Kelp quality sorting and color sorting management system and method based on artificial intelligence

Abstract

The invention relates to the technical field of kelp quality management, and discloses a kelp quality sorting and color sorting management system and method based on artificial intelligence, and the system comprises a kelp product color sorting information processing module, a kelp product color and band body quality grade evaluation module, and a kelp product quality evaluation and sorting execution module. The kelp product quality grade comprehensive judgment is performed according to the kelp product quality grade data corresponding to different appearance colors and band body quality grades and the kelp product appearance color and band body quality grade parameters, the kelp product quality grade parameters are efficiently and accurately constructed, and the kelp product quality grade intelligent evaluation is realized. Based on the kelp product quality grade parameters, kelp product quality classification and sorting operation is efficiently and intelligently executed, and the efficiency and accuracy of kelp product quality sorting are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of kelp quality management, and in particular to a kelp quality sorting and color selection management system and method based on artificial intelligence. Background Art

[0002] The kelp product harvesting process requires manual quality sorting and color selection of kelp products according to their appearance color and kelp body defect characteristics. However, manual quality sorting and color selection of kelp products is not only inefficient but also has a high error rate.

[0003] A Chinese invention patent application with publication number CN117726278A discloses a method and system for managing the flow of agricultural products, which obtains agricultural product information and supplier information of bulk agricultural products, determines an inspection plan, controls an inspection device based on the inspection plan to obtain inspection data of a storage unit, determines a quality evaluation of the storage unit based on the inspection data, and sorts the bulk agricultural products based on the quality evaluation; in response to the number of sorted unqualified products being less than a first threshold, increases the transmission speed of a conveyor belt; in response to the number of agricultural products meeting a third preset condition being greater than a third threshold, sorts the agricultural products separately; the third preset condition is that the quality grade of the agricultural products is higher than the overall quality grade; the sorted agricultural products are flow managed according to different sorting grades, thereby improving the quality management efficiency and accuracy of the agricultural products; however, the above technical scheme cannot perform a refined evaluation and analysis of the product quality of each agricultural product, thereby reducing the accuracy of agricultural product quality management. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In order to solve the problem that the manual quality sorting and color selection of kelp products is not only inefficient but also has a high error rate in the quality sorting and color selection of kelp products, the above purposes are achieved: accurately collecting characteristic images of kelp product appearance, scientifically establishing sample images of kelp product appearance, intelligently evaluating the color quality grade of kelp product appearance, accurately analyzing the quality grade of kelp product body, comprehensively evaluating the quality grade of kelp products, and efficiently performing kelp product sorting and color selection operations.

[0006] (II) Technical solution

[0007] The present invention is implemented by the following technical scheme: a kelp quality sorting and color selection management method, the method comprising the following steps:

[0008] S1, collecting kelp product appearance feature image data and performing image preprocessing to generate standard kelp product appearance feature image data;

[0009] S2, performing a process of generating a kelp product appearance image sample on the standard kelp product appearance feature image data to generate kelp product sample appearance image data;

[0010] S3, performing appearance color feature analysis processing on the kelp product sample according to the appearance image data of the kelp product sample and the appearance color image data of the qualified kelp product, and generating appearance color analysis data of the kelp product sample;

[0011] S4, based on the kelp product sample appearance color analysis data, weight numerical measurement processing is performed on the kelp product qualified appearance color samples, weight data of kelp product qualified appearance color samples is generated, and the appearance color quality grade analysis processing of the kelp product is performed with the weight threshold of the qualified appearance color samples of high-quality kelp products, so as to construct the kelp product appearance color quality grade data;

[0012] S5, performing body defect feature recognition processing of the kelp product sample according to the appearance image data of the kelp product sample and the body defect feature image data of the kelp product, and generating body defect recognition data of the kelp product sample;

[0013] S6, based on the kelp product sample body defect identification data, weight numerical measurement processing of kelp product body defect-free samples is performed to generate weight data of kelp product body defect-free samples, and the kelp product body quality grade analysis processing is performed with the weight threshold of high-quality kelp product body defect-free samples to construct kelp product body quality grade data;

[0014] S7. Perform comprehensive judgment on the quality grade of kelp products based on the quality grade parameters of kelp product appearance color and strip body and the kelp product quality grade data corresponding to different appearance colors and strip body quality grades, construct kelp product quality grade data and perform kelp product quality sorting operations.

[0015] Preferably, the steps of collecting the kelp product appearance feature image data and performing image preprocessing to generate the standard kelp product appearance feature image data are as follows:

[0016] S11. Collect the appearance feature images of the kelp products passing through the monitoring port online through the cloud camera installed on the material conveyor belt, and generate the appearance feature image data of the kelp products ;

[0017] S12, using an adaptive filtering method to filter the kelp product appearance feature image data After image noise reduction preprocessing, the standard kelp product appearance feature image data is generated .

[0018] Preferably, the standard kelp product appearance feature image data is processed to generate a kelp product appearance image sample, and the steps of generating the kelp product sample appearance image data are as follows:

[0019] S21, the standard kelp product appearance feature image data A square with a side length of L is used to perform grid division processing on the appearance feature image, and a data set of kelp product sample appearance images is generated. , ;in Indicates Appearance image data of kelp product samples, Indicates the total number of kelp product samples.

[0020] Preferably, the appearance color feature analysis processing of the kelp product sample is performed based on the appearance image data of the kelp product sample and the appearance color image data of the qualified kelp product to generate the appearance color analysis data of the kelp product sample in the following steps:

[0021] S31. Establishing a data set of appearance color images of qualified kelp products , ;in Indicates Appearance color image data of qualified kelp products, Indicates the maximum number of appearance color types of qualified kelp products. Appearance color types of qualified kelp products include brown-green, khaki, dark green and dark brown;

[0022] S32, collecting the kelp product sample appearance image data set Appearance image data of kelp product samples described in The qualified kelp product appearance color image data set is ordered according to the number of kelp product sample appearance images. Appearance color image data of qualified kelp products described in Perform image feature matching and generate a data set of kelp product sample appearance color analysis based on the image feature matching results , execute to generate kelp product sample appearance color analysis data set The specific steps are as follows:

[0023] S321, initializing the algorithm iteration data and executing the ocean current simulation process; setting the maximum algorithm iteration data to T, the ocean current simulation process is to collect the color image data of the appearance of qualified kelp products There are ocean currents in the search space that contain image data related to the appearance of kelp product samples Matching qualified kelp product appearance color image data Nutrients, appearance color search jellyfish kelp product sample appearance image data Matching qualified kelp product appearance color image data Nutrient attraction, the direction of the ocean current is determined by the average of all vectors from each appearance color searching jellyfish in the ocean toward the appearance color searching jellyfish located at the optimal position;

[0024] S322, during the algorithm execution process, the appearance color search jellyfish group appearance color search jellyfish in the qualified kelp product appearance color image data set Searching for image data related to the appearance of kelp product samples by moving around their own positions in the search space Matching qualified kelp product appearance color image data And update the position, the corresponding position update formula for each appearance color search jellyfish is as follows: ,in Search for jellyfish individuals by appearance color No. After iterations, the qualified kelp product appearance color image dataset The position in the search space, Search for jellyfish individuals by appearance color No. After iterations, the qualified kelp product appearance color image dataset The position in the search space, Indicates value Random functions in the interval; and Represents the upper and lower bounds of the search space, that is, the appearance color image data set of qualified kelp products Search the search space for the image data related to the appearance of kelp product samples Matching qualified kelp product appearance color image data The upper and lower bounds of the data, =0.1 indicates kinematic data;

[0025] S323, algorithm execution, appearance color search, appearance color search, jellyfish group, appearance color search, jellyfish in qualified kelp product appearance color image data set Search the search space for the image data related to the appearance of kelp product samples Matching qualified kelp product appearance color image data The control time of the behavior. The control time measurement formula is as follows: ,in Indicates The control time required to search for jellyfish individuals by appearance color after iterations, is the maximum iteration data;

[0026] S324, the algorithm executes to meet the boundary conditions, when the appearance color search jellyfish moves out of the qualified kelp product appearance color image data set with boundaries When searching the space, the appearance color search for jellyfish returns to the qualified kelp product appearance color image data set. The opposite search area in the search space continues to search for the image data of the kelp product sample appearance Matching qualified kelp product appearance color image data , the appearance color search jellyfish moving boundary condition satisfies the following formula:

[0027] ,in Search for jellyfish individuals by appearance color exist The updated position in the dimensional space, that is, in the space with the dimension A collection of qualified kelp product appearance color image data The updated position in the search space, Search for jellyfish individuals by appearance color exist The position before updating in the dimension space, that is, in the space dimension A collection of qualified kelp product appearance color image data The position before the update in the search space;

[0028] S325: When the algorithm meets the maximum iteration number data, output the appearance image data of the kelp product sample Image data of the appearance and color of the qualified kelp products The image feature matching results are used to generate a data set for the appearance color analysis of kelp product samples ,in Indicates the appearance image data of kelp product samples Corresponding kelp product sample appearance color analysis data;

[0029] when and If the image feature matching is successful, it means that the appearance color of the kelp product sample is qualified, and the appearance color analysis data of the kelp product sample is output. To be qualified;

[0030] when and If the image feature matching fails, it means that the appearance color of the kelp product sample is unqualified, then the appearance color analysis data of the kelp product sample is output. Is unqualified.

[0031] Preferably, based on the kelp product sample appearance color analysis data, weight numerical measurement processing of qualified kelp product appearance color samples is performed to generate weight data of qualified kelp product appearance color samples and perform appearance color quality grade analysis processing of kelp products with weight thresholds of qualified appearance color samples of high-quality kelp products to construct appearance color quality grade data of kelp products in the following steps:

[0032] S41. Establish the weight threshold of qualified appearance color samples of high-quality kelp products , the weight threshold of qualified appearance color samples of high-quality kelp products represents the minimum value of the weight of qualified appearance color samples of high-quality kelp products among all appearance samples;

[0033] S42, using a breadth-first search algorithm to analyze the appearance color data set of the kelp product sample Search for the appearance color analysis data of the kelp product sample The number of qualified kelp product samples and the number of qualified appearance color samples of kelp products are generated ;

[0034] S43, the number of qualified appearance color samples of the kelp product Total number of kelp product samples Perform numerical quotient measurement processing to generate weight data of qualified appearance color samples of kelp products ,in ;

[0035] S44, the weight data of qualified appearance color samples of the kelp product Weight threshold of qualified appearance color samples of high-quality kelp products Perform numerical comparison and construct the kelp product appearance color quality grade data based on the numerical comparison results ;

[0036] when ≥ , indicating that the appearance color of the kelp product meets the weight requirements of qualified appearance color samples in the appearance color of high-quality kelp products, then the appearance color quality grade data of the kelp product is output is level one;

[0037] when < , indicating that the appearance color of the kelp product does not meet the weight requirements of qualified appearance color samples in the appearance color of high-quality kelp products, then the appearance color quality grade data of the kelp product is output It is level two.

[0038] Preferably, the steps of performing body defect feature recognition processing of the kelp product sample according to the appearance image data of the kelp product sample and the body defect feature image data of the kelp product sample to generate body defect recognition data of the kelp product sample are as follows:

[0039] S51. Establishing a dataset of defect feature images of kelp products , ;in Indicates The defect feature image data of kelp products. Indicates the maximum number of kelp product body defect types, including body scratches, body black spots, body holes and body breaks;

[0040] S52, when the kelp product appearance color quality grade data When the construction is completed, the kelp product sample appearance image data set is collected using a unified cost search algorithm Appearance image data of kelp product samples described in The image data set of the kelp product body defect feature is ordered according to the number of the kelp product sample appearance images. The kelp product body defect feature image data described in Perform image feature matching and generate a kelp product sample body defect recognition data set based on the image feature matching results ,in Indicates the appearance image data of kelp product samples Corresponding kelp product sample body defect identification data;

[0041] when and If the image feature matching is successful, it means that the kelp product sample has the belt defect feature, then the kelp product sample belt defect recognition data is output. for existence;

[0042] when and If the image feature matching fails, it means that the kelp product sample does not have the belt defect feature, then the kelp product sample belt defect recognition data is output. Does not exist.

[0043] Preferably, based on the kelp product sample strip defect identification data, weighted numerical measurement processing of kelp product strip defect-free samples is performed to generate weighted data of kelp product strip defect-free samples and analyze the strip quality grade of kelp products with the weight threshold of high-quality kelp product strip defect-free samples to construct the kelp product strip quality grade data in the following steps:

[0044] S61. Establish a sample weight threshold for high-quality kelp products without body defects , the weight threshold of the samples without body defects of high-quality kelp products represents the minimum value of the weight of the samples without body defects of high-quality kelp products among all appearance samples;

[0045] S62, using a breadth-first search algorithm to identify the kelp product sample body defect data set Search for the kelp product sample's kelp body defect identification data The number of kelp product samples that do not exist, and the number of kelp product samples without body defects ;

[0046] S63, the number of samples of the kelp product without body defects Total number of kelp product samples Perform numerical quotient measurement processing to generate sample weight data for kelp products without kelp body defects ,in ;

[0047] S64, the kelp product does not have the weight data of the sample with body defects The sample weight threshold for the high-quality kelp products without body defects Carry out numerical comparison and construct the kelp product body quality grade data based on the numerical comparison results ;

[0048] when ≥ , indicating that the kelp product strip defect meets the sample weight requirement of no strip defect in the high-quality kelp product strip defect, then the kelp product strip quality grade data is output is level one;

[0049] when < , indicating that the kelp product strip defect does not meet the sample weight requirement of the high-quality kelp product strip defect, then the kelp product strip quality grade data is output. It is level two.

[0050] Preferably, the steps of performing comprehensive judgment on the quality grade of kelp products based on the quality grade parameters of the kelp product appearance color and the strip body and the kelp product quality grade data corresponding to different appearance colors and strip body quality grades, constructing the kelp product quality grade data and performing the kelp product quality sorting operation are as follows:

[0051] S71. Establish a data set of kelp product quality grades corresponding to different appearance colors and kelp body quality grades ,in Indicates the appearance color quality grade data of the kelp product And the kelp product body quality grade data The corresponding data of kelp product quality grades for different appearance colors and kelp body quality grades are as follows: , Indicates that the quality grade of kelp products is Grade One; Indicates the appearance color quality grade data of the kelp product The kelp product body quality grade data is Grade II or when the kelp product appearance color quality grade data The kelp product body quality grade data is The corresponding data of different appearance colors and kelp quality grades when it is level 1 are as follows: , Indicates that the quality grade of kelp products is Level 2; Indicates the appearance color quality grade data of the kelp product And the kelp product body quality grade data The corresponding data of kelp product quality grades for different appearance colors and kelp body quality grades are as follows: , It means that the quality grade of kelp products is Grade III;

[0052] S72, the kelp product appearance color quality grade data constructed in step S44 and step S64 And the kelp product body quality grade data A data set of kelp product quality grades corresponding to the different appearance colors and kelp body quality grades The kelp product quality grade data corresponding to the different appearance colors and belt quality grades described in the above are matched according to the appearance color and belt quality grade keywords to search for the kelp product appearance color quality grade data And the kelp product body quality grade data The corresponding different appearance colors and belt body quality grades correspond to the kelp product quality grade data and the kelp product quality grade data is constructed through data identification. ;

[0053] S73, according to the kelp product quality grade data The kelp products are sorted by quality through the material conveyor belt.

[0054] An artificial intelligence-based kelp quality sorting and color sorting management system, used to implement the kelp quality sorting and color sorting management method, the system includes a kelp product color sorting information processing module, a kelp product color and kelp body quality grade assessment module, and a kelp product quality assessment and sorting execution module;

[0055] The kelp product color sorting information processing module includes a kelp product appearance feature image acquisition unit, a kelp product appearance feature image preprocessing unit, and a kelp product sample appearance image generation unit;

[0056] The kelp product appearance feature image acquisition unit acquires kelp product appearance feature image data through a cloud camera; the kelp product appearance feature image preprocessing unit performs image preprocessing on the kelp product appearance feature image data to generate standard kelp product appearance feature image data; the kelp product sample appearance image generation unit performs kelp product appearance image sample generation processing on the standard kelp product appearance feature image data to generate kelp product sample appearance image data;

[0057] The kelp product color and belt body quality grade evaluation module includes a qualified kelp product appearance color image storage unit, a kelp product sample appearance color analysis unit, a kelp product qualified appearance color sample weight measurement unit, a high-quality kelp product qualified appearance color sample weight threshold storage unit, and a kelp product appearance color quality grade analysis unit; a kelp product belt body defect feature image storage unit, a kelp product sample belt body defect recognition unit, a kelp product non-belt body defect sample weight measurement unit, a high-quality kelp product non-belt body defect sample weight threshold storage unit, and a kelp product belt body quality grade analysis unit;

[0058] The qualified kelp product appearance color image storage unit is used to store qualified kelp product appearance color image data; the kelp product sample appearance color analysis unit performs appearance color feature analysis on the kelp product sample appearance image data and the qualified kelp product appearance color image data based on artificial intelligence to generate kelp product sample appearance color analysis data; the kelp product qualified appearance color sample weight measurement unit performs weight value measurement processing on the kelp product qualified appearance color samples based on the kelp product sample appearance color analysis data to generate kelp product qualified appearance color sample weight data; the high-quality kelp product qualified appearance color sample weight threshold storage unit is used to store the high-quality kelp product qualified appearance color sample weight threshold; the kelp product appearance color quality grade analysis unit performs appearance color quality grade analysis on the kelp product according to the kelp product qualified appearance color sample weight data and the high-quality kelp product qualified appearance color sample weight threshold to construct the kelp product appearance color quality grade data; the kelp product strip defect characteristic image storage unit is used to store the kelp product strip defect characteristic image data; the kelp product sample strip defect recognition unit performs strip defect characteristic recognition processing of the kelp product sample according to the kelp product sample appearance image data and the kelp product strip defect characteristic image data, and generates the kelp product sample strip defect recognition data; the kelp product non-existent strip defect sample weight measurement unit performs weight value measurement processing of the kelp product non-existent strip defect sample based on the kelp product sample strip defect recognition data, and generates the kelp product non-existent strip defect sample weight data; the high-quality kelp product non-existent strip defect sample weight threshold storage unit is used to store the high-quality kelp product non-existent strip defect sample weight threshold; the kelp product strip quality grade analysis unit performs kelp product strip quality grade analysis processing on the kelp product non-existent strip defect sample weight data and the high-quality kelp product non-existent strip defect sample weight threshold, and constructs the kelp product strip quality grade data;

[0059] The kelp product quality assessment and sorting execution module includes a kelp product quality grade storage unit corresponding to different appearance colors and kelp body quality grades, a kelp product quality grade comprehensive judgment unit, and a kelp product quality sorting operation execution unit;

[0060] The storage unit for kelp product quality grades corresponding to different appearance colors and strip quality grades is used to store kelp product quality grade data corresponding to different appearance colors and strip quality grades; the kelp product quality grade comprehensive judgment unit performs comprehensive judgment processing on the quality grade of kelp products based on the kelp product appearance color, strip quality grade parameters and kelp product quality grade data corresponding to different appearance colors and strip quality grades, and constructs kelp product quality grade data; the kelp product quality sorting operation execution unit executes kelp product quality sorting operations according to the kelp product quality grade data.

[0061] (III) Beneficial effects

[0062] The present invention provides a kelp quality sorting and color sorting management system and method based on artificial intelligence. It has the following beneficial effects:

[0063] 1. Through the cooperation between the kelp product appearance characteristic image acquisition unit and the kelp product appearance characteristic image preprocessing unit, the cloud lens is used to dynamically acquire the kelp product appearance characteristic image parameters and perform image noise reduction preprocessing, accurately generate the standard kelp product appearance characteristic image parameters, and improve the authenticity of the kelp product appearance characteristic image acquisition; the kelp product sample appearance image generation unit performs image grid division processing on the kelp product appearance characteristic image parameters, scientifically constructs the image samples of the kelp products to be tested, and realizes the refined operation of kelp product quality sorting and color selection.

[0064] 2. Through the cooperation of the kelp product sample appearance color analysis unit and the kelp product sample strip defect recognition unit, the artificial intelligence algorithm is used to perform intelligent and standardized analysis of the appearance color and strip defects of the kelp product image samples, thereby improving the accuracy and efficiency of kelp product quality sorting and color selection; the kelp product qualified appearance color sample weight measurement unit and the kelp product non-strip defect sample weight measurement unit cooperate with each other to digitally count the weights of qualified appearance color samples and samples without strip defects in the kelp product image samples, thereby realizing accurate quantitative processing of the appearance color and strip defect characteristics in kelp products; the kelp product appearance color quality grade analysis unit and the kelp product strip quality grade analysis unit cooperate with each other to compare the kelp product appearance color weight and strip defect weight with the appearance color and strip quality thresholds, scientifically analyze the appearance color and strip quality grade of the kelp product, thereby improving the scientific nature of kelp quality sorting and color selection.

[0065] 3. Through the cooperation between the kelp product quality grade storage unit corresponding to different appearance colors and strip quality grades and the kelp product quality grade comprehensive judgment unit, the standard preset kelp product quality grade data corresponding to different appearance colors and strip quality grades and the kelp product appearance color and strip quality grade parameters are used to make a comprehensive judgment on the kelp product quality grade, and the kelp product quality grade parameters are efficiently and accurately constructed to realize the intelligent evaluation of kelp product quality grade; the kelp product quality sorting operation execution unit efficiently and intelligently executes kelp product quality classification and sorting operations based on the kelp product quality grade parameters, thereby improving the efficiency and accuracy of kelp product quality sorting. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A schematic diagram of a module of the artificial intelligence-based kelp quality sorting and color selection management system provided by the present invention;

[0067] Figure 2 The present invention provides a flow chart of the kelp quality sorting and color selection management method. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] The embodiments of the kelp quality sorting and color selection management system and method based on artificial intelligence are as follows:

[0070] Embodiment 1:

[0071] See also Figure 1 - Figure 2 , a kelp quality sorting and color selection management method, the method comprises the following steps:

[0072] S1, collecting kelp product appearance feature image data and performing image preprocessing to generate standard kelp product appearance feature image data;

[0073] S2, performing a process of generating a kelp product appearance image sample on the standard kelp product appearance feature image data to generate kelp product sample appearance image data;

[0074] S3, performing appearance color feature analysis processing on the kelp product sample according to the appearance image data of the kelp product sample and the appearance color image data of the qualified kelp product, and generating appearance color analysis data of the kelp product sample;

[0075] S4, based on the appearance color analysis data of kelp product samples, weight numerical measurement processing of qualified appearance color samples of kelp products is performed to generate weight data of qualified appearance color samples of kelp products, and the appearance color quality grade analysis processing of kelp products is performed with the weight threshold of qualified appearance color samples of high-quality kelp products to construct appearance color quality grade data of kelp products;

[0076] S5, performing body defect feature recognition processing of the kelp product sample according to the appearance image data of the kelp product sample and the body defect feature image data of the kelp product, and generating body defect recognition data of the kelp product sample;

[0077] S6. Based on the kelp product sample body defect identification data, weight numerical measurement processing is performed on the kelp product body defect-free samples, weight data of kelp product body defect-free samples is generated, and the kelp product body quality grade analysis processing is performed with the weight threshold of high-quality kelp product body defect-free samples, to construct kelp product body quality grade data;

[0078] S7. Perform comprehensive judgment on the quality grade of kelp products based on the quality grade parameters of kelp product appearance color and strip body and the kelp product quality grade data corresponding to different appearance colors and strip body quality grades, construct kelp product quality grade data and perform kelp product quality sorting operations.

[0079] For further information, see Figure 1 - Figure 2 , collect kelp product appearance feature image data and perform image preprocessing, and generate standard kelp product appearance feature image data as follows:

[0080] S11. Collect the appearance feature images of the kelp products passing through the monitoring port online through the cloud camera installed on the material conveyor belt, and generate the appearance feature image data of the kelp products ;

[0081] S12, using adaptive filtering method to filter the appearance feature image data of kelp products After image noise reduction preprocessing, the standard kelp product appearance feature image data is generated .

[0082] The standard kelp product appearance feature image data is processed to generate a kelp product appearance image sample. The steps for generating the kelp product sample appearance image data are as follows:

[0083] S21. Standard kelp product appearance feature image data A square with a side length of L is used to perform grid division processing on the appearance feature image, and a data set of kelp product sample appearance images is generated. , ;in Indicates Appearance image data of kelp product samples, Indicates the total number of kelp product samples.

[0084] Through the cooperation of the kelp product appearance characteristic image acquisition unit and the kelp product appearance characteristic image preprocessing unit, the cloud lens is used to dynamically acquire the kelp product appearance characteristic image parameters and perform image noise reduction preprocessing, and the standard kelp product appearance characteristic image parameters are accurately generated, thereby improving the authenticity of the kelp product appearance characteristic image acquisition; the kelp product sample appearance image generation unit performs image grid division processing on the kelp product appearance characteristic image parameters, scientifically constructs the image samples of the kelp products to be tested, and realizes the refined operation of kelp product quality sorting and color selection.

[0085] For further information, see Figure 1 - Figure 2 According to the appearance image data of the kelp product sample and the appearance color image data of the qualified kelp product, the appearance color feature analysis and processing of the kelp product sample is performed, and the operation steps of generating the appearance color analysis data of the kelp product sample are as follows:

[0086] S31. Establishing a data set of appearance color images of qualified kelp products , ;in Indicates Appearance color image data of qualified kelp products, Indicates the maximum number of appearance color types of qualified kelp products. Appearance color types of qualified kelp products include brown-green, khaki, dark green and dark brown;

[0087] S32, collecting kelp product sample appearance image data Zhonghai kelp product sample appearance image data A dataset of appearance color images of kelp products in order of number and quality Appearance color image data of qualified kelp products Perform image feature matching and generate a data set of kelp product sample appearance color analysis based on the image feature matching results , execute to generate kelp product sample appearance color analysis data set The specific steps are as follows:

[0088] S321, initializing the algorithm iteration data and executing the ocean current simulation process; setting the maximum algorithm iteration data to T, the ocean current simulation process is to collect the color image data of the appearance of qualified kelp products There are ocean currents in the search space that contain image data related to the appearance of kelp product samples Matching qualified kelp product appearance color image data Nutrients, appearance color search jellyfish kelp product sample appearance image data Matching qualified kelp product appearance color image data Nutrient attraction, the direction of the ocean current is determined by the average of all vectors from each appearance color searching jellyfish in the ocean toward the appearance color searching jellyfish located at the optimal position;

[0089] S322, during the algorithm execution process, the appearance color search jellyfish group appearance color search jellyfish in the qualified kelp product appearance color image data set Searching for image data related to the appearance of kelp product samples by moving around their own positions in the search space Matching qualified kelp product appearance color image data And update the position, the corresponding position update formula for each appearance color search jellyfish is as follows: ,in Search for jellyfish individuals by appearance color No. After iterations, the qualified kelp product appearance color image dataset The position in the search space, Search for jellyfish individuals by appearance color No. After iterations, the qualified kelp product appearance color image dataset The position in the search space, Indicates value Random functions in the interval; and Represents the upper and lower bounds of the search space, that is, the appearance color image data set of qualified kelp products Search the search space for the image data related to the appearance of kelp product samples Matching qualified kelp product appearance color image data The upper and lower bounds of the data, =0.1 indicates kinematic data;

[0090] S323, algorithm execution, appearance color search, appearance color search, jellyfish group, appearance color search, jellyfish in qualified kelp product appearance color image data set Search the search space for the image data related to the appearance of kelp product samples Matching qualified kelp product appearance color image data The control time of the behavior. The control time measurement formula is as follows: ,in Indicates The control time required to search for jellyfish individuals by appearance color after iterations, is the maximum iteration data;

[0091] S324, the algorithm executes to meet the boundary conditions, when the appearance color search jellyfish moves out of the qualified kelp product appearance color image data set with boundaries When searching the space, the appearance color search for jellyfish returns to the qualified kelp product appearance color image data set. The opposite search area in the search space continues to search for the image data of the kelp product sample appearance Matching qualified kelp product appearance color image data , the appearance color search jellyfish moving boundary condition satisfies the following formula:

[0092] ,in Search for jellyfish individuals by appearance color exist The updated position in the dimensional space, that is, in the space with the dimension A collection of qualified kelp product appearance color image data The updated position in the search space, Search for jellyfish individuals by appearance color exist The position before updating in the dimension space, that is, in the space dimension A collection of qualified kelp product appearance color image data The position before the update in the search space;

[0093] S325. When the algorithm meets the maximum iteration number data, output the appearance image data of the kelp product sample Appearance color image data of qualified kelp products The image feature matching results are used to generate a data set for the appearance color analysis of kelp product samples ,in Indicates the appearance image data of kelp product samples Corresponding kelp product sample appearance color analysis data;

[0094] when and If the image feature matching is successful, it means that the appearance color of the kelp product sample is qualified, and the appearance color analysis data of the kelp product sample is output. To be qualified;

[0095] when and If the image feature matching fails, it means that the appearance color of the kelp product sample is unqualified, then the appearance color analysis data of the kelp product sample is output. Is unqualified.

[0096] Based on the appearance color analysis data of kelp product samples, weight numerical measurement processing of qualified appearance color samples of kelp products is performed to generate weight data of qualified appearance color samples of kelp products and analyze the appearance color quality grade of kelp products with the weight threshold of qualified appearance color samples of high-quality kelp products. The operation steps for constructing appearance color quality grade data of kelp products are as follows:

[0097] S41. Establish the weight threshold of qualified appearance color samples of high-quality kelp products , the weight threshold of qualified appearance color samples of high-quality kelp products indicates the minimum value of qualified appearance color samples of high-quality kelp products in the weight of all appearance samples;

[0098] S42, using breadth-first search algorithm in kelp product sample appearance color analysis data set Search for kelp product sample appearance color analysis data The number of qualified kelp product samples and the number of qualified appearance color samples of kelp products are generated ;

[0099] S43. The number of qualified appearance color samples of kelp products Total number of kelp product samples Perform numerical quotient measurement processing to generate weight data of qualified appearance color samples of kelp products ,in ;

[0100] S44. Weight data of qualified appearance color samples of kelp products Weight threshold of qualified appearance color samples of high-quality kelp products Perform numerical comparison and construct the kelp product appearance color quality grade data based on the numerical comparison results ;

[0101] when ≥ , indicating that the appearance color of the kelp product meets the weight requirements of qualified appearance color samples in the appearance color of high-quality kelp products, then the appearance color quality grade data of the kelp product is output is level one;

[0102] when < , indicating that the appearance color of the kelp product does not meet the weight requirements of qualified appearance color samples in the appearance color of high-quality kelp products, then the appearance color quality grade data of the kelp product is output It is level two.

[0103] The steps of performing body defect feature recognition processing on the kelp product sample according to the appearance image data of the kelp product sample and the body defect feature image data of the kelp product sample to generate body defect recognition data of the kelp product sample are as follows:

[0104] S51. Establishing a dataset of defect feature images of kelp products , ;in Indicates The defect feature image data of kelp products. Indicates the maximum number of kelp product body defect types, including body scratches, body black spots, body holes and body breaks;

[0105] S52, Appearance color quality grade data of kelp products When the construction is completed, the unified cost search algorithm is used to collect the appearance image data of kelp product samples Zhonghai kelp product sample appearance image data The image data set of kelp product samples is sorted by number according to the appearance image quantity and the image data set of kelp product body defects Image data of defects of Zhonghai kelp products Perform image feature matching and generate a kelp product sample body defect recognition data set based on the image feature matching results ,in Indicates the appearance image data of kelp product samples Corresponding kelp product sample body defect identification data;

[0106] when and If the image feature matching is successful, it means that the kelp product sample has the belt defect feature, then the kelp product sample belt defect recognition data is output. for existence;

[0107] when and If the image feature matching fails, it means that the kelp product sample does not have the belt defect feature, then the kelp product sample belt defect recognition data is output. Does not exist.

[0108] Based on the kelp product sample strip defect identification data, weighted numerical measurement processing is performed on the kelp product strip defect-free samples, weighted data on kelp product strip defect-free samples is generated, and the strip quality grade of kelp products is analyzed and processed with the weight threshold of high-quality kelp product strip defect-free samples. The operation steps for constructing the kelp product strip quality grade data are as follows:

[0109] S61. Establish a sample weight threshold for high-quality kelp products without body defects , the weight threshold of samples without body defects in high-quality kelp products indicates the minimum weight of samples without body defects in high-quality kelp products among all appearance samples;

[0110] S62, using breadth-first search algorithm in kelp product sample belt defect recognition data set Search for kelp product sample kelp body defect identification data The number of kelp product samples that do not exist, and the number of kelp product samples without body defects ;

[0111] S63. Number of samples of kelp products without body defects Total number of kelp product samples Perform numerical quotient measurement processing to generate sample weight data for kelp products without kelp body defects ,in ;

[0112] S64. Weight data of samples without kelp body defects Sample weight threshold for high-quality kelp products without body defects Carry out numerical comparison and construct the kelp product body quality grade data based on the numerical comparison results ;

[0113] when ≥ , indicating that the kelp product strip defect meets the sample weight requirement of no strip defect in the high-quality kelp product strip defect, then the kelp product strip quality grade data is output is level one;

[0114] when < , indicating that the kelp product strip defect does not meet the sample weight requirement of the high-quality kelp product strip defect, then the kelp product strip quality grade data is output. It is level two.

[0115] Through the cooperation between the kelp product sample appearance color analysis unit and the kelp product sample strip defect recognition unit, the artificial intelligence algorithm is used to perform intelligent and standardized analysis of the appearance color and strip defects of the kelp product image samples, thereby improving the accuracy and efficiency of kelp product quality sorting and color selection; the kelp product qualified appearance color sample weight measurement unit and the kelp product non-strip defect sample weight measurement unit cooperate with each other to digitally count the weights of qualified appearance color samples and samples without strip defects in the kelp product image samples, thereby realizing accurate quantitative processing of the appearance color and strip defect characteristics in kelp products; the kelp product appearance color quality grade analysis unit and the kelp product strip quality grade analysis unit cooperate with each other to compare the kelp product appearance color weight and strip defect weight with the appearance color and strip quality thresholds, scientifically analyze the appearance color and strip quality grade of the kelp product, thereby improving the scientific nature of kelp quality sorting and color selection.

[0116] For further information, see Figure 1 - Figure 2 Based on the quality grade parameters of kelp product appearance color and strip body and the kelp product quality grade data corresponding to different appearance colors and strip body quality grades, the quality grade of kelp products is comprehensively judged and processed, and the quality grade data of kelp products is constructed and the operation steps of kelp product quality sorting operation are as follows:

[0117] S71. Establish a data set of kelp product quality grades corresponding to different appearance colors and kelp body quality grades ,in Indicates the appearance, color and quality grade data of kelp products Quality grade data of kelp products The corresponding data of kelp product quality grades for different appearance colors and kelp body quality grades are as follows: , Indicates that the quality grade of kelp products is Grade One; Indicates the appearance, color and quality grade data of kelp products Grade 1, kelp product quality grade data Grade II or when the kelp product appearance color quality grade data Grade II, kelp product quality grade data The corresponding data of different appearance colors and kelp quality grades when it is level 1 are as follows: , Indicates that the quality grade of kelp products is Level 2; Indicates the appearance, color and quality grade data of kelp products Quality grade data of kelp products The corresponding data of kelp product quality grades for different appearance colors and kelp body quality grades are as follows: , It means that the quality grade of kelp products is Grade III;

[0118] S72, the kelp product appearance color quality grade data constructed by steps S44 and S64 Quality grade data of kelp products Data set of kelp product quality grades corresponding to different appearance colors and kelp body quality grades The quality grade data of kelp products corresponding to different appearance colors and strip quality grades in the search results are matched by the keywords of appearance color and strip quality grade, and the appearance color quality grade data of kelp products are searched. Quality grade data of kelp products The corresponding different appearance colors and belt quality grades correspond to the kelp product quality grade data and the kelp product quality grade data is constructed through data identification. ;

[0119] S73, based on kelp product quality grade data The kelp products are sorted by quality through the material conveyor belt.

[0120] Through the cooperation of the kelp product quality grade storage unit corresponding to different appearance colors and strip quality grades and the kelp product quality grade comprehensive judgment unit, the standard preset kelp product quality grade data corresponding to different appearance colors and strip quality grades and the kelp product appearance color and strip quality grade parameters are used to make a comprehensive judgment on the kelp product quality grade, and the kelp product quality grade parameters are efficiently and accurately constructed to realize the intelligent evaluation of the kelp product quality grade; the kelp product quality sorting operation execution unit efficiently and intelligently executes the kelp product quality classification and sorting operations based on the kelp product quality grade parameters, thereby improving the efficiency and accuracy of kelp product quality sorting.

[0121] Embodiment 2:

[0122] See also Figure 1 - Figure 2 , a kelp quality sorting and color sorting management system based on artificial intelligence is used to implement a kelp quality sorting and color sorting management method. The system includes a kelp product color sorting information processing module, a kelp product color and kelp body quality grade evaluation module, and a kelp product quality evaluation and sorting execution module;

[0123] The kelp product color sorting information processing module includes a kelp product appearance feature image acquisition unit, a kelp product appearance feature image preprocessing unit, and a kelp product sample appearance image generation unit;

[0124] A kelp product appearance feature image acquisition unit, which acquires kelp product appearance feature image data through a cloud lens; a kelp product appearance feature image preprocessing unit, which performs image preprocessing on the kelp product appearance feature image data to generate standard kelp product appearance feature image data; a kelp product sample appearance image generation unit, which performs kelp product appearance image sample generation processing on the standard kelp product appearance feature image data to generate kelp product sample appearance image data;

[0125] The kelp product color and body quality grade evaluation module includes a qualified kelp product appearance color image storage unit, a kelp product sample appearance color analysis unit, a kelp product qualified appearance color sample weight measurement unit, a high-quality kelp product qualified appearance color sample weight threshold storage unit, and a kelp product appearance color quality grade analysis unit; a kelp product body defect feature image storage unit, a kelp product sample body defect recognition unit, a kelp product non-body defect sample weight measurement unit, a high-quality kelp product non-body defect sample weight threshold storage unit, and a kelp product body quality grade analysis unit;

[0126] A qualified kelp product appearance color image storage unit is used to store qualified kelp product appearance color image data; a kelp product sample appearance color analysis unit performs appearance color feature analysis on kelp product sample appearance image data and qualified kelp product appearance color image data based on artificial intelligence to generate kelp product sample appearance color analysis data; a kelp product qualified appearance color sample weight measurement unit performs weight value measurement processing on kelp product qualified appearance color samples based on kelp product sample appearance color analysis data to generate kelp product qualified appearance color sample weight data; a high-quality kelp product qualified appearance color sample weight threshold storage unit is used to store the weight threshold of high-quality kelp product qualified appearance color samples; a kelp product appearance color quality grade analysis unit performs appearance color quality grade analysis on kelp product based on the weight data of kelp product qualified appearance color samples and the weight threshold of high-quality kelp product qualified appearance color samples to construct a kelp product appearance color quality grade. data; a kelp product body defect feature image storage unit, used to store kelp product body defect feature image data; a kelp product sample body defect recognition unit, which performs body defect feature recognition processing of kelp product samples according to kelp product sample appearance image data and kelp product body defect feature image data, and generates kelp product sample body defect recognition data; a kelp product body defect sample weight measurement unit, which performs weight value measurement processing of kelp product body defect sample based on kelp product sample body defect recognition data, and generates kelp product body defect sample weight data; a high-quality kelp product body defect sample weight threshold storage unit, which is used to store the high-quality kelp product body defect sample weight threshold; a kelp product body quality grade analysis unit, which performs body quality grade analysis processing of kelp product body defect sample weight data and high-quality kelp product body defect sample weight threshold, and constructs kelp product body quality grade data;

[0127] The kelp product quality assessment and sorting execution module includes a kelp product quality grade storage unit corresponding to different appearance colors and kelp body quality grades, a kelp product quality grade comprehensive judgment unit, and a kelp product quality sorting operation execution unit;

[0128] The storage unit for kelp product quality grades corresponding to different appearance colors and strip quality grades is used to store the quality grade data of kelp products corresponding to different appearance colors and strip quality grades; the comprehensive judgment unit for kelp product quality grade performs comprehensive judgment processing on the quality grade of kelp products based on the quality grade parameters of the appearance color and strip of kelp products and the quality grade data of kelp products corresponding to different appearance colors and strip quality grades, and constructs the quality grade data of kelp products; the execution unit for kelp product quality sorting operations executes kelp product quality sorting operations according to the quality grade data of kelp products.

[0129] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for managing kelp quality sorting and color selection, characterized in that: The method comprises the following steps: S1, collecting kelp product appearance feature image data and performing image preprocessing to generate standard kelp product appearance feature image data; S2, performing a process of generating a kelp product appearance image sample on the standard kelp product appearance feature image data to generate kelp product sample appearance image data; S3, performing appearance color feature analysis processing on the kelp product sample according to the appearance image data of the kelp product sample and the appearance color image data of the qualified kelp product, and generating appearance color analysis data of the kelp product sample; S4, based on the kelp product sample appearance color analysis data, weight numerical measurement processing is performed on the kelp product qualified appearance color samples, weight data of kelp product qualified appearance color samples is generated, and the appearance color quality grade analysis processing of the kelp product is performed with the weight threshold of the qualified appearance color samples of high-quality kelp products, so as to construct the kelp product appearance color quality grade data; S5, performing body defect feature recognition processing of the kelp product sample according to the appearance image data of the kelp product sample and the body defect feature image data of the kelp product, and generating body defect recognition data of the kelp product sample; S6, based on the kelp product sample body defect identification data, weight numerical measurement processing of kelp product body defect-free samples is performed to generate weight data of kelp product body defect-free samples, and the kelp product body quality grade analysis processing is performed with the weight threshold of high-quality kelp product body defect-free samples to construct kelp product body quality grade data; S7. Perform comprehensive judgment on the quality grade of kelp products based on the quality grade parameters of kelp product appearance color and strip body and the kelp product quality grade data corresponding to different appearance colors and strip body quality grades, construct kelp product quality grade data and perform kelp product quality sorting operations.

2. The kelp quality sorting and color selection management method according to claim 1 is characterized in that: The S1 comprises the following steps: S11. Collect the appearance feature images of the kelp products passing through the monitoring port online through the cloud camera installed on the material conveyor belt, and generate the appearance feature image data of the kelp products ; S12, using an adaptive filtering method to filter the kelp product appearance feature image data After image noise reduction preprocessing, the standard kelp product appearance feature image data is generated .

3. The kelp quality sorting and color selection management method according to claim 2 is characterized in that: The S2 comprises the following steps: S21, the standard kelp product appearance feature image data A square with a side length of L is used to perform grid division processing on the appearance feature image, and a data set of kelp product sample appearance images is generated. , ;in Indicates Appearance image data of kelp product samples, Indicates the total number of kelp product samples.

4. The kelp quality sorting and color selection management method according to claim 3 is characterized in that: The S3 comprises the following steps: S31. Establishing a data set of appearance color images of qualified kelp products , ;in Indicates Appearance color image data of qualified kelp products, Indicates the maximum number of appearance color types of qualified kelp products; S32, collecting the kelp product sample appearance image data set Appearance image data of kelp product samples described in The qualified kelp product appearance color image data set is ordered according to the number of kelp product sample appearance images. Appearance color image data of qualified kelp products described in Perform image feature matching and generate a data set of kelp product sample appearance color analysis based on the image feature matching results , execute to generate kelp product sample appearance color analysis data set The specific steps are as follows: S321, initializing the algorithm iteration data and executing the ocean current simulation process; setting the maximum algorithm iteration data to T; S322, during the algorithm execution process, the appearance color search jellyfish group appearance color search jellyfish in the qualified kelp product appearance color image data set Searching for image data related to the appearance of kelp product samples by moving around their own positions in the search space Matching qualified kelp product appearance color image data and update the location; S323, algorithm execution, appearance color search, appearance color search, jellyfish group, appearance color search, jellyfish in qualified kelp product appearance color image data set Search the search space for the image data related to the appearance of kelp product samples Matching qualified kelp product appearance color image data The duration of the behavior; S324, the algorithm executes to meet the boundary conditions, when the appearance color search jellyfish moves out of the qualified kelp product appearance color image data set with boundaries When searching the space, the appearance color search for jellyfish returns to the qualified kelp product appearance color image data set. The opposite search area in the search space continues to search for the image data of the kelp product sample appearance Matching qualified kelp product appearance color image data ; S325: When the algorithm meets the maximum iteration number data, output the appearance image data of the kelp product sample Image data of the appearance and color of the qualified kelp products The image feature matching results are used to generate a data set for the appearance color analysis of kelp product samples ,in Indicates the appearance image data of kelp product samples Corresponding kelp product sample appearance color analysis data; when and If the image feature matching is successful, the appearance color analysis data of the kelp product sample will be output. To be qualified; when and If the image features are not matched successfully, the appearance color analysis data of the kelp product sample will be output. Is unqualified.

5. The kelp quality sorting and color selection management method according to claim 4 is characterized in that: The S4 comprises the following steps: S41. Establish the weight threshold of qualified appearance color samples of high-quality kelp products ; S42, using a breadth-first search algorithm to analyze the appearance color data set of the kelp product sample Search for the appearance color analysis data of the kelp product sample The number of qualified kelp product samples and the number of qualified appearance color samples of kelp products are generated ; S43, the number of qualified appearance color samples of the kelp product Total number of kelp product samples Perform numerical quotient measurement processing to generate weight data of qualified appearance color samples of kelp products ,in ; S44, the weight data of qualified appearance color samples of the kelp product Weight threshold of qualified appearance color samples of high-quality kelp products Perform numerical comparison and construct the kelp product appearance color quality grade data based on the numerical comparison results ; when ≥ , then output the kelp product appearance color quality grade data is level one; when < , then output the kelp product appearance color quality grade data It is level two.

6. The kelp quality sorting and color selection management method according to claim 5 is characterized in that: The S5 comprises the following steps: S51. Establishing a dataset of defect feature images of kelp products , ;in Indicates The defect feature image data of kelp products. It indicates the maximum value of the number of defect types of kelp products; S52, when the kelp product appearance color quality grade data When the construction is completed, the kelp product sample appearance image data set is collected using a unified cost search algorithm Appearance image data of kelp product samples described in The image data set of the kelp product body defect feature is ordered according to the number of the kelp product sample appearance images. The kelp product body defect feature image data described in Perform image feature matching and generate a kelp product sample body defect recognition data set based on the image feature matching results ,in Indicates the appearance image data of kelp product samples Corresponding kelp product sample body defect identification data; when and If the image feature matching is successful, the kelp product sample body defect recognition data will be output. for existence; when and If the image features are not matched successfully, the kelp product sample body defect recognition data is output Does not exist.

7. The kelp quality sorting and color selection management method according to claim 6 is characterized in that: The S6 comprises the following steps: S61. Establish a sample weight threshold for high-quality kelp products without body defects ; S62, using a breadth-first search algorithm to identify the kelp product sample body defect data set Search for the kelp product sample's kelp body defect identification data The number of kelp product samples that do not exist, and the number of kelp product samples without body defects ; S63, the number of samples of the kelp product without body defects Total number of kelp product samples Perform numerical quotient measurement processing to generate sample weight data for kelp products without kelp body defects ,in ; S64, the kelp product does not have the weight data of the sample with body defects The sample weight threshold for the high-quality kelp products without body defects Carry out numerical comparison and construct the kelp product body quality grade data based on the numerical comparison results ; when ≥ , then output the kelp product body quality grade data is level one; when < , then output the kelp product body quality grade data It is level two.

8. The kelp quality sorting and color selection management method according to claim 7 is characterized in that: The S7 comprises the following steps: S71. Establish a data set of kelp product quality grades corresponding to different appearance colors and kelp body quality grades ,in Indicates the appearance color quality grade data of the kelp product And the kelp product body quality grade data The corresponding data of kelp product quality grades for different appearance colors and kelp body quality grades are as follows: , Indicates that the quality grade of kelp products is Grade One; Indicates the appearance color quality grade data of the kelp product The kelp product body quality grade data is Grade II or when the kelp product appearance color quality grade data The kelp product body quality grade data is The corresponding data of different appearance colors and kelp quality grades when it is level 1 are as follows: , Indicates that the quality grade of kelp products is Level 2; Indicates the appearance color quality grade data of the kelp product And the kelp product body quality grade data The corresponding data of kelp product quality grades for different appearance colors and kelp body quality grades are as follows: , It means that the quality grade of kelp products is Grade III; S72, the kelp product appearance color quality grade data constructed in step S44 and step S64 And the kelp product body quality grade data A data set of kelp product quality grades corresponding to the different appearance colors and kelp body quality grades The kelp product quality grade data corresponding to the different appearance colors and belt quality grades described in the above are matched according to the appearance color and belt quality grade keywords to search for the kelp product appearance color quality grade data And the kelp product body quality grade data The corresponding different appearance colors and belt body quality grades correspond to the kelp product quality grade data and the kelp product quality grade data is constructed through data identification. ; S73, according to the kelp product quality grade data The kelp products are sorted by quality through the material conveyor belt.

9. An artificial intelligence-based kelp quality sorting and color sorting management system, used to implement the kelp quality sorting and color sorting management method according to any one of claims 1 to 8, characterized in that: The system comprises a kelp product color sorting information processing module, a kelp product color and kelp body quality grade evaluation module, and a kelp product quality evaluation and sorting execution module.

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