Dynamic simulation system for influence of climate change on ecology

Abstract

The invention relates to the technical field of ecological impact analysis, in particular to a dynamic simulation system for the impact of climate change on ecology, which comprises a data collection and preprocessing module, a micro ecological process simulation module, a climate dynamic prediction module, an ecological response analysis module, a comprehensive impact evaluation module, a real-time feedback adjustment module and a strategy generation and recommendation module. The ecological response analysis module draws an ecological response curve by using a machine learning technology, deeply evaluates the sensitivity and adaptability of an ecological system to climate changes, provides effective management suggestions, helps decision makers and protection institutions to formulate policy measures with higher pertinence and operability, and improves the reliability of the system. And the real-time feedback adjustment module adopts a dynamic adjustment strategy based on a feedback control theory to continuously optimize the accuracy and prediction capability of the model, so that the simulation system has self-learning and continuous evolution capabilities, and the performance and applicability of the model are continuously improved by adjusting simulation parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological impact analysis, and particularly to a dynamic simulation system for the impact of climate change on the ecosystem. Background Art

[0002] Under the background of increasingly severe current climate change, the stability and adaptability of the ecosystem are crucial for the future of humanity and the planet. Phenomena such as rising temperatures, changing precipitation patterns, and frequent extreme weather events caused by climate change have had a profound impact on the structure and function of the ecosystem. Due to the complexity and uncertainty of climate change, predicting the response of the ecosystem to climate change and formulating corresponding adaptation and protection strategies face huge challenges.

[0003] Traditional climate models and ecosystem simulation tools often have low accuracy, are difficult to accurately capture the complex dynamic responses of the ecosystem, have low spatio-temporal resolution, are unable to simulate local ecological changes in detail, and lack consideration of dynamic changes and feedback mechanisms, resulting in insufficient reliability and practicality of the simulation results. Summary of the Invention

[0004] Based on the above purpose, the present invention provides a dynamic simulation system for the impact of climate change on the ecosystem.

[0005] The dynamic simulation system for the impact of climate change on the ecosystem includes a data collection and preprocessing module, a micro-ecological process simulation module, a climate dynamic prediction module, an ecological response analysis module, a comprehensive impact assessment module, a real-time feedback adjustment module, and a strategy generation and recommendation module, where;

[0006] The data collection and preprocessing module is responsible for collecting climate and ecological data, including satellite remote sensing data, ground observation station data, and historical climate records, and performing data cleaning and standardization processing;

[0007] The micro-ecological process simulation module simulates the biodiversity changes and population dynamics of specific ecological regions through high-resolution grids, and uses dynamic equations to describe the increase and decrease of biological populations;

[0008] The climate dynamic prediction module uses a physically based climate model to perform short-term and long-term climate change predictions;

[0009] The ecological response analysis module evaluates the sensitivity and adaptability of the ecosystem to climate change according to the data output by the climate dynamic prediction module;

[0010] The comprehensive impact assessment module comprehensively analyzes the interactive impacts of climate change and the ecosystem, and evaluates the ecological impacts and environmental consequences under different climate scenarios;

[0011] The real-time feedback adjustment module adjusts the simulation parameters according to the real-time monitoring data and simulation output to optimize the accuracy and prediction ability of the model. This module adopts a dynamic adjustment strategy based on feedback control theory to continuously optimize the model response;

[0012] The strategy generation and recommendation module uses artificial intelligence technology to generate management suggestions for ecosystems and climate change patterns, and provides customized climate adaptation and ecological protection strategies based on the simulation results.

[0013] Furthermore, the data collection includes:

[0014] Satellite remote sensing data collection: Using Earth observation satellites to collect data on surface temperature, vegetation index, soil moisture, and climate variables. Satellite data can provide large-scale and continuous observations, which are suitable for remote and vast areas that are difficult to measure directly;

[0015] Ground observation station data collection: Through the global meteorological station network, collect ground meteorological parameters, including temperature, precipitation, and wind speed. Through the biodiversity monitoring network, collect ecological data, including the number of species and niche occupancy;

[0016] Historical climate data collection: Obtain historical climate change data from climate data archives. Historical data helps to establish long-term trends in climate patterns and ecological responses;

[0017] The data cleaning includes:

[0018] Error detection and correction: Identify and correct data errors, including outliers and readings that significantly deviate from the physically feasible range;

[0019] Missing value handling: Use interpolation methods (such as linear interpolation for time series or more complex statistical methods such as Kriging) to fill in missing data points to ensure data continuity;

[0020] The standardization processing includes:

[0021] Unit unification: Ensure that all data sets use a unified measurement unit. Temperature is uniformly used in degrees Celsius, and precipitation is uniformly used in millimeters;

[0022] Spatial and temporal resolution adjustment: Adjust data from different sources to a unified spatial and temporal resolution;

[0023] Data formatting: Convert the collected data into the model CSV file format so that other modules can effectively read and process it.

[0024] Furthermore, the micro-ecological process simulation module simulates the biodiversity changes and population dynamics in specific ecological regions through high-resolution grids, including:

[0025] Grid division: The study area is divided into multiple grid units, and each grid unit represents a specific geographical location and ecological environment;

[0026] Population model: A dynamic model is established for the biological population in each grid unit, and the dynamic model is based on the Logistic equation;

[0027] The calculation formula of the Logistic equation is:

[0028] Where: represents the change rate of the population quantity over time;

[0029] N represents the population quantity;

[0030] r represents the intrinsic growth rate of the population;

[0031] K represents the environmental capacity (i.e., the maximum carrying capacity of the ecosystem).

[0032] Furthermore, the climate model of the physical basis includes an atmospheric model, an ocean model, and a land model, where;

[0033] Atmospheric model: Describes the air flow movement, heat transfer, and energy balance in the atmosphere;

[0034] Ocean model: Describes the dynamic and thermodynamic processes in the ocean, including ocean surface temperature, ocean current, ocean circulation, and simulates the three-dimensional movement and ocean circulation of the ocean;

[0035] Land model: Describes the hydrological cycle, vegetation growth, and energy exchange processes on the land surface, including soil moisture model, vegetation dynamic model, and surface energy balance model.

[0036] Furthermore, the short-term climate change prediction includes:

[0037] Initial state setting: The climate model obtains the initial states of the current atmospheric, oceanic, and land systems, including temperature, humidity, wind speed, ocean surface temperature. The initial states are usually obtained through observational data or simulation results of the previous time period;

[0038] Simulation run: The climate model runs physical equations according to the initial state and known external driving factors (such as solar radiation, surface albedo, etc.) to simulate the evolution of the atmospheric and oceanic systems in the next few days to weeks. The physical equations include the Navier-Stokes equation, the thermodynamic equation, and the radiation transfer equation;

[0039] Output result: After the simulation run is completed, future short-term climate change prediction results are generated, including meteorological elements such as temperature, precipitation, wind direction, and wind speed. These results are used for short-term weather forecasting and the monitoring and early warning of climate events;

[0040] The long-term climate change prediction includes:

[0041] Setting of external driving factors: For long-term climate change prediction, set the external driving factors for the next few decades to several centuries, including greenhouse gas emissions and solar radiation intensity;

[0042] Simulation run: After setting the external driving factors, the model runs physical equations to simulate climate change within the next few decades to several centuries;

[0043] Output result: After the simulation run is completed, the model will generate climate prediction results within the next few decades to several centuries, including trends of global average temperature increase, precipitation pattern change, and increasing frequency of extreme climate events.

[0044] Furthermore, the ecological response analysis module evaluates the sensitivity and adaptability of the ecosystem to climate change based on the data output by the climate dynamics prediction module, including:

[0045] Data processing and feature extraction: Preprocess the data output by the climate dynamics prediction module, including data cleaning, missing value handling, and feature extraction. The data includes climate variables (such as temperature, precipitation, etc.) and ecosystem response variables (such as vegetation growth rate, species diversity index, etc.);

[0046] Establish an ecological response model: Use factor analysis machine learning technology to establish an ecological response model to evaluate the sensitivity and adaptability of the ecosystem to climate change;

[0047] The mathematical expression of factor analysis is: Suppose there are p observed variables x1, x2, …, x p , through factor analysis, try to find m latent factors f1, f2, …, f m , where m < p, and the corresponding factor loadings λ ij describe the relationship between the observed variables and the latent factors. The factor analysis model is expressed as:

[0048] where: x i is the observed value of the i-th observed variable, μ i is the mean of the observed variable x i , λ ij is the factor loading between the observed variable x i and the latent factor f j , f j is the j-th latent factor, ∈ iis the observed variable x i is the special variance (error term);

[0049] Model training and validation: The preprocessed dataset is divided into a training set and a test set. The ecological response model is trained using the training set, and the test set is used for model validation and evaluation. Cross-validation techniques are used to avoid overfitting and improve the generalization ability of the model;

[0050] Ecological response curve plotting: Using the trained ecological response model, different climate change scenarios are simulated, and ecological response curves are plotted.

[0051] Furthermore, the comprehensive impact assessment module comprehensively analyzes the interactive impacts of climate change and the ecosystem, and evaluates the ecological impacts and environmental consequences under different climate scenarios, including:

[0052] Climate scenario setting: Based on the simulation results of climate models and the predictions of international climate change research, different climate scenarios are selected;

[0053] Ecosystem simulation: Using the climate dynamics prediction module to simulate climate changes under different climate scenarios, and inputting the simulation results into the ecological response model. The ecological response model usually includes modeling of vegetation, animals, soil, and water resource elements, as well as their interactions and feedback mechanisms;

[0054] Ecological impact assessment: According to the results of the ecological response model, evaluate the changes in the ecological response model under different climate scenarios, involving evaluations of changes in vegetation types and distributions, changes in species diversity, and changes in the supply and demand of ecosystem services;

[0055] Environmental consequence analysis: Analyze the impacts and consequences of ecosystem changes on the environment, including evaluations of environmental indicators such as soil erosion, water resource availability, biodiversity loss, and sea-level rise, as well as impact analyses on agriculture, water resource management, and natural disaster risks;

[0056] Uncertainty analysis: Analyze and handle the uncertainty of the evaluation results;

[0057] Formulate response strategies: According to the evaluation results, formulate response strategies for different climate scenarios and ecosystem responses.

[0058] Furthermore, the real-time feedback adjustment module adjusts the simulation parameters according to real-time monitoring data and simulation outputs to optimize the accuracy and prediction ability of the model, including:

[0059] Data Monitoring and Collection: Real-time monitor the indicators and environmental parameters of the ecosystem, including temperature, humidity, precipitation, vegetation cover, and animal population, and collect the simulation results output by the ecological response model, including ecosystem status, changes in biological populations, and environmental response information;

[0060] Data Comparison and Analysis: Compare and analyze the real-time monitoring data with the simulation output, evaluate the matching degree of the ecological response model to the actual situation, and identify the biases or deficiencies in the prediction of the ecological response model by comparing the differences between the simulation results of the ecological response model and the actual observation data;

[0061] Parameter Adjustment and Optimization: Based on the feedback control theory, adjust and optimize the parameters of the ecological response model according to the differences between the real-time monitoring data and the output of the ecological response model, including adjusting the initial conditions, parameter values, model structure, or model parameterization scheme of the ecological response model, reducing the differences between the ecological response model and the actual observation data, and improving the accuracy and prediction ability of the ecological response model;

[0062] Real-time Feedback Adjustment: According to the adjusted parameters of the ecological response model, re-run the simulation and compare it with the real-time monitoring data, continuously monitor and adjust the parameters of the ecological response model until the simulation results meet the predetermined compliance with the actual observation data;

[0063] Dynamic Update and Continuous Optimization: Continuously update and optimize the ecological response model to adapt to the dynamic changes of the ecosystem and the continuously updated monitoring data.

[0064] Furthermore, the strategy generation and recommendation module uses artificial intelligence technology to generate management suggestions for the ecosystem and climate change patterns, and provides customized climate adaptation and ecological protection strategies based on the simulation results, including:

[0065] Data Analysis and Pattern Recognition: Use machine learning and data mining techniques to analyze and mine historical simulation results and actual observation data to identify the response patterns and factors of the ecosystem to climate change;

[0066] Decision Trees and Rule Engines: Based on the results of data analysis, construct decision trees or rule engines, and generate corresponding management suggestions and strategies according to different ecosystem states and climate change scenarios. The decision tree or rule engine automatically derives the best decision-making scheme according to the pre-set rules and conditions;

[0067] Model Prediction and Optimization: According to the climate change prediction results of the climate dynamics prediction module, put forward corresponding management suggestions;

[0068] Knowledge Graphs and Expert Systems: Construct knowledge graphs and expert systems, integrate professional knowledge and experience in the field, and provide support and reference for decision-making.

[0069] Further, the decision tree and rule engine include:

[0070] Data preparation and feature selection: Obtain a dataset for constructing a decision tree or rule engine through data analysis and pattern recognition, including features and labels;

[0071] Construct a decision tree: Use the features in the dataset to divide the dataset into different subsets, recursively apply node splitting to each subset until a stopping condition is met (such as reaching the maximum depth, the number of samples is less than the threshold, etc.), and through pruning techniques, avoid overfitting and improve the generalization ability of the model;

[0072] Construct a rule engine: Generate a series of rules based on the data analysis results, where each rule includes conditions and conclusions;

[0073] Rule optimization: Optimize the number and form of rules to make them concise and effective, while maintaining accuracy and interpretability;

[0074] Model evaluation and optimization: Use cross-validation techniques to evaluate the performance of the model, ensure that the model has good generalization ability, and according to the evaluation results, tune the decision tree or rule engine, including adjusting parameters, pruning, adding samples, etc.;

[0075] Model application and interpretation: Apply the constructed decision tree or rule engine to new data for decision-making or prediction, and provide understandable decision-making basis for users and decision-makers.

[0076] The ecological response analysis module of the present invention uses machine learning technology to draw ecological response curves, deeply evaluate the sensitivity and adaptability of ecosystems to climate change. Based on the analysis results, the strategy generation and recommendation module will provide personalized climate adaptation and ecological protection strategies. These strategies will target specific ecosystems and climate change patterns and provide effective management suggestions to help decision-makers and conservation agencies formulate more targeted and operable policy measures.

[0077] The real-time feedback adjustment module of the present invention adopts a dynamic adjustment strategy based on feedback control theory. According to real-time monitoring data and simulation outputs, it continuously optimizes the accuracy and prediction ability of the model, enabling the simulation system to have the ability of self-learning and continuous evolution. It can timely discover the deficiencies of the model and continuously improve the performance and applicability of the model by adjusting simulation parameters.

[0078] The present invention collects a large amount of high-quality climate and ecological data through a data collection and preprocessing module, and after precise processing, combined with the dynamic simulation ability of the micro-ecological process simulation module, it can accurately predict the responses of the ecosystem under different climate scenarios, including changes in vegetation cover, changes in animal population numbers, etc., providing reliable predictions and evaluations for future ecological environment changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0080] Figure 1 It is a schematic diagram of the system flow of the embodiment of the present invention;

[0081] Figure 2 It is a schematic diagram of the process of the ecological response analysis module of the embodiment of the present invention;

[0082] Figure 3 It is a schematic diagram of the process of the real-time feedback adjustment module of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0083] To make the objectives, technical solutions, and advantages of the present invention clearer, the following further details the present invention in conjunction with specific embodiments.

[0084] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meaning understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second", and similar terms used in the present invention do not denote any order, quantity, or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0085] Such as Figures 1 - 3As shown, the dynamic simulation system for the ecological impact of climate change includes a data collection and preprocessing module, a micro-ecological process simulation module, a climate dynamics prediction module, an ecological response analysis module, a comprehensive impact assessment module, a real-time feedback adjustment module, and a strategy generation and recommendation module. Among them;

[0086] The data collection and preprocessing module is responsible for collecting climate and ecological data, including satellite remote sensing data, ground observation station data, and historical climate records, and performing data cleaning and standardization processing;

[0087] The micro-ecological process simulation module simulates the biodiversity changes and population dynamics of specific ecological regions through high-resolution grids, and uses dynamic equations to describe the increase and decrease of biological populations;

[0088] The climate dynamics prediction module uses a physically based climate model to perform short-term and long-term climate change predictions;

[0089] The ecological response analysis module evaluates the sensitivity and adaptability of the ecosystem to climate change based on the data output by the climate dynamics prediction module;

[0090] The comprehensive impact assessment module comprehensively analyzes the interactive impacts of climate change and the ecosystem, and evaluates the ecological impacts and environmental consequences under different climate scenarios;

[0091] The real-time feedback adjustment module adjusts the simulation parameters according to real-time monitoring data and simulation outputs to optimize the accuracy and prediction ability of the model. This module adopts a dynamic adjustment strategy based on feedback control theory to continuously optimize the model response;

[0092] The strategy generation and recommendation module uses artificial intelligence technology to generate management suggestions for the ecosystem and climate change patterns, and provides customized climate adaptation and ecological protection strategies based on simulation results.

[0093] Data collection includes:

[0094] Satellite remote sensing data collection: Using Earth observation satellites, collect data on surface temperature, vegetation index, soil moisture, and climate variables. Satellite data can provide large-scale and continuous observations, which are suitable for remote and vast areas that are difficult to measure directly;

[0095] Ground observation station data collection: Through the global meteorological station network, collect ground meteorological parameters, including temperature, precipitation, and wind speed. Through the biodiversity monitoring network, collect ecological data, including species number and niche occupancy;

[0096] Historical climate data collection: Obtain historical climate change data from the climate data archive center. Historical data helps to establish long-term trends in climate patterns and ecological responses;

[0097] Data cleaning includes:

[0098] Error detection and correction: Identify and correct data errors, including outliers and readings that significantly deviate from the physically feasible range;

[0099] Missing value handling: Fill in missing data points using interpolation methods (such as linear interpolation for time series or more complex statistical methods like Kriging) to ensure data continuity;

[0100] Standardization processing includes:

[0101] Unit unification: Ensure that all data sets use a unified measurement unit, with temperature uniformly in degrees Celsius and precipitation uniformly in millimeters;

[0102] Spatial and temporal resolution adjustment: Adjust data from different sources to a unified spatial and temporal resolution. For example, align the spatial resolution of satellite data with ground data and synchronize time data to the daily or hourly level;

[0103] Data formatting: Convert the collected data into the model CSV file format so that other modules can effectively read and process it;

[0104] The data collection and preprocessing module provides a comprehensive, accurate, and consistent data foundation for the dynamic simulation system of the ecological impact of climate change, enabling subsequent modules to perform effective dynamic simulations and analyses.

[0105] The micro-ecological process simulation module simulates the biodiversity changes and population dynamics in specific ecological regions through high-resolution grids, including:

[0106] Grid division: Divide the research area into multiple grid cells, with each grid cell representing a specific geographical location and ecological environment;

[0107] Population model: Establish a dynamic model for the biological populations within each grid cell. The dynamic model is based on the Logistic equation and describes the change in population quantity over time;

[0108] The calculation formula of the Logistic equation is:

[0109] Where: represents the change rate of population quantity over time;

[0110] N represents the population quantity;

[0111] r represents the intrinsic growth rate of the population;

[0112] K represents the environmental capacity (i.e., the maximum carrying capacity of the ecosystem);

[0113] Intrinsic growth rate r: r represents the growth rate of a population per unit time in the absence of resource limitations. It depends on the biological characteristics of the biological population, such as the reproduction rate, growth rate, etc.;

[0114] Environmental capacity K: K represents the maximum population size that an ecosystem can support. When the population approaches the environmental capacity, the population growth rate tends to zero because resources become scarce and ecological pressure increases, resulting in limited growth of the population;

[0115] The first term rN on the right side of the equation describes the natural growth rate of the population, which increases as the population size N increases;

[0116] The second term on the right side of the equation describes the impact of the environmental capacity on population growth, which decreases as the population size approaches the environmental capacity K. When the population size reaches the environmental capacity, this term becomes zero and population growth stops;

[0117] By applying the dynamic equation to the population in each grid cell within the ecological region, the increase and decrease process of the biological population is simulated, thereby realizing the simulation of biodiversity changes and population dynamics.

[0118] Physical-based climate models include atmospheric models, ocean models, and land models, among which;

[0119] Atmospheric model: Describes the air flow movement, heat transfer, and energy balance in the atmosphere;

[0120] Ocean model: Describes the dynamic and thermodynamic processes in the ocean, including ocean surface temperature, ocean currents, ocean circulation, and simulates the three-dimensional movement and ocean circulation of the ocean;

[0121] The ocean model can simulate various ocean phenomena from large-scale global circulation to local sea areas, such as the El Niño phenomenon, deep circulation, etc.;

[0122] Land model: Describes the hydrological cycle, vegetation growth, and energy exchange processes on the land surface, including soil moisture models, vegetation dynamics models, and surface energy balance models;

[0123] The land model can simulate processes such as soil temperature, soil moisture, vegetation growth, and transpiration, and model the surface energy and water balance.

[0124] Short-term climate change prediction includes:

[0125] Initial state setting: The climate model obtains the initial states of the current atmosphere, ocean, and land systems, including temperature, humidity, wind speed, ocean surface temperature. The initial states are usually obtained through observational data or simulation results of the previous time period;

[0126] Simulation Run: Based on the initial state and known external driving factors (such as solar radiation, surface albedo, etc.), the climate model runs physical equations to simulate the evolution of the atmospheric and oceanic systems over the next few days to weeks. The physical equations include the Navier-Stokes equations, the thermodynamic equations, and the radiation transfer equations;

[0127] The Navier-Stokes equations describe the motion of fluids. In the atmospheric model, the Navier-Stokes equations can be written in the following form:

[0128] where: v is the fluid velocity vector, ρ is the fluid density, p is the fluid pressure, g is the gravitational acceleration vector, F visc is the viscous force, and F ext is the external force;

[0129] In the climate model, the thermodynamic equations are usually expressed as:

[0130]

[0131] where: T is the temperature, C p is the specific heat capacity, K is the thermal conductivity tensor, and Φ is the volume source term (such as heat source or heat sink);

[0132] In the climate model, the radiation transfer equations are usually written in the following form:

[0133] where: I v is the radiation intensity at frequency v, s is the radiation path, k v is the absorption coefficient, ∈ v is the emission coefficient, and B v (T) is the blackbody radiation intensity;

[0134] Output Results: After the simulation run is completed, short-term future climate change prediction results are generated, including meteorological elements such as temperature, precipitation, wind direction, and wind speed. These results can be used for short-term weather forecasting and the monitoring and early warning of climate events;

[0135] Long-term climate change prediction includes:

[0136] Setting of External Driving Factors: For long-term climate change prediction, the external driving factors for the next few decades to centuries are set, including greenhouse gas emissions and solar radiation intensity;

[0137] Simulation Run: After setting the external driving factors, the model runs physical equations to simulate climate change over the next few decades to centuries. These simulations consider the long-term evolution of the atmospheric, oceanic, and terrestrial systems, as well as climate change trends such as global warming and sea-level rise;

[0138] The physical equations include the energy balance equation:

[0139] where C is the surface heat capacity, R is the net radiative energy input, H is the heat transfer, L is the latent heat release, and S is the convective heat transport;

[0140] and the moist air motion equation:

[0141]

[0142] where: ρ is the density of moist air, v is the velocity vector of moist air, p is the pressure of moist air, g is the gravitational acceleration vector, f is the external force, q is the specific humidity of moist air, and S is the water vapor source;

[0143] Output results: After the simulation runs are completed, the model will generate climate prediction results for the next few decades to centuries, including the trends of increasing global average temperature, changing precipitation patterns, and increasing frequencies of extreme climate events. These results are of great significance for the assessment of climate change and the formulation of future response strategies.

[0144] The ecological response analysis module evaluates the sensitivity and adaptability of ecosystems to climate change based on the data output by the climate dynamics prediction module, including:

[0145] Data processing and feature extraction: Preprocess the data output by the climate dynamics prediction module, including data cleaning, missing value handling, and feature extraction. The data includes climate variables (such as temperature, precipitation, etc.) and ecosystem response variables (such as vegetation growth rate, species diversity index, etc.);

[0146] Establish an ecological response model: Use factor analysis machine learning techniques to establish an ecological response model to evaluate the sensitivity and adaptability of ecosystems to climate change;

[0147] Factor analysis is a commonly used statistical method for discovering latent factors or hidden variables in a dataset and interpreting the observed data as a linear combination of these factors. The mathematical expression of factor analysis is: Suppose there are p observed variables x1, x2,..., x p , through factor analysis, try to find m latent factors f1, f2,..., f m , where m < p, and the corresponding factor loadings λ ij describe the relationship between the observed variables and the latent factors. The factor analysis model is expressed as:

[0148] where: x i is the observed value of the i-th observed variable, μ i is the mean of the observed variable x i and λ ijis the observed variable x i and the latent factor f j The factor loading between them, f j is the j-th latent factor, ∈ i is the observed variable x i The specific variance (error term) of;

[0149] Factor analysis also standardizes the variance of the factor f j so that the variance of the factor is 1, so that the factor loading λ ij is expressed as the change of x i in units of standard deviation of the unit. The goal of factor analysis is to estimate the parameters by minimizing the sum of squared residuals between the observed data and the factors. Usually, maximum likelihood estimation or principal component analysis methods are used to solve it. The relationship between the factor loading and the factor can be obtained by methods such as matrix factorization or gradient descent;

[0150] Generally speaking, factor analysis can help discover the latent structure in the observed data, extract important features, and simplify the data set for further analysis and interpretation;

[0151] Model training and validation: Divide the preprocessed data set into a training set and a test set. Use the training set to train the ecological response model and use the test set for model validation and evaluation. Cross-validation techniques are used to avoid overfitting and improve the generalization ability of the model;

[0152] Ecological response curve plotting: Use the trained ecological response model to simulate different climate change scenarios and plot the ecological response curve. The ecological response curve reflects the relationship between the ecological system response variables and the climate variables, such as the influence curve of temperature on the vegetation growth rate, etc.

[0153] The comprehensive impact assessment module comprehensively analyzes the interactive impacts of climate change and the ecosystem, and evaluates the ecological impacts and environmental consequences under different climate scenarios, including:

[0154] Climate scenario setting: Based on the simulation results of climate models and the predictions of international climate change research, different climate scenarios are selected;

[0155] Ecosystem simulation: Use the climate dynamics prediction module to simulate climate change under different climate scenarios, and input the simulation results into the ecological response model. The ecological response model usually includes the modeling of vegetation, animals, soil and water resources elements, as well as their interactions and feedback mechanisms;

[0156] Ecological impact assessment: According to the results of the ecological response model, evaluate the changes in the ecological response model under different climate scenarios, involving the evaluation of changes in vegetation types and distributions, changes in species diversity, and changes in the supply and demand of ecosystem services;

[0157] Analysis of environmental consequences: Analyze the impacts and consequences of ecosystem changes on the environment, including the assessment of environmental indicators such as soil erosion, water resource availability, biodiversity loss, and sea-level rise, as well as the impact analysis on agriculture, water resource management, and natural disaster risks;

[0158] Uncertainty analysis: Analyze and handle the uncertainty of the assessment results. Since both the climate system and the ecosystem are affected by multiple uncertainties, the assessment results are uncertain. Sensitivity analysis is used to reduce uncertainty;

[0159] Formulate response strategies: Based on the assessment results, formulate response strategies for different climate scenarios and ecosystem responses, involving ecological protection measures, resource management policies, and measures and policy recommendations in urban planning and infrastructure construction.

[0160] The real-time feedback adjustment module adjusts the simulation parameters according to the real-time monitoring data and simulation output to optimize the accuracy and prediction ability of the model, including:

[0161] Data monitoring and collection: Real-time monitor the indicators and environmental parameters of the ecosystem, including temperature, humidity, precipitation, vegetation cover, and animal numbers, and collect the simulation results output by the ecological response model, including the state of the ecosystem, changes in biological populations, and environmental response information;

[0162] Data comparison and analysis: Compare and analyze the real-time monitoring data with the simulation output, evaluate the matching degree of the ecological response model to the actual situation, and identify the biases or deficiencies in the prediction of the ecological response model by comparing the differences between the simulation results of the ecological response model and the actual observed data;

[0163] Parameter adjustment and optimization: Based on the feedback control theory, adjust and optimize the parameters of the ecological response model according to the differences between the real-time monitoring data and the output of the ecological response model, including adjusting the initial conditions, parameter values, model structure, or model parameterization scheme of the ecological response model, reducing the differences between the ecological response model and the actual observed data, and improving the accuracy and prediction ability of the ecological response model;

[0164] Real-time feedback adjustment: According to the adjusted parameters of the ecological response model, re-run the simulation and compare it with the real-time monitoring data, continuously monitor and adjust the parameters of the ecological response model until the simulation results and the actual observed data reach a predetermined degree of conformity;

[0165] Dynamic Updates and Continuous Optimization: Continuously update and optimize the ecological response model to adapt to the dynamic changes of the ecosystem and continuously updated monitoring data. As new observational data accumulates and the prediction results of the ecological response model improve, the model parameters and structure can be dynamically adjusted and updated to ensure the effectiveness and adaptability of the model;

[0166] Through the continuous optimization of the real-time feedback adjustment module, the accuracy and prediction ability of the ecosystem model can be continuously improved, the simulation effect of the model on the dynamic changes of complex ecosystems can be enhanced, and more reliable scientific support can be provided for ecological management and decision-making.

[0167] The strategy generation and recommendation module uses artificial intelligence technology to generate management suggestions for the ecosystem and climate change patterns, and provides customized climate adaptation and ecological protection strategies based on the simulation results, including:

[0168] Data Analysis and Pattern Recognition: Use machine learning and data mining techniques to analyze and mine historical simulation results and actual observational data to identify the response patterns and factors of the ecosystem to climate change. By pattern recognition and feature extraction of the data, understand the characteristics and trends of the ecosystem under different climate scenarios;

[0169] Decision Trees and Rule Engines: Based on the results of data analysis, construct decision trees or rule engines. According to different ecosystem states and climate change scenarios, generate corresponding management suggestions and strategies. The decision tree or rule engine automatically derives the best decision-making plan according to the pre-set rules and conditions;

[0170] Model Prediction and Optimization: According to the climate change prediction results of the climate dynamics prediction module, put forward corresponding management suggestions;

[0171] Knowledge Graphs and Expert Systems: Construct knowledge graphs and expert systems, integrate professional knowledge and experience in the field, and provide support and reference for decision-making. The knowledge graph can associate and integrate knowledge in different fields to form a knowledge network, while the expert system provides professional solutions for specific problems based on this knowledge;

[0172] The strategy generation and recommendation module uses artificial intelligence technology to generate management suggestions for specific ecosystems and climate change patterns through data analysis, pattern recognition, decision trees, model prediction, and knowledge graph methods to help decision-makers better cope with the challenges of climate change and the ecological environment.

[0173] The decision tree and rule engine include:

[0174] Data Preparation and Feature Selection: Obtain a dataset for constructing a decision tree or rule engine through data analysis and pattern recognition, including features and labels. Based on the results of data analysis, select features as the nodes of the decision tree or the conditions of the rule engine;

[0175] Constructing a Decision Tree: Use the features in the dataset to divide the dataset into different subsets. Select the best features and splitting methods to make the samples within the subsets as pure as possible. Recursively apply node splitting to each subset until the stopping conditions are met (such as reaching the maximum depth, the number of samples is less than the threshold, etc.). Through pruning techniques, avoid overfitting and improve the generalization ability of the model;

[0176] Constructing a Rule Engine: Generate a series of rules based on the results of data analysis. Each rule includes conditions and conclusions. The conditions are judgment conditions based on features, and the conclusions are the corresponding decisions or prediction results;

[0177] Rule Optimization: Optimize the number and form of rules to make them concise and effective, while maintaining accuracy and interpretability;

[0178] Model Evaluation and Optimization: Use cross-validation techniques to evaluate the performance of the model to ensure that the model has good generalization ability. According to the evaluation results, tune the decision tree or rule engine, including adjusting parameters, pruning, increasing samples, etc.;

[0179] Model Application and Interpretation: Apply the constructed decision tree or rule engine to new data for decision-making or prediction, and provide understandable decision-making basis for users and decision-makers;

[0180] A decision tree or rule engine applicable to specific problems and scenarios can be constructed based on the results of data analysis, thereby realizing data-driven decision-making and prediction.

[0181] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present invention is limited to these examples; under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and the steps can be implemented in any order, and there are many other variations in different aspects of the present invention as described above. For the sake of brevity, they are not provided in detail.

[0182] The present invention aims to cover all such substitutions, modifications, and variations that fall within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A dynamic simulation system for the impact of climate change on ecology, characterized by: It includes data collection and preprocessing module, micro-ecological process simulation module, climate dynamic prediction module, ecological response analysis module, comprehensive impact assessment module, real-time feedback adjustment module, strategy generation and recommendation module, among which; The data collection and preprocessing module is responsible for collecting climate and ecological data, including satellite remote sensing data, ground observation station data and historical climate records, and performing data cleaning and standardization processing; The micro-ecological process simulation module simulates the biodiversity changes and population dynamics of a specific ecological region through a high-resolution grid, and uses dynamic equations to describe the increase and decrease of biological populations; The climate dynamic prediction module uses a physical-based climate model to make short-term climate change predictions and long-term climate change predictions; The ecological response analysis module evaluates the sensitivity and adaptability of the ecosystem to climate change based on the data output by the climate dynamic prediction module; The comprehensive impact assessment module comprehensively analyzes the interactive effects of climate change and ecosystems, and assesses the ecological impacts and environmental consequences under different climate scenarios; The real-time feedback adjustment module adjusts simulation parameters according to real-time monitoring data and simulation output to optimize the accuracy and predictive ability of the model. The module adopts a dynamic adjustment strategy based on feedback control theory to continuously optimize the model response; The strategy generation and recommendation module uses artificial intelligence technology to generate management recommendations for ecosystems and climate change patterns, and provides customized climate adaptation and ecological protection strategies based on simulation results.

2. The dynamic simulation system for the impact of climate change on ecology according to claim 1, characterized in that: The data collection includes: Satellite remote sensing data collection: using earth observation satellites to collect data on surface temperature, vegetation index, soil moisture and climate variables; Data collection from ground observation stations: Through the global network of weather stations, ground meteorological parameters including temperature, precipitation, and wind speed are collected; through the biodiversity monitoring network, ecological data including species numbers and niche occupancy are collected; Historical climate data collection: Obtain historical climate change data from the climate data archive center; The data cleaning includes: Error detection and correction: Identify and correct data errors, including outliers and readings that significantly deviate from the physically feasible range; Missing value processing: missing data points are filled by interpolation method to ensure data continuity; The standardization process includes: Unit consistency: Ensure that all datasets use consistent measurement units, such as Celsius for temperature and millimeters for precipitation; Spatial and temporal resolution adjustment: adjust data from different sources to a uniform spatial and temporal resolution; Data formatting: Convert the collected data into the model CSV file format so that other modules can read and process it effectively.

3. The dynamic simulation system for the impact of climate change on ecology according to claim 1, characterized in that: The micro-ecological process simulation module simulates the biodiversity changes and population dynamics of specific ecological regions through high-resolution grids, including: Grid division: Divide the study area into multiple grid units, each grid unit represents a specific geographical location and ecological environment; Population model: A dynamic model is established for the biological population in each grid unit. The dynamic model is based on the Logistic equation. The calculation formula of the Logistic equation is: in: It represents the rate of change of population size over time; N represents the population size; r represents the intrinsic growth rate of the population; K represents the capacity of the environment (i.e. the maximum carrying capacity of the ecosystem).

4. The dynamic simulation system for the impact of climate change on ecology according to claim 3, characterized in that: The physical-based climate model includes an atmospheric model, an ocean model and a land model, wherein; Atmospheric model: describes the air flow, heat transfer and energy balance in the atmosphere; Ocean models: describe the dynamic and thermodynamic processes in the ocean, including sea surface temperature, ocean currents, ocean circulation, and simulate the three-dimensional movement and circulation of the ocean; Land model: describes the hydrological cycle, vegetation growth and energy exchange processes on the land surface, including soil moisture model, vegetation dynamics model and surface energy balance model.

5. The dynamic simulation system for the impact of climate change on ecology according to claim 1, characterized in that: The short-term climate change projections include: Initial state setting: The climate model obtains the initial state of the current atmosphere, ocean, and land systems, including temperature, humidity, wind speed, and sea surface temperature; Simulation operation: The climate model runs physical equations based on the initial state and known external driving factors to simulate the evolution of the atmosphere and ocean system in the next few days to weeks. The physical equations include the Navier-Stokes equations, thermodynamic equations, and radiation transfer equations. Output results: After the simulation is completed, the prediction results of future short-term climate change are generated, including temperature, precipitation, wind direction and wind speed meteorological elements; The long-term climate change projections include: External driver setting: For long-term climate change predictions, external drivers are set for decades to hundreds of years in the future, including greenhouse gas emissions and solar radiation intensity; Simulation run: After setting the external driving factors, the model runs the physical equations to simulate climate change in the next few decades to hundreds of years; Output results: After the simulation is completed, the model will produce climate predictions for the next few decades to hundreds of years, including rising global average temperatures, changes in precipitation patterns, and an increasing trend in the frequency of extreme climate events.

6. The dynamic simulation system for the impact of climate change on ecology according to claim 1, characterized in that: The ecological response analysis module evaluates the sensitivity and adaptability of the ecosystem to climate change based on the data output by the climate dynamic prediction module, including: Data processing and feature extraction: Preprocess the data output by the climate dynamic prediction module, including data cleaning, missing value processing and feature extraction. The data includes climate variables and ecosystem response variables; Building ecological response models: Using factor analysis machine learning techniques to build ecological response models to assess the sensitivity and adaptability of ecosystems to climate change; The mathematical expression of factor analysis is as follows: Suppose there are p observed variables x1, x2, …, x p , through factor analysis, we attempt to find m latent factors f1, f2, …, f m , where m < p, and the corresponding factor loadings λ ij describe the relationship between the observed variables and the latent factors. The factor analysis model is expressed as: Where: x i is the observed value of the ith observed variable, μ i is the observed variable x i The mean of ij is the observed variable x i and the latent factor f j The factor loading between j is the jth latent factor, ∈ i is the observed variable x i The special variance of Model training and validation: The preprocessed data set is divided into a training set and a test set. The training set is used to train the ecological response model, and the test set is used to validate and evaluate the model. Cross-validation technology is used to avoid overfitting and improve the generalization ability of the model. Drawing of ecological response curves: Using the trained ecological response model, different climate change scenarios are simulated and ecological response curves are drawn.

7. The dynamic simulation system for the impact of climate change on ecology according to claim 6, characterized in that: The comprehensive impact assessment module comprehensively analyzes the interactive effects of climate change and ecosystems, and assesses the ecological impacts and environmental consequences under different climate scenarios, including: Climate scenario setting: Select different climate scenarios based on the simulation results of climate models and the predictions of international climate change research; Ecosystem simulation: Use the climate dynamics prediction module to simulate climate change under different climate scenarios and input the simulation results into the ecological response model; Ecological impact assessment: Based on the results of the ecological response model, evaluate the changes in the ecological response model under different climate scenarios, involving changes in vegetation types and distribution, changes in species diversity, and changes in the supply and demand of ecosystem services; Environmental consequence analysis: Analyze the impacts and consequences of ecosystem changes on the environment, including assessments of environmental indicators such as soil erosion, water availability, biodiversity loss, and sea level rise, as well as impact analysis on agriculture, water resource management, and natural disaster risks; Uncertainty analysis: Analyze and process the uncertainty of the evaluation results; Develop response strategies: Based on the assessment results, formulate response strategies for different climate scenarios and ecosystem responses.

8. The dynamic simulation system for the impact of climate change on ecology according to claim 1, characterized in that: The real-time feedback adjustment module adjusts the simulation parameters according to the real-time monitoring data and simulation output to optimize the accuracy and predictive ability of the model, including: Data monitoring and collection: real-time monitoring of ecosystem indicators and environmental parameters, including temperature, humidity, precipitation, vegetation cover, and animal population, and collection of simulation results output by ecological response models, including ecosystem status, biological population changes, and environmental response information; Data comparison and analysis: Compare and analyze real-time monitoring data with simulation outputs to evaluate the degree of match between the ecological response model and actual conditions. By comparing the differences between the simulation results of the ecological response model and the actual observation data, the deviations or deficiencies in the predictions of the ecological response model can be identified. Parameter adjustment and optimization: Based on feedback control theory, according to the difference between real-time monitoring data and the output of the ecological response model, the parameters of the ecological response model are adjusted and optimized, including adjusting the initial conditions, parameter values, model structure or model parameterization scheme of the ecological response model; Real-time feedback adjustment: Re-run the simulation based on the adjusted ecological response model parameters and compare them with the real-time monitoring data. Continue to monitor and adjust the ecological response model parameters until the simulation results and the actual observation data reach a predetermined degree of consistency; Dynamic updating and continuous optimization: Continuously update and optimize the ecological response model to adapt to the dynamic changes of the ecosystem and the constantly updated monitoring data.

9. The dynamic simulation system for the impact of climate change on ecology according to claim 1, characterized in that: The strategy generation and recommendation module uses artificial intelligence technology to generate management recommendations for ecosystems and climate change patterns, and provides customized climate adaptation and ecological protection strategies based on simulation results, including: Data analysis and pattern recognition: Use machine learning and data mining techniques to analyze and mine historical simulation results and actual observational data to identify patterns and factors in ecosystem responses to climate change; Decision trees and rule engines: Based on the results of data analysis, decision trees or rule engines are constructed to generate corresponding management recommendations and strategies according to different ecosystem states and climate change scenarios; Model prediction and optimization: Propose corresponding management suggestions based on the climate change prediction results of the climate dynamic prediction module; Knowledge graphs and expert systems: Build knowledge graphs and expert systems to integrate professional knowledge and experience in the field and provide support and reference for decision-making.

10. The dynamic simulation system for the impact of climate change on ecology according to claim 9, characterized in that: The decision tree and rule engine include: Data preparation and feature selection: Obtaining data sets for building decision trees or rule engines, including features and labels, through data analysis and pattern recognition; Construct a decision tree: Use the features in the data set to divide the data set into different subsets, recursively apply node splitting to each subset until the stopping condition is met, and use pruning technology to avoid overfitting and improve the generalization ability of the model; Build a rule engine: Generate a series of rules based on data analysis results, each rule includes conditions and conclusions; Rule optimization: optimize the number and form of rules to make them concise and effective while maintaining accuracy and interpretability; Model evaluation and optimization: Use cross-validation techniques to evaluate the performance of the model to ensure that the model has good generalization capabilities. Based on the evaluation results, tune the decision tree or rule engine. Model application and interpretation: Apply the constructed decision tree or rule engine to new data to make decisions or predictions, and provide decision-making basis for users and decision makers.

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