Blue crab saline-alkali land seedling desalination domestication interactive training system, salinity regulation model optimization method, equipment and medium

Abstract

The application provides a mud crab salt marsh seed desalination domestication interactive training system, a salinity regulation model optimization method, equipment and a medium. A mud crab seed salinity stress response model is established, and parameter estimation and calibration are performed based on actual cultivation data to obtain a dynamic physiological response model. Global sensitivity analysis is performed according to the dynamic physiological response model to determine the physiological tolerance interval of key regulation factors related to mud crab seed desalination domestication salinity. Numerical optimization calculation is performed with salinity daily drop and stage stable time as decision variables and maximum seed survival rate as an optimization objective to obtain an optimal combination. The optimal target salinity setting value of each desalination stage is determined according to the physiological tolerance interval and the optimization result, and a gradient desalination regulation strategy is constructed. The scheme of the application can optimize the salinity regulation model and improve the stability and efficiency of mud crab seed desalination domestication.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blue crab brackish water seed desalination domestication, more specifically, the present application relates to a blue crab brackish water seed desalination domestication interactive training system, a salinity regulation model optimization method, equipment and medium. BACKGROUND

[0002] In the blue crab breeding industry, seed desalination domestication, as a key link of brackish water breeding, is of great significance to the adaptive transfer of blue crab from seawater to low salinity environment, and to the improvement of survival rate and growth performance. Traditional desalination domestication methods rely on empirical salinity regulation, such as gradually reducing salinity and observing seed response, but lack quantitative analysis and optimization support for physiological stress mechanisms, making it difficult to cope with the variability of brackish water environment and individual differences of seed, resulting in large fluctuations in seed survival rate and unstable molting period. With the expansion of brackish water breeding scale and the improvement of fine management demand, intelligent salinity regulation model has gradually become a core technical demand to improve seed domestication efficiency.

[0003] The existing mainstream blue crab seed desalination domestication method generally has the problems of staticity and experience dependence in model design, which is difficult to adapt to the dynamic influence of salinity change on seed osmotic pressure regulation, ion balance and physiological tolerance. Especially in the scenario of excessive daily salinity reduction or insufficient stage stabilization time, the survival rate of seed is easily affected by stress accumulation and significantly decreased. The root cause of this defect is that the traditional method fails to introduce a dynamic model mechanism that reflects the nonlinear correlation between salinity regulation and physiological response, lacks sensitivity analysis and optimization strategy for key parameters, and thus the regulation scheme is difficult to adapt to environmental changes and cannot fully exert the maximum potential to improve seed survival rate. Therefore, how to optimize the salinity regulation model to improve the stability and efficiency of blue crab seed desalination domestication has become a difficult problem in the industry. SUMMARY

[0004] The present application provides a blue crab brackish water seed desalination domestication interactive training system, a salinity regulation model optimization method, equipment and medium, which can optimize the salinity regulation model and improve the stability and efficiency of blue crab seed desalination domestication.

[0005] In the first aspect, the present application provides a salinity regulation model optimization method for blue crab seed desalination domestication, which comprises the following steps:

[0006] Establishing a blue crab seed salinity stress response model, based on the observed values of seed survival rate in actual breeding data, performing parameter estimation and calibration on the salinity stress response model, and establishing a calibrated dynamic physiological response model;

[0007] According to the dynamic physiological response model, global sensitivity analysis is performed on the daily salinity reduction amplitude, the stage stable time and the initial salinity, and according to the global sensitivity analysis result, a physiological tolerance interval of a key regulation factor related to the salinity of the mud crab fry desalination acclimation is determined;

[0008] With the daily salinity reduction amplitude and the stage stable time as the decision variables and the maximum survival rate of the fry as the optimization objective, numerical optimization calculation of the decision variables is performed based on the dynamic physiological response model, and the optimal combination of the daily salinity reduction amplitude and the stable time of each stage and the corresponding expected maximum survival rate are obtained.

[0009] According to the physiological tolerance interval of the key regulation factor and the optimal daily salinity reduction amplitude and stable time obtained by optimization, the optimal target salinity setting value of each desalination stage is determined, and a gradient desalination regulation strategy is constructed according to the optimal target salinity setting value and the regulation objective of the expected maximum survival rate.

[0010] Preferably, the establishment of the mud crab fry salinity stress response model specifically includes:

[0011] The survival rate data, physiological index data, ion concentration data and molting cycle data of the mud crab fry under different salinity gradients are collected;

[0012] The influence coefficient of the salinity change rate on the osmoregulation and ion balance of the fry is calculated according to the survival rate data, physiological index data, ion concentration data and molting cycle data;

[0013] The mud crab fry salinity stress response model, i.e. a nonlinear dynamic response model between salinity change, ion concentration and fry survival rate, is constructed through the influence coefficient.

[0014] Preferably, based on the observed values of the survival rate of the fry in the actual breeding data, parameter estimation and calibration are performed on the salinity stress response model, and the calibrated dynamic physiological response model specifically includes:

[0015] The time series observation sequences of the survival rate, ion concentration and molting rate of the fry in multiple desalination cycles are extracted from the actual breeding records;

[0016] The model parameters of the salinity stress response model are estimated based on the time series observation sequences;

[0017] The model structure of the salinity stress response model is adjusted according to the estimated parameters, and the calibrated and optimized dynamic physiological response model is established.

[0018] Preferably, according to the dynamic physiological response model, global sensitivity analysis is performed on the daily salinity reduction amplitude, the stage stable time, the initial salinity, the ion concentration, the temperature and the dissolved oxygen, and according to the global sensitivity analysis result, the physiological tolerance interval of a key regulation factor related to the salinity is determined specifically includes:

[0019] defining parameter input ranges of salinity daily reduction, phase stable time, initial salinity, ion concentration, temperature and dissolved oxygen in the dynamic physiological response model;

[0020] calculating main effect index and total effect index of each input parameter on seedling survival rate output;

[0021] screening sensitive parameters according to the main effect index and total effect index, and determining the physiological tolerance interval of the key regulatory factor related to salinity based on the variation range of the sensitive parameters.

[0022] Preferably, taking salinity daily reduction, phase stable time and ion concentration as decision variables, maximizing seedling survival rate and molting rate as optimization objectives, numerical optimization calculation of the decision variables is carried out based on the dynamic physiological response model to obtain the optimal combination of salinity daily reduction, stable time and ion concentration of each stage and the corresponding expected maximum survival rate, which specifically includes:

[0023] Setting numerical search boundaries of salinity daily reduction, phase stable time and ion concentration;

[0024] Multi-objective iterative solution of the decision variables is carried out within the numerical search boundaries;

[0025] According to the solution result, the optimal values of salinity daily reduction, stable time and ion concentration of each desalination stage are extracted, and the corresponding maximum seedling survival rate is simulated and calculated.

[0026] Preferably, according to the physiological tolerance interval of the key regulatory factor and the optimal salinity daily reduction and stable time obtained by optimization, the optimal target salinity setting value and ion concentration setting value of each desalination stage are determined, which specifically includes:

[0027] Determining the starting salinity of each desalination stage, taking the initial breeding salinity as the starting point of the first desalination stage, and the starting salinity of the subsequent desalination stage as the target salinity of the previous stage;

[0028] Multiply the optimal salinity daily reduction obtained by optimization by the stable time of the stage to obtain the total amount of salinity that can be reduced in the stage;

[0029] Subtract the total amount of salinity from the starting salinity of the stage, and if the result is within the physiological tolerance interval of the key regulatory factor, it is the optimal target salinity setting value of the stage.

[0030] Preferably, the salinity stress response model is a semi-mechanism semi-experience model or a mechanism model.

[0031] In the second aspect, the application provides a mangrove crab salt and alkali land seedling desalination domestication interactive training system, which comprises a salinity regulation model optimization unit, and the salinity regulation model optimization unit specifically comprises:

[0032] The modeling module is configured to establish a model of a response of a mud crab seed to salinity stress, perform parameter estimation and calibration on the model of the response of the mud crab seed to the salinity stress based on observed values of survival rates of the seed in actual cultivation data, and establish a calibrated dynamic physiological response model.

[0033] The processing module is configured to perform global sensitivity analysis on a daily salinity reduction, a stable time, an initial salinity, an ion concentration, a temperature, and dissolved oxygen according to the dynamic physiological response model, and determine a physiological tolerance interval of a key regulation factor related to a salinity of the mud crab seed in a desalination and domestication process according to a result of the global sensitivity analysis.

[0034] The processing module is further configured to perform numerical optimization calculation on the decision variables based on the dynamic physiological response model, so as to obtain an optimal combination of the daily salinity reduction, the stable time, and the ion concentration in each stage and a corresponding expected maximum survival rate.

[0035] The execution module is configured to determine optimal target salinity set values and ion concentration set values of each desalination stage according to the physiological tolerance interval of the key regulation factor and the optimal daily salinity reduction, stable time, and ion concentration, and construct a gradient desalination regulation strategy according to the optimal target salinity set values, the ion concentration set values, the expected maximum survival rate, and a regulation target of nutritional intervention.

[0036] In a third aspect, the present application provides a computer device, which comprises a memory and a processor, the memory stores a code, and the processor is configured to acquire the code and execute the above-mentioned salinity regulation model optimization method for desalination and domestication of mud crab seeds.

[0037] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above-mentioned salinity regulation model optimization method for desalination and domestication of mud crab seeds.

[0038] The technical scheme provided by the embodiments of the present application has the following beneficial effects:

[0039] In the embodiments of the present application, a model of salt stress response of mud crab fry is established, the model is a dynamic response relationship model between the change of the salinity of the aquaculture water and the survival rate of the mud crab fry; based on the observed value of the survival rate of the fry in the actual aquaculture data, parameter estimation and calibration are performed on the salt stress response model to establish a calibrated dynamic physiological response model; according to the dynamic physiological response model, global sensitivity analysis is performed on the daily salinity reduction, the stage stable time and the initial salinity, and according to the global sensitivity analysis result, the physiological tolerance interval of the key control factor related to the salinity of the mud crab fry desalination domestication is determined; taking the daily salinity reduction and the stage stable time as the decision variables and maximizing the survival rate of the fry as the optimization objective, numerical optimization calculation is performed on the decision variables based on the dynamic physiological response model to obtain the optimal combination of the daily salinity reduction and the stable time of each stage and the corresponding expected maximum survival rate; according to the physiological tolerance interval of the key control factor and the optimal daily salinity reduction and stable time obtained by optimization, the optimal target salinity setting value of each desalination stage is determined, and a gradient desalination control strategy is constructed according to the optimal target salinity setting value and the control objective of the expected maximum survival rate.

[0040] It can be seen that, in the present application, numerical optimization calculation is performed on the daily salinity reduction and the stage stable time as the decision variables to obtain the optimal combination, and a gradient desalination control strategy is constructed based on the physiological tolerance interval. First, by establishing the salt stress response model of the mud crab fry, the dynamic influence of the salinity change on the survival rate can be revealed on the basis of physiological mechanism, so as to establish a control framework with biological basis. The model construction process integrates the survival rate, physiological indicators and ion data to realize quantitative expression of the stress response, and parameter calibration is performed on this basis to enhance the adaptability of the model to the actual aquaculture data from the source. Second, the physiological tolerance interval of the key control factor related to the salinity of the mud crab fry desalination domestication is determined by global sensitivity analysis, which can identify the parameter range that has a significant influence on the survival rate, effectively avoid the blindness of the control strategy, highlight the optimization priority of the daily salinity reduction and the stable time, and realize targeted selection of the control factor. Then, the decision variable optimization is performed with the maximum survival rate as the objective, and the optimal combination of the daily salinity reduction and the stable time is solved by using numerical calculation method, so as to output the control parameters with quantitative support, which significantly improves the efficiency and survival rate expectation of the desalination process. Finally, the target salinity setting value is determined according to the tolerance interval and the optimization result, and a gradient control strategy is constructed, so that the control scheme further strengthens the physiological adaptation mechanism in the multi-stage desalination, and maximally reduces the negative influence of stress accumulation on the fry. This mechanism constructs an adaptive path of salinity control in the salinized land domestication scene through dynamic optimization, which effectively overcomes the problem of unstable survival rate caused by experience dependence in the traditional method. In summary, the present application can optimize the salinity control model to improve the stability and efficiency of the mud crab fry desalination domestication. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 is an example flow chart of a salinity regulation model optimization method for green crab fry desalination domestication according to some embodiments of the present application;

[0042] Figure 2 is a flow chart of establishing a calibrated dynamic physiological response model according to some embodiments of the present application;

[0043] Figure 3 is a flow chart of implementing numerical optimization calculation of decision variables according to some embodiments of the present application;

[0044] Figure 4 is a structural diagram of a salinity regulation model optimization unit according to some embodiments of the present application;

[0045] Figure 5 is a structural diagram of a computer device for implementing a salinity regulation model optimization method for green crab fry desalination domestication according to some embodiments of the present application. DETAILED DESCRIPTION

[0046] In order to better understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below in combination with the drawings in the specification and specific embodiments.

[0047] Reference Figure 1 The figure is an example flow chart of a salinity regulation model optimization method for green crab fry desalination domestication according to some embodiments of the present application, which mainly includes the following steps:

[0048] In step 101, a green crab fry salinity stress response model is established, and based on the observed value of the survival rate of fry in actual breeding data, parameter estimation and calibration are performed on the salinity stress response model to establish a calibrated dynamic physiological response model.

[0049] It should be noted that the green crab fry salinity stress response model in the present application is a dynamic model based on the physiological mechanism of green crab fry, and the green crab fry salinity stress response model can be implemented by using a semi-mechanism semi-experience model or a mechanism model.

[0050] In some embodiments, the model of the response of the mud crab seed salt stress can be established by collecting survival rate data, physiological index data (such as osmotic pressure, enzyme activity), ion concentration data (such as Na+and K+concentration) and molting cycle data of the mud crab seed under different salt gradients, and synchronously recording temperature and dissolved oxygen time series data; calculating the influence coefficient of the rate of change of the salt on the osmotic pressure regulation and ion balance of the seed and the interactive effect of temperature-salt and dissolved oxygen-salt according to the survival rate data, the physiological index data, the ion concentration data and the molting cycle data; and constructing the model of the response of the mud crab seed salt stress, i.e. a nonlinear dynamic response model between the rate of change of the salt, the ion concentration, the temperature, the dissolved oxygen and the survival rate of the seed, by the influence coefficient and the interactive effect.

[0051] wherein the temperature time series data are time series data of the temperature of the water for cultivation recorded in the time dimension continuously in the process of the desalination domestication of the mud crab seed, and the dissolved oxygen time series data are time series data of the dissolved oxygen content of the water for cultivation recorded in the time dimension continuously in the process of the desalination domestication of the mud crab seed, which are not described herein again.

[0052] It should be noted that the influence coefficient of the rate of change of the salt on the osmotic pressure regulation and ion balance of the seed and the interactive effect coefficient of temperature-salt and dissolved oxygen-salt can be calculated according to the survival rate data, the physiological index data, the ion concentration data and the molting cycle data in the following manner:

[0053] The collected survival rate, physiological indicators, ion concentration and molting cycle data under different salinity gradients are classified according to the salinity change rate to obtain multiple groups of data corresponding to different salinity change rates, for example, grouping according to different rates of daily reduction of 0.5 ppt, 1 ppt, 1.5 ppt, 2 ppt, etc. Ensure that each group of data only corresponds to one salinity change rate, analyze the influence of a single factor of salinity, and then analyze the interaction of temperature, dissolved oxygen and salinity. Then, for the data corresponding to each salinity change rate, record the osmotic pressure change of the seedlings (for example, whether the osmotic pressure is maintained in the stable interval, the amplitude of exceeding the stable interval) and the ion concentration change (for example, whether the Na+ and K+ concentrations are unbalanced, and the numerical range of the imbalance), while combining the molting cycle (for example, whether the molting cycle is extended when the rate is too fast) and the survival rate (for example, whether the survival rate decreases when the rate is too high), to determine the direct correlation between the salinity change rate and the osmotic pressure regulation ability and ion balance state. Further, from the data corresponding to each salinity change rate, the core information that can directly reflect the influence of the salinity change rate on the osmotic pressure / ion balance is selected, for example, in the 1 ppt daily reduction group, the records of maintaining the osmotic pressure at 200-220 mOsm / kg and the Na+ concentration fluctuation ≤5 mmol / L are retained. This is only an example to illustrate, not as a limitation of the present application. Further, by comparing the core information of different rate groups, the change amplitude of the osmotic pressure regulation ability and the ion balance state is calculated when the salinity change rate changes by one unit (such as 0.5 ppt / day), and the change amplitude is further quantified to obtain the change amplitude of the osmotic pressure regulation ability and the ion balance state when the salinity change rate changes by one unit, the temperature changes by 1°C, and the dissolved oxygen changes by 1 mg / L. The influence coefficient and the temperature-salinity interaction effect coefficient and the dissolved oxygen-salinity interaction effect coefficient are obtained by further quantifying the change amplitude. In specific implementation, the idea of fixed condition measurement, difference calculation and constant coefficient can be used, for example:

[0054] Fixing the temperature and dissolved oxygen, only changing the salinity reduction amplitude, testing the physiological indicator change of the seedlings:

[0055] Salinity reduction 0.5 ppt / day, Na+ increase 4 mmol / L;

[0056] Salinity reduction 1.0 ppt / day, Na+ increase 6 mmol / L;

[0057] Salinity reduction 1.5 ppt / day, Na+ increase 9 mmol / L;

[0058] Calculate the average change of Na+ corresponding to each 1 ppt salinity reduction. This difference is the influence coefficient of salinity on ion balance. The linear calculation result is used as the initial fitting basis for the low stress segment of the piecewise function.

[0059] The influence coefficient is calculated by using a segmented function: for example, low stress segment (0.5≤daily drop≤1.2 ppt): effect coefficient=5mmol / L·ppt (linear fitting); high stress segment (1.2<daily drop≤1.5 ppt): effect coefficient=20×daily drop-19 (nonlinear fitting, such as 1.5 ppt: 20×1.5-19=11mmol / L·ppt); forbidden segment (daily drop>1.5 ppt): effect coefficient=+∞ (considered as infeasible and directly excluded); cumulative stress effect is added: if high stress segment is continuously present for 2 days, the effect coefficient is increased by 20% (simulating the superimposed damage of continuous stress), that is, the physiological regulation ability of mud crab fry under different salinity stress intensities is matched, the biological response law of "compensation - decompensation - fatalness" is matched by using a segmented function to match the three-stage change of physiological regulation ability, and the superimposed damage of continuous stress is simulated by using a cumulative stress effect. Here, only an example is given, and the specific limitation of the present application is not intended.

[0060] Similarly, for the temperature-salinity interaction effect coefficient, for example:

[0061] Keep the salinity drop 1.0 ppt / day, and the dissolved oxygen 5 mg / L, only change the temperature:

[0062] When the temperature is 25℃, Na⁺ rises by 6mmol / L (salinity alone);

[0063] When the temperature is 30℃, the measured Na⁺ rises by 7mmol / L (salinity+high temperature combined effect);

[0064] The difference between the two (7-6=1mmol / L) is the temperature-salinity interaction effect coefficient (representing that when the temperature rises by 5℃, the ion imbalance effect of salinity is additionally enhanced by 1mmol / L).

[0065] Similarly, the dissolved oxygen-salinity interaction effect coefficient, for example:

[0066] Keep the salinity drop 1.0 ppt / day, and the temperature 25℃, only change the dissolved oxygen:

[0067] When the dissolved oxygen is 5mg / L, Na⁺ rises by 6mmol / L (salinity alone);

[0068] When the dissolved oxygen is 3mg / L, the measured Na⁺ rises by 8mmol / L (salinity+low oxygen combined effect);

[0069] The difference between the two (8-6=2mmol / L) is the dissolved oxygen-salinity interaction effect coefficient (representing that when the dissolved oxygen decreases by 2mg / L, the ion imbalance effect of salinity is additionally enhanced by 2mmol / L).

[0070] It should be noted that the obtained influence coefficient and interaction effect coefficient also need to be verified with the actual situation until the influence coefficient and interaction effect coefficient match the actual situation, which will not be repeated here.

[0071] The influence coefficient and interaction effect coefficient are used to construct the blue crab fry salinity stress response model, which can be implemented in the following way:

[0072] First, the ion concentration dynamic calculation rule is established by the influence coefficient and the interaction effect coefficient, that is:

[0073] The ion concentration change calculated daily is composed of the sum of three parts:

[0074] The basic salinity effect: the salinity reduction of the day is multiplied by the salinity influence coefficient.

[0075] Temperature interaction effect: the difference between the temperature of the day and the reference temperature is multiplied by the temperature-salinity interaction effect coefficient.

[0076] Dissolved oxygen interaction effect: the difference between the reference dissolved oxygen and the dissolved oxygen of the day is multiplied by the dissolved oxygen-salinity interaction effect coefficient.

[0077] The ion concentration at the end of the day is equal to the concentration of the previous day plus the ion concentration change calculated for the day.

[0078] Enzyme activity correlation: add Na⁺-K⁺-ATPase activity threshold (for example, when the daily reduction is ≤1.5 ppt, the enzyme activity is maintained at more than 80%, and the effect coefficient is 11 mmol / L·ppt; when the daily reduction is >1.5 ppt, the enzyme activity is reduced to 50%, and the effect coefficient is 5 mmol / L·ppt) in the model, which reflects the nonlinear physiological response that the higher the enzyme activity, the stronger the ion absorption ability.

[0079] Secondly, the mapping relationship between ion imbalance and seedling survival rate is established, that is, based on historical observation data, a nonlinear mapping relationship between the imbalance degree of ion concentration and the survival rate of seedlings is established. For example, first, extract the matching data of ion concentration (such as Na+) + corresponding time seedling survival rate from historical observation data, exclude interference data of abnormal fluctuations of temperature and salinity (ensure that only the influence of ion imbalance is reflected); define the imbalance degree, that is, first determine the normal physiological range of ions, then calculate the ion concentration deviation value of each data point to quantify the imbalance degree; then group and statistically analyze the trend, group by deviation value size, for example, slight deviation ± 10 mmol / L, moderate deviation ± 10-20 mmol / L, and severe deviation ± 20 mmol / L or more, and statistically analyze the average survival rate of each group to preliminarily identify the nonlinear trend that the heavier the imbalance, the faster the survival rate decreases; finally, nonlinear fitting: use mathematical methods (such as Logistic curve, piecewise function) to fit the grouping data of imbalance degree-average survival rate into a calculable relationship (such as a curve), fix the rules of slight deviation slow decline, moderate deviation sudden drop, and severe deviation near zero, and finally use historical data that did not participate in fitting to verify the fitting result. If the survival rate prediction error is < a preset threshold (such as 5%), the mapping relationship takes effect; otherwise, adjust the grouping or fitting method, which is not described here.

[0080] Thirdly, the ion dynamic calculation rule and the mapping relationship between the ion concentration imbalance degree and the seedling survival rate determined above are integrated to build a complete dynamic response model, that is, a nonlinear dynamic response model between salinity change, ion concentration, temperature, dissolved oxygen, and seedling survival rate.

[0081] In specific implementation, actual farming data that did not participate in modeling can be used to verify the model, daily environmental data is input into the model to obtain a predicted survival rate curve, and the predicted survival rate curve is compared with an actually observed survival rate curve. According to the deviation of the comparison result, the model is fine-tuned and calibrated until the predicted result of the model is highly consistent with the actual data, which is not described here.

[0082] In some embodiments, based on the observed value of the seedling survival rate in the actual farming data, parameter estimation and calibration are performed on the salinity stress response model to establish a calibrated dynamic physiological response model.

[0083] In some embodiments, referring to Figure 2 , based on the observed value of the seedling survival rate in the actual farming data, parameter estimation and calibration are performed on the salinity stress response model to establish a calibrated dynamic physiological response model, which can be implemented in the following way:

[0084] In step 1011, time series observation sequences of juvenile survival rate, ion concentration and molting rate in a plurality of desalination periods are extracted from the actual breeding records, wherein the time series observation sequences are key data sequences related to salinity stress recorded continuously in time in the desalination and domestication of mangrove crab juveniles, such as juvenile survival rate data sequences, ion concentration data sequences, which are not limited here;

[0085] In step 1012, the model parameters of the salinity stress response model are estimated based on the time series observation sequences, which can be implemented in the following manner, that is:

[0086] First, the parameters to be estimated are determined, which can be, for example, influence coefficients or ion imbalance thresholds, which are not limited here;

[0087] Second, according to the physiological characteristics of the mangrove crab, the statistical characteristics of the time series observation sequences are combined to set the initial value of the parameter to be estimated, which is used as the starting benchmark for subsequent iterative optimization. In actual implementation, for example, it is known that the normal range of Na⁺ concentration when the ion balance of mangrove crab juveniles is 180-220 mmol / L, and physiological disorder is prone to occur when the daily salinity drop is more than 2 ppt. This is used as the physiological boundary for parameter setting. Then the statistical characteristics of the time series observation sequences are extracted, that is, the mean value of the salinity change rate (for example, the daily average drop of 1.2 ppt), the average value of the ion concentration (Na⁺) (for example, 205 mmol / L), and the correlation trend between the two (for example, for every 1 ppt drop in salinity, the average Na⁺ rises by 8-10 mmol / L) are calculated from the time series observation sequence data. Combined with the two, the initial value of the parameter is set, for example, the ion imbalance threshold is set to 1.5 ppt, referring to the physiological tolerance threshold (2 ppt) and the 90% quantile of the observed salinity drop (1.8 ppt). The "salinity-ion concentration influence coefficient" is set to 9 mmol / L・ppt according to the observed correlation trend, ensuring that the initial value of the parameter conforms to the physiological law and fits the data characteristics of the time series observation sequences;

[0088] Finally, the parameter value of the parameter to be estimated is adjusted step by step through iteration to minimize the sum of squares of residuals between the model predicted value and the actual observed value of the time series observation sequences until the iteration converges, and the final parameter estimation value is obtained. Preferably, the iteration can use the weighted least squares method, and other iterative algorithms can also be used in practice, which are not limited here, that is, the initial value of the parameter is taken as the starting point, and the value of the parameter to be estimated is adjusted step by step through iteration. The sum of squares of residuals between the predicted value of the model based on the current parameter and the actual observed value in the time series observation sequences is continuously calculated during the process, and the parameter is optimized with the core target of minimizing the sum of squares of residuals. When the iteration meets the preset convergence condition, the iteration is stopped, and the parameter value obtained at this time is the final parameter estimation value, which is not described here.

[0089] It should be noted that when the weighted least squares method is used for iteration, the weights can be set according to the reciprocal square of the error rate of each index, the error of the survival rate is 3%, the weight is 11, the error of the ion concentration is 4%, the weight is 6, and the error of the molting rate is 10%, the weight is 1; after calibration, the normality of the residual error can be verified by Shapiro-Wilk test, and the heteroscedasticity can be eliminated by White test, which will not be described here.

[0090] In step 1013, the model structure of the salinity stress response model is adjusted according to the parameter estimation value, and a calibrated dynamic physiological response model is established.

[0091] In some embodiments, adjusting the model structure of the salinity stress response model according to the parameter estimation value can be adjusted based on statistical test method, which is to judge the model parameters by statistical test. Specifically, first, determine the variable to be tested, that is, select the variable to be tested from the input parameters of the salinity stress response model, for example, the input parameters include daily salinity drop, stage stabilization time, ion concentration, temperature, dissolved oxygen, and the temperature can be selected as the variable to be tested to judge whether the temperature has a significant influence on the survival rate of seedlings. Secondly, select a statistical test method, which can have multiple ways for different parameters and the same parameter. Taking the temperature as an example, the statistical test method can be a parameter significance test method or a likelihood ratio test method. For example, for the parameter significance test method, it is to judge whether a single parameter is significant. If it is significant, the parameter value is retained. If it is not significant, the parameter value is deleted. Finally, adjust the model structure according to the test result. For example, if a parameter has no significant influence after testing, delete the parameter from the salinity stress response model; if there is multicollinearity among multiple variables, retain the variable that has more significant influence on the survival rate and simplify the model structure.

[0092] It should be noted that for the adjusted model, the matching degree of the predicted value of the adjusted model and the actual observation sequence during cultivation needs to be tested. If the matching degree meets the standard (i.e. the error is less than the preset threshold), the final model structure is determined; if it does not meet the standard, the model is adjusted again. For example, the daily salinity drop of 1.2 ppt, the Na⁺ concentration of 205 mmol / L, and the actual survival rate of 93% are substituted into the adjusted dynamic physiological response model, and the predicted survival rate is calculated as 92.3%, with an error of only 0.7%. The actual survival rate on the 15th day is 87%, and the model predicts 86.7%, with an error of 0.3%. The error is less than the preset threshold (2%), which can be used as the final calibrated and optimized dynamic physiological response model.

[0093] In step 102, the salinity daily drop, the stage stabilization time, and the initial salinity are subjected to global sensitivity analysis according to the dynamic physiological response model, and the physiological tolerance interval of the key control factor related to the salinity of the juvenile mud crab is determined according to the global sensitivity analysis result.

[0094] In some embodiments, according to the dynamic physiological response model, a global sensitivity analysis is performed on the daily salinity drop, the stage stable time, the initial salinity, the ion concentration, the temperature and the dissolved oxygen, and according to the results of the global sensitivity analysis, the physiological tolerance interval of the key regulating factor related to salinity can be determined in the following manner: the parameter input range of the daily salinity drop, the stage stable time, the initial salinity, the ion concentration, the temperature and the dissolved oxygen is defined in the dynamic physiological response model, the main effect / total effect of the controllable regulating factors (such as the daily salinity drop, the stage stable time, the ion concentration set value) is calculated first, and the parameters with main effect > 0.5 and total effect > 0.7 are selected as the key regulating factors; then the robustness index (such as the coefficient of variation of survival rate when the temperature fluctuates between 20-30°C) of the uncontrollable environmental factors (temperature, dissolved oxygen) is calculated, and if the robustness index > 10%, an environmental compensation term (such as the daily salinity drop decreases by 0.1 ppt for every 1°C increase in temperature) is added in the optimization; the main effect index and the total effect index of each input parameter on the seedling survival rate output are calculated; the sensitive parameters are selected according to the main effect index and the total effect index, and the physiological tolerance interval of the key regulating factor related to salinity is determined based on the variation range of the sensitive parameters.

[0095] It should be noted that the main effect index reflects the degree of influence of the independent change of a single parameter on the seedling survival rate output, that is, the main effect index excludes the interaction of other parameters. Taking the daily salinity drop as an example, the influence is reflected by the change amplitude of the seedling survival rate, other parameters are fixed, a low first daily salinity drop and a high second daily salinity drop are taken within the stage time, and the corresponding seedling survival rate is obtained by inputting the first daily salinity drop and the second daily salinity drop into the dynamic physiological response model: the seedling survival rate is 95% at the first daily salinity drop, and the seedling survival rate is 75% at the second daily salinity drop, the daily salinity drop increases from low to high, and the seedling survival rate decreases by 20%, which indicates that the independent influence of the parameter is strong, and the main effect index of the parameter can be simplified as 0.9, wherein the closer to 1, the greater the independent influence of the parameter, which is only used for illustration and is not a specific limitation.

[0096] In addition, the total effect index in this application is the comprehensive influence degree of the independent change of a single parameter plus the interaction of the parameter with other parameters on the survival rate. Taking the interaction of temperature and daily salinity drop as an example, the temperature is taken as 20°C (low) and 30°C (high), and other parameters are still fixed, and 4 groups of survival rates are calculated: salinity 0.5 ppt + 20°C, seedling survival rate 94%; salinity 0.5 ppt + 30°C, seedling survival rate 96%, i.e. the survival rate increases slightly at high temperature, which reflects the interaction; salinity drop 2 ppt + 20°C, seedling survival rate 70%; salinity 2 ppt + 30°C, seedling survival rate 78%, i.e. the survival rate still increases slightly at high temperature, which is obvious interaction.

[0097] Calculate the total effect index: From low to high daily salinity decrease, after adding temperature interaction, the seedling survival rate decreased by a maximum of 26% (96%-70%), which is greater than the effect of daily salinity decrease alone (20%), indicating a strong comprehensive impact. The total effect index is simplified to 0.95, where the closer it is to 1, the greater the comprehensive impact.

[0098] It should be noted that the quantitative standards for the main effect index and the total effect index are determined according to the relative contribution of the parameters to the seedling survival rate output. No specific restrictions are made here. The key regulatory factors must simultaneously meet the requirements of controllability and significant main effect, excluding environmental factors.

[0099] In addition, the selection of sensitive parameters based on the main effect index and the total effect index is based on the standard that the higher the index, the more significant the impact on the survival rate. Based on the main effect index and the total effect index of each input parameter, with the total effect index as the main index and the main effect index as the secondary index, parameters with both high total effect index and high main effect index are classified as sensitive parameters.

[0100] The physiological tolerance range of key salinity-related regulatory factors is determined based on the variation range of sensitive parameters using the following method:

[0101] Determine the variation range of the selected sensitive parameters in the dynamic physiological response model, such as a daily salinity decrease of 0.5-2 ppt / day and a stable phase time of 3-7 days. Within this variation range, simulate the seedling survival rate corresponding to different parameter values ​​through the model, and find the parameter value range that can maintain the survival rate at the expected high level. This parameter value range can be regarded as the physiological tolerance range of the key salinity-related regulatory factors. It should be noted that the parameter value range of the expected high level is the parameter value range close to the expected maximum survival rate.

[0102] In step 103, with the daily salinity decrease and the stage stabilization time as decision variables and the maximization of seedling survival rate as the optimization objective, the decision variables are numerically optimized based on the dynamic physiological response model to obtain the optimal combination of daily salinity decrease and stabilization time for each stage and the corresponding expected maximum survival rate.

[0103] In some embodiments, reference Figure 3 As shown in the figure, this is a flowchart illustrating the numerical optimization calculation of decision variables in some embodiments of this application. In this embodiment, the daily salinity decrease, stage stabilization time, and ion concentration are used as decision variables, and the seedling survival rate and molting rate are maximized as optimization objectives. Based on the dynamic physiological response model, the numerical optimization calculation of decision variables is performed to obtain the optimal combination of daily salinity decrease, stabilization time, and ion concentration at each stage, as well as the corresponding expected maximum survival rate. This can be achieved through the following steps:

[0104] In step 1031, the salinity daily reduction, the stage stable time and the numerical search boundary of ion concentration are set, wherein the numerical search boundary is the decision variable range set by the physiological tolerance interval of the key control factor and the actual scene condition of the salt-tolerant crab seedling desalination domestication, and the search boundary is set according to the seedling stage, for example, the salinity daily reduction of the large-eyed larva is 0.5-1.2 ppt / day, and the stage stable time is 5-7 days; the salinity daily reduction of the juvenile crab I is 0.8-1.5 ppt / day, and the stage stable time is 4-6 days; the salinity daily reduction of the juvenile crab II is 1.2-2.0 ppt / day, and the stage stable time is 3-5 days; the model identifies the seedling stage through the molting rate (molting rate ≥ 30% is determined as the juvenile crab I); for example, the upper limit is determined according to the physiological tolerance interval, the osmoregulation of the salt-tolerant crab seedling is limited, if the salinity daily reduction is more than 2 ppt / day, the survival rate of the seedling will decrease sharply, so 2 ppt / day is set as the upper limit;

[0105] The lower limit is determined according to the actual scene of the salt-tolerant crab seedling desalination domestication, that is, in the salt-tolerant crab breeding, if the reduction is less than 0.5 ppt / day, the desalination period will be too long, and the cost will be increased, so 0.5 ppt / day can be set as the lower limit, and finally the numerical search boundary of the salinity daily reduction is set as 0.5-2 ppt / day, the above is only used for illustration, and does not limit the present application;

[0106] In step 1032, the decision variables are solved iteratively in the numerical search boundary, and in the implementation, the multi-objective is set first, for example, the objectives of high survival rate and reasonable desalination period; then the initial value (such as 0.5, 1, 1.5, 2) is selected in the numerical search boundary, for example, in the salinity daily reduction of 0.5-2 ppt / day, and the corresponding seedling survival rate and desalination period are calculated by substituting the model; the seedling survival rate and desalination period corresponding to each initial value are compared, and the optimal value is reserved, for example, the salinity daily reduction of 1.5 ppt / day is good, and the iteration is continued, for example, the salinity daily reduction of 1.4 ppt / day and the salinity daily reduction of 1.6 ppt / day are iteratively calculated, until the optimal value of the double objectives is found, for example, 1.2-1.5 ppt / day is the optimal value;

[0107] It should be noted that the NSGA-II algorithm can output the Pareto optimal solution set (for example, the optimal schemes including the survival rate of 95%+desalination period of 15 days or the survival rate of 92%+desalination period of 12 days), and the target weight can be adjusted according to the scene (for example, the survival rate weight of the seedling period is 0.7, and the molting rate weight of the growing period is 0.6, which is not described here.

[0108] In step 1033, the optimal values of the salinity daily reduction, the stable time and the ion concentration of each desalination stage are extracted according to the solving result, and the maximum value of the corresponding seedling survival rate is simulated and calculated.

[0109] In a specific implementation, for example, from the multi-target iterative solution result, according to the classification of the pre-dilution, mid-dilution and post-dilution, the salinity daily reduction that can meet the high survival rate + reasonable dilution period is screened in each stage, for example, the pre-dilution is 0.8 ppt / day, the mid-dilution is 1.2 ppt / day; the stable time, for example, the pre-dilution is 5 days, the mid-dilution is 4 days; the ion concentration, for example, the pre-dilution is 210 mmol / L, the mid-dilution is 200 mmol / L, are taken as the optimal values in each stage, the optimal values in each stage are substituted into the dynamic physiological response model, the model will simulate and calculate the corresponding seedling survival rate according to the optimal values in each stage, and the output results of all parameter combinations are compared, and the highest value is the maximum seedling survival rate. Here, it is not repeated.

[0110] In step 104, according to the physiological tolerance interval of the key control factor and the optimal salinity daily reduction and stable time obtained by optimization, the optimal target salinity setting value of each dilution stage is determined, and a gradient dilution control strategy is constructed according to the optimal target salinity setting value and the control target of the expected maximum survival rate.

[0111] In some embodiments, according to the physiological tolerance interval of the key control factor and the optimal salinity daily reduction and stable time obtained by optimization, the optimal target salinity setting value of each dilution stage is determined. In a specific implementation, the starting salinity of each dilution stage is first determined, that is, the initial breeding salinity is taken as the starting point of the first dilution stage, and the starting salinity of the subsequent dilution stage is the target salinity of the previous stage;

[0112] Each dilution stage is divided into an adaptation period (the first 2 days, daily reduction = 0.8*optimal daily reduction) and a stable reduction period (the remaining days, daily reduction = 1.2*optimal daily reduction), and a 1-day buffer period (salinity remains unchanged) is set between the two stages. The amount of salinity reduction in the stage is determined, that is, the total amount of salinity that can be reduced in this stage is calculated by (adaptation period daily reduction*2 days) + (stable reduction period daily reduction*(stable time-2 days)) (wherein the adaptation period daily reduction = 0.8*optimal daily reduction, and the stable reduction period daily reduction = 1.2*optimal daily reduction).

[0113] The target salinity is checked and set, that is, the starting salinity of the stage is reduced by the total amount of salinity, and if the result is within the physiological tolerance interval of the key control factor, it is the optimal target salinity of the stage.

[0114] The gradient dilution control strategy can be constructed according to the optimal target salinity setting value and the expected maximum survival rate, which can be implemented in the following way, that is, first, the target salinity gradient is determined in stages: the dilution stages are divided into pre-dilution, mid-dilution and post-dilution, the optimal target salinity of each dilution stage is arranged in turn to form a gradually decreasing salinity gradient;

[0115] Secondly, configure the core parameters of each stage, that is, match the optimized salinity daily drop and stabilization time for each salinity gradient, and adjust the ion concentration to ensure that the core parameters of each stage adapt to the target salinity of the stage; finally, verify the survival rate constraint, that is, combine the parameters of each stage into the model to confirm that each step can maintain the expected maximum survival rate, that is, form a complete gradient desalination control strategy, which will not be described here.

[0116] In this application, the key salinity control factors are first screened through global sensitivity analysis, and then quantitatively optimized by combining numerical optimization methods. This process effectively avoids the blindness of traditional empirical control, significantly improves the stability of seedling survival rate and molting rate. Compared with the static control mode used in the prior art, the present scheme effectively reduces the negative impact of stress accumulation on the physiological state of seedlings by dynamically adjusting the decision variables such as salinity daily drop and stage stabilization time, thereby improving the overall domestication efficiency and economic benefits of aquaculture, and effectively meeting the fine control needs of salt and alkali land mud crab seed desalination domestication.

[0117] On the other hand, in some embodiments, the present application provides an interactive training system for salt and alkali land mud crab seed desalination domestication, which comprises a salinity control model optimization unit, which is used to Figure 4 The figure is a structural schematic diagram of a salinity control model optimization unit according to some embodiments of the present application. The salinity control model optimization unit 400 comprises a modeling module 401, a processing module 402 and an execution module 403, which are described as follows:

[0118] The modeling module 401 is mainly used to establish a mud crab seed salinity stress response model in the present application. Based on the observed values of seedling survival rate in actual aquaculture data, the salinity stress response model is parameterized and calibrated to establish a calibrated dynamic physiological response model;

[0119] The processing module 402 is used to perform global sensitivity analysis on salinity daily drop, stage stabilization time, initial salinity, ion concentration, temperature and dissolved oxygen according to the dynamic physiological response model in the present application. According to the global sensitivity analysis results, the physiological tolerance interval of the key control factors related to the salinity of mud crab seed desalination domestication is determined;

[0120] In addition, the processing module 402 is also used to take salinity daily drop, stage stabilization time and ion concentration as decision variables, maximize seedling survival rate and molting rate as optimization objectives, and perform numerical optimization calculation of decision variables based on the dynamic physiological response model to obtain the optimal combination of salinity daily drop, stabilization time and ion concentration of each stage and the corresponding expected maximum survival rate;

[0121] The execution module 403 is mainly used for determining optimal target salinity setting values and ion concentration setting values of each desalination stage according to the physiological tolerance interval of the key regulatory factor, the optimal salinity daily drop, the stable time and the ion concentration obtained by optimization, and constructing a gradient desalination regulation strategy according to the optimal target salinity setting values, the ion concentration setting values, the expected maximum survival rate and the regulation target of nutritional intervention.

[0122] In addition, the present application also provides a computer device, which comprises a memory and a processor, the memory stores code, and the processor is configured to acquire the code and execute the above-mentioned salinity regulation model optimization method for juvenile blue crab desalination domestication.

[0123] In some embodiments, reference is made to Figure 5 The figure is a structural schematic diagram of a computer device for implementing the salinity regulation model optimization method for juvenile blue crab desalination domestication according to some embodiments of the present application. The salinity regulation model optimization method for juvenile blue crab desalination domestication in the above-mentioned embodiments can be implemented by the computer device shown in the figure, which comprises at least one processor 501, a communication bus 502, a memory 503 and at least one communication interface 504. Figure 4

[0124] The processor 501 can be a general central processing unit (CPU) or an application specific integrated circuit (ASIC).

[0125] The communication bus 502 can be used to transmit information between the above-mentioned components.

[0126] ​The memory 503 can be a readonly memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM), or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable readonly memory (EEPROM), a compact disc readonly memory (CDROM) or other optical disk storage, a magneto-optical disk storage, a magnetic disk or other magnetic storage device, or any other medium capable of storing desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto. The memory 503 can exist independently of the processor 501, and can be connected to the processor 501 via the communication bus 502. The memory 503 can also be integrated with the processor 501.

[0127] The memory 503 is configured to store program codes for implementing the solutions of the present application, and the processor 501 is configured to execute the program codes stored in the memory 503. The program codes can include one or more software modules. The salt concentration regulation model optimization method for blue crab fry desalination domestication in the above embodiments can be implemented by one or more software modules in the program codes of the processor 501 and the memory 503.

[0128] The communication interface 504 is configured to communicate with other devices or communication networks, such as an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc., using any transceiver-like mechanism.

[0129] In specific implementations, as an example, the computer device can include multiple processors, each of which can be a single CPU processor or a multi-CPU processor. The processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0130] The computer device described above can be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device can be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.

[0131] In addition, the present application also provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by a processor to realize the salinity regulation model optimization method for the saltwater culture of blue crab fry.

[0132] Although the preferred embodiments of the present application have been described, those skilled in the art who are familiar with the basic inventive concept can make additional changes and modifications to the embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0133] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A salinity regulation model optimization method for the desalination domestication of blue crab larvae, characterized by, The method comprises the following steps: A model of the salt stress response of the blue crab fry is established, and based on the observed values of the survival rate of the fry in actual breeding data, parameter estimation and calibration are performed on the salt stress response model to establish a calibrated dynamic physiological response model; Based on the dynamic physiological response model, global sensitivity analysis is performed on the daily salt reduction amplitude, stage stable time and initial salinity, and based on the global sensitivity analysis results, the physiological tolerance interval of the key control factor related to the salinity of the blue crab fry is determined; Taking the daily salt reduction amplitude and the stage stable time as the decision variables and maximizing the survival rate of the fry as the optimization objective, numerical optimization calculation is performed on the decision variables based on the dynamic physiological response model to obtain the optimal combination of the daily salt reduction amplitude and the stable time of each stage and the corresponding expected maximum survival rate; Based on the physiological tolerance interval of the key control factor and the optimal daily salt reduction amplitude and stable time obtained by optimization, the optimal target salinity setting value of each desalination stage is determined, and a gradient desalination control strategy is constructed based on the optimal target salinity setting value and the control objective of the expected maximum survival rate.

2. The method of claim 1, wherein, The establishment of the salt stress response model of the blue crab fry specifically comprises: Collecting survival rate data, physiological index data, ion concentration data and molting cycle data of the blue crab fry under different salinity gradients; Calculating the influence coefficient of the salinity change rate on the osmoregulation and ion balance of the fry based on the survival rate data, physiological index data, ion concentration data and molting cycle data; Building the salt stress response model of the blue crab fry, i.e. a nonlinear dynamic response model between salinity change, ion concentration and fry survival rate, through the influence coefficient.

3. The method of claim 1, wherein, Based on the observed values of the survival rate of the fry in actual breeding data, parameter estimation and calibration are performed on the salt stress response model to establish a calibrated dynamic physiological response model specifically comprising: Extracting time series observation sequences of the survival rate of the fry, ion concentration and molting rate in multiple desalination cycles from actual breeding records; Estimating the model parameters of the salt stress response model based on the time series observation sequences; Adjusting the model structure of the salt stress response model based on the estimated parameters to establish a calibrated and optimized dynamic physiological response model.

4. The method of claim 1, wherein, Based on the dynamic physiological response model, global sensitivity analysis is performed on the daily salt reduction amplitude, stage stable time, initial salinity, ion concentration, temperature and dissolved oxygen, and based on the global sensitivity analysis results, the physiological tolerance interval of the key control factor related to the salinity specifically comprises: Defining the parameter input range of the daily salt reduction amplitude, stage stable time, initial salinity, ion concentration, temperature and dissolved oxygen in the dynamic physiological response model; Calculating the main effect index and total effect index of each input parameter on the survival rate output of the fry; Based on the main effect index and total effect index, sensitive parameters are screened, and based on the variation range of the sensitive parameters, the physiological tolerance interval of the key control factor related to the salinity is determined.

5. The method of claim 1 wherein, The numerical optimization calculation of the decision variables is performed based on the dynamic physiological response model, taking the salinity daily reduction, the stage stable time and the ion concentration as the decision variables, and maximizing the survival rate and the molting rate of the seedlings as the optimization target, to obtain the optimal combination of the salinity daily reduction, the stable time and the ion concentration in each stage and the corresponding expected maximum survival rate, specifically including: Setting the numerical search boundary of the salinity daily reduction, the stage stable time and the ion concentration; Multi-objective iterative solving of the decision variables is performed within the numerical search boundary; According to the solving result, the optimal values of the salinity daily reduction, the stable time and the ion concentration in each desalination stage are extracted, and the corresponding maximum survival rate of the seedlings is simulated and calculated.

6. The method of claim 1, wherein, According to the physiological tolerance interval of the key control factor and the optimal salinity daily reduction and stable time obtained by optimization, the optimal target salinity setting value of each desalination stage is determined, specifically including: The starting salinity of each desalination stage is determined, taking the initial breeding salinity as the starting point of the first desalination stage, and the starting salinity of the subsequent desalination stage is the target salinity of the previous stage; The optimal salinity daily reduction obtained by optimization is multiplied by the stable time of the stage to obtain the total amount of salinity that can be reduced in this stage; The starting salinity of the stage is subtracted by the total amount of salinity, and if the result is within the physiological tolerance interval of the key control factor, it is the optimal target salinity setting value of the stage.

7. The method of claim 1 wherein, The salinity stress response model is a semi-mechanism semi-experience model or a mechanism type model.

8. An interactive training system for desalinization and domestication of blue crab seedlings in saline-alkali soil, comprising a salinity control model optimization unit, characterized in that, The salinity control model optimization unit specifically includes: A modeling module is configured to establish a salinity stress response model of mud crab seedlings, and perform parameter estimation and calibration on the salinity stress response model based on the observed values of the survival rate of seedlings in actual breeding data, to establish a calibrated dynamic physiological response model; A processing module is configured to perform global sensitivity analysis on the salinity daily reduction, the stage stable time, the initial salinity, the ion concentration, the temperature and the dissolved oxygen according to the dynamic physiological response model, and determine the physiological tolerance interval of the key control factor related to the salinity of mud crab seedlings desalination acclimation according to the global sensitivity analysis result; The processing module is further configured to perform numerical optimization calculation of the decision variables based on the dynamic physiological response model, taking the salinity daily reduction, the stage stable time and the ion concentration as the decision variables, and maximizing the survival rate and the molting rate of the seedlings as the optimization target, to obtain the optimal combination of the salinity daily reduction, the stable time and the ion concentration in each stage and the corresponding expected maximum survival rate; An execution module is configured to determine the optimal target salinity setting value and the ion concentration setting value of each desalination stage according to the physiological tolerance interval of the key control factor and the optimal salinity daily reduction, the stable time and the ion concentration obtained by optimization, and construct a gradient desalination control strategy according to the optimal target salinity setting value, the ion concentration setting value, the expected maximum survival rate and the control target of nutritional intervention. 9.A computer device, comprising a memory and a processor, wherein the memory stores code, and the code comprises the following steps: The processor is configured to obtain the code and execute the salinity control model optimization method for mud crab seedling desalination acclimation according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the salinity control model optimization method for mud crab seedling desalination acclimation according to any one of claims 1 to 7.

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