A method and device for quantitatively analyzing the quality and quantity of tobacco grades, an electronic device, and a storage medium

Abstract

This invention discloses a method, apparatus, electronic device, and storage medium for quantitative analysis of tobacco leaf grade quality. The method includes: acquiring raw data of the tobacco leaves to be analyzed and preprocessing the raw data; wherein the raw data of the tobacco leaves to be analyzed includes a first grade code and a second grade code, the first grade code referring to the original grade code of the tobacco leaves, and the second grade code referring to the grade code after the tobacco leaves have been graded; using the first grade code as the standard, determining the tobacco leaf subgroup and positive group of the second grade code according to expert knowledge rules to reclassify the second grade code and determine the reclassified tobacco leaf type; assigning weights and penalty coefficients to the reclassified tobacco leaf type to quantitatively analyze the grade quality of the tobacco leaves to be analyzed. Using this method, the accuracy and reliability of tobacco leaf grade quality analysis are significantly improved, providing a scientific and efficient quality assessment basis for tobacco leaf acquisition, processing, and distribution.

Description

Technical Field

[0001] This invention relates to the field of tobacco leaf quality analysis technology, and in particular to a method, apparatus, electronic device, and storage medium for quantitative analysis of tobacco leaf grade quality. Background Technology

[0002] In the tobacco processing sector, the quality of tobacco leaves serves as the core foundation for ensuring the stability of cigarette product quality, maintaining the consistency of style and characteristics, and optimizing and improving production processes. Its accurate assessment and scientific management are of paramount strategic importance for enhancing the overall efficiency of the tobacco industry chain.

[0003] However, current traditional methods of analyzing tobacco leaf quality still primarily rely on manual data interpretation and statistical analysis. This model has revealed several significant drawbacks in practical applications: First, due to the lack of systematic and standardized objective judgment criteria, subjective human judgment is easily influenced by multiple variables such as experience differences and environmental factors, resulting in low judgment efficiency and difficulty in ensuring consistency of results. Second, manual statistical analysis involves a large amount of repetitive work, which not only consumes human resources but also makes it difficult to avoid errors caused by operational fatigue. Third, manual analysis is usually limited to post-event statistics and lacks the ability to perceive quality fluctuations during the production process in real time, resulting in significant time lag and making it difficult to meet the needs of real-time control in modern production processes. Fourth, the manual data transmission chain is lengthy, information flow is inefficient, and data leakage and tampering are prone to occur during multi-stage handover, posing a potential threat to the integrity and confidentiality of quality data.

[0004] The aforementioned problems have become key bottlenecks restricting the tobacco industry from achieving precise quality control and intelligent management. Therefore, there is an urgent need to establish a scientific, efficient, objective, and impartial quantitative analysis algorithm for tobacco leaf quality to overcome the limitations of traditional manual analysis methods and provide accurate and reliable quality assessment basis for all links in the tobacco industry chain. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for quantitative analysis of tobacco leaf quality grades, in order to solve the problems of low efficiency, high cost, time lag, inconsistency, and safety risks in traditional manual quality assessment methods.

[0006] According to one aspect of the present invention, a method for quantitative analysis of tobacco leaf grade quality is provided, the method comprising: The original data of the tobacco leaves to be analyzed is obtained and preprocessed; wherein, the original data of the tobacco leaves to be analyzed includes a first grade code and a second grade code, the first grade code refers to the original grade code of the tobacco leaves, and the second grade code refers to the grade code after the tobacco leaves are classified into grades; Using the first-level code as the standard, the second-level code is used to determine the tobacco leaf subgroup and tobacco leaf main group according to expert knowledge rules, so as to reclassify the second-level code and determine the tobacco leaf type after reclassification. Weights and penalty coefficients were assigned to the reclassified tobacco leaf types to quantify the grade and quality of the tobacco leaves to be analyzed.

[0007] According to another aspect of the present invention, a tobacco leaf grade quality quantification analysis device is provided, the device comprising: The data acquisition and preprocessing module is used to acquire the original data of the tobacco leaves to be analyzed and to preprocess the original data of the tobacco leaves to be analyzed; wherein, the original data of the tobacco leaves to be analyzed includes a first grade code and a second grade code, the first grade code refers to the original grade code of the tobacco leaves, and the second grade code refers to the grade code after the tobacco leaves are classified. The expert knowledge rule embedding module is used to determine the tobacco subgroup and tobacco positive group of the second-level code based on the first-level code and expert knowledge rules, so as to reclassify the second-level code and determine the tobacco type after reclassification. The tobacco leaf grade quality quantitative analysis module is used to assign weights and penalty coefficients to the reclassified tobacco leaf types in order to quantitatively analyze the grade quality of the tobacco leaves to be analyzed.

[0008] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the tobacco leaf grade quality quantification analysis method according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the tobacco leaf grade quality quantitative analysis method according to any embodiment of the present invention.

[0010] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the tobacco leaf grade quality quantification analysis method as described in any embodiment of the present invention.

[0011] The technical solution of this invention involves acquiring and preprocessing the raw data of the tobacco leaves to be analyzed. The raw data includes a first-grade code and a second-grade code. The first-grade code refers to the original grade code of the tobacco leaves, and the second-grade code refers to the grade code after the tobacco leaves have been graded. Using the first-grade code as the standard, the second-grade code is reclassified into a subgroup or positive group based on expert knowledge rules to determine the reclassified tobacco leaf type. Weights and penalty coefficients are assigned to the reclassified tobacco leaf types to quantitatively analyze the grade quality of the tobacco leaves to be analyzed. This technical solution significantly improves the accuracy and reliability of tobacco leaf grade quality analysis, solving the problems of low efficiency due to the lack of standardized logic and reliance on manual labor in existing technologies. It provides a scientific and efficient quality assessment basis for tobacco leaf acquisition, processing, and distribution.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of a method for quantitative analysis of tobacco leaf grade quality according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of a method for quantitative analysis of tobacco leaf grade quality according to Embodiment 2 of the present invention; Figure 3 This is a flowchart of another method for quantitative analysis of tobacco leaf grade quality provided in Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of an expert knowledge rule determination process provided in Embodiment 2 of the present invention; Figure 5 This is a schematic diagram of a tobacco leaf grade quality quantification analysis device provided in Embodiment 3 of the present invention; Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0016] The acquisition, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. It should be noted that the terms "first," "second," "target," and "original," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising," "etc.," and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] Example 1 Figure 1 This invention provides a flowchart of a method for quantitative analysis of tobacco leaf grade quality according to Embodiment 1. This embodiment is applicable to situations where tobacco leaves are graded according to expert knowledge rules, and subgroup and positive group determinations are performed to quantitatively analyze the grade quality. This method can be executed by a tobacco leaf grade quality quantitative analysis device, which can be implemented in hardware and / or software. This tobacco leaf grade quality quantitative analysis device can be configured in any electronic device with network communication capabilities. Figure 1 As shown, the method includes: S110. Obtain the raw data of the tobacco leaves to be analyzed and preprocess the raw data of the tobacco leaves to be analyzed.

[0018] The raw data of tobacco leaves to be analyzed refers to the initial data used to analyze the quality of tobacco leaf grades. The raw data includes a first grade code and a second grade code. The first grade code refers to the original grade code of the tobacco leaves, which can be a standard positive group grade code before the tobacco leaves are graded. In this embodiment, the original grade code is C3F. The second grade code refers to the grade code after the tobacco leaves have been graded. The grade code after grading includes the original grade code and other grade codes. In this embodiment, the grade codes after grading include, but are not limited to, C3F, B3F, C4F, C3L, C3V, GY1, and CX1K. Optionally, the above-mentioned tobacco leaf grades conform to the tobacco leaf grading standards.

[0019] Preprocessing refers to intelligently repairing missing and outlier values ​​in the acquired raw tobacco leaf data to be analyzed, as well as format conversion. Since the raw tobacco leaf data to be analyzed is obtained from multiple dimensions, it needs to be converted into a unified format to ensure consistency between data from different sources.

[0020] S120. Using the first-level code as the standard, the second-level code is used to determine the tobacco leaf subgroup and tobacco leaf main group according to expert knowledge rules, so as to reclassify the second-level code and determine the tobacco leaf type after reclassification.

[0021] Among them, expert knowledge rules refer to standardized rules for judging the quality and group of tobacco leaves, summarized by senior technical personnel in the tobacco industry based on long-term practical experience and scientific research. These rules include, but are not limited to, judgment thresholds for multiple dimensions such as tobacco leaf color, part, oil content, leaf structure, maturity, and damage, and are the core basis for tobacco leaf grading. The expert knowledge rules described in this embodiment of the invention include, but are not limited to, rules for judging the positive group of tobacco leaves and rules for judging the secondary group of tobacco leaves.

[0022] Tobacco by-products refer to tobacco leaves that have obvious defects due to problems in growth, processing, or storage, such as discoloration, damage, or smooth leaves, but still have some use value. These tobacco leaves usually require special processing before they can be used in specific blends. The corresponding rules for determining tobacco by-products can refer to using the first-grade code as a standard to determine whether the second-grade code contains mixed colors, mixed green colors, or mixed smooth leaves.

[0023] The term "standard tobacco group" refers to tobacco leaves that have grown and developed normally, undergone proper processing, and whose appearance quality meets mainstream industrial requirements. These tobacco leaves have no obvious defects and can be directly used as core raw materials for cigarette formulations, such as the common lower-middle lemon-yellow group and upper orange-yellow group. The corresponding standard tobacco group determination rule refers to using the first-grade code as the standard to determine whether the second-grade code contains mixed parts, mixed colors, or mixed grades.

[0024] Reclassification can refer to the process of re-judging and adjusting the primary group, secondary group, and specific type of tobacco leaves based on expert knowledge rules when the secondary grade code does not meet the standard of the primary grade code. The purpose is to make the classification of tobacco leaf grades more accurate and more in line with the needs of industrial production.

[0025] S130. Assign weights and penalty coefficients to the reclassified tobacco leaf types to conduct a quantitative analysis of the grade quality of the tobacco leaves to be analyzed.

[0026] The weighting value refers to the priority of the impact of different tobacco leaf defect types on the overall quality grade. It is a core parameter set based on industry standards, market demand, and production pain points. The greater the negative impact of the defect on the quality of the tobacco leaf, the higher the weighting value. For example, blending directly affects the aroma and combustibility of the tobacco leaf, causing the greatest damage to the quality of the tobacco leaf. Therefore, the weighting value can be set relatively high. On the other hand, the impact of blending grades on appearance and internal quality is relatively indirect, so the weighting value can be relatively low.

[0027] The penalty coefficient can be a correction parameter set for the severity of a single defect, used to distinguish the impact of minor and serious defects; the more serious the defect, the higher the penalty coefficient. For example, in the case of blended tobacco, a blend containing less than 3% of slightly green tobacco leaves is considered a minor defect, and the penalty coefficient can be set to 1; a blend containing more than 3% of slightly green tobacco leaves is considered a serious defect, and the penalty coefficient can be set to 2 or even higher.

[0028] Quantitative analysis refers to the process of constructing mathematical models to transform qualitative defects in tobacco leaves into comparable and traceable quantitative scores, ultimately achieving standardized assessment of quality grades. By replacing empirical judgment with data, it addresses the subjectivity and inconsistency issues of traditional manual grading.

[0029] This invention provides a method for quantitative analysis of tobacco leaf grade quality. The method involves acquiring and preprocessing raw data of the tobacco leaves to be analyzed. The raw data includes a first grade code and a second grade code. The first grade code refers to the original grade code of the tobacco leaves, and the second grade code refers to the grade code after the tobacco leaves have been graded. Using the first grade code as the standard, the second grade code is reclassified into a subgroup or main group based on expert knowledge rules, thus determining the reclassified tobacco leaf type. Weights and penalty coefficients are assigned to the reclassified tobacco leaf types to quantitatively analyze the grade quality of the tobacco leaves. This invention significantly improves the accuracy and reliability of tobacco leaf grade quality analysis, solving the problems of low efficiency due to lack of standardized logic and reliance on manual labor in existing technologies. It provides a scientific and efficient quality assessment basis for tobacco leaf acquisition, processing, and distribution.

[0030] Example 2 Figure 2 This is a flowchart of a method for quantitative analysis of tobacco leaf grade quality provided in Embodiment 2 of the present invention. This embodiment further optimizes the aforementioned embodiments, and can be combined with various optional solutions from one or more of the above embodiments. For example... Figure 2 As shown, the method includes: S210. Obtain the raw data of the tobacco leaves to be analyzed and preprocess the raw data of the tobacco leaves to be analyzed.

[0031] This involves acquiring raw data of the tobacco leaves to be analyzed in real time and preprocessing the raw data.

[0032] As an optional but non-limiting implementation, the acquisition of raw tobacco leaf data to be analyzed and the preprocessing of the raw tobacco leaf data include, but are not limited to, steps A1-A2: Step A1: Connect with the tobacco system through a standardized interface to obtain the original data of the tobacco leaves to be analyzed; wherein, the original data of the tobacco leaves to be analyzed includes the first grade code, the second grade code, the total number of samples, and the number of second grade tobacco leaves sampled, and the sum of the number of second grade tobacco leaves sampled is equal to the total number of samples. Step A2: Use an adaptive data cleaning algorithm to preprocess the raw tobacco leaf data to be analyzed; wherein, the preprocessing includes repairing missing values ​​and outliers, and format conversion.

[0033] Among them, see Figure 3 The system connects to the tobacco system via a standardized interface to obtain the original data of the tobacco leaves to be analyzed. The tobacco system can refer to a tobacco enterprise management information system, an intelligent warehouse management system, or a mobile testing terminal. The original data of the tobacco leaves to be analyzed includes, but is not limited to, the original grade code of the tobacco leaves, the grade codes after grade classification, the total number of samples taken, and the number of samples taken from the second grade. For example, if the original grade code of the tobacco leaves is C3F, the total number of samples taken is 100 bundles, and the grade codes after grade classification and the corresponding sample data are: C3F: 70 bundles (positive group), B3F: 5 bundles (positive group), C4F: 10 bundles (positive group), C3L: 8 bundles (positive group), C3V: 3 bundles (sub-group, including "V"), GY1: 2 bundles (sub-group, including "GY"), CX1K: 2 bundles (sub-group, including "K").

[0034] In the data preprocessing stage, an adaptive data cleaning algorithm is used to intelligently repair missing and outlier values, and a coding conversion engine is used to uniformly map 42-level standard codes to the internal coding system of the algorithm to ensure the consistency of data formats from different sources.

[0035] Optionally, embodiments of the present invention further include verifying the first grade code and the second grade code to determine whether the tobacco leaf grades included in the first grade code and the second grade code meet the tobacco leaf grade standards; and verifying the sampling quantity to determine whether the sampling quantity of second grade tobacco leaves is equal to the total sampling quantity.

[0036] S220. Based on the expert knowledge rules, the second-level code is used to make the first determination of the tobacco subgroup, and the first determination result is obtained.

[0037] In this process, the second-level code is used to determine the tobacco leaf subgroup, prioritizing the determination of tobacco leaves with obvious defects in the second-level code to determine the first determination result.

[0038] As an optional but non-limiting implementation, the first determination of the tobacco subgroup based on the expert knowledge rules to obtain the first determination result includes, but is not limited to, steps B1-B3: Step B1: Determine whether there are mixed green tobacco leaves in the tobacco leaves represented by the second-level code, and determine the quantity of mixed green tobacco leaves. Step B2: Determine the mixed color of tobacco leaves in the second-level code to determine whether there are mixed-color tobacco leaves in the tobacco leaves represented by the second-level code, and determine the number of mixed-color tobacco leaves; Step B3: Determine whether there are mixed smooth tobacco leaves in the tobacco leaves represented by the second level code, and determine the number of mixed smooth tobacco leaves.

[0039] The first determination of the tobacco subgroup for the second-level code includes determining whether the tobacco leaves are mixed with green, mixed with color, or mixed with smoothness. See also Figure 4When determining the subgroup of tobacco leaves for the second-level code, the order of determination of mixed greenness, mixed color, and mixed smoothness can be disregarded. For example, the second-level code can be determined by first determining mixed greenness to identify mixed green tobacco leaves; then, the second-level code can be determined by determining mixed color to identify mixed colored tobacco leaves; and finally, the second-level code can be determined by determining mixed smoothness to identify mixed smooth tobacco leaves. Taking the second-grade codes, including C3F: 70 bundles, B3F: 5 bundles, C4F: 10 bundles, C3L: 8 bundles, C3V: 3 bundles, GY1: 2 bundles, and CX1K: 2 bundles, as an example, we determine whether the tobacco grade code in the second-grade code contains "V" or "GY" to determine whether it contains mixed green tobacco leaves and the quantity of mixed green tobacco leaves. That is, there are 5 bundles of mixed green tobacco leaves, namely C3V and GY1. We also determine whether the tobacco grade code in the second-grade code contains "K" to determine whether it contains mixed colored tobacco leaves. That is, there are 2 bundles of mixed colored tobacco leaves, namely CX1K. Finally, we determine whether the tobacco grade code in the second-grade code contains "S" to determine whether it contains mixed smooth tobacco leaves and the corresponding quantity. In this designation, B represents upper leaves, C represents middle leaves, X represents lower leaves, F represents orange-yellow, L represents lemon-yellow, R represents reddish-brown, H represents fully ripe, V represents slightly greenish, GY represents greenish-yellow, CXK represents variegated leaves, and S represents smooth leaves.

[0040] In this embodiment of the invention, the subgroup of tobacco leaves is determined to roughly identify the subgroup defects and their corresponding quantities in the second-level code.

[0041] S230. Using the first-level code as the standard, the second-level code is judged according to the expert knowledge rules to obtain the second judgment result of the tobacco leaf positive group.

[0042] Specifically, the second-level code is judged based on the first-level code to determine whether there are tobacco leaves of mixed parts, colors, and grades in the second-level code, so as to determine the second judgment result.

[0043] As a limited implementation, the method of using the first-level code as the standard and performing a second determination of the second-level code on the tobacco leaf positive group according to expert knowledge rules to obtain the second determination result includes, but is not limited to, steps C1-C3: Step C1: Using the first-level code as the standard, determine the mixed parts of tobacco leaves for the second-level code. If the tobacco leaf part characteristic characters in the first-level code and the second-level code are not equal, then determine that the tobacco leaf represented by the second-level code is a mixed part tobacco leaf, and determine the quantity of mixed part tobacco leaves. Step C2: If the tobacco leaf represented by the second-level code is a non-mixed part tobacco leaf, then the second-level code is used to determine the tobacco leaf color. If the tobacco leaf color feature characters in the first-level code and the second-level code are not equal, then the tobacco leaf represented by the second-level code is determined to be a mixed-color tobacco leaf, and the quantity of mixed-color tobacco leaves is determined. Step C3: If the tobacco leaf represented by the second-level code is a non-mixed-color tobacco leaf, then the second-level code is used to determine the mixed-level tobacco leaf. If the tobacco leaf level feature characters in the first-level code and the second-level code are not equal, then the tobacco leaf represented by the second-level code is determined to be a mixed-level tobacco leaf, and the quantity of mixed-level tobacco leaves is determined; otherwise, the second-level code is determined to be the same as the first-level code.

[0044] The second determination of the second grade code for tobacco leaves includes determining the mixed parts, mixed colors, and mixed grades of the tobacco leaves, and the determination of the order of determination is important.

[0045] When determining the correct group of tobacco leaves for the second-level code, the determination must be performed in sequence. For example, the determination can be performed in the order of mixed part, mixed color, and mixed grade. The specific order is limited. For example, when determining the correct group of tobacco leaves for B3F, the determination can be performed in the order of mixed part, mixed color, and mixed grade. That is, it is compared with the first-level code C to determine the mixed part. If it is determined to be a mixed part tobacco leaf, the subsequent steps are not continued, and B3F is confirmed as a mixed part tobacco leaf. When determining the correct group of tobacco leaves for C4F, the determination can be performed in the order of mixed part, mixed color, and mixed grade. That is, it is compared with the first-level code C to determine the mixed part. If the part character is the same, the mixed color F is determined. If the color character is the same, the mixed grade is determined. If it is different from the first-level code level 3, it is determined to be a mixed grade tobacco leaf.

[0046] In the embodiment of the present invention, the determination is carried out in the order of mixed part determination, mixed color determination, and mixed grade determination. The determination process and results of some tobacco leaf positive groups are shown in Table 1. In this embodiment of the invention, the second-level code is used to determine the correct group of tobacco leaves, so as to refine the detection of the second-level code and determine the problems and corresponding quantities of tobacco leaves in the second-level code.

[0047] S240. Based on the first and second judgment results, the second grade code is reclassified, and the reclassified tobacco leaf type is determined.

[0048] Among them, the expert knowledge rules include the rules for determining the subgroup and the rules for determining the main group.

[0049] The first and second judgment results are combined to determine the tobacco leaf type and corresponding quantity after reclassification for the second-level code. See Table 2 for the reclassified tobacco leaf quantity determined according to expert knowledge rules. The expert knowledge rules in this invention not only cover the judgment rule of prioritizing secondary and then primary grades, that is, prioritizing the judgment of mixed green, mixed colors, smoothness, etc. in the graded tobacco leaves, but also construct a progressive judgment logic of part, color and grade for the primary group of tobacco leaves, which ensures the accuracy and consistency of the judgment results. It transforms the judgment process that originally relied on human experience into a standardized and automated intelligent judgment process, effectively solving the problems of strong subjectivity and low efficiency of traditional methods.

[0050] S250: Assign weights and penalty coefficients to the reclassified tobacco leaf types to conduct a quantitative analysis of the grade quality of the tobacco leaves to be analyzed.

[0051] In this process, weights and penalty coefficients are assigned to the reclassified tobacco leaf types to quantify the grade and quality of the tobacco leaves to be analyzed.

[0052] As an optional but non-limiting implementation, the assignment of weights and penalty coefficients to the reclassified tobacco leaf types for quantitative analysis of the grade quality of the tobacco leaves to be analyzed includes, but is not limited to, steps D1-D2: Step D1: Assign weights and penalty coefficients to the reclassified tobacco leaf types, and determine the proportion of each tobacco leaf type after reclassification; Step D2: Based on the weights, penalty coefficients, and proportions of each tobacco leaf type, conduct a quantitative analysis of the tobacco leaf grade quality to determine the quality score of each tobacco leaf type after reclassification and the total quality score of the tobacco leaves to be analyzed.

[0053] The process involves assigning weights to the reclassified tobacco leaf types. These weights can be allocated based on specific emphases, for example, assigning higher weights to defects in the tobacco leaf subgroups. Optionally, the weights assigned to the reclassified tobacco leaf types could be: Mixed green tobacco W1 = 0.8, mixed colored tobacco W2 = 0.7, mixed smooth tobacco W3 = 0.6, mixed part tobacco W4 = 0.5, mixed color tobacco W5 = 0.4, and mixed grade tobacco W6 = 0.3. A penalty coefficient k is also included. i It can be set to 1, and the specific setting can be adjusted according to the actual situation.

[0054] The proportion of each tobacco leaf type after reclassification can be determined based on the ratio of the quantity of each tobacco leaf type after reclassification to the total quantity sampled. For example, the proportion of original grade tobacco leaves. The proportion of mixed green tobacco leaves The proportion of mixed-color tobacco leaves The proportion of blended smooth tobacco leaves The proportion of mixed parts of tobacco leaves The proportion of mixed-color tobacco leaves The proportion of blended tobacco leaves Optionally, the constraints corresponding to the proportion of each tobacco leaf type after reclassification can be set as follows: .

[0055] After determining the weights, penalty coefficients, and proportions of tobacco leaf types after reclassification, the quality scores of each tobacco leaf type and the total quality score of the tobacco leaves to be analyzed are determined.

[0056] As an optional but non-limiting implementation, the quantitative analysis of tobacco leaf quality grades based on the weights, penalty coefficients, and proportions of each tobacco leaf type, to determine the quality scores of each tobacco leaf type after reclassification and the total quality score of the tobacco leaves to be analyzed, includes, but is not limited to, steps E1-E2: Step E1: Based on the penalty coefficient and proportion of each tobacco leaf type, conduct a quantitative analysis of the tobacco leaf grade quality and determine the quality score of each tobacco leaf type after reclassification. Step E2: Set adjustment coefficients for each tobacco leaf type, and perform quantitative analysis of tobacco leaf grade quality based on the weight, penalty coefficient and proportion of each tobacco leaf type to determine the total quality score of the tobacco leaf to be analyzed; wherein, the adjustment coefficients are used to balance the impact of mixing defects on the overall quality of tobacco leaves.

[0057] Specifically, based on the penalty coefficient and proportion of each tobacco leaf type, a quantitative analysis of the tobacco leaf grade quality is performed to determine the quality score of each tobacco leaf type after reclassification; the quality score of each tobacco leaf type after reclassification can be expressed as: The specific calculation results are shown in Table 3.

[0058] After determining the quality scores of each tobacco leaf type after reclassification, the total quality score of the tobacco leaves to be analyzed is determined; the total quality score can be expressed as: Where T represents the adjustment coefficient, a correction parameter used to balance the impact of mixed-type defects on the overall quality. Essentially, it linearly adjusts the penalty intensity to make the scoring results more closely match the needs of the actual scenario. In this embodiment, the adjustment coefficient can be set to 0.5, but can be set according to actual conditions; no specific limitation is imposed in this embodiment. The final determined total quality score is 62.95. As shown in Table 3, the total quality score of this batch of original grade C3F tobacco leaves was 62.95 points. The main problems were the mixing of green leaves and color, and targeted quality rectification was required to address these issues.

[0059] In this embodiment of the invention, the quantitative analysis of tobacco leaf quality adopts a multi-dimensional weighted scoring model. This model, based on basic weights, introduces penalty coefficients and adjustment coefficients to quantify the negative impact of different types of mixed items on tobacco leaf quality, achieving refined and dynamic quality assessment. In particular, the flexible setting mechanism of the penalty coefficient can apply exponential penalties to severely mixed items through convex function characteristics, or adapt to normal quality fluctuation scenarios through linear penalties, significantly improving the scoring system's adaptability to complex quality issues. Optionally, a quality warning threshold can also be set, automatically triggering a warning mechanism when the tobacco leaf quality score falls below a set value.

[0060] In one optional embodiment of the invention, the results of the tobacco leaf quality assessment are compiled and displayed visually using a dual-mode approach: a visual dashboard and a structured report. The dashboard uses a 3D bar chart to intuitively display the proportion of various mixed items, while the structured report records in detail the judgment criteria, scoring calculation process, and improvement suggestions, and supports export to multiple formats such as PDF and Excel. By visually displaying detailed information such as the quantity, proportion, and quality score of various tobacco leaf samples, a comprehensive and accurate tobacco leaf grade quality analysis report is provided to relevant personnel in the tobacco leaf purchasing, processing, and distribution processes.

[0061] In this embodiment of the invention, based on expert knowledge rules, the sub-group of tobacco leaves is first determined to roughly identify the sub-group defects of the tobacco leaves to be analyzed. Then, the positive group of tobacco leaves is determined according to the rule that the impact on tobacco leaf quality gradually decreases, thereby improving the accuracy and consistency of the determination results and effectively solving the problems of strong subjectivity and low efficiency of traditional methods. Through a multi-dimensional weighted quality scoring model, the quality assessment is refined and dynamic, significantly improving the adaptability of the scoring system to complex quality problems. The technical solution of this embodiment of the invention can significantly improve the accuracy and reliability of tobacco leaf grade quality analysis, reduce labor costs, and provide a scientific and efficient quality assessment basis for tobacco leaf acquisition, processing, and circulation.

[0062] Example 3 Figure 5 This is a schematic diagram of a tobacco leaf grade quality quantification analysis device provided in Embodiment 3 of the present invention. Figure 5 As shown, the device includes: The data acquisition and preprocessing module 510 is used to acquire the original data of the tobacco leaves to be analyzed and to preprocess the original data of the tobacco leaves to be analyzed; wherein, the original data of the tobacco leaves to be analyzed includes a first grade code and a second grade code, the first grade code refers to the original grade code of the tobacco leaves, and the second grade code refers to the grade code after the tobacco leaves are classified. The expert knowledge rule embedding module 520 is used to determine the tobacco subgroup and tobacco positive group of the second-level code based on the first-level code and the expert knowledge rules, so as to reclassify the second-level code and determine the tobacco type after reclassification. The tobacco leaf grade quality quantitative analysis module 530 is used to assign weights and penalty coefficients to the reclassified tobacco leaf types in order to perform quantitative analysis on the grade quality of the tobacco leaves to be analyzed.

[0063] Optional, a data acquisition and preprocessing module, specifically used for: Data is exchanged with the tobacco system through a standardized interface to obtain the original data of the tobacco leaves to be analyzed; wherein, the original data of the tobacco leaves to be analyzed includes the first grade code, the second grade code, the total number of samples, and the number of second grade tobacco leaves sampled, and the sum of the number of second grade tobacco leaves sampled is equal to the total number of samples. An adaptive data cleaning algorithm is used to preprocess the raw tobacco leaf data to be analyzed; wherein, the preprocessing includes repairing missing values ​​and outliers, and format conversion.

[0064] Optional, the expert knowledge rule embedding module is specifically used for: Based on the expert knowledge rules, the second-level code was used to make the first determination of the tobacco subgroup, and the first determination result was obtained; Using the first-level code as the standard, the second-level code is judged according to the expert knowledge rules to obtain the second judgment result of the tobacco leaf positive group; Based on the first and second judgment results, the second-level codes are reclassified, and the reclassified tobacco leaf type is determined; among them, the expert knowledge rules include subgroup judgment rules and main group judgment rules.

[0065] Optionally, the expert knowledge rule embedding module is also specifically used for: The second-level code is used to determine whether there are mixed green tobacco leaves in the tobacco leaves represented by the second-level code, and to determine the quantity of mixed green tobacco leaves. The second-level code is used to determine the presence of mixed-color tobacco leaves, and the quantity of mixed-color tobacco leaves is determined. The second-level code is used to determine whether there are mixed smooth tobacco leaves in the tobacco leaves represented by the second-level code, and the number of mixed smooth tobacco leaves is determined. The first determination of the second-level code for tobacco subgroup includes determining the mixed greenness, mixed color, and mixed smoothness of the second-level code.

[0066] Optionally, the expert knowledge rule embedding module is also specifically used for: Using the first-level code as the standard, the second-level code is used to determine the mixed parts of tobacco leaves. If the tobacco leaf part characteristic characters in the first-level code and the second-level code are not equal, the tobacco leaf represented by the second-level code is determined to be a mixed part tobacco leaf, and the quantity of mixed part tobacco leaves is determined. If the tobacco leaf represented by the second-level code is a non-mixed part tobacco leaf, then the second-level code is used to determine the tobacco leaf color. If the tobacco leaf color feature characters in the first-level code and the second-level code are not equal, then the tobacco leaf represented by the second-level code is determined to be a mixed-color tobacco leaf, and the quantity of mixed-color tobacco leaves is determined. If the tobacco leaf represented by the second-level code is a non-mixed-color tobacco leaf, then the second-level code is used to determine the mixed-level tobacco leaf. If the tobacco leaf level feature characters in the first-level code and the second-level code are not equal, then the tobacco leaf represented by the second-level code is determined to be a mixed-level tobacco leaf, and the number of mixed-level tobacco leaves is determined; otherwise, the second-level code is determined to be the same as the first-level code. The second determination of the second grade code for tobacco leaves includes determining the mixed parts, mixed colors, and mixed grades of the tobacco leaves, and the determination of the order of determination is important.

[0067] Optional, a tobacco leaf grade quality quantification analysis module, specifically used for: Assign weights and penalty coefficients to the reclassified tobacco leaf types, and determine the proportion of each tobacco leaf type after reclassification; Based on the weights, penalty coefficients, and proportions of each tobacco leaf type, a quantitative analysis of the tobacco leaf grade quality is conducted to determine the quality score of each tobacco leaf type after reclassification and the total quality score of the tobacco leaves to be analyzed.

[0068] Optionally, the tobacco leaf grade quality quantitative analysis module is also specifically used for: Based on the penalty coefficients and proportions of each tobacco leaf type, a quantitative analysis of the quality of tobacco leaf grades is conducted to determine the quality score of each tobacco leaf type after reclassification. An adjustment coefficient is set for each type of tobacco leaf, and the quality of the tobacco leaf grade is quantitatively analyzed based on the weight, penalty coefficient and proportion of each type of tobacco leaf to determine the total quality score of the tobacco leaf to be analyzed; wherein, the adjustment coefficient is used to balance the impact of mixing defects on the overall quality of the tobacco leaf. The total quality score of the tobacco leaves to be analyzed is expressed as follows: ; Where F represents the total quality score of the tobacco leaves to be analyzed. The percentage of original grade tobacco leaves, where T is the adjustment coefficient. This represents the percentage of each tobacco leaf type after reclassification. k represents the weights for each tobacco leaf type. i This is the penalty coefficient; These respectively represent mixed blue, mixed color, mixed smoothness, mixed parts, mixed colors, and mixed levels.

[0069] The tobacco leaf grade quality quantification analysis device provided in the embodiments of the present invention can execute the tobacco leaf grade quality quantification analysis method provided in any of the embodiments of the present invention, and has the corresponding functions and beneficial effects of executing the tobacco leaf grade quality quantification analysis method. For details, please refer to the relevant operations of the tobacco leaf grade quality quantification analysis method in the foregoing embodiments.

[0070] Example 4 Figure 6 A schematic diagram of an electronic device 10, which can be used to implement embodiments 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 processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0071] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0072] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0073] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for quantitative analysis of tobacco leaf grade quality.

[0074] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0075] In some embodiments, the tobacco leaf grade quality quantification analysis method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may 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 grade quality quantification analysis method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the tobacco leaf grade quality quantification analysis method by any other suitable means (e.g., by means of firmware).

[0076] Various embodiments of the systems and techniques described above 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-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0077] Computer programs used to implement 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 executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0078] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0079] 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 provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0080] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0081] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0082] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0083] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for quantitative analysis of tobacco leaf grade quality, characterized in that, The method includes: The original data of the tobacco leaves to be analyzed is obtained and preprocessed; wherein, the original data of the tobacco leaves to be analyzed includes a first grade code and a second grade code, the first grade code refers to the original grade code of the tobacco leaves, and the second grade code refers to the grade code after the tobacco leaves are classified into grades; Using the first-level code as the standard, the second-level code is used to determine the tobacco leaf subgroup and tobacco leaf main group according to expert knowledge rules, so as to reclassify the second-level code and determine the tobacco leaf type after reclassification. Weights and penalty coefficients were assigned to the reclassified tobacco leaf types to quantify the grade and quality of the tobacco leaves to be analyzed.

2. The method according to claim 1, characterized in that, The process of acquiring and preprocessing the raw data of the tobacco leaves to be analyzed includes: Data is exchanged with the tobacco system through a standardized interface to obtain the original data of the tobacco leaves to be analyzed; wherein, the original data of the tobacco leaves to be analyzed includes the first grade code, the second grade code, the total number of samples, and the number of second grade tobacco leaves sampled, and the sum of the number of second grade tobacco leaves sampled is equal to the total number of samples. An adaptive data cleaning algorithm is used to preprocess the raw tobacco leaf data to be analyzed; wherein, the preprocessing includes repairing missing values ​​and outliers, and format conversion.

3. The method according to claim 1, characterized in that, The process of using the first-level code as the standard, and determining the tobacco leaf subgroup and positive group of the second-level code according to expert knowledge rules to reclassify the second-level code and determine the reclassified tobacco leaf type includes: Based on the expert knowledge rules, the second-level code was used to make the first determination of the tobacco subgroup, and the first determination result was obtained; Using the first-level code as the standard, the second-level code is judged according to the expert knowledge rules to obtain the second judgment result of the tobacco leaf positive group; Based on the first and second judgment results, the second-level codes are reclassified, and the reclassified tobacco leaf type is determined; among them, the expert knowledge rules include subgroup judgment rules and main group judgment rules.

4. The method according to claim 3, characterized in that, The first determination of the tobacco subgroup based on the expert knowledge rules for the second-level code, and the resulting first determination result, include: The second-level code is used to determine whether there are mixed green tobacco leaves in the tobacco leaves represented by the second-level code, and to determine the quantity of mixed green tobacco leaves. The second-level code is used to determine the presence of mixed-color tobacco leaves, and the quantity of mixed-color tobacco leaves is determined. The second-level code is used to determine whether there are mixed smooth tobacco leaves in the tobacco leaves represented by the second-level code, and the number of mixed smooth tobacco leaves is determined. The first determination of the second-level code for tobacco subgroup includes determining the mixed greenness, mixed color, and mixed smoothness of the second-level code.

5. The method according to claim 3, characterized in that, The second determination of tobacco leaf positive group based on the first-level code and expert knowledge rules, using the first-level code as the standard, yields the second determination result, including: Using the first-level code as the standard, the second-level code is used to determine the mixed parts of tobacco leaves. If the tobacco leaf part characteristic characters in the first-level code and the second-level code are not equal, the tobacco leaf represented by the second-level code is determined to be a mixed part tobacco leaf, and the quantity of mixed part tobacco leaves is determined. If the tobacco leaf represented by the second-level code is a non-mixed part tobacco leaf, then the second-level code is used to determine the tobacco leaf color. If the tobacco leaf color feature characters in the first-level code and the second-level code are not equal, then the tobacco leaf represented by the second-level code is determined to be a mixed-color tobacco leaf, and the quantity of mixed-color tobacco leaves is determined. If the tobacco leaf represented by the second-level code is a non-mixed-color tobacco leaf, then the second-level code is used to determine the mixed-level tobacco leaf. If the tobacco leaf level feature characters in the first-level code and the second-level code are not equal, then the tobacco leaf represented by the second-level code is determined to be a mixed-level tobacco leaf, and the number of mixed-level tobacco leaves is determined; otherwise, the second-level code is determined to be the same as the first-level code. The second determination of the second grade code for tobacco leaves includes determining the mixed parts, mixed colors, and mixed grades of the tobacco leaves, and the determination of the order of determination is important.

6. The method according to claim 1, characterized in that, The assignment of weights and penalty coefficients to the reclassified tobacco leaf types for quantitative analysis of the grade quality of the tobacco leaves to be analyzed includes: Assign weights and penalty coefficients to the reclassified tobacco leaf types, and determine the proportion of each tobacco leaf type after reclassification; Based on the weights, penalty coefficients, and proportions of each tobacco leaf type, a quantitative analysis of the tobacco leaf grade quality is conducted to determine the quality score of each tobacco leaf type after reclassification and the total quality score of the tobacco leaves to be analyzed.

7. The method according to claim 6, characterized in that, The process involves quantitatively analyzing the quality of tobacco leaves based on their weights, penalty coefficients, and proportions, determining the quality scores for each reclassified tobacco leaf type and the total quality score of the tobacco leaves to be analyzed, including: Based on the penalty coefficients and proportions of each tobacco leaf type, a quantitative analysis of the quality of tobacco leaf grades is conducted to determine the quality score of each tobacco leaf type after reclassification. An adjustment coefficient is set for each type of tobacco leaf, and the quality of the tobacco leaf grade is quantitatively analyzed based on the weight, penalty coefficient and proportion of each type of tobacco leaf to determine the total quality score of the tobacco leaf to be analyzed; wherein, the adjustment coefficient is used to balance the impact of mixing defects on the overall quality of the tobacco leaf. The total quality score of the tobacco leaves to be analyzed is expressed as follows: ； Where F represents the total quality score of the tobacco leaves to be analyzed. The percentage of original grade tobacco leaves, where T is the adjustment coefficient. This represents the percentage of each tobacco leaf type after reclassification. k represents the weights for each tobacco leaf type. i This is the penalty coefficient; These respectively represent mixed blue, mixed color, mixed smoothness, mixed parts, mixed colors, and mixed levels.

8. A device for quantitative analysis of tobacco leaf grade quality, characterized in that, The device includes: The data acquisition and preprocessing module is used to acquire the original data of the tobacco leaves to be analyzed and to preprocess the original data of the tobacco leaves to be analyzed; wherein, the original data of the tobacco leaves to be analyzed includes a first grade code and a second grade code, the first grade code refers to the original grade code of the tobacco leaves, and the second grade code refers to the grade code after the tobacco leaves are classified. The expert knowledge rule embedding module is used to determine the tobacco subgroup and tobacco positive group of the second-level code based on the first-level code and expert knowledge rules, so as to reclassify the second-level code and determine the tobacco type after reclassification. The tobacco leaf grade quality quantitative analysis module is used to assign weights and penalty coefficients to the reclassified tobacco leaf types in order to quantitatively analyze the grade quality of the tobacco leaves to be analyzed.

9. An electronic device, characterized in that, The electronic device includes: 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 being executed by the at least one processor to enable the at least one processor to perform the tobacco leaf grade quality quantification analysis method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for quantitative analysis of tobacco leaf grade quality as described in any one of claims 1-7.

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