A new type of Longjing tea processing method

Abstract

The invention provides a Longjing tea processing method with a novel mechanism, which belongs to the technical field of tea processing, and specifically comprises: when it is determined that similar processing data does not exist for Longjing tea based on the distribution data of Longjing tea in different leaf intervals, reference processing data are determined by using the distribution data in different leaf intervals; based on the reference processing data in different processing methods, fluctuation data of defective rates corresponding to different processing methods are determined; screening processing methods in the processing methods are determined by using the fluctuation data of the defective rate and the number of historical processing times corresponding to the reference processing data; matching processing methods for Longjing tea are determined based on the defective rates of the historical processing times corresponding to the reference processing data in different screening processing methods; and the Longjing tea is processed by using the matching processing method, thereby achieving effective control of the defective rate of processing.

Description

Technical Field

[0001] The invention belongs to the technical field of tea processing, and in particular relates to a new type of Longjing tea processing method. Background Art

[0002] In order to realize the preparation and processing of Longjing tea, in the invention patent application CN202111674948.1 "A tea processing method and Longjing tea", the tea is processed through the steps of spreading, frying, screening, and moisture regain, so that the loss of tea is less, the flavor is better, the defective rate is lower, and the economic benefits are better. However, the following technical problems exist through analysis:

[0003] When processing tea leaves, the existing technical solutions have neglected to determine the processing method based on the distribution of tea leaves. When the same processing method is used for tea leaves of different sizes, there are large differences in the processing results of the tea leaves. Therefore, if differentiated processing methods cannot be used based on the distribution of tea leaves, it is impossible to achieve satisfactory results in reducing the loss and defective rate of tea leaves.

[0004] In response to the above technical problems, the present application specifically provides a Longjing tea processing method with a new mechanism. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] A new type of Longjing tea processing method, specifically comprising:

[0007] S1: After the Longjing tea is spread out, the leaf distribution of the Longjing tea is identified by using an image recognition model to obtain an identification result. When it is determined based on the identification result that the leaf distribution of the Longjing tea does not meet the requirements, the next step is entered;

[0008] S2: determining distribution data in different leaf intervals based on the leaf distribution of the Longjing tea, and when it is determined that similar processing data does not exist for the Longjing tea based on the distribution data in different leaf intervals, determining reference processing data using the distribution data in different leaf intervals;

[0009] S3: determining the fluctuation data of the defective rate corresponding to the different processing methods based on the reference processing data in the different processing methods, and determining the screening processing method in the processing methods by using the fluctuation data of the defective rate and the historical processing times corresponding to the reference processing data;

[0010] S4 determines the matching processing method of the Longjing tea based on the defective rate of historical processing times corresponding to the reference processing data in different screening processing methods, and processes the Longjing tea using the matching processing method.

[0011] The beneficial effects of the present invention are:

[0012] The screening processing methods among the processing methods are determined by utilizing the fluctuation data of the defective rate and the historical processing times corresponding to the reference processing data, thereby realizing the identification of the screening processing methods with smaller fluctuations in the defective rate in the reference processing data and larger historical processing times. This not only avoids the technical problem of insufficient reliability in identifying the fluctuation data of the defective rate in the reference processing data due to the small number of historical processing times, but also avoids the technical problem of processing results not meeting the requirements due to the high fluctuation degree of the defective rate, and also lays the foundation for further realizing the determination of matching processing methods for Longjing tea.

[0013] The matching processing method of Longjing tea is determined based on the defective rate of historical processing times corresponding to the reference processing data in different screening and processing methods. The defective rate of different screening and processing methods corresponding to the reference processing data is fully taken into consideration, and the screening of matching processing methods with lower defective rates is achieved, thus ensuring the reliability of tea processing of Longjing tea.

[0014] A further technical solution is that the recognition result includes the distribution of leaf sizes of the Longjing tea.

[0015] A further technical solution is to determine that the leaf distribution of the Longjing tea does not meet the requirements, specifically including:

[0016] Based on the recognition result, determining the distribution of leaf sizes of the Longjing tea;

[0017] Determining the distribution quantity ratio of the Longjing tea in different leaf intervals according to the distribution of leaf sizes;

[0018] Based on the distribution quantity proportion, the high proportion interval within the leaf interval is determined, and the quantity of the high proportion interval is used to determine whether the leaf distribution of the Longjing tea meets the requirements.

[0019] A further technical solution is that the high-proportion interval is a blade interval in which the distribution quantity proportion is greater than a preset quantity proportion.

[0020] A further technical solution is that when the number of the high-proportion intervals is greater than the number of preset intervals, it is determined that the leaf distribution of the Longjing tea does not meet the requirements.

[0021] A further technical solution is that, when the leaf distribution of the Longjing tea meets the requirements, the preset processing method corresponding to the leaf interval with the largest distribution quantity is used as the matching processing method of the Longjing tea.

[0022] A further technical solution is to determine that the leaf distribution of the Longjing tea does not meet the requirements, specifically including:

[0023] Based on the recognition result, determining the distribution of leaf sizes of the Longjing tea;

[0024] Determining the distribution quantity ratio of the Longjing tea in different leaf intervals according to the distribution of leaf sizes;

[0025] According to the maximum value of the distribution quantity ratio in different leaf intervals, it is determined whether the leaf distribution of the Longjing tea meets the requirements.

[0026] A further technical solution is that the preset defect rate interval is a defect rate interval in which the defect rate is greater than the preset defect rate.

[0027] A further technical solution is that the method for determining the matching processing method of the Longjing tea is:

[0028] The average value of the defective rates of the historical processing times corresponding to the reference processing data in different screening processing methods is taken as the average defective rate;

[0029] The matching processing method of the Longjing tea is determined according to the average defective rate.

[0030] A further technical solution is that the matching processing method is a screening processing method with the smallest average defective rate.

[0031] A further technical solution is that the method for determining the matching processing method of the Longjing tea is:

[0032] The average value of the defective rates of the historical processing times corresponding to the reference processing data in different screening processing methods is taken as the average defective rate;

[0033] When there is a screening and processing method with an average defect rate less than a preset defect rate threshold, the screening and processing method with the smallest average defect rate is used as the matching processing method for the Longjing tea;

[0034] When there is no screening and processing method whose average defective rate is less than the preset defective rate threshold, the average defective rate is used to determine the secondary screening and processing method in the screening and processing method, and the matching processing method of the Longjing tea is determined based on the average values ​​of the control parameters of different secondary processing methods in different operation steps.

[0035] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings;

[0038] Figure 1 It is a flow chart of a new type of Longjing tea processing method;

[0039] Figure 2 It is a flow chart for determining that the leaf distribution of Longjing tea does not meet the requirements;

[0040] Figure 3 is a flow chart of a method for determining reference processing data;

[0041] Figure 4 It is a flow chart of a method for determining a screening processing method in a processing method;

[0042] Figure 5 It is a flow chart of a method for determining a matching processing method for Longjing tea. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0044] The defective rate of tea leaves varies greatly when the same processing method is used for tea leaves of different sizes. Therefore, differentiated processing methods are needed according to the size of the tea leaves to effectively control the defective rate of tea leaves.

[0045] The distribution ratio of Longjing tea in different leaf intervals is determined according to the distribution of leaf sizes, and the leaf intervals with a distribution ratio greater than 0.3 are regarded as high-ratio intervals. When the number of high-ratio intervals is more than 2, it is determined that the leaf distribution of Longjing tea does not meet the requirements.

[0046] Similar processing data is determined based on historical processing times whose deviations from the distribution quantity proportions in different blade intervals are all within a preset proportion deviation range, specifically, processing data under historical processing times that are all within a preset proportion deviation range.

[0047] The reference processing data is processing data corresponding to historical processing times in which the deviation rate of the distribution quantity ratio in different blade intervals is less than 0.1.

[0048] The historical processing times under the reference processing data corresponding to the processing method are taken as the matching processing times. Based on the fluctuation data of the defective rates of different matching processing times, the matching processing times within different defective rate intervals are determined. The defective rate interval in which the matching processing times are greater than the preset processing times is taken as the screening defective rate interval. When the number of screening defective rate intervals is only one, the processing method is determined to be the screening processing method.

[0049] The average value of the defective rates of the historical processing times corresponding to the reference processing data in different screening processing methods is taken as the average defective rate, and the matching processing method is the screening processing method with the smallest average defective rate.

[0050] Embodiment 1 To solve the above-mentioned problem, according to one aspect of the present invention, Figure 1 As shown, a new type of Longjing tea processing method is provided, which specifically includes:

[0051] S1: After the Longjing tea is spread out, the leaf distribution of the Longjing tea is identified by using an image recognition model to obtain an identification result. When it is determined based on the identification result that the leaf distribution of the Longjing tea does not meet the requirements, the next step is entered;

[0052] Furthermore, the identification result includes the distribution of leaf sizes of the Longjing tea.

[0053] Specifically, Figure 2 As shown, determining that the leaf distribution of the Longjing tea does not meet the requirements specifically includes:

[0054] Based on the recognition result, determining the distribution of leaf sizes of the Longjing tea;

[0055] Determining the distribution quantity ratio of the Longjing tea in different leaf intervals according to the distribution of leaf sizes;

[0056] Based on the distribution quantity proportion, the high proportion interval within the leaf interval is determined, and the quantity of the high proportion interval is used to determine whether the leaf distribution of the Longjing tea meets the requirements.

[0057] It should be noted that the high-proportion interval is a blade interval in which the distribution quantity ratio is greater than the preset quantity ratio.

[0058] It can be understood that when the number of the high-proportion intervals is greater than the preset number of intervals, it is determined that the leaf distribution of the Longjing tea does not meet the requirements.

[0059] Specifically, when the leaf distribution of the Longjing tea meets the requirements, the preset processing method corresponding to the leaf interval with the largest distribution quantity is used as the matching processing method of the Longjing tea.

[0060] It should also be noted that determining that the leaf distribution of the Longjing tea does not meet the requirements specifically includes:

[0061] Based on the recognition result, determining the distribution of leaf sizes of the Longjing tea;

[0062] Determining the distribution quantity ratio of the Longjing tea in different leaf intervals according to the distribution of leaf sizes;

[0063] According to the maximum value of the distribution quantity ratio in different leaf intervals, it is determined whether the leaf distribution of the Longjing tea meets the requirements.

[0064] Furthermore, when the maximum value of the distribution quantity proportion in different leaf intervals is greater than a preset proportion threshold, it is determined that the leaf distribution of the Longjing tea meets the requirements.

[0065] Optionally, determining that the leaf distribution of the Longjing tea does not meet the requirements specifically includes:

[0066] S11 determines the distribution of leaf sizes of the Longjing tea based on the recognition result, and determines the distribution quantity ratio of the Longjing tea in different leaf intervals according to the distribution of leaf sizes;

[0067] S12 determines the distribution dispersion coefficient in different leaf intervals based on the deviation between the leaf size in different leaf intervals and the preset leaf size corresponding to the leaf interval;

[0068] S13 determines the weight coefficients of different leaf intervals based on the distribution quantity ratio, determines the comprehensive dispersion coefficient in combination with the distribution dispersion coefficients in different leaf intervals, and uses the comprehensive dispersion coefficient to determine whether the leaf distribution of the Longjing tea meets the requirements.

[0069] Optionally, the above step S11 includes the following contents:

[0070] S111 determines the distribution of the leaf sizes of the Longjing tea based on the recognition result, and determines the distribution quantity ratio of the Longjing tea in different leaf intervals according to the distribution of the leaf sizes. When there is no leaf interval whose distribution quantity ratio is greater than a preset quantity ratio threshold, it is determined that the leaf distribution of the Longjing tea does not meet the requirements. When there is a leaf interval whose distribution quantity ratio is greater than the preset quantity ratio threshold, the process proceeds to step S112.

[0071] S112: when there is a leaf interval whose distribution quantity accounts for a greater proportion than the preset proportion threshold, it is determined that the leaf distribution of the Longjing tea meets the requirement; when there is no leaf interval whose distribution quantity accounts for a greater proportion than the preset proportion threshold, the process proceeds to step S113;

[0072] S113 regards the leaf intervals whose distribution quantity ratio is greater than the preset quantity ratio threshold as high-ratio intervals. When the number of the high-ratio intervals is greater than the preset interval quantity threshold, it is determined that the leaf distribution of the Longjing tea does not meet the requirements. When the number of the high-ratio intervals is not greater than the preset interval quantity threshold, proceed to step S12.

[0073] Optionally, the above step S12 includes the following contents:

[0074] S121: taking the high-proportion interval with the largest distribution quantity as the screening proportion interval; when the deviation between the distribution quantity proportion of the screening proportion interval and other high-proportion intervals is less than the preset deviation threshold, it is determined that the leaf distribution of the Longjing tea does not meet the requirements; when the deviation between the distribution quantity proportion of the screening proportion interval and other high-proportion intervals is not less than the preset deviation threshold, proceeding to step S122;

[0075] S122 determines the distribution dispersion coefficients in different leaf intervals based on the deviations between the leaf sizes in different leaf intervals and the preset leaf sizes corresponding to the leaf intervals. When the distribution dispersion coefficients in the high-proportion intervals all meet the requirements, it is determined that the leaf distribution of the Longjing tea meets the requirements. When there is a high-proportion interval where the distribution dispersion coefficient does not meet the requirements, proceed to step S13.

[0076] S2: determining distribution data in different leaf intervals based on the leaf distribution of the Longjing tea, and when it is determined that similar processing data does not exist for the Longjing tea based on the distribution data in different leaf intervals, determining reference processing data using the distribution data in different leaf intervals;

[0077] Furthermore, the distribution data within the blade interval includes the distribution quantity and the proportion of the distribution quantity in different blade intervals.

[0078] Specifically, the similar processing data is determined based on the historical processing times whose deviations from the distribution quantity proportions in different blade intervals are all within the preset proportion deviation range, specifically, the processing data under the historical processing times that are all within the preset proportion deviation range.

[0079] It should be noted that the processing data includes the processing methods and defective rates corresponding to the historical processing times.

[0080] It can be understood that when similar processing data exists, the matching processing method of the Longjing tea is determined by using the processing method with the lowest defective rate in the historical processing times corresponding to the similar processing data.

[0081] Specifically, Figure 3 As shown, the method for determining the reference processing data is:

[0082] Based on the distribution data in different leaf intervals, determine the distribution quantity ratio in different leaf intervals;

[0083] Determine the deviation of the quantity proportion in different blade intervals based on the deviation of the distribution data corresponding to the historical processing times and the distribution quantity proportion in different blade intervals;

[0084] According to the average values ​​of the quantity proportion deviations in different blade intervals, it is determined whether the processing data corresponding to the historical processing times is the reference processing data.

[0085] Further, when the average value of the deviation of the quantity proportions in different blade intervals is less than a preset value of the deviation, the processing data corresponding to the historical processing times is determined as the reference processing data.

[0086] In another embodiment, the method for determining the reference processing data is:

[0087] Based on the distribution data in different leaf intervals, determine the distribution quantity ratio in different leaf intervals;

[0088] Determine the deviation of the quantity proportion in different blade intervals based on the deviation of the distribution data corresponding to the historical processing times and the distribution quantity proportion in different blade intervals;

[0089] The deviation blade interval is determined according to the deviation of the quantity proportion in different blade intervals, and whether the processing data corresponding to the historical processing times is the reference processing data is determined based on the quantity of the deviation blade interval.

[0090] Further, when the number of the deviation blade intervals meets the requirement, the processing data corresponding to the historical processing times is determined as the reference processing data.

[0091] Optionally, the method for determining the reference processing data is:

[0092] S21 determines the distribution quantity proportions in different blade intervals based on the distribution data in different blade intervals, and determines the quantity proportion deviations in different blade intervals based on the deviations between the distribution quantity proportions in different blade intervals and the distribution data corresponding to the historical processing times;

[0093] S22 determines the distribution deviation in different blade intervals by using the deviation of the distribution data of the blade sizes in different blade intervals, and determines the comprehensive deviation in different blade intervals based on the quantity proportion deviation and the distribution deviation;

[0094] S23 determines the weight coefficients in different blade intervals based on the proportion of the distribution quantity in different blade intervals, and determines the data deviation coefficient of the processing data corresponding to the historical processing times in combination with the comprehensive deviation amount in different blade intervals, and uses the data deviation coefficient to determine whether the processing data corresponding to the historical processing times is the reference processing data.

[0095] Furthermore, the data deviation coefficient is determined according to the sum of the products of the comprehensive deviation amounts and the weight coefficients in different blade intervals.

[0096] Specifically, when the data deviation coefficient does not meet the requirement, it is determined that the processing data corresponding to the historical processing times does not belong to the reference processing data.

[0097] Optionally, the above step S21 includes the following contents:

[0098] S211 determines the distribution quantity proportions in different blade intervals based on the distribution data in different blade intervals, and determines the quantity proportion deviations in different blade intervals based on the deviations between the distribution quantity proportions in different blade intervals and the distribution data corresponding to the historical processing times;

[0099] S212: When the average value of the deviation of the number of proportions in different blade intervals meets the requirement, the process proceeds to step S213; when the average value of the deviation of the number of proportions in different blade intervals does not meet the requirement, it is determined that the processing data corresponding to the historical processing times does not belong to the reference processing data;

[0100] S213 determines that there is no blade interval whose quantity ratio deviation does not meet the requirement based on the quantity ratio deviation in different blade intervals, then determines that the processing data corresponding to the historical processing times belongs to the reference processing data, and when there is a blade interval whose quantity ratio deviation does not meet the requirement, proceeds to step S214;

[0101] S214 When the number of blade intervals whose quantity ratio deviation does not meet the requirements is greater than the preset blade interval quantity threshold, it is determined that the processing data corresponding to the historical processing times does not belong to the reference processing data; when the number of blade intervals whose quantity ratio deviation does not meet the requirements is not greater than the preset blade interval quantity threshold, proceed to step S22.

[0102] Optionally, the above step S22 includes the following contents:

[0103] S221 uses the deviation of the distribution data of the blade sizes in different blade intervals to determine the distribution deviation in different blade intervals, and determines the comprehensive deviation in different blade intervals based on the quantity proportion deviation and the distribution deviation. When the comprehensive deviations in different blade intervals meet the requirements, it is determined that the processing data corresponding to the historical processing times belongs to the reference processing data. When there is a blade interval whose comprehensive deviation does not meet the requirements, it proceeds to step S222;

[0104] S222: when the number of blade intervals whose comprehensive deviation does not meet the requirement is greater than the preset interval number setting value, it is determined that the processing data corresponding to the historical processing times does not belong to the reference processing data; when the number of blade intervals whose comprehensive deviation does not meet the requirement is not greater than the preset interval number setting value, the process proceeds to step S223;

[0105] S223 determines that the processing data corresponding to the historical processing times does not belong to the reference processing data when the sum of the comprehensive deviation amounts of the blade intervals whose comprehensive deviation amounts do not meet the requirements does not meet the requirements. When the sum of the comprehensive deviation amounts of the blade intervals whose comprehensive deviation amounts do not meet the requirements meets the requirements, proceed to step S23.

[0106] S3: determining the fluctuation data of the defective rate corresponding to the different processing methods based on the reference processing data in the different processing methods, and determining the screening processing method in the processing methods by using the fluctuation data of the defective rate and the historical processing times corresponding to the reference processing data;

[0107] It should be noted that the fluctuation data of the defect rate is determined according to the defect rate of the historical processing times corresponding to the processing method.

[0108] It is understandable that if Figure 4 As shown, the method for determining the screening processing method in the processing method is:

[0109] The historical processing times under the reference processing data corresponding to the processing method are used as the matching processing times;

[0110] Based on the fluctuation data of the defective rate of different matching processing times, the matching processing times within different defective rate ranges are determined;

[0111] The defect rate interval in which the matching processing times is greater than the preset processing times is used as a screening defect rate interval, and the number of the screening defect rate intervals is used to determine whether the processing method is a screening processing method.

[0112] Furthermore, when the number of the screening defective rate intervals is greater than a preset defective rate interval number threshold, it is determined that the processing method does not belong to the screening processing method.

[0113] In another embodiment, the method for determining the screening processing method in the processing method is:

[0114] The historical processing times under the reference processing data corresponding to the processing method are used as the matching processing times, and when the matching processing times are less than a preset matching times threshold, it is determined that the processing method does not belong to the screening processing method;

[0115] When the matching processing times are not less than the preset matching times threshold:

[0116] Based on the fluctuation data of the defective rates of different matching processing times, the matching processing times within different defective rate intervals are determined, and when the matching processing times within the preset defective rate interval is greater than the preset processing times threshold, it is determined that the processing method does not belong to the screening processing method;

[0117] When the matching processing times within the preset defect rate range is not greater than the preset processing times threshold:

[0118] Determine the defect rate matching coefficient of the preset defect rate interval based on the number of matching processing times and the number ratio of the matching processing times within the preset defect rate interval, and when the defect rate matching coefficient is greater than the preset matching coefficient threshold, determine that the processing method does not belong to the screening processing method;

[0119] When the defect rate matching coefficient is not greater than the preset matching coefficient threshold:

[0120] When there is no defect rate interval in which the matching processing times is greater than the preset processing times, it is determined that the processing method does not belong to the screening processing method;

[0121] When there is a defect rate interval where the matching processing times are greater than the preset processing times:

[0122] The defect rate interval in which the matching processing times is greater than the preset processing times is used as the screening defect rate interval, and when the number of the screening defect rate intervals does not meet the requirement, it is determined that the processing method does not belong to the screening processing method;

[0123] When the number of the screening failure rate intervals meets the requirements:

[0124] The defect rate interval with the largest number of matching processing times is taken as the target interval, and the defect rate fluctuation coefficient of the processing method is determined by using the matching processing times within the target interval and the proportion of the matching processing times. The defect rate fluctuation coefficient is used to determine whether the processing method is a screening processing method.

[0125] Furthermore, the preset defective rate interval is a defective rate interval in which the defective rate is greater than the preset defective rate.

[0126] S4 determines the matching processing method of the Longjing tea based on the defective rate of historical processing times corresponding to the reference processing data in different screening processing methods, and processes the Longjing tea using the matching processing method.

[0127] Specifically, Figure 5 As shown, the method for determining the matching processing method of Longjing tea is:

[0128] The average value of the defective rates of the historical processing times corresponding to the reference processing data in different screening processing methods is taken as the average defective rate;

[0129] The matching processing method of the Longjing tea is determined according to the average defective rate.

[0130] Furthermore, the matching processing method is a screening processing method with the smallest average defective rate.

[0131] In another embodiment, the method for determining the matching processing method of Longjing tea is:

[0132] The average value of the defective rates of the historical processing times corresponding to the reference processing data in different screening processing methods is taken as the average defective rate;

[0133] When there is a screening and processing method with an average defect rate less than a preset defect rate threshold, the screening and processing method with the smallest average defect rate is used as the matching processing method for the Longjing tea;

[0134] When there is no screening and processing method whose average defective rate is less than the preset defective rate threshold, the average defective rate is used to determine the secondary screening and processing method in the screening and processing method, and the matching processing method of the Longjing tea is determined based on the average values ​​of the control parameters of different secondary processing methods in different operation steps.

[0135] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0136] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0137] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A new type of Longjing tea processing method, characterized in that: Specifically include: After the Longjing tea is spread out, the leaf distribution of the Longjing tea is identified by using an image recognition model to obtain an identification result. When it is determined based on the identification result that the leaf distribution of the Longjing tea does not meet the requirements, the next step is entered; Based on the leaf distribution of the Longjing tea, the size distribution data in different leaf intervals are determined, and when it is determined based on the size distribution data in different leaf intervals that similar processing data does not exist for the Longjing tea, the distribution data in different leaf intervals are used to determine the reference processing data; Based on reference processing data in different processing methods, determining the fluctuation data of defective rates corresponding to the different processing methods, and determining the screening processing method in the processing methods using the fluctuation data of the defective rates and the historical processing times corresponding to the reference processing data; Determine a matching processing method for the Longjing tea according to the defective rates of the historical processing times corresponding to the reference processing data in different screening processing methods, and process the Longjing tea using the matching processing method; The similar processing data is determined based on the number of historical processing times whose deviations from the distribution quantity ratios in different blade intervals are all within a preset ratio deviation range; Based on the size distribution data in different blade intervals, the distribution quantity ratio in different blade intervals is determined. Based on the deviation between the distribution quantity ratio in different blade intervals and the distribution data corresponding to the historical processing times, the quantity ratio deviation in different blade intervals is determined. When the average value of the quantity ratio deviation in different blade intervals is less than the preset value of the deviation, the processing data corresponding to the historical processing times is determined as the reference processing data.

2. The novel mechanism-based Longjing tea processing method as claimed in claim 1, characterized in that: The identification result includes the distribution of leaf sizes of the Longjing tea.

3. The novel mechanism-based Longjing tea processing method as claimed in claim 1, characterized in that: Determining that the leaf distribution of the Longjing tea does not meet the requirements specifically includes: Based on the recognition result, determining the distribution of leaf sizes of the Longjing tea; Determining the distribution quantity ratio of the Longjing tea in different leaf intervals according to the distribution of leaf sizes; Based on the distribution quantity proportion, the high proportion interval within the leaf interval is determined, and the quantity of the high proportion interval is used to determine whether the leaf distribution of the Longjing tea meets the requirements.

4. The novel mechanism-based Longjing tea processing method as claimed in claim 3, characterized in that: The high-proportion interval is a blade interval in which the distribution quantity ratio is greater than the preset quantity ratio.

5. The novel mechanism-based Longjing tea processing method as claimed in claim 1, characterized in that: When the leaf distribution of the Longjing tea meets the requirements, the preset processing method corresponding to the leaf interval with the largest distribution quantity is used as the matching processing method of the Longjing tea.

6. The novel mechanism-based Longjing tea processing method as claimed in claim 1, characterized in that: The distribution data within the leaf intervals includes the distribution quantity and the distribution quantity ratio within different leaf intervals.

7. The novel mechanism-based Longjing tea processing method as claimed in claim 1, characterized in that: The method for determining the matching processing method of the Longjing tea is: The average value of the defective rates of the historical processing times corresponding to the reference processing data in different screening processing methods is taken as the average defective rate; The matching processing method of the Longjing tea is determined according to the average defective rate.

8. The novel mechanism-based Longjing tea processing method as claimed in claim 7, characterized in that: The matching processing method is a screening processing method with the smallest average defective rate.

9. The novel mechanism-based Longjing tea processing method as claimed in claim 1, characterized in that: The method for determining the matching processing method of the Longjing tea is: The average value of the defective rates of the historical processing times corresponding to the reference processing data in different screening processing methods is taken as the average defective rate; When there is a screening and processing method with an average defect rate less than a preset defect rate threshold, the screening and processing method with the smallest average defect rate is used as the matching processing method for the Longjing tea; When there is no screening and processing method whose average defective rate is less than the preset defective rate threshold, the average defective rate is used to determine the secondary screening and processing method in the screening and processing method, and the matching processing method of the Longjing tea is determined based on the average values ​​of the control parameters of different secondary processing methods in different operation steps.

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