Climate change prediction system based on deep learning model

Abstract

The invention discloses a climate change prediction system based on a deep learning model, and the system comprises a data integration and processing module which is used for collecting, integrating and preprocessing multi-source climate data from a satellite, a ground observation station, climate model simulation and social media; the deep learning model construction and optimization module is used for constructing a multi-layer and multi-mode climate change prediction model based on a deep neural network architecture comprising a convolutional neural network, a recurrent neural network, a variant long-short term memory network of the recurrent neural network, a gating circulation unit and an attention mechanism; the model is optimized through transfer learning, hyper-parameter tuning and integrated learning methods; and the spatio-temporal feature extraction and fusion module efficiently extracts spatio-temporal features of climate data by using a deep learning model, and enhances the prediction ability of the model through a multi-source data fusion strategy including feature-level fusion and decision-level fusion.

Description

Technical Field

[0001] The present invention relates to the technical field of climate prediction, and in particular to a climate change prediction system based on a deep learning model. Background Art

[0002] As global climate change becomes increasingly severe, accurately predicting climate change trends and their impacts has become a focus of widespread international attention. Traditional climate change prediction methods mainly rely on physical climate models. Although these models can simulate the complex interactions of the earth system such as the atmosphere and ocean, they are often difficult to achieve high-precision, real-time predictions of climate change due to bottlenecks in computing resources, data acquisition and processing technology.

[0003] In recent years, with the rapid development of big data and artificial intelligence technology, especially the widespread application of deep learning technology, new ideas and methods have been provided for climate change prediction. Deep learning models have achieved remarkable results in image recognition, speech recognition, natural language processing and other fields with their powerful data processing, nonlinear mapping and pattern recognition capabilities. Applying deep learning technology to climate change prediction is expected to break through the limitations of traditional prediction methods and improve the accuracy and timeliness of predictions.

[0004] However, the application of deep learning technology to the field of climate change prediction still faces many challenges. First, climate change data has the characteristics of multi-source, heterogeneity and spatiotemporal correlation. How to effectively collect, integrate and preprocess these multi-source climate data is the premise and basis for building a deep learning prediction model. Second, climate change is a complex nonlinear system. Its prediction requires the construction of a multi-level, multi-modal deep learning model to capture the deep characteristics and changing laws of climate data. In addition, the optimization of deep learning models, the extraction and fusion of spatiotemporal features, the quantification of uncertainty in prediction results, and the visualization are also technical challenges for achieving high-precision climate change predictions. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, one object of the present invention is to propose a climate change prediction system based on a deep learning model, comprising:

[0006] Data integration and processing module, used to collect, integrate and pre-process climate data from multiple sources including satellites, ground observation stations, climate model simulations and social media;

[0007] Deep learning model building and optimization module, based on deep neural network architecture including: convolutional neural network, recurrent neural network and its variant long short-term memory network, gated recurrent unit, and attention mechanism, to build a multi-level, multi-modal climate change prediction model, and optimize the model through transfer learning, hyperparameter tuning and ensemble learning methods;

[0008] The spatiotemporal feature extraction and fusion module uses deep learning models to efficiently extract the spatiotemporal features of climate data, and enhances the model's prediction capabilities through multi-source data fusion strategies including feature-level fusion and decision-level fusion;

[0009] Uncertainty quantification and risk management module, which quantifies the uncertainty of prediction results through Bayesian networks and Monte Carlo simulation, and provides risk management suggestions;

[0010] The forecast result visualization and decision support module displays the forecast results intuitively in the form of charts, maps, and animations, and combines multi-dimensional information on social economy and ecological environment to provide decision makers with customized climate change response strategies.

[0011] Preferably, the data integration and processing module includes:

[0012] Data cleaning and verification unit, used to identify and correct errors, outliers and missing values ​​in the data;

[0013] Data standardization and normalization unit converts data from different sources and dimensions into a unified format to improve model training efficiency;

[0014] The feature engineering unit extracts valuable features for climate change prediction from raw data based on statistical analysis and machine learning algorithms.

[0015] Preferably, the deep learning model construction and optimization module includes:

[0016] The convolutional neural network is used to extract spatial features of climate data;

[0017] The recurrent neural network, the variant long short-term memory network and the gated recurrent unit are used to capture the long-term dependencies of time series data;

[0018] The attention mechanism is used to dynamically adjust the model's attention to different time steps and spatial positions.

[0019] Preferably, the spatiotemporal feature extraction and fusion module includes:

[0020] The spatiotemporal convolution unit combines the convolution operations of the time dimension and the space dimension to extract the spatiotemporal characteristics of climate data;

[0021] The feature fusion strategy unit adopts feature splicing and weighted summation to effectively fuse features from different data sources.

[0022] Preferably, the uncertainty quantification and risk management module includes:

[0023] The forecast uncertainty assessment unit calculates the forecast error and confidence interval based on the probability distribution of the forecast results;

[0024] The risk management recommendation generation unit provides targeted risk mitigation measures and adaptation strategies based on the uncertainty assessment results.

[0025] Preferably, the prediction result visualization and decision support module includes:

[0026] Visualization tool integration unit, supporting a variety of visualization technologies, including heat maps, trend maps, and animation simulations;

[0027] The customized decision support unit provides personalized decision suggestions based on user needs and climate change characteristics of specific regions.

[0028] Preferably, it also includes: a model performance monitoring and iteration optimization module, which is used to continuously monitor the predictive performance of the deep learning model and iteratively optimize the model based on newly collected data and user demand feedback.

[0029] Preferably, it also includes: supporting multi-scale, multi-variable climate change predictions, and being able to simultaneously predict the changing trends of climate variables such as temperature, precipitation, wind speed, and sea level rise.

[0030] Preferably, it also includes: a user interaction interface and an intelligent recommendation system, through which users can input prediction requirements, and based on user input and historical behavior data, the system intelligently recommends suitable prediction models and visualization solutions.

[0031] Preferably, it also includes: cross-platform compatibility, the ability to run on different operating systems and different hardware devices, and support cloud computing and edge computing to meet the needs of large-scale data processing and real-time prediction.

[0032] The above solution of the present invention includes at least the following beneficial effects:

[0033] Through the data integration and processing module, the system can comprehensively and quickly collect and integrate climate data from multiple channels, such as satellites, ground observation stations, climate model simulations and social media, effectively solving the problem of wide data sources and diverse formats, and providing a high-quality data foundation for subsequent model construction; at the same time, the module also has data preprocessing functions, which can remove noise, fill in missing values, etc., further improving data quality;

[0034] Using deep neural network architectures such as convolutional neural networks, recurrent neural networks and their variants, such as long short-term memory networks, gated recurrent units and attention mechanisms, we have built multi-level, multi-modal climate change prediction models. These models can deeply explore the inherent laws and potential connections of climate data and improve the accuracy of predictions. In addition, through transfer learning, hyperparameter tuning and ensemble learning methods, the system can continuously optimize model performance and make it more adaptable to complex and changing climate change scenarios.

[0035] The spatiotemporal feature extraction and fusion module uses deep learning models to efficiently extract the spatiotemporal features of climate data, and uses multi-source data fusion strategies such as feature-level fusion and decision-level fusion to achieve complementarity and enhancement between data. This function not only improves the prediction ability of the model, but also can more comprehensively reflect the spatiotemporal distribution and evolution trend of climate change, providing more accurate information support for decision makers.

[0036] The uncertainty of the forecast results is quantified through methods such as Bayesian networks and Monte Carlo simulation, providing decision makers with an intuitive assessment of the reliability of the forecast results. At the same time, combined with risk management recommendations, the system can help decision makers better cope with the uncertainties and risks brought about by climate change and develop more scientific and reasonable response strategies.

[0037] The forecast result visualization and decision support module displays the forecast results in the form of charts, maps, animations, etc., so that decision makers can quickly understand the trends and laws of climate change. In addition, the module also combines multi-dimensional information such as social economy and ecological environment to provide decision makers with customized climate change response strategies, which helps to improve the scientificity and effectiveness of decision-making.

[0038] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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 description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0040] Figure 1 is a flow chart of a climate change prediction system based on a deep learning model provided in an embodiment of the present invention;

[0041] Figure 2 is a flow chart of a data integration and processing module provided in an embodiment of the present invention;

[0042] Figure 3 is a flowchart of a deep learning model construction and optimization module provided in an embodiment of the present invention;

[0043] Figure 4 is a flow chart of a spatiotemporal feature extraction and fusion module provided in an embodiment of the present invention;

[0044] Figure 5 is a flow chart of an uncertainty quantification and risk management module provided in an embodiment of the present invention;

[0045] Figure 6 It is a flow chart of a prediction result visualization and decision support module provided in an embodiment of the present invention.

[0046] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but are not to be construed as limiting the present invention. All other embodiments obtained by ordinary technicians in the field without creative work based on the embodiments of the present invention are within the scope of protection of the present invention.

[0048] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "circumferential", "radial" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0049] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0050] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0052] The following describes in detail a climate change prediction system based on a deep learning model according to an embodiment of the present invention with reference to the accompanying drawings.

[0053] See also Figure 1 to Figure 6 In this embodiment, it includes: a data integration and processing module, which is used to collect, integrate and preprocess multi-source climate data from satellites, ground observation stations, climate model simulations and social media; a deep learning model construction and optimization module, which is based on the deep neural network architecture including: convolutional neural network, recurrent neural network and its variant long short-term memory network, gated recurrent unit, and attention mechanism, to build a multi-level, multi-modal climate change prediction model, and optimize the model through transfer learning, hyperparameter tuning and ensemble learning methods; a spatiotemporal feature extraction and fusion module, which uses a deep learning model to efficiently extract the spatiotemporal features of climate data, and uses multi-source data fusion strategies including: feature-level fusion and decision-level fusion to enhance the prediction ability of the model; an uncertainty quantification and risk management module, which quantifies the uncertainty of the prediction results through Bayesian networks and Monte Carlo simulations, and provides risk management suggestions; a prediction result visualization and decision support module, which intuitively displays the prediction results in the form of charts, maps, and animations, and combines multi-dimensional information on social economy and ecological environment to provide decision makers with customized climate change response strategies;

[0054] Through the data integration and processing module, the system can comprehensively and quickly collect and integrate climate data from multiple channels, such as satellites, ground observation stations, climate model simulations and social media, effectively solving the problem of wide data sources and diverse formats, and providing a high-quality data foundation for subsequent model construction; at the same time, the module also has data preprocessing functions, which can remove noise, fill in missing values, etc., further improving data quality;

[0055] Using deep neural network architectures such as convolutional neural networks, recurrent neural networks and their variants, such as long short-term memory networks, gated recurrent units and attention mechanisms, we have built multi-level, multi-modal climate change prediction models. These models can deeply explore the inherent laws and potential connections of climate data and improve the accuracy of predictions. In addition, through transfer learning, hyperparameter tuning and ensemble learning methods, the system can continuously optimize model performance and make it more adaptable to complex and changing climate change scenarios.

[0056] The spatiotemporal feature extraction and fusion module uses deep learning models to efficiently extract the spatiotemporal features of climate data, and uses multi-source data fusion strategies such as feature-level fusion and decision-level fusion to achieve complementarity and enhancement between data. This function not only improves the prediction ability of the model, but also can more comprehensively reflect the spatiotemporal distribution and evolution trend of climate change, providing more accurate information support for decision makers.

[0057] The uncertainty of the forecast results is quantified through methods such as Bayesian networks and Monte Carlo simulation, providing decision makers with an intuitive assessment of the reliability of the forecast results. At the same time, combined with risk management recommendations, the system can help decision makers better cope with the uncertainties and risks brought about by climate change and develop more scientific and reasonable response strategies.

[0058] The forecast result visualization and decision support module displays the forecast results in the form of charts, maps, animations, etc., so that decision makers can quickly understand the trends and laws of climate change. In addition, the module also combines multi-dimensional information such as social economy and ecological environment to provide decision makers with customized climate change response strategies, which helps to improve the scientificity and effectiveness of decision-making.

[0059] In this embodiment, the data integration and processing module includes: a data cleaning and verification unit, which is used to identify and correct errors, abnormal values ​​and missing values ​​in the data; the data cleaning and verification unit uses preset algorithms and rules to accurately identify and correct errors, abnormal values ​​and missing values ​​in the data to ensure the accuracy and reliability of the data;

[0060] The data standardization and normalization unit converts data from different sources and dimensions into a unified format to improve the efficiency of model training. The data standardization and normalization unit converts data from different sources and dimensions into a unified format through a series of conversion operations, which not only improves the efficiency of model training, but also enhances the generalization ability of the model.

[0061] The feature engineering unit extracts valuable features for climate change prediction from raw data based on statistical analysis and machine learning algorithms; the feature engineering unit deeply mines valuable features in raw data based on statistical analysis and machine learning algorithms, which are crucial for climate change prediction;

[0062] Through a series of preprocessing steps including data cleaning and verification unit, data standardization and normalization unit and feature engineering unit, the system can generate high-quality data sets, providing strong support for the subsequent model construction and optimization.

[0063] In this embodiment, the deep learning model construction and optimization module includes:

[0064] Convolutional neural networks are used to extract spatial features of climate data; Convolutional neural networks are good at extracting spatial features of climate data, such as temperature distribution, precipitation patterns, etc.

[0065] Recurrent neural networks, variant long short-term memory networks, and gated recurrent units are used to capture long-term dependencies of time series data; recurrent neural networks and their variants can capture long-term dependencies of time series data, such as temperature trends, precipitation periodicity, etc.

[0066] The attention mechanism is used to dynamically adjust the model's attention to different time steps and spatial positions; the attention mechanism can dynamically adjust the model's attention to different time steps and spatial positions, further improving the model's prediction ability;

[0067] Through transfer learning, hyperparameter tuning and integrated learning methods, the system can continuously optimize model performance, making it more adaptable to complex and changeable climate change scenarios.

[0068] In this embodiment, the spatiotemporal feature extraction and fusion module includes:

[0069] The spatiotemporal convolution unit combines the convolution operations of the time dimension and the space dimension to extract the spatiotemporal characteristics of climate data; The spatiotemporal convolution unit combines the convolution operations of the time dimension and the space dimension to accurately capture the spatiotemporal characteristics of climate data;

[0070] The feature fusion strategy unit adopts the method of feature splicing and weighted summation to effectively fuse the features from different data sources; The feature fusion strategy unit adopts the method of feature splicing and weighted summation to effectively fuse the features from different data sources;

[0071] The spatiotemporal convolution unit and the feature fusion strategy unit not only improve the prediction ability of the model, but also can more comprehensively reflect the spatiotemporal distribution and evolution trend of climate change; through the optimization processing of the spatiotemporal convolution unit and the feature fusion strategy unit, the system can generate more accurate and comprehensive prediction results, providing more powerful information support for decision makers.

[0072] In this embodiment, the uncertainty quantification and risk management module includes:

[0073] The prediction uncertainty assessment unit calculates the prediction error and confidence interval based on the probability distribution of the prediction results; The prediction uncertainty assessment unit calculates the prediction error and confidence interval based on the probability distribution of the prediction results, providing decision makers with an intuitive assessment of the reliability of the prediction results; so that decision makers can more clearly understand the uncertainty range of the prediction results and make more scientific decisions;

[0074] The risk management recommendation generation unit provides targeted risk mitigation measures and adaptation strategies based on the uncertainty assessment results; the risk management recommendation generation unit provides targeted risk mitigation measures and adaptation strategies based on the uncertainty assessment results; these recommendations are not only based on the forecast results, but also combine multi-dimensional information such as social economy, ecological environment, etc., which will help decision makers better deal with the uncertainties and risks brought about by climate change.

[0075] In this embodiment, the prediction result visualization and decision support module includes:

[0076] The visualization tool integration unit supports a variety of visualization technologies, including heat maps, trend maps, and animation simulations. The visualization tool integration unit supports a variety of visualization technologies, including heat maps, trend maps, and animation simulations, and can provide a variety of display methods according to different needs. These visualization tools enable decision makers to intuitively understand the trends and laws of climate change, so as to make more accurate decisions.

[0077] The customized decision support unit provides personalized decision-making suggestions based on user needs and climate change characteristics of specific regions; the customized decision support unit provides personalized decision-making suggestions based on user needs and climate change characteristics of specific regions; these suggestions are not only based on prediction results, but also combine multi-dimensional information such as social economy, ecological environment, etc., providing decision makers with comprehensive and scientific decision support.

[0078] In this embodiment, it also includes: a model performance monitoring and iteration optimization module, which is used to continuously monitor the predictive performance of the deep learning model and iteratively optimize the model according to newly collected data and user demand feedback; the model performance monitoring and iteration optimization module can continuously monitor the predictive performance of the deep learning model and iteratively optimize it according to newly collected data and user demand feedback; through this mechanism, the system can continuously adapt to new climate change scenarios and data features, and improve the accuracy and precision of the prediction; at the same time, the module can also promptly discover and resolve problems and defects in the model to ensure the stability and reliability of the system; with the support of the model performance monitoring and iterative optimization module, the system can maintain its leading position and provide users with better quality climate change prediction services.

[0079] In this embodiment, it also includes: supporting multi-scale, multi-variable climate change predictions, and being able to simultaneously predict the changing trends of climate variables such as temperature, precipitation, wind speed, and sea level rise; the system can simultaneously predict the changing trends of multiple climate variables such as temperature, precipitation, wind speed, and sea level rise, providing decision makers with more comprehensive information support; this function enables the system to more accurately reflect the complexity and diversity of climate change, and provide a more powerful scientific basis for responding to climate change; at the same time, the system can also make customized predictions based on user needs to meet the specific needs of different fields and regions; by supporting multi-scale, multi-variable climate change predictions, the system can better serve global climate change research and response work.

[0080] In this embodiment, it also includes: a user interaction interface and an intelligent recommendation system. Users can input forecasting requirements through the interface, and the system intelligently recommends suitable forecasting models and visualization solutions based on user input and historical behavior data; users can input forecasting requirements through the interface, and the system intelligently recommends suitable forecasting models and visualization solutions based on user input and historical behavior data; this function not only simplifies the user's operation process, but also improves the accuracy and practicality of the forecast results. The intelligent recommendation system can also be continuously optimized and improved based on user feedback and needs, providing users with a more personalized service experience; through the support of the user interaction interface and the intelligent recommendation system, users can more conveniently obtain climate change forecast information, providing more powerful support for responding to climate change.

[0081] In this embodiment, it also includes: cross-platform compatibility, the ability to run on different operating systems and different hardware devices, and support cloud computing and edge computing to meet the needs of large-scale data processing and real-time prediction; the system can run on different operating systems and different hardware devices, and support cloud computing and edge computing technologies; so that the system can flexibly adapt to different application scenarios and computing environments, and meet the needs of large-scale data processing and real-time prediction; at the same time, the system also supports remote access and collaboration functions, allowing users to obtain prediction results anytime and anywhere and provide decision support; through the support of cross-platform compatibility, the system can provide users with more convenient and efficient climate change prediction services, and provide more powerful technical support for addressing global climate change challenges.

[0082] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0083] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A climate change prediction system based on a deep learning model, characterized by: include: Data integration and processing module, used to collect, integrate and pre-process climate data from multiple sources including satellites, ground observation stations, climate model simulations and social media; Deep learning model building and optimization module, based on deep neural network architecture including: convolutional neural network, recurrent neural network and its variant long short-term memory network, gated recurrent unit, and attention mechanism, to build a multi-level, multi-modal climate change prediction model, and optimize the model through transfer learning, hyperparameter tuning and ensemble learning methods; The spatiotemporal feature extraction and fusion module uses deep learning models to efficiently extract the spatiotemporal features of climate data, and enhances the model's prediction capabilities through multi-source data fusion strategies including feature-level fusion and decision-level fusion; Uncertainty quantification and risk management module, which quantifies the uncertainty of prediction results through Bayesian networks and Monte Carlo simulation, and provides risk management suggestions; The forecast result visualization and decision support module displays the forecast results intuitively in the form of charts, maps, and animations, and combines multi-dimensional information on social economy and ecological environment to provide decision makers with customized climate change response strategies.

2. The climate change prediction system based on deep learning model according to claim 1, characterized in that: The data integration and processing module includes: Data cleaning and verification unit, used to identify and correct errors, outliers and missing values ​​in the data; Data standardization and normalization unit converts data from different sources and dimensions into a unified format to improve model training efficiency; The feature engineering unit extracts valuable features for climate change prediction from raw data based on statistical analysis and machine learning algorithms.

3. The climate change prediction system based on deep learning model according to claim 1, characterized in that: The deep learning model building and optimization modules include: The convolutional neural network is used to extract spatial features of climate data; The recurrent neural network, the variant long short-term memory network and the gated recurrent unit are used to capture the long-term dependencies of time series data; The attention mechanism is used to dynamically adjust the model's attention to different time steps and spatial positions.

4. The climate change prediction system based on deep learning model according to claim 1, characterized in that: The spatiotemporal feature extraction and fusion module includes: The spatiotemporal convolution unit combines the convolution operations of the time dimension and the space dimension to extract the spatiotemporal characteristics of climate data; The feature fusion strategy unit adopts feature splicing and weighted summation to effectively fuse features from different data sources.

5. The climate change prediction system based on deep learning model according to claim 1, characterized in that: The uncertainty quantification and risk management module includes: The forecast uncertainty assessment unit calculates the forecast error and confidence interval based on the probability distribution of the forecast results; The risk management recommendation generation unit provides targeted risk mitigation measures and adaptation strategies based on the uncertainty assessment results.

6. The climate change prediction system based on deep learning model according to claim 1, characterized in that: The prediction result visualization and decision support module includes: Visualization tool integration unit, supporting a variety of visualization technologies, including heat maps, trend maps, and animation simulations; The customized decision support unit provides personalized decision suggestions based on user needs and climate change characteristics of specific regions.

7. The climate change prediction system based on deep learning model according to claim 1, characterized in that: Also includes: The model performance monitoring and iteration optimization module is used to continuously monitor the predictive performance of the deep learning model and iteratively optimize the model based on newly collected data and user demand feedback.

8. The climate change prediction system based on deep learning model according to claim 1, characterized in that: Also includes: It supports multi-scale and multi-variable climate change predictions, and can simultaneously predict the changing trends of climate variables such as temperature, precipitation, wind speed, and sea level rise.

9. The climate change prediction system based on deep learning model according to claim 1, characterized in that: Also includes: User interaction interface and intelligent recommendation system. Users can input prediction requirements through the interface, and the system will intelligently recommend suitable prediction models and visualization solutions based on user input and historical behavior data.

10. The climate change prediction system based on deep learning model according to claim 1, characterized in that: Also includes: It has cross-platform compatibility, can run on different operating systems and different hardware devices, and supports cloud computing and edge computing to meet the needs of large-scale data processing and real-time prediction.

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