curve together to obtain the characteristic data for predicting the growth part to which the tobacco product raw material belongs.

[0101] The following shows schematically, in Table 1, the characteristic data (also known as "characteristic parameters") obtained based on these characteristic points.

[0102] Table 1

[0103] Continuing to refer to Figure 3 , the curve obtained after curve fitting based on the 15 characteristic points A1, A2, B1 - B5, C1 - C4, and D1 - D4 is as shown by the dashed line in Figure 3 . It can be seen that the trend of the curve fitted according to these 15 characteristic points is basically the same as that of the actual DTG curve. This indicates that the 30 characteristic data obtained using these 15 characteristic points can quite accurately reflect the pyrolysis characteristics of the tobacco product raw material during the pyrolysis process. Therefore, using these characteristic data can further help accurately predict the growth part to which the tobacco product raw material belongs.

[0104] In some embodiments, the chemical composition data of the tobacco product raw material may further include the ratios of the contents of different chemical components among various chemical components in the tobacco product raw material. For example, the various chemical components may include total nitrogen, nicotine, total sugar, reducing sugar, potassium, and chlorine. The ratios of the contents of different chemical components among the various chemical components may include at least one of the potassium - to - chlorine ratio (i.e., the ratio of the contents of potassium and chlorine), the nitrogen - to - nicotine ratio (i.e., the ratio of the contents of total nitrogen and nicotine), the sugar - to - nicotine ratio (i.e., the ratio of the contents of total sugar and nicotine), and the reducing - sugar - to - total - sugar ratio (i.e., the ratio of the contents of reducing sugar and total sugar).

[0105] In this way, by analyzing the ratios of the contents of different chemical components in the tobacco product raw material, the characteristics of the growth part to which the tobacco product raw material belongs can be comprehensively judged. In the process of predicting the growth part to which the tobacco product raw material belongs, further considering the contents and the ratios of the contents of various chemical components in the tobacco product raw material can more accurately predict the growth part to which the tobacco product raw material belongs.

[0106] The foregoing introduced the relevant implementation of predicting the growth part to which the tobacco product raw material belongs using the trained prediction model. The following further introduces the training method of the prediction model for predicting the growth part to which the tobacco product raw material belongs.

[0107] Figure 4 The flowchart showing some embodiments of the training method of the prediction model of the present disclosure is as shown in Figure 4As shown, for example, the method for training a prediction model may include steps 410 to 430. Among them, the prediction model is used to predict the growth part to which the tobacco product raw material belongs.

[0108] In step 410, obtain the thermogravimetric analysis data and chemical composition data of each tobacco product raw material sample among a plurality of tobacco product raw material samples from multiple different growth parts.

[0109] The thermogravimetric analysis data of each tobacco product raw material sample includes a plurality of sample pyrolysis temperatures in the pyrolysis process of each tobacco product raw material sample and the mass change rate corresponding to each sample pyrolysis temperature among the plurality of sample pyrolysis temperatures. The chemical composition data of each tobacco product raw material sample includes the content of each chemical component among a plurality of chemical components in each tobacco product raw material sample.

[0110] In some embodiments, select tobacco product raw materials from different growth parts in the same production area to construct a training data set. For example, the selected tobacco product raw material samples cover upper tobacco leaves, middle tobacco leaves, and lower tobacco leaves, and the number of tobacco product raw material samples for each selected growth part is not less than a certain amount, so as to construct a training data set.

[0111] In this way, the constructed training data set can more comprehensively cover the characteristics of tobacco product raw materials in each growth part. Using such a training data set to train the prediction model can help the prediction model learn more extensive characteristics of tobacco product raw materials, improve the generalization ability of the model, and thus contribute to improving the prediction performance of the model.

[0112] In step 420, determine the characteristic data of each tobacco product raw material sample from the thermogravimetric analysis data of each tobacco product raw material sample.

[0113] The characteristic data of each tobacco product raw material sample includes the sample characteristic temperature among the plurality of sample pyrolysis temperatures and the mass change rate corresponding to the sample characteristic temperature.

[0114] The selection method of the characteristic data of the tobacco product raw material sample during the training process of the prediction model is similar to the selection method of the characteristic data of the tobacco product raw material during the prediction process using the prediction model. For specific descriptions, reference can be made to the relevant embodiments above. Details are not described here again.

[0115] In step 430, the prediction model is trained with at least one parameter of the sample characteristic temperature and the mass change rate corresponding to the sample characteristic temperature and the chemical composition data of each tobacco product raw material sample as inputs and the predicted growth location to which each tobacco product raw material sample belongs as the output until the training end condition is met. The training end condition can be, for example, that the number of training times reaches a specified number and / or the difference between the predicted growth location to which each tobacco product raw material sample belongs and the actual growth location to which each tobacco product raw material sample belongs is less than a threshold value.

[0116] It can be understood here that the types of data input into the prediction model during the training process of the prediction model are the same as the types of data input into the prediction model during the process of applying the prediction model for prediction.

[0117] For example, if the prediction model is trained with the sample characteristic temperature and the chemical composition data of each tobacco product raw material sample as inputs and the predicted growth location to which each tobacco product raw material sample belongs as the output, then during the process of using the trained prediction model for prediction, the characteristic temperature in the characteristic data and the chemical composition data of the tobacco product raw material are jointly input into the prediction model to obtain the prediction result of the growth location to which the tobacco product raw material belongs.

[0118] For example, if the prediction model is trained with the sample characteristic temperature, the mass change rate corresponding to the sample characteristic temperature, and the chemical composition data of each tobacco product raw material sample as inputs and the predicted growth location to which each tobacco product raw material sample belongs as the output, then during the process of using the trained prediction model for prediction, the characteristic temperature in the characteristic data, the mass change rate corresponding to the characteristic temperature, and the chemical composition data of the tobacco product raw material are jointly input into the prediction model to obtain the prediction result of the growth location to which the tobacco product raw material belongs.

[0119] In some embodiments, the prediction model may include a random forest model. Since the random forest model has good interpretability and is more suitable for large-scale data sets and structured data, using the random forest model to predict the growth location to which the tobacco product raw material belongs can obtain better prediction results.

[0120] Figure 5 The flowchart showing some other embodiments of the training method of the prediction model of the present disclosure.

[0121] As Figure 5 shown, for example, the training method of the prediction model may include steps 510 to 580. This method can be executed as Figure 4 a specific implementation of the method in

[0122] In step 510, tobacco product raw material samples are selected. For example, tobacco product raw materials from different growing parts of the same production area are selected to construct a training data set. For example, the selected tobacco product raw material samples (such as tobacco leaf samples) include upper tobacco leaves, middle tobacco leaves, and lower tobacco leaves, and the number of tobacco product raw material samples selected from each growing part is not less than 30.

[0123] In step 520, chemical composition data for each tobacco product raw material sample is obtained.

[0124] For example, the chemical composition data of each tobacco product raw material sample is obtained by a continuous flow method. The chemical composition data of each tobacco product raw material sample may include total nitrogen, nicotine, total sugar, reducing sugar, potassium, chlorine, potassium-chlorine ratio, nitrogen-alkali ratio, sugar-alkali ratio and two sugar ratio.

[0125] In step 530, a pyrolysis experiment is performed on each tobacco product raw material sample.

[0126] For example, each tobacco product raw material sample is placed in a constant temperature and humidity chamber for a period of time (e.g., 48 hours). After being taken out and ground, it is sieved and fully mixed. For example, 20 mg of tobacco powder is weighed as a sample and placed in an alumina flat crucible. The sample is heated from room temperature to 873K in a nitrogen atmosphere in a thermal analyzer.

[0127] In step 540, thermogravimetric analysis data is obtained for each tobacco product raw material sample.

[0128] For example, for each tobacco product raw material sample, with a step length of 0.5 K, a total of 801 data between 401.138 K and 801.138 K are taken as the thermogravimetric analysis data of the tobacco product raw material sample.

[0129] In step 550, characteristic data of each tobacco product raw material sample is determined from the thermogravimetric analysis data of each tobacco product raw material sample. For example, 30 characteristic data are selected from the DTG curve to characterize the thermogravimetric analysis data of the tobacco product raw material sample.

[0130] For example, refer to the previous article Figures 2 to 3 In the relevant example, the 30 characteristic data of each tobacco product raw material sample can be determined by selecting the starting point A1, the ending point A2, the characteristic points B1~B5, the "half-peak points" C1~C4 and D1~D4 from the DTG curve of each tobacco product raw material sample, and then determining the values ​​of the horizontal and vertical coordinates of these 15 characteristic points in the DTG curve.

[0131] In step 560, a training data set is constructed. For example, the training data set is constructed in the form of "feature data-chemical composition data-growth location", and model training is performed. For example, a random forest model is used for model training.

[0132] For example, the prediction model is a random forest model. Using the sample characteristic temperature, the mass change rate corresponding to the sample characteristic temperature, and the chemical composition data of each tobacco product raw material sample in the characteristic data of each tobacco product raw material sample as inputs, and using the predicted growth location to which each tobacco product raw material sample belongs as the output, the random forest model is trained until the number of training times reaches the specified number of times and / or the difference between the predicted growth location to which each tobacco product raw material sample belongs and the actual growth location to which each tobacco product raw material sample belongs is less than the threshold value.

[0133] In step 570, a trained prediction model (also known as a "growth location prediction model") is obtained.

[0134] In step 580, the characteristic data and chemical composition data of the tobacco product raw material sample used as the test sample are input into the trained prediction model to obtain the prediction result output by the prediction model.

[0135] It should be recognized that in Figure 2 , Figure 3 and Figure 5 's examples, the pyrolysis temperature and the number of characteristic points (characteristic temperature and the corresponding mass change rate) are only exemplary.

[0136] For more embodiments of the training method of the prediction model provided by the embodiments of the present disclosure, reference can be made to the description of the method and its embodiments for predicting the growth location to which the tobacco product raw material belongs in the foregoing text.

[0137] Next, in combination with some embodiments, the performance of the prediction model trained by using the training method of the present disclosure and the prediction effect of predicting the growth location to which the tobacco product raw material belongs by using the prediction model are exemplarily described.

[0138] 149 tobacco product raw material samples from 2 different growth locations in origin A are selected, and 143 tobacco product raw material samples from 2 different growth locations in origin B are selected.

[0139] The continuous flow method is used to measure the 10 chemical composition data of each tobacco product raw material sample in a total of 292 tobacco product raw material samples from origin A and origin B. These 10 chemical composition data include total nitrogen, nicotine, total sugar, reducing sugar, potassium, chlorine, potassium-chlorine ratio, nitrogen-base ratio, sugar-base ratio, and two-sugar ratio.

[0140] For each tobacco product raw material sample among 292 tobacco product raw material samples, a corresponding tobacco powder sample was selected for thermogravimetric testing. In a thermal analyzer, the tobacco powder sample was heated from room temperature to 873 K at a certain heating rate (10 K / min) under a nitrogen atmosphere (carrier gas flow rate of 100 mL / min), and the mass change of the tobacco powder sample during heating was measured to obtain thermogravimetric data (i.e., the data corresponding to the TG curve).

[0141] 801 thermogravimetric analysis data (i.e., the data corresponding to the DTG curve) were obtained by differentiating the thermogravimetric data.

[0142] 30 thermogravimetric analysis data were selected from the 801 thermogravimetric analysis data as characteristic data (also known as "thermogravimetric characteristics") to represent these 801 thermogravimetric analysis data. Each characteristic data includes a characteristic temperature and the mass change rate corresponding to the characteristic temperature.

[0143] A dataset was constructed in the form of "characteristic data - chemical composition data - growth location" sample pairs, and the dataset was divided into a training set and a test set in a ratio of 8:2.

[0144] To construct a prediction model, the growth location to which the sample belongs was used as the target value of the model output. On this basis, the following five types of input data were selected for model training: (1) 10 chemical composition data of each tobacco product raw material sample; (2) 801 thermogravimetric analysis data of each tobacco product raw material sample; (3) 30 thermogravimetric characteristics of each tobacco product raw material sample; (4) 801 thermogravimetric analysis data and 10 chemical composition data of each tobacco product raw material sample; (5) 30 thermogravimetric characteristics and 10 chemical composition data of each tobacco product raw material sample. Among them, the data in categories (1) to (3) are also known as non-fused data, and the data in categories (4) to (5) are also known as fused data.

[0145] Subsequently, the random forest algorithm was used to perform model training on the above five types of input data respectively, and the classification accuracy was used as the evaluation index for the prediction performance of the trained model.

[0146] The five-fold cross-validation method was used to divide the above constructed dataset, and the dataset was evenly divided into five subsets with equal data volumes. In each validation process, four of the subsets were selected for model training, and the remaining one subset was used for model testing. This division and validation process were repeated five times, labeled as k1, k2, k3, k4, and k5 respectively. In each validation, a different subset was selected as the test set, and the remaining subsets were used as the training set. The prediction accuracy of the trained model on each test set was recorded, and finally the average value of the prediction accuracies of the five validations was calculated as the comprehensive evaluation index for the model prediction performance.

[0147] Table 2 shows the prediction results of predicting the growth location to which the tobacco product raw material samples from origin A belong.

[0148] Table 2 Prediction Results of the Random Forest Algorithm for Tobacco Product Raw Material Samples of Two Growth Locations in Origin A

[0149]

[0150] As shown in Table 2, the effect of using the fusion data of chemical composition data and thermogravimetric characteristics as the input of the model for predicting the growth location to which the tobacco product raw material belongs is optimal, and its average prediction accuracy reaches 93.93%. This indicates that using the fusion data of chemical composition data and thermogravimetric characteristics can accurately achieve the prediction of the growth location to which the tobacco product raw material belongs.

[0151] Table 3 shows the prediction results of predicting the growth location to which the tobacco product raw material samples from origin B belong.

[0152] Table 3 Prediction Results of the Random Forest Algorithm for Tobacco Product Raw Material Samples of Two Growth Locations in Origin B

[0153] It can also be seen from the results shown in Table 3 that the average prediction accuracy of using only thermogravimetry-related data (such as thermogravimetric analysis data and thermogravimetric characteristics) for predicting the growth location to which the tobacco product raw material belongs is significantly higher than the average prediction performance of using only chemical composition data for predicting the growth location to which the tobacco product raw material belongs. Among them, the effect of using the fusion data of chemical composition data and thermogravimetric characteristics as the input of the model for predicting the growth location to which the tobacco product raw material belongs is optimal, and its average prediction accuracy reaches 94.74%.

[0154] Generally speaking, the prediction method using fusion data has better prediction performance than the prediction method using non-fusion data in terms of prediction effect, that is, it can more accurately predict the growth location to which the tobacco product raw material belongs. This verifies the technical solution of using the characteristic data that can characterize the pyrolysis characteristics of the tobacco product raw material and the chemical composition data of the tobacco product raw material provided by the embodiments of the present disclosure for predicting the growth location to which the tobacco product raw material belongs, which can effectively improve the prediction accuracy.

[0155] Figure 6 The block diagrams of some embodiments of the device for predicting the growth location to which the tobacco product raw material belongs according to the present disclosure are shown.

[0156] As Figure 6 shown, the device 600 for predicting the growth location to which the tobacco product raw material belongs includes an acquisition module 601, a determination module 602, and a prediction module 603.

[0157] The acquisition module 601 can be configured to acquire the thermogravimetric analysis data and chemical composition data of the tobacco product raw material. The thermogravimetric analysis data of the tobacco product raw material includes multiple pyrolysis temperatures in the pyrolysis process of the tobacco product raw material and the mass change rate of the tobacco product raw material corresponding to each pyrolysis temperature among the multiple pyrolysis temperatures. The chemical composition data of the tobacco product raw material includes the content of each chemical component among multiple chemical components in the tobacco product raw material.

[0158] The determination module 602 can be configured to determine characteristic data from the thermogravimetric analysis data. The characteristic data includes the characteristic temperature among the multiple pyrolysis temperatures and the mass change rate corresponding to the characteristic temperature.

[0159] The prediction module 603 can be configured to predict the growth location to which the tobacco product raw material belongs based on at least one parameter among the characteristic temperature and the mass change rate corresponding to the characteristic temperature and the chemical composition data, using the trained prediction model.

[0160] In some embodiments, the apparatus 600 for predicting the growth location to which the tobacco product raw material belongs may further include other modules that perform other operations in the relevant embodiments of the method for predicting the growth location to which the tobacco product raw material belongs described above.

[0161] Figure 7 A block diagram showing some embodiments of the training apparatus of the prediction model of the present disclosure.

[0162] As Figure 7 shown, the training apparatus 700 of the prediction model includes an acquisition module 701, a determination module 702, and a training module 703. The training apparatus 700 of the prediction model is used to predict the growth location to which the tobacco product raw material belongs.

[0163] The acquisition module 701 is configured to acquire the thermogravimetric analysis data and chemical composition data of each tobacco product raw material sample from multiple tobacco product raw material samples from multiple different growth locations. The thermogravimetric analysis data of each tobacco product raw material sample includes multiple sample pyrolysis temperatures in the pyrolysis process of each tobacco product raw material sample and the mass change rate corresponding to each sample pyrolysis temperature among the multiple sample pyrolysis temperatures. The chemical composition data of each tobacco product raw material sample includes the content of each chemical component among multiple chemical components in each tobacco product raw material sample.

[0164] The determination module 702 is configured to determine the characteristic data of each tobacco product raw material sample from the thermogravimetric analysis data of each tobacco product raw material sample. The characteristic data of each tobacco product raw material sample includes the sample characteristic temperature among the multiple sample pyrolysis temperatures and the mass change rate corresponding to the sample characteristic temperature.

[0165] The training module 703 is configured to train the prediction model with at least one parameter of the sample characteristic temperature and the mass change rate corresponding to the sample characteristic temperature and the chemical composition data of each tobacco product raw material sample as the input and the predicted growth site to which each tobacco product raw material sample belongs as the output until the training end condition is met.

[0166] In some embodiments, the training device 700 of the prediction model may further include other modules that perform other operations in the relevant embodiments of the foregoing method for training the prediction model.

[0167] Figure 8 The block diagram showing some embodiments of the electronic device of the present disclosure.

[0168] As Figure 8 shown, the electronic device 800 of this embodiment includes: a memory 801 and a processor 802 coupled to the memory 801. The processor 802 is configured to execute the method in any one of the embodiments of the present disclosure based on the instructions stored in the memory 801.

[0169] Among them, the memory 801 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory stores, for example, an operating system, application programs, a boot loader, a database, and other programs.

[0170] In some embodiments, the electronic device 800 may be used as a device for predicting the growth site to which a tobacco product raw material belongs for performing the operations in any one of the foregoing embodiments. In other embodiments, the electronic device 800 may be used as a training device for the prediction model for performing the operations in any one of the foregoing embodiments.

[0171] Figure 9 The block diagram showing other embodiments of the electronic device of the present disclosure.

[0172] As Figure 9 shown, the electronic device 900 of this embodiment includes: a memory 901 and a processor 902 coupled to the memory 901. The processor 902 is configured to execute the method in any one of the foregoing embodiments based on the instructions stored in the memory 901.

[0173] The memory 901 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory stores, for example, an operating system, application programs, a boot loader, and other programs.

[0174] The electronic device 900 may further include an input / output interface 903, a network interface 904, a storage interface 905, etc. These interfaces 903, 904, 905 and the memory 901 and the processor 902 may be connected, for example, via a bus 906. Among them, the input / output interface 903 provides a connection interface for input / output devices such as a display, a mouse, a keyboard, a touch screen, a microphone, a speaker, etc. The network interface 904 provides a connection interface for various networking devices. The storage interface 905 provides a connection interface for external storage devices such as an SD card and a USB flash drive.

[0175] In some embodiments, the electronic device 900 may be used as a device for predicting the growth part to which a tobacco product raw material belongs and performing the operations of any of the above embodiments. In other embodiments, the electronic device 900 may be used as a training device for a prediction model that performs the operations of any of the above embodiments.

[0176] The embodiments of the present disclosure also provide a computer-readable storage medium, including computer program instructions, which when executed by a processor, implement the method of any of the above embodiments.

[0177] The embodiments of the present disclosure also provide a computer program product, including a computer program, which when executed by a processor, implement the method of any of the above embodiments.

[0178] Those skilled in the art should understand that the embodiments of the present disclosure may be provided as a method, a system, or a computer program product. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0179] So far, the technical solutions for predicting the growth part to which a tobacco product raw material belongs and the technical solutions for training a prediction model according to the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein based on the above description.

[0180] The methods and systems of the present disclosure may be implemented in many ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the methods is for illustration only, and the steps of the methods of the present disclosure are not limited to the specific order described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, and these programs include machine-readable instructions for implementing the methods according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the methods according to the present disclosure.

[0181] Although some specific embodiments of the present disclosure have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustration only and not for limiting the scope of the present disclosure. Those skilled in the art should understand that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for predicting the growth position of tobacco product raw materials, comprising: Acquire thermogravimetric analysis data and chemical composition data of a tobacco product raw material, wherein the thermogravimetric analysis data includes a plurality of pyrolysis temperatures during the pyrolysis process of the tobacco product raw material and a mass change rate of the tobacco product raw material corresponding to each of the plurality of pyrolysis temperatures, and the chemical composition data includes the content of each of the plurality of chemical components in the tobacco product raw material; Determining characteristic data from the thermogravimetric analysis data, the characteristic data comprising characteristic temperatures among the plurality of pyrolysis temperatures and mass change rates corresponding to the characteristic temperatures; Based on at least one parameter of the characteristic temperature and the mass change rate corresponding to the characteristic temperature and the chemical composition data, a trained prediction model is used to predict the growth site to which the tobacco product raw material belongs.

2. The method according to claim 1, wherein: The multiple pyrolysis temperatures form a temperature interval, and the mass change rate corresponding to the characteristic temperature includes one or more extreme values ​​of the mass change rate of the tobacco product raw material within the temperature interval.

3. The method according to claim 1, wherein: The multiple pyrolysis temperatures form a first temperature interval and a second temperature interval adjacent to each other. The mass change rate corresponding to the characteristic temperature includes an intermediate value between a first extreme value of the mass change rate of the tobacco product raw material in the first temperature interval and a second extreme value of the mass change rate of the tobacco product raw material in the second temperature interval. The characteristic temperature includes a first temperature corresponding to the intermediate value in the thermogravimetric analysis data, a temperature corresponding to the first extreme value is lower than a temperature corresponding to the second extreme value, and the first temperature is higher than a temperature corresponding to the first extreme value and lower than a temperature corresponding to the second extreme value.

4. The method according to claim 3, wherein: The characteristic temperature includes a second temperature corresponding to the intermediate value in the thermogravimetric analysis data, where the second temperature is less than a temperature corresponding to the first extreme value or greater than a temperature corresponding to the second extreme value.

5. The method according to any one of claims 1 to 4, wherein: The characteristic temperature includes a starting temperature or an ending temperature among the plurality of pyrolysis temperatures.

6. The method according to any one of claims 1 to 4, wherein: The chemical composition data also includes the ratio of the content of different chemical components among the multiple chemical components in the tobacco product raw material.

7. The method according to any one of claims 1 to 4, wherein: The trained prediction model is trained in the following manner: Obtaining thermogravimetric analysis data and chemical composition data of each tobacco product raw material sample from a plurality of tobacco product raw material samples from a plurality of different growth parts, wherein the thermogravimetric analysis data of each tobacco product raw material sample includes a plurality of sample pyrolysis temperatures during the pyrolysis process of each tobacco product raw material sample and a mass change rate corresponding to each sample pyrolysis temperature among the plurality of sample pyrolysis temperatures, and the chemical composition data of each tobacco product raw material sample includes the content of each chemical component among the plurality of chemical components in each tobacco product raw material sample; Determining characteristic data of each tobacco product raw material sample from the thermogravimetric analysis data of each tobacco product raw material sample, wherein the characteristic data of each tobacco product raw material sample includes a sample characteristic temperature among the plurality of sample pyrolysis temperatures and a mass change rate corresponding to the sample characteristic temperature; and The prediction model is trained with the sample characteristic temperature and at least one parameter of the mass change rate corresponding to the sample characteristic temperature and the chemical composition data of each tobacco product raw material sample as input and the predicted growth position to which each tobacco product raw material sample belongs as output until the training end condition is met.

8. The method according to any one of claims 1 to 4, wherein: The trained prediction model includes a random forest model.

9. The method according to any one of claims 1 to 4, wherein: The plurality of chemical components include at least one of total nitrogen, nicotine, total sugar, reducing sugar, potassium and chlorine.

10. A method for training a prediction model, the prediction model being used to predict the growth position to which tobacco product raw materials belong, the training method comprising: Obtaining thermogravimetric analysis data and chemical composition data of each tobacco product raw material sample from a plurality of tobacco product raw material samples from a plurality of different growth parts, wherein the thermogravimetric analysis data of each tobacco product raw material sample includes a plurality of sample pyrolysis temperatures during the pyrolysis process of each tobacco product raw material sample and a mass change rate corresponding to each sample pyrolysis temperature among the plurality of sample pyrolysis temperatures, and the chemical composition data of each tobacco product raw material sample includes the content of each chemical component among the plurality of chemical components in each tobacco product raw material sample; Determining characteristic data of each tobacco product raw material sample from the thermogravimetric analysis data of each tobacco product raw material sample, wherein the characteristic data of each tobacco product raw material sample includes a sample characteristic temperature among the plurality of sample pyrolysis temperatures and a mass change rate corresponding to the sample characteristic temperature; and The prediction model is trained with at least one parameter of the sample characteristic temperature and the mass change rate corresponding to the sample characteristic temperature and the chemical composition data of each tobacco product raw material sample as input and the predicted growth position of each tobacco product raw material sample as output until the training end conditions are met.

11. A device for predicting the growth position of tobacco product raw materials, comprising: an acquisition module, configured to acquire thermogravimetric analysis data and chemical composition data of a tobacco product raw material, wherein the thermogravimetric analysis data includes a plurality of pyrolysis temperatures during the pyrolysis process of the tobacco product raw material and a mass change rate of the tobacco product raw material corresponding to each of the plurality of pyrolysis temperatures, and the chemical composition data includes a content of each of the plurality of chemical components in the tobacco product raw material; A determination module configured to determine characteristic data from the thermogravimetric analysis data, wherein the characteristic data includes a characteristic temperature among the plurality of pyrolysis temperatures and a mass change rate corresponding to the characteristic temperature; The prediction module is configured to predict the growth site of the tobacco product raw material using a trained prediction model based on at least one parameter of the characteristic temperature and the mass change rate corresponding to the characteristic temperature and the chemical composition data.

12. An electronic device, comprising: Memory; and A processor coupled to the memory, the processor being configured to execute the method for predicting the growth location of tobacco product raw materials as described in any one of claims 1-9 or the training method of the prediction model as described in claim 10 based on instructions stored in the memory.

13. A computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the method for predicting the growth site of tobacco product raw materials as described in any one of claims 1 to 9 or the training method of the prediction model as described in claim 10.

14. A computer program product, comprising instructions, which, when executed by a processor, cause the processor to execute the method for predicting the growth location of tobacco product raw materials according to any one of claims 1 to 9 or the training method of the prediction model according to claim 10.

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