Numerical simulation method and system for lateolabrax japonicus with rapid mature gonad in complex sea area system model

Abstract

The invention discloses a numerical simulation method and system for lateolabrax japonicus with rapidly mature gonads in a complex sea area system model, and relates to the technical field of fish numerical simulation. Comprising the following steps: selecting same-age lateolabrax japonicus to randomly group, feeding each group of test samples under water samples with different salinity, and keeping other environmental parameters consistent; by randomly detecting the gonad development key hormone level and key gene expression ability level of lateolabrax japonicus in each group of test samples, establishing a prediction model taking salinity as input and gonad development related indexes as output; inputting the salinity of a sea area to be bred into the model to obtain predicted values of key hormone level and key gene expression ability level of gonad development of lateolabrax japonicus, and generating a primary gonad development index in combination with environmental parameters; and correcting the gonad development index based on the sea area disturbance parameters to obtain an accurate gonad development index to finish simulation of the gonad development degree of lateolabrax japonicus. According to the method, the gonad development degree of lateolabrax japonicus in a complex sea area environment can be accurately predicted, and guidance is provided for scientific culture.

Description

Technical Field

[0001] The invention relates to the technical field of fish numerical simulation, and in particular to a method and system for numerical simulation of Lateolabrax japonicus with rapid gonadal maturation in a complex sea system model. Background Art

[0002] Lateolabrax japonicus is one of the important economic marine fish species in China. It is widely distributed in coastal waters of my country and is valued by farmers for its delicious meat, fast growth and strong adaptability. However, with the expansion of the scale of lateolabrax farming and the complexity of the ecological environment in natural sea areas, how to promote the gonadal development of lateolabrax japonicus and improve its reproductive efficiency has become a core issue in the current aquaculture and fishery resource management. During the gonadal development of lateolabrax japonicus, salinity, temperature, light, nutrition and other environmental disturbances (such as sea surface wind speed, ocean current circulation, etc.) have a significant impact on its gonadal maturation and reproductive results. For example, salinity fluctuations can significantly affect the secretion of key hormones such as gonadotropin-releasing hormone, follicle-stimulating hormone and luteinizing hormone by regulating the endocrine system and gene expression of fish. Changes in these hormone levels further lead to the upregulation or downregulation of the expression levels of genes related to gonadal development (such as aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor), ultimately affecting the maturation process and quality of gonadal development.

[0003] At present, research on gonadal development in marine ecology and aquaculture, especially for fish with complex growth cycles such as striped seabass, lacks a systematic modeling method that can accurately reflect the impact of environmental changes on individual organisms. Most existing studies focus on the role of a single environmental factor, while ignoring the comprehensive effects of multiple factor interactions on the growth and gonadal development of striped seabass. Therefore, a new method is urgently needed to monitor and simulate the gonadal development process of striped seabass in real time, especially the changes under different salinities and sea conditions. This requires combining environmental changes with the growth characteristics of individual organisms to propose a method that can quickly and efficiently predict the level of gonadal development of striped seabass.

[0004] As a powerful data processing and prediction tool, neural network models also have wide application potential in the field of aquaculture, but they are currently less used, especially in the process of gonadal development, which is still in its infancy. Therefore, building a gonadal development prediction model for Lateolabrax japonicus based on neural network technology and combining it with environmental factors for real-time monitoring and simulation can effectively improve the accuracy and application scope of gonadal development prediction, thereby providing strong support for marine ecological protection and aquaculture management.

[0005] In the prior art, the publication number CN117350068B discloses a method and device for numerical simulation of herbivorous fish in a complex water ecosystem model, which collects the basic data required for constructing a complex water ecosystem; based on the PCLake+ model, according to the collected basic data, a PCLake+Gras numerical simulation model of a complex water ecosystem is constructed; according to the collected physiological and biochemical process data of herbivorous fish, various parameters of herbivorous fish in the PCLake+Gras model are calibrated to obtain a calibrated PCLake+Gras model; the collected measured lake water quality and water ecology data are input into the calibrated PCLake+Gras model, and the water ecosystem simulation result of the simulation object is output. This scheme requires a large amount of accurate basic data, including water quality, water ecology, physiological and biochemical processes of herbivorous fish in complex water ecosystems, etc. However, in practical applications, data collection may not cover the spatial heterogeneity of the entire lake ecosystem (such as differences in water quality and fish distribution in different regions), or reflect the trend of long-term dynamic changes. At the same time, in complex aquatic ecosystems, many key ecological processes are highly nonlinear or random, but this solution may not fully consider these factors, which limits the model's predictive ability. Therefore, the accuracy and effectiveness of the simulation results are reduced.

[0006] 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

[0007] The object of the present invention is to provide a method and system for numerical simulation of rapid gonadal maturation of Lateolabrax japonicus in a complex marine system model, so as to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: A numerical simulation method for the rapid maturation of gonads of Lateolabrax japonicus in a complex marine system model, the specific steps comprising: Select several striped seabass of the same age to be reared, randomly group the selected striped seabass groups of the same age to form several groups of test samples, set different salinity water samples for different groups of test samples, and keep the other environmental parameters of each group of test samples consistent except salinity. Raise the striped seabass of each group of test samples for a period of time. ; After the feeding experiment is completed, the key hormone level of gonad development and the key gene expression ability level of each seabass in each group of test samples are randomly tested, wherein the key hormone level of gonad development is calculated by calculating the content of gonadotropin-releasing hormone, follicle-stimulating hormone and luteinizing hormone, and the key gene expression ability level is calculated by calculating the content of aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor; Based on the key hormone levels and key gene expression levels of gonadal development of each striped sea bass in the test sample, a neural network prediction model was established. The salinity of water samples from different groups of test samples was used as the model input, and the model was trained with the corresponding key hormone levels and key gene expression levels of gonadal development as labels to obtain a gonadal development prediction model. The salinity of the sea area where the japonica sea bass is to be reared is collected, and the salinity is input into the trained gonad development prediction model to obtain the predicted values ​​of the key hormone levels and key gene expression ability levels of the japonica sea bass gonad development in this sea area, and the japonica sea bass gonad development index is generated by combining the environmental parameters of the sea area where the japonica sea bass is to be reared; The disturbance parameters in the sea area where the striped seabass to be raised are obtained, and the striped seabass gonad development index is corrected based on the disturbance parameters to obtain the accurate striped seabass gonad development index. The simulation of the gonad development degree of the striped seabass is completed according to the accurate striped seabass gonad development index, wherein the disturbance parameters include the maximum wind speed on the sea surface and the sea circulation speed.

[0009] Furthermore, different salinity water samples are set for different groups of test samples, and other environmental parameters except salinity are kept consistent among the test samples of each group, wherein the other environmental parameters include water temperature, dissolved oxygen concentration, average daily light intensity and average daily nutrient content; Each group of test samples were reared for a period of , wherein the length of time of feeding Calibrated as ,in, Indicates the start of the feeding experiment, represents the length of the time period in days, and , Is a positive integer.

[0010] Furthermore, based on the key hormone levels and key gene expression levels of gonadal development of each striped seabass in the test sample, a neural network prediction model was established. A neural network prediction model was established based on the LSTM model, and the activation function and optimization algorithm were selected. The Tanh function was selected as the activation function, and Adam was selected as the optimization algorithm of the LSTM model. The formula of the Tanh function is: ; In the formula, Represents the Tanh function, independent variable Represents the weighted sum of the neuron's input, that is, the result of weighted summation of the input received by the neuron from the previous layer; At the same time, the hyperparameters of the LSTM model are set, and the hyperparameters of the LSTM model include: the number of network layers, the number of iterations, the learning rate, the batch size, the number of training times, the batch processing number, and the number of hidden layer neurons; The number of network layers is set to a 3-layer network structure, the number of iterations is set to 200, the learning rate is set to 0.001, the batch size is set to 32, the number of training times is set to 100, the batch size is set to 256, and the number of hidden layer neurons is 32.

[0011] Furthermore, according to the predicted values ​​of the key hormone levels and key gene expression ability levels of the striped sea bass gonad development in this sea area, combined with the environmental parameters of the sea area where the striped sea bass is to be raised, the striped sea bass gonad development index is generated, wherein the specific formula for calculating the striped sea bass gonad development index is: ; In the formula, represents the gonadal development index of Lateolabrax japonicus. and are the predicted values ​​of key hormone levels and key gene expression levels for gonadal development of Leptospermum japonicum, To detect the sea water temperature, The water temperature for the breeding experiment is is the average daily light intensity of the feeding experiment, To detect the average daily light intensity in the sea area, To detect the dissolved oxygen concentration in the sea area, To detect the average daily nutrient content of the sea area; The levels of key hormones and key gene expression in the gonadal development of sea bass and Characterized by the produced hormone content and related proteins, the specific calculation formula is: ; ; In the formula, is the predicted value of gonadotropin-releasing hormone content after the same period of time as the feeding experiment, is the predicted value of follicle-stimulating hormone content during the same period as the feeding experiment, is the predicted value of luteinizing hormone content in the same period as the feeding experiment, , and They are the predicted values ​​of aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor contents during the same period as the feeding experiment, respectively.

[0012] Furthermore, the average daily nutrient content of the sea area is detected The specific calculation formula is: ; In the formula, and To detect the average nitrate and phosphate content in the sea area, To detect the number of biological species in the sea, is the abundance of zooplankton, and are the weight coefficients of the number of biological species in the detected sea area and the sum of the mean nitrate content and the mean phosphate content, respectively. ,and and are all greater than 0, and the abundance of zooplankton The calculation is based on the formula: ; In the formula, is the total mass of zooplankton in the sample water collected, The volume of water sample to filter zooplankton, is the capture efficiency correction factor.

[0013] Furthermore, the gonad development index of the striped seabass was corrected based on the disturbance parameters to obtain the accurate gonad development index of the striped seabass, wherein the formula for calculating the accurate gonad development index of the striped seabass is: ; In the formula, To accurately indices the gonadal development of Lateolabrax japonicus. is the maximum wind speed on the sea surface, is the ocean circulation velocity, and are the weight coefficients of the maximum wind speed on the sea surface and the sea circulation speed, respectively. and and All are greater than 0; Set the threshold for judging the gonadal development of Lateolabrax to , where the logic for judging the gonadal development status of Lateolabrax japonicus is: when When the gonad development of the japonica seabass is good, it means that the gonad development of the japonica seabass in this sea area is fast and suitable for breeding. when When the gonad development of the japonica seabass is good, it is judged that the gonad development of the japonica seabass to be reared in this sea area is good; when , it is judged that the gonad development of the striped seabass is poor, indicating that in this sea area, the gonad development speed of the striped seabass to be raised is slow and it is not suitable for raising.

[0014] The present invention also provides a numerical simulation system for a striped sea bass with rapid gonadal maturation in a complex sea system model, and the numerical simulation system for a striped sea bass with rapid gonadal maturation in a complex sea system model is used to execute the numerical simulation method for a striped sea bass with rapid gonadal maturation in a complex sea system model, comprising: The test sample preparation module is used to select several striped bass of the same age of the species to be raised, randomly group the selected striped bass groups of the same age to form several groups of test samples, set different salinity water samples for different groups of test samples, and keep the other environmental parameters of each group of test samples consistent except salinity, and raise the striped bass of each group of test samples for a period of time. ; The gene and hormone analysis module is used to randomly detect the key hormone level of gonadal development and the key gene expression ability level of each striped seabass in each group of test samples after the feeding experiment is completed, wherein the key hormone level of gonadal development is obtained by calculating the content of gonadotropin-releasing hormone, follicle-stimulating hormone and luteinizing hormone, and the key gene expression ability level is obtained by calculating the content of aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor; The prediction model training module is used to establish a neural network prediction model based on the key hormone levels and key gene expression levels of gonadal development of each striped sea bass in the test sample, take the salinity of water samples of different groups of test samples as model input, and use the corresponding key hormone levels and key gene expression levels of gonadal development as labels to train the model, and obtain a gonadal development prediction model; The gonad development index calculation module is used to collect the salinity of the sea area where the japonica sea bass is to be raised, input the salinity into the trained gonad development prediction model, obtain the predicted values ​​of the key hormone levels and key gene expression ability levels of the japonica sea bass gonad development in the sea area, and generate the japonica sea bass gonad development index in combination with the environmental parameters of the sea area where the japonica sea bass is to be raised; The precise development index simulation module is used to obtain the disturbance parameters in the sea area where the striped seabass to be raised grows, correct the striped seabass gonad development index based on the disturbance parameters to obtain the precise striped seabass gonad development index, and complete the simulation of the striped seabass gonad development degree according to the precise striped seabass gonad development index. The disturbance parameters include the maximum wind speed on the sea surface and the sea circulation speed.

[0015] Compared with the prior art, the present invention has the following beneficial effects: First, through random grouping and multi-salinity experimental design, the effects of salinity on key hormone levels and gene expression during the gonadal development of L. japonicus, including aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor, were systematically studied, and the multi-dimensional regulatory mechanism of salinity on gonadal development was fully revealed. Through deep learning and fitting of experimental data by neural network model, the complex nonlinear relationship between salinity and gonadal development indicators can be effectively captured, overcoming the limitation of traditional analysis methods that can only evaluate simple linear relationships. Secondly, the model can not only make predictions under different salinity conditions, but also accurately evaluate the levels of key hormones and gene expression capacity of gonadal development when other environmental factors (such as temperature, nutrients, etc.) remain unchanged. Finally, the dynamic effects of complex marine environment on gonadal development of L. japonicus were considered. In traditional studies, the effects of disturbance factors such as wind speed and circulation on reproductive physiology are often ignored, resulting in large deviations from the actual environment. This scheme uses disturbance parameters in natural sea areas such as maximum sea surface wind speed and ocean circulation speed as correction factors to make secondary corrections to the gonadal development index, which significantly improves the applicability and accuracy of model prediction. It provides technical reference for aquaculture and fishery management, and promotes the efficient and sustainable development of the sea bass industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] 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.

[0019] Example: See also Figure 1, the present invention provides a technical solution: A numerical simulation method for the rapid maturation of gonads of Lateolabrax japonicus in a complex marine system model, the specific steps comprising: Step 1: Select several striped perch of the same age to be raised, randomly group the selected striped perch groups of the same age to form several groups of test samples, set different salinity water samples for different groups of test samples, and keep the other environmental parameters of each group of test samples consistent except salinity. Raise the striped perch of each group of test samples for a period of time. .

[0020] Different salinity water samples were set for different groups of test samples. Except for salinity, other environmental parameters of each group of test samples were kept consistent, wherein the other environmental parameters included water temperature, dissolved oxygen concentration, average daily light intensity and average daily nutrient content; Each group of test samples were reared for a period of , wherein the length of time of feeding Calibrated as ,in, Indicates the start time of the feeding experiment, represents the length of the time period in days, and , Is a positive integer.

[0021] The striped seabass of the same age are generally selected as one-year-old striped seabass. One-year-old striped seabass has important scientific significance and practical value as the research object. One-year-old striped seabass refers to individual striped seabass that grows for about one year after birth. The fish population at this stage is relatively stable in physiological and ecological characteristics, and it is a key stage for studying the dynamic changes of gonadal development of striped seabass. One-year-old striped seabass is usually in a stage of rapid growth, and its gonadal development begins to enter the initial stage, which is an important time window for studying the laws of reproductive development. From a physiological point of view, the reproductive system of fish at this stage is not yet fully mature, but the gonads have already shown certain developmental characteristics.

[0022] From an ecological perspective, one-year-old japonicus usually have good adaptability and respond significantly to changes in environmental factors (such as salinity, temperature, and light). Therefore, choosing one-year-old japonicus helps to clearly observe the impact of the external environment on its gonadal development.

[0023] Step 2: After the feeding experiment is completed, the key hormone levels for gonadal development and the key gene expression ability levels of each striped seabass in each group of test samples are randomly tested, wherein the key hormone levels for gonadal development are calculated by calculating the contents of gonadotropin-releasing hormone, follicle-stimulating hormone and luteinizing hormone, and the key gene expression ability levels are calculated by calculating the contents of aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor.

[0024] The key hormone levels of gonadal development and the key gene expression ability levels of each striped seabass in each group of test samples were randomly tested. The key hormone levels of gonadal development were characterized by the produced hormone content, and the key gene expression ability levels were characterized by the content of related proteins of the parameters, including aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor.

[0025] In order to accurately determine the key hormone levels and key gene expression capacity of the gonadal development of sea bass, it is necessary to randomly select individuals from the experimental samples to collect tissue and blood samples. The detection method is enzyme-linked immunosorbent assay (ELISA), and the specific steps are: select a commercial ELISA kit for the content of gonadotropin-releasing hormone, follicle-stimulating hormone and luteinizing hormone to ensure targeting and sensitivity, dilute and process the serum sample according to the kit instructions, add it to a 96-well plate, and perform antibody binding, washing, color development and other steps in sequence, use an enzyme reader (450nm) to read the absorbance value, and calculate the hormone content according to the standard curve.

[0026] The expression level of key genes is characterized by the content of related proteins of the parameters, including aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor. The content of key proteins (aromatase, follicle-stimulating hormone receptor, luteinizing hormone receptor) reflects the final expression product level of the gene. Commonly used techniques are Western Blot or enzyme-linked immunosorbent assay (ELISA), and the specific steps include: extracting total protein from gonadal tissue, measuring protein concentration (such as BCA method), separating proteins by SDS-PAGE gel electrophoresis, transferring to PVDF membrane, detecting with specific antibodies against target proteins, developing with chemiluminescence (ECL), recording signal band intensity, and calculating relative protein levels by normalization with internal reference (such as β-actin).

[0027] Step 3: Based on the key hormone levels and key gene expression levels of gonadal development of each striped seabass in the experimental samples, a neural network prediction model was established. The salinity of water samples in different groups of experimental samples was used as the model input, and the model was trained with the corresponding key hormone levels and key gene expression levels of gonadal development as labels to obtain the gonadal development prediction model.

[0028] Based on the key hormone levels and key gene expression levels of gonadal development of each sea bass in the test sample, a neural network prediction model was established. A neural network prediction model was established based on the LSTM model, and the activation function and optimization algorithm were selected. The Tanh function was selected as the activation function, and Adam was selected as the optimization algorithm of the LSTM model. The formula of the Tanh function is: ; In the formula, Represents the Tanh function, independent variable Represents the weighted sum of the neuron's input, that is, the result of weighted summation of the input received by the neuron from the previous layer; At the same time, the hyperparameters of the LSTM model are set, and the hyperparameters of the LSTM model include: the number of network layers, the number of iterations, the learning rate, the batch size, the number of training times, the batch processing number, and the number of hidden layer neurons; The number of network layers is set to a 3-layer network structure, the number of iterations is set to 200, the learning rate is set to 0.001, the batch size is set to 32, the number of training times is set to 100, the batch size is set to 256, and the number of hidden layer neurons is 32.

[0029] LSTM can capture the dynamic regulation of hormone and gene expression during gonadal development. This time-based dynamic modeling capability can make up for the shortcomings of traditional statistical models (such as linear regression or multivariate regression) in dealing with time correlation. LSTM neural networks can fit highly complex nonlinear relationships through multiple layers of neurons and weight adjustment. The effect of salinity on key indicators of gonadal development may be regulated by multiple feedback mechanisms, and LSTM can capture these complex relationships more accurately.

[0030] Finally, the input is the salinity of the water in the growth environment of the striped seabass, and the output is the key hormone levels and key gene expression ability levels of the striped seabass' gonadal development within a specific time range.

[0031] Step 4: Collect the salinity of the sea area where the striped seabass to be raised grows, and input the salinity into the trained gonad development prediction model to obtain the predicted values ​​of the key hormone levels and key gene expression ability levels of the striped seabass gonad development in this sea area. Combined with the environmental parameters of the sea area where the striped seabass to be raised grows, generate the striped seabass gonad development index.

[0032] According to the predicted values ​​of the key hormone levels and key gene expression levels of the striped perch gonad development in this sea area, combined with the environmental parameters of the sea area where the striped perch will grow, the striped perch gonad development index is generated. The specific formula for calculating the striped perch gonad development index is: ; In the formula, represents the gonadal development index of Lateolabrax japonicus. and are the predicted values ​​of key hormone levels and key gene expression levels for gonadal development of Leptospermum japonicum, To detect the sea water temperature, The water temperature for the breeding experiment is is the average daily light intensity of the feeding experiment, To detect the average daily light intensity in the sea area, To detect the dissolved oxygen concentration in the sea area, To detect the average daily nutrient content of the sea area; Where, the gonadal development index of Lateolabrax japonicus The gonadal development of L. japonicus was characterized by combining the key hormone levels and key gene expression capacity predicted values ​​of L. japonicus gonadal development with environmental factors. The larger it is, the more mature the gonads of the striped seabass are and the faster the gonads develop.

[0033] It reflects the direct regulatory effect of the endocrine system of L. japonicus on gonadal development, and reveals the hormonal drive of gonadal development by measuring the levels of gonadotropin (gonadotropin-releasing hormone, follicle-stimulating hormone, and luteinizing hormone). Characterizing the transcription and expression capacity of genes related to gonadal development (such as aromatase, gonadotropin receptors, etc.) is the molecular basis of gonadal development. These are key biological indicators reflecting the physiological process of gonadal development in striped seabass. The progress of gonadal development is closely related to the levels of sex hormones (such as gonadal hormones, sex hormone receptors, etc.), and the level of gene expression capacity can also affect gonadal development. The combination of the two is used to quantify the level of gonadal development in striped seabass. Hormone levels and gene expression capacity are the core driving forces of gonadal development, so and Gonadal development index of L. japonicum The two are directly proportional, and are introduced in the form of a product, which reflects the importance of their synergistic effect, that is, only when hormone levels and gene expression reach high levels at the same time can they support good gonadal development.

[0034] Water temperature is a key environmental factor in the development of the gonads of Lateolabrax japonicus, directly affecting their metabolic rate, enzyme activity, and hormone secretion. The larger the value, the less likely the sea bass will be able to adapt, and the gonadal development will be inhibited. Gonadal development index of Lateolabrax japonicus The water temperature difference is added to the denominator to play a penalty role, indicating that the greater the water temperature deviation, the greater the gonadal development index of the sea bass. The lower.

[0035] Light affects the circadian rhythm of organisms, thereby regulating the levels of hormones related to gonadal development (such as melatonin secreted by the pineal gland). If the actual light intensity is significantly different from the experimental conditions, it may cause biological rhythm disorders, thereby affecting gonadal development. Gonadal development index of L. japonicum Inversely proportional, adding the light difference in the denominator has a penalizing effect, similar to the water temperature difference.

[0036] Among them, the culture water temperature of the feeding experiment Average daily light intensity in the feeding experiment Generally, these are the optimal temperature and optimal daily light intensity for the growth and development of striped sea bass. The specific values ​​can be set by referring to relevant materials and combining expert experience.

[0037] The dissolved oxygen concentration in the tested sea area is an important environmental factor for the metabolism, energy consumption and gonadal development of the striped sea bass. The higher the dissolved oxygen level, the more sufficient the oxygen supply in the water body, the more active the physiological functions of the striped sea bass (such as oxidative metabolism and germ cell development), thus promoting gonadal development. Therefore, the dissolved oxygen concentration in the tested sea area is Gonadal development index of L. japonicum Proportional, in logarithmic form The introduction of weakens the impact of extremely high dissolved oxygen values, while ensuring that the negative impact at low dissolved oxygen concentrations is significantly amplified.

[0038] Nutrients are an important material basis for the development of the gonads of Lateolabrax japonicus. In particular, the formation of yolk protein and the growth of gonadal tissue have high energy and nutrient requirements. Nutritional status is crucial to the gonadal development of Lateolabrax japonicus. The average daily nutrient content directly affects the growth rate and gonadal development of fish. The reproductive health of fish is closely related to its nutritional status. Therefore, the average daily nutrient content of the sea area is tested. Gonadal development index of L. japonicum Proportional, exponential form The introduction of can highlight the positive effect of nutrient content while avoiding excessive amplification of extreme high values.

[0039] Among them, the key hormone levels and key gene expression levels of Lateolabrax japonicus gonadal development and Characterized by the produced hormone content and related proteins, the specific calculation formula is: ; ; In the formula, is the predicted value of gonadotropin-releasing hormone content after the same period of time as the feeding experiment, is the predicted value of follicle-stimulating hormone content during the same period as the feeding experiment, is the predicted value of luteinizing hormone content in the same period as the feeding experiment, , and They are the predicted values ​​of aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor contents during the same period as the feeding experiment, respectively.

[0040] The average daily nutrient content of the tested sea area The specific calculation formula is: ; In the formula, and To detect the average nitrate and phosphate content in the sea area, To detect the number of biological species in the sea, is the abundance of zooplankton, and are the weight coefficients of the number of biological species in the detected sea area and the sum of the mean nitrate content and the mean phosphate content, respectively. ,and and Both are greater than 0.

[0041] The average daily nutritional content It is characterized by the inorganic nutrients, zooplankton and species richness in the sea area. The larger the value, the richer the resources in the sea area, the more suitable it is for the growth of striped seabass, and the higher the degree of gonadal development of striped seabass.

[0042] Inorganic nutrients include and They are the main nutrient source for the growth of primary producers, phytoplankton, which is the direct nutrient source for the bait organisms of the sea bass. Therefore, the nitrate and phosphate contents in the tested sea area are consistent with the daily average nutrient content in the tested sea area. Directly proportional.

[0043] Abundance of zooplankton The larger the value, the more zooplankton there is. Zooplankton is the direct food source of sea bass, so the abundance of zooplankton is The average daily nutrient content of the tested sea area Directly proportional.

[0044] The number of biological species in the detected sea area , which can be obtained through field investigation, and its value represents the number of secondary producers. High abundance will consume the abundance of primary productivity phytoplankton and zooplankton, thereby reducing the nutrients available to the sea bass, resulting in a strong competitive effect. Therefore, the number of biological species in the detection area The average daily nutrient content of the tested sea area Inversely proportional.

[0045] Among them, the higher the content of inorganic nutrients, the more primary producer phytoplankton it can produce, the faster the production rate will be, and the greater the impact on the nutrition supply of the detection sea area. Therefore, ,and and Both are greater than 0.

[0046] The abundance of zooplankton The calculation is based on the formula: ; In the formula, is the total mass of zooplankton in the sample water collected, The volume of water sample to filter zooplankton, is the capture efficiency correction factor. In the natural environment, the capture efficiency of zooplankton may vary depending on the mesh size and operation method. To improve the calculation accuracy, the capture efficiency correction factor is added, where the capture efficiency correction factor Generally it is 0.6-0.9, depending on the type of net and experimental conditions.

[0047] Step 5: Obtain disturbance parameters in the sea area where the striped seabass to be raised grows, correct the striped seabass gonad development index based on the disturbance parameters to obtain an accurate striped seabass gonad development index, and complete the simulation of the striped seabass gonad development degree according to the accurate striped seabass gonad development index, wherein the disturbance parameters include the maximum sea surface wind speed and the sea circulation speed.

[0048] The gonad development index of striped sea bass was corrected based on the disturbance parameters to obtain the accurate gonad development index of striped sea bass. The formula for calculating the accurate gonad development index of striped sea bass is: ; In the formula, To accurately indices the gonadal development of Lateolabrax japonicus. is the maximum wind speed on the sea surface, is the ocean circulation velocity, and are the weight coefficients of the maximum wind speed on the sea surface and the sea circulation speed, respectively. and and Both are greater than 0.

[0049] Among them, the precise gonadal development index of Lateolabrax japonicus In order to integrate the disturbance parameters of complex marine environment, the precise value of the gonad development index of Lateolabrax japonicus under the disturbance parameters was described. The larger the value, the higher the maturity of the gonad development of Lateolabrax japonicus and the faster the gonad development.

[0050] The maximum wind speed on the sea surface is an important parameter affecting ocean disturbances. High wind speeds can cause water mixing, affect the vertical transport of nutrients, and affect the mixing degree of the water area and the distribution of nutrients, which in turn affects the growth and gonadal development of L. japonicus. Therefore, the maximum wind speed on the sea surface is inversely proportional to the precise gonadal development index of L. japonicus. The exponential function shows that the effect on gonadal development increases significantly when the wind speed is too high.

[0051] Sea circulation speed , which can significantly affect the stability of local habitats. Excessive circulation speed may lead to the dispersion of bait organisms and habitat destruction, thereby inhibiting gonadal development. Therefore, the circulation speed of the sea area should be It is inversely proportional to the precise gonad development index of striped sea bass, and the logarithmic function shows that the impact on gonad development increases significantly when the circulation speed is too large.

[0052] The maximum sea surface wind speed refers to the maximum value of the wind speed on the sea surface within a specified time. It is usually used to assess the environmental dynamic conditions and potential meteorological impacts of the sea area. The specific method of obtaining it is: deploy anemometers (such as ultrasonic anemometers or mechanical anemometers) in specific sea areas to directly measure the sea surface wind speed; the time series of wind speed needs to be recorded during measurement, and the maximum wind speed value is obtained through statistical analysis.

[0053] The ocean circulation velocity can be measured by deploying ADCP equipment in the ocean and using the acoustic Doppler effect to measure the flow rate and direction of different water layers.

[0054] Since the ocean circulation speed can directly change the distribution of bait organisms and nutrients, and and Both are greater than 0.

[0055] Set the threshold for judging the gonadal development of Lateolabrax to , where the logic for judging the gonadal development status of Lateolabrax japonicus is: when When the gonad development of the japonica seabass is good, it means that the gonad development of the japonica seabass in this sea area is fast and suitable for breeding. when When the gonad development of the japonica seabass is good, it is judged that the gonad development of the japonica seabass to be reared in this sea area is good; when When the gonad development of the japonica seabass is poor, it means that the gonad development of the japonica seabass species to be reared in this sea area is slow and unsuitable for rearing. The gonad development judgment threshold of the japonica seabass is set by consulting relevant materials and combining expert experience.

[0056] See also Figure 2 The present invention also provides a numerical simulation system for the rapid maturation of gonads of striped sea bass in a complex sea system model, and the numerical simulation system for the rapid maturation of gonads of striped sea bass in a complex sea system model is used to execute the numerical simulation method for the rapid maturation of gonads of striped sea bass in a complex sea system model, comprising: The test sample preparation module is used to select several striped bass of the same age of the species to be raised, randomly group the selected striped bass groups of the same age to form several groups of test samples, set different salinity water samples for different groups of test samples, and keep the other environmental parameters of each group of test samples consistent except salinity, and raise the striped bass of each group of test samples for a period of time. ; The gene and hormone analysis module is used to randomly detect the key hormone level of gonadal development and the key gene expression ability level of each striped seabass in each group of test samples after the feeding experiment is completed, wherein the key hormone level of gonadal development is obtained by calculating the content of gonadotropin-releasing hormone, follicle-stimulating hormone and luteinizing hormone, and the key gene expression ability level is obtained by calculating the content of aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor; The prediction model training module is used to establish a neural network prediction model based on the key hormone levels and key gene expression levels of gonadal development of each striped sea bass in the test sample, take the salinity of water samples of different groups of test samples as model input, and use the corresponding key hormone levels and key gene expression levels of gonadal development as labels to train the model, and obtain a gonadal development prediction model; The gonad development index calculation module is used to collect the salinity of the sea area where the japonica sea bass is to be raised, input the salinity into the trained gonad development prediction model, obtain the predicted values ​​of the key hormone levels and key gene expression ability levels of the japonica sea bass gonad development in the sea area, and generate the japonica sea bass gonad development index in combination with the environmental parameters of the sea area where the japonica sea bass is to be raised; The precise development index simulation module is used to obtain the disturbance parameters in the sea area where the striped seabass to be raised grows, correct the striped seabass gonad development index based on the disturbance parameters to obtain the precise striped seabass gonad development index, and complete the simulation of the striped seabass gonad development degree according to the precise striped seabass gonad development index. The disturbance parameters include the maximum wind speed on the sea surface and the sea circulation speed.

[0057] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by 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 electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0058] 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.

[0059] 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 numerical simulation method for the rapid maturation of gonads of Lateolabrax japonicus in a complex marine system model, characterized in that: The specific steps include: Select several striped seabass of the same age to be reared, randomly group the selected striped seabass groups of the same age to form several groups of test samples, set different salinity water samples for different groups of test samples, and keep the other environmental parameters of each group of test samples consistent except salinity. Raise the striped seabass of each group of test samples for a period of time. ; After the feeding experiment is completed, the key hormone level of gonad development and the key gene expression ability level of each seabass in each group of test samples are randomly tested, wherein the key hormone level of gonad development is calculated by calculating the content of gonadotropin-releasing hormone, follicle-stimulating hormone and luteinizing hormone, and the key gene expression ability level is calculated by calculating the content of aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor; Based on the key hormone levels and key gene expression levels of gonadal development of each striped sea bass in the test sample, a neural network prediction model was established. The salinity of water samples from different groups of test samples was used as the model input, and the model was trained with the corresponding key hormone levels and key gene expression levels of gonadal development as labels to obtain a gonadal development prediction model. The salinity of the sea area where the japonica sea bass is to be reared is collected, and the salinity is input into the trained gonad development prediction model to obtain the predicted values ​​of the key hormone levels and key gene expression ability levels of the japonica sea bass gonad development in this sea area, and the japonica sea bass gonad development index is generated by combining the environmental parameters of the sea area where the japonica sea bass is to be reared; The disturbance parameters in the sea area where the striped seabass to be raised are obtained, and the striped seabass gonad development index is corrected based on the disturbance parameters to obtain the accurate striped seabass gonad development index. The simulation of the gonad development degree of the striped seabass is completed according to the accurate striped seabass gonad development index, wherein the disturbance parameters include the maximum wind speed on the sea surface and the sea circulation speed.

2. The method for numerically simulating the rapid gonadal maturation of Lateolabrax japonicus in a complex marine system model according to claim 1, characterized in that: Different salinity water samples were set for different groups of test samples. Except for salinity, other environmental parameters of each group of test samples were kept consistent, wherein the other environmental parameters included water temperature, dissolved oxygen concentration, average daily light intensity and average daily nutrient content; Each group of test samples were reared for a period of , wherein the length of time of feeding Calibrated as ,in, Indicates the start time of the feeding experiment, represents the length of the time period in days, and , Is a positive integer.

3. The method for numerically simulating the rapid gonadal maturation of Lateolabrax japonicus in a complex marine system model according to claim 2, characterized in that: Based on the key hormone levels and key gene expression levels of gonadal development of each sea bass in the test sample, a neural network prediction model was established. A neural network prediction model was established based on the LSTM model, and the activation function and optimization algorithm were selected. The Tanh function was selected as the activation function, and Adam was selected as the optimization algorithm of the LSTM model. The formula of the Tanh function is: ； In the formula, Represents the Tanh function, independent variable Represents the weighted sum of the neuron's input, that is, the result of weighted summation of the input received by the neuron from the previous layer; At the same time, the hyperparameters of the LSTM model are set, and the hyperparameters of the LSTM model include: the number of network layers, the number of iterations, the learning rate, the batch size, the number of training times, the batch processing number, and the number of hidden layer neurons; The number of network layers is set to a 3-layer network structure, the number of iterations is set to 200, the learning rate is set to 0.001, the batch size is set to 32, the number of training times is set to 100, the batch size is set to 256, and the number of hidden layer neurons is 32.

4. The method for numerically simulating the rapid maturation of gonads of Lateolabrax japonicus in a complex marine system model according to claim 3, characterized in that: According to the predicted values ​​of the key hormone levels and key gene expression levels of the striped perch gonad development in this sea area, combined with the environmental parameters of the sea area where the striped perch will grow, the striped perch gonad development index is generated. The specific formula for calculating the striped perch gonad development index is: ； In the formula, represents the gonadal development index of Lateolabrax japonicus. and are the predicted values ​​of key hormone levels and key gene expression levels for gonadal development of Leptospermum japonicum, To detect the sea water temperature, The water temperature for the breeding experiment is is the average daily light intensity of the feeding experiment, To detect the average daily light intensity in the sea area, To detect the dissolved oxygen concentration in the sea area, To detect the average daily nutrient content of the sea area; The levels of key hormones and key gene expression in the gonadal development of sea bass and Characterized by the produced hormone content and related proteins, the specific calculation formula is: ； ； In the formula, is the predicted value of gonadotropin-releasing hormone content after the same period of time as the feeding experiment, is the predicted value of follicle-stimulating hormone content during the same period as the feeding experiment, is the predicted value of luteinizing hormone content in the same period as the feeding experiment, , and They are the predicted values ​​of aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor contents during the same period as the feeding experiment, respectively.

5. The method for numerically simulating the rapid gonadal maturation of Lateolabrax japonicus in a complex marine system model according to claim 4, characterized in that: The average daily nutrient content of the tested sea area The specific calculation formula is: ； In the formula, and To detect the average nitrate and phosphate content in the sea area, To detect the number of biological species in the sea, is the abundance of zooplankton, and are the weight coefficients of the number of biological species in the detected sea area and the sum of the mean nitrate content and the mean phosphate content, respectively. ,and and are all greater than 0, and the abundance of zooplankton The calculation is based on the formula: ； In the formula, is the total mass of zooplankton in the sample water collected, The volume of water sample to filter zooplankton, is the capture efficiency correction factor.

6. The method for numerically simulating the rapid maturation of gonads of Lateolabrax japonicus in a complex marine system model according to claim 4, characterized in that: The gonad development index of striped sea bass was corrected based on the disturbance parameters to obtain the accurate gonad development index of striped sea bass. The formula for calculating the accurate gonad development index of striped sea bass is: ； In the formula, To accurately indices the gonadal development of Lateolabrax japonicus. is the maximum wind speed on the sea surface, is the ocean circulation velocity, and are the weight coefficients of the maximum wind speed on the sea surface and the sea circulation speed, respectively. and and All are greater than 0; Set the threshold for judging the gonadal development of Lateolabrax to , where the logic for judging the gonadal development status of Lateolabrax japonicus is: when When the gonad development of the japonica seabass is good, it means that the gonad development of the japonica seabass in this sea area is fast and suitable for breeding. when When the gonad development of the japonica seabass is good, it is judged that the gonad development of the japonica seabass to be reared in this sea area is good; when , it is judged that the gonad development of the striped seabass is poor, indicating that in this sea area, the gonad development speed of the striped seabass to be raised is slow and it is not suitable for raising.

7. A numerical simulation system for the rapid maturation of gonads of Lateolabrax japonicus in a complex marine system model, characterized by: The numerical simulation system for the rapid maturation of gonads of Lateolabrax japonicus in a complex sea system model is used to execute the numerical simulation method for the rapid maturation of gonads of Lateolabrax japonicus in a complex sea system model according to any one of claims 1 to 6, comprising: The test sample preparation module is used to select several striped bass of the same age of the species to be raised, randomly group the selected striped bass groups of the same age to form several groups of test samples, set different salinity water samples for different groups of test samples, and keep the other environmental parameters of each group of test samples consistent except salinity, and raise the striped bass of each group of test samples for a period of time. ; The gene and hormone analysis module is used to randomly detect the key hormone level of gonadal development and the key gene expression ability level of each striped seabass in each group of test samples after the feeding experiment is completed, wherein the key hormone level of gonadal development is obtained by calculating the content of gonadotropin-releasing hormone, follicle-stimulating hormone and luteinizing hormone, and the key gene expression ability level is obtained by calculating the content of aromatase, follicle-stimulating hormone receptor and luteinizing hormone receptor; The prediction model training module is used to establish a neural network prediction model based on the key hormone levels and key gene expression levels of gonadal development of each striped sea bass in the test sample, take the salinity of water samples of different groups of test samples as model input, and use the corresponding key hormone levels and key gene expression levels of gonadal development as labels to train the model, and obtain a gonadal development prediction model; The gonad development index calculation module is used to collect the salinity of the sea area where the japonica sea bass is to be raised, input the salinity into the trained gonad development prediction model, obtain the predicted values ​​of the key hormone levels and key gene expression ability levels of the japonica sea bass gonad development in the sea area, and generate the japonica sea bass gonad development index in combination with the environmental parameters of the sea area where the japonica sea bass is to be raised; The precise development index simulation module is used to obtain the disturbance parameters in the sea area where the striped seabass to be raised grows, correct the striped seabass gonad development index based on the disturbance parameters to obtain the precise striped seabass gonad development index, and complete the simulation of the striped seabass gonad development degree according to the precise striped seabass gonad development index. The disturbance parameters include the maximum wind speed on the sea surface and the sea circulation speed.

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