Antarctic krill yield analysis system and method based on multi-source big data

Abstract

The invention discloses an Antarctic krill yield analysis system and method based on multi-source big data, and relates to the technical field of marine fishery. The system comprises the following components: a data acquisition and management module, which acquires Antarctic sea area ocean current, ocean environment, ocean food chain and ecosystem structure data through satellite remote sensing, an ocean monitoring station and a scientific research and investigation ship, and stores the data in a distributed database after preprocessing; according to the method, multi-source big data from satellite remote sensing, an ocean monitoring station, a scientific research and investigation ship and the like are integrated, the influence of various complex factors such as ocean current change, ocean environment parameters, food chain dynamic balance and ecological system structure functions on the yield of the euphausia superba is comprehensively considered, and a fluid dynamic model and a numerical simulation technology are utilized; and by combining a high-precision ocean current simulation result, the krill yield fluctuation under different ocean current scenes can be more accurately predicted, and a scientific and prospective decision basis is provided for a fishery production department.

Description

Technical Field

[0001] The present invention relates to the field of marine fishery technology, and in particular to an Antarctic krill production analysis system and method based on multi-source big data. Background Art

[0002] Antarctic krill, as a key species in the Antarctic ecosystem, is not only an important part of the Antarctic marine ecosystem, but also an important source of global fishery resources. The environment of the Antarctic waters is complex and changeable. Changes in ocean currents have a significant impact on the distribution, foraging, migration and survival of krill. In addition, krill, as a basic link in the food chain, its dynamic changes play a vital role in the balance of the entire Antarctic marine ecosystem. Therefore, accurate prediction of Antarctic krill production is of great significance to the sustainable utilization of fishery resources and ecological protection.

[0003] Traditional krill production prediction methods and ecosystem research mostly rely on a single data source or simple mathematical models. These methods cannot fully and accurately reflect the complex changes in marine ecosystems and the comprehensive effects of ocean currents on the food chain. Due to the complexity and variability of the Antarctic marine environment, a single data source often cannot capture all key information, resulting in poor accuracy and reliability of prediction results. At the same time, simple models cannot fully consider the interactions between organisms and the dynamic impact of ocean currents on the food chain, which makes the understanding of the dynamic balance of the ecosystem have great limitations. Therefore, traditional prediction and analysis methods have obvious shortcomings when dealing with Antarctic krill production prediction and ecosystem research.

[0004] In view of the above problems, it is necessary to optimize the existing Antarctic krill production analysis system, simulate the ocean currents with high precision by using fluid dynamics models and numerical simulation technology, and construct a dynamic equilibrium model of the Antarctic marine food chain with krill as the core to achieve high-precision prediction of Antarctic krill production. Therefore, it is of great significance to develop an Antarctic krill production analysis system and method based on multi-source big data that can comprehensively realize the above characteristics. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and provide an Antarctic krill production analysis system and method based on multi-source big data. It can integrate data from multiple channels such as satellite remote sensing, ocean monitoring stations, scientific research survey ships, etc., and use advanced fluid dynamics models and numerical simulation technologies to simulate the ocean currents in the Antarctic waters with high precision, and construct an Antarctic marine food chain dynamic equilibrium model with krill as the core. By deeply analyzing the impact of ocean current changes on krill ecology and the interaction between organisms in the food chain, the present invention can achieve high-precision prediction of krill production and reveal the impact mechanism of ocean currents on the dynamic equilibrium of the food chain. This comprehensive analysis method and system not only provides scientific and forward-looking guidance for fishery production, but also provides strong theoretical support for the protection of the Antarctic ecosystem.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a system for analyzing Antarctic krill production based on multi-source big data, the system comprises the following components: Data collection and management module: collects data on Antarctic ocean currents, marine environment, marine food chain and ecosystem structure through satellite remote sensing, marine monitoring stations and scientific research survey ships, and stores them in a distributed database after preprocessing; Ocean current and ecological simulation module: Based on the geographical information and environmental parameters of the Antarctic waters, a fluid dynamics model is constructed to simulate ocean currents and update them in real time. A food chain dynamic equilibrium model with krill as the core is constructed by combining marine food chain and ecological data. The results of ocean current simulation are integrated to simulate the impact of ocean currents on the food chain and the laws of chain reactions under different scenarios. Krill Ecological Analysis Module: Integrates ocean current and ecological simulation results and other relevant data to analyze krill's foraging areas and food availability, migration paths and distribution changes under the influence of ocean currents. Consider environmental and biological factors to build an evaluation model to analyze krill's adaptability to the living environment and survival probability. Krill production forecasting module: Based on krill ecological analysis and biological characteristics, the key factors affecting krill production are determined and quantified, a forecasting model is constructed, and training and optimization are performed using historical data. Data from different ocean current change scenarios are input to predict production fluctuations and conduct uncertainty analysis. Result Visualization and Application Module: Develop software tools to visualize simulation analysis results in the form of charts, maps or dynamic presentations, and design user interaction interfaces to assist different users in making fishery production decisions, formulating fishing strategies and carrying out Antarctic ecosystem protection work.

[0007] Furthermore, the data acquisition and management module collects historical and real-time ocean current data, marine environment data, marine food chain data and ecosystem structure and function data through satellite remote sensing, ocean monitoring stations and scientific research survey ships, wherein the historical and real-time ocean current data include but are not limited to ocean current speed, direction and temperature, the marine environment data include but are not limited to salinity, dissolved oxygen and light, the marine food chain data include but are not limited to the distribution of krill's food sources, the distribution of natural enemies and the interaction relationship between various organisms, and the ecosystem structure and function data include the number and distribution of different biological populations.

[0008] Furthermore, the ocean current and ecological simulation module constructs a fluid dynamics model based on the geographic information and environmental parameters of the Antarctic waters to simulate ocean currents and updates the model in real time. The model formula is: ,in, is the ocean current velocity vector, indicating the ocean current speed and direction at a certain location in the Antarctic Ocean. is time, used to describe the change of ocean current speed over time, is the density of seawater, determined by the temperature and salinity of the Antarctic Ocean. is the seawater pressure, which is related to the depth and density of the seawater. It is the kinematic viscosity of seawater, which is determined by the physical properties of seawater and affects the viscosity and energy dissipation within the ocean current. is the gradient operator, which is used to calculate the rate of change of physical quantities in space. is the external force term, It is a newly added force term that takes into account the influence of seabed terrain roughness, and is calculated by combining high-precision seabed terrain data and terrain roughness model.

[0009] Furthermore, the ocean current and ecological simulation module combines the marine food chain and ecological data to construct a food chain dynamic equilibrium model with krill as the core, and the model formula is: ,in, Indicates The population size of various organisms, including krill and other related organisms, is time, used to describe the changes in the number of biological populations over time, It is The intrinsic growth rate of an organism reflects its ability to grow under ideal conditions. It is The environmental carrying capacity of a species depends on the ecological environment conditions in which the species lives, including food resources and habitat space. is the interaction coefficient between organisms, when hour, Indicates Species The effect of a certain organism on the organism. A positive number indicates a promoting effect, while a negative number indicates an inhibiting effect. It is The sensitivity coefficient of an organism to environmental fluctuations is determined according to the ecological adaptability and tolerance of the organism. It is the amount of environmental fluctuation, which comprehensively considers the changes in ocean temperature, salinity and ocean currents, and is obtained through the analysis and calculation of multi-source environmental data.

[0010] Furthermore, the krill ecological analysis module establishes a model to analyze the foraging area and food availability, migration path and distribution changes of krill under the influence of ocean currents. Specifically, the simulation results output by the ocean current and ecological simulation module are deeply integrated with the marine food chain and ecosystem structure and function data in the data acquisition and management module. Based on the integrated data set, the distribution of food particles carried by the ocean current is analyzed to determine the food availability in different areas and at different times. At the same time, the relationship between the foraging behavior of krill and food distribution is analyzed. According to the feeding habits and foraging strategies of krill, the probability of krill foraging at a certain location is determined. The probability calculation formula is: ,in, is the probability that krill will forage at a certain location, is the food abundance at that location, measured and calculated from the quantity and distribution of krill food sources, is the water diffusion coefficient at that location, taking into account the diffusion and transport effects of ocean currents on food particles, and is calculated by analyzing the ocean current speed, direction, and diffusion model. is the temperature suitability of the location, It is the competitive pressure index of the location, which comprehensively considers the competition of other organisms for food resources and is obtained by analyzing the population and distribution of organisms that compete with krill for food in the area. , , , It is the weight coefficient of the corresponding factor, which is determined by the hierarchical analysis method or machine learning method according to the actual data and analysis purpose, and is used to adjust the relative importance of each factor in the foraging probability calculation.

[0011] Furthermore, the krill ecological analysis module comprehensively considers environmental and biological factors to construct an evaluation model to analyze the adaptability of krill to the living environment and the probability of survival. Specifically, the krill migration prediction algorithm is used to predict the migration path and distribution changes of krill, analyze the survival challenges faced by krill during migration, and evaluate the impact of these factors on the krill population and reproductive success rate, in combination with the direction and speed data of ocean currents and the distribution and behavior change data of other organisms in the food chain. The algorithm formula is: ,in, It is a comprehensive assessment value of the krill living environment, used to measure the overall suitability and survival pressure of the krill environment. is the total number of environmental and biological factors considered, including temperature, salinity, dissolved oxygen, number of natural enemies, and food availability. It is Factors at the time The weight coefficient changes dynamically over time and is determined by a dynamic modeling method based on the changing trends of environmental factors and the needs and sensitivity of krill to various factors at different growth stages. It is The estimated value of a factor is obtained by standardizing the actual measured value of the factor and the tolerance range of krill to the factor.

[0012] Furthermore, the krill production prediction module determines and quantifies the key factors affecting krill production based on krill ecological analysis and biological characteristics, and constructs a prediction model, the prediction model of which is: ,in, yes The krill production predicted at any given moment is the predicted target value. is the activation function used to transform the input into a value within the output range, is the number of historical time steps considered, and indicates the use of the past The influencing factor data of time steps predict the krill production at the current moment. It is Factors affecting the historical time step The weight coefficient of yes The variables affecting krill production at each moment include calculated food availability, habitat assessments, and ocean current speeds. is the total number of spatial regions, dividing the Antarctic Ocean into different spatial regions to take into account the differences in krill production in different regions. is the number of hidden layers of the recurrent neural network, It is a spatial region No. Hidden Layers The weight coefficient of It is a spatial region exist The hidden layer state at the moment represents the spatial region exist The hidden layer state at each moment captures the spatiotemporal dependencies through a recurrent neural network. is the bias term, which is used to adjust the output of the model.

[0013] On the other hand, a method for analyzing Antarctic krill production based on multi-source big data comprises the following specific steps: Data collection and preprocessing: collect relevant data through the data collection and management module, clean, filter and standardize the data, remove outliers and erroneous data, and perform storage management; Ocean current and ecological simulation: Input the preprocessed data into the ocean current and ecological simulation module, use the fluid dynamics model and numerical simulation technology to simulate the ocean currents in the Antarctic waters, obtain the ocean current simulation results, and build the Antarctic marine food chain dynamic equilibrium model and integrate the ocean current simulation results to simulate the impact of ocean currents on the food chain under different conditions and the dynamic change process of the food chain, and analyze the chain reaction and evolution law; Krill Ecological Analysis: Combine the results of the Ocean Current and Ecological Simulation Module with the relevant data in the Data Collection and Management Module, and use data analysis algorithms and models to analyze the foraging, migration, and living environment of krill; Krill production forecast: Based on the results of krill ecological analysis, the model established by the krill production forecast module is used to input simulation data under different ocean current change scenarios to predict krill production fluctuations and conduct uncertainty analysis; Result display and application: The prediction results and the dynamic changes of the food chain are visualized through the result visualization and application module and provided to relevant users to assist them in making fishery production decisions, formulating fishing strategies and carrying out Antarctic ecosystem protection work.

[0014] Compared with the prior art, the Antarctic krill production analysis system and method based on multi-source big data has the following beneficial effects: 1. The present invention integrates multi-source big data from satellite remote sensing, ocean monitoring stations, scientific research survey ships, etc., comprehensively considers the impact of multiple complex factors such as ocean current changes, marine environmental parameters, dynamic balance of food chains, and structural functions of ecosystems on Antarctic krill production, and uses fluid dynamics models and numerical simulation technology, combined with high-precision ocean current simulation results, to more accurately predict krill production fluctuations under different ocean current scenarios, providing scientific and forward-looking decision-making basis for fishery production departments.

[0015] 2. This invention constructs a dynamic equilibrium model of the Antarctic marine food chain with krill as the core, deeply explores the impact of ocean current changes on various biological populations in the food chain and its chain reactions, and provides comprehensive data support and theoretical basis for scientific research institutions by simulating and analyzing the interaction between ocean currents and ecosystems under different conditions, which helps to deeply understand the complexity and dynamic equilibrium mechanism of the Antarctic marine ecosystem, and provides a solid scientific foundation for further formulating effective ecological protection measures and maintaining the stability and diversity of the Antarctic ecosystem.

[0016] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a schematic diagram of the structure of an Antarctic krill production analysis system based on multi-source big data; Figure 2 This is a flow chart of an Antarctic krill production analysis method based on multi-source big data. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0020] Embodiment 1: During a certain period, abnormal ocean current steering occurred in the Antarctic waters, which may have a significant impact on the living environment and production of krill. Through satellite remote sensing, ocean monitoring stations, and scientific research survey ships, the ocean currents, marine environment, marine food chain, and ecosystem structure and function data of the Antarctic waters are collected through multiple channels. After cleaning to remove outliers and erroneous data, screening key data fields, and using formulas for standardization, the data are stored in a distributed database and access rights are set. Specifically, through cooperation with a number of satellite remote sensing agencies, high-resolution ocean current data covering the entire Antarctic waters are obtained, including parameters such as speed and flow direction, as well as sea surface temperature data. At the same time, the observation frequency of abnormal ocean current areas is increased to ensure real-time grasp of ocean current changes, and a real-time data transmission channel is established with 12 ocean monitoring stations distributed in the Antarctic waters to collect salinity, dissolved oxygen, and The system collects marine environmental data such as light intensity and water depth, and conducts a preliminary quality check on the data. In addition, it dispatches two scientific research survey ships to conduct a week-long field investigation along the abnormal ocean current path and its surrounding areas. Sampling equipment, such as high-precision water samplers and new plankton nets, are used to collect marine food chain samples, focusing on analyzing the number and distribution changes of krill's food sources (such as diatoms, copepods and other plankton), as well as the activity range and number changes of natural enemies (such as penguins, sharks, etc.). All collected data are transmitted to the data center, and outliers (such as extreme data beyond the normal range) and erroneous data (such as data caused by format errors or sensor failures) are removed. According to the needs of krill production forecasts, key data fields are screened out, such as the rate of change of ocean current speed, changes in food source biomass, etc., and finally stored in a distributed database, and detailed data indexes are established and access permissions are set.

[0021] Based on the latest Antarctic waters geographic information data and ocean environment parameters, a high-precision fluid dynamics model is constructed , simulate the impact of abnormal ocean current changes on seawater flow, and continuously adjust model parameters to match them with actual observation data to improve the accuracy of simulation. Based on the geographical information and environmental parameters of the Antarctic waters, a fluid dynamics model is constructed to simulate ocean currents. A food chain dynamic equilibrium model with krill as the core is constructed in combination with marine food chain and ecological data, and the results of ocean current simulation are integrated into it. A variety of dynamic conditions and environmental parameter combinations are set to simulate ecological changes under different scenarios. Specifically, based on the collected marine food chain data and ecosystem structure and function data, a food chain dynamic equilibrium model with krill as the core is constructed. , considering the impact of abnormal ocean currents on biological distribution and interactions, the parameters in the model are updated and adjusted in real time to simulate the dynamic changes of the food chain under abnormal conditions.

[0022] The results of ocean current and ecological simulation and the data of data collection and management modules are integrated, and a multi-factor weighted model is used to analyze the probability of krill foraging. A model that considers multiple factors is established to analyze the migration path and survival challenges of krill. A comprehensive evaluation model of assisted living weights is constructed to evaluate the adaptability and probability of the krill living environment. Specifically, the output results of the ocean current and ecological simulation modules are deeply integrated with the data of the data collection and management module to form a comprehensive krill ecological analysis data set. Based on the fused data set, a variety of initial conditions are set to analyze the distribution of food particles carried by the ocean current, determine the food availability in different areas and at different times, and analyze the relationship between krill foraging behavior and food distribution. According to the feeding habits and foraging strategies of krill, the probability of krill foraging at a certain location is determined. The probability calculation formula is: , through machine learning algorithms, determine the weights , , analyze the changes in krill's foraging areas and food acquisition capabilities under the influence of abnormal ocean currents, predict krill's food intake, combine ocean current simulation results and food chain dynamics, consider multiple factors to analyze krill migration paths and survival challenges, build a dynamic weighted comprehensive assessment model to assess the responsiveness and probability of krill's living environment, predict krill's migration paths and new distribution areas under the influence of abnormal ocean currents, and the survival challenges they may face during migration, such as food shortages and threats from natural enemies, and build a comprehensive assessment model for krill's living environment , dynamically adjust the weight of each factor based on real-time environmental data and the ecological needs of krill , evaluate the survival adaptability and survival probability of krill in different regions under the influence of abnormal ocean currents, and provide a basis for production prediction.

[0023] The results of krill ecological analysis were comprehensively analyzed to determine the key factors affecting krill production, and a prediction model integrating spatiotemporal features was constructed. The model parameters were optimized using historical data training. The data of different ocean current change scenarios were input to predict production and conduct uncertainty analysis. Specifically, the results of the krill ecological analysis module were comprehensively analyzed to determine that the key factors affecting krill production were changes in food source distribution caused by abnormal ocean currents, changes in krill migration paths, and deterioration of the living environment. A prediction model integrating spatiotemporal features was constructed. , determine the model parameters by training historical data and current real-time data (considering data from the past five time steps), (dividing the Antarctic Ocean into 4 spatial regions), (The number of hidden layers of the recursive neural network is 3), the simulated abnormal ocean current changes and related environmental data are input into the prediction model to predict the changes in krill production in different regions under abnormal conditions, and uncertainty analysis is performed to provide a confidence interval for the prediction results.

[0024] Based on the prediction results, they are visualized in the form of charts, maps, dynamic demonstrations, etc., and a user-friendly interactive interface is designed to realize data query, screening, comparison and customized display, and the results are provided to the fishery company to assist decision-making. According to the prediction results, the fishery company adjusted its fishing plan, reduced the catch in areas severely affected by abnormal ocean currents, and increased fishing operations in areas where krill may gather.

[0025] Embodiment 2 The sea ice coverage and distribution in the Antarctic waters will change significantly in different seasons, which has an important impact on the survival and reproduction of krill. A marine research institution cooperates with a fishery management department to use the system and method of the present invention to predict the impact of sea ice changes in different seasons on krill production. By collecting satellite remote sensing data for each season in the past ten years, including information such as sea ice coverage, thickness, edge position, and corresponding ocean current speed, direction, and sea surface temperature data, and cooperating with 10 ocean monitoring stations that monitor the Antarctic waters for a long time, marine environmental data such as salinity, dissolved oxygen, light intensity, and water depth are obtained in each season, and the data are sorted and calibrated. At the same time, multiple scientific research survey ships are organized to conduct field surveys in different seasons, and professional equipment is used to collect marine food chain samples. The changes in the number and distribution of krill food sources (such as ice algae, planktonic bacteria, etc.) under the influence of sea ice, as well as the seasonal activity patterns of natural enemies (such as seals, seabirds, etc.) are analyzed. The collected historical and real-time data are transmitted to the data center, data cleaning is performed, noise data and outliers are removed, and the data is stored in a distributed database, and a data backup and recovery mechanism is established.

[0026] Based on historical and real-time sea ice data, ocean geographic information and environmental parameters, a fluid dynamics model that can reflect seasonal sea ice changes is constructed , simulate the impact of sea ice changes on ocean currents, and the indirect effect of ocean current changes on the living environment of krill, and build a dynamic balance model of seasonal food chain with krill as the core , considering the impact of sea ice changes on biological habitats, food resources and interactions between organisms, adjusting model parameters and simulating the dynamic evolution of food chains in different seasons.

[0027] By integrating the results of the ocean current and ecological simulation module with the data from the data collection and management module, a complete krill ecological data set is formed, and the krill foraging analysis model is used , through statistical analysis of historical data, determine the weights for different seasons For example, in seasons with high sea ice cover, increasing The weight of (food abundance) is used because the food source is relatively concentrated near the sea ice at this time. The foraging behavior and food acquisition of krill in different seasons are analyzed to determine the changes in foraging areas. The impact of seasonal changes in sea ice on krill migration is considered, such as the expansion and contraction of sea ice will change the migration route and time of krill. The migration path and distribution changes of krill in different seasons are predicted, as well as the interaction with food sources and natural enemies during migration, and a comprehensive assessment model of krill living environment is constructed. , dynamically adjust the weights of each factor based on seasonal sea ice changes and the ecological needs of krill , evaluate the living environment quality and survival probability of krill in different seasons and regions, and provide a basis for production prediction.

[0028] Based on the results of the krill ecological analysis module, we determined that the key factors affecting krill production in different seasons are changes in sea ice coverage, seasonal distribution of food sources, krill migration patterns, and seasonal changes in the living environment. We then built a krill production prediction model based on seasonal changes. , through training and verification of historical production data and seasonal environmental data, the model parameters for different seasons are determined , , For example, in seasons with large sea ice changes, it is appropriate to increase The value of is used to consider data with more historical time steps, and the simulated sea ice changes in different seasons and related environmental data are input into the prediction model to predict the trend of krill production in different seasons and regions. A sensitivity analysis is also carried out to determine the impact of key factors on production prediction.

[0029] Based on the forecast results, fishery management departments have formulated seasonal fishing quotas, appropriately increasing catches in seasons and regions with higher krill production, and implementing protective measures in seasons and regions with lower production.

[0030] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. An Antarctic krill production analysis system based on multi-source big data, characterized in that: The system consists of the following components: Data collection and management module: collects data on Antarctic ocean currents, marine environment, marine food chain and ecosystem structure through satellite remote sensing, marine monitoring stations and scientific research survey ships, and stores them in a distributed database after preprocessing; Ocean current and ecological simulation module: Based on the geographical information and environmental parameters of the Antarctic waters, a fluid dynamics model is constructed to simulate ocean currents and update them in real time. A food chain dynamic equilibrium model with krill as the core is constructed by combining marine food chain and ecological data. The results of ocean current simulation are integrated to simulate the impact of ocean currents on the food chain and the laws of chain reactions under different scenarios. Krill Ecological Analysis Module: Integrates ocean current and ecological simulation results and other relevant data to analyze krill's foraging areas and food availability, migration paths and distribution changes under the influence of ocean currents. Consider environmental and biological factors to build an evaluation model to analyze krill's adaptability to the living environment and survival probability. Krill production forecasting module: Based on krill ecological analysis and biological characteristics, the key factors affecting krill production are determined and quantified, a forecasting model is constructed, and training and optimization are performed using historical data. Data from different ocean current change scenarios are input to predict production fluctuations and conduct uncertainty analysis. Result Visualization and Application Module: Develop software tools to visualize simulation analysis results in the form of charts, maps or dynamic presentations, and design user interaction interfaces to assist different users in making fishery production decisions, formulating fishing strategies and carrying out Antarctic ecosystem protection work.

2. The Antarctic krill production analysis system based on multi-source big data according to claim 1, characterized in that: The data acquisition and management module collects historical and real-time ocean current data, marine environment data, marine food chain data and ecosystem structure and function data through satellite remote sensing, ocean monitoring stations and scientific research survey ships, wherein the historical and real-time ocean current data include but are not limited to ocean current speed, direction and temperature, the marine environment data include but are not limited to salinity, dissolved oxygen and light, the marine food chain data include but are not limited to the distribution of krill's food sources, the distribution of natural enemies and the interaction relationship between various organisms, and the ecosystem structure and function data include the number and distribution of different biological populations.

3. The Antarctic krill production analysis system based on multi-source big data according to claim 1, characterized in that: The ocean current and ecological simulation module constructs a fluid dynamics model based on the geographic information and environmental parameters of the Antarctic waters to simulate ocean currents and update them in real time. The model formula is: ,in, is the ocean current velocity vector, indicating the ocean current speed and direction at a certain location in the Antarctic Ocean. is time, used to describe the change of ocean current speed over time, is the density of seawater, determined by the temperature and salinity of the Antarctic Ocean. is the seawater pressure, which is related to the depth and density of the seawater. It is the kinematic viscosity of seawater, which is determined by the physical properties of seawater and affects the viscosity and energy dissipation within the ocean current. is the gradient operator, which is used to calculate the rate of change of physical quantities in space. is the external force term, It is a newly added force term that takes into account the influence of seabed terrain roughness, and is calculated by combining high-precision seabed terrain data and terrain roughness model.

4. The Antarctic krill production analysis system based on multi-source big data according to claim 1, characterized in that: The ocean current and ecological simulation module combines the marine food chain and ecological data to construct a food chain dynamic equilibrium model with krill as the core, and the model formula is: ,in, Indicates The population size of various organisms, including krill and other related organisms, is time, used to describe the changes in the number of biological populations over time, It is The intrinsic growth rate of an organism reflects its ability to grow under ideal conditions. It is The environmental carrying capacity of a species depends on the ecological environment conditions in which the species lives, including food resources and habitat space. is the interaction coefficient between organisms, when hour, Indicates Species The effect of a certain organism on the organism. A positive number indicates a promoting effect, while a negative number indicates an inhibiting effect. It is The sensitivity coefficient of an organism to environmental fluctuations is determined according to the ecological adaptability and tolerance of the organism. It is the amount of environmental fluctuation, which comprehensively considers the changes in ocean temperature, salinity and ocean currents, and is obtained through the analysis and calculation of multi-source environmental data.

5. The Antarctic krill production analysis system based on multi-source big data according to claim 1, characterized in that: The krill ecological analysis module establishes a model to analyze the foraging area and food availability, migration path and distribution changes of krill under the influence of ocean currents. Specifically, the simulation results output by the ocean current and ecological simulation module are deeply integrated with the marine food chain and ecosystem structure and function data in the data acquisition and management module. Based on the integrated data set, the distribution of food particles carried by the ocean current is analyzed to determine the food availability in different areas and at different times. At the same time, the relationship between the foraging behavior of krill and the distribution of food is analyzed. According to the feeding habits and foraging strategies of krill, the probability of krill foraging at a certain location is determined. The probability calculation formula is: ,in, is the probability that krill will forage at a certain location, is the food abundance at that location, measured and calculated from the quantity and distribution of krill food sources, is the water diffusion coefficient at that location, taking into account the diffusion and transport effects of ocean currents on food particles, and is calculated by analyzing the ocean current speed, direction, and diffusion model. is the temperature suitability of the location, It is the competitive pressure index of the location, which comprehensively considers the competition of other organisms for food resources and is obtained by analyzing the population and distribution of organisms that compete with krill for food in the area. , , , It is the weight coefficient of the corresponding factor, which is determined by the hierarchical analysis method or machine learning method according to the actual data and analysis purpose, and is used to adjust the relative importance of each factor in the foraging probability calculation.

6. The Antarctic krill production analysis system based on multi-source big data according to claim 1, characterized in that: The krill ecological analysis module comprehensively considers environmental and biological factors to construct an evaluation model to analyze the adaptability of krill to the living environment and the probability of survival. Specifically, it combines the direction and speed data of ocean currents and the distribution and behavior change data of other organisms in the food chain, and uses the krill migration prediction algorithm to predict the migration path and distribution changes of krill, analyze the survival challenges faced by krill during migration, and evaluate the impact of these factors on the krill population and reproductive success rate. The algorithm formula is: ,in, It is a comprehensive assessment value of the krill living environment, used to measure the overall suitability and survival pressure of the krill environment. is the total number of environmental and biological factors considered, including temperature, salinity, dissolved oxygen, number of natural enemies, and food availability. It is Factors at the time The weight coefficient changes dynamically over time and is determined by a dynamic modeling method based on the changing trends of environmental factors and the needs and sensitivity of krill to various factors at different growth stages. It is The estimated value of a factor is obtained by standardizing the actual measured value of the factor and the tolerance range of krill to the factor.

7. The Antarctic krill production analysis system based on multi-source big data according to claim 1, characterized in that: The krill production prediction module determines and quantifies the key factors affecting krill production based on krill ecological analysis and biological characteristics, and constructs a prediction model. The prediction model is: ,in, yes The krill production predicted at any given moment is the predicted target value. is the activation function, which is used to transform the input into a value within the output range. is the number of historical time steps considered, indicating the use of past The influencing factor data of time steps predict the krill production at the current moment. It is Factors affecting the historical time step The weight coefficient of yes The variables affecting krill production at each moment include calculated food availability, habitat assessments, and ocean current speeds. is the total number of spatial regions, dividing the Antarctic Ocean into different spatial regions to take into account the differences in krill production in different regions. is the number of hidden layers of the recurrent neural network, It is a spatial region No. Hidden Layers The weight coefficient of It is a spatial region exist The hidden layer state at the moment represents the spatial region exist The hidden layer state at each moment captures the spatiotemporal dependencies through a recurrent neural network. is the bias term, which is used to adjust the output of the model.

8. A method for analyzing Antarctic krill production based on multi-source big data, the method being applicable to the Antarctic krill production analysis system based on multi-source big data as claimed in any one of claims 1 to 7, characterized in that: The method comprises the following specific steps: Data collection and preprocessing: collect relevant data through the data collection and management module, clean, filter and standardize the data, remove outliers and erroneous data, and perform storage management; Ocean current and ecological simulation: Input the preprocessed data into the ocean current and ecological simulation module, use the fluid dynamics model and numerical simulation technology to simulate the ocean currents in the Antarctic waters, obtain the ocean current simulation results, and build the Antarctic marine food chain dynamic equilibrium model and integrate the ocean current simulation results to simulate the impact of ocean currents on the food chain under different conditions and the dynamic change process of the food chain, and analyze the chain reaction and evolution law; Krill Ecological Analysis: Combine the results of the Ocean Current and Ecological Simulation Module with the relevant data in the Data Collection and Management Module, and use data analysis algorithms and models to analyze the foraging, migration, and living environment of krill; Krill production forecast: Based on the results of krill ecological analysis, the model established by the krill production forecast module is used to input simulation data under different ocean current change scenarios to predict krill production fluctuations and conduct uncertainty analysis; Result display and application: The prediction results and the dynamic changes of the food chain are visualized through the result visualization and application module and provided to relevant users to assist them in making fishery production decisions, formulating fishing strategies and carrying out Antarctic ecosystem protection work.

Images