Tobacco leaf proportion design method and device, computer equipment and computer readable storage medium

Abstract

The invention relates to a tobacco leaf proportion design method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: determining a thermal weight loss prediction equation corresponding to various tobacco leaves based on thermogravimetric analysis; acquiring information of multiple groups of tobacco leaf groups; the tobacco leaf group information comprises various tobacco leaves and corresponding tobacco leaf proportions; determining thermal weight loss statistical information corresponding to each group of tobacco leaf group information according to the thermal weight loss prediction equation of each type of tobacco leaves and the multiple groups of tobacco leaf group information; and according to the thermal weight loss statistical information and a preset condition, determining a tobacco leaf ratio corresponding to the optimal tobacco leaf group. By adopting the method, the thermal weight loss rate of the tobacco can be accurately predicted, and the tobacco ratio of the tobacco group is optimized according to the thermal weight loss statistical information and the preset conditions; the problem that the sensory quality and the smoke release characteristic of a heated cigarette product are difficult to keep consistent due to the fact that the fluctuation of the thermal weight loss rate of the heated cigarette product is large is solved.

Description

Technical Field

[0001] The present application relates to the technical field of tobacco products, and in particular to a tobacco leaf ratio design method, device, computer equipment, and computer-readable storage medium. Background Art

[0002] As a new tobacco product, heated cigarettes release tobacco ingredients through heating rather than combustion, effectively reducing the formation of harmful substances and holding promising market prospects. The quality stability of heated cigarettes primarily depends on the thermal weight loss of tobacco during heating, and the rate of thermal weight loss is a key factor influencing the product's sensory quality and smoke release characteristics.

[0003] At present, due to the inability to accurately predict the thermal weight loss rate of tobacco in relevant technologies and the lack of methods to optimize the tobacco leaf ratio of the tobacco leaf group, heated cigarette products are prone to large fluctuations in the thermal weight loss rate, resulting in difficulty in maintaining consistency in the sensory quality and smoke release characteristics of heated cigarette products, affecting consumers' usage experience. Summary of the Invention

[0004] Based on this, it is necessary to provide a tobacco ratio design method, tobacco ratio design device and computer equipment that can accurately predict the thermal weight loss rate of tobacco and optimize the tobacco ratio of the tobacco group to address the above technical problems.

[0005] In a first aspect, the present application provides a tobacco leaf blend design method. The method comprises: determining, based on thermogravimetric analysis, a thermogravimetric prediction equation corresponding to multiple tobacco leaves; obtaining multiple sets of tobacco leaf group information; the tobacco leaf group information comprising multiple tobacco leaves and corresponding tobacco leaf blends; determining, based on the thermogravimetric prediction equation for each tobacco leaf and the multiple sets of tobacco leaf group information, thermogravimetric statistical information corresponding to each set of tobacco leaf group information; and determining, based on the thermogravimetric statistical information and preset conditions, a tobacco leaf blend corresponding to an optimal tobacco leaf group.

[0006] In one embodiment, determining the thermal weight loss prediction equations corresponding to multiple tobacco leaves based on thermogravimetric analysis includes: obtaining the actual heating temperature of the tobacco leaves; determining the thermal weight loss rate surface prediction equations corresponding to the multiple tobacco leaves based on thermogravimetric analysis; the thermal weight loss rate surface prediction equation includes: a surface prediction equation consisting of thermal weight loss rate, thermal weight loss analysis temperature and thermal weight loss analysis time; and determining the thermal weight loss prediction equation for each tobacco leaf based on the actual heating temperature and the thermal weight loss rate surface prediction equation for each tobacco leaf.

[0007] In one embodiment, determining the thermal weight loss prediction equation for each tobacco leaf based on the actual heating temperature and the thermal weight loss rate surface prediction equation for each tobacco leaf includes: substituting the actual heating temperature into the thermal weight loss rate surface prediction equation for each tobacco leaf to determine the thermal weight loss prediction equation for each tobacco leaf; the thermal weight loss prediction equation includes a prediction equation consisting of the thermal weight loss rate and the thermal weight loss analysis time.

[0008] In one embodiment, determining the thermal weight loss statistical information corresponding to each group of tobacco leaf group information based on the thermal weight loss prediction equation of each tobacco leaf and multiple groups of tobacco leaf group information includes: obtaining the thermal weight loss prediction equations of multiple target tobacco leaves in the target tobacco leaf group information; the target tobacco leaf group information is any one group of tobacco leaf group information among the multiple groups of tobacco leaf group information; determining the average thermal weight loss rate corresponding to the target tobacco leaf group information based on the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equation of multiple target tobacco leaves; determining the standard deviation of the thermal weight loss rate corresponding to the target tobacco leaf group information based on the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equation of multiple target tobacco leaves.

[0009] In one embodiment, determining the average thermal weight loss rate corresponding to the target tobacco group information based on the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of multiple target tobacco leaves includes: obtaining the actual heating time; summing the thermal weight loss prediction equations of multiple target tobacco leaves with the target tobacco leaf ratio, and then integrating them with the actual heating time to obtain the sum of the thermal weight loss rates; and determining the average thermal weight loss rate corresponding to the target tobacco leaf group information based on the sum of the thermal weight loss rates and the actual heating time.

[0010] In one embodiment, determining the standard deviation of the thermal weight loss rate corresponding to the target tobacco group information based on the target tobacco ratio in the target tobacco group information and the thermal weight loss prediction equations of multiple target tobacco leaves includes: obtaining the actual heating time; within the actual heating time range, sampling the thermal weight loss prediction equations of multiple target tobacco leaves respectively to obtain a thermal weight loss rate group for each target tobacco leaf; determining the standard deviation of the thermal weight loss rate corresponding to the target tobacco group information based on the target tobacco ratio and the thermal weight loss rate group for each target tobacco leaf.

[0011] In one embodiment, determining the tobacco ratio corresponding to the optimal tobacco group based on the thermal weight loss statistical information and preset conditions includes: determining the tobacco ratio corresponding to the optimal tobacco group based on the tobacco group information whose average thermal weight loss rate is greater than or equal to a preset threshold and whose thermal weight loss rate standard deviation is the smallest.

[0012] In a second aspect, the present application further provides a tobacco leaf ratio design device, the device comprising:

[0013] An analysis module is used to determine the thermogravimetric loss prediction equations corresponding to various tobacco leaves based on thermogravimetric analysis;

[0014] An acquisition module is used to acquire information of multiple tobacco leaf groups; the tobacco leaf group information includes: multiple tobacco leaves and corresponding tobacco leaf ratios;

[0015] a calculation module, configured to determine the thermal weight loss statistical information corresponding to each group of tobacco leaf information based on the thermal weight loss prediction equation for each tobacco leaf and multiple groups of tobacco leaf group information;

[0016] The determination module is used to determine the tobacco leaf ratio corresponding to the optimal tobacco leaf group based on the thermal weight loss statistical information and preset conditions.

[0017] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements any one of the methods in the first aspect when executing the computer program.

[0018] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements any one of the methods in the above-mentioned first aspect when executed by a processor.

[0019] The above-mentioned tobacco leaf ratio design method, tobacco leaf ratio design device and computer equipment perform thermogravimetric analysis on multiple tobacco leaves to obtain thermal weight loss prediction equations corresponding to each tobacco leaf. After obtaining multiple groups of tobacco leaf group information, thermal weight loss statistical information corresponding to each tobacco leaf group is calculated based on the thermal weight loss prediction equations corresponding to each tobacco leaf and the multiple groups of tobacco leaf group information. Finally, based on the thermal weight loss statistical information and preset conditions, the tobacco leaf ratio corresponding to the optimal tobacco leaf group is screened out from the multiple groups of tobacco leaf group information. The thermal weight loss rate of tobacco is accurately predicted by the thermal weight loss prediction equations corresponding to each tobacco leaf obtained by performing thermogravimetric analysis on multiple tobacco leaves. The tobacco leaf ratio of the tobacco group is optimized by screening out the tobacco leaf ratio corresponding to the optimal tobacco leaf group from the multiple groups of tobacco leaf group information based on the thermal weight loss statistical information and preset conditions, thereby preventing the problem of difficulty in maintaining consistent sensory quality and smoke release characteristics of heated cigarette products due to large fluctuations in the thermal weight loss rate of heated cigarette products. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of a process for designing tobacco leaf ratios in one embodiment;

[0021] Figure 2A schematic diagram of a process for determining the thermal weight loss statistical information corresponding to each group of tobacco leaf group information based on the thermal weight loss prediction equation of each tobacco leaf and multiple groups of tobacco leaf group information in one embodiment;

[0022] Figure 3 A schematic flow chart of a tobacco leaf ratio design method according to another embodiment;

[0023] Figure 4 This is a graph showing the thermal weight loss rates of tobacco raw materials A, B, and the optimal leaf group formula when operated at 260° C. for 6 minutes in a tobacco leaf ratio design method in another embodiment;

[0024] Figure 5 This is a graph showing the thermal weight loss rate of tobacco raw materials C, D, E and the optimal leaf group formula when operated at 230° C. for 7 minutes in a tobacco leaf ratio design method in another embodiment;

[0025] Figure 6 This is a graph showing the thermal weight loss rate of tobacco raw materials C, D, E, F and the optimal leaf group formula when operated at 230° C. for 7 minutes in a tobacco leaf ratio design method in another embodiment;

[0026] Figure 7 This is a structural block diagram of a tobacco leaf ratio design device in one embodiment;

[0027] Figure 8 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0029] At present, the design of the ratio of each tobacco in the tobacco group of heated cigarettes mostly relies on experience or simple thermogravimetric analysis data, and usually only considers the influence of a single factor (such as temperature or time) on the thermal weight loss rate, which makes it difficult to fully reflect the complex thermal weight loss process of tobacco during the heating process. In addition, since the relevant technology cannot accurately predict the thermal weight loss rate of tobacco, and there is a lack of methods to optimize the tobacco ratio of the tobacco group, heated cigarette products are prone to large fluctuations in the thermal weight loss rate, resulting in the difficulty in maintaining consistency in the sensory quality and smoke release characteristics of heated cigarette products, affecting the consumer's experience. At the same time, the design of the ratio of each tobacco in the tobacco group of traditional heated cigarettes mostly relies on experience, and it is difficult to optimize the proportion of tobacco raw materials used, resulting in waste of raw materials and increased production costs.

[0030] To address the above issues, there is a lack of a tobacco leaf ratio design method that can comprehensively consider the influence of multiple factors such as temperature and time, and accurately optimize the tobacco leaf ratio of the tobacco leaf group based on the thermal weight loss rate, so as to improve the thermal weight loss rate prediction accuracy, optimize the formula design, improve product quality stability, and reduce production costs.

[0031] In order to solve the above problem, in one embodiment of the present application, Figure 1 As shown, a tobacco leaf ratio design method is provided, comprising the following steps:

[0032] Step 201: Determine thermogravimetric loss prediction equations corresponding to various tobacco leaves based on thermogravimetric analysis.

[0033] Thermogravimetric analysis refers to studying the thermal weight loss process of a substance by measuring its mass change during heating. In this embodiment, the substance is a variety of tobacco leaves, and the thermal weight loss prediction equation is a curve equation that plots the thermal weight loss rate of the tobacco leaves during the thermal weight loss process versus the thermal weight loss analysis time. That is, thermogravimetric analysis is performed on each of the various tobacco leaves to obtain multiple thermal weight loss prediction equations corresponding to the various tobacco leaves. The differences between the various tobacco leaves lie in their type, variety, origin, location, nicotine content, reducing sugar content, and sensory evaluation quality. Thermogravimetric analysis is performed on the various tobacco leaves using a thermogravimetric analyzer.

[0034] Step 202: Acquire multiple groups of tobacco leaf group information; the tobacco leaf group information includes: multiple types of tobacco leaves and corresponding tobacco leaf ratios.

[0035] The tobacco leaf group information includes a variety of tobacco leaf types and the ratio of each type of tobacco leaf. The purpose of obtaining multiple groups of tobacco leaf group information is to determine the tobacco leaf ratio corresponding to the optimal tobacco leaf group from each group of tobacco leaf group information based on the thermal weight loss statistical information of each group of tobacco leaf group information and preset conditions, thereby optimizing the tobacco leaf ratio of the tobacco leaf group. Each tobacco leaf group information actually represents the formula of a cigarette product, that is, the types of tobacco leaves included and the ratio between each tobacco leaf. For example, one group of tobacco leaf group information in the multiple groups of tobacco leaf group information includes tobacco leaf A and tobacco leaf B, with the ratio of tobacco leaf A being 0.3 and the ratio of tobacco leaf B being 0.7.

[0036] Step 203: Determine the thermal weight loss statistical information corresponding to each group of tobacco leaf information based on the thermal weight loss prediction equation of each tobacco leaf and multiple groups of tobacco leaf group information.

[0037] Based on a set of tobacco leaf group information from multiple sets of tobacco leaf group information, the various tobacco leaves corresponding to the tobacco leaf group are first determined. The corresponding thermal weight loss prediction equations for the various tobacco leaves are then searched for within the thermal weight loss prediction equations for each tobacco leaf, and all thermal weight loss prediction equations corresponding to the tobacco leaf group information are determined. The determined thermal weight loss prediction equations are then statistically analyzed to determine thermal weight loss statistical information for the corresponding tobacco leaf group information. The above analysis is performed on each set of tobacco leaf group information from the multiple sets of tobacco leaf group information to obtain thermal weight loss statistical information corresponding to each set of tobacco leaf group information. The thermal weight loss statistical information includes thermal weight loss parameters corresponding to each set of tobacco leaf group information, such as the average thermal weight loss rate and the standard deviation of the thermal weight loss rate.

[0038] Step 204: Determine the tobacco leaf ratio corresponding to the optimal tobacco leaf group based on the thermogravimetric statistical information and preset conditions.

[0039] The preset conditions are preset constraints related to the thermal weight loss statistical information, that is, according to the thermal weight loss statistical information and the preset conditions, the tobacco leaf ratio corresponding to the optimal tobacco leaf group is determined based on the thermal weight loss statistical information corresponding to each group of tobacco leaf group information. According to the preset conditions, the thermal weight loss statistical information that meets the preset conditions is screened out, and the tobacco leaf ratio in the tobacco leaf group information corresponding to the screened thermal weight loss statistical information is used as the formula of the optimal tobacco leaf group.

[0040] In the above-mentioned tobacco leaf ratio design method, thermogravimetric analysis is performed on multiple tobacco leaves respectively to obtain thermal weight loss prediction equations corresponding to each tobacco leaf. After obtaining multiple groups of tobacco leaf group information, thermal weight loss statistical information corresponding to each tobacco leaf group is calculated based on the thermal weight loss prediction equations corresponding to each tobacco leaf and the multiple groups of tobacco leaf group information. Finally, based on the thermal weight loss statistical information and preset conditions, the tobacco leaf ratio corresponding to the optimal tobacco leaf group is screened out from the multiple groups of tobacco leaf group information. The thermal weight loss rate of tobacco is accurately predicted by the thermal weight loss prediction equations corresponding to each tobacco leaf obtained by performing thermogravimetric analysis on multiple tobacco leaves respectively. The tobacco leaf ratio of the tobacco group is optimized by screening out the tobacco leaf ratio corresponding to the optimal tobacco leaf group from the multiple groups of tobacco leaf group information based on the thermal weight loss statistical information and preset conditions, so as to prevent the problem of difficulty in maintaining consistent sensory quality and smoke release characteristics of heated cigarette products due to large fluctuations in the thermal weight loss rate of heated cigarette products.

[0041] In other embodiments of the present application, the thermogravimetric analysis-based determination of the thermogravimetric loss prediction equations corresponding to the various tobacco leaves includes:

[0042] Step 1: Obtain the actual heating temperature of the tobacco leaves.

[0043] The actual heating temperature is the heating temperature of the cigarette product during use. In this embodiment, the actual heating temperature ranges from 200°C to 300°C, such as 230°C or 260°C.

[0044] Step 2: Based on thermogravimetric analysis, determine the thermal weight loss rate surface prediction equations corresponding to various tobacco leaves.

[0045] The thermal weight loss rate surface prediction equation includes a surface prediction equation for the thermal weight loss rate, the thermal weight loss analysis temperature, and the thermal weight loss analysis time. Specifically, the thermal weight loss rate surface prediction equation is determined for each type of tobacco leaf based on the thermal weight loss analysis temperature and time. Specifically, the thermal weight loss rate surface prediction equations are determined for each type of tobacco leaf based on the thermal weight loss analysis. Specifically, the thermal weight loss analysis conditions involve subjecting the tobacco leaves to different heating temperatures and times, and recording their thermal weight loss rates. The thermal weight loss data for each type of tobacco leaf is obtained through thermogravimetric analysis. The thermal weight loss data includes the thermal weight loss rates of the tobacco leaves at different heating temperatures and times. Based on the thermal weight loss data for each type of tobacco leaf, the thermal weight loss rate surface prediction equations for each type of tobacco leaf are then determined. Based on the temperature and time data obtained from the thermogravimetric analysis and the tobacco thermal weight loss rates, a thermal weight loss rate surface prediction equation for heated cigarette tobacco is established.

[0046] For example, the thermal weight loss rate surface prediction equation is:

[0047]

[0048] Among them, z(x,y) is the predicted value of tobacco thermal weight loss rate, k1 to k 10 is the fitting parameter of the nonlinear prediction equation, x is the thermogravimetric analysis temperature, and y is the thermogravimetric analysis time.

[0049] Step 3: Determine the thermal weight loss prediction equation for each tobacco leaf based on the actual heating temperature and the thermal weight loss rate surface prediction equation for each tobacco leaf.

[0050] According to the actual heating temperature and the thermal weight loss rate surface prediction equation of each tobacco leaf, the thermal weight loss prediction equation of each tobacco leaf is determined. Specifically, the actual heating temperature is substituted into the thermal weight loss rate surface prediction equation of each tobacco leaf to determine the thermal weight loss prediction equation of each tobacco leaf; the thermal weight loss prediction equation includes a prediction equation composed of the thermal weight loss rate and the thermal weight loss analysis time.

[0051] Exemplarily, the thermogravimetric prediction equation is:

[0052]

[0053] Among them, z(y) is the predicted value of tobacco thermal weight loss rate, k1 to k 10 is the fitting parameter of the nonlinear prediction equation, w is the actual heating temperature, and y is the thermogravimetric analysis time.

[0054] In other embodiments of the present application, Figure 2 As shown, the determining of the thermal weight loss statistical information corresponding to each group of tobacco leaf group information based on the thermal weight loss prediction equation of each tobacco leaf and multiple groups of tobacco leaf group information includes:

[0055] Step 301: Obtain thermal weight loss prediction equations for multiple target tobacco leaves in the target tobacco leaf group information.

[0056] The target tobacco leaf group information is any one of the multiple tobacco leaf group information. For example, when the target tobacco leaf group includes three target tobacco leaves and tobacco blends corresponding to the three target tobacco leaves, the thermal weight loss prediction equations corresponding to the three target tobacco leaves are obtained respectively.

[0057] It should be noted that in this embodiment, any one group of tobacco leaf group information among the multiple groups of tobacco leaf group information is used as the target tobacco leaf group information in order to facilitate the specific explanation of the specific process of determining the thermal weight loss statistical information corresponding to each group of tobacco leaf group information based on the thermal weight loss prediction equation of each tobacco leaf and the multiple groups of tobacco leaf group information. In actual circumstances, it is necessary to use each group of tobacco leaf group information among the multiple groups of tobacco leaf group information as the target tobacco leaf group to execute the relevant steps of this embodiment.

[0058] Step 302: Determine the average value of the thermal weight loss rate corresponding to the target tobacco leaf group information based on the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of the multiple target tobacco leaves.

[0059] The target tobacco leaf ratio is the tobacco leaf ratio in the target tobacco leaf group information. For example, when the target tobacco leaf group includes target tobacco leaf A, target tobacco leaf B, and target tobacco leaf C, the ratio of target tobacco leaf A is 0.46, the ratio of target tobacco leaf B is 0.31, and the ratio of target tobacco leaf C is 0.23. The multiple target tobacco leaves are the multiple tobacco leaves in the target tobacco leaf group information, and the average thermal weight loss rate is the average thermal weight loss rate of the multiple target tobacco leaves in the target tobacco leaf group.

[0060] Step 303: Determine the standard deviation of the thermal weight loss rate corresponding to the target tobacco leaf group information based on the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of the multiple target tobacco leaves.

[0061] The standard deviation of the thermal weight loss rate is the standard deviation of the thermal weight loss rates of the multiple target tobacco leaves in the target tobacco group. For example, when the target tobacco group includes target tobacco leaves A, target tobacco leaves B, and target tobacco leaves C, and the blend ratio of target tobacco leaves A is 0.46, the blend ratio of target tobacco leaves B is 0.31, and the blend ratio of target tobacco leaves C is 0.23, the average thermal weight loss rate of the tobacco group is 600.1476% / min, and the standard deviation is 74.99% / min.

[0062] In other embodiments of the present application, according to the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of multiple target tobacco leaves, determining the average thermal weight loss rate corresponding to the target tobacco leaf group information includes:

[0063] Step 1: Get the actual heating time.

[0064] The actual heating time is the heating time of the cigarette product during its use. In this embodiment, the actual heating time ranges from 5 minutes to 8 minutes, such as 6 minutes or 7 minutes.

[0065] Step 2: summing the thermal weight loss prediction equations of multiple target tobacco leaves with the target tobacco leaf ratio, and then integrating them with the actual heating time to obtain the sum of the thermal weight loss rates.

[0066] The total weight loss rate is the sum of the weight loss rates of the target tobacco leaves in the target tobacco group. The weight loss prediction equations for the target tobacco leaves are summed based on the target tobacco leaf ratios and then integrated by the actual heating time. The total weight loss rate is obtained by summing the weight loss prediction equations for the target tobacco leaves based on the target tobacco leaf ratios and then integrating the summed result by the actual heating time.

[0067] Step 3: Determine the average value of the thermal weight loss rate corresponding to the target tobacco leaf group information based on the total thermal weight loss rate and the actual heating time.

[0068] According to the total weight loss rate and the actual heating time, the average weight loss rate corresponding to the target tobacco group information is determined as follows: the total weight loss rate is divided by the actual heating time to obtain the average weight loss rate corresponding to the target tobacco group information.

[0069] For example, when the target tobacco leaf group information includes tobacco leaf A and tobacco leaf B, and the ratio of tobacco leaf raw material A in the tobacco leaf group is α, and the ratio of tobacco leaf raw material B in the tobacco leaf group is β, the average value of the thermal weight loss rate is:

[0070]

[0071] Among them, F avg is the average value of thermal weight loss rate, L is the actual heating time, Z(y) A is the prediction equation for the thermal weight loss of tobacco leaf A, Z(y) B is the prediction equation for the thermal gravimetric loss of tobacco leaf B.

[0072] It should be noted that the above formula only illustrates the calculation of the average thermal weight loss rate of the target tobacco leaf group information by taking the target tobacco leaf group information including two types of tobacco leaves as an example. This application does not limit the number of multiple tobacco leaves contained in the target tobacco leaf group information.

[0073] In other embodiments of the present application, determining the standard deviation of the thermal weight loss rate corresponding to the target tobacco leaf group information according to the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of the multiple target tobacco leaves includes:

[0074] Step 1: Get the actual heating time.

[0075] Step 2: Within the actual heating time range, the thermal weight loss prediction equations of multiple target tobacco leaves are sampled to obtain a thermal weight loss rate group for each target tobacco leaf.

[0076] Within the actual heating time range, the thermal weight loss prediction equations of multiple target tobacco leaves are sampled respectively to obtain a thermal weight loss rate group for each target tobacco leaf: based on the actual heating time and a preset number, a heating moment group is obtained, and each heating moment in the heating moment group is substituted into the thermal weight loss prediction equations of the multiple target tobacco leaves to obtain the thermal weight loss rate of each target tobacco leaf corresponding to each heating moment. Based on each heating moment of each target tobacco leaf and the thermal weight loss rate at the corresponding moment, a thermal weight loss rate group for each target tobacco leaf is constructed. The heating moment group includes a number of heating moments, and the number of heating moments is equal to the preset number. The preset number is the array size in the preset heating moment group, and the preset number can be manually set or randomly generated.

[0077] Step 3: Determine the standard deviation of the thermal weight loss rate corresponding to the target tobacco leaf group information based on the target tobacco leaf ratio and the thermal weight loss rate group of each target tobacco leaf.

[0078] For example, when the target tobacco leaf group information includes tobacco leaf A and tobacco leaf B, and the ratio of tobacco leaf raw material A in the tobacco leaf group is α, and the ratio of tobacco leaf raw material B in the tobacco leaf group is β, the standard deviation of the thermal weight loss rate is:

[0079]

[0080] Among them, F std is the standard deviation of the rate of thermal weight loss, N is a preset number, preferably 1000, Z(y i ) A is the thermal weight loss rate obtained by substituting the target time data in the heating time group into the thermal weight loss prediction equation of tobacco leaf A, Z(y i ) B It is the thermal weight loss rate obtained by substituting the target time data in the heating time group into the thermal weight loss prediction equation of tobacco leaf B. The target time data in the heating time group is any data in the heating time group.

[0081] It should be noted that the above formula only illustrates the calculation of the standard deviation of the thermal weight loss rate of the target tobacco leaf group information by taking the target tobacco leaf group information including two types of tobacco leaves as an example. This application does not limit the number of multiple tobacco leaves contained in the target tobacco leaf group information.

[0082] In other embodiments of the present application, the tobacco leaf ratio corresponding to the optimal tobacco leaf group is determined based on the thermogravimetric statistical information and preset conditions, specifically:

[0083] The tobacco blending ratio corresponding to the optimal tobacco group is determined based on the tobacco group information whose average thermal weight loss rate is greater than or equal to a preset threshold and whose thermal weight loss rate standard deviation is the smallest. The preset conditions are: the average thermal weight loss rate is greater than or equal to the preset threshold and the thermal weight loss rate standard deviation is the smallest.

[0084] The preset threshold value is a preset target value of the average value of the thermal weight loss rate, such as 500% / min, 600% / min.

[0085] For example, the preset conditions are:

[0086] min F std subject to F avg ≥M

[0087] Among them, F avg is the average value of thermal weight loss rate, M is the preset threshold, F std is the standard deviation of the thermal weight loss rate.

[0088] In other embodiments of the present application, Figure 3 As shown, the tobacco leaf ratio design method includes:

[0089] Step 1: Based on the temperature and time data obtained from thermogravimetric analysis, a surface prediction model for the thermal weight loss rate of heated cigarette tobacco is constructed.

[0090] Step 2: Perform dimensionality reduction processing on the surface prediction equation to obtain a curve prediction equation of the tobacco thermal weight loss rate with respect to the analysis time.

[0091] That is, the actual working temperature is substituted into the surface prediction equation to obtain the curve prediction equation of tobacco thermal weight loss rate with respect to analysis time.

[0092] Step 3: Calculate the average value and standard deviation of the tobacco thermal weight loss rate within the operating time range of the heating device.

[0093] Step 4: With the goal of minimizing the design value and standard deviation of the thermal weight loss rate, design a heated tobacco leaf group formula and determine the optimal usage ratio of tobacco leaf raw materials.

[0094] It should be noted that the tobacco leaf ratio design method establishes a surface prediction equation for the thermal weight loss rate, combines it with the actual parameters of the heating device to generate a thermal weight loss prediction equation, and quantifies the mean and standard deviation of the thermal weight loss rate of the target tobacco leaf group information. This provides a scientific basis for tobacco leaf ratio design and significantly improves the stability of the thermal weight loss rate of cigarette products during the thermal weight loss process. Furthermore, the tobacco leaf ratio design method optimizes tobacco leaf proportions, reduces costs, and can be adapted to different product types by adjusting the actual parameters of the heating device. Scientifically calculating and optimizing the tobacco leaf usage ratio reduces raw material waste, improves resource utilization, and reduces production costs. Furthermore, by controlling the standard deviation of the thermal weight loss rate of the target tobacco leaf group information, the tobacco leaf ratio design method improves product sensory quality and smoke release consistency, meeting consumer needs and enhancing market competitiveness. Finally, this application designs and provides the above-mentioned tobacco leaf ratio design method based on relevant data and mathematical models from thermogravimetric analysis. This tobacco leaf ratio design method is simple and easy to implement, making it easy to promote in the tobacco industry, providing technical support for product research and development and production, and has broad application prospects.

[0095] In other embodiments of the present application, a specific implementation of the tobacco leaf ratio design method in the above embodiment is provided, and the method includes the following steps:

[0096] Step 1: Perform thermogravimetric analysis on tobacco leaf raw materials A and B to obtain thermogravimetric analysis temperature, thermogravimetric analysis time, and thermogravimetric rate data, and establish a thermogravimetric rate surface prediction equation for tobacco leaf raw materials A and B:

[0097] z(x,y) A =0.3168+1.785×x+1.064×y+4.099×y 2 -1.678×y 3 -0.4939×x×y+0.08912×x×y 2

[0098]

[0099] Among them, z(x, y) A is the surface prediction equation for the thermal weight loss rate of tobacco raw material A, z(x, y) B is the prediction equation of the thermal gravimetric rate surface of tobacco raw material B, x is the thermal gravimetric analysis temperature, and y is the thermal gravimetric analysis time.

[0100] Step 2: Based on the actual heating temperature of the heating device for heating cigarette products (260°C), the thermal weight loss rate surface prediction equation in step 1 is subjected to dimensionality reduction processing to obtain the curve prediction equation of the thermal weight loss rate of tobacco raw materials A and B with respect to the thermogravimetric analysis time (i.e., the thermal weight loss prediction equation mentioned above):

[0101] z(y) A =-1.678×y 3 +27.2702×y 2 -127.35×y+464.4168

[0102] z(y) B =-2.829×y 3 +44.597×y 2 -204.698×y+820.3954

[0103] Among them, z(x, y) A is the surface prediction equation for the thermal weight loss rate of tobacco raw material A, z(x, y) B is the surface prediction equation for the thermal weight loss rate of tobacco raw material B.

[0104] Step 3: Assume that the formula usage ratio of tobacco leaf raw material A is α and the formula usage ratio of tobacco leaf raw material B is β. Calculate the average thermal weight loss rate of the tobacco leaf group consisting of tobacco leaf raw materials A and B within the actual heating time range of 0-6 minutes of the heating device:

[0105]

[0106] Among them, F avg is the average value of the thermal weight loss rate, α is the proportion of tobacco raw material A in the tobacco group, and β is the proportion of tobacco raw material B in the tobacco group.

[0107] Step 4: Calculate the standard deviation of the thermal weight loss rate of the tobacco leaf group consisting of tobacco leaf raw materials A and B within the actual heating time range of 0-6 minutes of the heating device:

[0108]

[0109] Among them, F std = is the standard deviation of the thermal weight loss rate. Specifically, within the actual heating time range of the heating device, a set of equally spaced data is generated based on the thermal weight loss rate curve prediction equation. This data is then substituted into the thermal weight loss prediction equation for each tobacco leaf raw material and multiplied by the proportion of each tobacco leaf in the tobacco group. Finally, the standard deviation of the tobacco group information is calculated. In this embodiment, the number of data in this set is 1000.

[0110] Step 5: Set the target value (i.e., preset threshold) of the thermal weight loss rate of the heated cigarette product to 500% / min, requiring that the average thermal weight loss rate of the tobacco group be greater than the target value while the standard deviation of the thermal weight loss rate be minimized:

[0111] min F std subject to F avg ≥500

[0112] That is, the tobacco leaf group information that satisfies the conditions that the average value of the thermal weight loss rate is greater than or equal to the preset threshold and the standard deviation of the thermal weight loss rate is the smallest is found as the optimal tobacco leaf group.

[0113] Through optimization calculation, when the usage ratios of tobacco leaf raw materials A and B in the tobacco leaf group are 0.67 and 0.33 respectively, the average thermal weight loss rate of the tobacco leaf group is 500.231% / min and the standard deviation is 63.8857% / min. At this time, the thermal weight loss rate and stability of the tobacco leaf group reach the optimal level. Figure 4 Designing and producing tobacco leaf groups using the above optimized ratios can significantly improve the smoke release stability of heated cigarette products and meet the requirements for product quality consistency.

[0114] In other embodiments of the present application, a specific implementation of the tobacco leaf ratio design method in the above embodiment is provided, and the method includes the following steps:

[0115] Step 1: Perform thermogravimetric analysis on tobacco leaf raw materials C, D, and E to obtain thermogravimetric analysis temperature, thermogravimetric analysis time, and thermogravimetric rate data, and establish a thermogravimetric rate surface prediction equation for tobacco leaf raw materials C, D, and E:

[0116] z(x,y) C =0.3448+5.7×x+1.171×y+3.539×y 2 -3.895×y 3 -1.112×x×y+0.2163×x×y 2

[0117] z(x,y) D =0.2944+2.543×x+0.9826×y+3.364×y 2 -2.09×y 3 -0.5885×x×y+0.1132×x×y 2

[0118] z(x,y) E =0.3103+0.9447×x+1.077×y+4.266×y 2 -1.313×y 3 -0.3661×x×y+0.06702×x×y 2

[0119] Among them, z(x, y) C is the surface prediction equation for the thermal weight loss rate of tobacco leaf raw material C, z(x, y) D is the surface prediction equation for the thermal weight loss rate of tobacco raw material D, z(x, y) Eis the prediction equation of the thermal gravimetric rate surface of tobacco raw material E, x is the thermal gravimetric analysis temperature, and y is the thermal gravimetric analysis time.

[0120] Step 2: Based on the actual heating temperature of the heating device for heating cigarette products (230°C), the thermal weight loss rate surface prediction equation in step 1 is subjected to dimensionality reduction processing to obtain the curve prediction equation of the thermal weight loss rate of tobacco raw materials C, D, and E with respect to the thermogravimetric analysis time (i.e., the thermal weight loss prediction equation mentioned above):

[0121] z(y) C =-3.895×y 3 +53.288×y 2 -254.589×y+1311.3448

[0122] z(y) D =-2.09×y 3 +29.4×y 2 -134.3724×y+585.1844

[0123] z(y)E=-1.313×y 3 +19.6806×y 2 -83.126×y+217.5913

[0124] Among them, z(y) C is the curve prediction equation of tobacco leaf raw material C, z(y) D is the curve prediction equation of tobacco raw material D, z(y) E is the curve prediction equation of tobacco raw material E.

[0125] Step 3: Assume that the formula usage ratio of tobacco leaf raw material C is α, the formula usage ratio of tobacco leaf raw material D is β, and the formula usage ratio of tobacco leaf raw material E is γ. Within the actual heating time range of 0-7 minutes of the heating device, calculate the average thermal weight loss rate of the tobacco leaf group consisting of tobacco leaf raw materials C, D, and E:

[0126]

[0127] Among them, F avg is the average value of the thermal weight loss rate, α is the ratio of tobacco leaf raw material C in the tobacco leaf group, β is the ratio of tobacco leaf raw material D in the tobacco leaf group, and γ is the ratio of tobacco leaf raw material E in the tobacco leaf group.

[0128] Step 4: Calculate the standard deviation of the thermal weight loss rate of the tobacco group consisting of tobacco raw materials C, D, and E within the range of 0-7 minutes of the actual heating time of the heating device:

[0129]

[0130] Among them, F std = is the standard deviation of the thermal weight loss rate. Specifically, within the actual heating time range of the heating device, a set of equally spaced data is generated based on the thermal weight loss rate curve prediction equation. This data is then substituted into the thermal weight loss prediction equation for each tobacco leaf raw material and multiplied by the proportion of each tobacco leaf in the tobacco group. Finally, the standard deviation of the tobacco group information is calculated. In this embodiment, the number of data in this set is 1000.

[0131] Step 5: Set the target value (i.e., the preset threshold) of the thermal weight loss rate of the heated cigarette product to 600% / min, requiring that the average thermal weight loss rate of the tobacco group be greater than the target value while the standard deviation of the thermal weight loss rate be minimized:

[0132] min F std subject to F avg ≥600

[0133] That is, the tobacco leaf group information that satisfies the conditions that the average value of the thermal weight loss rate is greater than or equal to the preset threshold and the standard deviation of the thermal weight loss rate is the smallest is found as the optimal tobacco leaf group.

[0134] Through optimization calculation, when the usage ratios of tobacco leaf raw materials C, D, and E in the tobacco leaf group are 0.46, 0.31, and 0.23 respectively, the average thermal weight loss rate of the tobacco leaf group is 600.1476% / min, and the standard deviation is 74.99% / min. At this time, the thermal weight loss rate and stability of the tobacco leaf group reach the optimal level. Figure 5 Designing and producing tobacco leaf groups using the above optimized ratios can significantly improve the smoke release stability of heated cigarette products and meet the requirements for product quality consistency.

[0135] In other embodiments of the present application, a specific implementation of the tobacco leaf ratio design method in the above embodiment is provided, and the method includes the following steps:

[0136] Step 1: Perform thermogravimetric analysis on tobacco leaf raw materials C, D, E, and F to obtain thermogravimetric analysis temperature, thermogravimetric analysis time, and thermogravimetric rate data, and establish a thermogravimetric rate surface prediction equation for tobacco leaf raw materials C, D, E, and F:

[0137] z(x,y) C =0.3448+5.7×x+1.171×y+3.539×y 2 -3.895×y 3 -1.112×x×y+0.2163×x×y 2

[0138] z(x,y) D =0.2944+2.543×x+0.9826×y+3.364×y2 -2.09×y 3 -0.5885×x×y+0.1132×x×y 2

[0139] z(x,y) E =0.3103+0.9447×x+1.077×y+4.266×y 2 -1.313×y 3 -0.3661×x×y+0.06702×x×y 2

[0140] z(x,y) F =0.2582+5.163×x+0.8469×y+1.652×y 2 -3.103×y 3 -0.932×x×y+0.1787×x×y 2

[0141] Among them, z(x, y) C is the surface prediction equation for the thermal weight loss rate of tobacco leaf raw material C, z(x, y) D is the surface prediction equation for the thermal weight loss rate of tobacco raw material D, z(x, y) E is the surface prediction equation for the thermal weight loss rate of tobacco raw material E, z(x, y) F is the prediction equation of the thermal gravimetric rate surface of tobacco raw material F, x is the thermal gravimetric analysis temperature, and y is the thermal gravimetric analysis time.

[0142] Step 2: Based on the actual heating temperature of the heating device for heating cigarette products (230°C), the thermal weight loss rate surface prediction equation in step 1 is subjected to dimensionality reduction processing to obtain the curve prediction equation of the thermal weight loss rate of tobacco raw materials C, D, E, and F with respect to the thermogravimetric analysis time (i.e., the thermal weight loss prediction equation mentioned above):

[0143] z(y) C =-3.895×y 3 +53.288×y 2 -254.589×y+1311.3448

[0144] z(y) D =-2.09×y 3 +29.4×y 2 -134.3724×y+585.1844

[0145] z(y) E =-1.313×y 3 -19.6806×y 2-83.126×y+217.5913

[0146] z(y) F =-3.103×y 3 +42.753×y 2 -213.5131×y+1187.7482

[0147] Among them, z(y) C is the curve prediction equation of tobacco leaf raw material C, z(y) D is the curve prediction equation of tobacco raw material D, z(y) E is the curve prediction equation of tobacco raw material E, z(y) F is the curve prediction equation of tobacco leaf raw material F.

[0148] Step 3: Assume that the formulation ratio of tobacco leaf raw material C is α, the formulation ratio of tobacco leaf raw material D is β, the formulation ratio of tobacco leaf raw material E is γ, and the formulation ratio of tobacco leaf raw material F is η. Within the actual heating time range of 0-7 minutes of the heating device, calculate the average thermal weight loss rate of the tobacco group consisting of tobacco leaf raw materials C, D, E, and F:

[0149]

[0150] Among them, F avg is the average value of the thermal weight loss rate, α is the ratio of tobacco leaf raw material C in the tobacco leaf group, β is the ratio of tobacco leaf raw material D in the tobacco leaf group, γ is the ratio of tobacco leaf raw material E in the tobacco leaf group, and η is the ratio of tobacco leaf raw material F in the tobacco leaf group.

[0151] Step 4: Calculate the standard deviation of the thermal weight loss rate of the tobacco group consisting of tobacco raw materials C, D, E, and F within the range of 0-7 minutes of the actual heating time of the heating device:

[0152]

[0153] Among them, F std = is the standard deviation of the thermal weight loss rate. Specifically, within the actual heating time range of the heating device, a set of equally spaced data is generated based on the thermal weight loss rate curve prediction equation. This data is then substituted into the thermal weight loss prediction equation for each tobacco leaf raw material and multiplied by the proportion of each tobacco leaf in the tobacco group. Finally, the standard deviation of the tobacco group information is calculated. In this embodiment, the number of data in this set is 1000.

[0154] Step 5: Set the target value (i.e., the preset threshold) of the thermal weight loss rate of the heated cigarette product to 600% / min, requiring that the average thermal weight loss rate of the tobacco group be greater than the target value while the standard deviation of the thermal weight loss rate be minimized:

[0155] min Fstd subject to F avg ≥600

[0156] That is, the tobacco leaf group information that satisfies the conditions that the average value of the thermal weight loss rate is greater than or equal to the preset threshold and the standard deviation of the thermal weight loss rate is the smallest is found as the optimal tobacco leaf group.

[0157] Through optimization calculation, when the usage ratios of tobacco raw materials C, D, E, and F in the tobacco group are 0.36, 0.55, 0.07, and 0.02 respectively, the average thermal weight loss rate of the tobacco group is 600.0608% / min, and the standard deviation is 74.87% / min. At this time, the thermal weight loss rate and stability of the tobacco group reach the optimal level. Figure 6 Designing and producing tobacco leaf groups using the above optimized ratios can significantly improve the smoke release stability of heated cigarette products and meet the requirements for product quality consistency.

[0158] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0159] Based on the same inventive concept, the present application also provides a tobacco leaf ratio design device for implementing the aforementioned tobacco leaf ratio design method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more tobacco leaf ratio design device embodiments provided below can be found in the above-mentioned limitations of the tobacco leaf ratio design method and will not be further elaborated here.

[0160] In one embodiment of the present application, Figure 7 As shown, a tobacco leaf ratio design device is provided, comprising:

[0161] The analysis module 100 is used to determine thermogravimetric prediction equations corresponding to various tobacco leaves based on thermogravimetric analysis.

[0162] The acquisition module 200 is used to obtain multiple groups of tobacco leaf group information; the tobacco leaf group information includes: multiple types of tobacco leaves and corresponding tobacco leaf ratios.

[0163] The calculation module 300 is used to determine the thermal weight loss statistical information corresponding to each group of tobacco leaf group information based on the thermal weight loss prediction equation of each tobacco leaf and multiple groups of tobacco leaf group information.

[0164] The determination module 400 is used to determine the tobacco leaf ratio corresponding to the optimal tobacco leaf group based on the thermogravimetric statistical information and preset conditions.

[0165] In other embodiments of the present application, the analysis module 100 is further used to obtain the actual heating temperature of the tobacco leaves; based on thermogravimetric analysis, determine the thermal weight loss rate surface prediction equations corresponding to multiple tobacco leaves respectively; the thermal weight loss rate surface prediction equation includes: a surface prediction equation consisting of thermal weight loss rate, thermal weight loss analysis temperature and thermal weight loss analysis time; based on the actual heating temperature and the thermal weight loss rate surface prediction equation of each tobacco leaf, determine the thermal weight loss prediction equation for each tobacco leaf.

[0166] In other embodiments of the present application, determining the thermal weight loss prediction equation for each tobacco leaf based on the actual heating temperature and the thermal weight loss rate surface prediction equation for each tobacco leaf includes: substituting the actual heating temperature into the thermal weight loss rate surface prediction equation for each tobacco leaf to determine the thermal weight loss prediction equation for each tobacco leaf; the thermal weight loss prediction equation includes a prediction equation consisting of the thermal weight loss rate and the thermal weight loss analysis time.

[0167] In other embodiments of the present application, the calculation module 300 is further used to obtain the thermal weight loss prediction equations of multiple target tobacco leaves in the target tobacco leaf group information; the target tobacco leaf group information is any one group of tobacco leaf group information among the multiple groups of tobacco leaf group information; based on the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of the multiple target tobacco leaves, the average thermal weight loss rate corresponding to the target tobacco leaf group information is determined; based on the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of the multiple target tobacco leaves, the standard deviation of the thermal weight loss rate corresponding to the target tobacco leaf group information is determined.

[0168] In other embodiments of the present application, determining the average thermal weight loss rate corresponding to the target tobacco group information based on the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of multiple target tobacco leaves includes: obtaining the actual heating time; summing the thermal weight loss prediction equations of multiple target tobacco leaves with the target tobacco leaf ratio, and then integrating them with the actual heating time to obtain the sum of the thermal weight loss rates; and determining the average thermal weight loss rate corresponding to the target tobacco leaf group information based on the sum of the thermal weight loss rates and the actual heating time.

[0169] In other embodiments of the present application, determining the standard deviation of the thermal weight loss rate corresponding to the target tobacco group information based on the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of multiple target tobacco leaves includes: obtaining the actual heating time; within the actual heating time range, sampling the thermal weight loss prediction equations of multiple target tobacco leaves respectively to obtain a thermal weight loss rate group for each target tobacco leaf; determining the standard deviation of the thermal weight loss rate corresponding to the target tobacco leaf group information based on the target tobacco leaf ratio and the thermal weight loss rate group for each target tobacco leaf.

[0170] In other embodiments of the present application, determining the tobacco leaf ratio corresponding to the optimal tobacco leaf group based on the thermal weight loss statistical information and preset conditions includes: determining the tobacco leaf ratio corresponding to the optimal tobacco leaf group based on the tobacco leaf group information whose average thermal weight loss rate is greater than or equal to a preset threshold and whose thermal weight loss rate standard deviation is the smallest.

[0171] Each module in the tobacco blend design device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0172] In one embodiment of the present application, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store all relevant data for executing a tobacco leaf ratio design method. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a tobacco leaf ratio design method is implemented.

[0173] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0174] In one embodiment of the present application, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the tobacco leaf ratio design method in the above embodiment are implemented.

[0175] In one embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. The computer program is executed by a processor to implement the steps of the tobacco leaf ratio design method in the above-mentioned method embodiments.

[0176] In one embodiment of the present application, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of the tobacco leaf ratio design method in the above-mentioned method embodiments.

[0177] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0178] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0179] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A tobacco leaf ratio design method, characterized in that: The method comprises: Based on thermogravimetric analysis, the corresponding thermogravimetric loss prediction equations for various tobacco leaves were determined; Acquire multiple groups of tobacco leaf group information; the tobacco leaf group information includes: multiple tobacco leaves and corresponding tobacco leaf ratios; Determining the thermal weight loss statistical information corresponding to each group of tobacco leaf group information according to the thermal weight loss prediction equation of each tobacco leaf and the multiple groups of tobacco leaf group information; The tobacco leaf ratio corresponding to the optimal tobacco leaf group is determined based on the thermogravimetric statistical information and preset conditions.

2. The tobacco leaf ratio design method according to claim 1, characterized in that: The thermogravimetric analysis-based method for determining the thermogravimetric loss prediction equations corresponding to various tobacco leaves includes: Obtaining the actual heating temperature of tobacco leaves; Based on thermogravimetric analysis, a surface prediction equation for thermal weight loss rate corresponding to a plurality of tobacco leaves is determined; the surface prediction equation for thermal weight loss rate includes a surface prediction equation consisting of thermal weight loss rate, thermogravimetric analysis temperature, and thermogravimetric analysis time; The thermal weight loss prediction equation for each tobacco leaf is determined based on the actual heating temperature and the thermal weight loss rate surface prediction equation for each tobacco leaf.

3. The tobacco leaf ratio design method according to claim 2, characterized in that: Determining the thermal weight loss prediction equation for each tobacco leaf according to the actual heating temperature and the thermal weight loss rate surface prediction equation for each tobacco leaf includes: The actual heating temperature is respectively introduced into the thermal weight loss rate surface prediction equation of each tobacco leaf to determine the thermal weight loss prediction equation of each tobacco leaf; the thermal weight loss prediction equation includes a prediction equation consisting of the thermal weight loss rate and the thermal weight loss analysis time.

4. The tobacco leaf ratio design method according to claim 1, characterized in that: Determining the thermal weight loss statistical information corresponding to each group of tobacco leaf group information based on the thermal weight loss prediction equation for each tobacco leaf and the multiple groups of tobacco leaf group information includes: Obtaining the thermal weight loss prediction equations of multiple target tobacco leaves in the target tobacco leaf group information; the target tobacco leaf group information is any one group of tobacco leaf group information among the multiple groups of tobacco leaf group information; Determine the average value of the thermal weight loss rate corresponding to the target tobacco leaf group information according to the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of the multiple target tobacco leaves; According to the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of multiple target tobacco leaves, the standard deviation of the thermal weight loss rate corresponding to the target tobacco leaf group information is determined.

5. The tobacco leaf ratio design method according to claim 4, characterized in that: According to the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of the multiple target tobacco leaves, determining the average thermal weight loss rate corresponding to the target tobacco leaf group information includes: Get the actual heating time; Adding the thermal weight loss prediction equations of multiple target tobacco leaves according to the target tobacco leaf ratio, and then integrating them with the actual heating time to obtain the sum of thermal weight loss rates; According to the sum of the thermal weight loss rates and the actual heating time, the average thermal weight loss rate corresponding to the target tobacco leaf group information is determined.

6. The tobacco leaf ratio design method according to claim 4, characterized in that: Determining the standard deviation of the thermal weight loss rate corresponding to the target tobacco leaf group information according to the target tobacco leaf ratio in the target tobacco leaf group information and the thermal weight loss prediction equations of the multiple target tobacco leaves includes: Get the actual heating time; Within the actual heating time range, sampling the thermal weight loss prediction equations of multiple target tobacco leaves respectively to obtain a thermal weight loss rate group for each target tobacco leaf; According to the target tobacco leaf ratio and the thermal weight loss rate group of each target tobacco leaf, the thermal weight loss rate standard deviation corresponding to the target tobacco leaf group information is determined.

7. The tobacco leaf ratio design method according to claim 6, characterized in that: Determining the tobacco leaf ratio corresponding to the optimal tobacco leaf group based on the thermogravimetric statistical information and preset conditions includes: The tobacco leaf ratio corresponding to the optimal tobacco leaf group is determined based on the tobacco leaf group information whose average thermal weight loss rate is greater than or equal to a preset threshold and whose thermal weight loss rate standard deviation is the smallest.

8. A tobacco leaf proportion design device, characterized in that: The device comprises: An analysis module is used to determine the thermogravimetric loss prediction equations corresponding to various tobacco leaves based on thermogravimetric analysis; An acquisition module is used to acquire information of multiple tobacco leaf groups; the tobacco leaf group information includes: multiple tobacco leaves and corresponding tobacco leaf ratios; a calculation module, configured to determine the thermal weight loss statistical information corresponding to each group of tobacco leaf information based on the thermal weight loss prediction equation for each tobacco leaf and multiple groups of tobacco leaf group information; The determination module is used to determine the tobacco leaf ratio corresponding to the optimal tobacco leaf group based on the thermal weight loss statistical information and preset conditions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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