Breeding environment regulation and control method for promoting rapid maturation of sexual glands of lateolabrax japonicus

Abstract

The invention provides a breeding environment regulation and control method for promoting rapid maturation of sexual glands of lateolabrax japonicus, and relates to the technical field of lateolabrax japonicus breeding environment regulation and control, and the method specifically comprises the following steps: under different breeding environment parameter combinations, respectively carrying out lateolabrax japonicus breeding tests, constructing a lateolabrax japonicus development prediction model, and obtaining growth state parameters and sexual gland development parameters of lateolabrax japonicus; the method comprises the following steps: establishing a functional relation among body length, body height, body weight and growth character coefficients, establishing a functional relation among gonad weight, gonad index and gonad development coefficients, generating gonad maturation coefficients, performing iterative optimization on the gonad maturation coefficients by adopting a genetic algorithm, screening out individuals corresponding to the gonad development degree reaching the IV stage, and establishing a functional relation among body length, body height, body weight and growth character coefficients. And taking the individual corresponding to the maximum value of the gonad maturation coefficient as an optimal breeding environment parameter combination. According to the method, the optimal breeding environment parameter combination is found out through iterative optimization of the genetic algorithm, so that the aim of promoting rapid maturation of sexual glands of lateolabrax japonicus is achieved, and the breeding efficiency and benefits are improved.

Description

Technical Field

[0001] The invention relates to the technical field of culture environment regulation of striped seabass, and in particular to a culture environment regulation method for promoting rapid maturation of gonads of striped seabass. Background Art

[0002] The striped seabass is an important economic fish species, widely distributed in tropical and subtropical waters. Due to its delicious meat and rich nutrition, the market demand is growing. Therefore, studying its growth and reproduction characteristics is of great significance to improving breeding efficiency and meeting market demand. In recent years, with the advancement of aquaculture technology, many environmental control technologies have been introduced into the breeding of striped seabass, including automated water quality monitoring and regulation equipment, intelligent feed feeding systems, and lighting control systems. The application of these technologies enables breeders to more accurately control the breeding environment, providing good conditions for the rapid maturation of the gonads of the striped seabass.

[0003] In the prior art, a method for breeding sea bass with reproductive regulation provided by announcement number CN105494210B comprises the following steps: light regulation, temperature regulation, salinity regulation and nutrition regulation. A special incubation device is used when incubating fertilized eggs. The southern broodstock are regulated by gradually extending the light time, stimulating temperature changes, stimulating salinity changes, etc., so as to promote the gonadal development of the southern broodstock, so that the sexual maturity time of the southern broodstock is advanced from November to January to October to December. At the same time, the light for the northern broodstock is reduced, and the low temperature is used for cultivation to delay the gonadal development of the broodstock, so that the sexual maturity time of the northern broodstock is delayed from September to November to October to December, so that the gonadal development of the southern broodstock and the northern broodstock are synchronized, and ovulation and sperm production are synchronized.

[0004] However, there are still the following deficiencies. From the above statements, it can be seen that although the existing technology has regulated light, temperature, salinity and nutrition, it often considers the influence of a certain factor alone, without comprehensively considering the combined effects of salinity, temperature, light cycle and nutrient content, resulting in an incomplete assessment of the impact on the gonadal development of the striped seabass, and poor setting of environmental parameters to promote the rapid maturation of the gonads of the striped seabass, resulting in long reproductive cycles and breeding cycles, making it difficult to achieve optimal breeding benefits.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The object of the present invention is to provide a method for regulating the aquaculture environment for promoting rapid maturation of the gonads of Lateolabrax japonicus, so as to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A method for regulating and controlling the aquaculture environment for promoting rapid maturation of the gonads of Lateolabrax japonicus, comprising the following specific steps: S1. Under different combinations of aquaculture environment parameters, a culture experiment of striped seabass was conducted, the culture experiment lasted for a total of T time period, and the striped seabass with gonad development degree at stage I was used for the culture experiment, and the growth state parameters and gonad development parameters of the striped seabass were obtained after the experiment. The growth trait parameters of the striped seabass included body length, body height and body weight, the gonad development parameters included gonad weight and gonad index, the culture environment parameters included salinity, temperature, photoperiod and nutrient content, and the gonad development degree was determined based on the gonad weight; S2, constructing a development prediction model for Lateolabrax japonicus, taking different combinations of aquaculture environment parameters as input, and using growth status parameters and gonad development parameters as label training models, and training the development prediction model for Lateolabrax japonicus; S3. Establishing constraints on aquaculture environment parameters, randomly combining aquaculture environment parameters, constructing individuals of an initial population of aquaculture environment parameters, inputting individuals of the initial population of aquaculture environment parameters into a development prediction model for sea bass, and obtaining growth status parameters and gonad development parameters; S4. Construct the functional relationship between body length, height, body weight and growth trait coefficient, construct the functional relationship between gonad weight, gonad index and gonad development coefficient, construct the functional relationship between growth trait coefficient, gonad development coefficient and gonad maturity coefficient. The gonad maturity coefficient is used to comprehensively evaluate the degree of gonad maturity of Lateolabrax japonicus. A genetic algorithm is used to select, crossover and mutate the gonad maturity coefficient until the predetermined number of iterations is reached. The individuals corresponding to the gonad development level reaching stage IV are screened out, and the individuals corresponding to the maximum value of the gonad maturity coefficient are used as the optimal breeding environment parameter combination.

[0008] Furthermore, the aquaculture environment parameters are randomly combined to construct individuals of the initial population of aquaculture environment parameters. The specific process is as follows: Set the duration of time period T to 6 months and mark the initial population as , and the initial population , is the first Individuals, is the index of the individual in the initial population, and , is the number of individuals in the initial population, ,in, Respectively salinity, temperature, photoperiod and nutrient content of each individual.

[0009] Furthermore, the development prediction model of the striped seabass is composed of a deep learning network based on a multilayer perceptron, and the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, and the first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function; The process of training the Lateolabrax development prediction model is as follows: Different combinations of breeding environment parameters are used as input, growth state parameters and gonad development parameters are used as output labels for training, and mean square error is used as the loss function. When the range is within the range, the training of the development prediction model of striped seabass is completed.

[0010] Furthermore, a functional relationship between body length, body height, body weight and growth trait coefficients was constructed, and the formula is as follows: ; in, For the The growth trait coefficient of each individual, For the The body length of each individual, For the The height of each individual, For the The weight of an individual, For the The weight coefficient of the combined parameters of body length and height of each individual, For the The weight coefficient of each individual's weight, , .

[0011] Furthermore, a functional relationship between gonad weight, gonad index and gonad development coefficient was constructed, and the formula is as follows: ; ; in, For the The gonadal development coefficient of each individual, For the The gonad weight of each individual, For the The gonadal index of each individual, For the The weight of an individual.

[0012] Furthermore, the functional relationship between the growth trait coefficient, gonad development coefficient and gonad maturity coefficient is constructed according to the following formula: ; in, For the The gonadal maturity coefficient of each individual, For the The weight coefficient of the growth trait coefficient of each individual, For the The weight coefficient of the gonadal development coefficient of each individual, and , The specific value of is determined by the hierarchical analysis method.

[0013] Furthermore, a genetic algorithm is used to select, crossover, and mutate the gonadal maturity coefficient until a predetermined number of iterations is reached to determine the optimal breeding environment parameter combination. The specific process is as follows: Iterative optimization is performed on the individuals of the initial population of aquaculture environment parameters. During the iterative optimization process, the constraints of the aquaculture environment parameters should be set, that is, the maximum and minimum values ​​of salinity, temperature, photoperiod, and nutrient content should be set respectively. Within the constraints of salinity, temperature, photoperiod, and nutrient content, the aquaculture environment parameters are iteratively optimized. Specifically, the gonad maturity coefficient Sort from large to small, select gland maturation coefficient The individuals in the front row are taken as the parents. Through crossover and mutation operations, the genes of the parent individuals are exchanged, combined and mutated to generate new individuals. The growth state parameters and gonad development parameters of the newly generated individuals are obtained by using the development prediction model of striped seabass, and the gonad maturity coefficient is calculated. The new individuals and the parents are taken as the new population, and the selection, crossover and mutation operations are repeated until the predetermined number of iterations is reached. The individuals corresponding to the gonad development level reaching stage IV are screened out, and the individuals corresponding to the maximum gonad maturity coefficient among them are used as the optimal breeding environment parameter combination, and the individuals corresponding to the maximum gonad maturity coefficient are calibrated as , then the optimal aquaculture environment parameter combination is salinity ,temperature , photoperiod and nutritional content .

[0014] Compared with the prior art, the present invention has the following beneficial effects: The invention conducts culture experiments of striped seabass under different culture environment parameter combinations, constructs a development prediction model of striped seabass, takes different culture environment parameter combinations as input, outputs growth state parameters and gonad development parameters of striped seabass, constructs functional relationship expressions among body length, body height, body weight and growth trait coefficients, constructs functional relationship expressions among gonad weight, gonad index and gonad development coefficient, generates gonad maturity coefficients for comprehensively evaluating gonad maturity of striped seabass, adopts a genetic algorithm, selects, crosses and mutates the gonad maturity coefficients, and screens out individuals whose gonad development degree reaches stage IV after reaching a predetermined number of iterations, and uses individuals corresponding to the maximum value of the gonad maturity coefficient as the optimal culture environment parameter combination. The gonad maturity coefficient provides a clear evaluation standard for culture managers, and finds out the best culture environment parameter combination through iterative optimization of the genetic algorithm, so as to achieve the purpose of promoting rapid gonad maturation of striped seabass, shortening the reproductive cycle and breeding cycle, and improving the efficiency and benefit of culture. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0017] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Embodiment 1: See also Figure 1 , the present invention provides a technical solution: A method for regulating and controlling the aquaculture environment for promoting rapid maturation of the gonads of Lateolabrax japonicus, comprising the following specific steps: S1. Under different combinations of aquaculture environment parameters, a culture experiment of striped seabass was conducted, the culture experiment lasted for a total of T time period, and the striped seabass with gonad development degree at stage I was used for the culture experiment. After the experiment, several striped seabass were randomly captured and averaged to obtain the growth state parameters and gonad development parameters of the striped seabass. The growth trait parameters of the striped seabass included body length, body height and body weight, the gonad development parameters included gonad weight and gonad index, the culture environment parameters included salinity, temperature, photoperiod and nutrient content, and the gonad development degree was determined based on the gonad weight; S2, constructing a development prediction model for Lateolabrax japonicus, taking different combinations of aquaculture environment parameters as input, and using growth status parameters and gonad development parameters as label training models, and training the development prediction model for Lateolabrax japonicus; S3. Establishing constraints on aquaculture environment parameters, randomly combining aquaculture environment parameters, constructing individuals of an initial population of aquaculture environment parameters, inputting individuals of the initial population of aquaculture environment parameters into a development prediction model for sea bass, and obtaining growth status parameters and gonad development parameters; S4. Construct the functional relationship between body length, height, body weight and growth trait coefficient, construct the functional relationship between gonad weight, gonad index and gonad development coefficient, construct the functional relationship between growth trait coefficient, gonad development coefficient and gonad maturity coefficient. The gonad maturity coefficient is used to comprehensively evaluate the degree of gonad maturity of Lateolabrax japonicus. A genetic algorithm is used to select, crossover and mutate the gonad maturity coefficient until the predetermined number of iterations is reached. The individuals corresponding to the gonad development level reaching stage IV are screened out, and the individuals corresponding to the maximum value of the gonad maturity coefficient are used as the optimal breeding environment parameter combination.

[0019] On the basis of the above embodiment, the aquaculture environment parameters are randomly combined to construct individuals of the initial population of aquaculture environment parameters. The specific process is as follows: Set the duration of time period T to 6 months and mark the initial population as , and the initial population , is the first Individuals, is the index of the individual in the initial population, and , is the number of individuals in the initial population, ,in, Respectively salinity, temperature, photoperiod and nutrient content of each individual.

[0020] On the basis of the above embodiment, the development prediction model of the striped seabass is composed of a deep learning network based on a multilayer perceptron, and the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, and the first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function; In this embodiment, the input features of the deep learning network of the multilayer perceptron include four features: salinity, temperature, photoperiod and nutrient content.

[0021] The structure of the deep learning network of multi-layer perceptron is: Input layer: receives input of 4 features; The first hidden layer has 64 neurons and uses ReLU as the activation function. The second hidden layer has 32 neurons and also uses the ReLU activation function. The third hidden layer has 16 neurons and uses the ReLU activation function. Output layer: has 2 neurons, growth state parameters and gonad development parameters.

[0022] The process of training the Lateolabrax development prediction model is as follows: Different combinations of breeding environment parameters are used as input, growth state parameters and gonad development parameters are used as output labels for training, and mean square error is used as the loss function. When the range is within the range, the training of the development prediction model of striped seabass is completed.

[0023] Based on the above examples, the correlation between the body length, body height, body weight and the growth quality of the striped seabass is as follows: The body length of the striped seabass is positively correlated with the growth quality of the striped seabass. Because the increase in body length usually means fast growth, which reflects that the striped seabass can effectively absorb nutrients in the breeding environment. Good nutritional intake can not only promote the growth of body length, but also help improve the overall growth quality.

[0024] The height of the striped bass is positively correlated with its growth quality, because the increase in height usually means the increase in the size of the striped bass, and the larger size of the striped bass is conducive to better storage of energy and nutrients, thereby improving the quality of growth.

[0025] The weight of the spotted seabass is positively correlated with the quality of its growth. Because the increase in weight usually reflects the growth rate of the spotted seabass, a heavier spotted seabass usually means that it has accumulated more biomass in a specific period of time, which indicates that it is in good growth condition and has improved its quality of growth.

[0026] According to the correlation between the body length, height, weight and the growth quality of the striped seabass, the functional relationship between the body length, height, weight and growth trait coefficients was constructed, and the formula is as follows: ; in, For the The growth trait coefficient of each individual, For the The body length of each individual, For the The height of each individual, For the The weight of an individual, For the The weight coefficient of the combined parameters of body length and height of each individual, For the The weight coefficient of each individual's body weight; No. Growth trait coefficients of individuals The value range is ; when Close to , indicating that the growth performance of the individual is poor, indicating that the breeding environment of the individual is not suitable; when Close to , indicating that the growth performance of the individual is excellent, indicating that the breeding environment of the individual is suitable; Therefore, the growth trait coefficient The larger it is, the better the growth of the individual.

[0027] The reasons for using the above function form to express the functional relationship between the body length, height, weight and growth trait coefficients of the sea bass are as follows: First, due to the length of the flower bass , body height ,weight and growth trait coefficients The functional relationship is not linear, that is, with the body length , body height or weight The increase in growth trait coefficient The change of is not in a fixed ratio, so an exponential function of nonlinear relationship is used to express it.

[0028] Second, body length and body height The product of This reflects the interaction between these two parameters. The growth of L. japonica is not the result of a single parameter, but the combined effect of multiple parameters. By multiplying them, the body length can be more effectively characterized. and body height How do the combinations of jointly affect the growth traits of Lateolabrax japonicus.

[0029] Third, weight Related to energy intake and expenditure, a larger body weight requires more energy to maintain physiological processes and growth, therefore, body weight With body length and body height The combination of products can be multiplied to emphasize weight An important role in the growth process, by multiplying the weight With body length and body height The combination of It can more comprehensively reflect the individual's growth potential.

[0030] Fourth, weight coefficient is a scalar used to adjust the body length and body height The product of the growth trait coefficient The degree of contribution Multiply The role is to regulate the overall contribution of body length and height to growth traits by introducing The body length can be flexibly adjusted according to the needs of different individuals or different environments. and body height relative importance to growth performance; There are significant differences in body weight among different individuals of L. and body height The growth of the child is outstanding, but the weight gain is slow. In this case, through appropriate Value, can increase weight The impact on growth traits can more comprehensively reflect the growth characteristics of individual striped sea bass.

[0031] Weight coefficient of the combined parameters of body length and height of an individual and the weight coefficient of the individual's weight The size relationship is set as follows: First, due to the length of the flower bass and body height are considered to be the key factors determining growth potential, and their product It can reflect the space occupation and growth capacity of individual sea bass. and body height This means that individuals have certain advantages in resource competition, so these two indicators dominate growth performance. and body height The combined effects of these factors can significantly affect an individual's growth performance; Second, the growth priority of individual sea bass is body length. and body height growth, especially in the early stages of growth, because individuals in their early years need to increase their body length and height to enhance their survival competitiveness and adaptability.

[0032] In the above case, the body length and body height The product of the growth trait coefficient The correlation is greater, weight and growth trait coefficients The correlation is small, so The value of is set larger. The value of is set to be smaller, that is, Based on .

[0033] As an implementation method, The value range is 0.5-0.6. The value range is 0.3-0.45. The specific value is set by the technicians according to the actual situation and is not limited here.

[0034] On the basis of the above examples, the correlation between the gonad weight, gonad index and gonad development quality of Lateolabrax japonicus is as follows: The gonad weight of Lateolabrax japonicus is positively correlated with the quality of gonad development. Because gonads are the main organs that produce germ cells (sperm or eggs), the increase in gonad weight corresponds to the increase in the number of mature germ cells, and the increase in the number of mature germ cells means that more mature germ cells can improve the individual's reproductive potential, ensure that more eggs or sperm are available for mating and fertilization, and the physiological function is more active, which can effectively respond to environmental and endocrine stimuli and promote reproductive behavior.

[0035] The gonad index of Lateolabrax is positively correlated with the quality of gonad development. Because the gonad index is the ratio of gonad weight to body weight, it can effectively reflect the reproductive capacity of an individual. An increase in the gonad index means that the individual can produce more mature reproductive cells (such as sperm or eggs), which not only improves the individual's reproductive capacity, but also increases the success rate of reproduction. With the good development of the gonads, the reproductive capacity is further enhanced, thus forming a positive feedback loop, making the gonad development better and better.

[0036] According to the correlation between the gonad weight, gonad index and gonad development of Lateolabrax japonicus, the functional relationship between the gonad weight, gonad index and gonad development coefficient was constructed as follows: ; ; in, For the The gonadal development coefficient of each individual, For the The gonad weight of each individual, For the The gonadal index of each individual, For the The weight of each individual; No. Gonadal development coefficient The value range is ; when Close to , indicating that the gonad development of the individual is poor, which means that the individual has poor reproductive ability; when Close to , indicating that the individual's gonads are well developed, which means that the individual has strong reproductive ability and can show good reproductive status during the breeding season.

[0037] Therefore, the gonadal development coefficient The larger it is, the better the gonadal development of the individual.

[0038] The reasons for using the above function form to express the functional relationship between the gonad weight, gonad index and gonad development coefficient of the striped sea bass are as follows: First, gonad weight The increase is related to changes in hormone levels. During development, gonads (such as testicles or ovaries) secrete more sex hormones (such as testosterone and estrogen). These hormones have an important influence on the development, maturation and function of the reproductive system. The increase in hormone levels further promotes reproductive ability, making the gonad development coefficient The gonadal development will go through several important stages. During the critical period of gonadal development, the gonadal weight The increase in the amount of induced a series of physiological changes, which are the result of cumulative effects, making the gonadal development coefficient It shows an exponential growth, so through the exponential function Express the functional relationship between gonad weight and gonad development coefficient.

[0039] Second, gonadal index is the gonadal weight Individual weight This ratio can eliminate the effect of body weight on gonadal development, making the comparison between individuals of different body weights more reasonable; a higher gonadal index Means relative to body weight , the gonadal development of the individual is good. By introducing the ratio of gonadal weight to individual body weight into the formula, the gonadal index The correction of individual growth status is realized. Increase, only when gonadal weight When the gonadal development coefficient increases faster It will improve significantly.

[0040] Third, gonadal development coefficient is a composite index that combines gonad weight and gonadal index , these two factors together reflect the individual's gonadal development status and can be used to assess the reproductive capacity and health level of the organism; the function in this formula It ensures that even when the gonad index and gonad weight are extremely large, the output value will not exceed 1, which can prevent the results from losing comparability and interpretability due to extreme values.

[0041] On the basis of the above embodiment, the growth trait coefficient and the gonad development coefficient are processed to generate a gonad maturity coefficient for comprehensively evaluating the gonad maturity of Lateolabrax japonicus, according to the following formula: ; in, For the The gonadal maturity coefficient of each individual is used to combine the growth trait coefficient of the individual and gonadal development coefficient , in order to comprehensively evaluate the maturity of the gonads of Lateolabrax japonicus, and the gonad maturity coefficient The larger it is, the more mature the gonads of the sea bass are, and the more conducive it is to reproduction. It should be noted that, as can be seen from the above description, the growth trait coefficient of an individual The larger the size, the better the growth, and the individual's gonadal development coefficient The larger the size, the better the gonad development, so the gonad maturity coefficient and growth trait coefficient , gonadal development coefficient are positively correlated, so the gonadal maturity coefficient in the above weighted sum form is set Calculation formula; In the formula, For the The weight coefficient of the growth trait coefficient of each individual, For the The weight coefficient of the gonadal development coefficient of each individual, and , The specific value of is determined by the hierarchical analysis method, and the specific logic is as follows: The two indicators of individual growth trait coefficient and gonad development coefficient are marked, and the relative importance between them is determined by the nine-scale method to construct a judgment matrix, in which the index of the growth trait coefficient is marked as 1 and the index of the gonad development coefficient is marked as 2. The constructed judgment matrix for: ; in, , denotes the index of the coefficient, and , , indicating that the index is The importance of the coefficient of index v on the gonadal maturation coefficient, The specific value is determined by relevant experts using a 1-9 scoring method. Indicates that the index is The coefficient of is more important for the gonadal maturation coefficient than the coefficient with index v. Indicates that the index is The coefficient of is extremely unimportant for the gonadal maturation coefficient compared to the coefficient with index v; Each element value in the judgment matrix is ​​divided by the sum of its columns to obtain a normalized judgment matrix. The mean of the element values ​​in each row of the normalized judgment matrix is ​​calculated, and the mean of the element values ​​in the first row is used as the weight coefficient of the individual's growth trait coefficient, and the mean of the element values ​​in the second row is used as the weight coefficient of the individual's gonad development coefficient. With the constraint that the sum of the scaled values ​​is equal to 1, the two weight coefficients are scaled in equal proportion, and the values ​​obtained after scaling are used as the weights of the corresponding coefficients.

[0042] On the basis of the above embodiment, a genetic algorithm is used to select, cross and mutate the gonadal maturity coefficient until a predetermined number of iterations is reached to determine the optimal breeding environment parameter combination. The specific process is as follows: Iterative optimization is performed on the individuals of the initial population of aquaculture environment parameters. During the iterative optimization process, the constraints of the aquaculture environment parameters should be set, that is, the maximum and minimum values ​​of salinity, temperature, photoperiod, and nutrient content should be set respectively. Within the constraints of salinity, temperature, photoperiod, and nutrient content, the aquaculture environment parameters are iteratively optimized. Specifically, the gonad maturity coefficient Sort from large to small, select gland maturation coefficient The individuals in the front row are regarded as the parents, and the front row refers to the individuals in the gonadal maturity coefficient. For the first 50% of individuals, the genes of the parent individuals are exchanged, combined and mutated through crossover and mutation operations to generate new individuals. The growth state parameters and gonad development parameters of the newly generated individuals are obtained using the flower seabass development prediction model, and their gonad maturity coefficients are calculated. The new individuals and the parents are used as new populations, and the selection, crossover and mutation operations are repeated until the predetermined number of iterations is reached. Since the gonad development degree of flower seabass is divided into four developmental stages: stage I, stage II, stage III, and stage IV, when the gonad development degree reaches stage IV, the gonad weight of males is 5-15 grams, and the gonad weight of females is 7-20 grams. From stage I to stage IV, as the gonad develops, the gonad weight gradually increases. Individuals whose gonad weight reaches the stage IV standard are screened out, and the individuals corresponding to the maximum gonad maturity coefficient among them are used as the optimal breeding environment parameter combination, and the individuals corresponding to the maximum gonad maturity coefficient are calibrated as , then the optimal aquaculture environment parameter combination is salinity ,temperature , photoperiod and nutritional content .

[0043] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0044] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by computer software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0045] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and 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 solution of this embodiment.

[0046] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for regulating the aquaculture environment for promoting rapid maturation of the gonads of Lateolabrax japonicus, characterized in that: The specific steps include: S1. Under different combinations of aquaculture environment parameters, a culture experiment of striped seabass was conducted, the culture experiment lasted for a total of T time period, and the striped seabass with gonad development degree at stage I was used for the culture experiment, and the growth state parameters and gonad development parameters of the striped seabass were obtained after the experiment. The growth trait parameters of the striped seabass included body length, body height and body weight, the gonad development parameters included gonad weight and gonad index, the culture environment parameters included salinity, temperature, photoperiod and nutrient content, and the gonad development degree was determined based on the gonad weight; S2, constructing a development prediction model for Lateolabrax japonicus, taking different combinations of aquaculture environment parameters as input, and using growth status parameters and gonad development parameters as label training models, and training the development prediction model for Lateolabrax japonicus; S3. Establishing constraints on aquaculture environment parameters, randomly combining aquaculture environment parameters, constructing individuals of an initial population of aquaculture environment parameters, inputting individuals of the initial population of aquaculture environment parameters into a development prediction model for sea bass, and obtaining growth status parameters and gonad development parameters; S4. Construct the functional relationship between body length, height, body weight and growth trait coefficient, construct the functional relationship between gonad weight, gonad index and gonad development coefficient, construct the functional relationship between growth trait coefficient, gonad development coefficient and gonad maturity coefficient. The gonad maturity coefficient is used to comprehensively evaluate the degree of gonad maturity of Lateolabrax japonicus. A genetic algorithm is used to select, crossover and mutate the gonad maturity coefficient until the predetermined number of iterations is reached. The individuals corresponding to the gonad development level reaching stage IV are screened out, and the individuals corresponding to the maximum value of the gonad maturity coefficient are used as the optimal breeding environment parameter combination.

2. The method for regulating the aquaculture environment for promoting rapid maturation of the gonads of Lateolabrax japonicus according to claim 1, characterized in that: The aquaculture environment parameters are randomly combined to construct individuals of the initial population of aquaculture environment parameters. The specific process is as follows: Set the duration of time period T to 6 months and mark the initial population as , and the initial population , is the first Individuals, is the index of the individual in the initial population, and , is the number of individuals in the initial population, ,in, Respectively salinity, temperature, photoperiod and nutrient content of each individual.

3. The method for regulating the aquaculture environment for promoting rapid maturation of the gonads of Lateolabrax japonicus according to claim 2, characterized in that: The development prediction model of the striped sea bass is composed of a deep learning network based on a multi-layer perceptron, wherein the deep neural network of the multi-layer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, wherein the first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function; The process of training the Lateolabrax development prediction model is as follows: Different combinations of breeding environment parameters are used as input, growth state parameters and gonad development parameters are used as output labels for training, and mean square error is used as the loss function. When the range is within the range, the training of the development prediction model of striped seabass is completed.

4. The method for regulating the aquaculture environment for promoting rapid maturation of the gonads of Lateolabrax japonicus according to claim 3, characterized in that: The functional relationship between body length, body height, body weight and growth trait coefficients is constructed as follows: ； in, For the The growth trait coefficient of each individual, For the The body length of each individual, For the The height of each individual, For the The weight of an individual, For the The weight coefficient of the combined parameters of body length and height of each individual, For the The weight coefficient of each individual's weight, , .

5. The method for regulating the aquaculture environment for promoting rapid maturation of the gonads of Lateolabrax japonicus according to claim 4, characterized in that: The functional relationship between gonad weight, gonad index and gonad development coefficient was constructed as follows: ； ； in, For the The gonadal development coefficient of each individual, For the The gonad weight of each individual, For the The gonadal index of each individual, For the The weight of an individual.

6. The method for regulating the aquaculture environment for promoting rapid maturation of the gonads of Lateolabrax japonicus according to claim 5, characterized in that: The functional relationship between growth trait coefficient, gonad development coefficient and gonad maturity coefficient is constructed based on the following formula: ； in, For the The gonadal maturity coefficient of each individual, For the The weight coefficient of the growth trait coefficient of each individual, For the The weight coefficient of the gonadal development coefficient of each individual, and , The specific value of is determined by the hierarchical analysis method.

7. The method for regulating the aquaculture environment for promoting rapid maturation of the gonads of Lateolabrax japonicus according to claim 6, characterized in that: The genetic algorithm is used to select, cross and mutate the gonadal maturity coefficient until the predetermined number of iterations is reached to determine the optimal combination of breeding environment parameters. The specific process is as follows: Iterative optimization is performed on the individuals of the initial population of aquaculture environment parameters. During the iterative optimization process, the constraints of the aquaculture environment parameters should be set, that is, the maximum and minimum values ​​of salinity, temperature, photoperiod, and nutrient content should be set respectively. Within the constraints of salinity, temperature, photoperiod, and nutrient content, the aquaculture environment parameters are iteratively optimized. Specifically, the gonad maturity coefficient Sort from large to small, select gland maturation coefficient The individuals in the front row are taken as the parents. Through crossover and mutation operations, the genes of the parent individuals are exchanged, combined and mutated to generate new individuals. The growth state parameters and gonad development parameters of the newly generated individuals are obtained by using the development prediction model of striped seabass, and the gonad maturity coefficient is calculated. The new individuals and the parents are taken as the new population, and the selection, crossover and mutation operations are repeated until the predetermined number of iterations is reached. The individuals corresponding to the gonad development level reaching stage IV are screened out, and the individuals corresponding to the maximum gonad maturity coefficient among them are used as the optimal breeding environment parameter combination, and the individuals corresponding to the maximum gonad maturity coefficient are calibrated as , then the optimal aquaculture environment parameter combination is salinity ,temperature , photoperiod and nutritional content .

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