A marine ecological environment evaluation and early warning system based on big data and AI

Abstract

The present application relates to the technical field of marine ecological environment monitoring, and particularly relates to a marine ecological environment assessment and early warning system based on big data and AI, comprising a multi-source data fusion module, a dynamic feature extraction module, a meteorological disturbance coefficient calculation module, an ecological index generation module and a coupling early warning generation module; wherein: the multi-source data fusion module is used for real-time collection of multi-source data; the dynamic feature extraction module is used for extraction of 72-hour change gradients of three types of core parameters; the meteorological disturbance coefficient calculation module is used for calculation and output of meteorological disturbance coefficients; the ecological index generation module is used for generation of water quality deterioration indexes and biological stress indexes; and the coupling early warning generation module is used for output of ecological risk grade signals. Through real-time reduction of meteorological interference and weighted fusion of water quality deterioration and biological stress indexes, the present application can accurately assess marine ecological environment risks and provide scientific support for ecological protection and pollution early warning.

Description

Technical Field

[0001] This invention relates to the field of marine ecological environment monitoring technology, and in particular to a marine ecological environment assessment and early warning system based on big data and AI. Background Technology

[0002] Marine water pollution and biological stress have become urgent environmental problems that need to be addressed globally. Key ecological parameters such as dissolved oxygen concentration, chlorophyll concentration, and plankton biomass are core indicators for measuring the health of marine water bodies. At the same time, meteorological factors such as wind speed, precipitation, and temperature also have a significant impact on the marine ecological environment.

[0003] While existing marine ecological assessment methods can analyze some basic ecological indicators, they lack effective real-time early warning and risk assessment capabilities when considering the complex impacts of meteorological factors on water quality and biological communities. Therefore, there is an urgent need for a marine ecological environment assessment and early warning system based on big data and AI to solve the above problems and achieve accurate real-time assessment of the marine ecological environment. Summary of the Invention

[0004] To achieve the above objectives, this invention provides a marine ecological environment assessment and early warning system based on big data and AI.

[0005] A marine ecological environment assessment and early warning system based on big data and AI includes a multi-source data fusion module, a dynamic feature extraction module, a meteorological disturbance coefficient calculation module, an ecological indicator generation module, and a coupled early warning generation module; wherein:

[0006] Multi-source data fusion module: used to collect satellite remote sensing data, marine sensor network data and meteorological station data in real time, and generate standardized ecological data streams through spatiotemporal alignment processing;

[0007] Dynamic feature extraction module: used to receive standardized ecological data streams and use a sliding window mechanism to extract the 72-hour variation gradients of three core parameters: dissolved oxygen concentration, chlorophyll concentration, and plankton biomass;

[0008] Meteorological disturbance coefficient calculation module: Based on the meteorological disturbance sensitivity matrix established by the historical disaster database, it calculates and outputs the meteorological disturbance coefficient;

[0009] Ecological index generation module: It is used to receive the 72-hour change gradient of three core parameters, generate a water quality deterioration index based on the negative correlation between dissolved oxygen concentration and chlorophyll concentration, and generate a biological stress index based on the positive correlation between chlorophyll concentration and plankton biomass.

[0010] Coupled early warning generation module: When the meteorological disturbance coefficient exceeds the preset threshold, it proportionally reduces the meteorological disturbance component in the water quality deterioration index and the biological stress index, and then performs weighted fusion on the reduced index to output the ecological risk level signal.

[0011] Optionally, the multi-source data fusion module includes a satellite remote sensing data acquisition unit, a marine sensor network data acquisition unit, a meteorological station data acquisition unit, and a spatiotemporal alignment unit; wherein:

[0012] Satellite remote sensing data acquisition unit: used to receive satellite remote sensing data from the satellite remote sensing system in real time through satellite sensors, and to obtain ocean surface temperature, salinity and sea level height;

[0013] Marine sensor network data acquisition unit: used to collect marine sensor data in real time, including parameters such as water temperature, dissolved oxygen, plankton biomass, and chlorophyll concentration, through a sensor network deployed in the ocean;

[0014] Meteorological station data acquisition unit: used to acquire real-time meteorological station data from meteorological stations, including wind speed, temperature and humidity;

[0015] Spatiotemporal alignment unit: used to perform spatiotemporal alignment processing on satellite remote sensing data, marine sensor network data and meteorological station data. By uniformly processing the timestamps and geographic location information of different data sources, the data is made consistent in time and space, thereby generating a standardized ecological data stream.

[0016] Optionally, the dynamic feature extraction module includes a data receiving unit, a sliding window mechanism unit, a gradient calculation unit, and a result output unit; wherein:

[0017] Data receiving unit: used to receive standardized ecological data streams output from the multi-source data fusion module, including time-series data of dissolved oxygen concentration, chlorophyll concentration and plankton biomass;

[0018] Sliding window mechanism unit: used to apply the sliding window mechanism to the received standardized ecological data stream, with the window size set to 72 hours, and the sliding window moving gradually over time.

[0019] The gradient calculation unit is used to calculate the rate of change of three core parameters—dissolved oxygen concentration, chlorophyll concentration, and plankton biomass—within each sliding window, and obtain the gradient of change of each parameter over 72 hours.

[0020] Results output unit: Used to output the 72-hour gradient of the three core parameters calculated for each sliding window.

[0021] Optionally, the gradient calculation unit includes:

[0022] Dissolved oxygen concentration change rate calculation subunit: It is used to receive dissolved oxygen concentration data from the sliding window mechanism unit, and obtain the gradient of dissolved oxygen concentration change by solving the ratio of the hourly dissolved oxygen concentration change value to the time interval.

[0023] Chlorophyll concentration change rate calculation subunit: It is used to receive chlorophyll concentration data from the sliding window mechanism unit, and obtain the chlorophyll concentration change gradient by differentiating the chlorophyll concentration change at each time point and calculating the change rate according to the time interval.

[0024] The phytoplankton biomass change rate calculation subunit is used to receive phytoplankton biomass data from the sliding window mechanism unit and obtain the phytoplankton biomass change gradient by calculating the ratio of the difference in phytoplankton biomass between each time point to the time interval.

[0025] Gradient Integration Subunit: Used to integrate the rate of change calculated from the three subunits to generate the gradient of change of each parameter over 72 hours.

[0026] Optionally, the meteorological disturbance coefficient calculation module includes a historical disaster data receiving unit, a meteorological disturbance sensitivity matrix construction unit, and a meteorological disturbance coefficient calculation unit; wherein:

[0027] Historical disaster data receiving unit: used to receive meteorological disturbance data from the historical disaster database, including historical meteorological disaster events related to marine ecology;

[0028] Meteorological disturbance sensitivity matrix construction unit: Based on meteorological disturbance data, a meteorological disturbance sensitivity matrix is ​​established;

[0029] Meteorological Disturbance Coefficient Calculation Unit: Used to calculate and output the meteorological disturbance coefficient based on the meteorological disturbance sensitivity matrix and real-time collected meteorological station data. .

[0030] Optionally, the ecological index generation module includes a water quality deterioration index generation unit and a biological stress index generation unit; wherein:

[0031] Water quality deterioration index generation unit: It is used to receive gradient data of changes in dissolved oxygen concentration and chlorophyll concentration, calculate the negative correlation between dissolved oxygen concentration and chlorophyll concentration, determine their correlation strength, and thus generate a water quality deterioration index.

[0032] Biological stress index generation unit: It is used to receive gradient data of changes in chlorophyll concentration and plankton biomass, analyze the correlation between chlorophyll concentration and plankton biomass, determine the strength of their positive correlation, and thus generate a biological stress index.

[0033] Optionally, the water quality deterioration index generation unit includes:

[0034] Data receiving subunit: used to receive gradient data of changes in dissolved oxygen concentration and chlorophyll concentration;

[0035] Correlation calculation subunit: used to calculate the negative correlation coefficient between dissolved oxygen concentration and chlorophyll concentration;

[0036] Correlation strength calculation subunit: Based on the calculated negative correlation coefficient, the correlation strength between dissolved oxygen concentration and chlorophyll concentration is obtained and standardized to a value between 0 and 1;

[0037] Water quality deterioration index generation sub-unit: The final water quality deterioration index is calculated by weighting the values ​​based on the correlation strength, using the following formula: Among them, WQI is the water quality deterioration index; For coefficients; This indicates the strength of the negative correlation between dissolved oxygen concentration and chlorophyll concentration.

[0038] Optionally, the biological stress index generation unit includes:

[0039] Data receiving subunit: used to receive gradient data on changes in chlorophyll concentration and plankton biomass;

[0040] Correlation calculation subunit: used to analyze the correlation between chlorophyll concentration and plankton biomass, and quantifies the strength of the relationship between the two by calculating the Pearson correlation coefficient;

[0041] Correlation strength calculation subunit: Based on the calculation results of Pearson correlation coefficient, the positive correlation strength between chlorophyll concentration and plankton biomass is obtained and standardized to a value between 0 and 1;

[0042] Biological stress index generation sub-unit: The biological stress index is calculated by weighting the values ​​of the correlation strengths to obtain the final biological stress index, as shown in the formula: Among them, BSI is the biological stress index. For coefficients, This indicates the strength of the positive correlation between chlorophyll concentration and plankton biomass.

[0043] Optionally, the coupled early warning generation module includes a meteorological interference component reduction unit, a weighted fusion unit, and an ecological risk level signal generation unit; wherein:

[0044] Meteorological disturbance component reduction unit: When the meteorological disturbance coefficient exceeds a preset threshold, it calculates the meteorological disturbance component that needs to be reduced based on the comparison between the meteorological disturbance coefficient and the preset threshold, and then adjusts the water quality deterioration index and biological stress index; the calculation formula is:

[0045] ;

[0046] ;in, It is an adjustment factor; This represents the maximum value of the meteorological disturbance coefficient; This is the meteorological disturbance coefficient; This is a preset threshold value;

[0047] Weighted fusion unit: used to weight and fuse the reduced water quality deterioration index and biological stress index to calculate the ecological risk value ERS;

[0048] Ecological risk level signal generation unit: used to receive ecological risk value ERS and map it to the corresponding ecological risk level signal according to preset rules.

[0049] Optionally, mapping it to the corresponding ecological risk level signal according to preset rules includes:

[0050] When the ecological risk value ERS is less than or equal to 0.3, a low-risk signal is generated;

[0051] When the ecological risk value ERS is greater than 0.3 and less than or equal to 0.5, a medium-to-low risk signal is generated.

[0052] When the ecological risk value ERS is greater than 0.5 and less than or equal to 0.7, a medium-risk signal is generated;

[0053] When the ecological risk value ERS is greater than 0.7 and less than or equal to 0.85, a medium-to-high risk signal is generated.

[0054] When the Ecological Risk Value (ERS) is greater than 0.85, a high-risk signal is generated.

[0055] The beneficial effects of this invention are:

[0056] This invention, by reducing meteorological interference components and combining a weighted fusion of water quality deterioration index and biological stress index, can effectively identify and assess the impact of meteorological factors on the marine ecological environment. Through real-time adjustment of indicator weights, the system can accurately reflect the changing trends of the marine ecological environment under different meteorological conditions, providing a dynamic and comprehensive risk assessment, and solving the problem that existing technologies cannot process meteorological impacts and provide early warnings in real time. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of a marine ecological environment assessment and early warning system according to an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of the coupling early warning generation module in an embodiment of the present invention. Detailed Implementation

[0060] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0061] like Figures 1-2 As shown, a marine ecological environment assessment and early warning system based on big data and AI includes a multi-source data fusion module, a dynamic feature extraction module, a meteorological disturbance coefficient calculation module, an ecological indicator generation module, and a coupled early warning generation module; wherein:

[0062] Multi-source data fusion module: used to collect satellite remote sensing data, marine sensor network data and meteorological station data in real time, and generate standardized ecological data streams through spatiotemporal alignment processing;

[0063] Dynamic feature extraction module: used to receive standardized ecological data streams and use a sliding window mechanism to extract the 72-hour variation gradients of three core parameters: dissolved oxygen concentration, chlorophyll concentration, and plankton biomass;

[0064] Meteorological disturbance coefficient calculation module: Based on the meteorological disturbance sensitivity matrix established by the historical disaster database, it calculates and outputs the meteorological disturbance coefficient;

[0065] Ecological index generation module: It is used to receive the 72-hour change gradient of three core parameters, generate a water quality deterioration index based on the negative correlation between dissolved oxygen concentration and chlorophyll concentration, and generate a biological stress index based on the positive correlation between chlorophyll concentration and plankton biomass.

[0066] Coupled early warning generation module: When the meteorological disturbance coefficient exceeds the preset threshold, it proportionally reduces the meteorological disturbance component in the water quality deterioration index and the biological stress index, and then performs weighted fusion on the reduced index to output the ecological risk level signal.

[0067] The multi-source data fusion module includes a satellite remote sensing data acquisition unit, a marine sensor network data acquisition unit, a meteorological station data acquisition unit, and a spatiotemporal alignment unit; among which:

[0068] Satellite remote sensing data acquisition unit: used to receive satellite remote sensing data from the satellite remote sensing system in real time through satellite sensors, and to obtain ocean surface temperature, salinity and sea level height;

[0069] Marine sensor network data acquisition unit: used to collect marine sensor data in real time, including parameters such as water temperature, dissolved oxygen, plankton biomass, and chlorophyll concentration, through a sensor network deployed in the ocean;

[0070] Meteorological station data acquisition unit: used to acquire real-time meteorological station data from meteorological stations, including wind speed, temperature and humidity, to ensure comprehensive environmental data support;

[0071] Spatiotemporal alignment unit: Used to perform spatiotemporal alignment processing on satellite remote sensing data, marine sensor network data, and meteorological station data. By uniformly processing the timestamps and geographic location information of different data sources, the data achieves consistency in time and space, thereby generating a standardized ecological data stream and ensuring that data from different sources can be effectively compared and analyzed. Through the combination of multi-source data acquisition units, the system can collect data from satellite remote sensing, marine sensor networks, and meteorological stations in real time, providing comprehensive marine ecological monitoring data. Spatiotemporal alignment ensures the consistency of data in time and space, thereby realizing the generation of a standardized ecological data stream and providing a reliable data foundation for subsequent feature extraction and evaluation.

[0072] The dynamic feature extraction module includes a data receiving unit, a sliding window mechanism unit, a gradient calculation unit, and a result output unit; wherein:

[0073] Data receiving unit: used to receive standardized ecological data streams output from the multi-source data fusion module, including time-series data of dissolved oxygen concentration, chlorophyll concentration and plankton biomass;

[0074] Sliding window mechanism unit: used to apply the sliding window mechanism to the received standardized ecological data stream, with the window size set to 72 hours. The sliding window moves gradually over time, and the gradient of the changes of three core parameters is calculated through the data in each window.

[0075] The gradient calculation unit is used to calculate the rate of change of three core parameters—dissolved oxygen concentration, chlorophyll concentration, and plankton biomass—within each sliding window, and obtain the gradient of change of each parameter over 72 hours.

[0076] The results output unit outputs the 72-hour gradient of the three core parameters calculated in each sliding window for use by the subsequent evaluation module. Through the sliding window mechanism, the dynamic feature extraction module can efficiently extract the gradient of key ecological parameters over 72 hours from the standardized ecological data stream. This method can capture the dynamic trend of marine ecological environment changes in real time, providing high-precision input data for subsequent ecological risk assessment and early warning, and helping to identify environmental changes in a timely manner and make scientific predictions.

[0077] The gradient calculation unit includes:

[0078] Dissolved oxygen concentration change rate calculation subunit: It is used to receive dissolved oxygen concentration data from the sliding window mechanism unit, and obtain the gradient of dissolved oxygen concentration change by solving the ratio of the hourly dissolved oxygen concentration change value to the time interval.

[0079] Chlorophyll concentration change rate calculation subunit: It is used to receive chlorophyll concentration data from the sliding window mechanism unit, and obtain the chlorophyll concentration change gradient by differentiating the chlorophyll concentration change at each time point and calculating the change rate according to the time interval.

[0080] The phytoplankton biomass change rate calculation subunit is used to receive phytoplankton biomass data from the sliding window mechanism unit and obtain the phytoplankton biomass change gradient by calculating the ratio of the difference in phytoplankton biomass between each time point to the time interval.

[0081] The gradient integration subunit is used to integrate the rate of change calculated by the three subunits and generate the gradient of change of each parameter over 72 hours for use by subsequent modules. By calculating and integrating the rate of change of the three core parameters one by one, the gradient calculation unit can accurately extract the changing trends of dissolved oxygen concentration, chlorophyll concentration, and plankton biomass. This function ensures that the dynamic changes of each parameter can be scientifically and accurately quantified, providing detailed data support for subsequent ecological assessments.

[0082] The meteorological disturbance coefficient calculation module includes a historical disaster data receiving unit, a meteorological disturbance sensitivity matrix construction unit, and a meteorological disturbance coefficient calculation unit; among which:

[0083] Historical disaster data receiving unit: used to receive meteorological disturbance data from the historical disaster database, including historical meteorological disaster events related to marine ecology (such as storms, typhoons, etc.).

[0084] Meteorological disturbance sensitivity matrix construction unit: Based on meteorological disturbance data, a meteorological disturbance sensitivity matrix is ​​established;

[0085] The steps for constructing a meteorological disturbance sensitivity matrix are as follows:

[0086] Data collection: Collect historical disaster data, including typhoons, rainstorms, droughts, heat waves, etc.

[0087] Collect relevant ecological and environmental data, including data on target ecological parameters such as dissolved oxygen concentration, chlorophyll concentration, and plankton biomass;

[0088] Select meteorological factors: Identify the main meteorological factors affecting the ecological environment, including wind speed, air pressure, precipitation, temperature, etc.

[0089] Determine the impact relationships: First, based on historical disaster data and ecological environment data, analyze the impact of each meteorological factor on the target ecological environment parameters; then, use statistical analysis methods to quantify the strength of the relationship between meteorological factors and target parameters; finally, generate a value of the degree of influence to represent the strength of the influence of meteorological factors on ecological environment parameters, which can be represented by a value between 0 and 1.

[0090] Construct a sensitivity matrix: Organize the relationship between all meteorological factors and target ecological and environmental parameters into a matrix. Each element of the matrix represents the sensitivity of a certain meteorological factor to a certain target ecological parameter. The rows of the matrix represent different meteorological factors, and the columns represent different ecological parameters.

[0091] Standardization: Standardize each level of influence to ensure that the elements in the matrix have a relatively uniform scale;

[0092] Final Matrix: The final meteorological disturbance sensitivity matrix is ​​obtained, which can reflect the intensity of the impact of different meteorological factors on the ecological environment.

[0093] Given n meteorological factors and m ecological environment parameters, the meteorological disturbance sensitivity matrix S is an n×m matrix, where each element of the matrix... This represents the intensity of the influence of the i-th meteorological factor on the j-th ecological and environmental parameter; its expression is as follows:

[0094] ;

[0095] Each of them The value is obtained from the analysis of actual data; the sensitivity value can be a positive number (indicating a positive correlation) or a negative number (indicating a negative correlation). The higher the sensitivity, the greater the impact of changes in meteorological factors on ecological and environmental parameters.

[0096] Meteorological Disturbance Coefficient Calculation Unit: Used to calculate and output the meteorological disturbance coefficient based on the meteorological disturbance sensitivity matrix and real-time collected meteorological station data. The calculation formula is as follows: ,in, The elements in the meteorological disturbance sensitivity matrix represent the sensitivity of meteorological parameter i to ecosystem parameter j; This represents the real-time change value of meteorological parameter i (such as current wind speed, precipitation, etc.). denoted as the meteorological disturbance coefficient; n represents the total number of meteorological parameters.

[0097] The ecological indicator generation module includes a water quality deterioration index generation unit and a biological stress index generation unit; among which:

[0098] Water quality deterioration index generation unit: It is used to receive gradient data of changes in dissolved oxygen concentration and chlorophyll concentration, calculate the negative correlation between dissolved oxygen concentration and chlorophyll concentration, determine their correlation strength, and thus generate a water quality deterioration index.

[0099] The biological stress index generation unit receives gradient data on changes in chlorophyll concentration and phytoplankton biomass. By analyzing the correlation between chlorophyll concentration and phytoplankton biomass, it determines the strength of their positive correlation and generates a biological stress index. Through calculating the water quality deterioration index and the biological stress index, the ecological index generation module can assess the trends of water quality deterioration and ecological stress in real time, providing effective data support, helping to identify changes in the marine ecological environment in a timely manner, and providing a scientific basis for subsequent risk warning and management decisions.

[0100] The water quality deterioration index generation unit includes:

[0101] Data receiving subunit: used to receive gradient data of changes in dissolved oxygen concentration and chlorophyll concentration;

[0102] Correlation calculation subunit: Used to calculate the negative correlation coefficient between dissolved oxygen concentration and chlorophyll concentration. The relationship between them is quantified by calculating the Pearson correlation coefficient, as shown in the formula:

[0103] ,in, and , representing the values ​​of dissolved oxygen concentration and chlorophyll concentration at time point i, respectively. and , respectively, are the mean values ​​of dissolved oxygen concentration and chlorophyll concentration, and r is the negative correlation coefficient between the two; the negative correlation is indicated by the sign of the result value, with a negative value indicating a negative correlation between dissolved oxygen concentration and chlorophyll concentration;

[0104] Correlation strength calculation subunit: Based on the calculated negative correlation coefficient, the correlation strength between dissolved oxygen concentration and chlorophyll concentration is obtained and standardized to a value between 0 and 1. The higher the strength, the more obvious the negative correlation.

[0105] Water quality deterioration index generation sub-unit: The final water quality deterioration index is calculated by weighting the values ​​based on the correlation strength, using the following formula: Among them, WQI is the water quality deterioration index; The coefficient is used to adjust the scale of the exponent value; This indicates the strength of the negative correlation between dissolved oxygen concentration and chlorophyll concentration.

[0106] The biological stress index generation unit includes:

[0107] Data receiving subunit: used to receive gradient data on changes in chlorophyll concentration and plankton biomass;

[0108] Correlation calculation subunit: used to analyze the correlation between chlorophyll concentration and plankton biomass, and quantifies the strength of the relationship between the two by calculating the Pearson correlation coefficient;

[0109] Correlation strength calculation subunit: Based on the calculation results of Pearson correlation coefficient, the positive correlation strength between chlorophyll concentration and plankton biomass is obtained and standardized to a value between 0 and 1. The higher the strength, the more obvious the positive correlation between the two.

[0110] Biological stress index generation sub-unit: The biological stress index is calculated by weighting the values ​​of the correlation strengths to obtain the final biological stress index, as shown in the formula: Among them, BSI is the biological stress index. The coefficient is used to adjust the scale of the exponent value. This indicates the strength of the positive correlation between chlorophyll concentration and plankton biomass. By calculating the positive correlation between chlorophyll concentration and plankton biomass, and generating a biological stress index based on this, the biological stress index generation unit can accurately assess the relationship between algal growth and plankton in the ecosystem, providing a scientific basis for the monitoring and early warning of biological stress.

[0111] The coupled early warning generation module includes a meteorological interference component reduction unit, a weighted fusion unit, and an ecological risk level signal generation unit; among which:

[0112] Meteorological disturbance component reduction unit: When the meteorological disturbance coefficient exceeds a preset threshold, it calculates the meteorological disturbance component that needs to be reduced based on the comparison between the meteorological disturbance coefficient and the preset threshold, and then adjusts the water quality deterioration index and biological stress index; the calculation formula is:

[0113] ;

[0114] ;in, It is an adjustment factor; This represents the maximum value of the meteorological disturbance coefficient; This is the meteorological disturbance coefficient; The preset threshold is used; this process reduces the meteorological influence component in the water quality deterioration index and biological stress index through a proportional reduction method;

[0115] Weighted fusion unit: Used to weight and fuse the reduced water quality deterioration index and biological stress index to calculate the ecological risk value ERS; the weighted fusion is based on the set weight coefficients. and The ecological risk value is calculated by weighting the two adjusted indices, as shown in the following formula:

[0116] ,in, and The weighting coefficients for the water quality deterioration index and the biological stress index are respectively, satisfying... This is to ensure that the weighted fusion result reasonably reflects the relative importance of the two indicators;

[0117] Weights are dynamically assigned based on the current dissolved oxygen concentration:

[0118] When dissolved oxygen concentration is ≥5 mg / L, the weight of water quality deterioration is... =0.6, biological stress weight =0.4;

[0119] When dissolved oxygen concentration is <5 mg / L, water quality deterioration weighting =0.3, biological stress weight =0.7;

[0120] Ecological risk level signal generation unit: This unit receives the ecological risk value ERS and maps it to the corresponding ecological risk level signal according to preset rules. By reducing meteorological interference components and weighting and fusing the water quality deterioration index and biological stress index, this unit, coupled with the early warning generation module, can accurately assess the risk level of the marine ecological environment. The weights are dynamically adjusted according to real-time data, and the generated ecological risk level signal provides scientific support for marine ecological protection and pollution early warning, which helps to achieve early warning and effective response.

[0121] According to preset rules, it is mapped to the corresponding ecological risk level signals, including:

[0122] When the ecological risk value ERS is less than or equal to 0.3, it indicates that the ecological environment is in a low-risk state, and a low-risk signal is generated.

[0123] When the ecological risk value ERS is greater than 0.3 and less than or equal to 0.5, it indicates that the ecological environment is in a medium-to-low risk state, and a medium-to-low risk signal is generated.

[0124] When the ecological risk value ERS is greater than 0.5 and less than or equal to 0.7, it indicates that the ecological environment is in a medium-risk state, and a medium-risk signal is generated.

[0125] When the ecological risk value ERS is greater than 0.7 and less than or equal to 0.85, it indicates that the ecological environment is in a medium-to-high risk state, generating a medium-to-high risk signal;

[0126] When the ecological risk value ERS is greater than 0.85, it indicates that the ecological environment is in a high-risk state, generating a high-risk signal. By mapping the ecological risk value to specific risk level signals, the ecological risk level signal generation unit can provide clear risk indications, helping decision-makers to understand the risk level of the marine ecological environment in real time. These risk level signals provide a strong basis for ecological environment management and early warning issuance.

[0127] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0128] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A marine ecological environment assessment and early warning system based on big data and AI, characterized in that, It includes a multi-source data fusion module, a dynamic feature extraction module, a meteorological disturbance coefficient calculation module, an ecological indicator generation module, and a coupled early warning generation module; among which: Multi-source data fusion module: used to collect satellite remote sensing data, marine sensor network data and meteorological station data in real time, and generate standardized ecological data streams through spatiotemporal alignment processing; Dynamic feature extraction module: used to receive standardized ecological data streams and use a sliding window mechanism to extract the 72-hour variation gradients of three core parameters: dissolved oxygen concentration, chlorophyll concentration, and plankton biomass; Meteorological disturbance coefficient calculation module: Based on the meteorological disturbance sensitivity matrix established by the historical disaster database, it calculates and outputs the meteorological disturbance coefficient; Ecological index generation module: It is used to receive the 72-hour change gradient of three core parameters, generate a water quality deterioration index based on the negative correlation between dissolved oxygen concentration and chlorophyll concentration, and generate a biological stress index based on the positive correlation between chlorophyll concentration and plankton biomass. The ecological index generation module includes a water quality deterioration index generation unit and a biological stress index generation unit; wherein: Water quality deterioration index generation unit: It is used to receive gradient data of changes in dissolved oxygen concentration and chlorophyll concentration, calculate the negative correlation between dissolved oxygen concentration and chlorophyll concentration, determine their correlation strength, and thus generate a water quality deterioration index. Biological stress index generation unit: It is used to receive gradient data of changes in chlorophyll concentration and plankton biomass, analyze the correlation between chlorophyll concentration and plankton biomass, determine the strength of their positive correlation, and thus generate a biological stress index. The water quality deterioration index generation unit includes: Data receiving subunit: used to receive gradient data of changes in dissolved oxygen concentration and chlorophyll concentration; Correlation calculation subunit: used to calculate the negative correlation coefficient between dissolved oxygen concentration and chlorophyll concentration; Correlation strength calculation subunit: Based on the calculated negative correlation coefficient, the correlation strength between dissolved oxygen concentration and chlorophyll concentration is obtained and standardized to a value between 0 and 1; Water quality deterioration index generation sub-unit: The final water quality deterioration index is calculated by weighting the values ​​based on the correlation strength, using the following formula: ,in, The water quality deterioration index; For coefficients; This indicates the strength of the negative correlation between dissolved oxygen concentration and chlorophyll concentration; The biological stress index generation unit includes: Data receiving subunit: used to receive gradient data on changes in chlorophyll concentration and plankton biomass; Correlation calculation subunit: used to analyze the correlation between chlorophyll concentration and plankton biomass, and quantifies the strength of the relationship between the two by calculating the Pearson correlation coefficient; Correlation strength calculation subunit: Based on the calculation results of Pearson correlation coefficient, the positive correlation strength between chlorophyll concentration and plankton biomass is obtained and standardized to a value between 0 and 1; Biological stress index generation sub-unit: The biological stress index is calculated by weighting the values ​​of the correlation strengths to obtain the final biological stress index, as shown in the formula: ,in, As a biological stress index, For coefficients, This indicates the strength of the positive correlation between chlorophyll concentration and plankton biomass. Coupled early warning generation module: When the meteorological disturbance coefficient exceeds the preset threshold, it proportionally reduces the meteorological disturbance component in the water quality deterioration index and the biological stress index, and then performs weighted fusion on the reduced index to output the ecological risk level signal. The coupled early warning generation module includes a meteorological interference component reduction unit, a weighted fusion unit, and an ecological risk level signal generation unit; wherein: Meteorological disturbance component reduction unit: When the meteorological disturbance coefficient exceeds a preset threshold, it calculates the meteorological disturbance component that needs to be reduced based on the comparison between the meteorological disturbance coefficient and the preset threshold, and then adjusts the water quality deterioration index and biological stress index; the calculation formula is: ； ;in, It is an adjustment factor; This represents the maximum value of the meteorological disturbance coefficient; This is the meteorological disturbance coefficient; This is a preset threshold value; Weighted fusion unit: Used to weight and fuse the reduced water quality deterioration index and biological stress index to calculate the ecological risk value. ; Ecological risk level signal generation unit: used to receive ecological risk values And map it to the corresponding ecological risk level signal according to the preset rules; The process of mapping the data to the corresponding ecological risk level signal according to preset rules includes: When ecological risk value A low-risk signal is generated when the value is less than or equal to 0.

3. When ecological risk value When the value is greater than 0.3 and less than or equal to 0.5, a low to medium risk signal is generated. When ecological risk value A medium-risk signal is generated when the value is greater than 0.5 and less than or equal to 0.

7. When ecological risk value A value greater than 0.7 and less than or equal to 0.85 generates a medium-to-high risk signal. When ecological risk value A value greater than 0.85 generates a high-risk signal.

2. The marine ecological environment assessment and early warning system based on big data and AI according to claim 1, characterized in that, The multi-source data fusion module includes a satellite remote sensing data acquisition unit, a marine sensor network data acquisition unit, a meteorological station data acquisition unit, and a spatiotemporal alignment unit; wherein: Satellite remote sensing data acquisition unit: used to receive satellite remote sensing data from the satellite remote sensing system in real time through satellite sensors, and to obtain ocean surface temperature, salinity and sea level height; Marine sensor network data acquisition unit: used to collect marine sensor data in real time, including parameters such as water temperature, dissolved oxygen, plankton biomass, and chlorophyll concentration, through a sensor network deployed in the ocean; Meteorological station data acquisition unit: used to acquire real-time meteorological station data from meteorological stations, including wind speed, temperature and humidity; Spatiotemporal alignment unit: used to perform spatiotemporal alignment processing on satellite remote sensing data, marine sensor network data and meteorological station data. By uniformly processing the timestamps and geographic location information of different data sources, the data is made consistent in time and space, thereby generating a standardized ecological data stream.

3. The marine ecological environment assessment and early warning system based on big data and AI according to claim 1, characterized in that, The dynamic feature extraction module includes a data receiving unit, a sliding window mechanism unit, a gradient calculation unit, and a result output unit; wherein: Data receiving unit: used to receive standardized ecological data streams output from the multi-source data fusion module, including time-series data of dissolved oxygen concentration, chlorophyll concentration and plankton biomass; Sliding window mechanism unit: used to apply the sliding window mechanism to the received standardized ecological data stream, with the window size set to 72 hours, and the sliding window moving gradually over time. The gradient calculation unit is used to calculate the rate of change of three core parameters—dissolved oxygen concentration, chlorophyll concentration, and plankton biomass—within each sliding window, and obtain the gradient of change of each parameter over 72 hours. Results output unit: Used to output the 72-hour gradient of the three core parameters calculated for each sliding window.

4. The marine ecological environment assessment and early warning system based on big data and AI according to claim 3, characterized in that, The gradient calculation unit includes: Dissolved oxygen concentration change rate calculation subunit: It is used to receive dissolved oxygen concentration data from the sliding window mechanism unit, and obtain the gradient of dissolved oxygen concentration change by solving the ratio of the hourly dissolved oxygen concentration change value to the time interval. Chlorophyll concentration change rate calculation subunit: It is used to receive chlorophyll concentration data from the sliding window mechanism unit, and obtain the chlorophyll concentration change gradient by differentiating the chlorophyll concentration change at each time point and calculating the change rate according to the time interval. The phytoplankton biomass change rate calculation subunit is used to receive phytoplankton biomass data from the sliding window mechanism unit and obtain the phytoplankton biomass change gradient by calculating the ratio of the difference in phytoplankton biomass between each time point to the time interval. Gradient Integration Subunit: Used to integrate the rate of change calculated from the three subunits to generate the gradient of change of each parameter over 72 hours.

5. A marine ecological environment assessment and early warning system based on big data and AI according to claim 1, characterized in that, The meteorological disturbance coefficient calculation module includes a historical disaster data receiving unit, a meteorological disturbance sensitivity matrix construction unit, and a meteorological disturbance coefficient calculation unit; wherein: Historical disaster data receiving unit: used to receive meteorological disturbance data from the historical disaster database, including historical meteorological disaster events related to marine ecology; Meteorological disturbance sensitivity matrix construction unit: Based on meteorological disturbance data, a meteorological disturbance sensitivity matrix is ​​established; Meteorological Disturbance Coefficient Calculation Unit: Used to calculate and output the meteorological disturbance coefficient based on the meteorological disturbance sensitivity matrix and real-time collected meteorological station data. .

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