Kelp drying parameter self-adaptive regulation and control system and method based on AI algorithm

Abstract

The invention relates to the technical field of kelp drying control, and discloses a kelp drying parameter self-adaptive regulation and control system and method based on an AI algorithm, and the system comprises a kelp drying process selection module, a kelp drying stage evaluation module, and a kelp drying temperature regulation module. Kelp drying stage dynamic judgment processing is carried out on the basis of kelp drying real-time humidity parameters, kelp drying accumulated duration parameters, an intelligent recognition algorithm and scientifically preset drying humidity time parameters of different kelp drying stages, and dynamic intelligent monitoring of the kelp drying stages in the kelp drying process is achieved. Accurate matching of drying temperatures required by different kelp drying stages is performed according to judgment result information of the kelp drying stages in combination with an artificial intelligence recognition algorithm and scientifically preset drying temperature parameters of different kelp drying stages, so that scientific adjustment of the kelp drying temperature parameters is realized, and AI-based adaptive adjustment of the kelp drying temperature is realized; and the kelp drying quality is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of kelp drying control, and specifically to an adaptive regulation system and method for kelp drying parameters based on an AI algorithm. Background Art

[0002] The kelp drying process can be divided into two categories: natural sun drying and mechanical drying according to the technical path. There are significant differences in efficiency, quality, and applicable scenarios between different methods. The specific technical points are as follows: The natural sun drying method includes the traditional ground sun drying and the improved three-dimensional hanging sun drying process; among them, the traditional ground sun drying is to spread the kelp flat on the drying ground after cleaning and turn it regularly until it is completely dry, which takes 3 - 7 days. The traditional ground sun drying depends on sunny weather, is easily contaminated by flies and insects, has a high sediment adhesion rate, and requires a large site. The improved three-dimensional hanging sun drying process; uses the terrain height difference to erect steel cables, and suspends the kelp on the mountainside through electric pulleys to form a stacked drying structure. The improved three-dimensional hanging sun drying process has improved ventilation, shortened the cycle to 1.5 - 2 days; avoided ground contact, reduced sediment pollution, and maintained cleanliness and dryness. The mechanical drying method includes the heat pump drying technology and the electric oven drying method; among them, the process flow of the heat pump drying technology includes the heating stage (50 - 55°C): 1.5 - 3 hours, the high humidity and low temperature stage (50 - 60°C): 1.5 - 3 hours, with intermittent moisture discharge; the medium humidity and medium temperature stage (50 - 60°C): 3 - 3.5 hours, adjusting the inlet / return air valves to control the humidity; the shaping stage (70 - 80°C): continuing until completely dry, with a total duration of 8 - 12 hours. The heat pump drying technology has multi-temperature zone hierarchical temperature control, uniform quality; low energy consumption and supports automatic operation. The electric oven drying method passes through pretreatment: cut and spread evenly to avoid stacking; drying parameters: initially 60 - 70°C (2 - 3 hours), then raise the temperature to 70 - 80°C (3 - 4 hours), with a total duration of 5 - 7 hours; turning requirements: manually adjust every 1 - 2 hours to ensure uniform heating; in the existing kelp heat pump drying process, it is impossible to accurately judge the kelp drying stage based on the drying humidity and drying time, nor can it adaptively control the drying temperature of the heat pump system based on the kelp drying stage, reducing the efficiency and quality of kelp drying.

[0003] The Chinese invention patent with the publication number CN112524800B and the publication date of March 8, 2022 discloses a control method, device, and storage medium for a heat pump drying device, which controls the compressor output load of the heat pump system by matching the baking control strategy corresponding to the baking treatment stage; according to the fresh air control strategy corresponding to the drying control process, controls the proportional opening of the fresh air valve of the fresh air moisture discharge device to adjust the humidity in the baking room. However, the above technical solutions cannot intelligently analyze the baking treatment stage of the heat pump drying device. Summary of the Invention

[0004] (1) Technical problems to be solved

[0005] To solve the problem that the existing kelp heat pump drying process cannot accurately judge the drying stage of kelp based on the drying humidity and drying time, nor can it adaptively control the drying temperature of the heat pump system based on the drying stage of kelp, which reduces the efficiency and quality of kelp drying. The purpose is to realize the personalized matching of kelp drying process information, intelligently and real-time judge the drying stage of kelp, accurately control the drying temperature of kelp, realize the adaptive intelligent control of the kelp drying process, and improve the quality and efficiency of kelp drying.

[0006] (2) Technical solutions

[0007] The present invention is realized through the following technical solutions: an adaptive regulation method for kelp drying parameters based on an AI algorithm, and the method includes the following steps:

[0008] S1. Collect characteristic image data of kelp to be dried;

[0009] S2. Identify the cutting appearance shape type of the kelp to be dried according to the characteristic image data of the kelp to be dried and the characteristic image data of kelp with different cutting shapes, and generate identification data of the cutting shape type of the kelp to be dried;

[0010] S3. Match the drying process plan of the kelp to be dried based on the identification data of the cutting shape type of the kelp to be dried and the drying process data of kelp with different cutting shapes, and generate matching data of the drying process of the kelp to be dried;

[0011] S4. Collect and process the drying humidity and cumulative drying time of the kelp drying process according to the matching data of the drying process of the kelp to be dried, and generate real-time humidity data of kelp drying and cumulative drying duration data of kelp respectively;

[0012] S5. Judge the drying stage of the kelp drying operation based on the real-time humidity data of kelp drying, the cumulative drying duration data of kelp, and the drying humidity-time data of different kelp drying stages, and generate judgment data of the kelp drying stage;

[0013] S6. Analyze the drying temperature required for different drying stages of kelp based on the judgment data of the kelp drying stage and the drying temperature data of different kelp drying stages, and generate analysis data of the drying temperature of the kelp drying operation;

[0014] S7. Construct real-time temperature adjustment data for kelp drying and perform temperature adjustment operations during the kelp drying process.

[0015] Preferably, the operation steps for collecting the characteristic image data of the kelp to be dried are as follows:

[0016] S11. Install a camera lens at the inlet of the heat pump drying system to collect the image information of the cut appearance shape of the kelp to be dried online, and generate the characteristic image data of the kelp to be dried. The characteristic image data of the kelp to be dried represents the appearance shape image information after segmentation and preprocessing before drying the kelp to be dried.

[0017] Preferably, the operation steps of identifying the cutting appearance shape type of the kelp to be dried based on the characteristic image data of the kelp to be dried and the characteristic image data of kelp with different cutting shapes, and generating the identification data of the cutting shape type of the kelp to be dried are as follows:

[0018] S21. Establish a set of characteristic image data of kelp with different cutting shapes , ; where represents the characteristic image data of kelp with different cutting shapes corresponding to the th kelp segmentation shape type, represents the maximum value of the number of kelp segmentation shape types; the kelp segmentation shape types include strip shape, block shape and flake shape; the characteristic image data of kelp with different cutting shapes represents the standard kelp appearance characteristic image information set for different kelp segmentation shape types;

[0019] S22. Use the ORB image feature matching algorithm to match the characteristic image data of the kelp to be dried with the characteristic image data of kelp with different cutting shapes in the set of characteristic image data of kelp with different cutting shapes to perform kelp image feature matching, search for the text information of the kelp segmentation shape type corresponding to the characteristic image data of kelp with different cutting shapes matched with the characteristic image data of the kelp to be dried , and generate the identification data of the cutting shape type of the kelp to be dried through data identification . .

[0020] Preferably, the operation steps of matching the drying process plan of the kelp to be dried based on the identification data of the cutting shape type of the kelp to be dried and the drying process data of kelp with different cutting shapes, and generating the matching data of the drying process of the kelp to be dried are as follows:

[0021] S31. Establish a set of drying process data of kelp with different cutting shapes , where represents the The drying process data of kelp with different cutting shapes corresponding to the kelp segmentation shape types, and the characteristic image data of the kelp with different cutting shapes represents the optimal kelp heat pump drying process plan information for the kelp heat pump drying treatment set for different kelp segmentation shape types; wherein the optimal kelp heat pump drying process plan information includes the operation steps of kelp heat pump drying and the equipment information required for kelp heat pump drying processing;

[0022] S32. Use the Sorensen-Morris-Pratt search algorithm to identify the data of the cutting shape type of the kelp to be dried and the set of drying process data of kelp with different cutting shapes in the drying process data of kelp with different cutting shapes for character matching of the kelp segmentation shape type, and search for the drying process data of the kelp with different cutting shapes corresponding to the data of the cutting shape type of the kelp to be dried and construct the drying process matching data of the kelp to be dried .

[0023] Preferably, the operation steps of collecting and processing the drying humidity and the cumulative drying time in the kelp drying process according to the drying process matching data of the kelp to be dried to generate the real-time humidity data of kelp drying and the cumulative drying duration data of kelp are as follows:

[0024] S41. The heat pump drying management terminal controls the heat pump drying system to perform drying operations on the kelp to be dried according to the drying process matching data of the kelp to be dried . During the kelp drying operation, the humidity parameter of the flowing air inside the heat pump drying system and the cumulative time length parameter of the kelp drying operation are collected online through a humidity sensor and a time counting module, and the real-time humidity data of kelp drying and the cumulative drying duration data of kelp are generated respectively, where is in grams per cubic meter, is in hours.

[0025] Preferably, the operation steps of performing drying stage judgment processing on the kelp drying operation based on the real-time humidity data of kelp drying, the cumulative drying duration data of kelp and the drying humidity time data of different kelp drying stages to generate the drying stage judgment data of kelp are as follows:

[0026] S51. Establish a set of drying humidity time data for different kelp drying stages , ; where represents the different drying humidity time data of the th type of kelp drying stage type,​ Represents the maximum value of the number of types of kelp drying stages; , where Represents the th type of kelp drying stage type corresponding to the drying humidity range of the kelp drying stage, , where and respectively represent the minimum drying humidity and the maximum drying humidity of the kelp drying stage in the drying humidity range of the kelp drying stage , and the units of and are both grams per cubic meter; where Represents the th type of kelp drying stage type corresponding to the drying time range of the kelp drying stage, , where and respectively represent the minimum drying time and the maximum drying time of the kelp drying stage in the drying time range of the kelp drying stage , and the units of and are both hours. The drying humidity and time data of different kelp drying stages represent the information on the standard drying humidity range of the air flowing inside the heat pump drying system and the information on the standard cumulative drying duration range during the heat pump drying process of kelp for different types of kelp drying stages; the types of kelp drying stages include a heating stage, a high-humidity and low-temperature constant-speed drying and moisture exhaust stage, a medium-humidity and medium-temperature constant-speed drying and moisture exhaust stage, and a shaping stage;

[0027] S52. Compare the real-time kelp drying humidity data and the cumulative kelp drying duration data with the drying humidity and time data of different kelp drying stages in the set of drying humidity and time data of different kelp drying stages respectively, and compare the drying humidity range and the drying time range of the kelp drying stage in the drying humidity and time data of different kelp drying stages to search for the text information of the kelp drying stage corresponding to the drying humidity and time data of different kelp drying stages and that match the real-time kelp drying humidity data , and generate kelp drying stage judgment data through data identification.

[0028] Preferably, according to the kelp drying stage judgment data and the drying temperature data of different kelp drying stages, the operation steps for analyzing the drying temperature required for different kelp drying stages to generate the drying temperature analysis data for the kelp drying operation are as follows:

[0029] S61. Establish a set of drying temperature data for different kelp drying stages , where represents the drying temperature data of different kelp drying stages corresponding to the th type of kelp drying stage. Among them, the unit of is degree Celsius, and the drying temperature data of different kelp drying stages represents the optimal kelp heat pump drying temperature parameters set for different kelp drying stage types;

[0030] S62. Perform keyword matching of the kelp drying stage judgment data with the drying temperature data of different kelp drying stages in the set of drying temperature data of different kelp drying stages to search for the drying temperature data of different kelp drying stages corresponding to the kelp drying stage judgment data , and construct the drying temperature analysis data for the kelp drying operation . The operation steps for constructing the drying temperature analysis data for the kelp drying operation are as follows: S621. Parameter initialization: The population size of the drying temperature search bats is

[0031] , the maximum number of iterations is T, the objective function is F, the position of the drying temperature search bat individual in the search space of the set of drying temperature data of different kelp drying stages is , and the speed is , the sound wave frequency is , the sound wave loudness is , and the frequency is , and the sound wave loudness is and the frequency is ;

[0032] S622. Search for the position of the optimal drying temperature search bat individual in the population of drying temperature search bats in the search space of the set of drying temperature data of different kelp drying stages , that is, the population of drying temperature search bats searches for the drying temperature data of different kelp drying stages that is most matched with the kelp drying stage judgment data in the search space of the set of drying temperature data of different kelp drying stages , and the drying temperature data of different kelp drying stages most matched with the kelp drying stage judgment data The position is determined, and the speed and position are updated. The speed and position update formulas are as follows: , , where represents the drying temperature search bat individual at iterations later, the speed in the search space of the drying temperature data set for different kelp drying stages ; represents the drying temperature search bat individual at iterations later, the speed in the search space of the drying temperature data set for different kelp drying stages ; represents the drying temperature search bat individual at iterations later, the position in the search space of the drying temperature data set for different kelp drying stages ; represents the drying temperature search bat individual at iterations later, the position in the search space of the drying temperature data set for different kelp drying stages ;

[0033] S623. Generate random numbers rand1 and rand1 is a random number in the interval [0, 1]. When rand1 > , select an optimal drying temperature search bat individual from the best drying temperature search bat individuals, and search in the search space of the drying temperature data set for different kelp drying stages for the position of the different kelp drying stage drying temperature data that best matches the kelp drying stage judgment data . Near the selected optimal drying temperature search bat individual, through the formulas , , generate a local solution, and search in the search space of the drying temperature data set for different kelp drying stages for the position of the different kelp drying stage drying temperature data that best matches the kelp drying stage judgment data , where represents the sound intensity of the drying temperature search bat individual at iterations later in the search space of the drying temperature data set for different kelp drying stages ; represents the drying temperature search bat individual at After the iterations, the drying temperature data set at different kelp drying stages The frequency in the search space of Indicates drying temperature to search for individual bats exist After the iterations, the drying temperature data set at different kelp drying stages The initial frequency in the search space of ; otherwise, according to the formula , , update the drying temperature to search for bat positions, and collect drying temperature data at different kelp drying stages Update the search space to search for the kelp drying stage judgment data Matching drying temperature data of different kelp drying stages The location of Indicates drying temperature to search for individual bats exist After the iterations, the drying temperature data set at different kelp drying stages The loudness of the sound wave in the search space of Indicates drying temperature to search for individual bats exist After the iterations, the drying temperature data set at different kelp drying stages The frequency in the search space of Indicates value The random function of and is a constant and 0﹤ ﹤1, >0;

[0034] S624, generate another random number rand2, rand2 is a random number on [0, 1]; when rand2< , and the fitness of the objective function F is better than the new solution in S623, in the drying temperature data set of different kelp drying stages Search the search space to find the data related to the kelp drying stage judgment Matching drying temperature data of different kelp drying stages The fitness is better than the drying temperature data of different kelp drying stages matched in S623 , then accept the drying temperature data of different kelp drying stages Drying temperature data set at different kelp drying stages The position of the search space is updated, and the position update formula is as follows: , and according to the formula , , synchronous adjustment and ; where represents the search bat individual for the drying temperature at a new position in the search space of the drying temperature data set for different seaweed drying stages ; represents the search bat individual for the drying temperature at an old position in the search space of the drying temperature data set for different seaweed drying stages ; represents any number within the range [-1, 1];

[0035] S625. Sort the fitness values of all individuals in the search bat population for the drying temperature, and find the current best , and search for the position of the drying temperature data for different seaweed drying stages in the search space that best matches the seaweed drying stage judgment data among the drying temperature data for different seaweed drying stages ;

[0036] S626. Repeat steps S622 to S625. When the maximum number of iterations T is satisfied, output the drying temperature data for different seaweed drying stages that matches the seaweed drying stage judgment data ;

[0037] S627. Based on the drying temperature data for different seaweed drying stages output in step S626 that matches the seaweed drying stage judgment data and through data identification, construct the drying temperature analysis data for the seaweed drying operation , where the unit of is degrees Celsius.

[0038] Preferably, the operation steps for constructing the real-time temperature adjustment data for seaweed drying and performing the temperature adjustment operation during the seaweed drying process are as follows:

[0039] S71. Through data identification, construct the real-time temperature adjustment data for seaweed drying from the drying temperature analysis data for the seaweed drying operation , where the unit of is degrees Celsius;

[0040] S72. The heat pump drying management terminal adjusts the drying temperature of the seaweed inside the heat pump drying system according to the real-time temperature adjustment data for seaweed drying to perform the temperature adjustment operation during the seaweed drying process.

[0041] An adaptive regulation system for kelp drying parameters based on AI algorithms, which is used to implement the adaptive regulation method for kelp drying parameters based on AI algorithms. The system includes a kelp drying process selection module, a kelp drying stage evaluation module, and a kelp drying temperature adjustment module;

[0042] The kelp drying process selection module includes a characteristic image acquisition unit for kelp to be dried, a storage unit for characteristic images of kelp with different cutting shapes, a recognition unit for the cutting shape of kelp to be dried, a storage unit for drying processes of kelp with different cutting shapes, and a matching unit for kelp drying processes;

[0043] The characteristic image acquisition unit for kelp to be dried collects characteristic image data of kelp to be dried through a shooting lens; the storage unit for characteristic images of kelp with different cutting shapes is used to store characteristic image data of kelp with different cutting shapes; the recognition unit for the cutting shape of kelp to be dried performs recognition processing on the cutting appearance shape type of kelp to be dried based on the characteristic image data of kelp to be dried and the characteristic image data of kelp with different cutting shapes, and generates recognition data for the cutting shape type of kelp to be dried; the storage unit for drying processes of kelp with different cutting shapes is used to store drying process data of kelp with different cutting shapes; the matching unit for kelp drying processes performs matching processing on the drying process plan of kelp to be dried based on the recognition data for the cutting shape type of kelp to be dried and the drying process data of kelp with different cutting shapes, and generates matching data for the drying process of kelp to be dried;

[0044] The kelp drying stage evaluation module includes a humidity acquisition unit for kelp drying, a time acquisition unit for kelp drying, a storage unit for drying humidity-time information in different kelp drying stages, a judgment unit for kelp drying stages, a storage unit for drying temperatures in different kelp drying stages, and an analysis unit for kelp drying temperatures;

[0045] The humidity acquisition unit for kelp drying collects real-time humidity data of kelp drying through a humidity sensor; the time acquisition unit for kelp drying collects cumulative duration data of kelp drying through a time counting module; the storage unit for drying humidity-time information in different kelp drying stages is used to store drying humidity-time data in different kelp drying stages; the judgment unit for kelp drying stages performs judgment processing on the drying stage of kelp drying operations based on the real-time humidity data of kelp drying, the cumulative duration data of kelp drying, and the drying humidity-time data in different kelp drying stages, and generates judgment data for kelp drying stages; the storage unit for drying temperatures in different kelp drying stages is used to store drying temperature data in different kelp drying stages; the analysis unit for kelp drying temperatures performs analysis processing on the drying temperatures required in different kelp drying stages based on the judgment data for kelp drying stages and the drying temperature data in different kelp drying stages, and generates analysis data for the drying temperature of kelp drying operations;

[0046] The kelp drying temperature adjustment module includes a kelp drying real-time temperature parameter construction unit and a kelp drying temperature adjustment unit;

[0047] The kelp drying real-time temperature parameter construction unit constructs kelp drying real-time temperature adjustment data based on the drying temperature analysis parameters of the kelp drying operation and data processing; the kelp drying temperature adjustment unit performs temperature adjustment operations during the kelp drying process according to the kelp drying real-time temperature adjustment data and in combination with the heat pump drying management terminal and the heat pump drying system.

[0048] (III) Beneficial effects

[0049] The present invention provides an AI algorithm-based adaptive regulation system and method for kelp drying parameters. It has the following beneficial effects:

[0050] First, by dynamically collecting the characteristic image information of the kelp to be dried through a camera lens, it provides reliable data support for accurately analyzing the kelp drying process; based on the characteristic image information of the kelp to be dried, combined with an intelligent recognition algorithm and the characteristic image information of kelp with different cutting shapes stored in a large database, scientific identification and processing of the cutting appearance shape type of the kelp to be dried are carried out to realize intelligent identification of the cutting shape of the dried kelp; based on the recognition information of the cutting shape type of the kelp to be dried, combined with an intelligent search algorithm and the drying process information of kelp with different cutting shapes established by standards, intelligent matching of the kelp drying process plan is carried out to realize personalized customization of the optimal kelp drying process information based on the spatial characteristics of kelp cutting, improving the reliability of kelp drying.

[0051] Second, through a humidity sensor and a time counting module, the real-time humidity and cumulative drying duration parameters inside the kelp heat pump drying system are efficiently monitored online, providing real data support for intelligently judging the kelp drying stage; based on the real-time humidity parameter of kelp drying and the cumulative drying duration parameter of kelp, combined with an intelligent recognition algorithm and the scientifically preset drying humidity time parameters of different kelp drying stages, dynamic judgment processing of the kelp drying stage is carried out to realize dynamic intelligent monitoring of the kelp drying stage during the kelp drying process; according to the judgment result information of the kelp drying stage, combined with an artificial intelligence recognition algorithm and the scientifically preset drying temperature parameters of different kelp drying stages, accurate matching of the drying temperature required for different kelp drying stages is carried out to realize scientific adjustment of the kelp drying temperature parameters, realizing AI-based adaptive adjustment of the kelp drying temperature and improving the quality of kelp drying.

[0052] Third, by accurately constructing the real-time temperature adjustment parameters of kelp drying based on the drying temperature analysis parameters of the kelp drying operation and data processing, efficient collection of kelp drying parameters is realized; according to the real-time temperature adjustment parameters of kelp drying and in combination with the heat pump drying management terminal and the heat pump drying system, the temperature adjustment operation during the kelp drying process is autonomously and accurately performed, realizing adaptive and precise control of the kelp drying parameters and improving the efficiency and output of kelp drying. Description of the drawings

[0053] Figure 1 Schematic diagram of the modules of the kelp drying parameter adaptive regulation system based on AI algorithm provided by the present invention;

[0054] Figure 2 Flow chart of the method for adaptively regulating kelp drying parameters based on AI algorithm provided by the present invention. Specific embodiments

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] Embodiments of the kelp drying parameter adaptive regulation system and method based on AI algorithm are as follows:

[0057] Embodiment 1:

[0058] Please refer to Figure 1 - Figure 2 , the method for adaptively regulating kelp drying parameters based on AI algorithm, and the method includes the following steps:

[0059] S1. Collect characteristic image data of kelp to be dried;

[0060] S2. Identify the cutting appearance shape type of the kelp to be dried based on the characteristic image data of the kelp to be dried and the characteristic image data of kelp with different cutting shapes, and generate identification data of the cutting shape type of the kelp to be dried;

[0061] S3. Match the drying process plan of the kelp to be dried based on the identification data of the cutting shape type of the kelp to be dried and the drying process data of kelp with different cutting shapes, and generate matching data of the drying process of the kelp to be dried;

[0062] S4. Collect and process the drying humidity and cumulative drying time during the kelp drying process according to the matching data of the drying process of the kelp to be dried, and generate real-time humidity data of kelp drying and cumulative duration data of kelp drying respectively;

[0063] S5. Judge the drying stage of the kelp drying operation based on the real-time humidity data of kelp drying, the cumulative duration data of kelp drying and the drying humidity time data of different kelp drying stages, and generate judgment data of the kelp drying stage;

[0064] S6. Analyze and process the drying temperature required for different drying stages of kelp based on the kelp drying stage judgment data and the drying temperature data of different kelp drying stages, and generate the drying temperature analysis data for kelp drying operations.

[0065] S7. Construct the real-time temperature adjustment data for kelp drying and perform the temperature adjustment operation during the kelp drying process.

[0066] Furthermore, please refer to Figure 1 - Figure 2 , and the operation steps for collecting the characteristic image data of the kelp to be dried are as follows:

[0067] S11. Install a shooting lens at the inlet of the heat pump drying system to collect the image information of the cut appearance shape of the kelp to be dried online, and generate the characteristic image data of the kelp to be dried. , and the characteristic image data of the kelp to be dried represents the appearance shape image information after segmentation and preprocessing before drying the kelp to be dried.

[0068] The operation steps for identifying the cutting appearance shape type of the kelp to be dried based on the characteristic image data of the kelp to be dried and the characteristic image data of kelp with different cutting shapes, and generating the identification data of the cutting shape type of the kelp to be dried are as follows:

[0069] S21. Establish a set of characteristic image data of kelp with different cutting shapes. , ; where represents the characteristic image data of kelp with different cutting shapes corresponding to the th kelp segmentation shape type, represents the maximum value of the number of kelp segmentation shape types; the kelp segmentation shape types include strip, block, and flake; the characteristic image data of kelp with different cutting shapes represents the standard kelp appearance characteristic image information set for different kelp segmentation shape types.

[0070] S22. Use the ORB image feature matching algorithm to match the characteristic image data of the kelp to be dried with the characteristic image data of kelp with different cutting shapes in the set of characteristic image data of kelp with different cutting shapes to perform kelp image feature matching, search for the text information of the kelp segmentation shape type corresponding to the characteristic image data of kelp with different cutting shapes that matches the characteristic image data of the kelp to be dried , and generate the identification data of the cutting shape type of the kelp to be dried through data identification. .

[0071] Based on the identification data of the cutting shape type of the kelp to be dried and the drying process data of the kelp with different cutting shapes, the drying process scheme of the kelp to be dried is matched, and the operation steps of generating the drying process matching data of the kelp to be dried are as follows:

[0072] S31. Establishing a data set of kelp drying process with different cutting shapes ,in Indicates The kelp drying process data of different cutting shapes corresponding to the kelp segmentation shape types, and the kelp feature image data of different cutting shapes represent the optimal kelp heat pump drying process scheme information of kelp heat pump drying treatment set for different kelp segmentation shape types; wherein the optimal kelp heat pump drying process scheme information includes the kelp heat pump drying operation steps and the equipment information required for the kelp heat pump drying process;

[0073] S32, using the Sorensen-Morris-Pratt search algorithm to identify the shape type of the kelp to be dried Kelp drying process data set with different cutting shapes Drying process data of kelp in different cutting shapes Perform kelp segmentation shape type character matching to search for the kelp cutting shape type identification data to be dried Corresponding drying process data of kelp with different cutting shapes , and build the drying process matching data of the kelp to be dried .

[0074] The characteristic image acquisition unit of the kelp to be dried uses a camera to dynamically collect characteristic image information of the kelp to be dried, providing reliable data support for the accurate analysis of the kelp drying process; the cutting shape recognition unit of the kelp to be dried performs scientific recognition and processing of the cutting appearance shape type of the kelp to be dried based on the characteristic image information of the kelp to be dried combined with an intelligent recognition algorithm and characteristic image information of kelp with different cutting shapes based on big data storage, thereby realizing intelligent recognition of the cutting shape of the dried kelp; the kelp drying process matching unit performs intelligent matching of kelp drying process plans based on the recognition information of the cutting shape type of the kelp to be dried combined with an intelligent search algorithm and the drying process information of kelp with different cutting shapes established by the standard, thereby realizing personalized customization of the optimal kelp drying process information based on the spatial characteristics of kelp cutting, thereby improving the reliability of kelp drying.

[0075] For further information, see Figure 1 - Figure 2 According to the drying process matching data of the kelp to be dried, the drying humidity and cumulative drying time of the kelp drying process are collected and processed, and the operating steps of generating the real-time humidity data of kelp drying and the cumulative drying time data of kelp drying are as follows:

[0076] S41. The heat pump drying management terminal matches data according to the drying process of the kelp to be dried to control the heat pump drying system to perform drying operations on the kelp to be dried. During the kelp drying operation, the humidity parameter of the flowing air inside the heat pump drying system and the cumulative time length parameter of the kelp drying operation are collected online through a humidity sensor and a time counting module, and the real-time humidity data of the kelp drying and the cumulative drying duration data of the kelp are generated respectively , where is in grams per cubic meter, and

[0077] is in hours. Based on the real-time humidity data of the kelp drying, the cumulative drying duration data of the kelp, and the drying humidity time data of different kelp drying stages, the operation steps for judging the drying stage of the kelp drying operation and generating the kelp drying stage judgment data are as follows:

[0078] S51. Establish a set of drying humidity time data for different kelp drying stages , where represents the different drying humidity time data corresponding to the th type of kelp drying stage type, represents the maximum value of the number of kelp drying stage types; , where represents the drying humidity interval of the kelp drying stage corresponding to the th type of kelp drying stage type, , where and respectively represent the minimum drying humidity and the maximum drying humidity in the drying humidity interval of the kelp drying stage, and are both in grams per cubic meter; where represents the drying time interval of the kelp drying stage corresponding to the th type of kelp drying stage type, , where and respectively represent the minimum drying time and the maximum drying time in the drying time interval of the kelp drying stage, and The unit is hours. The drying humidity time data for different kelp drying stages represents the information on the standard drying humidity range of the internal flowing air in the heat pump drying system and the standard cumulative drying duration range during the kelp heat pump drying process for different types of kelp drying stages. The types of kelp drying stages include the heating stage, the high-humidity and low-temperature constant-speed drying and moisture removal stage, the medium-humidity and medium-temperature constant-speed drying and moisture removal stage, and the shaping stage.

[0079] S52. Compare the real-time humidity data of kelp drying and the cumulative drying duration data of kelp drying with the drying humidity time data sets for different kelp drying stages inside, and compare the drying humidity time data for different kelp drying stages in the drying humidity interval of the kelp drying stage and the drying time interval of the kelp drying stage to search for the drying humidity time data for different kelp drying stages that match the real-time humidity data of kelp drying and the cumulative drying duration data of kelp drying , and generate the text information of the kelp drying stage corresponding to the matched drying humidity time data for different kelp drying stages , and generate the kelp drying stage judgment data through data identification.

[0080] The operation steps for analyzing the drying temperature required for different kelp drying stages based on the kelp drying stage judgment data and the drying temperature data for different kelp drying stages to generate the drying temperature analysis data for the kelp drying operation are as follows:

[0081] S61. Establish a data set of drying temperature data for different kelp drying stages , ; where represents the drying temperature data for different kelp drying stages corresponding to the th type of kelp drying stage type. The unit of is degrees Celsius, and the drying temperature data for different kelp drying stages represents the optimal heat pump drying temperature parameters for different kelp drying stage types.

[0082] S62. Match the kelp drying stage judgment data with the drying temperature data for different kelp drying stages in the data set of drying temperature data for different kelp drying stages to search for the drying temperature data for different kelp drying stages corresponding to the kelp drying stage judgment data , and construct the drying temperature analysis data for the kelp drying operation ​​, perform the operation of constructing the analysis data of the drying temperature for kelp drying The operation steps are as follows:

[0083] S621. Parameter initialization: The population size of the search bats for the drying temperature is , the maximum number of iterations T, the objective function F, the position of the search bat individual for the drying temperature in the search space of the drying temperature data set at different kelp drying stages ; and the velocity , ; and the sound wave frequency , the sound wave loudness , and the frequency ; ;

[0084] S622. Search for the position of the optimal search bat individual for the drying temperature in the search space of the drying temperature data set at different kelp drying stages , that is, search for the position in the search space of the drying temperature data set at different kelp drying stages that is most matched with the kelp drying stage judgment data of the search bat population for the drying temperature, and update the velocity and position. The velocity and position update formulas are as follows: , , where represents the velocity of the search bat individual for the drying temperature in the search space of the drying temperature data set at different kelp drying stages after iterations, represents the velocity of the search bat individual for the drying temperature in the search space of the drying temperature data set at different kelp drying stages after iterations; represents the position of the search bat individual for the drying temperature in the search space of the drying temperature data set at different kelp drying stages after iterations, represents the position of the search bat individual for the drying temperature in the search space of the drying temperature data set at different kelp drying stages after iterations; represents the position of the search bat individual for the drying temperature in the search space of the drying temperature data set at different kelp drying stages after iterations, represents the position of the search bat individual for the drying temperature in the search space of the drying temperature data set at different kelp drying stages after iterations; ;

[0085] S623. Generate random numbers rand1, rand1 is a random number in the interval [0, 1]. When rand1 > , select an optimal drying temperature search bat individual from the bat individuals searching for the optimal drying temperature, and in the search space of the drying temperature data set at different kelp drying stages , search for the drying temperature data of different kelp drying stages that best matches the kelp drying stage judgment data . , near the selected optimal drying temperature search bat individual, through the formula , , , generate a local solution, and search for the drying temperature data of different kelp drying stages that best matches the kelp drying stage judgment data in the search space of the drying temperature data set at different kelp drying stages , . Among them, represents the sound wave loudness of the drying temperature search bat individual at iterations in the search space of the drying temperature data set at different kelp drying stages , represents the frequency of the drying temperature search bat individual at iterations in the search space of the drying temperature data set at different kelp drying stages ; represents the initial frequency of the drying temperature search bat individual at iterations in the search space of the drying temperature data set at different kelp drying stages ; Otherwise, according to the formula , , update the position of the drying temperature search bat, and update and search for the drying temperature data of different kelp drying stages that matches the kelp drying stage judgment data in the search space of the drying temperature data set at different kelp drying stages , . Among them, represents the sound wave loudness of the drying temperature search bat individual at iterations in the search space of the drying temperature data set at different kelp drying stages , represents the frequency of the drying temperature search bat individual at iterations in the search space of the drying temperature data set at different kelp drying stages ; represents the random function with the value , and is a constant and 0 < < 1, > 0;

[0086] S624. Then generate a random number rand2, where rand2 is a random number on [0, 1]; when rand2 < , and at this time the fitness of the objective function F is better than the new solution in S623, search in the search space of the drying temperature data set for different drying temperature data of kelp in different drying stages that match the kelp drying stage judgment data ; if the fitness of the different drying temperature data of kelp in different drying stages is better than the fitness of the different drying temperature data of kelp in different drying stages that match in S623 , then accept the position of the different drying temperature data of kelp in different drying stages in the search space of the drying temperature data set of different drying stages of kelp, and update the position. The position update formula is as follows: , and according to the formula , , synchronously adjust and ; where represents the new position of the bat individual searching for the drying temperature in the search space of the drying temperature data set of different drying stages of kelp, represents the old position of the bat individual searching for the drying temperature in the search space of the drying temperature data set of different drying stages of kelp, represents any number in the interval [-1, 1];

[0087] S625. Sort the fitness values of all individuals in the bat population searching for the drying temperature, and find the current best , and search in the search space of the drying temperature data set for the position of the different drying temperature data of kelp in different drying stages that best matches the kelp drying stage judgment data ; ;

[0088] S626. Repeat S622 to S625. When the maximum number of iterations T is satisfied, output the different drying temperature data of kelp in different drying stages that match the kelp drying stage judgment data ; ;

[0089] S627. According to the different drying temperature data of kelp in different drying stages output in step S626 that match the kelp drying stage judgment data Drying temperature data for different drying stages of kelp that match And through data identification, the drying temperature analysis data of the kelp drying operation is constructed , where The unit is degrees Celsius

[0090] Through the cooperation of the kelp drying humidity acquisition unit and the kelp drying time acquisition unit, the humidity sensor and the time counting module are used to efficiently and online monitor the real-time drying humidity and the cumulative drying duration parameters inside the kelp heat pump drying system, providing real data support for the intelligent judgment of the kelp drying stage; the kelp drying stage judgment unit is based on the real-time drying humidity parameter of the kelp and the cumulative drying duration parameter of the kelp, combined with the intelligent recognition algorithm and the scientifically preset drying humidity time parameters for different kelp drying stages to perform dynamic judgment processing of the kelp drying stage, realizing dynamic intelligent monitoring of the kelp drying stage during the kelp drying process; the kelp drying temperature analysis unit accurately matches the drying temperature required for different kelp drying stages based on the kelp drying stage judgment result information, combined with the artificial intelligence recognition algorithm and the scientifically preset drying temperature parameters for different kelp drying stages, realizing the scientific adjustment of the kelp drying temperature parameters, realizing the AI-based adaptive adjustment of the kelp drying temperature, and improving the quality of kelp drying

[0091] Furthermore, please refer to Figure 1 - Figure 2 , and the operation steps for constructing the real-time temperature adjustment data of kelp drying and performing the temperature adjustment operation during the kelp drying process are as follows

[0092] S71. The drying temperature analysis data of the kelp drying operation Through data identification, the real-time temperature adjustment data of kelp drying is constructed , where The unit is degrees Celsius

[0093] S72. The heat pump drying management terminal adjusts the drying temperature of the kelp inside the heat pump drying system according to the real-time temperature adjustment data of the kelp drying To perform the temperature adjustment operation during the kelp drying process

[0094] Through the real-time temperature parameter construction unit of kelp drying, based on the drying temperature analysis parameters of the kelp drying operation and combined with data processing, the real-time temperature adjustment parameters of kelp drying are accurately constructed, realizing the efficient collection of kelp drying parameters; the kelp drying temperature adjustment unit accurately performs the temperature adjustment operation during the kelp drying process according to the real-time temperature adjustment parameters of the kelp drying, combined with the heat pump drying management terminal and the heat pump drying system, realizing the adaptive and accurate control of the kelp drying parameters, and improving the efficiency and output of kelp drying

[0095] Example 2

[0096] Please refer to Figure 1 - Figure 2, an AI algorithm-based adaptive control system for kelp drying parameters, which is used to implement the AI algorithm-based method for adaptively controlling kelp drying parameters. The system includes a kelp drying process selection module, a kelp drying stage evaluation module, and a kelp drying temperature adjustment module;

[0097] The kelp drying process selection module includes a unit for collecting characteristic images of kelp to be dried, a unit for storing characteristic images of kelp with different cutting shapes, a unit for identifying the cutting shape of kelp to be dried, a unit for storing drying processes of kelp with different cutting shapes, and a unit for matching kelp drying processes;

[0098] The unit for collecting characteristic images of kelp to be dried collects data of characteristic images of kelp to be dried through a shooting lens; the unit for storing characteristic images of kelp with different cutting shapes is used to store data of characteristic images of kelp with different cutting shapes; the unit for identifying the cutting shape of kelp to be dried performs identification processing on the cutting appearance shape type of kelp to be dried based on the data of characteristic images of kelp to be dried and the data of characteristic images of kelp with different cutting shapes, and generates identification data of the cutting shape type of kelp to be dried; the unit for storing drying processes of kelp with different cutting shapes is used to store drying process data of kelp with different cutting shapes; the unit for matching kelp drying processes performs matching processing on the drying process plan of kelp to be dried based on the identification data of the cutting shape type of kelp to be dried and the drying process data of kelp with different cutting shapes, and generates matching data of the drying process of kelp to be dried;

[0099] The kelp drying stage evaluation module includes a unit for collecting the humidity of kelp drying, a unit for collecting the drying time of kelp, a unit for storing drying humidity-time information of different kelp drying stages, a unit for judging the kelp drying stage, a unit for storing drying temperatures of different kelp drying stages, and a unit for analyzing the drying temperature of kelp;

[0100] The unit for collecting the humidity of kelp drying collects real-time humidity data of kelp drying through a humidity sensor; the unit for collecting the drying time of kelp collects cumulative drying duration data of kelp drying through a time counting module; the unit for storing drying humidity-time information of different kelp drying stages is used to store drying humidity-time data of different kelp drying stages; the unit for judging the kelp drying stage performs judgment processing on the drying stage of kelp drying operation based on the real-time humidity data of kelp drying, the cumulative drying duration data of kelp drying, and the drying humidity-time data of different kelp drying stages, and generates judgment data of the kelp drying stage; the unit for storing drying temperatures of different kelp drying stages is used to store drying temperature data of different kelp drying stages; the unit for analyzing the drying temperature of kelp performs analysis processing on the drying temperature required for different drying stages of kelp based on the judgment data of the kelp drying stage and the drying temperature data of different kelp drying stages, and generates analysis data of the drying temperature of kelp drying operation;

[0101] The kelp drying temperature adjustment module includes a unit for constructing real-time temperature parameters of kelp drying and a unit for adjusting the drying temperature of kelp;

[0102] The kelp drying real-time temperature parameter construction unit constructs the kelp drying real-time temperature adjustment data based on the drying temperature analysis parameters of the kelp drying operation in combination with data processing; the kelp drying temperature adjustment unit performs the temperature adjustment operation during the kelp drying process according to the kelp drying real-time temperature adjustment data in combination with the heat pump drying management end and the heat pump drying system.

[0103] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An adaptive regulation method for the kelp drying parameters based on an AI algorithm, characterized in that, The method includes the following steps: S1. Collect the characteristic image data of the kelp to be dried; S2. Perform the recognition process of the cutting appearance shape type of the kelp to be dried, and generate the recognition data of the cutting shape type of the kelp to be dried; S3. Perform the matching process of the drying process plan of the kelp to be dried, and generate the drying process matching data of the kelp to be dried; The S3 includes the following steps: S31. Establish a data set of kelp drying processes with different cutting shapes , where the includes ; among them represents the drying process data of kelp with different cutting shapes corresponding to the th type of kelp segmentation shape S32. Use the Sorensen-Morris-Pratt search algorithm to with the described in for kelp segmentation shape type character matching, and search for the corresponding , and construct the drying process matching data for the kelp to be dried , where it represents the recognition data of the cutting shape type of the kelp to be dried; S4. According to the drying process matching data of the kelp to be dried, perform the collection process of the drying humidity and the cumulative drying time during the kelp drying process, and respectively generate the real-time humidity data of the kelp drying and the cumulative duration data of the kelp drying; S5. Perform the judgment process of the drying stage of the kelp drying operation, and generate the judgment data of the kelp drying stage; S6. Perform the analysis process of the drying temperature required for different drying stages of the kelp, and generate the drying temperature analysis data of the kelp drying operation; S7. Construct the real-time temperature adjustment data of the kelp drying and execute the temperature adjustment operation during the kelp drying process.

2. The method for adaptively regulating the drying parameters of kelp based on the AI algorithm according to claim 1, wherein: The S1 includes the following steps: S11. Install a shooting lens at the feed inlet of the heat pump drying system to collect the image information of the cut appearance shape of the kelp to be dried online, and generate the characteristic image data of the kelp to be dried .

3. The method for adaptively regulating kelp drying parameters based on the AI algorithm according to claim 2, wherein: The S2 includes the following steps: S21. Establish a data set of characteristic images of kelp with different cutting shapes , where the includes ; among which represents the characteristic image data of kelp with different cutting shapes corresponding to the th type of kelp segmentation shape type; S22. Using the ORB image feature matching algorithm, match the with the described in the to perform kelp image feature matching, search for the kelp segmentation shape type text information that matches the matched , and generate the identification data of the cutting shape type of the kelp to be dried through data identification .

4. The method for adaptively regulating the drying parameters of kelp based on the AI algorithm according to claim 1, wherein: The S4 includes the following steps: S41. The heat pump drying management terminal, according to the control, the heat pump drying system performs drying operations on the kelp to be dried. During the kelp drying operation, the humidity parameter of the flowing air inside the heat pump drying system and the cumulative time length parameter of the kelp drying operation are collected online through a humidity sensor and a time counting module, and the real-time humidity data of the kelp drying is generated respectively and the cumulative duration data of the kelp drying , where is in grams per cubic meter, is in hours.

5. The kelp drying parameter adaptive regulation method based on the AI algorithm according to claim 4, characterized in that: The S5 includes the following steps: S51. Establish a data set of drying humidity time for different kelp drying stages , where the includes , among which represents the drying humidity time data of different kelp drying stage types corresponding to the th type of kelp drying stage, represents the maximum value of the number of kelp drying stage types; the consists of and ; among which represents the drying humidity interval of the kelp drying stage corresponding to the th type of kelp drying stage, represents the drying time interval of the kelp drying stage corresponding to the th type of kelp drying stage; S52. Connect the and the respectively to the inside the in the and the for comparison of the numerical values of the drying humidity and drying time, search for the text information of the kelp drying stage corresponding to the and the that match, and generate the kelp drying stage judgment data through data identification .

6. The method for adaptively regulating the drying parameters of kelp based on the AI algorithm according to claim 5, characterized in that: The S6 includes the following steps: S61. Establish a drying temperature data set for different drying stages of kelp , where the includes , among which represents the drying temperature data of different drying stages of kelp corresponding to the th type of kelp drying stage, where is in degrees Celsius; S62. Match the with the described in for keyword matching in the kelp drying stage, search for the corresponding , and construct the analysis data of the drying temperature of the kelp drying operation. The operation steps for constructing the analysis data of the drying temperature of the kelp drying operation are as follows: S621. Parameter initialization: The population size of the bat population searching for the drying temperature is , the maximum number of iterations T, the objective function F, and the position of the individual bat searching for the drying temperature in the search space of ; and the velocity , the sound wave frequency ; the sound wave loudness , and the frequency ; ​ S622. Search for the optimal drying temperature search bat individual position in the drying temperature search bat population within the search space of , that is, the drying temperature search bat population searches for the position that best matches the within the search space of , and updates the speed and position; ​ S623. Generate a random number rand1, where rand1 is a random number in the interval [0, 1]. When rand1 > , select an optimal drying temperature search bat individual from the optimal drying temperature search bat individuals, and search in the search space for the position of the that best matches the . Near the selected optimal drying temperature search bat individual, generate a local solution, and search in the search space for the position of the that best matches the ; otherwise, update the position of the drying temperature search bat, and update and search in the search space for the position of the that matches the ; S624. Generate another random number rand2, where rand2 is a random number on [0, 1]; when rand2 < , and at this time the fitness of the objective function F is better than the new solution in S623, search in the search space of the to find the that matches the whose fitness is better than the matched in S623, then accept the at the position in the search space of the , update the position, and synchronously adjust the and ; S625. Sort the fitness values of all individuals in the bat population for the drying temperature search, and find the current best , and search in the search space of the for the position of the that best matches the ; S626. Repeat the execution of S622 to S625. When the maximum number of iterations T is satisfied, output the that matches the ; S627. Based on the matched in step S626 with the and constructing kelp drying operation drying temperature analysis data through data identification , where the unit of which is degree Celsius.

7. The method for adaptively regulating kelp drying parameters based on the AI algorithm according to claim 6, wherein: The S7 includes the following steps: S71. Construct the real-time temperature adjustment data for kelp drying by using the data identification, where the unit of is degree Celsius; S72. The heat pump drying management terminal performs the temperature adjustment operation for kelp drying according to the adjustment of the kelp drying temperature inside the heat pump drying system.

8. An adaptive regulation system for kelp drying parameters based on AI algorithms, which is used to implement the adaptive regulation method for kelp drying parameters based on AI algorithms according to any one of claims 1-7, and is characterized in that: The system includes a kelp drying process selection module, a kelp drying stage evaluation module, and a kelp drying temperature adjustment module.

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