Climate change response evaluation system and method based on marine environment parameters

Abstract

The invention relates to a climate change response evaluation system and method based on marine environment parameters, an acquisition device is arranged to acquire marine environment parameters and climate index data of a working sea area, and the marine environment parameters include sea surface temperature, chlorophyll-a concentration, sea level abnormal value and wind stress rotation; the climate index data comprises pacific ocean intergenerational oscillation, Atlantic ocean intergenerational oscillation and a dipole modal index, under the combined processing of the data processing device and the prediction device, multi-parameter coupling monitoring and climate oscillation phenomenon integration can be realized, the dynamic change of the marine ecosystem can be comprehensively evaluated, and the prediction accuracy of the marine ecosystem is improved. And the limitation of single parameter or single climatic factor analysis is avoided.

Description

Technical Field

[0001] The present invention relates to the field of computer model and system technology, and in particular to a climate change response assessment system and method based on marine environmental parameters. Background Art

[0002] As global climate change intensifies, marine ecosystems face unprecedented pressure. Marine environmental changes in regions such as the western Indian Ocean (Gulf of Oman) are having profound impacts on biodiversity and fishery resources. Existing marine environmental monitoring technologies primarily rely on monitoring single parameters, such as sea surface temperature (SST), chlorophyll-a concentration (Chl-a), or sea level anomaly (SLA), lacking comprehensive assessment of the coupled effects of multiple parameters.

[0003] For example, although the satellite remote sensing systems commonly used in existing technologies can provide high-precision sea surface temperature data, they cannot effectively combine parameters such as chlorophyll-a concentration and wind stress curl (WSC) for comprehensive assessment, resulting in incomplete and inaccurate predictions of changes in marine ecosystems.

[0004] In addition, existing technologies are insufficient to assess the interactions between climate oscillation phenomena (such as the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO)) and marine environmental parameters, making it difficult to assess the long-term impact of climate change on marine ecosystems. Summary of the Invention

[0005] The technical problem to be solved by the present invention is how to overcome the technical defects of existing marine environmental monitoring technology, such as the lack of comprehensive evaluation of multi-parameter coupling effects and difficulty in evaluating the long-term impact of climate change on marine ecosystems. In order to overcome the above defects of the existing technology, the present invention provides a climate change response evaluation system and method based on marine environmental parameters, including a climate change response evaluation system based on marine environmental parameters and a climate change response evaluation method based on marine environmental parameters.

[0006] The present invention provides a climate change response assessment system based on marine environmental parameters, comprising: The collection device is configured to obtain real-time data on marine environmental parameters and climate indices in the working sea area; a data processing device, in communication with the acquisition device, configured to first perform time synchronization and spatial matching on the ocean environment parameter and the climate index data to obtain integrated data, and then preprocess the integrated data to obtain preprocessed data; a prediction device, in communication with the data processing device, configured to use the preprocessed data to obtain correlations between parameters and temporal trends of the parameters using a statistical inference algorithm, and to use the preprocessed data to obtain a chlorophyll-a concentration trend for the next five years using an extreme gradient boosting algorithm; in, Said marine environmental parameters include sea surface temperature, chlorophyll-a concentration, sea level anomaly, and wind stress curl; The climate index data include the Pacific Decadal Oscillation, the Atlantic Multidecadal Oscillation and the Dipole Mode Index.

[0007] The disclosed climate change response assessment system based on marine environmental parameters addresses the technical deficiencies listed above. By providing a collection device to collect marine environmental parameter and climate index data from the working sea area, the marine environmental parameters include sea surface temperature, chlorophyll-a concentration, sea level anomaly, and wind stress curl; and the climate index data include the Pacific Decadal Oscillation, the Atlantic Multi-Decadal Oscillation, and the Dipole Mode Index. Combined processing by the provided data processing device and prediction device facilitates multi-parameter coupled monitoring and integration of climate oscillation phenomena, thereby enabling a comprehensive assessment of the dynamic changes in marine ecosystems and avoiding the limitations of single parameter or single climate factor analysis. Furthermore, the provided prediction device can utilize preprocessed data through a statistical inference algorithm to obtain the correlation between the various parameters and the temporal variation trends of each parameter. Furthermore, the provided prediction device can utilize the preprocessed data through an extreme gradient boosting algorithm to obtain the chlorophyll-a concentration variation trend for the next five years. Because the extreme gradient boosting algorithm can accurately predict the variation trends of marine environmental parameters, it improves the accuracy and reliability of the prediction, thereby achieving high-precision predictions and climate change assessments, providing a timely scientific basis for marine resource management and environmental protection. This overcomes the technical shortcomings of existing marine environmental monitoring technologies, such as the lack of comprehensive assessment of multi-parameter coupling effects and difficulty in assessing the long-term impact of climate change on marine ecosystems.

[0008] In a possible implementation, the collection device includes: The remote sensing data module is set to obtain the sea surface temperature, chlorophyll-a concentration, and sea level anomaly values of the working sea area in real time; The meteorological observation data module is set to obtain the wind stress curl of the working sea area in real time; The climate index data module is set to obtain the Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation and Dipole Mode Index in real time in the working sea area; This will enable real-time collection of sea surface temperature, chlorophyll-a concentration, sea level anomalies, wind stress curl Pacific decadal oscillation, Atlantic multi-decadal oscillation and dipole modal index, thereby further ensuring the implementation of multi-parameter coupled monitoring and integration of climate oscillation phenomena.

[0009] In a possible implementation, the remote sensing data module is a satellite remote sensing device for acquiring sea surface temperature, chlorophyll-a concentration, and sea level anomaly values from a satellite platform.

[0010] In a possible implementation, the remote sensing data module is a sensor module composed of a sea surface temperature sensor, a chlorophyll-a concentration sensor, and a sea level anomaly sensor arranged in the working sea area.

[0011] In one possible implementation, the data processing device includes: an integration module, communicating with the remote sensing data module, the meteorological observation data module, and the climate index data module simultaneously, and configured to perform time synchronization and spatial matching on sea surface temperature, chlorophyll-a concentration, sea level anomaly, wind stress curl, Pacific decadal oscillation, Atlantic multidecadal oscillation, and dipole mode index to obtain the integrated data; a preprocessing module, in communication with the integration module, and configured to preprocess the integrated data to obtain the preprocessed data; By setting up an integration module, the sea surface temperature, chlorophyll-a concentration, sea level anomaly, wind stress curl, Pacific decadal oscillation, Atlantic multi-decadal oscillation and dipole modal index can be synchronized in time and matched in space to achieve the effect of integrating data and obtain integrated data, thereby ensuring the implementation of multi-parameter coupling monitoring and integration of climate oscillation phenomena. In addition, by setting up a preprocessing module to preprocess the data, data filtering and denoising can be achieved, thereby ensuring the accuracy and efficiency of the prediction results.

[0012] In one possible implementation, the preprocessing module is configured to perform the following steps: A1: Cleaning the integrated data to remove outliers, supplement missing values, and eliminate noise data to obtain cleaned data; A2: normalizing the data of different parameters in the cleaned data to eliminate dimensional differences and obtain normalized data; A3: performing segmented time series data processing on different parameters in the normalized data to extract time variation features to obtain the preprocessed data; This solution not only ensures data filtering and denoising, but also extracts seasonal and interannual variation characteristics by segmenting time series data of different parameters in the normalized data. It also achieves the invisible effect of decomposing sea surface temperature data into seasonal components and long-term trend components, which facilitates subsequent analysis.

[0013] In one possible implementation, the prediction device includes: a statistical inference module, in communication with the preprocessing module, configured to utilize the preprocessed data to obtain correlations between parameters and temporal trends of the parameters through a statistical inference algorithm; The extreme gradient boosting module communicates with the preprocessing module and is configured to obtain a chlorophyll-a concentration variation trend for the next five years using the preprocessed data through an extreme gradient boosting algorithm.

[0014] In one possible implementation, the statistical inference module is configured to perform the following steps: B1: using the preprocessed data to obtain the correlation between the parameters through the Pearson correlation coefficient inference algorithm or the principal component inference algorithm; B2: Obtaining the time variation trend of each parameter using the preprocessed data through time series analysis; This scheme can not only obtain the correlation between various parameters, but also reveal the temporal variation trend of each parameter based on time series analysis.

[0015] Another technical solution of the present invention is to provide a method for evaluating climate change response based on marine environmental parameters, the method comprising the following steps: S1: Constructing a dataset for model parameter optimization using the extreme gradient boosting module, and dividing the dataset into a training set, a test set, and a validation set; S2: performing data cleaning and data normalization on the training set, the test set, and the validation set respectively to obtain optimized parameter data; S3 optimizes the model parameters of the extreme gradient boosting module based on the optimization parameter data using a mean square error loss function, and places the extreme gradient boosting module after the model parameter optimization in the prediction device; S4: Obtain ocean environmental parameters and climate index data of the working sea area in real time through the collection device; S5: performing time synchronization and spatial matching on the ocean environment parameter and the climate index data by a data processing device to obtain integrated data, and then preprocessing the integrated data to obtain preprocessed data; S6: The prediction device uses the pre-processed data with a statistical inference algorithm to obtain the correlation between the parameters and the time change trend of the parameters, and uses the extreme gradient boosting algorithm to obtain the chlorophyll-a concentration change trend in the next five years.

[0016] The method disclosed in this invention first constructs training, test, and validation sets to obtain optimized parameter data through data preprocessing. It then uses a mean squared error loss function to optimize the model parameters of an extreme gradient boosting module based on the optimized parameter data. The optimized extreme gradient boosting module is then deployed in a prediction device. Furthermore, a collection device collects marine environmental parameter and climate index data from the operating area. This data is then processed in a combined data processing and prediction device. This facilitates multi-parameter coupled monitoring and the integration of climate oscillation phenomena, enabling a comprehensive assessment of the dynamic changes in marine ecosystems, avoiding the limitations of single-parameter or single-climate factor analysis. Furthermore, the prediction device utilizes preprocessed data using a statistical inference algorithm to determine the correlations between parameters and their temporal trends. Furthermore, the prediction device also utilizes the preprocessed data using an extreme gradient boosting algorithm to determine the chlorophyll-a concentration trend for the next five years. Because the extreme gradient boosting algorithm accurately predicts the changing trends of marine environmental parameters, it improves the accuracy and reliability of predictions, thereby achieving high-precision predictions and enabling climate change assessments, providing timely scientific evidence for marine resource management and environmental protection. This overcomes the technical shortcomings of existing marine environmental monitoring technologies, such as the lack of comprehensive assessment of multi-parameter coupling effects and difficulty in assessing the long-term impact of climate change on marine ecosystems.

[0017] In one possible implementation, the data set is the Cartesian product of input data and rational output data; The input data are sea surface temperature and chlorophyll-a concentration for several years before each of multiple time years obtained from the Copernicus Marine and Environmental Monitoring Service satellite platform; The rational output data is the chlorophyll-a concentration change trend in each time year and the following 5 years among multiple time years; This solution can ensure the accuracy of extreme gradient boosting module model parameter optimization and ensure that the extreme gradient boosting module after model parameter optimization has the ability to make accurate predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of the structure of a climate change response assessment system based on marine environmental parameters disclosed in an embodiment of the present application; Figure 2 A flow chart of the method disclosed in the embodiments of this application; Figure 3This is a flowchart of the data set partitioning and cleaning disclosed in the embodiments of this application. DETAILED DESCRIPTION

[0019] First, those skilled in the art should understand that these embodiments are merely used to explain the technical principles of the embodiments of the present application and are not intended to limit the scope of protection of the embodiments of the present application. Those skilled in the art may adjust them as needed to suit specific application scenarios.

[0020] In the embodiments of the present application, unless otherwise clearly specified and limited, the communication or communication connection between the first feature and the second feature refers to the transmission of information between the first feature and the second feature. This information transmission can be either unidirectional or bidirectional, and the method of realizing the communication connection can be electrical connection of wires, radio connection, electrical connection of electromagnetic media (such as semiconductors), communication realized by channels, etc.

[0021] In the embodiments of the present application, unless otherwise expressly specified or limited, a first feature being “above,” “below,” “in front of,” or “behind” a second feature may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being “above,” “above,” or “above” a second feature may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being “below,” “below,” or “below” a second feature may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is lower in level than the second feature. A first feature being “before,” “in front of,” or “in front of” a second feature may mean that the first feature is directly in front of or diagonally in front of the second feature, or simply means that the first feature is prior to the second feature in sequence. A first feature being “behind,” “behind,” or “behind” a second feature may mean that the first feature is directly behind or diagonally behind the second feature, or simply means that the first feature is later in sequence than the second feature.

[0022] The technical solution of the present application is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] See also Figures 1 to 3 The present application discloses a climate change response assessment system based on marine environmental parameters. Figure 1 is a schematic diagram of the system structure, as shown in Figure 1 As shown, the system includes a collection device, a data processing device and a prediction device. The data processing device communicates with the collection device, and the prediction device communicates with the data processing device.

[0024] In this system, the collection device is set to obtain real-time ocean environmental parameters and climate index data of the working sea area. The ocean environmental parameters include sea surface temperature, chlorophyll-a concentration, sea level anomaly, and wind stress curl. The climate index data include Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and Dipole Mode Index. Figure 1 In this embodiment, the acquisition device includes a remote sensing data module, a meteorological observation data module and a climate index data module. The remote sensing data module is configured to obtain the sea surface temperature, chlorophyll-a concentration and sea level anomaly of the working sea area in real time; the meteorological observation data module is configured to obtain the wind stress curl of the working sea area in real time; and the climate index data module is configured to obtain the Pacific decadal oscillation, Atlantic multidecadal oscillation and dipole mode index of the working sea area in real time.

[0025] In the acquisition device, the remote sensing data module is a satellite remote sensing device used to retrieve sea surface temperature, chlorophyll-a concentration, and sea level anomaly values from a satellite platform; alternatively, the remote sensing data module is a sensor module composed of a sea surface temperature sensor, a chlorophyll-a concentration sensor, and a sea level anomaly sensor deployed in the operating sea area. In this embodiment, the remote sensing data module uses a satellite remote sensing data interface that retrieves sea surface temperature, chlorophyll-a concentration, and sea level anomaly values from a satellite platform to collect these data in real time, ensuring multi-parameter coupling.

[0026] Please continue to see Figure 1 In this system, the data processing device includes an integration module and a preprocessing module. The integration module communicates with the remote sensing data module, the meteorological observation data module, and the climate index data module simultaneously, and the preprocessing module communicates with the integration module. In this embodiment, the data processing device is configured to perform the following process: first, the integration module performs time synchronization and spatial matching on sea surface temperature, chlorophyll-a concentration, sea level anomaly, wind stress curl, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and Dipole Mode Index to obtain the integrated data; then, the preprocessing module preprocesses the integrated data to obtain the preprocessed data.

[0027] In the data processing device, the preprocessing module is configured to perform the following steps: A1: cleaning the integrated data to remove outliers, supplement missing values and eliminate noise data to obtain cleaned data; in this embodiment, the method of supplementing missing values is linear interpolation; A2: normalizing the data of different parameters in the cleaned data to eliminate dimensional differences to obtain normalized data; A3: segmenting the time series data of different parameters in the normalized data to extract time change characteristics to obtain the preprocessed data.

[0028] Please continue to see Figure 1In this system, the prediction device is configured to use preprocessed data using a statistical inference algorithm to determine the correlation between parameters and the temporal trend of each parameter, and to use an extreme gradient boosting algorithm using the preprocessed data to determine the chlorophyll-a concentration trend for the next five years. In this embodiment, the prediction device includes a statistical inference module and an extreme gradient boosting (XGBoost) module. The statistical inference module communicates with the preprocessing module, and the extreme gradient boosting module communicates with the preprocessing module.

[0029] In the prediction device, the statistical inference module is configured to use preprocessed data using a statistical inference algorithm to determine the correlations between parameters and the temporal trends of each parameter. Specifically, the statistical inference module is configured to perform the following steps: B1: Using the preprocessed data, the correlations between parameters are determined using a Pearson correlation coefficient inference algorithm or a principal component inference algorithm; B2: Using the preprocessed data, the temporal trends of each parameter are determined using time series analysis. The extreme gradient boosting module is configured to use the preprocessed data using an extreme gradient boosting algorithm to determine the chlorophyll-a concentration trend over the next five years.

[0030] See also Figure 2 and Figure 3 The following will further disclose the method for using the climate change response assessment system based on marine environmental parameters described in this embodiment. Figure 2 The overall flow chart of this method is as follows: Figure 3 The flowchart for data set partitioning and cleaning corresponds to steps S1 and S2. The method includes the following steps: S1: Construct a dataset for model parameter optimization similar to that of the extreme gradient boosting module, and divide the dataset into training set, test set, and validation set.

[0031] In step S1, the dataset is the Cartesian product of input data and rational output data. The input data is sea surface temperature and chlorophyll-a concentration for several years before each year in a multi-year period, acquired from the Copernicus Marine and Environmental Monitoring Service satellite platform CMEMS. The rational output data is the chlorophyll-a concentration trend for each year in the multi-year period and the five years thereafter.

[0032] S2: Perform data cleaning and normalization on the training set, test set, and validation set respectively to obtain optimized parameter data.

[0033] S3 uses the mean squared error loss function to optimize the model parameters of the extreme gradient boosting module based on the optimized parameter data. The optimized extreme gradient boosting module is then deployed in the prediction device. During the model parameter optimization process, the model is verified using cross-validation. Using cross-validation and other methods to verify the model ensures its reliability and accuracy.

[0034] When optimizing the model parameters of XGBoost in this embodiment, the constructed XGBoost grid hyperparameter search range is as follows: Parameter name Chinese instructions Optimal value n_estimators Number of trees [50,500] Step size 50 max_depth Maximum depth of the tree [1,15] Step 3 subsample The proportion of random sampling for each tree [0.6,1] Step size 0.05 learning_rate Learning rate (step size reduction factor) [0.01,0.2] Step size 0.02 gamma Minimum loss reduction required for a node to split [0,1] Step size 0.05 colsample_bytree The proportion of features randomly sampled by each tree [0.5,1] Step size 0.05 min_child_weight The minimum sample weight and the leaf node [1,10] Step 1 The final optimal parameters of XGBoost are as follows: Parameter name Optimal value n_estimators 350 max_depth 6 subsample 0.8 learning_rate 0.15 gamma 0.0 colsample_bytree 1 min_child_weight 5 The optimal model obtained has real-time monitoring and early warning capabilities. After accessing the latest ocean environmental parameter data through satellite and meteorological observation platforms, it can ensure the real-time nature of the monitoring system and guarantee dynamic early warning. If the visualization module is connected, the technical effect of visual display can also be obtained.

[0035] S4: Obtain the ocean environment parameters and climate index data of the working sea area in real time through the collection device.

[0036] S5: Performing time synchronization and spatial matching on the ocean environment parameter and climate index data through a data processing device to obtain integrated data, and then preprocessing the integrated data to obtain preprocessed data.

[0037] S6: Using the statistical inference algorithm of the prediction device to use the preprocessed data to obtain the correlation between the parameters and the time change trend of the parameters, and using the extreme gradient boosting algorithm to use the preprocessed data to obtain the chlorophyll-a concentration change trend in the next five years.

[0038] The climate change response assessment system based on marine environmental parameters disclosed in this embodiment is equipped with a collection device to collect marine environmental parameters and climate index data in the working sea area. The marine environmental parameters include sea surface temperature, chlorophyll-a concentration, sea level anomaly, and wind stress curl; the climate index data includes the Pacific Decadal Oscillation, the Atlantic Multi-Decadal Oscillation, and the Dipole Mode Index. Under the combined processing of the provided data processing device and prediction device, it helps to achieve multi-parameter coupled monitoring and the integration of climate oscillation phenomena, thereby enabling a comprehensive assessment of the dynamic changes of marine ecosystems, avoiding the limitations of single parameter or single climate factor analysis. In addition, the provided prediction device can use preprocessed data through a statistical inference algorithm to obtain the correlation between various parameters and the temporal variation trend of each parameter. In addition, the provided prediction device also uses the preprocessed data through an extreme gradient boosting algorithm to obtain the chlorophyll-a concentration variation trend for the next five years. Because the extreme gradient boosting algorithm can accurately predict the variation trend of marine environmental parameters, it improves the accuracy and reliability of the prediction, thereby achieving high-precision prediction and climate change assessment, providing a timely scientific basis for marine resource management and environmental protection. This overcomes the technical shortcomings of existing marine environmental monitoring technologies, such as the lack of comprehensive assessment of multi-parameter coupling effects and difficulty in assessing the long-term impact of climate change on marine ecosystems.

[0039] In the description of the embodiments of the present application, it should be noted that in the description of the present application, terms such as "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or component must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present application.

[0040] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "in the present embodiment", "specific example", or "some examples" means that the specific features, mechanisms, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, mechanisms, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0041] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A climate change response assessment system based on marine environmental parameters, characterized in that: include: The collection device is configured to obtain real-time data on marine environmental parameters and climate indices in the working sea area; a data processing device, in communication with the acquisition device, configured to first perform time synchronization and spatial matching on the ocean environment parameter and the climate index data to obtain integrated data, and then preprocess the integrated data to obtain preprocessed data; a prediction device, in communication with the data processing device, configured to use the preprocessed data to obtain correlations between parameters and temporal trends of the parameters using a statistical inference algorithm, and to use the preprocessed data to obtain a chlorophyll-a concentration trend for the next five years using an extreme gradient boosting algorithm; in, Said marine environmental parameters include sea surface temperature, chlorophyll-a concentration, sea level anomaly, and wind stress curl; The climate index data include the Pacific Decadal Oscillation, the Atlantic Multidecadal Oscillation and the Dipole Mode Index.

2. The climate change response assessment system based on marine environmental parameters according to claim 1, characterized in that: The collection device comprises: The remote sensing data module is set to obtain the sea surface temperature, chlorophyll-a concentration, and sea level anomaly values of the working sea area in real time; The meteorological observation data module is set to obtain the wind stress curl of the working sea area in real time; The climate index data module is set to obtain the Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation and Dipole Mode Index in the working sea area in real time.

3. The climate change response assessment system based on marine environmental parameters according to claim 2, characterized in that: The remote sensing data module is a satellite remote sensing device used to retrieve sea surface temperature, chlorophyll-a concentration, and sea level anomaly values from a satellite platform.

4. The climate change response assessment system based on marine environmental parameters according to claim 2, characterized in that: The remote sensing data module is a sensor module composed of a sea surface temperature sensor, a chlorophyll-a concentration sensor and a sea level anomaly sensor arranged in the working sea area.

5. The climate change response assessment system based on marine environmental parameters according to any one of claims 2 to 4, characterized in that: The data processing device includes: an integration module, communicating with the remote sensing data module, the meteorological observation data module, and the climate index data module simultaneously, and configured to perform time synchronization and spatial matching on sea surface temperature, chlorophyll-a concentration, sea level anomaly, wind stress curl, Pacific decadal oscillation, Atlantic multidecadal oscillation, and dipole mode index to obtain the integrated data; The preprocessing module is in communication with the integration module and is configured to preprocess the integrated data to obtain the preprocessed data.

6. The climate change response assessment system based on marine environmental parameters according to claim 5, characterized in that: The pre-processing module is configured to perform the following steps: A1: Cleaning the integrated data to remove outliers, supplement missing values, and eliminate noise data to obtain cleaned data; A2: normalizing the data of different parameters in the cleaned data to eliminate dimensional differences and obtain normalized data; A3: Performing segmented processing of time series data on different parameters in the normalized data to extract time variation features and obtain the preprocessed data.

7. The climate change response assessment system based on marine environmental parameters according to claim 6, characterized in that: The prediction device comprises: a statistical inference module, in communication with the preprocessing module, configured to utilize the preprocessed data to obtain correlations between parameters and temporal trends of the parameters through a statistical inference algorithm; The extreme gradient boosting module communicates with the preprocessing module and is configured to obtain a chlorophyll-a concentration variation trend for the next five years using the preprocessed data through an extreme gradient boosting algorithm.

8. The climate change response assessment system based on marine environmental parameters according to claim 7, characterized in that: The statistical inference module is configured to perform the following steps: B1: using the preprocessed data to obtain the correlation between the parameters through the Pearson correlation coefficient inference algorithm or the principal component inference algorithm; B2: Obtain the time variation trend of each parameter using the pre-processed data through time series analysis.

9. A method for evaluating climate change response based on marine environmental parameters, characterized in that: A climate change response assessment system based on marine environmental parameters applicable to any one of claims 1 to 9, comprising the following steps: S1: Constructing a dataset for model parameter optimization using the extreme gradient boosting module, and dividing the dataset into a training set, a test set, and a validation set; S2: performing data cleaning and data normalization on the training set, the test set, and the validation set respectively to obtain optimized parameter data; S3 optimizes the model parameters of the extreme gradient boosting module based on the optimization parameter data using a mean square error loss function, and places the extreme gradient boosting module after the model parameter optimization in the prediction device; S4: Obtain ocean environmental parameters and climate index data of the working sea area in real time through the collection device; S5: performing time synchronization and spatial matching on the ocean environment parameter and the climate index data by a data processing device to obtain integrated data, and then preprocessing the integrated data to obtain preprocessed data; S6: The prediction device uses the pre-processed data with a statistical inference algorithm to obtain the correlation between the parameters and the time change trend of the parameters, and uses the extreme gradient boosting algorithm to obtain the chlorophyll-a concentration change trend in the next five years.

10. The method for evaluating climate change response based on marine environmental parameters according to claim 9, characterized in that: The data set is the Cartesian product of the input data and the rational output data; The input data are sea surface temperature and chlorophyll-a concentration for several years before each of multiple time years obtained from the Copernicus Marine and Environmental Monitoring Service satellite platform; The rational output data is the chlorophyll-a concentration change trend of each time year among multiple time years and the following 5 years.

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