Marine carbon sink data collection and real-time analysis system

Abstract

This invention relates to the field of marine carbon sequestration data analysis technology, specifically to a marine carbon sequestration data acquisition and real-time analysis system. The system includes a data acquisition and preprocessing module, a seagrass bed carbon sequestration environment analysis module, a data analysis module, and an early warning module. Specifically, the data acquisition and preprocessing module acquires marine environmental parameters in real time; the seagrass bed carbon sequestration environment analysis module analyzes the correlation between seagrass bed carbon sequestration capacity and marine environmental parameters in real time, dynamically tracking the impact of environmental changes on seagrass bed carbon sequestration function; the data analysis module analyzes the dynamic changes in carbon sequestration capacity in real time; and the early warning module detects abnormal changes in carbon sequestration capacity and automatically issues an early warning when the carbon sequestration capacity drops to a preset threshold. This invention can quickly identify key environmental factors affecting carbon sequestration capacity, helping managers quickly respond to ecological risks brought about by environmental changes and providing more scientific carbon sequestration management decision support.

Description

Technical Field

[0001] The present invention relates to the technical field of ocean carbon sink data analysis, and in particular to an ocean carbon sink data collection and real-time analysis system. Background Art

[0002] As an important marine ecosystem, seagrass beds can effectively absorb and store carbon dioxide, and therefore make a significant contribution to the ocean's carbon sink capacity. Monitoring and management of the carbon sink capacity of seagrass beds can promote the balance of the global carbon cycle and effectively respond to climate change. However, due to the complexity and variability of the marine environment, real-time and efficient monitoring of the carbon sink capacity of seagrass beds and related environmental factors is still a major challenge facing current carbon sink management.

[0003] Existing marine carbon sink monitoring technologies usually rely on regular field sampling and laboratory analysis, and have problems such as poor real-time performance, limited coverage, and low monitoring frequency, making it difficult to cope with the rapidly changing marine environment. In addition, existing technologies do not conduct in-depth analysis of the correlation between seagrass bed carbon sink capacity and environmental parameters, and cannot effectively identify key environmental factors that affect carbon sink capacity, resulting in a lack of scientificity and foresight in management decisions, making it difficult to respond to ecological risks in a timely manner. At the same time, the existing system has limited abnormal warning functions, making it difficult to respond quickly and make specific recommendations for ecological restoration or environmental intervention.

[0004] The present invention aims to overcome the deficiencies of the prior art and provide a marine carbon sink data collection and real-time analysis system, which can take management measures in a timely manner according to the reports generated by the system, and effectively improve the scientificity, foresight and timeliness of decision-making of seagrass bed carbon sink management. Summary of the invention

[0005] The invention provides an ocean carbon sink data collection and real-time analysis system.

[0006] The marine carbon sink data acquisition and real-time analysis system includes a data acquisition and preprocessing module, a seagrass bed carbon sink environment analysis module, a data analysis module, and an early warning module, among which;

[0007] The data acquisition and preprocessing module collects ocean environmental parameters in real time through sensors deployed in the ocean, including seawater temperature, salinity, dissolved oxygen, nutrient concentration, and carbon dioxide concentration, and preprocesses the collected ocean environmental parameters, including cleaning and standardization;

[0008] The seagrass bed carbon sink environmental analysis module analyzes the relationship between the seagrass bed carbon sink capacity and the marine environmental parameters in real time based on the pre-processed marine environmental parameters, and dynamically tracks the impact of environmental changes on the seagrass bed carbon sink function;

[0009] The data analysis module combines the results of the correlation analysis between the carbon sink capacity of the seagrass bed and the marine environmental parameters to analyze the dynamic changes of the carbon sink capacity in real time and generate a real-time report on the carbon sink capacity, which includes the historical change trend, current status and predicted future changes of the carbon sink capacity of the seagrass bed;

[0010] The early warning module detects abnormal changes in carbon sink capacity based on the generated real-time report on carbon sink capacity. When the carbon sink capacity drops to a preset threshold, an early warning is automatically issued to prompt relevant departments to take management measures, including ecological restoration or environmental intervention.

[0011] Optionally, the data acquisition and preprocessing module includes:

[0012] Data collection: Deploy ocean temperature sensors to collect seawater temperature in real time, use conductivity sensors to measure seawater salinity, use dissolved oxygen sensors to monitor dissolved oxygen concentration in seawater in real time, use chemical sensors to measure nutrient concentrations in seawater, including nitrates, phosphates, and ammonia nitrogen, and use electrochemical sensors to monitor dissolved carbon dioxide concentrations in seawater;

[0013] Data cleaning: Use mean filtering to clean the collected marine environmental parameters and remove abnormal data and noise points;

[0014] Data standardization: The z-score standardization method is used to standardize the cleaned marine environmental parameters and unify the dimensions of different marine environmental parameters.

[0015] Optionally, the seagrass bed carbon sink environment analysis module includes:

[0016] Environmental parameter correlation analysis: Establish a correlation model between the pre-processed marine environmental parameters and the carbon sequestration capacity of seagrass beds to identify environmental factors that affect carbon sequestration capacity;

[0017] Dynamic assessment of carbon sequestration capacity: Based on the results of environmental parameter correlation analysis, the changing trend of the carbon sequestration capacity of seagrass beds is assessed in real time, and the carbon sequestration performance of seagrass beds under current environmental conditions is monitored;

[0018] Tracking the impact of environmental changes: By analyzing changes in marine environmental parameters over different time periods, the potential risks of environmental changes to the ecological health and carbon sequestration function of seagrass beds are assessed.

[0019] Optionally, the environmental parameter association analysis includes:

[0020] Data input and organization: The preprocessed marine environmental parameters were used as input data and the carbon sequestration capacity of seagrass beds was used as output variables to construct a multivariate data set;

[0021] Establishment of correlation model: Linear regression algorithm was used to establish the correlation model between marine environmental parameters and carbon sequestration capacity of seagrass beds;

[0022] Importance analysis and identification of influencing factors: In the case of using random forest model, the key environmental factors affecting carbon sequestration capacity were identified by calculating feature importance analysis.

[0023] Optionally, the dynamic assessment of carbon sequestration capacity includes:

[0024] Application of time series analysis model: Use ARIMA model to predict the changing trend of carbon sequestration capacity of seagrass beds based on historical marine environmental parameters and carbon sequestration capacity data;

[0025] Real-time assessment and monitoring: The current real-time collected marine environmental parameters are input into the correlation model, combined with the time series prediction results, to dynamically monitor the changes in the carbon sequestration capacity of the seagrass bed and evaluate the carbon sequestration performance under current environmental conditions.

[0026] Optionally, the environmental change impact tracking includes:

[0027] Time period division and environmental parameter change analysis: The marine environmental parameters are divided into , each time period contains different environmental data (such as temperature, salinity, dissolved oxygen, etc.), monitoring the changes in marine environmental parameters in different time periods;

[0028] Ecological Health Assessment: Evaluate the potential risks of environmental changes to the ecological health and carbon sequestration function of seagrass beds based on set environmental parameter thresholds.

[0029] Optionally, the data analysis module includes:

[0030] Analysis of historical trends and current status of carbon sink capacity: Combine the results of the correlation analysis between the carbon sink capacity of seagrass beds and environmental parameters to identify historical trends, and combine the input of current environmental parameters to calculate the carbon sink capacity at the current moment in real time;

[0031] Prediction of future changes in carbon sink capacity: Based on the analysis results of historical trends and current status, predict future carbon sink capacity and generate a forecast report.

[0032] Optionally, the analysis of historical trend and current status of carbon sequestration capacity includes:

[0033] Historical trend identification: Through the time series analysis model, the changing trend of the carbon sequestration capacity of seagrass beds is analyzed according to the historical marine environmental parameters;

[0034] Environmental parameter correlation analysis: Combine the correlation model between marine environmental parameters and carbon sink capacity, and input the current environmental parameters into the model for real-time calculation.

[0035] Optionally, the prediction of future changes in carbon sequestration capacity includes:

[0036] Future prediction: Based on the analysis results of historical trends and current status, an exponential smoothing model is used to predict the carbon sequestration capacity within a predetermined time in the future;

[0037] Generate forecast report: Based on the calculation results of the exponential smoothing model, generate a forecast report on changes in carbon sink capacity. The report includes the future upward or downward trend of carbon sink capacity and ecological risk assessment.

[0038] Optionally, the early warning module includes:

[0039] Carbon sink capacity monitoring: Real-time monitoring of changes in carbon sink capacity. By analyzing the data in the real-time report on carbon sink capacity, detect whether there are abnormal changes in carbon sink capacity, including decline in carbon sink capacity, fluctuation in carbon sink capacity, and mismatch between carbon sink capacity and environmental parameters;

[0040] Preset threshold judgment: the real-time value of carbon sink capacity With pre-set environmental parameter thresholds When compared, the carbon sink capacity Below threshold When the alarm is triggered, the warning signal is automatically triggered;

[0041] Issue early warning and management measures: When the carbon sequestration capacity is lower than the preset threshold, an early warning notification will be automatically sent to the relevant departments. The early warning notification will be conveyed via email, SMS or real-time notification of the management platform, and will be accompanied by management measures, including the implementation of ecological restoration or environmental intervention plans.

[0042] Beneficial effects of the present invention:

[0043] The present invention ensures the high quality and consistency of data and reduces the interference of noise and outliers by deploying multiple sensors and collecting marine environmental parameters in real time, combined with data cleaning and standardization processing. This not only optimizes the data basis for carbon sink capacity assessment, but also improves the system's real-time response capability to complex environmental changes, making data collection and processing more accurate and reliable.

[0044] The present invention combines the correlation analysis of the carbon sink capacity of seagrass beds with environmental parameters to evaluate the dynamic changes of carbon sink capacity in real time and track the long-term impact of environmental changes on the carbon sink function of seagrass beds. By using time series models and environmental parameter correlation models, the system can quickly identify key environmental factors that affect carbon sink capacity, help managers quickly respond to ecological risks brought about by environmental changes, and provide more scientific carbon sink management decision support.

[0045] The present invention, through the prediction and early warning module of future carbon sink capacity changes, can issue ecological risk warnings in a timely manner. In particular, when there are abnormal fluctuations in carbon sink capacity, the system will respond quickly and provide specific management suggestions. Based on prediction algorithms such as exponential smoothing models, the system can respond more quickly to changes in recent data, thereby improving the effectiveness of carbon sink management and the foresight of decision-making, thereby ensuring the carbon sink function of seagrass beds and the long-term stability of the ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the present invention or 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 in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 A schematic diagram of system function modules according to an embodiment of the present invention;

[0048] Figure 2 Schematic diagram of a seagrass bed carbon sink environment analysis module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0050] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0051] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0052] like Figure 1-Figure 2 As shown, the marine carbon sink data acquisition and real-time analysis system includes a data acquisition and preprocessing module, a seagrass bed carbon sink environment analysis module, a data analysis module and an early warning module, wherein;

[0053] The data acquisition and preprocessing module collects ocean environmental parameters in real time through sensors deployed in the ocean, including seawater temperature, salinity, dissolved oxygen, nutrient concentration, and carbon dioxide concentration, and preprocesses the collected ocean environmental parameters, including cleaning and standardization;

[0054] The seagrass bed carbon sink environmental analysis module analyzes the relationship between the seagrass bed carbon sink capacity and the marine environmental parameters in real time based on the pre-processed marine environmental parameters, and dynamically tracks the impact of environmental changes on the seagrass bed carbon sink function;

[0055] The data analysis module combines the results of the correlation analysis between the carbon sink capacity of seagrass beds and marine environmental parameters to analyze the dynamic changes of carbon sink capacity in real time and generate a real-time report on carbon sink capacity, including the historical trend of carbon sink capacity of seagrass beds, current status and predicted future changes;

[0056] The early warning module detects abnormal changes in carbon sequestration capacity based on the real-time report of carbon sequestration capacity generated. When the carbon sequestration capacity drops to the preset threshold, an early warning is automatically issued to prompt relevant departments to take management measures, including ecological restoration or environmental intervention;

[0057] Through the above content, it is possible to monitor the relationship between the carbon sequestration capacity of seagrass beds and environmental changes in real time and efficiently, provide accurate carbon sequestration dynamic reports and abnormal warnings, and provide decision makers with timely and reliable management basis through automated analysis and real-time feedback, effectively respond to the impact of environmental changes on the marine carbon sink function, and provide scientific support for ecological restoration and environmental intervention.

[0058] The data acquisition and preprocessing modules include:

[0059] Data collection: Deploy ocean temperature sensors to collect seawater temperature in real time, use conductivity sensors to measure seawater salinity, use dissolved oxygen sensors to monitor dissolved oxygen concentration in seawater in real time, use chemical sensors to measure nutrient concentrations in seawater, including nitrates, phosphates, and ammonia nitrogen, and use electrochemical sensors to monitor dissolved carbon dioxide concentrations in seawater;

[0060] Data cleaning: Use mean filtering to clean the collected marine environmental parameters and remove abnormal data and noise points, expressed as:

[0061] ;

[0062] in, is the cleaned data, N is the number of adjacent data points, is the value of the i-th data point after the t-th time;

[0063] Data standardization: The z-score standardization method is used to standardize the cleaned marine environmental parameters, and the different marine environmental parameters are unified in dimension, expressed as:

[0064] ;

[0065] Among them, x is the cleaned data, is the mean of the data, is the standard deviation of the data;

[0066] Through the above content, the reliability of the data is improved, the interference of noise and outliers is reduced, the basic data quality for subsequent analysis is optimized, and solid data support is provided for the evaluation and dynamic monitoring of carbon sink functions. At the same time, the design of automatically adjusting the collection frequency ensures the timeliness and coverage of the data.

[0067] Seagrass bed carbon sink environmental analysis module includes:

[0068] Environmental parameter correlation analysis: Establish a correlation model between the pre-processed marine environmental parameters and the carbon sequestration capacity of seagrass beds to identify environmental factors that affect carbon sequestration capacity;

[0069] Dynamic assessment of carbon sequestration capacity: Based on the results of environmental parameter correlation analysis, the changing trend of the carbon sequestration capacity of seagrass beds is assessed in real time, and the carbon sequestration performance of seagrass beds under current environmental conditions is monitored;

[0070] Tracking the impact of environmental changes: By analyzing the changes in marine environmental parameters over different time periods, the potential risks of environmental changes to the ecological health and carbon sequestration function of seagrass beds are assessed;

[0071] Through the above content, the dynamic changes in the carbon sequestration capacity of seagrass beds can be evaluated in real time, and the long-term impact of environmental changes on carbon sequestration function can be tracked. This can accurately identify the key environmental factors affecting carbon sequestration capacity, provide timely carbon sequestration capacity assessment, and help managers quickly respond to ecological risks brought about by environmental changes, thereby improving the scientific nature of seagrass bed carbon sequestration management and the effectiveness of decision-making.

[0072] Environmental parameter correlation analysis includes:

[0073] Data input and organization: The preprocessed marine environmental parameters were used as input data and the carbon sequestration capacity of seagrass beds was used as output variables to construct a multivariate data set;

[0074] Establishment of correlation model: A linear regression algorithm was used to establish the correlation model between marine environmental parameters and the carbon sequestration capacity of seagrass beds, which is expressed as:

[0075] ;

[0076] Where y represents the carbon sink capacity of seagrass beds, Represent different marine environmental parameters. is the bias term, is the regression coefficient of each environmental parameter, is the error term;

[0077] Importance analysis and identification of influencing factors: In the case of using the random forest model, the key environmental factors affecting carbon sequestration capacity are identified by calculating feature importance analysis, expressed as:

[0078] ;

[0079] in, is the importance of the jth feature (environmental parameter), represents the importance contribution in the tth tree, where T is the number of decision trees in the random forest;

[0080] Through the above content, key environmental factors that affect carbon sequestration capacity can be effectively identified, helping managers to accurately understand the contribution of different environmental variables to carbon sequestration capacity, providing data support for subsequent monitoring, management and carbon sequestration optimization, and improving the management effect and scientific decision-making of seagrass bed carbon sequestration capacity.

[0081] Dynamic assessment of carbon sequestration capacity includes:

[0082] Application of time series analysis model: The ARIMA model is used to predict the changing trend of seagrass bed carbon sink capacity based on historical marine environmental parameters and carbon sink capacity data, expressed as:

[0083] ;

[0084] in, represents the carbon sink capacity at time t, represents the carbon sink capacity at the previous i moments, represents the noise term at the jth moment, p and q are the orders of the autoregressive and moving average terms, respectively;

[0085] Real-time assessment and monitoring: The current real-time collected marine environmental parameters are input into the correlation model, combined with the time series prediction results, to dynamically monitor the changes in the carbon sink capacity of the seagrass bed and evaluate the carbon sink performance under the current environmental conditions, expressed as:

[0086] ;

[0087] in, is the predicted value of carbon sink capacity at the current time t, is the i-th environmental parameter value at the current moment, is the bias term, is the regression coefficient of each environmental parameter;

[0088] ;

[0089] in, is the revised predicted value of carbon sink capacity, is the bias term in the model, is the predicted value of carbon sink capacity at the previous moment, is the noise term at the past moment, p and q are the model orders;

[0090] Through the above content, it is possible to predict and monitor the changing trends of the carbon sequestration capacity of seagrass beds in real time, promptly reflect the impact of current environmental conditions on the carbon sequestration function, and make trend forecasts through historical data to provide accurate dynamic assessments. This not only improves the response speed of the system, but also helps managers identify potential risks in advance, make more scientific decisions, and improve the efficiency and accuracy of seagrass bed carbon sequestration management.

[0091] Tracking the impact of environmental changes includes:

[0092] Time period division and environmental parameter change analysis: The marine environmental parameters are divided into , each time period contains different environmental data (such as temperature, salinity, dissolved oxygen, etc.), monitoring the changes in marine environmental parameters in different time periods, expressed as:

[0093] ;

[0094] in, Indicates time period The value of the i-th environmental parameter in Indicates time period The value of the i-th environmental parameter in Indicates the changes in environmental parameters between different time periods;

[0095] Ecological health assessment: The potential risk of environmental changes to the ecological health and carbon sink function of seagrass beds is assessed based on set environmental parameter thresholds, expressed as:

[0096] ;

[0097] in, represents the i-th environmental parameter threshold, represents the potential risk of environmental change to the ecological health of seagrass beds, and n is the number of marine environmental parameters;

[0098] The i-th environmental parameter threshold and standard deviation Perform calculations to set safety thresholds for each environmental parameter , expressed as:

[0099] ;

[0100] in, is the historical average value of the i-th environmental parameter, is the standard deviation of the i-th environmental parameter, reflecting the fluctuation range of the parameter, k is the safety factor, which is set according to the actual situation and takes a value of 2 or 3;

[0101] Through the above content, we analyze the changes in marine environmental parameters in different periods, evaluate the potential risks of environmental fluctuations to the ecological health of seagrass beds, ensure timely identification of environmental anomalies, ensure the ecological stability of seagrass beds and the continued function of carbon sequestration, and effectively improve the scientific nature and timeliness of ecological protection.

[0102] Data analysis modules include:

[0103] Analysis of historical trends and current status of carbon sink capacity: Combine the results of the correlation analysis between the carbon sink capacity of seagrass beds and environmental parameters to identify historical trends, and combine the input of current environmental parameters to calculate the carbon sink capacity at the current moment in real time;

[0104] Prediction of future changes in carbon sink capacity: Based on the analysis results of historical trends and current status, predict future carbon sink capacity and generate a forecast report;

[0105] Through the above content, combined with the relationship between carbon sink capacity and environmental parameters, the current carbon sink performance can be calculated in real time, and future changes can be predicted based on these data. This can provide managers with a comprehensive historical data review, current status assessment and future predictions, help timely discover changing trends in carbon sink capacity, ensure the forward-looking and scientific nature of decision-making, and effectively improve the efficiency and accuracy of seagrass bed carbon sink management.

[0106] The historical trend and current status analysis of carbon sequestration capacity includes:

[0107] Historical trend identification: Through the time series analysis model, the changing trend of the carbon sequestration capacity of seagrass beds is analyzed according to the historical marine environmental parameters;

[0108] Environmental parameter correlation analysis: Combine the correlation model between marine environmental parameters and carbon sink capacity, and input the current environmental parameters into the model for real-time calculation;

[0109] Through the above content, combined with historical data and real-time input of current environmental parameters, it is possible to accurately identify the historical change trend of carbon sink capacity, and predict the current carbon sink capacity in real time based on the latest environmental conditions. It can provide comprehensive trend analysis and accurate real-time calculation, making carbon sink management forward-looking and timely, helping managers make quick and effective decisions in actual environmental changes, and ensuring the continuous optimization of carbon sink functions and ecological protection.

[0110] The forecast of future changes in carbon sequestration capacity includes:

[0111] Future prediction: Based on the analysis results of historical trends and current status, the exponential smoothing model is used to predict the carbon sequestration capacity within a predetermined time in the future, expressed as:

[0112] ;

[0113] in, For time The predicted value of carbon sink capacity at the time, is the smoothing coefficient, ranging from 0 to 1. The closer the value is to 1, the higher the weight of the most recent observation data. is the actual value of carbon sink capacity at the current time t, is the predicted value at the previous time point;

[0114] Generate forecast report: Generate forecast report on carbon sink capacity change based on the calculation results of exponential smoothing model, including the upward or downward trend of carbon sink capacity in the future and ecological risk assessment;

[0115] Through the above content, it is possible to respond more quickly to changes in recent data, thereby more accurately predicting future trends in carbon sink capacity and ensuring that the forecast results can reflect current trends in a timely manner. This flexibility and high responsiveness will help managers make more accurate decisions in the short term and respond quickly to potential risk changes, thereby improving the effectiveness and foresight of carbon sink management.

[0116] The early warning module includes:

[0117] Carbon sink capacity monitoring: Real-time monitoring of changes in carbon sink capacity. By analyzing the data in the real-time report on carbon sink capacity, detect whether there are abnormal changes in carbon sink capacity, including decline in carbon sink capacity, fluctuation in carbon sink capacity, and mismatch between carbon sink capacity and environmental parameters;

[0118] Preset threshold judgment: the real-time value of carbon sink capacity With pre-set environmental parameter thresholds When compared, the carbon sink capacity Below threshold When the alarm is triggered, the warning signal is automatically triggered;

[0119] Issue early warning and management measures: When the carbon sequestration capacity is lower than the preset threshold, an early warning notice will be automatically sent to the relevant departments. The early warning notice will be conveyed by email, SMS or real-time notification of the management platform, and will be accompanied by management measures, including the implementation of ecological restoration or environmental intervention plans;

[0120] Through the above content, it is possible to quickly identify situations that may threaten the health of seagrass beds, such as a sudden decline in carbon sequestration capacity, long-term fluctuations, or mismatches in environmental parameters. By providing early warning of potential ecological risks, it is ensured that managers can take timely ecological restoration or environmental intervention measures before the problem expands, thereby improving the timeliness and accuracy of decision-making and ensuring the sustainability of the carbon sequestration function of the seagrass beds and the stability of the ecosystem.

[0121] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0122] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Marine carbon sink data collection and real-time analysis system, characterized by: It includes data collection and preprocessing module, seagrass bed carbon sink environment analysis module, data analysis module and early warning module, among which; The data acquisition and preprocessing module collects ocean environmental parameters in real time through sensors deployed in the ocean, including seawater temperature, salinity, dissolved oxygen, nutrient concentration, and carbon dioxide concentration, and preprocesses the collected ocean environmental parameters, including cleaning and standardization; The seagrass bed carbon sink environmental analysis module analyzes the relationship between the seagrass bed carbon sink capacity and the marine environmental parameters in real time based on the pre-processed marine environmental parameters, and dynamically tracks the impact of environmental changes on the seagrass bed carbon sink function; The seagrass bed carbon sink environment analysis module includes: Environmental parameter correlation analysis: Establish a correlation model between the pre-processed marine environmental parameters and the carbon sequestration capacity of seagrass beds to identify environmental factors that affect carbon sequestration capacity; Dynamic assessment of carbon sequestration capacity: Based on the results of environmental parameter correlation analysis, the changing trend of the carbon sequestration capacity of seagrass beds is assessed in real time, and the carbon sequestration performance of seagrass beds under current environmental conditions is monitored; Tracking the impact of environmental changes: By analyzing the changes in marine environmental parameters over different time periods, the potential risks of environmental changes to the ecological health and carbon sequestration function of seagrass beds can be assessed; The environmental parameter association analysis includes: Data input and organization: The preprocessed marine environmental parameters were used as input data and the carbon sequestration capacity of seagrass beds was used as output variables to construct a multivariate data set; Establishment of correlation model: A linear regression algorithm was used to establish a correlation model between marine environmental parameters and the carbon sequestration capacity of seagrass beds; Importance analysis and identification of influencing factors: Using the random forest model, key environmental factors affecting carbon sequestration capacity were identified by calculating feature importance analysis; The dynamic assessment of carbon sequestration capacity includes: Application of time series analysis model: Use ARIMA model to predict the changing trend of carbon sequestration capacity of seagrass beds based on historical marine environmental parameters and carbon sequestration capacity data; Real-time assessment and monitoring: Input the current real-time collected marine environmental parameters into the correlation model, combine with the time series prediction results, dynamically monitor the changes in the carbon sequestration capacity of the seagrass bed, and evaluate the carbon sequestration performance under the current environmental conditions; The environmental change impact tracking includes: Time period division and environmental parameter change analysis: The marine environmental parameters are divided into T1, T2, …, T n , each time period contains different environmental data, monitoring the changes of marine environmental parameters in different time periods; Ecological health assessment: Evaluate the potential risks of environmental changes to the ecological health and carbon sequestration function of seagrass beds based on set environmental parameter thresholds; The data analysis module combines the results of the correlation analysis between the carbon sink capacity of the seagrass bed and the marine environmental parameters to analyze the dynamic changes of the carbon sink capacity in real time and generate a real-time report on the carbon sink capacity, which includes the historical change trend, current status and predicted future changes of the carbon sink capacity of the seagrass bed; The early warning module detects abnormal changes in carbon sink capacity based on the generated real-time report on carbon sink capacity. When the carbon sink capacity drops to a preset threshold, an early warning is automatically issued to prompt relevant departments to take management measures, including ecological restoration or environmental intervention.

2. The marine carbon sink data acquisition and real-time analysis system according to claim 1, characterized in that: The data acquisition and preprocessing module includes: Data collection: Deploy ocean temperature sensors to collect seawater temperature in real time, use conductivity sensors to measure seawater salinity, use dissolved oxygen sensors to monitor dissolved oxygen concentration in seawater in real time, use chemical sensors to measure nutrient concentrations in seawater, including nitrates, phosphates, and ammonia nitrogen, and use electrochemical sensors to monitor dissolved carbon dioxide concentrations in seawater; Data cleaning: Use mean filtering to clean the collected marine environmental parameters and remove abnormal data and noise points; Data standardization: The z-score standardization method is used to standardize the cleaned marine environmental parameters and unify the dimensions of different marine environmental parameters.

3. The marine carbon sink data acquisition and real-time analysis system according to claim 1 is characterized in that: The data analysis module includes: Analysis of historical trends and current status of carbon sink capacity: Combine the results of the correlation analysis between the carbon sink capacity of seagrass beds and environmental parameters to identify historical trends, and combine the input of current environmental parameters to calculate the carbon sink capacity at the current moment in real time; Prediction of future changes in carbon sink capacity: Based on the analysis results of historical trends and current status, predict future carbon sink capacity and generate a forecast report.

4. The marine carbon sink data acquisition and real-time analysis system according to claim 3 is characterized in that: The analysis of historical trends and current status of carbon sequestration capacity includes: Historical trend identification: Through the time series analysis model, the changing trend of the carbon sequestration capacity of seagrass beds is analyzed according to the historical marine environmental parameters; Environmental parameter correlation analysis: Combine the correlation model between marine environmental parameters and carbon sink capacity, and input the current environmental parameters into the model for real-time calculation.

5. The marine carbon sink data acquisition and real-time analysis system according to claim 4, characterized in that: The forecast of future changes in carbon sink capacity includes: Future prediction: Based on the analysis results of historical trends and current status, an exponential smoothing model is used to predict the carbon sequestration capacity within a predetermined time in the future; Generate forecast report: Based on the calculation results of the exponential smoothing model, generate a forecast report on changes in carbon sink capacity. The report includes the future upward or downward trend of carbon sink capacity and ecological risk assessment.

6. The marine carbon sink data acquisition and real-time analysis system according to claim 5, characterized in that: The early warning module comprises: Carbon sink capacity monitoring: Real-time monitoring of changes in carbon sink capacity. By analyzing the data in the real-time report on carbon sink capacity, detect whether there are abnormal changes in carbon sink capacity, including decline in carbon sink capacity, fluctuation in carbon sink capacity, and mismatch between carbon sink capacity and environmental parameters; Preset threshold judgment: the real-time value of carbon sink capacity y t The pre-set environmental parameter threshold V th When the carbon sink capacity y t Below the threshold V th When the alarm is triggered, the warning signal is automatically triggered; Issue early warning and management measures: When the carbon sequestration capacity is lower than the preset threshold, an early warning notification will be automatically sent to the relevant departments. The early warning notification will be conveyed via email, SMS or real-time notification of the management platform, and will be accompanied by management measures, including the implementation of ecological restoration or environmental intervention plans.

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