Tobacco similarity evaluation method and device, computer equipment and readable storage medium

Abstract

The invention relates to a tobacco similarity evaluation method and device, computer equipment and a readable storage medium. The method can comprehensively and accurately evaluate the similarity of tobaccos, and comprises the following steps: determining a first thermal weight loss prediction equation of target tobacco leaves and a second thermal weight loss prediction equation of substitute tobacco leaves based on thermogravimetric analysis; according to the first thermal weight loss prediction equation, the sum of the thermal weight loss rates of the target tobacco leaves is determined; according to the first thermal weight loss prediction equation and the second thermal weight loss prediction equation, determining a thermal weight loss rate difference value between the target tobacco leaves and the substitute tobacco leaves; according to the thermal weight loss rate sum and the thermal weight loss rate difference value, the similarity between the target tobacco leaves and the substitute tobacco leaves is determined. By quantifying the similarity between the target tobacco leaves and the substitute tobacco leaves, the tobacco similarity is comprehensively and accurately evaluated, and the tobacco similarity evaluation efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of tobacco products, and in particular to a tobacco similarity assessment method, apparatus, computer equipment, and readable storage medium. Background Art

[0002] With the continuous development of cigarette technology, heated cigarettes, as a new type of tobacco product, release flavor components during the heating process, which is a key factor affecting consumer experience. However, the output of tobacco leaves used to make tobacco leaves will be affected by the annual tobacco production. In the case of a reduction in the production of a certain tobacco leaf, it is necessary to find a substitute tobacco leaf to replace the reduced production leaf through tobacco similarity evaluation.

[0003] However, traditional tobacco similarity assessment methods are time-consuming and labor-intensive, and are unable to comprehensively and accurately assess tobacco similarity. Summary of the Invention

[0004] Based on this, it is necessary to provide a tobacco similarity assessment method, apparatus, computer device and readable storage medium that can comprehensively and accurately assess the similarity of tobacco in order to address the above technical issues.

[0005] In a first aspect, the present application provides a tobacco similarity assessment method. The method comprises: determining a first thermal weight loss prediction equation for a target tobacco leaf and a second thermal weight loss prediction equation for a substitute tobacco leaf based on thermogravimetric analysis; determining the sum of the thermal weight loss rates of the target tobacco leaf based on the first thermal weight loss prediction equation; determining the difference in thermal weight loss rates between the target tobacco leaf and the substitute tobacco leaf based on the first thermal weight loss prediction equation and the second thermal weight loss prediction equation; and determining the similarity between the target tobacco leaf and the substitute tobacco leaf based on the sum of the thermal weight loss rates and the difference in the thermal weight loss rates.

[0006] In one embodiment, determining a first thermal weight loss prediction equation for a target tobacco leaf and a second thermal weight loss prediction equation for a substitute tobacco leaf based on thermogravimetric analysis includes: obtaining an actual heating temperature of the tobacco leaf; determining a first curved surface prediction equation for the target tobacco leaf and a second curved surface prediction equation for the substitute tobacco leaf based on thermogravimetric analysis; the curved surface prediction equation includes a curved surface prediction equation consisting of a thermal weight loss rate, a thermal weight loss analysis temperature, and a thermal weight loss analysis time; determining the first thermal weight loss prediction equation for the target tobacco leaf and the second thermal weight loss prediction equation for the substitute tobacco leaf based on the actual heating temperature, the first curved surface prediction equation, and the second curved surface prediction equation; the thermal weight loss prediction equation includes a prediction equation consisting of a thermal weight loss rate and a thermal weight loss analysis time.

[0007] In one embodiment, determining the first thermal weight loss prediction equation of the target tobacco and the second thermal weight loss prediction equation of the substitute tobacco based on the actual heating temperature, the first curved surface prediction equation, and the second curved surface prediction equation includes: substituting the actual heating temperature into the first curved surface prediction equation to determine the first thermal weight loss prediction equation of the target tobacco; substituting the actual heating temperature into the second curved surface prediction equation to determine the second thermal weight loss prediction equation of the substitute tobacco.

[0008] In one embodiment, determining the total thermal weight loss rate of the target tobacco leaf according to the first thermal weight loss prediction equation includes: obtaining the actual heating time; integrating the first thermal weight loss prediction equation with the actual heating time to obtain the total thermal weight loss rate of the target tobacco leaf.

[0009] In one embodiment, determining the difference in thermal weight loss rate between the target tobacco and the substitute tobacco based on the first thermal weight loss prediction equation and the second thermal weight loss prediction equation includes: obtaining the actual heating time; subtracting the first thermal weight loss prediction equation from the second thermal weight loss prediction equation to obtain a thermal weight loss difference equation; and performing trapezoidal integration on the absolute value of the thermal weight loss difference equation using the actual heating time to determine the difference in thermal weight loss rate between the target tobacco and the substitute tobacco.

[0010] In one embodiment, the method further includes: obtaining first basic information of the target tobacco leaf and second basic information of all tobacco leaves; and determining one or more substitute tobacco leaves based on the first basic information and the second basic information.

[0011] In one embodiment, if there are multiple substitute tobacco leaves, the method further includes: obtaining the similarity between the target tobacco leaf corresponding to each substitute tobacco leaf and the substitute tobacco leaf; and selecting the substitute tobacco leaf corresponding to the maximum similarity among the multiple similarities as the target substitute tobacco leaf.

[0012] In a second aspect, the present application also provides a tobacco similarity assessment device. The device comprises:

[0013] An analysis module, configured to determine a first thermogravimetric prediction equation for target tobacco leaves and a second thermogravimetric prediction equation for substitute tobacco leaves based on thermogravimetric analysis;

[0014] a calculation module, configured to determine the sum of the thermal weight loss rates of the target tobacco leaves according to the first thermal weight loss prediction equation; and further configured to determine the difference in thermal weight loss rates between the target tobacco leaves and the substitute tobacco leaves according to the first thermal weight loss prediction equation and the second thermal weight loss prediction equation;

[0015] The comparison module is used to determine the similarity between the target tobacco leaf and the substitute tobacco leaf according to the total thermal weight loss rate and the thermal weight loss rate difference.

[0016] 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.

[0017] 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.

[0018] The above-mentioned tobacco similarity assessment method, device, computer equipment and readable storage medium perform thermogravimetric analysis on target tobacco leaves and substitute tobacco leaves to obtain a first thermogravimetric prediction equation corresponding to the target tobacco leaves and a second thermogravimetric prediction equation corresponding to the substitute tobacco leaves. Then, based on the first thermogravimetric prediction equation, the total thermogravimetric rate of the target tobacco leaves is calculated, and based on the first thermogravimetric prediction equation and the second thermogravimetric prediction equation, the difference in thermogravimetric rate between the target tobacco leaves and the substitute tobacco leaves is determined. Finally, based on the total thermogravimetric rate and the difference in thermogravimetric rate, the similarity between the target tobacco leaves and the substitute tobacco leaves is determined. This improves the efficiency of tobacco similarity assessment and comprehensively and accurately assesses tobacco similarity by quantifying the similarity between the target tobacco leaves and the substitute tobacco leaves. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of a process for evaluating tobacco similarity in one embodiment;

[0020] Figure 2 A schematic diagram of a process for calculating a thermogravimetric loss prediction equation in one embodiment;

[0021] Figure 3 A schematic diagram of a process for calculating the difference in thermal weight loss rate in one embodiment;

[0022] Figure 4 is a schematic flow chart of a tobacco similarity assessment method according to another embodiment;

[0023] Figure 5 Schematic diagram of the thermal weight loss rate curve and the total thermal weight loss rate of target tobacco leaf A when the heating device is operated at 230° C. for 10 minutes in one embodiment;

[0024] Figure 6 Schematic diagram of the thermal weight loss rate curves and the thermal weight loss rate difference of target tobacco leaf A and substitute tobacco leaf B when the heating device is operated at 230° C. for 10 minutes in one embodiment;

[0025] Figure 7 Schematic diagram of the thermal weight loss rate curves and the difference in thermal weight loss rates of target tobacco leaf A and substitute tobacco leaf C when the heating device is operated at 230° C. for 10 minutes in one embodiment;

[0026] Figure 8 Schematic diagram of the thermal weight loss rate curve and the total thermal weight loss rate of target tobacco leaf K when the heating device is operated at 290° C. for 4 minutes in one embodiment;

[0027] Figure 9 Schematic diagram of the thermal weight loss rate curves and the thermal weight loss rate difference of target tobacco leaf K and substitute tobacco leaf E when the heating device is operated at 290° C. for 4 minutes in one embodiment;

[0028] Figure 10 Schematic diagram of the thermal weight loss rate curves and the difference in thermal weight loss rates of target tobacco leaf K and substitute tobacco leaf F when the heating device is operated at 290° C. for 4 minutes in one embodiment;

[0029] Figure 11 Schematic diagram of the thermal weight loss rate curves and the difference in thermal weight loss rates of target tobacco leaf K and substitute tobacco leaf G when the heating device is operated at 290° C. for 4 minutes in one embodiment;

[0030] Figure 12 Schematic diagram of the thermal weight loss rate curves and the thermal weight loss rate difference of target tobacco leaf K and substitute tobacco leaf H when the heating device is operated at 290° C. for 4 minutes in one embodiment;

[0031] Figure 13 is a structural block diagram of a tobacco similarity evaluation device in one embodiment;

[0032] Figure 14 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0033] 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.

[0034] With the continuous development of cigarette technology, heated cigarettes, as a new type of tobacco product, have become a key factor influencing the consumer experience. The aroma components released during the heating process of tobacco cuts are therefore crucial for predicting and controlling the release rate of tobacco flavor components. Currently, heating appliances are primarily used to heat glycerin-containing tobacco to a specific temperature to meet aroma release requirements. However, the yield of tobacco leaves used to make cut tobacco is affected by annual tobacco production. In the event of a decrease in the production of a particular tobacco leaf, it is necessary to identify alternative tobacco leaves through tobacco similarity assessment to replace the reduced yield.

[0035] However, the traditional tobacco similarity assessment method is time-consuming and labor-intensive, and cannot comprehensively and accurately evaluate the changes in the release rate of flavor components of tobacco during the heating process, nor can it comprehensively and accurately assess the similarity of tobacco.

[0036] In order to solve the above problem, in one embodiment of the present application, Figure 1 As shown, a tobacco similarity evaluation method is provided, comprising the following steps:

[0037] Step 201: Based on thermogravimetric analysis, determine a first thermogravimetric loss prediction equation for target tobacco leaves and a second thermogravimetric loss prediction equation for substitute tobacco leaves.

[0038] Among them, thermogravimetric analysis refers to studying the thermal weight loss process of a substance by measuring the mass change of the substance during the heating process. In this embodiment, the substance is a target tobacco leaf or a substitute tobacco leaf. The first thermogravimetric analysis equation is a curve equation of the thermal weight loss rate of the target tobacco leaf during the thermal weight loss process and the thermogravimetric analysis time. The second thermogravimetric analysis equation is a curve equation of the thermal weight loss rate of the substitute tobacco leaf during the thermal weight loss process and the thermogravimetric analysis time. Specifically, thermogravimetric analysis is performed on the target tobacco leaf and the substitute tobacco leaf respectively to obtain the first thermogravimetric analysis equation corresponding to the target tobacco leaf and the second thermogravimetric analysis equation corresponding to the substitute tobacco leaf. Thermogravimetric analysis is a thermogravimetric analysis of multiple tobacco leaves using a thermogravimetric analyzer.

[0039] Step 202: Determine the total thermal weight loss rate of the target tobacco leaf according to the first thermal weight loss prediction equation.

[0040] The total thermal weight loss rate is the total thermal weight loss rate of the target tobacco leaf within a certain time period. That is, according to the first thermal weight loss prediction equation, the total thermal weight loss rate of the target tobacco leaf is determined by integrating the first thermal weight loss prediction equation to determine the total thermal weight loss rate of the target tobacco leaf.

[0041] Step 203: Determine the difference in thermal weight loss rate between the target tobacco leaf and the substitute tobacco leaf according to the first thermal weight loss prediction equation and the second thermal weight loss prediction equation.

[0042] The thermal weight loss rate difference is the difference in thermal weight loss rate between the target tobacco leaf and the substitute tobacco leaf over a certain time period. That is, based on the first thermal weight loss prediction equation and the second thermal weight loss prediction equation, the thermal weight loss rate difference between the target tobacco leaf and the substitute tobacco leaf is determined by integrating the difference between the first thermal weight loss prediction equation and the second thermal weight loss prediction equation to determine the thermal weight loss rate difference between the target tobacco leaf and the substitute tobacco leaf.

[0043] Step 204: Determine the similarity between the target tobacco leaf and the substitute tobacco leaf based on the sum of the thermal weight loss rates and the thermal weight loss rate difference.

[0044] The similarity is the similarity between the substitute tobacco leaf and the target tobacco leaf, and its value range is greater than or equal to 0 and less than or equal to 1, for example, 0.843 and 0.9159. That is, based on the sum of the thermal weight loss rates and the thermal weight loss rate difference, the similarity between the target tobacco leaf and the substitute tobacco leaf is determined by subtracting the sum of the thermal weight loss rates from the thermal weight loss rate difference, taking the absolute value of the difference, and dividing it by the sum of the thermal weight loss rates to obtain the similarity between the target tobacco leaf and the substitute tobacco leaf.

[0045] It should be noted that the greater the similarity L, the higher the similarity between the substitute tobacco and the target tobacco, and the maximum value of similarity L is 1. When 1 ≥ similarity L > 0.8, the similarity between the substitute tobacco and the target tobacco is "high"; when 0.8 ≥ similarity L > 0.6, the similarity between the substitute tobacco and the target tobacco is "relatively high"; when 0.6 ≥ similarity L > 0.4, the similarity between the substitute tobacco and the target tobacco is "medium"; when 0.4 ≥ similarity L > 0.2, the similarity between the substitute tobacco and the target tobacco is "relatively low"; and when L ≤ 0.2, the similarity between the substitute tobacco and the target tobacco is "low". Preferably, in the design of heated cigarette products, substitute tobacco with a "high" or "relatively high" similarity should be selected for leaf group formula maintenance to improve product quality stability.

[0046] It should be noted that the above-mentioned tobacco similarity assessment method performs thermogravimetric analysis on the target tobacco leaves and the substitute tobacco leaves to obtain a first thermogravimetric prediction equation corresponding to the target tobacco leaves and a second thermogravimetric prediction equation corresponding to the substitute tobacco leaves. Then, based on the first thermogravimetric prediction equation, the sum of the thermogravimetric loss rates of the target tobacco leaves is calculated, and based on the first thermogravimetric prediction equation and the second thermogravimetric prediction equation, the difference in thermogravimetric loss rates between the target tobacco leaves and the substitute tobacco leaves is determined. Finally, based on the sum of the thermogravimetric loss rates and the difference in the thermogravimetric loss rates, the similarity between the target tobacco leaves and the substitute tobacco leaves is determined, thereby comprehensively and accurately evaluating the similarity of tobacco.

[0047] In other embodiments of the present application, Figure 2As shown, the first thermogravimetric loss prediction equation for the target tobacco leaf and the second thermogravimetric loss prediction equation for the substitute tobacco leaf based on thermogravimetric analysis include:

[0048] Step 301: Obtain the actual heating temperature of the tobacco leaves.

[0049] The actual heating temperature is the heating temperature of the cigarette product during use. In this embodiment, the actual heating temperature is preferably 200°C to 300°C, such as 230°C or 290°C.

[0050] Step 302: Determine a first curved surface prediction equation for the target tobacco leaf and a second curved surface prediction equation for the substitute tobacco leaf based on thermogravimetric analysis.

[0051] The surface prediction equation includes a surface prediction equation consisting of the thermal weight loss rate, the thermal weight loss analysis temperature, and the thermal weight loss analysis time. That is, the surface prediction equation is a surface equation of the thermal weight loss rate with respect to the thermal weight loss analysis temperature and the thermal weight loss analysis time. Specifically, thermogravimetric analysis is performed on the target tobacco leaves and the substitute tobacco leaves to obtain thermal weight loss data for the target tobacco leaves and the substitute tobacco leaves. The thermal weight loss data includes the thermal weight loss rates of the tobacco leaves under different heating temperatures and heating times. Then, a first surface prediction equation corresponding to the target tobacco leaves is determined based on the thermal weight loss data of the target tobacco leaves, and a second surface prediction equation corresponding to the substitute tobacco leaves is determined based on the thermal weight loss data of the substitute tobacco leaves. The conditions for the thermogravimetric analysis are to place the tobacco leaves in an environment with different heating temperatures and different heating times and record the thermal weight loss rates of the tobacco leaves.

[0052] For example, the surface prediction equation is:

[0053]

[0054] Among them, z(x,y) is the predicted value of the thermal weight loss rate of tobacco leaves; k1 to k 10 is the fitting parameter of the nonlinear prediction equation; x is the thermogravimetric analysis temperature; y is the thermogravimetric analysis time.

[0055] Step 303: Determine a first thermal weight loss prediction equation for the target tobacco leaf and a second thermal weight loss prediction equation for the substitute tobacco leaf according to the actual heating temperature, the first curved surface prediction equation, and the second curved surface prediction equation.

[0056] The thermogravimetric loss prediction equation includes a prediction equation consisting of the thermogravimetric loss rate and the thermogravimetric analysis time. Specifically, the actual heating temperature is substituted into the first curved surface prediction equation to determine the first thermogravimetric loss prediction equation for the target tobacco leaf; the actual heating temperature is substituted into the second curved surface prediction equation to determine the second thermogravimetric loss prediction equation for the substitute tobacco leaf.

[0057] That is, the actual heating temperature is substituted into the first surface prediction equation and the second surface prediction equation as the thermogravimetric analysis temperature, and the first surface prediction equation and the second surface prediction equation are subjected to dimensionality reduction processing, thereby obtaining the first thermogravimetric prediction equation and the second thermogravimetric prediction equation respectively.

[0058] Exemplarily, the thermogravimetric prediction equation is:

[0059]

[0060] Among them, z(y) is the predicted value of the thermal weight loss rate of tobacco leaves, k1-k 10 is the fitting parameter of the nonlinear prediction equation, w is the actual heating temperature of the heating device, and y is the thermogravimetric analysis time.

[0061] It should be noted that the fitting parameters of the thermogravimetric prediction equation and / or the surface prediction equation are different between different tobacco leaves.

[0062] In other embodiments of the present application, determining the sum of the thermal weight loss rates of the target tobacco leaves according to the first thermal weight loss prediction equation 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 is preferably 3 minutes to 10 minutes, such as 4 minutes or 10 minutes.

[0065] Step 2: Integrate the first thermal weight loss prediction equation with the actual heating time to obtain the total thermal weight loss rate of the target tobacco leaf.

[0066] For example, the sum of the thermal weight loss rates of the target tobacco leaves is:

[0067]

[0068] Where M represents the total thermal weight loss rate of the target tobacco leaf, t is the actual heating time of the heating device, z(y) M This is the first thermogravimetric prediction equation for the target tobacco leaf.

[0069] The first thermogravimetric loss prediction equation is integrated with the actual heating time to obtain the sum of the thermogravimetric loss rates of the target tobacco leaves: the actual heating time is used as the thermogravimetric loss analysis time, the first thermogravimetric loss prediction equation is integrated to obtain the sum of the thermogravimetric loss rates of the target tobacco leaves during the actual heating time.

[0070] In other embodiments of the present application, Figure 3 As shown, according to the first thermogravimetric loss prediction equation and the second thermogravimetric loss prediction equation, determining the difference in thermogravimetric loss rate between the target tobacco leaf and the substitute tobacco leaf includes:

[0071] Step 401: Obtain actual heating time.

[0072] The actual heating time is the heating time of the cigarette product during its use. In this embodiment, the actual heating time is preferably 3 minutes to 10 minutes, such as 4 minutes or 10 minutes.

[0073] Step 402: Subtract the first thermal weight loss prediction equation from the second thermal weight loss prediction equation to obtain a thermal weight loss difference equation.

[0074] Step 403: Perform trapezoidal integration on the absolute value of the thermal weight loss difference equation using the actual heating time to determine the difference in thermal weight loss rate between the target tobacco leaf and the substitute tobacco leaf.

[0075] Step 403 is: using the actual heating time as the thermogravimetric analysis time, performing trapezoidal integration on the thermogravimetric difference equation, and obtaining the difference in thermogravimetric rate between the target tobacco leaf and the substitute tobacco leaf during the actual heating time.

[0076] Exemplarily, the difference in thermal weight loss rate is:

[0077] D=trapz(y,abs(z(y) M -z(y) N ));

[0078] Where D represents the difference in thermal weight loss rate between the target tobacco leaf and the substitute tobacco leaf, trapz is the trapezoidal integral function, abs is the absolute value function, z(y) M is the first thermogravimetric prediction equation for the target tobacco leaf, z(y) N The second thermal weight loss prediction equation for the substitute tobacco leaves.

[0079] In other embodiments of the present application, the method further includes:

[0080] Step 1: Obtain the first basic information of the target tobacco leaf and the second basic information of all tobacco leaves.

[0081] Step 2: Determine one or more substitute tobacco leaves based on the first basic information and the second basic information.

[0082] Basic information includes the type, variety, origin, part of the tobacco leaf, nicotine content, reducing sugar content, and sensory smoking quality. Specifically, based on the first basic information of the target tobacco leaf, the second basic information of all tobacco leaves is screened for similar information. One or more tobacco leaves corresponding to the screened second basic information are used as substitute tobacco leaves.

[0083] It should be noted that, in this embodiment, substitute tobacco leaves are determined by a pre-set large language model, specifically: the first basic information of the target tobacco leaves and the second basic information of all tobacco leaves are input into the pre-set large language model, and the large language model is guided by prompt words to screen the second basic information from the second basic information based on the first basic information, the second basic information whose similarity with the first basic information reaches a predetermined threshold, and one or more tobacco leaves corresponding to the screened second basic information are used as substitute tobacco leaves.

[0084] It should be noted that the step of determining one or more substitute tobacco leaves provided in this embodiment is to perform preliminary screening among all tobacco leaves, thereby screening out one or more tobacco leaves that have a certain degree of similarity with the target tobacco leaves as substitute tobacco leaves, thereby improving the efficiency of determining substitute tobacco leaves.

[0085] In other embodiments of the present application, if there are multiple tobacco substitutes, the method further includes:

[0086] Step 1: Obtain the similarity between the target tobacco leaf and the substitute tobacco leaf corresponding to each substitute tobacco leaf.

[0087] Step 2: Select the substitute tobacco leaf corresponding to the maximum similarity among multiple similarities as the target substitute tobacco leaf.

[0088] For example, the similarity between the first substitute tobacco leaf and the target tobacco leaf is 0.834, and the similarity between the second substitute tobacco leaf and the target tobacco leaf is 0.916. Based on the condition of selecting the substitute tobacco leaf corresponding to the maximum similarity with the target tobacco leaf as the target substitute tobacco leaf, the second substitute tobacco leaf is selected as the target substitute tobacco leaf. The target substitute tobacco leaf is the substitute tobacco leaf with the highest similarity to the target tobacco leaf among all the substitute tobacco leaves. In the event of a reduction in target tobacco leaf production, the target substitute tobacco leaf is used to replace the target tobacco leaf, thereby ensuring that the production volume of cigarette products is not reduced and the quality of cigarette products is not affected.

[0089] In other embodiments of the present application, Figure 4 As shown, the tobacco similarity assessment method includes:

[0090] Step 1: Take the tobacco thermal weight loss rate as the dependent variable, and the thermal weight loss analysis temperature and thermal weight loss analysis time as independent variables to establish a surface prediction equation for the tobacco thermal weight loss rate.

[0091] The tobacco thermal weight loss rate surface prediction equation includes the first surface prediction equation and the second surface prediction equation in the above embodiment. Specifically, based on the temperature and time data obtained by thermogravimetric analysis and the tobacco thermal weight loss rate, a thermal weight loss rate surface prediction equation for heated cigarette tobacco is established.

[0092] Step 2: Using the working parameters of the heating device to reduce the dimension of the surface prediction equation, a thermogravimetric curve prediction equation of the tobacco thermal weight loss rate with respect to the thermogravimetric analysis time is obtained.

[0093] The operating parameters of the heating device include the actual heating temperature and the actual heating time. The thermogravimetric curve prediction equation includes the first and second thermogravimetric loss prediction equations of the aforementioned embodiments. Specifically, based on the operating parameters of the heating device for heating the cigarette product, the thermogravimetric loss rate surface prediction equation obtained in the aforementioned steps is subjected to dimensionality reduction processing to obtain a thermogravimetric curve prediction equation for the tobacco thermogravimetric loss rate with respect to the thermogravimetric analysis time.

[0094] Step 3: Determine the prediction equation for the thermal weight loss rate of the target tobacco leaves, and calculate the total thermal weight loss rate of the target tobacco leaves.

[0095] The thermal weight loss rate prediction equation of the target tobacco leaf is determined as follows: the first thermal weight loss prediction equation of the target tobacco leaf is used as the thermal weight loss rate prediction equation of the target tobacco leaf.

[0096] Step 4: Establish a prediction equation for the thermal weight loss rate of the substitute tobacco leaves, calculate the difference in thermal weight loss rate between the substitute tobacco leaves and the target tobacco leaves, and evaluate the similarity between the substitute tobacco leaves and the target tobacco leaves.

[0097] The equation for predicting the thermal weight loss rate of a substitute tobacco leaf is established by using the second thermal weight loss prediction equation for the substitute tobacco leaf as the thermal weight loss prediction equation for the substitute tobacco leaf. Specifically, the substitute tobacco leaf is selected based on the characteristics of the target tobacco leaf, and a thermal weight loss prediction equation for the substitute tobacco leaf is established. The difference in thermal weight loss rate between the substitute tobacco leaf and the target tobacco leaf is calculated, and the similarity between the substitute tobacco leaf and the target tobacco leaf is evaluated, thereby replacing the tobacco leaf raw material for heated cigarette products.

[0098] It should be noted that the tobacco similarity assessment method quantitatively evaluates the similarity between the target tobacco and the substitute tobacco by establishing a surface prediction equation and a thermal weight loss prediction equation, making it more objective and scientific. Furthermore, by integrating the surface prediction equation and calculating the absolute error during the actual heating time of the heating device, the tobacco similarity assessment method can quickly obtain the difference in thermal weight loss rates and similarity assessment results between the substitute tobacco and the target tobacco, significantly improving evaluation efficiency. Furthermore, the tobacco similarity assessment method takes into account the basic information of the target tobacco and the operating parameters of the heating device, making the tobacco similarity assessment method more practical and providing a scientific tobacco similarity assessment method for tobacco raw materials used in alternative heated cigarette products. Finally, the tobacco similarity assessment method combines the calculation of the total thermal weight loss rate and the difference evaluation to comprehensively consider the differences in characteristics between the substitute tobacco and the target tobacco under heating conditions, evaluating similarity from multiple perspectives and ensuring the comprehensiveness and accuracy of the results of the tobacco similarity assessment method.

[0099] In other embodiments of the present application, Figures 5 to 7 As shown, a specific implementation of the tobacco similarity evaluation method in the above embodiment is provided, and the method includes the following steps: A tobacco similarity evaluation method based on thermal weight loss rate, the method includes the following steps:

[0100] Step 1: Perform thermogravimetric analysis on target tobacco leaf A to obtain thermogravimetric analysis temperature, thermogravimetric analysis time, and thermogravimetric loss rate data, and establish the first surface prediction equation for target tobacco leaf A:

[0101] z(x, y) A =0.3168+1.785×x+1.064×y+4.99×y 2 -1.678×y 3 -0.4939×x×y+0.08912×xy×y 2 ;

[0102] Among them, z(x, y) A is the surface prediction equation for the thermal gravimetric analysis rate of target tobacco leaf A (i.e., the first surface prediction equation), x is the thermal gravimetric analysis temperature, and y is the thermal gravimetric analysis time.

[0103] Step 2: For target tobacco leaf A, select substitute tobacco leaves B and C based on the same type, variety, origin, and part, perform thermogravimetric analysis, and establish the second surface prediction equation for substitute tobacco leaves B and C:

[0104]

[0105] z(x, y) C =0.5964+2.19×x+1.903×y+9.669×y 2 -2.525×y 3 -0.8493×x×y+0.1322×x×y 2 ;

[0106] Among them, z(x, y) B is the second surface prediction equation of tobacco leaf raw material B, z(x, y) C is the second surface prediction equation of tobacco raw material C.

[0107] Step 3: The actual heating temperature of the heating device for heating cigarette products is 230°C. According to the actual heating temperature, the dimension of each surface prediction equation is reduced to obtain the thermal weight loss prediction equation z(y) for the thermogravimetric analysis time of target tobacco leaf A, substitute tobacco leaf B, and substitute tobacco leaf C. A 、z(y) B and z(y) C :

[0108] z(y)A =-1.678×y 3 +24.5966×y 2 -112.533×y+ 4 10.8668;

[0109] z(y) B =-2.052×y 3 +31.965×y 2 -150.88×y+411.4497;

[0110] z(y) C =-2.525×y 3 +40.075×y 2 -193.436×y+504.2964;

[0111] Among them, z(y) A is the prediction equation for the thermal weight loss of tobacco raw material A, z(y) B is the prediction equation for the thermal weight loss of tobacco raw material B, z(y) C is the prediction equation for the thermal gravimetric loss of tobacco raw material C.

[0112] Step 4: Integrate the thermal weight loss prediction equation of target tobacco leaf A within the actual heating time range of 0-10 min of the heating device to obtain the total thermal weight loss rate M of target tobacco leaf A. A .

[0113]

[0114] Step 5: Calculate the thermal weight loss prediction equation z(y) of target tobacco leaf A A Thermogravimetric prediction equation z(y) for alternative tobacco leaves B and C B 、z(y) C The absolute error between them is calculated, and the trapezoidal integration is performed within the actual heating time range of 0-10min of the heating device to obtain the difference D in the thermal weight loss rate between the target tobacco leaf A and the substitute tobacco leaves B and C. AB 、D AC :

[0115] D AB =trapz(y,abs(z(y) A -z(y) B ))=390.3972;

[0116] D AC =trapz(y,abs(z(y) A -z(y) C ))=209.0631;

[0117] Among them, trapz() is the trapezoidal integral function, abs() is the absolute value function, D AB D is the difference in thermal weight loss rate between target tobacco leaf A and substitute tobacco leaf B. AC is the difference in thermal weight loss rate between target tobacco leaf A and substitute tobacco leaf C.

[0118] Step 6: Calculate the similarity L between the substitute tobacco leaves and the target tobacco leaves AB and L AC :

[0119]

[0120] L AB is the similarity between the substitute tobacco leaf A and the target tobacco leaf B, L AC Similarity between substitute tobacco leaf A and target tobacco leaf C.

[0121] The similarity between substitute tobacco leaves B and C and target tobacco leaf A was "high", and substitute tobacco leaf C had a greater similarity to target tobacco leaf A than substitute tobacco leaf B. Therefore, tobacco leaf raw material C was preferred to replace tobacco leaf raw material A, and leaf group formula design and production were carried out, which improved the quality stability of heated cigarette products.

[0122] In other embodiments of the present application, Figures 8 to 12 As shown, a specific implementation of the tobacco similarity evaluation method in the above embodiment is provided, and the method includes the following steps: A tobacco similarity evaluation method based on thermal weight loss rate, the method includes the following steps:

[0123] Step 1: Perform thermogravimetric analysis on the target tobacco leaf K to obtain thermogravimetric analysis temperature, thermogravimetric analysis time, and thermogravimetric loss rate data, and establish the first surface prediction equation for the target tobacco leaf K:

[0124] z(x, y) K =0.3368+2.158×x+1.071×y+5.124×y 2 -2.14×y 3 -0.6307×x×y+0.1143×x×y 2 ;

[0125] Among them, z(x, y) K is the surface prediction equation for the thermal gravimetric analysis rate of target tobacco leaf K (i.e., the first surface prediction equation), x is the thermal gravimetric analysis temperature, and y is the thermal gravimetric analysis time.

[0126] Step 2: For the target tobacco leaf K, select substitute tobacco leaves E, F, G, and H based on the same type, variety, and origin, perform thermogravimetric analysis, and establish the second surface prediction equation for the substitute tobacco leaves E, F, G, and H:

[0127] z(x,y)E =0.1876+2.694×x+0.5926×y+2.564×y 2 -2.276×y 3 -0.6053×x×y+0.1244×x×y 2 ;

[0128]

[0129] z(x, y) H =0.3687+2.38×x+1.159×y+5.005×y 2 -2.317×y 3 -0.6664×x×y+0.1241×x×y 2 ;

[0130] Among them, z(x, y) E is the second surface prediction equation of tobacco raw material E, z(x, y) F is the second surface prediction equation of tobacco leaf raw material F, z(x, y) G is the second surface prediction equation of tobacco raw material G, z(x, y) H is the second surface prediction equation of tobacco raw material H.

[0131] Step 3: The actual heating temperature of the heating device for heating the cigarette product is 290°C. Based on the actual heating temperature, the dimension of each surface prediction equation is reduced to obtain the thermal weight loss prediction equations for the thermogravimetric analysis time of the target tobacco leaf K and the substitute tobacco leaves E, F, G, and H:

[0132] z(y) K =-2.14×y 3 +38.271×y 2 -181.832×y+626.1568;

[0133] z(y) E =-2.276×y 3 +38.64×y 2 -174.9444×y+781.4476;

[0134] z(y) F =-1.909×y 3 +38.3818×y 2 -203.625×y+371.2009;

[0135] z(y) G =-2.829×y 3 +49.19×y 2-228.446×y+915.0154;

[0136] z(y) H =-2.317×y 3 +40.994×y 2 -192.097×y+915.0154;

[0137] Among them, z(y) K is the prediction equation for the thermal weight loss of tobacco leaf raw material K, z(y) E is the prediction equation for the thermal weight loss of tobacco leaf raw material E, z(y) F is the prediction equation for the thermal weight loss of tobacco leaf raw material F, z(y) G is the prediction equation for the thermal weight loss of tobacco leaf raw material G, z(y) H is the prediction equation for the thermal gravimetric loss of tobacco raw material H.

[0138] Step 4: Integrate the prediction equation of the thermal weight loss rate curve within the actual heating time range of 0-4 minutes of the heating device to obtain the total thermal weight loss rate M of the target tobacco leaf K. K .

[0139]

[0140] Step 5: Calculate the thermal weight loss prediction equation z(y) of the target tobacco leaf K K The absolute error between the thermal weight loss prediction equations of the target tobacco leaves E, F, G, and H is calculated, and the difference in thermal weight loss rate between the target tobacco leaf K and the substitute tobacco leaf is obtained by performing trapezoidal integration within the actual heating time range of 0-4 minutes of the heating device:

[0141] D KE =trapz(y,abs(z(y) K -z(y) E ))=675.432;

[0142] D KF =trapz(y,abs(z(y) K -z(y) F ))=1177.0199;

[0143] D KG =trapz(y,abs(z(y) K -z(y) G ))=971.3651;

[0144] D KH =trapz(y,abs(z(y) H -z(y) H ))=222.2903;

[0145] Among them, trapz() is the trapezoidal integral function, abs() is the absolute value function, D KE D is the difference in thermal weight loss rate between target tobacco leaf K and substitute tobacco leaf E. KF D is the difference in thermal weight loss rate between target tobacco leaf K and substitute tobacco leaf F. KG D is the difference in thermal weight loss rate between target tobacco leaf K and substitute tobacco leaf G, KH is the difference in thermal weight loss rate between target tobacco leaf K and substitute tobacco leaf H.

[0146] Step 6: Calculate the similarity between the substitute tobacco leaves and the target tobacco leaves:

[0147]

[0148] Among them, L KE is the similarity between target tobacco leaf K and substitute tobacco leaf E, L KF is the similarity between target tobacco leaf K and substitute tobacco leaf F, L KG is the similarity between target tobacco leaf K and substitute tobacco leaf G, L KH is the similarity between the target tobacco leaf K and the substitute tobacco leaf H.

[0149] Substitute tobacco leaf H had a "high" similarity to target tobacco leaf A, substitute tobacco leaf E had a "relatively high" similarity, substitute tobacco leaf G had a "medium" similarity, and substitute tobacco leaf F had a "low" similarity. Therefore, tobacco leaf raw material H was preferred to replace tobacco leaf raw material K, and leaf group formulation design and production were carried out, improving the quality stability of heated cigarette products.

[0150] 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.

[0151] Based on the same inventive concept, embodiments of the present application also provide a tobacco similarity assessment device for implementing the aforementioned tobacco similarity assessment 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 similarity assessment device embodiments provided below can be found in the above-described limitations of the tobacco similarity assessment method and will not be further elaborated here.

[0152] In one embodiment of the present application, Figure 13 As shown, a tobacco similarity evaluation device is provided, comprising:

[0153] The analysis module 100 is used to determine a first thermogravimetric prediction equation for target tobacco leaves and a second thermogravimetric prediction equation for substitute tobacco leaves based on thermogravimetric analysis.

[0154] The operation module 200 is used to determine the total thermal weight loss rate of the target tobacco leaves according to the first thermal weight loss prediction equation; and is also used to determine the difference in thermal weight loss rate between the target tobacco leaves and the substitute tobacco leaves according to the first thermal weight loss prediction equation and the second thermal weight loss prediction equation.

[0155] The comparison module 300 is used to determine the similarity between the target tobacco leaf and the substitute tobacco leaf according to the sum of the thermal weight loss rates and the thermal weight loss rate difference.

[0156] In one embodiment 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 a first surface prediction equation for the target tobacco leaves and a second surface prediction equation for the substitute tobacco leaves; the surface prediction equations include: 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, the first surface prediction equation, and the second surface prediction equation, determine the first thermal weight loss prediction equation for the target tobacco leaves and the second thermal weight loss prediction equation for the substitute tobacco leaves; the thermal weight loss prediction equations include: a prediction equation consisting of thermal weight loss rate and thermal weight loss analysis time.

[0157] In one embodiment of the present application, determining the first thermal weight loss prediction equation of the target tobacco and the second thermal weight loss prediction equation of the substitute tobacco according to the actual heating temperature, the first curved surface prediction equation, and the second curved surface prediction equation includes: substituting the actual heating temperature into the first curved surface prediction equation to determine the first thermal weight loss prediction equation of the target tobacco; substituting the actual heating temperature into the second curved surface prediction equation to determine the second thermal weight loss prediction equation of the substitute tobacco.

[0158] In one embodiment of the present application, the calculation module 200 is further configured to obtain actual heating time; integrate the first thermogravimetric loss prediction equation with the actual heating time to obtain the sum of the thermogravimetric loss rates of the target tobacco leaves.

[0159] In one embodiment of the present application, the operation module 200 is further used to obtain the actual heating time; subtract the first thermal weight loss prediction equation from the second thermal weight loss prediction equation to obtain a thermal weight loss difference equation; perform trapezoidal integration on the absolute value of the thermal weight loss difference equation based on the actual heating time to determine the difference in thermal weight loss rate between the target tobacco leaf and the substitute tobacco leaf.

[0160] In one embodiment of the present application, the comparison module 300 is further configured to obtain the first basic information of the target tobacco leaf and the second basic information of all tobacco leaves; and determine one or more substitute tobacco leaves based on the first basic information and the second basic information.

[0161] In one embodiment of the present application, if there are multiple substitute tobacco leaves, the comparison module is further used to obtain the similarity between the target tobacco leaf corresponding to each substitute tobacco leaf and the substitute tobacco leaf; and select the substitute tobacco leaf corresponding to the maximum similarity among the multiple similarities as the target substitute tobacco leaf.

[0162] Each module in the tobacco similarity assessment 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 the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0163] 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 14 As shown. The computer device includes a processor, a 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 the tobacco similarity assessment 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 similarity assessment method is implemented.

[0164] Those skilled in the art will understand that Figure 14The 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 similarity evaluation method, characterized in that: The tobacco similarity assessment includes: Based on the thermogravimetric analysis, a first thermogravimetric prediction equation for target tobacco leaves and a second thermogravimetric prediction equation for substitute tobacco leaves are determined; Determining the total thermal weight loss rate of the target tobacco leaf according to the first thermal weight loss prediction equation; Determining the difference in thermal weight loss rate between the target tobacco leaf and the substitute tobacco leaf according to the first thermal weight loss prediction equation and the second thermal weight loss prediction equation; The similarity between the target tobacco leaf and the substitute tobacco leaf is determined based on the sum of the thermal weight loss rates and the thermal weight loss rate difference.

2. The tobacco similarity evaluation method according to claim 1, characterized in that: The method of determining the first thermogravimetric loss prediction equation for the target tobacco leaf and the second thermogravimetric loss prediction equation for the substitute tobacco leaf based on thermogravimetric analysis includes: Obtaining the actual heating temperature of tobacco leaves; Based on thermogravimetric analysis, determining a first curved surface prediction equation for the target tobacco leaf and a second curved surface prediction equation for the substitute tobacco leaf; the curved surface prediction equations include: a curved surface prediction equation consisting of a thermogravimetric analysis rate, a thermogravimetric analysis temperature, and a thermogravimetric analysis time; According to the actual heating temperature, the first curved surface prediction equation and the second curved surface prediction equation, a first thermal weight loss prediction equation for the target tobacco leaf and a second thermal weight loss prediction equation for the substitute tobacco leaf are determined; the thermal weight loss prediction equation includes a prediction equation consisting of a thermal weight loss rate and a thermal weight loss analysis time.

3. The tobacco similarity evaluation method according to claim 2, characterized in that: Determining the first thermal weight loss prediction equation of the target tobacco leaf and the second thermal weight loss prediction equation of the substitute tobacco leaf according to the actual heating temperature, the first curved surface prediction equation, and the second curved surface prediction equation includes: Substituting the actual heating temperature into the first curved surface prediction equation to determine a first thermal weight loss prediction equation for the target tobacco leaf; The actual heating temperature is substituted into the second curved surface prediction equation to determine the second thermal weight loss prediction equation of the substitute tobacco leaf.

4. The tobacco similarity evaluation method according to claim 1, wherein: Determining the sum of the thermal weight loss rates of the target tobacco leaves according to the first thermal weight loss prediction equation includes: Get the actual heating time; The first thermal weight loss prediction equation is integrated with the actual heating time to obtain the total thermal weight loss rate of the target tobacco leaf.

5. The tobacco similarity evaluation method according to claim 1, wherein: Determining the difference in thermal weight loss rate between the target tobacco leaf and the substitute tobacco leaf according to the first thermal weight loss prediction equation and the second thermal weight loss prediction equation includes: Get the actual heating time; Subtracting the first thermal weight loss prediction equation from the second thermal weight loss prediction equation to obtain a thermal weight loss difference equation; The absolute value of the thermal weight loss difference equation is subjected to trapezoidal integration using the actual heating time to determine the difference in thermal weight loss rate between the target tobacco leaf and the substitute tobacco leaf.

6. The tobacco similarity evaluation method according to claim 1, characterized in that: The method further comprises: Acquiring the first basic information of the target tobacco leaf and the second basic information of all tobacco leaves; One or more tobacco substitutes are determined based on the first basic information and the second basic information.

7. The tobacco similarity evaluation method according to claim 6, characterized in that: If there are multiple tobacco substitutes, the method further comprises: Obtaining the similarity between the target tobacco leaf corresponding to each substitute tobacco leaf and the substitute tobacco leaf; The substitute tobacco leaf corresponding to the maximum similarity value is selected from multiple similarities as the target substitute tobacco leaf.

8. A tobacco similarity evaluation device, characterized in that: The device comprises: An analysis module, configured to determine a first thermogravimetric prediction equation for target tobacco leaves and a second thermogravimetric prediction equation for substitute tobacco leaves based on thermogravimetric analysis; a calculation module, configured to determine the sum of the thermal weight loss rates of the target tobacco leaves according to the first thermal weight loss prediction equation; and further configured to determine the difference in thermal weight loss rates between the target tobacco leaves and the substitute tobacco leaves according to the first thermal weight loss prediction equation and the second thermal weight loss prediction equation; The comparison module is used to determine the similarity between the target tobacco leaf and the substitute tobacco leaf according to the total thermal weight loss rate and the thermal weight loss rate difference.

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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