Tobacco leaf classification method, device, electronic device and storage medium

Abstract

The present invention discloses a tobacco leaf classification method, device, electronic device and storage medium, wherein the method comprises: filtering tobacco leaf samples to be processed and tobacco leaf samples to be compared according to sieves of different aperture sizes to obtain a first tobacco leaf weight and a second tobacco leaf weight corresponding to each aperture size; wherein the tobacco leaf samples to be processed include insect-infested samples and non-insect-infested samples, and the tobacco leaf samples to be compared include non-insect-infested samples; for each aperture size, determining a first weight attribute and a second weight attribute corresponding to the current aperture size according to the first tobacco leaf weight, the second tobacco leaf weight, the first tobacco leaf weight and the second tobacco leaf weight that are larger than the current aperture size; processing the first weight attribute and the second weight attribute respectively according to a preset distribution function to obtain attributes of the tobacco leaf to be processed and attributes of the tobacco leaf to be compared; and determining a damage category of the tobacco leaf sample to be processed according to the attributes of the tobacco leaf to be processed and the attributes of the tobacco leaf to be compared.

Description

Technical Field

[0001] The present invention relates to the technical field of the tobacco industry, and in particular to a tobacco leaf classification method, device, electronic equipment and storage medium. Background Art

[0002] Tobacco beetles have a wide diet, but primarily feed on tobacco leaves and tobacco products. The tobacco leaf borer, a common larvae of the tobacco plant, attacks tobacco leaves, preferring flue-cured and oriental tobacco, which are high in sugar and low in nicotine. They particularly favor raw tobacco, which has a high moisture content, and are more damaging to raw tobacco than the tobacco beetle. Globally, losses from tobacco leaf storage due to tobacco beetles and tobacco leaf borers exceed 1% annually, totaling over 600 million yuan.

[0003] At present, the existing technology only judges the prevention and control effect of tobacco leaf pests, and when judging the degree of insect damage in tobacco leaves, it is judged only based on the experience of technicians. Therefore, it may lead to problems such as strong subjectivity, low classification efficiency and low classification accuracy. Summary of the Invention

[0004] The present invention provides a tobacco leaf classification method, device, electronic equipment and storage medium, so as to achieve the effect of accurately, efficiently, objectively and stably classifying the damaged categories of tobacco leaves.

[0005] According to one aspect of the present invention, a tobacco leaf classification method is provided, the method comprising:

[0006] Filtering the tobacco leaf samples to be processed and the tobacco leaf samples to be compared using sieves of different aperture sizes to obtain a first tobacco leaf weight and a second tobacco leaf weight corresponding to each aperture size; wherein the tobacco leaf samples to be processed include insect-infested samples and non-insect-infested samples, and the tobacco leaf samples to be compared include non-insect-infested samples;

[0007] For each aperture size, determining a first weight attribute and a second weight attribute corresponding to the current aperture size based on the first tobacco leaf weight, the second tobacco leaf weight, the first tobacco leaf weight and the second tobacco leaf weight that are larger than the current aperture size;

[0008] The first weight attribute and the second weight attribute are processed respectively according to a preset distribution function to obtain attributes of the tobacco leaves to be processed and attributes of the tobacco leaves to be compared;

[0009] Determine the damage category of the tobacco leaf samples to be processed based on the properties of the tobacco leaves to be processed and the properties of the tobacco leaves to be compared.

[0010] According to another aspect of the present invention, a tobacco leaf sorting device is provided, the device comprising:

[0011] a tobacco leaf sample filtering module, configured to filter tobacco leaf samples to be processed and tobacco leaf samples to be compared using sieves of different pore sizes, to obtain a first tobacco leaf weight and a second tobacco leaf weight corresponding to each pore size; wherein the tobacco leaf samples to be processed include insect-infested samples and non-insect-infested samples, and the tobacco leaf samples to be compared include non-insect-infested samples;

[0012] a weight attribute determination module for determining, for each aperture size, a first weight attribute and a second weight attribute corresponding to the current aperture size based on the first tobacco leaf weight, the second tobacco leaf weight, and the first tobacco leaf weight and the second tobacco leaf weight greater than the current aperture size;

[0013] A weight attribute processing module, configured to process the first weight attribute and the second weight attribute according to a preset distribution function to obtain attributes of the tobacco leaves to be processed and attributes of the tobacco leaves to be compared;

[0014] The damage category determination module is used to determine the damage category of the tobacco leaf sample to be processed based on the properties of the tobacco leaf to be processed and the properties of the tobacco leaf to be compared.

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

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the tobacco leaf classification method described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the tobacco leaf classification method according to any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention first filters the tobacco leaf samples to be processed and the tobacco leaf samples to be compared using sieves of different aperture sizes to obtain the first tobacco leaf weight and the second tobacco leaf weight corresponding to each aperture size. Then, for each aperture size, the first tobacco leaf weight, the second tobacco leaf weight corresponding to the current aperture size, and the first tobacco leaf weight and the second tobacco leaf weight greater than the current aperture size are used to determine the first weight attribute and the second weight attribute corresponding to the current aperture size. Furthermore, the first weight attribute and the second weight attribute are processed according to a preset distribution function to obtain the attributes of the tobacco leaf to be processed and the attributes of the tobacco leaf to be compared. Finally, the damage category of the tobacco leaf to be processed is determined based on the attributes of the tobacco leaf to be processed and the attributes of the tobacco leaf to be compared. This solves the problem in the prior art of judging the degree of insect damage of tobacco leaves based solely on the experience of technicians, which may lead to strong subjectivity, low classification efficiency, and low classification accuracy. The invention achieves the effect of accurately, efficiently, objectively, and stably classifying the damage category of tobacco leaves. In addition, the degree of insect damage of tobacco leaves can be determined by the damage category of tobacco leaves, so that technicians can promptly determine countermeasures.

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

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

[0023] Figure 1 This is a flow chart of a tobacco leaf classification method provided according to the first embodiment of the present invention;

[0024] Figure 2 This is a flow chart of a tobacco leaf classification method provided according to the second embodiment of the present invention;

[0025] Figure 3 This is a schematic structural diagram of a tobacco leaf sorting device provided according to a third embodiment of the present invention;

[0026] Figure 4 The figure is a schematic diagram of the structure of an electronic device for implementing the tobacco leaf classification method according to an embodiment of the present invention. DETAILED DESCRIPTION

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

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

[0029] Example 1

[0030] Figure 1 This is a flowchart of a tobacco leaf classification method provided according to the first embodiment of the present invention. This embodiment is applicable to the classification of tobacco leaf insect damage. The method can be performed by a tobacco leaf classification device. The tobacco leaf classification device can be implemented in the form of hardware and / or software. The tobacco leaf classification device can be configured in a terminal and / or server. Figure 1 As shown, the method includes:

[0031] S110 , filtering the tobacco leaf samples to be processed and the tobacco leaf samples to be compared using sieves with different aperture sizes to obtain a first tobacco leaf weight and a second tobacco leaf weight corresponding to each aperture size.

[0032] In this embodiment, the tobacco leaf samples to be processed can be understood as tobacco leaf samples that need to be classified and processed for tobacco leaf insect infestation. The tobacco leaf samples to be processed include insect-infested samples and non-insect-infested samples. The tobacco leaf samples to be compared can be understood as normal tobacco leaf samples used as a control group. The tobacco leaf samples to be compared include non-insect-infested samples. Optionally, the sieves with different aperture sizes can be a screening device comprising multiple layers of sieves, each layer having a different aperture size, such as a rotary detection sieve. Alternatively, the sieves can be multiple independent screening devices, each equipped with a layer of sieves having different aperture sizes, etc., which are not specifically limited in this embodiment. For example, the device for filtering the tobacco leaf samples to be processed and the tobacco leaf samples to be compared can be a 10-layer rotary detection sieve with sieve aperture sizes of 8 mm, 6.7 mm, 5.6 mm, 4.75 mm, 4 mm, 3.35 mm, 2.8 mm, 2 mm, 1.4 mm, and 0.71 mm, respectively.

[0033] In practical applications, when sieves with different aperture sizes are used to filter the tobacco leaf samples to be processed, the filtered tobacco leaf samples to be processed will remain in each layer of the sieves. The weight of the tobacco leaf samples to be processed remaining in each layer of the sieves can be used as the first tobacco leaf weight. Similarly, when sieves with different aperture sizes are used to filter the tobacco leaf samples to be compared, the weight of the tobacco leaf samples to be compared remaining in each layer of the sieves can be used as the second tobacco leaf weight.

[0034] Optionally, the tobacco leaf samples to be processed and the tobacco leaf samples to be compared are filtered through sieves with different aperture sizes to obtain the first tobacco leaf weight and the second tobacco leaf weight corresponding to each aperture size, including: filtering the tobacco leaf samples to be processed through sieves with different aperture sizes, and obtaining the weight of the tobacco leaf samples remaining on each sieve to obtain the first tobacco leaf weight corresponding to each aperture size; and filtering the tobacco leaf samples to be compared through sieves with different aperture sizes, and obtaining the weight of the tobacco leaf samples remaining on each sieve to obtain the second tobacco leaf weight corresponding to each aperture size.

[0035] In a specific implementation, after obtaining the samples to be processed and the tobacco leaf samples to be compared, sieves with different aperture sizes are used to filter the tobacco leaf samples to be processed and the tobacco leaf samples to be compared respectively. For the tobacco leaf samples to be processed, the tobacco leaf samples remaining on each layer of the sieve are weighed to obtain the first tobacco leaf weight corresponding to each aperture size; for the tobacco leaf samples to be compared, the tobacco leaf samples remaining on each layer of the sieve are weighed to obtain the second tobacco leaf weight corresponding to each aperture size.

[0036] S120. For each aperture size, determine the first weight attribute and the second weight attribute corresponding to the current aperture size based on the first tobacco leaf weight, the second tobacco leaf weight, the first tobacco leaf weight and the second tobacco leaf weight that are larger than the current aperture size.

[0037] In this embodiment, the first weight attribute can be understood as the ratio of the first tobacco leaf weight corresponding to each layer of screen to the total tobacco leaf weight of the tobacco leaf sample to be processed. The second weight attribute can be understood as the ratio of the second tobacco leaf weight corresponding to each layer of screen to the total tobacco leaf weight of the tobacco leaf sample to be compared. For example, both the first weight attribute and the second weight attribute can be expressed as percentages.

[0038] It should be noted that after obtaining the first tobacco leaf weight corresponding to the tobacco leaf sample to be processed on the sieves with different aperture sizes and the second tobacco leaf weight corresponding to the tobacco leaf sample to be compared on the sieves with different aperture sizes, in order to determine the characteristic size distribution relationship of the tobacco leaf samples, it is necessary to further determine the ratio of the cumulative weight of the tobacco leaf sample to be processed and the tobacco leaf sample to be compared on the sieves with different aperture sizes to the total weight of the tobacco leaf samples, so that the characteristic distribution relationship of the tobacco leaf samples can be determined based on the tobacco leaf weight ratio and the corresponding aperture size.

[0039] Optionally, for each aperture size, the first weight attribute and the second weight attribute corresponding to the current aperture size are determined based on the first tobacco leaf weight, the second tobacco leaf weight, the first tobacco leaf weight and the second tobacco leaf weight greater than the current aperture size corresponding to the current aperture size, including: determining the total weight of the first tobacco leaf based on the first tobacco leaf weight corresponding to each aperture size; determining, for each aperture size, the first cumulative weight corresponding to the current aperture size based on the first tobacco leaf weight corresponding to the current aperture size and the first tobacco leaf weight greater than the current aperture size; determining the first weight attribute corresponding to the current aperture size based on the first cumulative weight and the total weight of the first tobacco leaf; and determining the total weight of the second tobacco leaf based on the second tobacco leaf weight corresponding to each aperture size; determining, for each aperture size, the second cumulative weight corresponding to the current aperture size based on the second tobacco leaf weight corresponding to the current aperture size and the second tobacco leaf weight greater than the current aperture size; determining the second weight attribute corresponding to the current aperture size based on the second cumulative weight and the total weight of the second tobacco leaf.

[0040] Among them, the first total weight of tobacco leaves can be understood as the weight of tobacco leaves obtained by accumulating the weight of the first tobacco leaves corresponding to each aperture size. The first cumulative weight can be understood as the weight of tobacco leaves obtained by accumulating the weight of the first tobacco leaves of the current aperture size and the weight of the first tobacco leaves larger than the current aperture size. For example, when the current aperture size is 6.7 mm, the aperture size larger than 6.7 mm is 8 mm. The first cumulative weight corresponding to 6.7 mm can be obtained by summing the weight of the first tobacco leaves corresponding to 6.7 mm and the weight of the first tobacco leaves corresponding to 8 mm; when the current aperture size is 5.6 mm, the aperture sizes larger than 5.6 mm are 6.7 mm and 8 mm respectively. Therefore, the first cumulative weight corresponding to 5.6 mm can be obtained by summing the weight of the first tobacco leaves corresponding to 5.6 mm, the weight of the first tobacco leaves corresponding to 6.7 mm, and the weight of the first tobacco leaves corresponding to 8 mm.

[0041] In practical applications, after obtaining the total weight of the first tobacco leaves and the first cumulative weight corresponding to the current aperture size, the first weight attribute corresponding to the current aperture size can be obtained by dividing the first cumulative weight corresponding to the aperture size by the total weight of the first tobacco leaves.

[0042] Accordingly, the second total tobacco leaf weight may be a tobacco leaf weight obtained by accumulating the second tobacco leaf weights corresponding to each aperture size. The second accumulated weight may be a tobacco leaf weight obtained by accumulating the second tobacco leaf weight corresponding to the current aperture size and the second tobacco leaf weight corresponding to an aperture size larger than the current aperture size. Specifically, after obtaining the second total tobacco leaf weight and the second accumulated weight for the current aperture size, the second accumulated weight is divided by the second total tobacco leaf weight to obtain the second weight attribute corresponding to the current aperture size.

[0043] S130. Process the first weight attribute and the second weight attribute respectively according to a preset distribution function to obtain attributes of the tobacco leaves to be processed and attributes of the tobacco leaves to be compared.

[0044] In this embodiment, the preset distribution function can be a pre-set function for determining the distribution of characteristic dimensions of tobacco leaves. The attribute of the tobacco leaves to be processed can be understood as the average characteristic dimension of the tobacco leaves corresponding to the tobacco leaf samples to be processed. The attribute of the tobacco leaves to be compared can be understood as the average characteristic dimension of the tobacco leaves corresponding to the tobacco leaf samples to be compared.

[0045] Optionally, the first weight attribute and the second weight attribute are processed respectively according to a preset distribution function to obtain the attributes of the tobacco leaves to be processed and the attributes of the tobacco leaves to be compared, including: for each aperture size, processing the first weight attribute and the current aperture size according to the preset distribution function to obtain the parameters of the tobacco leaves to be processed corresponding to the current aperture size; processing the parameters of the tobacco leaves to be processed according to the preset attribute function to determine the single attribute of the tobacco leaves to be processed corresponding to the current aperture size; performing mean processing on the single attributes of the tobacco leaves to be processed of each aperture size to obtain the attributes of the tobacco leaves to be processed; and, for each aperture size, processing the second weight attribute and the current aperture size according to the preset distribution function to obtain the parameters of the tobacco leaves to be compared corresponding to the current aperture size; performing mean processing on the parameters of the tobacco leaves to be compared according to the preset attribute function to determine the single attribute of the tobacco leaves to be compared corresponding to the current aperture size; performing mean processing on the single attributes of the tobacco leaves to be compared of each aperture size to obtain the attributes of the tobacco leaves to be compared.

[0046] Among them, the parameters of the tobacco leaves to be processed can be understood as characteristic distribution parameters associated with the tobacco leaf samples to be processed. Optionally, the parameters of the tobacco leaves to be processed include the size uniformity coefficient of the tobacco leaves to be processed. Among them, the size uniformity coefficient of the tobacco leaves to be processed can represent the overall uniformity and concentration of the size distribution of the tobacco leaf samples. The larger the value, the narrower the normal distribution interval of the tobacco leaf sample structure, the higher the concentration, and the more uniform the distribution of the tobacco leaf samples. The preset attribute function can be understood as a pre-set function expression for determining the characteristic size of the tobacco leaf samples. The single attribute of the tobacco leaves to be processed can be understood as the characteristic size value of the tobacco leaf samples to be processed corresponding to a single aperture size.

[0047] Accordingly, the tobacco leaf parameters to be compared may be characteristic distribution parameters associated with the tobacco leaf samples to be compared. Optionally, the tobacco leaf parameters to be compared include a size uniformity coefficient of the tobacco leaf to be compared. The single attribute of the tobacco leaf to be compared may be a characteristic size value of the tobacco leaf samples to be compared corresponding to a single aperture size.

[0048] It should be noted that, since the method for determining the properties of the tobacco leaves to be compared is the same as the method for determining the properties of the tobacco leaves to be processed, the method for determining the properties of the tobacco leaves to be processed is used as an example for explanation.

[0049] In a specific implementation, after obtaining the first weight attribute corresponding to each aperture size, the first weight attribute and the corresponding aperture size are substituted into the preset distribution function for each aperture size to obtain the parameters of the tobacco leaves to be processed corresponding to the current aperture size. Furthermore, the parameters of the tobacco leaves to be processed are substituted into the preset attribute function to obtain the single attribute of the tobacco leaves to be processed corresponding to the current aperture size. After determining the single attribute of the tobacco leaves to be processed corresponding to each aperture size based on the above method, the single attribute of the tobacco leaves to be processed of each aperture size is averaged to obtain the attributes of the tobacco leaves to be processed.

[0050] Exemplarily, a single attribute of the tobacco leaves to be processed can be determined by the following formula:

[0051]

[0052] Wherein, F represents the first weight attribute, a represents the function expression parameter, q represents the aperture size, and p represents the size uniformity coefficient of the tobacco leaves to be processed.

[0053] Further,

[0054]

[0055] Where d represents a single attribute of the tobacco leaves to be processed.

[0056] S140. Determine the damage category of the tobacco leaf sample to be processed based on the properties of the tobacco leaf to be processed and the properties of the tobacco leaf to be compared.

[0057] In this embodiment, the damage category can be understood as a classification based on the damage level of the tobacco leaf sample. For example, the damage category can be represented by a scale of 0-9, with the damage level increasing from low to high. For example, level 0 indicates that the tobacco leaf sample is not damaged, and level 9 indicates that the damage level of the tobacco leaf sample is greater than 30%.

[0058] It should be noted that the tobacco leaf samples to be processed are samples that need to be classified, and the tobacco leaf samples to be compared are samples that serve as a control for the classification process. Therefore, when classifying the damage categories of the tobacco leaf samples to be processed, it is necessary to implement it based on the properties of the tobacco leaves to be processed and the properties of the tobacco leaves to be compared.

[0059] Optionally, the damage category of the tobacco leaf sample to be processed is determined based on the attributes of the tobacco leaves to be processed and the attributes of the tobacco leaves to be compared, including: determining the difference value between the attributes of the tobacco leaves to be processed and the attributes of the tobacco leaves to be compared; based on the difference value and the attributes of the tobacco leaves to be compared, determining the damage degree attribute of the tobacco leaf sample to be processed, so as to determine the damage category based on the damage degree attribute.

[0060] The damage degree attribute can be understood as a numerical value used to characterize the damage degree of tobacco leaves.

[0061] In the specific implementation, after obtaining the attributes of the tobacco leaves to be processed and the attributes of the tobacco leaves to be compared, first, the attributes of the tobacco leaves to be processed and the attributes of the tobacco leaves to be compared are subjected to difference processing to obtain their corresponding difference values. Further, the difference value is divided by the attributes of the tobacco leaves to be compared, thereby finally obtaining the damage degree attribute of the tobacco leaf sample to be processed.

[0062] For example, the damage degree attribute of the tobacco leaf sample to be processed can be determined by the following formula:

[0063]

[0064] Among them, Q represents the damage degree attribute of the tobacco leaf sample to be processed, Indicates the properties of the tobacco leaves to be compared. Indicates the properties of tobacco leaves to be processed.

[0065] Furthermore, after determining the damage degree attribute of the tobacco leaf sample to be processed, the damage category to which the tobacco leaf sample to be processed belongs is determined based on the damage degree attribute.

[0066] Optionally, determining the damage category based on the damage degree attribute includes: determining the damage category of the tobacco leaf sample to be processed based on a pre-established correspondence between the damage degree and the damage category.

[0067] In a specific implementation, after obtaining the damage degree attribute of the tobacco leaf sample to be processed, the damage category of the tobacco leaf sample to be processed is determined based on the pre-established correspondence between the damage degree attribute and the damage category to realize the classification process of the tobacco leaf sample to be processed.

[0068] The technical solution of the embodiment of the present invention first filters the tobacco leaf samples to be processed and the tobacco leaf samples to be compared using sieves of different aperture sizes to obtain the first tobacco leaf weight and the second tobacco leaf weight corresponding to each aperture size. Then, for each aperture size, the first tobacco leaf weight, the second tobacco leaf weight corresponding to the current aperture size, and the first tobacco leaf weight and the second tobacco leaf weight greater than the current aperture size are used to determine the first weight attribute and the second weight attribute corresponding to the current aperture size. Furthermore, the first weight attribute and the second weight attribute are processed according to a preset distribution function to obtain the attributes of the tobacco leaf to be processed and the attributes of the tobacco leaf to be compared. Finally, the damage category of the tobacco leaf to be processed is determined based on the attributes of the tobacco leaf to be processed and the attributes of the tobacco leaf to be compared. This solves the problem in the prior art of judging the degree of insect damage of tobacco leaves based solely on the experience of technicians, which may lead to strong subjectivity, low classification efficiency, and low classification accuracy. The invention achieves the effect of accurately, efficiently, objectively, and stably classifying the damage category of tobacco leaves. In addition, the degree of insect damage of tobacco leaves can be determined by the damage category of tobacco leaves, so that technicians can promptly determine countermeasures.

[0069] Example 2

[0070] Figure 2 This is a flow chart of a tobacco leaf classification method provided by the second embodiment of the present invention. This embodiment adds the technical feature of sample construction on the basis of the above embodiment. Figure 2 As shown, the method includes:

[0071] S210: Obtain finished tobacco leaves, and process the finished tobacco leaves based on the tobacco leaf sample preparation method to obtain insect-infested samples and non-insect-infested samples.

[0072] In this embodiment, finished tobacco leaves can be understood as tobacco leaves with a moisture content of 11%-13%. It should be noted that finished tobacco leaves include both insect-infested and non-insect-infested tobacco leaves. Insect-infested samples can be understood as tobacco leaves that have been damaged by insects. Non-insect-infested samples can be understood as tobacco leaves that have not been infested and are intact.

[0073] In actual applications, when analyzing the damage category of tobacco leaves, there are specific standard requirements for the moisture content, temperature, and humidity of the tobacco leaves during the analysis process. Therefore, after obtaining the finished tobacco leaves, the finished tobacco leaves need to be processed according to the tobacco leaf sample preparation method to obtain tobacco leaf samples that meet the conditions.

[0074] Optionally, the tobacco leaf sample preparation method includes the following steps: loosening and conditioning the finished tobacco leaves to obtain tobacco leaves to be processed; shredding the tobacco leaves to be processed, and balancing them according to preset tobacco leaf preparation standards to obtain insect-infested samples and non-insect-infested samples.

[0075] Among them, the tobacco leaves to be processed may be tobacco leaves that need to be shredded. The preset tobacco leaf preparation standard may be a pre-set standard for limiting tobacco leaf samples. The loosening and rehydration treatment can be understood as beating the finished tobacco leaves so that they change from an initial compact state to a dispersed state, and then rehydrating them to increase the moisture content of the treated tobacco leaves. The advantage of such treatment is that in the subsequent shredding process, the problem of being unable to shred due to the tobacco leaves being too compact or too dry is avoided. Optionally, the preset tobacco leaf preparation standard includes a temperature standard, a humidity standard, and a moisture content standard. Exemplarily, the preset tobacco leaf preparation standard may be GB / T16447.

[0076] In the specific implementation, an appropriate amount of finished tobacco leaves are obtained, and the finished tobacco leaves are loosened and moisturized to obtain tobacco leaves to be processed that meet the conditions for shredding. Furthermore, the tobacco leaves to be processed are shredded to obtain shredded tobacco, and then the shredded tobacco is balanced according to the preset tobacco leaf preparation standards so that the temperature, humidity and moisture content of the shredded tobacco meet the preparation standard requirements, thereby finally obtaining insect-infested samples and non-insect-infested samples.

[0077] S220: constructing a sample to be compared based on the non-insect-infested sample; and constructing a sample to be processed based on the insect-infested sample and part of the non-insect-infested sample.

[0078] It should be noted that since the tobacco leaf samples to be compared are normal tobacco leaf samples that serve as a control for the damage category analysis, when constructing the tobacco leaf samples to be compared, they can be constructed based on samples that have not been infested by insects; and since the tobacco leaf samples to be processed are tobacco leaf samples that need to be classified and processed according to the damage category, and in the actual analysis of the degree of tobacco leaf damage, there may be undamaged tobacco leaves, therefore, when constructing the tobacco leaf samples to be processed, they can be constructed based on insect-infested samples and some non-insect-infested samples.

[0079] S230 , filtering the tobacco leaf samples to be processed and the tobacco leaf samples to be compared using sieves with different aperture sizes to obtain a first tobacco leaf weight and a second tobacco leaf weight corresponding to each aperture size.

[0080] S240. For each aperture size, determine the first weight attribute and the second weight attribute corresponding to the current aperture size based on the first tobacco leaf weight, the second tobacco leaf weight, the first tobacco leaf weight and the second tobacco leaf weight that are larger than the current aperture size.

[0081] S250: Process the first weight attribute and the second weight attribute respectively according to a preset distribution function to obtain attributes of the tobacco leaves to be processed and attributes of the tobacco leaves to be compared.

[0082] S260. Determine the damage category of the tobacco leaf sample to be processed based on the properties of the tobacco leaf to be processed and the properties of the tobacco leaf to be compared.

[0083] The technical solution of the embodiment of the present invention is to determine insect-infested samples and non-insect-infested samples, and then construct tobacco leaf samples to be processed and tobacco leaf samples to be compared based on the insect-infested samples and non-insect-infested samples, and then filter the tobacco leaf samples to be processed and the tobacco leaf samples to be compared according to sieves with different aperture sizes to obtain the first tobacco leaf weight and the second tobacco leaf weight corresponding to each aperture size. Furthermore, for each aperture size, according to the first tobacco leaf weight, the second tobacco leaf weight, the first tobacco leaf weight and the second tobacco leaf weight corresponding to the current aperture size, the first weight attribute and the second weight attribute corresponding to the current aperture size are determined, and then the first weight attribute and the second weight attribute are processed respectively according to a preset distribution function to obtain the attributes of the tobacco leaf to be processed and the attributes of the tobacco leaf to be compared. Finally, the damage category of the tobacco leaf samples to be processed is determined based on the properties of the tobacco leaves to be processed and the properties of the tobacco leaves to be compared. This solves the problem in the existing technology that when judging the degree of insect damage of tobacco leaves, the judgment is made only based on the experience of technicians. Therefore, it may lead to problems such as strong subjectivity, low classification efficiency and low classification accuracy. The damage category of tobacco leaves can be accurately, efficiently, objectively and stably classified. Moreover, the degree of insect damage of tobacco leaves can be determined by the damage category of tobacco leaves, so that technicians can determine the response measures in a timely manner.

[0084] Example 3

[0085] Figure 3 Schematic diagram of a tobacco leaf sorting device according to the third embodiment of the present invention. Figure 3 As shown, the device includes: a tobacco sample filtering module 310, a weight attribute determination module 320, a weight attribute processing module 330 and a damage category determination module 340.

[0086] The tobacco leaf sample filtering module 310 is configured to filter the tobacco leaf samples to be processed and the tobacco leaf samples to be compared using sieves of different pore sizes to obtain a first tobacco leaf weight and a second tobacco leaf weight corresponding to each pore size; the tobacco leaf samples to be processed include insect-infested samples and non-insect-infested samples, and the tobacco leaf samples to be compared include non-insect-infested samples;

[0087] A weight attribute determination module 320 is configured to determine, for each aperture size, a first weight attribute and a second weight attribute corresponding to the current aperture size based on the first tobacco leaf weight, the second tobacco leaf weight, and the first tobacco leaf weight and the second tobacco leaf weight that are larger than the current aperture size.

[0088] The weight attribute processing module 330 is used to process the first weight attribute and the second weight attribute according to a preset distribution function to obtain the attributes of the tobacco leaves to be processed and the attributes of the tobacco leaves to be compared;

[0089] The damage category determination module 340 is used to determine the damage category of the tobacco leaf sample to be processed based on the properties of the tobacco leaf to be processed and the properties of the tobacco leaf to be compared.

[0090] The technical solution of the embodiment of the present invention first filters the tobacco leaf samples to be processed and the tobacco leaf samples to be compared using sieves of different aperture sizes to obtain the first tobacco leaf weight and the second tobacco leaf weight corresponding to each aperture size. Then, for each aperture size, the first tobacco leaf weight, the second tobacco leaf weight corresponding to the current aperture size, and the first tobacco leaf weight and the second tobacco leaf weight greater than the current aperture size are used to determine the first weight attribute and the second weight attribute corresponding to the current aperture size. Furthermore, the first weight attribute and the second weight attribute are processed according to a preset distribution function to obtain the attributes of the tobacco leaf to be processed and the attributes of the tobacco leaf to be compared. Finally, the damage category of the tobacco leaf to be processed is determined based on the attributes of the tobacco leaf to be processed and the attributes of the tobacco leaf to be compared. This solves the problem in the prior art of judging the degree of insect damage of tobacco leaves based solely on the experience of technicians, which may lead to strong subjectivity, low classification efficiency, and low classification accuracy. The invention achieves the effect of accurately, efficiently, objectively, and stably classifying the damage category of tobacco leaves. In addition, the degree of insect damage of tobacco leaves can be determined by the damage category of tobacco leaves, so that technicians can promptly determine countermeasures.

[0091] Optionally, the device also includes: a sample construction module, used to obtain finished tobacco leaves, process the finished tobacco leaves based on the tobacco leaf sample preparation method, and obtain insect-infested samples and non-insect-infested samples; construct samples to be compared based on the non-insect-infested samples; and construct samples to be processed based on the insect-infested samples and some non-insect-infested samples.

[0092] Optionally, the tobacco leaf sample filtering module 310 is also used to filter the tobacco leaf samples to be processed according to sieves of different aperture sizes, and obtain the weight of the tobacco leaf samples remaining on each sieve to obtain a first tobacco leaf weight corresponding to each aperture size; and to filter the tobacco leaf samples to be compared according to sieves of different aperture sizes, and obtain the weight of the tobacco leaf samples remaining on each sieve to obtain a second tobacco leaf weight corresponding to each aperture size.

[0093] Optionally, the weight attribute determination module 320 is also used to determine the total weight of the first tobacco leaves based on the first tobacco leaf weight corresponding to each aperture size; for each aperture size, determine the first cumulative weight corresponding to the current aperture size according to the first tobacco leaf weight corresponding to the current aperture size and the first tobacco leaf weight greater than the current aperture size; determine the first weight attribute corresponding to the current aperture size according to the first cumulative weight and the total weight of the first tobacco leaves; and determine the total weight of the second tobacco leaves based on the second tobacco leaf weight corresponding to each aperture size; for each aperture size, determine the second cumulative weight corresponding to the current aperture size according to the second tobacco leaf weight corresponding to the current aperture size and the second tobacco leaf weight greater than the current aperture size; and determine the second weight attribute corresponding to the current aperture size according to the second cumulative weight and the total weight of the second tobacco leaves.

[0094] Optionally, the weight attribute processing module 330 is also used to process the first weight attribute and the current aperture size according to a preset distribution function for each aperture size to obtain the parameters of the tobacco leaves to be processed corresponding to the current aperture size, wherein the parameters of the tobacco leaves to be processed include the size uniformity coefficient of the tobacco leaves to be processed; process the parameters of the tobacco leaves to be processed according to the preset attribute function to determine the single attribute of the tobacco leaves to be processed corresponding to the current aperture size; perform mean processing on the single attribute of the tobacco leaves to be processed of each aperture size to obtain the attributes of the tobacco leaves to be processed; and, for each aperture size, process the second weight attribute and the current aperture size according to the preset distribution function to obtain the parameters of the tobacco leaves to be compared corresponding to the current aperture size, wherein the parameters of the tobacco leaves to be compared include the size uniformity coefficient of the tobacco leaves to be compared; perform mean processing on the parameters of the tobacco leaves to be compared according to the preset attribute function to determine the single attribute of the tobacco leaves to be compared corresponding to the current aperture size; and perform mean processing on the single attribute of the tobacco leaves to be compared of each aperture size to obtain the attributes of the tobacco leaves to be compared.

[0095] Optionally, the damage category determination module 340 includes a difference value determination unit and a damage degree attribute determination unit.

[0096] The difference value determination unit is used to determine the difference value between the attributes of the tobacco leaves to be processed and the attributes of the tobacco leaves to be compared; the damage degree attribute determination unit is used to determine the damage degree attribute of the tobacco leaf sample to be processed based on the difference value and the attributes of the tobacco leaves to be compared, so as to determine the damage category based on the damage degree attribute.

[0097] Optionally, the sample construction module includes a sample preparation unit, which is used to loosen and rehumidify the finished tobacco leaves to obtain tobacco leaves to be processed; cut the tobacco leaves to be processed, and balance them according to preset tobacco leaf preparation standards to obtain insect-infested samples and non-insect-infested samples, wherein the preset tobacco leaf preparation standards include temperature standards, humidity standards and moisture content standards.

[0098] The tobacco leaf classification device provided in the embodiment of the present invention can execute the tobacco leaf classification method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0099] Example 4

[0100] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0101] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0102] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0103] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the tobacco leaf classification method.

[0104] In some embodiments, the tobacco leaf classification method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the tobacco leaf classification method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the tobacco leaf classification method in any other suitable manner (e.g., via firmware).

[0105] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0109] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0110] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0111] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0112] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A tobacco leaf classification method, characterized in that: include: Filtering the tobacco leaf samples to be processed and the tobacco leaf samples to be compared using sieves of different aperture sizes to obtain a first tobacco leaf weight and a second tobacco leaf weight corresponding to each aperture size; wherein the tobacco leaf samples to be processed include insect-infested samples and non-insect-infested samples, and the tobacco leaf samples to be compared include non-insect-infested samples; For each aperture size, determining a first weight attribute and a second weight attribute corresponding to the current aperture size based on the first tobacco leaf weight, the second tobacco leaf weight, and the first tobacco leaf weight and the second tobacco leaf weight that are larger than the current aperture size; Processing the first weight attribute and the second weight attribute respectively according to a preset distribution function to obtain attributes of the tobacco leaves to be processed and attributes of the tobacco leaves to be compared; Determining the damage category of the tobacco leaf sample to be processed based on the properties of the tobacco leaf to be processed and the properties of the tobacco leaf to be compared; wherein the damage category is a category divided according to the damage condition of the tobacco leaf sample; The processing of the first weight attribute and the second weight attribute according to a preset distribution function to obtain the attributes of the tobacco leaves to be processed and the attributes of the tobacco leaves to be compared includes: For each aperture size, the first weight attribute and the current aperture size are processed according to a preset distribution function to obtain parameters of the tobacco leaf to be processed corresponding to the current aperture size; wherein the parameters of the tobacco leaf to be processed include a size uniformity coefficient of the tobacco leaf to be processed; the size uniformity coefficient of the tobacco leaf to be processed is used to characterize the overall uniformity and concentration of the size distribution of the tobacco leaf sample; Processing the parameters of the tobacco leaves to be processed according to a preset attribute function to determine a single attribute of the tobacco leaves to be processed corresponding to the current aperture size; Performing mean processing on the single attribute of the tobacco leaves to be processed of each aperture size to obtain the attribute of the tobacco leaves to be processed; and For each aperture size, the second weight attribute and the current aperture size are processed according to a preset distribution function to obtain the tobacco leaf parameters to be compared corresponding to the current aperture size; wherein the tobacco leaf parameters to be compared include a size uniformity coefficient of the tobacco leaf to be compared; The parameters of the tobacco leaves to be compared are processed according to a preset attribute function to determine a single attribute of the tobacco leaves to be compared corresponding to the current aperture size; The single attribute of the tobacco leaves to be compared of each aperture size is averaged to obtain the attribute of the tobacco leaves to be compared.

2. The method according to claim 1, characterized in that Also includes: Obtaining finished tobacco leaves, and processing the finished tobacco leaves based on the tobacco leaf sample preparation method to obtain insect-infested samples and non-insect-infested samples; Based on the non-insect-infested samples, construct a sample set to be compared; as well as, A sample set to be processed is constructed based on the insect-infested samples and part of the non-insect-infested samples.

3. The method according to claim 1, characterized in that The sieves with different aperture sizes are used to filter the tobacco leaf samples to be processed and the tobacco leaf samples to be compared, and obtain the first tobacco leaf weight and the second tobacco leaf weight corresponding to each aperture size, including: Filtering the tobacco leaf samples to be processed using sieves with different aperture sizes, and obtaining the weight of the tobacco leaf samples remaining on each sieve to obtain a first tobacco leaf weight corresponding to each aperture size; as well as, The tobacco leaf samples to be compared are filtered according to sieves with different aperture sizes, and the weight of the tobacco leaf samples remaining on each sieve is obtained to obtain the second tobacco leaf weight corresponding to each aperture size.

4. The method according to claim 1, wherein The determining, for each aperture size, of the first and second weight attributes corresponding to the current aperture size based on the first tobacco leaf weight, the second tobacco leaf weight, and the first and second tobacco leaf weights greater than the current aperture size, includes: determining a total weight of the first tobacco leaves based on the weights of the first tobacco leaves corresponding to the respective aperture sizes; For each aperture size, determining a first cumulative weight corresponding to the current aperture size according to a first tobacco leaf weight corresponding to the current aperture size and a first tobacco leaf weight larger than the current aperture size; determining a first weight attribute corresponding to the current aperture size based on the first cumulative weight and the first total weight of the tobacco leaves; as well as, determining a total weight of the second tobacco leaves based on the weights of the second tobacco leaves corresponding to the respective aperture sizes; For each aperture size, determining a second cumulative weight corresponding to the current aperture size based on the second tobacco leaf weight corresponding to the current aperture size and the second tobacco leaf weight greater than the current aperture size; A second weight attribute corresponding to the current aperture size is determined according to the second cumulative weight and the second total weight of the tobacco leaves.

5. The method according to claim 1, wherein The step of determining the damage category of the tobacco leaf sample to be processed based on the properties of the tobacco leaf to be processed and the properties of the tobacco leaf to be compared includes: Determining the difference between the properties of the tobacco leaves to be processed and the properties of the tobacco leaves to be compared; Based on the difference value and the attribute of the tobacco leaf to be compared, the damage degree attribute of the tobacco leaf sample to be processed is determined, so as to determine the damage category based on the damage degree attribute.

6. The method according to claim 5, characterized in that The determining the damage category based on the damage degree attribute includes: The damage category of the tobacco leaf sample to be processed is determined based on a pre-established correspondence between the damage degree attribute and the damage category.

7. The method according to claim 2, characterized in that The tobacco leaf sample preparation method comprises the following steps: performing a loosening and moisture-conditioning treatment on the finished tobacco leaves to obtain tobacco leaves to be processed; The tobacco leaves to be processed are shredded and balanced according to preset tobacco preparation standards to obtain the insect-infested samples and the non-insect-infested samples, wherein the preset tobacco preparation standards include temperature standards, humidity standards and moisture content standards.

8. A tobacco leaf sorting device, characterized in that: include: a tobacco leaf sample filtering module, configured to filter tobacco leaf samples to be processed and tobacco leaf samples to be compared using sieves of different pore sizes, to obtain a first tobacco leaf weight and a second tobacco leaf weight corresponding to each pore size; wherein the tobacco leaf samples to be processed include insect-infested samples and non-insect-infested samples, and the tobacco leaf samples to be compared include non-insect-infested samples; a weight attribute determination module, configured to determine, for each aperture size, a first weight attribute and a second weight attribute corresponding to the current aperture size based on the first tobacco leaf weight, the second tobacco leaf weight, and the first tobacco leaf weight and the second tobacco leaf weight greater than the current aperture size; a weight attribute processing module, configured to process the first weight attribute and the second weight attribute respectively according to a preset distribution function to obtain attributes of the tobacco leaves to be processed and attributes of the tobacco leaves to be compared; a damage category determination module, configured to determine the damage category of the tobacco leaf sample to be processed based on the properties of the tobacco leaf to be processed and the properties of the tobacco leaf to be compared; wherein the damage category is a category divided according to the damage condition of the tobacco leaf sample; The weight attribute processing module is further used to process the first weight attribute and the current aperture size according to a preset distribution function for each aperture size to obtain the parameters of the tobacco leaves to be processed corresponding to the current aperture size; wherein the parameters of the tobacco leaves to be processed include a size uniformity coefficient of the tobacco leaves to be processed; the size uniformity coefficient of the tobacco leaves to be processed is used to characterize the overall uniformity and concentration of the size distribution of the tobacco leaf samples; the parameters of the tobacco leaves to be processed are processed according to the preset attribute function to determine the single attribute of the tobacco leaves to be processed corresponding to the current aperture size; the parameters of the tobacco leaves to be processed for each aperture size are processed. Process the single attribute of the tobacco leaf and perform mean processing to obtain the attribute of the tobacco leaf to be processed; and, for each aperture size, process the second weight attribute and the current aperture size according to a preset distribution function to obtain the tobacco leaf parameters to be compared corresponding to the current aperture size; wherein, the tobacco leaf parameters to be compared include the uniformity coefficient of the tobacco leaf size to be compared; perform mean processing on the tobacco leaf parameters to be compared according to a preset attribute function to determine the single attribute of the tobacco leaf to be compared corresponding to the current aperture size; perform mean processing on the single attribute of the tobacco leaf to be compared of each aperture size to obtain the tobacco leaf attributes to be compared.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the tobacco leaf classification method according to any one of claims 1 to 7.

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