A method and system for predicting the survival rate of mandarin fish domestication based on ecological simulation

Abstract

This invention belongs to the field of mandarin fish farming technology, and relates to a method and system for predicting the survival rate of mandarin fish domestication based on ecological simulation. The method constructs multiple ecological simulation ponds to obtain time-series data of ecological factors and the survival rate of sample mandarin fish. Based on this, it establishes a quantitative relationship between the ecological factors and the slope of the survival rate change of sample mandarin fish to form an evaluation model. It generates historical survival rate curves and determines the risk status by comparing the current slope of the sample mandarin fish survival rate change with the slope of the historical survival rate curve in the corresponding time period. Based on the evaluation model, it predicts the change path of ecological factors in future time periods, generates a predicted survival rate curve, and outputs an analysis report containing the risk status judgment results. This application transforms the complex domestication process into a quantifiable data model, achieving early and accurate risk warning through dynamic analysis of survival rate change trends.

Description

Technical Field

[0001] This invention belongs to the field of mandarin fish farming technology, specifically relating to a method and system for predicting the survival rate of mandarin fish domestication based on ecological simulation. Background Technology

[0002] With the rapid development of modern aquaculture towards intensification and intelligence, the efficient and stable cultivation of high-economic-value fish has become a core issue in ensuring market supply and improving industry efficiency. As a high-quality freshwater fish favored by the market, the large-scale cultivation of mandarin fish is key to meeting consumer demand.

[0003] However, the current common methods for domesticating mandarin fish feed rely heavily on the personal experience of technicians and lack effective means to identify and analyze key factors affecting survival rates. Problems are usually only discovered after large-scale mortality of fry, making early warning and intervention impossible. This not only causes huge economic losses but also wastes valuable fry resources and time.

[0004] In view of this, the industry urgently needs a method for predicting the survival rate of mandarin fish domestication based on ecological simulation, so as to significantly improve the success rate, stability and efficiency of mandarin fish domestication. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for predicting the survival rate of mandarin fish domestication based on ecological simulation, so as to solve the problems in the prior art. This application simulates the ecological environment of mandarin fish to create a comfortable domestication environment for mandarin fish. At the same time, it continuously tracks the survival rate of mandarin fish so as to adjust the domestication environment in a timely manner.

[0006] To achieve the above objectives, the present invention employs the following technical solution: a method for predicting the survival rate of mandarin fish domestication based on ecological simulation, comprising:

[0007] Based on the time series data of ecological factors recorded in the ecological simulation pond and the corresponding survival rate of mandarin fish, a quantitative relationship between ecological factors and the slope of the change in the survival rate of mandarin fish is established to form an evaluation model.

[0008] Historical survival rate curves are generated based on the historical survival rates of mandarin fish samples. These historical survival rate curves are used to characterize the change in the slope of the survival rate of mandarin fish samples over time.

[0009] Furthermore, when the slope of the current survival rate change of the sample mandarin fish meets the slope of the historical survival rate curve in the corresponding time period, the sample mandarin fish is determined to be in a risky state, and the following actions are taken:

[0010] Based on the evaluation model, a predicted survival rate curve for sample mandarin fish is generated for a future time period, and an analysis report including the risk status assessment results is output.

[0011] For example, the fulfillment of risk triggering conditions includes:

[0012] The absolute value of the difference between the current slope of the survival rate change of mandarin fish and the slope of the historical survival rate curve in the corresponding time period is greater than the preset risk threshold.

[0013] For example, the steps to determine whether a sample of mandarin fish is at risk also include:

[0014] Based on the sign of the slope of the current survival rate change of the mandarin fish samples and the breeding stage of the mandarin fish samples, normal and abnormal areas are delineated, and the mandarin fish samples are determined to be in a risky state based on the normal and abnormal areas.

[0015] For example, the steps to form an evaluation model include:

[0016] Construct multiple ecological simulation pools with different initial state parameters;

[0017] Monitor and record the time-series data of ecological factors in each ecological simulation pool;

[0018] Furthermore, after releasing sample mandarin fish into the ecological simulation pond, the number of surviving mandarin fish was recorded at multiple time points, and the corresponding survival rate and slope of the survival rate were calculated based on the number of surviving mandarin fish to establish a quantitative relationship.

[0019] For example, the steps to generate a predicted survival rate curve include:

[0020] Based on the assessment model and the current survival rate of mandarin fish samples, the change path of ecological factors in the future time period is estimated;

[0021] Furthermore, based on the change path, a predicted survival rate curve is simulated and generated.

[0022] For example, the process of calculating the path of change also includes:

[0023] Identify high-fluctuation periods where the change value exceeds a preset fluctuation threshold from historical survival rate curves;

[0024] Furthermore, high-fluctuation periods are screened based on the allowable range of ecological factors to obtain reference data for extrapolation.

[0025] For example, the steps of generating the predicted survival rate curve also include:

[0026] Real-time collected ecological factor data are used as initial parameters input into the evaluation model;

[0027] Calculate the fluctuation range of the predicted survival rate curve, and recalculate after adjusting the initial parameters if the fluctuation range exceeds the reference fluctuation range threshold.

[0028] This application also provides a system for predicting the survival rate of mandarin fish domestication based on ecological simulation, including:

[0029] The model building module is used to establish a quantitative relationship between ecological factors and the slope of change in the survival rate of sample mandarin fish based on the time series data of ecological factors recorded in the ecological simulation pond and the corresponding survival rate of sample mandarin fish, so as to form an evaluation model. In addition, the model building module is also used to generate historical survival rate curves that characterize the change in the slope of the survival rate of sample mandarin fish over time.

[0030] The risk assessment module is used to monitor the slope of the current survival rate of the sample mandarin fish and compare it with the slope of the historical survival rate curve in the corresponding time period, so as to determine that the sample mandarin fish is in a risky state when the preset risk triggering conditions are met.

[0031] The survival rate prediction module is used to call the assessment model in response to the risk status determined by the risk assessment module, to generate the predicted survival rate curve of the sample mandarin fish in the future time period, and output an analysis report containing the risk status judgment results.

[0032] For example, the risk assessment module is used to determine that the sample mandarin fish is in a risky state when the absolute value of the difference between the slope of the current survival rate change of the sample mandarin fish and the slope of the historical survival rate curve in the corresponding time period is greater than a preset risk threshold.

[0033] For example, high-fluctuation periods are identified and screened from historical survival rate curves, and these high-fluctuation periods are screened according to the allowable range of ecological factors to obtain reference data for estimating the change path of ecological factors.

[0034] Furthermore, based on the evaluation model and the current survival rate of mandarin fish samples, the change path of ecological factors in the future time period is estimated, and the predicted survival rate curve is simulated and generated according to the change path. Beneficial effects

[0035] This invention constructs multiple ecological simulation ponds with different initial state parameters to obtain time series data of ecological factors. By combining the survival rate, number of surviving mandarin fish, and slope of the survival rate of mandarin fish samples, a quantitative relationship between ecological factors and the slope of the survival rate of mandarin fish samples is established, forming an evaluation model. Thus, the complex domestication process can be transformed into a quantifiable data model, which is beneficial to improving the accuracy of predicting the survival rate of mandarin fish domestication.

[0036] This invention generates historical survival rate curves during risk assessment and compares the slope of the current survival rate change of the sample mandarin fish with the slope of the historical survival rate curve in the corresponding time period. It also takes into account the specific breeding stage of the sample mandarin fish to comprehensively determine whether it is in a risky state. Thus, it can achieve early warning of risk before the survival rate of the sample mandarin fish declines significantly. Combining the specific breeding stage of the sample mandarin fish further improves the pertinence and accuracy of the warning. Attached Figure Description

[0037] Figure 1 This is a flowchart of the method provided by the present invention. Detailed Implementation

[0038] Example 1

[0039] Please see Figure 1 As shown in the figure, this embodiment provides a method for predicting the survival rate of mandarin fish domestication based on ecological simulation, which specifically includes the following steps:

[0040] Step S100: Based on the time series data of ecological factors recorded in the ecological simulation pond and the corresponding survival rate of sample mandarin fish, establish a quantitative relationship between ecological factors and the slope of change of the survival rate of sample mandarin fish to form an evaluation model.

[0041] Pre-defined ecological factors related to mandarin fish growth, such as water temperature, dissolved oxygen, pH, ammonia nitrogen content, nitrite concentration, and turbidity, were acquired. Multiple ecological simulation ponds were constructed based on these factors. To ensure the breadth and representativeness of the experimental data, different numerical parameters were used to configure the initial state of the ecological factors in each simulation pond. For example, one simulation pond was set to a high dissolved oxygen, standard pH environment, while another was set to a critically low dissolved oxygen, slightly acidic pH environment. This facilitated the coverage of various environmental stress combinations that mandarin fish might encounter during domestication.

[0042] It should be noted that an ecological simulation pond is a controlled system used to reproduce a specific aquatic ecological environment. Key ecological factors within it, such as water temperature and dissolved oxygen, can be precisely set and controlled to simulate different environmental conditions that mandarin fish may encounter during the domestication process.

[0043] For example, during acclimatization experiments, sensors deployed in various ecological simulation ponds can continuously monitor the changes in the values ​​of ecological factors over time in each pond, recording these changes as ecological factor time series data. This time series data is assigned timestamps and arranged chronologically to form an ecological factor sequence, providing a data foundation for establishing the correlation between environmental changes and biological responses. An ecological factor sequence is a collection of data sequences formed by arranging the values ​​of multiple ecological factors, such as water temperature and pH, continuously monitored within a specified time period according to their timestamps.

[0044] A predetermined number of uniformly sized mandarin fish samples are released into each ecological simulation pond. At multiple preset time points, such as every 6 hours, the number of surviving mandarin fish samples in each pond is recorded. The survival rate of the mandarin fish samples is calculated based on the number of survivors. The survival rate change rate is the amount of change in the survival rate of mandarin fish per unit time, used to measure the speed of changes in the health status of the population. Its calculation formula can be expressed as: ,in and They are time points respectively and Survival rate.

[0045] Based on baseline data and the survival rate of sample mandarin fish, an assessment model was established. During the process, the influence of various ecological factors on the survival rate, number of surviving mandarin fish, and the slope of the survival rate change was measured. The baseline data consisted of time-series data reflecting environmental conditions, collected and recorded by sensors in an ecological simulation pond before the assessment model was constructed. Among these, the slope of the survival rate change was a more sensitive early warning signal, revealing trends of deterioration or improvement in population health earlier than the survival rate itself.

[0046] Specifically, a quantitative relationship is established between the ecological factor sequence and the synchronously collected survival rate sequence using machine learning algorithms. The survival rate sequence includes the survival rate of the sample mandarin fish, the number of surviving fish, and the slope of the survival rate change. The ecological factor sequence and the survival rate sequence are time-stamped to form data pairs, which form the basis for model training. The quantitative relationship is saved to form an evaluation model, and the quantitative relationship, including data pairs, factor weights, and relationship functions, is saved as a standard dataset for easy model access and updates. A data pair is a paired data unit containing environmental state and biological response information, formed by matching data points in the ecological factor sequence with corresponding data points in the survival rate sequence at the same timestamp. The evaluation model formula is as follows:

[0047] ;

[0048] In the formula, This represents the predicted survival rate, which is the ratio of the predicted number of surviving individuals in the mandarin fish population to the initial total number under given ecological factors. This represents the vector of ecological factors, which indicates the value of each factor at a given time point. The collection of numerical values ​​for all ecological factors, such as water temperature, dissolved oxygen, and pH. ,in For the first Individual ecological factors at a given time point The measured value; This represents the factor weight vector, which is a set of coefficients representing the degree of influence of each ecological factor on the survival rate, obtained through model training. This represents the bias term, which is the baseline bias of the model and is used to adjust the threshold of the activation function.

[0049] Step S200: Generate a historical survival rate curve based on the historical survival rate of sample mandarin fish. The historical survival rate curve is used to characterize the change in the slope of the survival rate of sample mandarin fish over time.

[0050] Based on the data pairs formed in the above steps, the survival rate sequence is extracted from the data pairs, and the change in survival rate of the sample mandarin fish within a predetermined unit time period, such as every 24 hours, is calculated. A historical survival rate curve is generated based on the survival rate change. The survival rate change is the numerical change in the survival rate of mandarin fish within a predetermined unit time period, calculated as the survival rate at the end of the time period minus the survival rate at the beginning of the time period. The historical survival rate curve is a continuous curve reflecting the change in survival rate over time, formed by connecting the survival rate data recorded at various time points in the experiment or past breeding cycles.

[0051] Specifically, historical survival rate curves are analyzed to identify periods of high volatility. The survival rate changes at various time points are calculated, and periods where the absolute value of the survival rate change exceeds a preset volatility threshold are marked as high volatility periods. These high volatility periods characterize significant changes in population size. Based on preset screening criteria, intervals that do not meet the criteria are excluded from the high volatility periods. These criteria include setting a numerical allowable range for each ecological factor. This allowable range is a normal and safe numerical range for each ecological factor; when the factor's measured value falls outside this range, it is considered that the environment is abnormal. For any high volatility period, it is determined whether the corresponding ecological factor value exceeds its corresponding numerical allowable range. If so, the period is excluded from the high volatility period; otherwise, it is included in the reserved data segment. All the selected reserved data segments are arranged chronologically as reference data for predicting the change path of ecological factors in future time periods. Among them, the retained data segment is a valid data fragment selected from the high fluctuation period according to the screening conditions, such as all ecological factors being within the allowable range of values, which can represent the population response pattern under a specific combination of environmental pressures. The change path is a sequence of inferences or simulations based on the retained data segment in historical data, which is used to predict the possible change trend of the values ​​of each ecological factor in the future.

[0052] Step S300: When the slope of the current survival rate change of the sample mandarin fish and the slope of the historical survival rate curve in the corresponding time period meet the preset risk triggering conditions, it is determined that the sample mandarin fish is in a risk state, and the following is executed: Based on the evaluation model, a predicted survival rate curve of the sample mandarin fish in the future time period is generated, and an analysis report containing the risk state judgment results is output.

[0053] Specifically, in actual aquaculture monitoring, the current survival rate of the sample mandarin fish is obtained, and the slope of the current survival rate change is calculated. In order to accurately determine the risk, the current slope of the current survival rate change is compared with the slope of the historical survival rate curve in the corresponding time period. If the absolute value of the difference between the current slope of the current survival rate change and the slope of the historical survival rate curve is greater than the preset risk threshold, then the sample mandarin fish is determined to be in a risky state.

[0054] To improve the accuracy of the assessment, the steps for determining whether the sample mandarin fish is in a risky state also include: determining whether the current slope of the survival rate change of the sample mandarin fish is positive or negative; determining whether the culture stage of the sample mandarin fish is within the preset culture stage recorded in the assessment model, such as the fry stage, juvenile stage, or adult stage; and delineating normal and abnormal regions based on the sign of the current slope of the survival rate change and the culture stage. For example, in the fry culture stage, a negative slope below the preset threshold for that stage can be classified as an abnormal region, while in the adult culture stage, the same slope may still be within the normal region. Based on this classification, the sample mandarin fish is determined to be in a risky state. The normal region is the acceptable range of the survival rate change slope defined based on historical data and expert experience at a specific culture stage; a slope falling into this region is considered to indicate a stable population. The abnormal region is where, at a specific culture stage, the survival rate change slope exceeds the range of the normal region; a slope falling into this region indicates that the population may experience unexpected mortality or abnormal conditions.

[0055] When prediction is required, real-time ecological factor data is collected. Before inputting this data into the evaluation model, a verification step is performed: the real-time ecological factor data is compared with a preset standard template, and the difference between the two is calculated. If the difference exceeds a preset comparison threshold, real-time ecological factor data is collected again until the difference does not exceed the comparison threshold. The comparison threshold is used to determine the consistency between the real-time ecological factor data and the standard ecological factor template data. The standard ecological factor template data is a set of ecological factor values ​​representing an ideal or baseline aquaculture environment, used as a reference standard to verify the validity of the real-time collected data.

[0056] Specifically, if the difference is not greater than the comparison threshold, the real-time collected ecological factor data is used as the initial parameter input into the evaluation model. Based on the evaluation model and reference data, the change path of ecological factors in future time periods is estimated, and the predicted total survival of the sample mandarin fish in each subsequent simulation period is calculated through the evaluation model. A predicted survival rate curve is then generated based on the predicted total survival. To ensure the stability of the prediction results, the predicted total survival is also analyzed, and its fluctuation amplitude is calculated. The fluctuation amplitude is a specific indicator over a period of time, such as the difference between the maximum and minimum values ​​of the predicted total survival, used to measure the stability of this indicator. If the fluctuation amplitude is greater than the reference fluctuation amplitude threshold, it is considered a high-fluctuation state, and the initial parameters are adjusted before recalculation to obtain smoother and more reliable prediction results.

[0057] Based on the predicted survival rate curve and the risk status assessment results, an analysis report containing the risk status assessment results is output. The analysis report may include risk warnings, future survival rate trend charts, key influencing factor analysis, and control suggestions, providing decision support for aquaculture management.

[0058] Example 2

[0059] This embodiment provides a mandarin fish domestication survival rate prediction system based on ecological simulation, which is used to execute the mandarin fish domestication survival rate prediction method based on ecological simulation described above. It can assess risks in real time and predict future survival rate trends by establishing a quantitative relationship between ecological factors and survival rate, thereby providing decision support for the mandarin fish domestication process and improving the domestication survival rate.

[0060] This application can be deployed on servers, workstations, cloud platforms, or embedded computing devices, and communicates with various sensors (e.g., sensors for water temperature, dissolved oxygen, pH, ammonia nitrogen content, nitrite concentration, and turbidity) and data recording devices in the ecological simulation pond through data interfaces. The mandarin fish domestication survival rate prediction system of this application can be divided into the following collaborative modules:

[0061] The model building module is used to establish a quantitative relationship between ecological factors and the slope of the survival rate change of sample mandarin fish, and to generate historical survival rate curves for benchmark comparison. In a specific execution flow: it supports the construction or management of multiple ecological simulation ponds with different initial state parameters. These initial state parameters may include, but are not limited to, initial aquatic microbial populations, substrate type, water volume, and initial water quality indicators to simulate diverse aquaculture environments. It continuously monitors and records the time-series data of ecological factors collected by sensors in each ecological simulation pond, forming benchmark data. After releasing sample mandarin fish into the ecological simulation pond, it records the number of surviving fish at multiple preset time points (e.g., daily, every 12 hours), based on the initial... The survival rate of mandarin fish samples was calculated by comparing the number of fish released with the number of fish that survived at each time point. The slope of the survival rate was then obtained through differential calculation. Correlation analysis was performed between the collected ecological factor time-series data and the corresponding slope of the survival rate of mandarin fish samples. Algorithms such as multiple linear regression, support vector machines, or neural networks were used to establish quantitative relationships and solidify them into an evaluation model. Furthermore, the model building module was used to generate historical survival rate curves. Based on a large amount of historically successful domestication batch data, typical curves showing the change in the survival rate of mandarin fish samples over time throughout the entire domestication cycle were statistically analyzed and plotted. These historical survival rate curves represent the dynamic benchmark that a healthy and ideal domestication process should exhibit.

[0062] The risk assessment module is used to monitor and assess the ongoing mandarin fish domestication process in real time, determining whether the current sample mandarin fish are in a risky state. In a specific execution flow: it receives real-time survival rate data of the sample mandarin fish in the breeding pond and calculates the slope of the current survival rate change. This slope is then compared with the slope of the historical survival rate curve generated earlier for the same time period (e.g., day N of the domestication cycle). A preset risk trigger condition is used to determine the risk. Specifically, the absolute value of the difference between the current survival rate change slope and the slope of the historical survival rate curve for the corresponding time period is calculated. If this absolute value is greater than a preset risk threshold, the risk trigger condition is met, and the sample mandarin fish are preliminarily determined to be in a risky state.

[0063] To improve the accuracy of the judgment, a comprehensive assessment is made by combining information from other dimensions. Based on the sign of the slope of the current survival rate change of the sample mandarin fish (indicating whether the survival rate is increasing, decreasing, or stabilizing) and the specific breeding stage of the sample mandarin fish (e.g., whether it is in a stress adaptation period or a stable feeding period), a dynamic normal and abnormal zone is defined. For example, in the early stages of domestication, a slight negative slope may be within the normal zone, but in the stable period, any significant negative slope may be classified as abnormal. Only when the absolute difference in slope exceeds the limit and its state falls into the abnormal zone of the current stage is the sample mandarin fish ultimately determined to be in a risky state.

[0064] The survival rate prediction module is activated upon receiving a risk status signal determined by the risk assessment module. It generates a prediction of the future and outputs a complete analysis report. In a specific execution flow: it calls the assessment model built by the model building module. Based on the assessment model, the current survival rate of the sample mandarin fish, and real-time collected ecological factor data, it reverse-engineers the most likely path of change for the ecological factors leading to the current risk status in the future.

[0065] To make the predictions more reliable, before predicting the change path, high-fluctuation periods with survival rate changes exceeding a preset fluctuation threshold are first identified from historical survival rate curves. These high-fluctuation periods represent historical "crises" or "turning points." Based on the allowable range of values ​​for each ecological factor, these high-fluctuation periods are filtered out, and invalid data caused by sensor malfunctions are removed to obtain reference data. The reference data helps the model better predict the evolution trend of ecological factors under similar crises. After obtaining the predicted change path of ecological factors, it is used as input and the model is evaluated again for forward simulation to generate the predicted survival rate curve of the sample mandarin fish in the future time period.

[0066] To ensure the stability of the prediction results, a verification step is also performed. Real-time collected ecological factor data is used as initial parameters to input into the evaluation model for the first calculation. After calculating the predicted survival rate curve, the fluctuation range of the curve is evaluated. If the fluctuation range is greater than a reference fluctuation range threshold, the initial parameters are adjusted for smoothing or constraint processing, and the calculation is repeated until a prediction curve with reasonable fluctuation is generated. The risk status judgment results output by the risk assessment module are integrated with the generated predicted survival rate curve to form an analysis report containing the risk status judgment results, which is output to users to provide a scientific basis for farmers to take intervention measures.

[0067] Through the collaborative work of the aforementioned model building module, risk assessment module, and survival rate prediction module, the system in this embodiment can form a complete closed loop from historical data modeling to real-time risk identification and future trend prediction. It can dynamically and accurately monitor the domestication process of mandarin fish, achieve early risk warning, and is suitable for large-scale and standardized aquaculture farms. It can improve the success rate and economic benefits of mandarin fish domestication through data-driven methods.

[0068] The above description is merely an embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting the survival rate of domesticated mandarin fish based on ecological simulation, characterized in that, include: Based on the time series data of different ecological factors recorded in ecological simulation ponds with multiple different initial state parameter configurations and the corresponding survival rates of sample mandarin fish, a quantitative relationship between ecological factors and the slope of changes in the survival rate of sample mandarin fish is established to form an evaluation model. Historical survival rate curves are generated based on the historical survival rates of mandarin fish samples. These historical survival rate curves are used to characterize the change in the slope of the survival rate of mandarin fish samples over time. Furthermore, when the slope of the current survival rate change of the sample mandarin fish meets the slope of the historical survival rate curve in the corresponding time period, the sample mandarin fish is determined to be in a risky state, and the following actions are taken: Based on the evaluation model, a predicted survival rate curve for sample mandarin fish is generated in the future time period, and an analysis report containing the risk status judgment results is output. The steps for determining whether the sample mandarin fish is at risk also include: Based on the sign of the slope of the current survival rate change of the mandarin fish samples and the breeding stage of the mandarin fish samples, normal and abnormal areas are delineated, and the mandarin fish samples are determined to be in a risky state based on the normal and abnormal areas.

2. The method for predicting the survival rate of mandarin fish domestication based on ecological simulation according to claim 1, characterized in that, The fulfillment of risk triggering conditions includes: The absolute value of the difference between the current slope of the survival rate change of mandarin fish and the slope of the historical survival rate curve in the corresponding time period is greater than the preset risk threshold.

3. The method for predicting the survival rate of mandarin fish domestication based on ecological simulation according to claim 1, characterized in that, The steps involved in developing an evaluation model include: Construct multiple ecological simulation pools with different initial state parameters; Monitor and record the time-series data of ecological factors in each ecological simulation pool; Furthermore, after releasing sample mandarin fish into the ecological simulation pond, the number of surviving mandarin fish was recorded at multiple time points, and the corresponding survival rate and slope of the survival rate were calculated based on the number of surviving mandarin fish to establish a quantitative relationship.

4. The method for predicting the survival rate of mandarin fish domestication based on ecological simulation according to claim 1, characterized in that, The steps for generating the predicted survival rate curve include: Based on the assessment model and the current survival rate of mandarin fish samples, the change path of ecological factors in the future time period is estimated; Furthermore, based on the change path, a predicted survival rate curve is simulated and generated.

5. The method for predicting the survival rate of mandarin fish domestication based on ecological simulation according to claim 4, characterized in that, Before calculating the path of change, the following steps are also included: Identify high-fluctuation periods where the change value exceeds a preset fluctuation threshold from historical survival rate curves; Furthermore, high-fluctuation periods are screened based on the allowable range of ecological factors to obtain reference data for extrapolation.

6. The method for predicting the survival rate of mandarin fish domestication based on ecological simulation according to claim 5, characterized in that, The steps for generating the predicted survival rate curve also include: Real-time collected ecological factor data are used as initial parameters input into the evaluation model; Calculate the fluctuation range of the predicted survival rate curve, and recalculate after adjusting the initial parameters if the fluctuation range exceeds the reference fluctuation range threshold.

7. A system for predicting the survival rate of mandarin fish domestication based on ecological simulation, characterized in that, include: The model building module is used to establish a quantitative relationship between ecological factors and the slope of change of the survival rate of sample mandarin fish based on time series data of different ecological factors recorded in ecological simulation ponds with multiple different initial state parameter configurations and the corresponding survival rates of sample mandarin fish, so as to form an evaluation model. In addition, the model building module is also used to generate historical survival rate curves that characterize the change of the slope of the survival rate of sample mandarin fish over time. The risk assessment module is used to monitor the slope of the current survival rate of the sample mandarin fish and compare it with the slope of the historical survival rate curve in the corresponding time period, so as to determine that the sample mandarin fish is in a risky state when the preset risk triggering conditions are met. The survival rate prediction module is used to call the assessment model in response to the risk status determined by the risk assessment module, to generate the predicted survival rate curve of the sample mandarin fish in the future time period, and output an analysis report containing the risk status judgment results. The determination that the sample mandarin fish was at risk also included: Based on the sign of the slope of the current survival rate change of the mandarin fish samples and the breeding stage of the mandarin fish samples, normal and abnormal areas are delineated, and the mandarin fish samples are determined to be in a risky state based on the normal and abnormal areas.

8. A survival rate prediction system for domesticated mandarin fish based on ecological simulation according to claim 7, characterized in that, The risk assessment module is used to determine that the sample mandarin fish is in a risky state when the absolute value of the difference between the slope of the current survival rate change of the sample mandarin fish and the slope of the historical survival rate curve in the corresponding time period is greater than a preset risk threshold.

9. A survival rate prediction system for domesticated mandarin fish based on ecological simulation according to claim 8, characterized in that, The survival rate prediction module is also used for: High-fluctuation periods are identified and screened from historical survival rate curves. These high-fluctuation periods are then screened based on the allowable range of ecological factor values ​​to obtain reference data for estimating the change path of ecological factors. Furthermore, based on the evaluation model and the current survival rate of mandarin fish samples, the change path of ecological factors in the future time period is estimated, and the predicted survival rate curve is simulated and generated according to the change path.

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