Snail bait feeding optimization method and system based on growth cycle

Abstract

The invention discloses a snail bait feeding optimization method and system based on a growth cycle. Historical bait feeding schemes of snails in different growth periods are obtained, and a genetic algorithm is adopted to construct a bait feeding population of each period. In the experiment putting process, the fitness of individuals of each population is evaluated, the individuals with the fitness meeting the preset standard are selected as parents, crossover and mutation operation is carried out, and a plurality of bait putting schemes to be selected are generated. And the feeding activity of the snails is observed in real time through secondary test feeding of the to-be-selected bait feeding scheme. And selecting a bait feeding scheme of each growth cycle based on the activeness, and finally obtaining a preferable bait feeding scheme. Through the method, behavior differences of the snails in different growth periods are fully considered, the bait feeding strategy is scientifically adjusted, the bait utilization rate is increased, and the snail feeding effect is optimized.

Description

Technical Field

[0001] The invention relates to the technical field of snail breeding, and in particular to a method and system for optimizing the placement of snail bait based on a growth cycle. Background Art

[0002] Snail farming is an important aquaculture industry, and its success is directly related to production and quality. Traditional strategies for feeding snails are usually based on experience and observation, ignoring the differences in feeding behavior of snails in different growth cycles. Therefore, there are problems such as low bait utilization and unstable farming benefits.

[0003] At present, although some intelligent feeding systems have been applied in the field of aquaculture, most of them still lack accurate consideration of the individual differences of snails in different growth cycles. Therefore, a more intelligent and personalized bait delivery method is needed to better adapt to the growth characteristics of snails.

[0004] As an optimization algorithm, genetic algorithm performs well in complex environments and can simulate natural selection and genetic mechanisms. Applying genetic algorithm to the optimization of snail bait placement can optimize the bait placement plan and improve the breeding efficiency by simulating evolution.

[0005] The present invention uses genetic algorithms and experimental observations to construct personalized bait delivery plans in different growth cycles, making them more in line with the actual needs of snails, and ultimately improving the attractiveness and feeding efficiency of bait. This technological innovation is expected to bring higher economic benefits and sustainable development to the snail farming industry. Summary of the invention

[0006] In order to solve at least one of the above technical problems, the present invention proposes a method and system for optimizing the delivery of snail bait based on the growth cycle.

[0007] The first aspect of the present invention provides a method for optimizing the placement of snail bait based on the growth cycle, comprising:

[0008] Obtain multiple historical bait delivery plans for snails in different growth cycles, and initialize the historical bait delivery plans in each growth cycle as population individuals based on a genetic algorithm to obtain the bait delivery population for each growth cycle;

[0009] Experimentally releasing snails in different growth cycles according to the bait release population, and evaluating the fitness of each individual in the bait release population;

[0010] Selecting population individuals that meet preset fitness as parents to perform crossover and mutation operations to generate multiple bait delivery plans to be selected;

[0011] The plurality of bait delivery schemes to be selected are delivered for a second trial, and the feeding activity of the snails is observed in real time;

[0012] A bait delivery plan for each growth cycle of the snails is selected according to the activity of the feeding behavior to obtain an optimal bait delivery plan.

[0013] In this scheme, a plurality of historical bait delivery schemes of snails in different growth cycles are obtained, and the historical bait delivery schemes in each growth cycle are initialized as population individuals based on a genetic algorithm to obtain the bait delivery population in each growth cycle, specifically:

[0014] Obtain the bait delivery time, delivery quantity, and delivery location information of each delivery of bait in different historical growth cycles of snails in the target breeding environment, and obtain multiple historical bait delivery plans for snails in different growth cycles;

[0015] Binary encoding is performed on the bait delivery time, delivery quantity, and delivery location information of each historical bait delivery plan to form multiple digital historical bait delivery plans;

[0016] A genetic algorithm is introduced to randomly generate a preset number of individuals, and each bait placement factor in each digitized historical bait placement plan is imported into the individuals as individual genes to obtain the bait placement population of snails in each growth cycle.

[0017] In this scheme, the snails in different growth cycles are tested according to the bait release population to evaluate the fitness of each individual in the bait release population, specifically:

[0018] Each growth cycle of the snail is traversed in a loop, and multiple population individuals corresponding to each growth cycle are extracted according to the bait release population;

[0019] Traverse multiple population individuals corresponding to each growth cycle, extract the release conditions of each population individual, and obtain multiple bait test release plans for snails in each growth cycle;

[0020] The bait test delivery plan is used to deliver actual bait to the snails in the corresponding growth cycle, and the video data of the snails in each growth cycle after the actual bait delivery in a preset time period are recorded in real time by video camera technology;

[0021] Acquire image feature data of historical snail feeding behavior, and extract video frame image data of the snail video data at preset time intervals;

[0022] Comparing the image feature data with the video frame image data to obtain the snail feeding behavior data of each growth cycle within a preset time period;

[0023] Evaluate the individual size change of snails within a preset time period according to the video frame image data to obtain the individual size change data of snails;

[0024] Evaluating the growth rate of the snails in each growth cycle according to the individual size change data;

[0025] The fitness of each individual in the bait-released population is evaluated based on the snail feeding behavior data and growth rate of each growth cycle.

[0026] In this scheme, the population individuals that meet the preset fitness are selected as parents to perform crossover and mutation operations to generate multiple bait delivery schemes to be selected, specifically:

[0027] Set a preset fitness threshold, sort the fitness of each population individual, get the sorting result, and introduce the roulette wheel selection method to select the population individuals with fitness higher than the preset threshold as the parent generation;

[0028] The parent generations are randomly paired in pairs to obtain parent generation combinations, each parent generation combination is subjected to a population individual gene exchange operation according to a single-point crossover operator to obtain a new population individual, and the new population individual is used to replace the original parent generation to obtain a child generation individual;

[0029] Randomly selecting the gene loci of the offspring individuals for mutation operation to obtain the optimized offspring individuals in each growth cycle of the snail;

[0030] A natural language compilation operation is performed on the optimized offspring individuals to obtain a plurality of bait delivery plans to be selected for each growth cycle of the snails.

[0031] In this scheme, the multiple bait delivery schemes to be selected are subjected to a second test delivery, and the feeding activity of the snails is observed in real time, specifically:

[0032] Select a preset number of bait placement plans for each growth cycle of snails for secondary trial placement, and obtain video data after the secondary placement;

[0033] Analyze the aggregation, feeding frequency, and feeding duration of snails according to the video data;

[0034] The feeding activity of the snails is evaluated based on the aggregation, feeding frequency and feeding time of the snails.

[0035] In this scheme, the bait delivery scheme for each growth cycle of the snail is selected according to the feeding behavior activity to obtain the optimal bait delivery scheme, which is specifically:

[0036] According to the feeding behavior activity, the attractiveness of the selected bait delivery scheme to the snails is evaluated, and the utilization efficiency of the bait is determined according to the attractiveness;

[0037] Determining a bait delivery plan for each growth cycle of snails according to the utilization efficiency of the bait, and obtaining an optimal bait delivery plan;

[0038] The preferred bait delivery scheme is put into practical use, the actual effect of the preferred bait delivery scheme is evaluated, and real-time feedback and delivery parameters are adjusted according to the actual effect.

[0039] The second aspect of the present invention also provides a snail bait delivery optimization system based on the growth cycle, the system comprising: a memory, a processor, the memory comprising a snail bait delivery optimization method program based on the growth cycle, and the snail bait delivery optimization method program based on the growth cycle is executed by the processor to implement the following steps:

[0040] Obtain multiple historical bait delivery plans for snails in different growth cycles, and initialize the historical bait delivery plans in each growth cycle as population individuals based on a genetic algorithm to obtain the bait delivery population for each growth cycle;

[0041] Experimentally releasing snails in different growth cycles according to the bait release population, and evaluating the fitness of each individual in the bait release population;

[0042] Selecting population individuals that meet preset fitness as parents to perform crossover and mutation operations to generate multiple bait delivery plans to be selected;

[0043] The plurality of bait delivery schemes to be selected are delivered for a second trial, and the feeding activity of the snails is observed in real time;

[0044] A bait delivery plan for each growth cycle of the snails is selected according to the activity of the feeding behavior to obtain an optimal bait delivery plan.

[0045] In this scheme, the population individuals that meet the preset fitness are selected as parents to perform crossover and mutation operations to generate multiple bait delivery schemes to be selected, specifically:

[0046] Set a preset fitness threshold, sort the fitness of each population individual, get the sorting result, and introduce the roulette wheel selection method to select the population individuals with fitness higher than the preset threshold as the parent generation;

[0047] The parent generations are randomly paired in pairs to obtain parent generation combinations, each parent generation combination is subjected to a population individual gene exchange operation according to a single-point crossover operator to obtain a new population individual, and the new population individual is used to replace the original parent generation to obtain a child generation individual;

[0048] Randomly selecting the gene loci of the offspring individuals for mutation operation to obtain the optimized offspring individuals in each growth cycle of the snail;

[0049] A natural language compilation operation is performed on the optimized offspring individuals to obtain a plurality of bait delivery plans to be selected for each growth cycle of the snails.

[0050] In this scheme, the multiple bait delivery schemes to be selected are subjected to a second test delivery, and the feeding activity of the snails is observed in real time, specifically:

[0051] Select a preset number of bait placement plans for each growth cycle of snails for secondary trial placement, and obtain video data after the secondary placement;

[0052] Analyze the aggregation, feeding frequency, and feeding duration of snails according to the video data;

[0053] The feeding activity of the snails is evaluated based on the aggregation, feeding frequency and feeding time of the snails.

[0054] In this scheme, the bait delivery scheme for each growth cycle of the snail is selected according to the feeding behavior activity to obtain the optimal bait delivery scheme, which is specifically:

[0055] According to the feeding behavior activity, the attractiveness of the selected bait delivery scheme to the snails is evaluated, and the utilization efficiency of the bait is determined according to the attractiveness;

[0056] Determining a bait delivery plan for each growth cycle of snails according to the utilization efficiency of the bait, and obtaining an optimal bait delivery plan;

[0057] The preferred bait delivery scheme is put into practical use, the actual effect of the preferred bait delivery scheme is evaluated, and real-time feedback and delivery parameters are adjusted according to the actual effect.

[0058] The invention discloses a method and system for optimizing the bait placement of snails based on the growth cycle. By obtaining the historical bait placement schemes of snails in different growth cycles, a genetic algorithm is used to construct the bait placement population for each cycle. In the experimental placement, the fitness of each population individual is evaluated, and the individuals whose fitness meets the preset standard are selected as the parent generation, and crossover and mutation operations are performed to generate multiple bait placement schemes to be selected. And by performing a secondary test placement on the bait placement scheme to be selected, the feeding behavior activity of the snails is observed in real time. The bait placement scheme for each growth cycle is selected based on the activity, and finally the optimal bait placement scheme is obtained. Through this method, the behavioral differences of snails in different growth cycles are fully considered, the bait placement strategy is scientifically adjusted, the bait utilization rate is improved, and the snail placement effect is optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flow chart of a method for optimizing the placement of snail bait based on a growth cycle of the present invention is shown;

[0060] Figure 2 A flow chart showing the generation of multiple bait delivery schemes to be selected according to the present invention is shown;

[0061] Figure 3 A flow chart showing the method for obtaining an optimal bait delivery scheme according to the present invention is shown;

[0062] Figure 4 A block diagram of a snail bait delivery optimization system based on a growth cycle of the present invention is shown. DETAILED DESCRIPTION

[0063] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0064] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0065] Figure 1 A flow chart of a method for optimizing the placement of snail bait based on the growth cycle of the present invention is shown.

[0066] like Figure 1 As shown, the first aspect of the present invention provides a method for optimizing the placement of snail bait based on the growth cycle, comprising:

[0067] S102, obtaining a plurality of historical bait delivery plans for snails in different growth cycles, and initializing the historical bait delivery plans in each growth cycle as population individuals based on a genetic algorithm to obtain a bait delivery population for each growth cycle;

[0068] S104, conducting a test release of snails in different growth cycles according to the bait release population, and evaluating the fitness of each individual in the bait release population;

[0069] S106, selecting a population individual that meets a preset fitness as a parent generation to perform crossover and mutation operations to generate multiple bait delivery plans to be selected;

[0070] S108, conducting a secondary test of the multiple bait delivery schemes to be selected, and observing the feeding activity of the snails in real time;

[0071] S110, selecting a bait placement plan for each growth period of the snails according to the feeding behavior activity to obtain an optimal bait placement plan.

[0072] It should be noted that the historical bait release schemes are initialized as population individuals through genetic algorithms to obtain bait release populations. The genetic algorithm is used to search for the diversity of bait release schemes, which helps to cover a wide range of possibilities in the search space, thereby increasing the chance of finding the global optimal solution; experimental release of the bait release population and evaluation of the fitness of population individuals and selection of population individuals that meet the preset fitness as parents help to screen out release schemes that perform better in the current environment and provide excellent genes for the generation of the next generation; the genetic algorithm uses crossover and mutation operations to fuse and mutate the genetic information of parent individuals, The invention can generate multiple bait delivery plans to be selected, which simulates the crossover and variation mechanism in biological evolution, helps to introduce new changes and excellent gene combinations, and increases the breadth of the search space; the bait delivery plans to be selected are subjected to secondary experimental delivery, and the feeding activity of the snails is observed to finally obtain the optimal bait delivery plan for each growth cycle of the snails, so that the bait delivery can more accurately meet the needs of the snails in different growth cycles, and improve the delivery effect and the balance of the ecosystem; the genetic algorithm is an optimization algorithm that simulates the natural selection and genetic mechanism in the process of biological evolution, and is a heuristic optimization algorithm that searches for the optimal solution in a group.

[0073] According to an embodiment of the present invention, the method of obtaining a plurality of historical bait delivery schemes for snails in different growth cycles, and initializing the historical bait delivery schemes in each growth cycle as population individuals based on a genetic algorithm to obtain a bait delivery population for each growth cycle, is specifically as follows:

[0074] Obtain the bait delivery time, delivery quantity, and delivery location information of each delivery of bait in different historical growth cycles of snails in the target breeding environment, and obtain multiple historical bait delivery plans for snails in different growth cycles;

[0075] Binary encoding is performed on the bait delivery time, delivery quantity, and delivery location information of each historical bait delivery plan to form multiple digital historical bait delivery plans;

[0076] A genetic algorithm is introduced to randomly generate a preset number of individuals, and each bait placement factor in each digitized historical bait placement plan is imported into the individuals as individual genes to obtain the bait placement population of snails in each growth cycle.

[0077] It should be noted that the individual refers to an entity representing a solution to a problem, and each individual is a representation of a historical bait placement plan; the bait placement factors are the bait placement time, placement quantity, and placement location.

[0078] According to an embodiment of the present invention, the snails in different growth cycles are tested and released according to the bait release population to evaluate the fitness of each individual in the bait release population, specifically:

[0079] Each growth cycle of the snail is traversed in a loop, and multiple population individuals corresponding to each growth cycle are extracted according to the bait release population;

[0080] Traverse multiple population individuals corresponding to each growth cycle, extract the release conditions of each population individual, and obtain multiple bait test release plans for snails in each growth cycle;

[0081] The bait test delivery plan is used to deliver actual bait to the snails in the corresponding growth cycle, and the video data of the snails in each growth cycle after the actual bait delivery in a preset time period are recorded in real time by video camera technology;

[0082] Acquire image feature data of historical snail feeding behavior, and extract video frame image data of the snail video data at preset time intervals;

[0083] Comparing the image feature data with the video frame image data to obtain the snail feeding behavior data of each growth cycle within a preset time period;

[0084] Evaluate the individual size change of snails within a preset time period according to the video frame image data to obtain the individual size change data of snails;

[0085] Evaluating the growth rate of the snails in each growth cycle according to the individual size change data;

[0086] The fitness of each individual in the bait-released population is evaluated based on the snail feeding behavior data and growth rate of each growth cycle.

[0087] It should be noted that the feeding behavior and growth rate of snails are evaluated through video frame image data, and finally the fitness of each population individual is evaluated. Not only the feeding behavior of snails is considered, but also their growth status is paid attention to, which provides a basis for a more comprehensive fitness evaluation. Through multi-dimensional information analysis, the algorithm can more accurately measure the effectiveness of the bait delivery plan in each growth cycle; the fitness is a measure of the superiority of an individual in solving problems or achieving goals, that is, the superiority evaluation result of the impact of the bait delivery plan represented by each population individual on the feeding behavior and growth rate of snails.

[0088] Figure 2 The flowchart of the present invention for generating a plurality of bait delivery plans to be selected is shown.

[0089] According to an embodiment of the present invention, the selection of population individuals that meet the preset fitness as parents to perform crossover and mutation operations to generate multiple bait delivery schemes to be selected is specifically as follows:

[0090] S202, setting a preset fitness threshold, sorting the fitness of each population individual, obtaining a sorting result, and introducing a roulette wheel selection method to select a population individual with a fitness higher than the preset fitness threshold as a parent generation;

[0091] S204, performing a random combination and pairing operation on the parent generations in pairs to obtain a parent generation combination, performing a population individual gene exchange operation on each parent generation combination according to a single-point crossover operator to obtain a new population individual, and replacing the original parent generation with the new population individual to obtain a child generation individual;

[0092] S206, randomly selecting gene loci of the offspring individuals to perform mutation operations to obtain optimized offspring individuals in each growth cycle of the snail;

[0093] S208, performing a natural language compilation operation on the optimized offspring individuals to obtain a plurality of bait placement plans to be selected for each growth cycle of the snails.

[0094] It should be noted that the roulette wheel selection method is a selection operation used in genetic algorithms. In the roulette wheel selection method, each individual is assigned a selection probability, which is proportional to the individual's fitness. Individuals with higher fitness are assigned a greater probability and are therefore more likely to be selected as parents to participate in crossover and mutation operations; the single-point crossover operator selects a random crossover point, cuts the genes of the two parent individuals at this point, and then exchanges their respective gene fragments to obtain two new individuals; the population individual gene exchange operation is crossover, which simulates the crossover process of biological genes in nature to produce new individuals. In the present application, it represents the crossover and exchange operation of the bait release factors in the two bait release schemes in each growth cycle; the mutation operation is to introduce new gene changes in the population by making random, local, and limited changes to individual genes. The mutation in the present application is to perform numerical replacement operations on the gene sites of the offspring that are artificially screened for unreasonable gene sites.

[0095] According to an embodiment of the present invention, the multiple bait delivery schemes to be selected are subjected to a secondary test delivery, and the feeding activity of the snails is observed in real time, specifically:

[0096] Select a preset number of bait placement plans for each growth cycle of snails for secondary trial placement, and obtain video data after the secondary placement;

[0097] Analyze the aggregation, feeding frequency, and feeding duration of snails according to the video data;

[0098] The feeding activity of the snails is evaluated based on the aggregation, feeding frequency and feeding time of the snails.

[0099] It should be noted that a preset number of candidate bait delivery plans were selected and a second trial delivery was conducted. Such a selection ensured a more in-depth verification of multiple plans in the actual environment, improving the representativeness of the experiment; the feeding activity of snails was evaluated, including the determination of snail aggregation, the quantification of feeding frequency, and the measurement of feeding time, which can accurately evaluate the feeding activity of snails in the experimental environment and provide a scientific basis for the subsequent optimization of bait delivery plans.

[0100] Figure 3 A flow chart of obtaining an optimal bait delivery scheme according to the present invention is shown.

[0101] According to an embodiment of the present invention, the bait delivery scheme for each growth cycle of the snail is selected according to the feeding behavior activity to obtain the preferred bait delivery scheme, which is specifically:

[0102] S302, evaluating the attractiveness of the selected bait delivery scheme to the snails according to the feeding behavior activity, and determining the utilization efficiency of the bait according to the attractiveness;

[0103] S304, determining a bait placement plan for each growth period of the snails according to the utilization efficiency of the bait, and obtaining an optimal bait placement plan;

[0104] S306, putting the preferred bait delivery scheme into practical use, evaluating the actual effect of the preferred bait delivery scheme, and providing real-time feedback and adjusting delivery parameters according to the actual effect.

[0105] It should be noted that the snail bait delivery optimization method based on the growth cycle can scientifically evaluate the bait plan according to the activity of feeding behavior, determine the optimal plan and verify its effect in the actual environment, thereby realizing the intelligent adjustment and optimization of snail bait delivery.

[0106] Figure 4 A block diagram of a snail bait delivery optimization system based on a growth cycle of the present invention is shown.

[0107] The second aspect of the present invention further provides a snail bait delivery optimization system 4 based on the growth cycle, the system comprising: a memory 41, a processor 42, the memory comprising a snail bait delivery optimization method program based on the growth cycle, the snail bait delivery optimization method program based on the growth cycle when executed by the processor, implements the following steps:

[0108] Obtain multiple historical bait delivery plans for snails in different growth cycles, and initialize the historical bait delivery plans in each growth cycle as population individuals based on a genetic algorithm to obtain the bait delivery population for each growth cycle;

[0109] Experimentally releasing snails in different growth cycles according to the bait release population, and evaluating the fitness of each individual in the bait release population;

[0110] Selecting population individuals that meet preset fitness as parents to perform crossover and mutation operations to generate multiple bait delivery plans to be selected;

[0111] The plurality of bait delivery schemes to be selected are delivered for a second trial, and the feeding activity of the snails is observed in real time;

[0112] A bait delivery plan for each growth cycle of the snails is selected according to the activity of the feeding behavior to obtain an optimal bait delivery plan.

[0113] It should be noted that the historical bait release schemes are initialized as population individuals through genetic algorithms to obtain bait release populations. The genetic algorithm is used to search for the diversity of bait release schemes, which helps to cover a wide range of possibilities in the search space, thereby increasing the chance of finding the global optimal solution; experimental release of the bait release population and evaluation of the fitness of population individuals and selection of population individuals that meet the preset fitness as parents help to screen out release schemes that perform better in the current environment and provide excellent genes for the generation of the next generation; the genetic algorithm uses crossover and mutation operations to fuse and mutate the genetic information of parent individuals, The invention can generate multiple bait delivery plans to be selected, which simulates the crossover and variation mechanism in biological evolution, helps to introduce new changes and excellent gene combinations, and increases the breadth of the search space; the bait delivery plans to be selected are subjected to secondary experimental delivery, and the feeding activity of the snails is observed to finally obtain the optimal bait delivery plan for each growth cycle of the snails, so that the bait delivery can more accurately meet the needs of the snails in different growth cycles, and improve the delivery effect and the balance of the ecosystem; the genetic algorithm is an optimization algorithm that simulates the natural selection and genetic mechanism in the process of biological evolution, and is a heuristic optimization algorithm that searches for the optimal solution in a group.

[0114] According to an embodiment of the present invention, the method of obtaining a plurality of historical bait delivery schemes for snails in different growth cycles, and initializing the historical bait delivery schemes in each growth cycle as population individuals based on a genetic algorithm to obtain a bait delivery population for each growth cycle, is specifically as follows:

[0115] Obtain the bait delivery time, delivery quantity, and delivery location information of each delivery of bait in different historical growth cycles of snails in the target breeding environment, and obtain multiple historical bait delivery plans for snails in different growth cycles;

[0116] Binary encoding is performed on the bait delivery time, delivery quantity, and delivery location information of each historical bait delivery plan to form multiple digital historical bait delivery plans;

[0117] A genetic algorithm is introduced to randomly generate a preset number of individuals, and each bait placement factor in each digitized historical bait placement plan is imported into the individuals as individual genes to obtain the bait placement population of snails in each growth cycle.

[0118] It should be noted that the individual refers to an entity representing a solution to a problem, and each individual is a representation of a historical bait placement plan; the bait placement factors are the bait placement time, placement quantity, and placement location.

[0119] According to an embodiment of the present invention, the snails in different growth cycles are tested and released according to the bait release population to evaluate the fitness of each individual in the bait release population, specifically:

[0120] Each growth cycle of the snail is traversed in a loop, and multiple population individuals corresponding to each growth cycle are extracted according to the bait release population;

[0121] Traverse multiple population individuals corresponding to each growth cycle, extract the release conditions of each population individual, and obtain multiple bait test release plans for snails in each growth cycle;

[0122] The bait test delivery plan is used to deliver actual bait to the snails in the corresponding growth cycle, and the video data of the snails in each growth cycle after the actual bait delivery in a preset time period are recorded in real time by video camera technology;

[0123] Acquire image feature data of historical snail feeding behavior, and extract video frame image data of the snail video data at preset time intervals;

[0124] Comparing the image feature data with the video frame image data to obtain the snail feeding behavior data of each growth cycle within a preset time period;

[0125] Evaluate the individual size change of snails within a preset time period according to the video frame image data to obtain the individual size change data of snails;

[0126] Evaluating the growth rate of the snails in each growth cycle according to the individual size change data;

[0127] The fitness of each individual in the bait-released population is evaluated based on the snail feeding behavior data and growth rate of each growth cycle.

[0128] It should be noted that the feeding behavior and growth rate of snails are evaluated through video frame image data, and finally the fitness of each population individual is evaluated. Not only the feeding behavior of snails is considered, but also their growth status is paid attention to, which provides a basis for a more comprehensive fitness evaluation. Through multi-dimensional information analysis, the algorithm can more accurately measure the effectiveness of the bait delivery plan in each growth cycle; the fitness is a measure of the superiority of an individual in solving problems or achieving goals, that is, the superiority evaluation result of the impact of the bait delivery plan represented by each population individual on the feeding behavior and growth rate of snails.

[0129] According to an embodiment of the present invention, the selection of population individuals that meet the preset fitness as parents to perform crossover and mutation operations to generate multiple bait delivery schemes to be selected is specifically as follows:

[0130] Set a preset fitness threshold, sort the fitness of each population individual, get the sorting result, and introduce the roulette wheel selection method to select the population individuals with fitness higher than the preset threshold as the parent generation;

[0131] The parent generations are randomly paired in pairs to obtain parent generation combinations, each parent generation combination is subjected to a population individual gene exchange operation according to a single-point crossover operator to obtain a new population individual, and the new population individual is used to replace the original parent generation to obtain a child generation individual;

[0132] Randomly selecting the gene loci of the offspring individuals for mutation operation to obtain the optimized offspring individuals in each growth cycle of the snail;

[0133] A natural language compilation operation is performed on the optimized offspring individuals to obtain a plurality of bait delivery plans to be selected for each growth cycle of the snails.

[0134] It should be noted that the roulette wheel selection method is a selection operation used in genetic algorithms. In the roulette wheel selection method, each individual is assigned a selection probability, which is proportional to the individual's fitness. Individuals with higher fitness are assigned a greater probability and are therefore more likely to be selected as parents to participate in crossover and mutation operations; the single-point crossover operator selects a random crossover point, cuts the genes of the two parent individuals at this point, and then exchanges their respective gene fragments to obtain two new individuals; the population individual gene exchange operation is crossover, which simulates the crossover process of biological genes in nature to produce new individuals. In the present application, it represents the crossover and exchange operation of the bait release factors in the two bait release schemes in each growth cycle; the mutation operation is to introduce new gene changes in the population by making random, local, and limited changes to individual genes. The mutation in the present application is to perform numerical replacement operations on the gene sites of the offspring that are artificially screened for unreasonable gene sites.

[0135] According to an embodiment of the present invention, the multiple bait delivery schemes to be selected are subjected to a secondary test delivery, and the feeding activity of the snails is observed in real time, specifically:

[0136] Select a preset number of bait placement plans for each growth cycle of snails for secondary trial placement, and obtain video data after the secondary placement;

[0137] Analyze the aggregation, feeding frequency, and feeding duration of snails according to the video data;

[0138] The feeding activity of the snails is evaluated based on the aggregation, feeding frequency and feeding time of the snails.

[0139] It should be noted that a preset number of candidate bait delivery plans were selected and a second trial delivery was conducted. Such a selection ensured a more in-depth verification of multiple plans in the actual environment, improving the representativeness of the experiment; the feeding activity of snails was evaluated, including the determination of snail aggregation, the quantification of feeding frequency, and the measurement of feeding time, which can accurately evaluate the feeding activity of snails in the experimental environment and provide a scientific basis for the subsequent optimization of bait delivery plans.

[0140] According to an embodiment of the present invention, the bait delivery scheme for each growth cycle of the snail is selected according to the feeding behavior activity to obtain the preferred bait delivery scheme, which is specifically:

[0141] According to the feeding behavior activity, the attractiveness of the selected bait delivery scheme to the snails is evaluated, and the utilization efficiency of the bait is determined according to the attractiveness;

[0142] Determining a bait delivery plan for each growth cycle of snails according to the utilization efficiency of the bait, and obtaining an optimal bait delivery plan;

[0143] The preferred bait delivery scheme is put into practical use, the actual effect of the preferred bait delivery scheme is evaluated, and real-time feedback and delivery parameters are adjusted according to the actual effect.

[0144] It should be noted that the snail bait delivery optimization method based on the growth cycle can scientifically evaluate the bait plan according to the activity of feeding behavior, determine the optimal plan and verify its effect in the actual environment, thereby realizing the intelligent adjustment and optimization of snail bait delivery.

[0145] The invention discloses a method and system for optimizing the bait placement of snails based on the growth cycle. By obtaining the historical bait placement schemes of snails in different growth cycles, a genetic algorithm is used to construct the bait placement population for each cycle. In the experimental placement, the fitness of each population individual is evaluated, and the individuals whose fitness meets the preset standard are selected as the parent generation, and crossover and mutation operations are performed to generate multiple bait placement schemes to be selected. And by performing a secondary test placement on the bait placement scheme to be selected, the feeding behavior activity of the snails is observed in real time. The bait placement scheme for each growth cycle is selected based on the activity, and finally the optimal bait placement scheme is obtained. Through this method, the behavioral differences of snails in different growth cycles are fully considered, the bait placement strategy is scientifically adjusted, the bait utilization rate is improved, and the snail placement effect is optimized.

[0146] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0147] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0148] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0149] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

[0150] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0151] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing the placement of snail bait based on the growth cycle, characterized in that: The following steps are involved: Obtain multiple historical bait delivery plans for snails in different growth cycles, and initialize the historical bait delivery plans in each growth cycle as population individuals based on a genetic algorithm to obtain the bait delivery population for each growth cycle; Experimentally releasing snails in different growth cycles according to the bait release population, and evaluating the fitness of each individual in the bait release population; Selecting individuals of a population that meet a preset fitness as parents to perform crossover and mutation operations to generate multiple bait delivery plans to be selected; The plurality of bait delivery schemes to be selected are delivered for a second trial, and the feeding activity of the snails is observed in real time; A bait delivery plan for each growth cycle of the snails is selected according to the activity of the feeding behavior to obtain an optimal bait delivery plan.

2. A method for optimizing the placement of snail bait based on the growth cycle according to claim 1, characterized in that: The method of obtaining a plurality of historical bait delivery plans of snails in different growth cycles and initializing the historical bait delivery plans in each growth cycle as population individuals based on a genetic algorithm to obtain a bait delivery population in each growth cycle is specifically as follows: Obtain the bait delivery time, delivery quantity, and delivery location information of each delivery of bait in different historical growth cycles of snails in the target breeding environment, and obtain multiple historical bait delivery plans for snails in different growth cycles; Binary encoding is performed on the bait delivery time, delivery quantity, and delivery location information of each historical bait delivery plan to form multiple digital historical bait delivery plans; A genetic algorithm is introduced to randomly generate a preset number of individuals, and each bait placement factor in each digitized historical bait placement plan is imported into the individuals as individual genes to obtain the bait placement population of snails in each growth cycle.

3. A method for optimizing the placement of snail bait based on the growth cycle according to claim 1, characterized in that: The method of testing the release of snails in different growth cycles according to the bait release population to evaluate the fitness of each individual in the bait release population is as follows: Each growth cycle of the snail is traversed in a loop, and multiple population individuals corresponding to each growth cycle are extracted according to the bait release population; Traverse multiple population individuals corresponding to each growth cycle, extract the release conditions of each population individual, and obtain multiple bait test release plans for snails in each growth cycle; The bait test delivery plan is used to deliver actual bait to the snails in the corresponding growth cycle, and the video data of the snails in each growth cycle after the actual bait delivery in a preset time period are recorded in real time by video camera technology; Acquire image feature data of historical snail feeding behavior, and extract video frame image data of the snail video data at preset time intervals; Comparing the image feature data with the video frame image data to obtain the snail feeding behavior data of each growth cycle within a preset time period; Evaluate the individual size change of snails within a preset time period according to the video frame image data to obtain the individual size change data of snails; Evaluating the growth rate of the snails in each growth cycle according to the individual size change data; The fitness of each individual in the bait-released population is evaluated based on the snail feeding behavior data and growth rate of each growth cycle.

4. A method for optimizing the placement of snail bait based on the growth cycle according to claim 1, characterized in that: The population individuals that meet the preset fitness are selected as parents to perform crossover and mutation operations to generate multiple bait delivery schemes to be selected, specifically: Set a preset fitness threshold, sort the fitness of each population individual, get the sorting result, and introduce the roulette wheel selection method to select the population individuals with fitness higher than the preset threshold as the parent generation; The parent generations are randomly paired in pairs to obtain parent generation combinations, each parent generation combination is subjected to a population individual gene exchange operation according to a single-point crossover operator to obtain a new population individual, and the new population individual is used to replace the original parent generation to obtain a child generation individual; Randomly selecting the gene loci of the offspring individuals for mutation operation to obtain the optimized offspring individuals in each growth cycle of the snail; A natural language compilation operation is performed on the optimized offspring individuals to obtain a plurality of bait delivery plans to be selected for each growth cycle of the snails.

5. A method for optimizing the placement of snail bait based on the growth cycle according to claim 1, characterized in that: The multiple bait delivery schemes to be selected are subjected to a secondary test delivery, and the feeding activity of the snails is observed in real time, specifically: Select a preset number of bait placement plans for each growth cycle of snails for secondary trial placement, and obtain video data after the secondary placement; Analyze the aggregation, feeding frequency, and feeding duration of snails according to the video data; The feeding activity of the snails is evaluated based on the aggregation, feeding frequency and feeding time of the snails.

6. A method for optimizing the placement of snail bait based on the growth cycle according to claim 1, characterized in that: The bait delivery scheme for each growth period of the snail is selected according to the feeding behavior activity to obtain the optimal bait delivery scheme, which is specifically: According to the feeding behavior activity, the attractiveness of the selected bait delivery scheme to the snails is evaluated, and the utilization efficiency of the bait is determined according to the attractiveness; Determining a bait delivery plan for each growth cycle of snails according to the utilization efficiency of the bait, and obtaining an optimal bait delivery plan; The preferred bait delivery scheme is put into practical use, the actual effect of the preferred bait delivery scheme is evaluated, and real-time feedback and delivery parameters are adjusted according to the actual effect.

7. A snail bait delivery optimization system based on growth cycle, characterized in that: The snail bait delivery optimization system based on the growth cycle includes a storage device and a processor, wherein the storage device includes a snail bait delivery optimization method program based on the growth cycle, and when the snail bait delivery optimization method program based on the growth cycle is executed by the processor, the following steps are implemented: Obtain multiple historical bait delivery plans for snails in different growth cycles, and initialize the historical bait delivery plans in each growth cycle as population individuals based on a genetic algorithm to obtain the bait delivery population for each growth cycle; Experimentally releasing snails in different growth cycles according to the bait release population, and evaluating the fitness of each individual in the bait release population; Selecting individuals of a population that meet a preset fitness as parents to perform crossover and mutation operations to generate multiple bait delivery plans to be selected; The plurality of bait delivery schemes to be selected are delivered for a second trial, and the feeding activity of the snails is observed in real time; A bait delivery plan for each growth cycle of the snails is selected according to the activity of the feeding behavior to obtain an optimal bait delivery plan.

8. A snail bait delivery optimization system based on growth cycle according to claim 7, characterized in that: The population individuals that meet the preset fitness are selected as parents to perform crossover and mutation operations to generate multiple bait delivery schemes to be selected, specifically: Set a preset fitness threshold, sort the fitness of each population individual, get the sorting result, and introduce the roulette wheel selection method to select the population individuals with fitness higher than the preset threshold as the parent generation; The parent generations are randomly paired in pairs to obtain parent generation combinations, each parent generation combination is subjected to a population individual gene exchange operation according to a single-point crossover operator to obtain a new population individual, and the new population individual is used to replace the original parent generation to obtain a child generation individual; Randomly selecting the gene loci of the offspring individuals for mutation operation to obtain the optimized offspring individuals in each growth cycle of the snail; A natural language compilation operation is performed on the optimized offspring individuals to obtain a plurality of bait delivery plans to be selected for each growth cycle of the snails.

9. The snail bait delivery optimization system based on growth cycle according to claim 7, characterized in that: The multiple bait delivery schemes to be selected are subjected to a secondary test delivery, and the feeding activity of the snails is observed in real time, specifically: Select a preset number of bait placement plans for each growth cycle of snails for secondary trial placement, and obtain video data after the secondary placement; Analyze the aggregation, feeding frequency, and feeding duration of snails according to the video data; The feeding activity of the snails is evaluated based on the aggregation, feeding frequency and feeding time of the snails.

10. The snail bait delivery optimization system based on growth cycle according to claim 7, characterized in that: The bait delivery scheme for each growth period of the snail is selected according to the feeding behavior activity to obtain the optimal bait delivery scheme, which is specifically: According to the feeding behavior activity, the attractiveness of the selected bait delivery scheme to the snails is evaluated, and the utilization efficiency of the bait is determined according to the attractiveness; Determining a bait delivery plan for each growth cycle of snails according to the utilization efficiency of the bait, and obtaining an optimal bait delivery plan; The preferred bait delivery scheme is put into practical use, the actual effect of the preferred bait delivery scheme is evaluated, and real-time feedback and delivery parameters are adjusted according to the actual effect.

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