A method, medium and system for integrated monitoring and early warning of marine environment

Abstract

The present invention provides an integrated monitoring and early warning method, medium and system for the marine environment, which belongs to the field of electronic digital data processing technology, including: firstly, collecting remote sensing monitoring data such as seawater temperature, salinity, pollutant concentration and flow rate, and obtaining environmental stability and change components through multivariate mixed decomposition. Based on the flow rate data, a diffusion dynamics model of N time scales is established, and a diffusion matrix group is calculated. The environmental change component and the diffusion matrix are cascaded and time-series weighted fused to obtain a comprehensive environmental change trend matrix, and a risk vector containing temperature, salinity and pollutant components is constructed accordingly. Finally, a matching operation is performed with the early warning threshold matrix group established with historical data to obtain an early warning index and output an early warning level. The present invention overcomes the shortcomings of strong empiricism and subjectivity in traditional methods by using mathematical and physical models, and solves the technical problem that the existing technology is difficult to achieve effective conversion of static remote sensing data to dynamic environmental risk early warning.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic digital data processing, and in particular, relates to an integrated monitoring and early warning method, medium and system for marine environment. Background Art

[0002] Marine environmental monitoring is the fundamental work of marine ecosystem protection. With the development of remote sensing technology, marine environmental monitoring technology based on satellite, aviation and underwater remote sensing platforms has made significant progress, and can obtain large-scale observation data of environmental parameters including seawater temperature, salinity, pollutant concentration, etc. However, the current marine environmental monitoring field faces a fundamental technical problem: how to convert static remote sensing monitoring data into dynamic environmental risk warning information. The core of this technical problem is that the environmental data obtained by remote sensing monitoring is essentially a "snapshot" of the state of the marine environment, that is, a static observation result at a certain moment. However, the formation and evolution of marine environmental risks is a dynamic process, involving the complex interaction of multiple environmental factors in time and space dimensions. The current status of technological development is:

[0003] 1. In terms of data acquisition, remote sensing technology can already provide high-quality environmental parameter observation data. Satellite remote sensing can obtain surface parameters such as sea surface temperature and chlorophyll concentration; aerial remote sensing can provide regional observations with higher spatial resolution; and underwater remote sensing platforms can obtain the distribution of environmental parameters in vertical sections. These technical means have greatly improved the spatial coverage capability of marine environmental monitoring.

[0004] 2. At the data processing level, relatively mature remote sensing data interpretation methods have been developed. Various algorithms can be used to extract the spatial distribution characteristics of environmental elements such as temperature field, salinity field, and pollutant concentration field from remote sensing data, and can achieve high extraction accuracy.

[0005] 3. At the environmental assessment level, a variety of environmental assessment models based on remote sensing data have been established. These models can use remote sensing observation data to assess the status of the marine environment, identify abnormal areas, and provide a basis for decision-making in environmental management.

[0006] However, current technologies still have the following key deficiencies:

[0007] 1. Lack of effective dynamic feature extraction methods. Existing technologies make it difficult to extract the evolution trend information of environmental elements from static remote sensing data. Although environmental changes can be analyzed by comparing remote sensing data at different times, this simple time series analysis cannot reveal the internal mechanism of environmental evolution.

[0008] 2. It is difficult to characterize the transport and diffusion process of environmental factors. Marine environmental factors will migrate and diffuse with the movement of water bodies. This dynamic process plays a decisive role in the formation and evolution of environmental risks. However, existing technologies often regard environmental factors as independent scalar fields, ignoring the transport and diffusion effects under the action of flow fields.

[0009] 3. The early warning model lacks physical mechanism support. Current environmental risk early warnings are mostly based on empirical threshold judgments and lack a deep understanding of the dynamic evolution of environmental factors. The scientificity and reliability of this early warning method are relatively limited.

[0010] These technical defects have led to a series of problems in marine environmental monitoring and early warning work: the lag of early warning is obvious, and early warnings are often issued after environmental risks have already formed; the accuracy of early warnings is insufficient, and false alarms or missed reports often occur; the foresight of early warnings is insufficient, and it is difficult to identify potential environmental risks at an early stage. These problems have seriously restricted the effectiveness of marine environmental protection work.

[0011] Therefore, there is an urgent need to develop new technical methods to achieve the effective transformation from static remote sensing data to dynamic environmental risk warnings. This is a core scientific problem that needs to be urgently solved in the current field of marine environmental monitoring and early warning. Summary of the invention

[0012] In view of this, the present invention provides a method, medium and system for integrated monitoring and early warning of marine environment, which can solve the technical problem that the existing technology is difficult to achieve effective conversion of static remote sensing data into dynamic environmental risk early warning.

[0013] The present invention is achieved in that:

[0014] A first aspect of the present invention provides an integrated marine environment monitoring and early warning method, comprising the following steps:

[0015] S10, collecting ocean remote sensing monitoring data, wherein the ocean remote sensing monitoring data includes seawater temperature data, seawater salinity data, seawater pollutant concentration data, and seawater flow rate data;

[0016] S20, performing multivariate mixed decomposition calculation on the seawater temperature data, seawater salinity data, and seawater pollutant concentration data to obtain an environmental stability component and an environmental change component;

[0017] S30, setting the number of diffusion steps to N, establishing a seawater diffusion kinetic model of N time scales based on the seawater velocity data, and calculating a seawater diffusion matrix group corresponding to the N time scales according to Fick's diffusion theorem;

[0018] S40, performing cascade matrix operations on the environmental change components and the seawater diffusion matrices of the N time scales respectively to obtain an environmental change trend matrix of the N time scales;

[0019] S50, performing time-series weighted fusion on the environmental change trend matrices of the N time scales to obtain a comprehensive environmental change trend matrix;

[0020] S60, establishing a marine environmental risk vector according to the comprehensive environmental change trend matrix, wherein the marine environmental risk vector includes a temperature risk component, a salinity risk component and a pollutant risk component;

[0021] S70, establishing a multi-scale risk warning threshold matrix group based on historical monitoring data to characterize the risk level of environmental parameters at different time scales;

[0022] S80, performing a time series matching operation on the marine environment risk vector and the multi-scale risk warning threshold matrix group to obtain a comprehensive risk warning index;

[0023] S90. Determine whether to trigger a warning signal based on the comprehensive risk warning index, and output a corresponding warning level.

[0024] Specifically, step S10 is to use a multi-spectral remote sensing instrument to obtain detailed data of the temperature matrix, salinity matrix and pollutant concentration matrix of the sea area. These data can constitute an environmental parameter matrix. At the same time, the detailed data of the seawater velocity matrix can also be obtained by remote sensing Doppler radar. These monitoring data provide a basis for subsequent environmental parameter decomposition, diffusion dynamics model establishment and risk assessment.

[0025] Specifically, step S20 uses a multivariate mixed decomposition method to decompose the original environmental parameter matrix into an environmental stability component and an environmental change component. The environmental stability component reflects the long-term average characteristics of the marine environmental state, including the historical average values ​​of temperature, salinity, and pollutant concentration. The environmental change component characterizes the short-term dynamic change trend of environmental parameters, including the rate of change of temperature, salinity, and pollutant concentration over time and space. This decomposition helps to more carefully depict the evolution mechanism of the marine environment.

[0026] Specifically, step S30 is to establish a seawater diffusion dynamics model of N time scales based on the obtained seawater flow rate data. Each time scale corresponds to a seawater diffusion matrix, which describes the diffusion characteristics of pollutant concentrations in seawater at that time scale. These diffusion matrices reflect the propagation laws of environmental parameters at different time scales, providing a basis for subsequent environmental change trend analysis. Among them, the values ​​of parameters such as diffusion coefficient and Laplace operator need to refer to relevant hydrodynamic theories and empirical data.

[0027] Specifically, step S40 is to perform cascade matrix operations on the environmental change component and the seawater diffusion matrix of N time scales to obtain the environmental change trend matrix of N time scales. This process simulates the spatial propagation process of environmental parameters affected by seawater flow. The environmental change trend matrix reflects the dynamic change characteristics of environmental parameters at different time scales. It should be noted that the spatial correlation coefficient is also introduced here to consider the spatiotemporal coupling effect of environmental parameters.

[0028] Specifically, step S50 is to perform time-series weighted fusion on the environmental change trend matrices of N time scales to obtain a comprehensive environmental change trend matrix. This step aims to integrate environmental change information at different time scales to reflect the overall evolution trend of the marine environment. In the fusion process, the corresponding weight coefficient needs to be determined according to the importance of each time scale, and the time-space coupling effect also needs to be considered.

[0029] Specifically, step S60 is to establish a marine environmental risk vector based on the comprehensive environmental change trend matrix. The vector includes a temperature risk component, a salinity risk component, and a pollutant risk component, which respectively describe the potential harm of these three environmental parameters to the marine environment. When calculating the risk components, it is necessary to introduce corresponding risk weight coefficients to reflect the contribution of different environmental parameters to the overall risk.

[0030] Specifically, step S70 is to establish a multi-scale risk warning threshold matrix group based on historical monitoring data. These threshold matrices represent the critical values ​​of environmental parameters reaching different risk levels at different time scales. When determining these thresholds, it is necessary to refer to relevant environmental quality standards and historical monitoring data, and it is also necessary to introduce some adjustment coefficients to adapt to the actual conditions of different sea areas.

[0031] Specifically, step S80 is to perform a time-series matching operation on the marine environmental risk vector and the multi-scale risk warning threshold matrix group to obtain a comprehensive risk warning index. This step is intended to comprehensively assess the overall risk status of the marine environment and provide a basis for subsequent warning decisions. When calculating the comprehensive risk warning index, it is necessary to consider the risk contribution degree of different time scales, and also to dynamically adjust the coefficient to adapt to environmental changes.

[0032] Specifically, step S90 is to determine whether to trigger a warning signal based on the comprehensive risk warning index and output the corresponding warning level. When the warning index is lower than the first threshold, it means that the environment is in a normal state; when the index is between the first and second thresholds, it means a mild warning; when the index is between the second and third thresholds, it means a moderate warning; when the index exceeds the third threshold, it means a severe warning. These thresholds need to be adjusted and optimized according to actual conditions.

[0033] Among them, in step S10, the seawater temperature data, seawater salinity data, seawater pollutant concentration data, and seawater flow rate data are all expressed in matrix form, as follows:

[0034] , , ; ;

[0035] In the formula, each element in the matrix represents a monitoring point. For the Line The temperature values ​​of the monitoring points are listed in °C, which are directly obtained by multi-spectral remote sensing instruments; For the Line The salinity values ​​of the monitoring points are listed in ‰, which are directly obtained by multispectral remote sensing instruments; For the Line The pollutant concentration values ​​at the monitoring points are listed in mg / L, which are directly obtained through multi-spectral remote sensing instruments; For the Line The seawater velocity at the monitoring points in the column, in m / s, is obtained by an acoustic Doppler current meter (ADCP); is the number of rows of the monitoring area in the collected ocean remote sensing monitoring data; is the number of columns in the monitoring area.

[0036] For step S20, the multivariate mixed decomposition calculation process is as follows:

[0037] 1. Environmental stability Calculation:

[0038] ;

[0039] In the formula For the Line List the environmental stability component values ​​of the monitoring points; is the temperature weight coefficient, with a value range of [0,1], and is calculated by principal component analysis method: ,in is the eigenvalue of the covariance matrix; is the salinity weight coefficient, with a value range of [0,1] and the calculation method is the same as ; is the pollutant concentration weight coefficient, with a value range of [0,1] and a calculation method similar to ; is the temperature historical average matrix, which is obtained by calculating the arithmetic mean of the temperature at the same time in the past 30 days; is the salinity historical average matrix, and the calculation method is the same as ; is the historical average matrix of pollutant concentration, and the calculation method is the same as ; is the spatial weight coefficient, with a value range of [0,1], and is calculated using the Kriging interpolation method: ,in is the distance between monitoring points, is the distance power, usually 2; is the partial derivative of temperature with respect to time, indicating the rate of change of temperature with time; is the partial derivative of salinity with respect to time, indicating the rate of change of salinity with time; is the partial derivative of pollutant concentration with respect to time, indicating the rate of change of pollutant concentration with time; is the error term, which follows a normal distribution .

[0040] 2. Environmental change component Calculation:

[0041] ;

[0042] In the formula, For the Line List the environmental change component values ​​of the monitoring points; is the overall environmental component matrix, which is equal to the measured value matrix; is the spatial variation coefficient, with a value range of [0,1], and is calculated using the variation function method: ,in The distance is The number of sample points, is the observed value; is the second-order partial derivative of temperature with respect to space, indicating the acceleration of spatial variation of temperature; is the second-order partial derivative of salinity with respect to space, indicating the acceleration of spatial variation of salinity; is the second-order partial derivative of pollutant concentration with respect to space, indicating the acceleration of spatial variation of pollutant concentration; is the error term, which follows a normal distribution In the formula, only the partial derivative in the x direction is considered to reduce the amount of calculation. At the same time, since the x direction can already describe the changes in each parameter, it has little impact on the overall calculation accuracy, and the partial derivative in the y direction can be temporarily ignored. x represents the rows of the matrix, and y represents the columns of the matrix.

[0043] Furthermore, the weight coefficient in the environmental stability component ( , , ) is obtained through the following steps: 1) constructing the environmental parameter correlation matrix; 2) calculating the eigenvalues ​​and eigenvectors; 3) determining the weight value according to the principal component contribution rate. Spatial weight coefficient The determination method is as follows: 1) calculate the distance matrix between monitoring points; 2) use the inverse distance weighted method to calculate the spatial weight; 3) perform weight normalization processing.

[0044] Environmental stability component The equation contains three main parts: 1) Weighted average term Reflects the basic level of environmental parameters; 2) Time variation items Characterize the time evolution characteristics of the parameters; 3) Error term Used to compensate for errors.

[0045] Environmental change component The equation consists of the following parts: 1) The difference between the overall environmental quantity and the stable component ;2) Spatial gradient term Characterize the spatial evolution characteristics of parameters; 3) Error compensation term Compensate for errors.

[0046] For step S30, the seawater diffusion matrix set of N time scales is Calculation:

[0047] ;

[0048] In the formula, is the time scale serial number; For the The time scale Line Diffusion coefficients of the monitoring points; is the initial diffusion coefficient, in m² / s, which can be determined by tracer experiments. The general range is ; is the time scale serial number, the value range is [1, N]; is the time step, in seconds, determined according to the monitoring frequency, usually Hour; is the characteristic time scale, in seconds, determined by the physical properties of the diffusion process, usually Hour; is the diffusion coefficient, in ,The value range is [0,1], determined by diffusion experiments; is the Laplace operator, expressed as: ; is the error term, which follows a normal distribution .

[0049] This equation is based on Fick's diffusion theorem and consists of the following components:

[0050] 1) Time decay term Characterize the decay law of diffusion intensity over time;

[0051] 2) Flow rate modulation item characterize the effects of ocean currents on dispersion;

[0052] 3) Concentration diffusion term Describe the diffusion process of pollutants, where: is the diffusion coefficient, which is related to the nature of the pollutant and the characteristics of seawater.

[0053] For step S40, the environmental change trend matrix Calculation:

[0054] ;

[0055] In the formula, represents the Hadamard product (element-wise multiplication); For the The time scale Line List the environmental change trend values ​​of the monitoring points; is the spatial correlation coefficient, with a value range of [-1,1], calculated by Moran's I index: ,in is the spatial weight; is the error term, which follows a normal distribution .

[0056] This equation reflects the coupling effect of environmental changes and diffusion processes, including:

[0057] 1) Hadamard product term Characterize the direct coupling of environmental changes and diffusion;

[0058] 2) Spatial related items Describe spatial correlation effects.

[0059] For step S50, the comprehensive environment change trend matrix Calculation:

[0060] ;

[0061] In the formula, It is an element of the comprehensive environmental change trend matrix, reflecting the overall change trend of marine environmental parameters at different time scales. is the time scale weight coefficient, and its value range is ,and . Indicates the contribution of different time scales to the final result. is the time-space coupling coefficient, and its value range is , represents the interaction between temporal variation and spatial variation. is the error term, which follows a normal distribution , which reflects the uncertainty in the calculation process of the comprehensive environmental change trend matrix.

[0062] For step S60, the marine environment risk vector Calculation:

[0063] ;

[0064] In the formula, is the marine environmental risk vector, including the temperature risk component , salinity risk component and pollutant risk components These three components reflect the risk levels of various parameters of the marine environment. are the risk weight coefficients of temperature, salinity and pollutants, respectively, and their value range is ,and These weight coefficients reflect the relative importance of different environmental parameters to the overall risk. Appropriate values ​​can be set according to actual conditions. are the partial derivatives of temperature, salinity, and pollutant concentration with respect to time, respectively, indicating the rate of change of these parameters over time. They can be calculated by numerical difference. are the partial derivatives of temperature, salinity, and pollutant concentration with respect to space, respectively, indicating the rate of change of these parameters in space. They can also be calculated by numerical difference. is the error term, which follows a normal distribution , which reflects the uncertainty in the calculation process of marine environmental risk vector.

[0065] For step S70, the multi-scale risk warning threshold matrix group Calculation:

[0066] ;

[0067] In the formula, For the The risk warning threshold matrix at each time scale includes three warning level thresholds for the three risk indicators of temperature, salinity and pollutants. They are the average values ​​of historical temperature, salinity, and pollutant risk, respectively, and can be obtained by statistical calculation of past monitoring data. arrive is the threshold adjustment coefficient, and its value range is , used to adjust the warning thresholds at different time scales. These coefficients can be calibrated based on actual experience and monitoring data. is the error term, which follows a normal distribution , which reflects the uncertainty in the calculation process of the risk warning threshold matrix.

[0068] For step S80, the comprehensive risk warning index Calculation:

[0069] ;

[0070] In the formula, It is a comprehensive risk warning index that comprehensively reflects the risk level of various parameters of the marine environment. is the comprehensive weight of the time scale, and its value range is ,and . Indicates the contribution of different time scales to the final warning results. is the early warning dynamic adjustment coefficient, and its value range is , used to adjust the early warning index at different time scales. is the error term, which follows a normal distribution , which reflects the uncertainty in the calculation process of the comprehensive risk warning index.

[0071] For step S90, the warning signal is judged: ;

[0072] In the formula, is the warning level (0-normal, 1-mild, 2-moderate, 3-severe); is the warning level threshold, which is set based on experience. The default setting method is to divide the value range of I into 4 equal intervals. The dividing point of the interval.

[0073] Furthermore, the value range of N is 8~64, and the default value is 16.

[0074] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned integrated monitoring and early warning method for the marine environment.

[0075] A third aspect of the present invention provides an integrated marine environment monitoring and early warning system, which includes the above-mentioned computer-readable storage medium.

[0076] Compared with the prior art, the beneficial effects of the integrated marine environment monitoring and early warning method, medium and system provided by the present invention are:

[0077] 1. Accurate extraction of dynamic features of environmental elements is achieved. The present invention decomposes environmental monitoring data into stable components and variable components through a multivariate hybrid decomposition algorithm. The stable component reflects the background field characteristics of environmental elements, while the variable component contains dynamic information of environmental changes. This decomposition method overcomes the limitation of traditional technology that can only obtain static features, and lays the foundation for subsequent dynamic analysis.

[0078] 2. A dynamic evolution model based on physical mechanism is constructed. Based on Fick's diffusion theorem, the present invention establishes a multi-time scale seawater diffusion dynamics model. By solving the diffusion equation, a diffusion matrix group characterizing the transport and diffusion process of environmental elements is obtained. This method introduces flow field information into the analysis of environmental element evolution, so that the dynamic evolution prediction of environmental elements has a solid physical basis.

[0079] 3. A mathematical framework for dynamic risk warning is established. The present invention couples the change components of environmental factors with the diffusion matrix through cascade matrix operations to obtain the environmental change trend matrix on different time scales. Through time series weighted fusion, the change trends of multiple time scales are integrated into a unified trend feature, solving the technical problem that the existing technology is difficult to achieve effective conversion of static remote sensing data to dynamic environmental risk warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A flow chart of the method provided by the present invention;

[0081] Figure 2 It is a comparison diagram of the effects of the method of the present invention and the traditional method in the embodiment. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0083] like Figure 1 As shown, it is a flow chart of a method for integrated monitoring and early warning of marine environment provided by the first aspect of the present invention, and the method comprises the following steps:

[0084] S10. Collecting ocean remote sensing monitoring data, including seawater temperature data, seawater salinity data, seawater pollutant concentration data, and seawater flow rate data;

[0085] S20, performing multivariate mixed decomposition calculation on the seawater temperature data, the seawater salinity data, and the seawater pollutant concentration data to obtain an environmental stability component and an environmental change component;

[0086] S30, setting the number of diffusion steps to N, establishing a seawater diffusion kinetic model of N time scales based on seawater velocity data, and calculating a seawater diffusion matrix group corresponding to the N time scales according to Fick diffusion theorem;

[0087] S40, performing cascade matrix operations on the environmental change components and the seawater diffusion matrices of N time scales respectively to obtain an environmental change trend matrix of N time scales;

[0088] S50, performing time series weighted fusion on the environmental change trend matrices of N time scales to obtain a comprehensive environmental change trend matrix;

[0089] S60, establishing a marine environmental risk vector according to the comprehensive environmental change trend matrix, wherein the marine environmental risk vector includes a temperature risk component, a salinity risk component and a pollutant risk component;

[0090] S70, establishing a multi-scale risk warning threshold matrix group based on historical monitoring data to characterize the risk level of environmental parameters at different time scales;

[0091] S80, performing a time series matching operation on the marine environmental risk vector and the multi-scale risk warning threshold matrix group to obtain a comprehensive risk warning index;

[0092] S90. Determine whether to trigger a warning signal based on the comprehensive risk warning index, and output a corresponding warning level.

[0093] The specific implementation methods of the above steps are described in detail below:

[0094] The specific implementation of step S10 is to collect marine environment data using remote sensing monitoring technology. It includes the following sub-steps:

[0095] First, multispectral remote sensing instruments are used to obtain spatial distribution data of temperature, salinity and pollutant concentration in the sea area. These data can reflect the basic physical and chemical characteristics of the marine environment. For example, infrared remote sensing technology is used to measure seawater temperature, microwave remote sensing technology is used to measure seawater salinity, and hyperspectral remote sensing technology is used to monitor the concentration distribution of pollutants in seawater. These data can form an environmental parameter matrix. Secondly, Doppler radar is used to obtain velocity field data in the sea area. Velocity field information can describe the dynamic process of seawater and provide basic data for subsequent diffusion simulation.

[0096] Specifically, the method for obtaining each parameter is described as follows:

[0097] Temperature Matrix The acquisition methods are as follows: 1) using infrared remote sensing technology to obtain sea surface temperature data through satellite-borne thermal infrared sensors; 2) using acoustic Doppler profilers (ADCP) to measure the vertical temperature distribution of water bodies; 3) deploying CTD (conductivity-temperature-depth) instruments at buoy sites for fixed-point temperature monitoring.

[0098] Salinity Matrix The acquisition method is as follows: 1) using satellite microwave radiometer to invert sea surface salinity; 2) using CTD instrument to measure salinity values ​​at different depths; 3) using salinity meter to measure salinity at sampling points on site.

[0099] Pollutant Concentration Matrix The acquisition method is as follows: 1) using multispectral remote sensing images to invert water quality parameters such as chlorophyll a; 2) using an automatic water quality monitoring system for continuous monitoring; 3) conducting regular sampling and analysis to determine the content of pollutants such as heavy metals and organic matter.

[0100] Flow rate matrix The acquisition methods are as follows: 1) using high-frequency ground wave radar to monitor the surface flow field; 2) using ADCP to measure the vertical velocity profile of the water body; 3) using a drifting buoy to track and measure the flow velocity.

[0101] Through the above steps, comprehensive marine environment remote sensing monitoring data including temperature, salinity, pollutant concentration and flow field can be obtained. These data provide necessary input conditions for subsequent environmental change trend analysis and risk assessment.

[0102] The specific implementation of step S20 is to perform multivariate mixed decomposition calculation on the acquired marine environment monitoring data, with the purpose of separating the stable component and the variable component from the overall environmental data. This process includes the following sub-steps:

[0103] First, the environmental parameter matrix is ​​decomposed into two parts using the matrix decomposition method: the environmental stability component matrix and the environmental change component matrix .in It reflects the basic spatial distribution characteristics of environmental parameters. The time evolution characteristics of environmental parameters are characterized.

[0104] Specifically, It consists of three parts: the first is the basic data matrix term, which describes the spatial distribution of each monitoring parameter; the second is the spatial gradient term, which captures the spatial variation of the parameter through the second-order partial derivative; and the third is the Gaussian error term, which reflects the observation and calculation errors. It consists of three parts: the time derivative term characterizes the time evolution characteristics of the parameters, the spatial Gaussian term describes the spatial correlation of the changes, and the error correction term compensates for the calculation and observation errors.

[0105] Compared with simple scalar equations, this matrix expression has the following advantages: first, it can fully describe the distribution characteristics of space; second, it includes the space-time coupling effect; third, it introduces nonlinear terms to better describe complex environmental processes; and fourth, it takes into account multi-scale effects.

[0106] In general, the purpose of step S20 is to separate the stable component and the variable component from the overall environmental monitoring data through multivariate mixed decomposition, laying the foundation for subsequent dynamic modeling and risk assessment.

[0107] Specific implementation of step S30: Establishing a seawater diffusion kinetic model at N time scales The purpose of this step is to establish a kinetic model describing the seawater diffusion process based on the acquired seawater velocity data. Specifically, it includes the following sub-steps:

[0108] Set the number of diffusion steps N to represent different time scales. The time scale here can be 1 hour, 6 hours, 12 hours, 24 hours, etc., to reflect the short-term, medium-term and long-term characteristics of environmental changes.

[0109] According to Fick's diffusion law, a group of seawater diffusion matrices with N time scales is established. Fick's law describes the diffusion process of substances under the action of concentration gradient and can be expressed by partial differential equations. Here, the seawater velocity field information is incorporated into it to construct a diffusion dynamics model that is closer to reality.

[0110] Specifically, each diffusion matrix The following key factors are included: initial diffusion coefficient , describing the diffusion characteristics of substances in static water. Time decay factor , reflects the time-varying characteristics of the diffusion process, where is the time step, is the characteristic time scale. Velocity field matrix , describes the seawater dynamics and affects the diffusion process. The concentration gradient term , characterizes the driving force of diffusion. Diffusion coefficient , describing the diffusion intensity at different time scales. Gaussian error term , taking into account uncertainties in observations and calculations.

[0111] By combining these factors, the seawater diffusion matrix at N different time scales can be established. , providing a basis for subsequent analysis of environmental change trends.

[0112] Specific implementation of step S40: Calculate the environmental change trend matrix of N time scales based on the environmental change component matrix obtained in step S20 and the seawater diffusion matrix set established in step S30 , the environmental change trend matrix under N time scales can be calculated The core of this step is to combine environmental change information with diffusion dynamics to reflect the evolution characteristics of environmental parameters at different time scales. It includes the following sub-steps:

[0113] For each time scale , calculate the environmental change trend matrix It consists of two parts:

[0114] Environmental change component Diffusion Matrix The Hadamard product describes the basic changing trend of environmental parameters under the action of diffusion.

[0115] The product of the environmental change component and the second-order partial derivative of the diffusion matrix describes the spatial correlation of changes in environmental parameters.

[0116] Gaussian error term , considering the uncertainty factors.

[0117] In this way, the environmental change trend matrix under N time scales is obtained , which contains the evolution characteristics of environmental parameters in time and space.

[0118] Specific implementation of step S50: Comprehensive environmental change trends at N time scales Although the environmental change trend matrix at N time scales is obtained through the previous step, these matrices need to be further integrated to reflect the comprehensive environmental change characteristics. The purpose of this step is to perform time-series weighted integration on these N matrices to obtain the final comprehensive environmental change trend matrix . Specifically includes the following sub-steps:

[0119] Determine the weight coefficients for each time scale These weight coefficients reflect the relative importance of different time scales and can be determined by methods such as principal component analysis.

[0120] The environmental change trend matrix of N time scales According to the weight coefficient Perform weighted summation to obtain the comprehensive environmental change trend matrix .

[0121] In order to further describe the spatiotemporal coupling characteristics of environmental changes, in the matrix A second-order partial derivative term is added to . This term describes the coupling relationship between environmental change trends in time and space.

[0122] Finally, add the Gaussian error term , to reflect the uncertainty in the calculation and observation process.

[0123] Through the above steps, the comprehensive environmental change trend matrix is ​​obtained ,It contains the evolution characteristics of marine environmental parameters in time and space, laying the foundation for subsequent risk assessment.

[0124] Specific implementation of step S60: Constructing the marine environmental risk vector based on the comprehensive environmental change trend matrix calculated above , we can further construct the marine environmental risk vector This vector includes three components: temperature risk, salinity risk, and pollutant risk, reflecting the contribution of different environmental parameters to the overall risk. It includes the following sub-steps:

[0125] Temperature risk component Calculation:

[0126] Using the Matrix Elements in , combined with the temperature matrix The spatial and temporal derivatives of are used to calculate the risk contribution corresponding to the temperature change.

[0127] The temperature risk weight factor is introduced here , used to adjust the relative importance of temperature risks.

[0128] Salinity risk component Calculation:

[0129] Similar to temperature risk, using Elements in And the salinity matrix The spatial and temporal derivatives of are used to calculate the risk contribution corresponding to the salinity change.

[0130] Introducing salinity risk weight factor to adjust risk weights.

[0131] Pollutant risk factor Calculation:

[0132] Still using a similar method, using Elements in and the pollutant concentration matrix The spatial and temporal derivatives of are used to calculate the risk contribution caused by pollutant changes.

[0133] Introducing pollutant risk weight coefficients to adjust risk weights.

[0134] The above three risk components are combined into the marine environmental risk vector , and add the Gaussian error term .

[0135] In this way, a vector that comprehensively reflects the marine environmental risk is obtained. , including risk information in three aspects: temperature, salinity and pollutants. Subsequent risk warning will be based on this vector.

[0136] Specific implementation of step S70: Establishing a multi-scale risk warning threshold matrix group In order to carry out effective risk warning, it is necessary to determine the risk level thresholds of each environmental parameter at different time scales in advance. Therefore, the purpose of this step is to establish a multi-scale risk warning threshold matrix group based on historical monitoring data. . Specifically includes the following sub-steps:

[0137] For each time scale , construct a 3×3 risk warning threshold matrix .

[0138] matrix Each element of Both contain two parts:

[0139] Based on historical environmental risk average The linear term reflects the average risk level.

[0140] Based on historical environmental risk standard deviation The linear term reflects the discrete degree of risk.

[0141] These two parts of the linear terms have corresponding adjustment coefficients, such as arrive , used to adjust the risk thresholds at different time scales.

[0142] Finally, add the Gaussian error term , to cover both computational and observational uncertainties.

[0143] Through the above steps, we get the risk warning threshold matrix group under N time scales: This set of matrices can be used to characterize the risk levels of various environmental parameters at different time scales and provide a basis for subsequent comprehensive risk warnings.

[0144] Specific implementation of step S80: Calculate the comprehensive risk warning index with the marine environmental risk vector established in the previous step And the multi-scale risk warning threshold matrix group , we can calculate the comprehensive risk warning index This index comprehensively reflects the overall risk level of each environmental parameter at different time scales, providing a basis for the final early warning decision. It specifically includes the following sub-steps:

[0145] The risk vector With each time scale The warning threshold matrix Perform matrix product operations. This will yield comprehensive risk scores at different time scales.

[0146] The time-scale weighted summation of these comprehensive risk scores is used to obtain the final comprehensive risk warning index. The weight coefficient here is It reflects the relative importance of each time scale.

[0147] In order to consider the dynamic characteristics of risk thresholds at different time scales, A second-order partial derivative term is added to the calculation of . This term describes the temporal trend of the risk threshold and can reflect the dynamic characteristics of risk warning.

[0148] Finally, add the Gaussian error term , to cover the uncertainty in the whole calculation process.

[0149] Through the above steps, we get the comprehensive risk warning index This index comprehensively considers the risk contribution of various environmental parameters at different time scales, providing an important basis for subsequent early warning decisions.

[0150] Specific implementation of step S90: Early warning signal judgment has a comprehensive risk early warning index , it is possible to determine whether the warning signal is triggered based on the set warning threshold. This includes the following sub-steps:

[0151] Set three risk warning thresholds , corresponding to the four warning levels of normal, mild, moderate and severe.

[0152] The calculated comprehensive risk warning index Compare with these three thresholds to get the specific warning level :

[0153] if , it is judged as a normal warning;

[0154] if , it is judged as a mild warning;

[0155] if , it is judged as a moderate warning;

[0156] if , it is judged as a severe warning.

[0157] According to the warning level , output corresponding warning signals.

[0158] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned integrated monitoring and early warning method for the marine environment.

[0159] A third aspect of the present invention provides an integrated marine environment monitoring and early warning system, which includes the above-mentioned computer-readable storage medium.

[0160] Specifically, the principle of the present invention is:

[0161] 1. Basic data acquisition and processing principle: This technology first obtains key parameter data of the marine environment through remote sensing monitoring, including basic data such as seawater temperature, salinity, pollutant concentration, and seawater flow rate. These data together constitute a comprehensive description of the state of the marine environment. The innovation of the technology lies in the use of a multivariate mixed decomposition calculation method to decompose the environmental parameter data into two important components: an environmental stability component and an environmental change component. The environmental stability component reflects the basic background characteristics of the marine environment and represents the average state or long-term change trend of the environmental parameters; while the environmental change component contains short-term fluctuations and dynamic change information of environmental parameters. This decomposition method achieves the effective separation of environmental signals and lays the foundation for subsequent dynamic analysis.

[0162] 2. Principle of constructing diffusion dynamics model: After obtaining basic data, the second innovation of the technology is to construct a seawater diffusion dynamics model based on Fick's diffusion theorem. The special feature of this model is that it introduces the concept of multiple time scales. By setting the number of diffusion steps N, diffusion dynamics models of N different time scales are established. At each time scale, the diffusion equation is solved based on the seawater velocity data to obtain the corresponding seawater diffusion matrix. This method fully considers the transport and diffusion characteristics of marine environmental elements at different time scales, so that the prediction of the dynamic evolution of environmental elements has a solid physical basis.

[0163] 3. Principle of environmental change trend analysis: The third innovation of the technology is to couple the environmental change components with the seawater diffusion matrix of different time scales through cascade matrix operations. This operation method realizes the organic combination of environmental change information and material transport process, and can quantitatively describe the evolution characteristics of environmental factors at different time scales. In this way, environmental change trend matrices of N time scales can be obtained, and each matrix reflects the change law of environmental factors at a specific time scale.

[0164] 4. Principle of time series fusion mechanism: In order to obtain a unified representation of environmental change trends, the technology adopts a time series weighted fusion method to integrate the environmental change trend matrices of N time scales. The characteristic of this fusion mechanism is that by reasonably setting the weight coefficients of different time scales, it not only retains the environmental evolution characteristics on each time scale, but also realizes the differentiated treatment of the importance of different time scales. The final comprehensive environmental change trend matrix has become an important basis for building environmental risk early warning indicators.

[0165] 5. Principles of risk warning system construction: Based on the comprehensive environmental change trend matrix, the technology establishes a marine environmental risk vector composed of temperature risk component, salinity risk component and pollutant risk component. This risk vector is a quantitative expression of the change trend of each environmental factor. At the same time, the technology also establishes a multi-scale risk warning threshold matrix group based on historical monitoring data to characterize the risk level of environmental parameters at different time scales. This warning threshold system comprehensively considers the statistical characteristics of historical data, the ecological thresholds of environmental factors and the characteristic fluctuation range of different time scales.

[0166] 6. Principle of early warning signal generation: Finally, the technology compares the environmental risk vector with the multi-scale risk early warning threshold matrix group through time series matching operation, and calculates the comprehensive risk early warning index. This operation method realizes the comprehensive assessment of the risks of multiple environmental factors, unifies the risk characteristics of different time scales, and can quantitatively determine the early warning level. When the comprehensive risk early warning index exceeds a specific threshold, the system will trigger an early warning signal of the corresponding level.

[0167] The core principle of this technology is to convert static remote sensing monitoring data into dynamic environmental risk warning information. The environmental signals are effectively separated through multivariate mixed decomposition, the transport process of environmental elements is described using a multiscale diffusion dynamics model, dynamic feature extraction is achieved using cascade matrix operations, and a unified representation of the changing trend is obtained through time series weighted fusion, and finally a risk warning system based on physical mechanisms is established. The entire technical solution is based on a solid foundation of physics and mathematics, overcoming the shortcomings of strong empiricism and subjectivity in traditional methods, and significantly improving the scientificity and reliability of marine environmental monitoring and early warning.

[0168] The following is an example of a specific application scenario of the present invention: Taking a sea area along the coast of Shandong as an example, this sea area is often affected by environmental problems such as red tides and hypoxia, so it is necessary to carry out integrated marine environmental monitoring and early warning work.

[0169] First, the method of the present invention starts with the collection of ocean remote sensing monitoring data. Parameter data such as temperature, salinity and pollutant concentration in the sea area can be obtained through multi-spectral remote sensing instruments. Taking September 1, 2024 as an example, the temperature matrix of the sea area , salinity matrix and the pollutant concentration matrix As shown in Table 1. At the same time, the seawater velocity matrix of the sea area is obtained by using HF radar and other technologies. , as shown in Table 2. The data in Tables 1 and 2 represent the spatial distribution of the sea area along the x and y coordinate axes. These monitoring data provide a basis for subsequent environmental change analysis and early warning.

[0170] Table 1 Environmental parameter matrix of a certain sea area on September 1, 2024

[0171]

[0172] Table 2 Seawater velocity matrix in a certain sea area on September 1, 2024

[0173]

[0174] Secondly, the above collected data are subjected to multivariate mixed decomposition calculation to obtain the environmental stability component and environmental change component For environmental stability components First, determine the weight coefficient , , , and determined using principal component analysis. At the same time, the historical average matrix of temperature, salinity and pollutant concentration was calculated based on historical monitoring data. , and Substituting these parameters into the formula, we can get the environmental stability component The calculation results are shown in Table 3.

[0175] Table 3 Environmental stability component matrix of a certain sea area on September 1, 2024

[0176]

[0177] For environmental change First, calculate , and then calculate according to the formula , as shown in Table 4.

[0178] Table 4 Component matrix of environmental changes in a certain sea area on September 1, 2024

[0179]

[0180] Next, based on the seawater velocity data , establish a seawater diffusion dynamics model with N time scales. Assume that the number of diffusion steps is , time step Hours, characteristic time scale hours, initial diffusion coefficient m / s, diffusion coefficient m / s. Substituting these parameters into the formula, we can calculate the seawater diffusion matrix of five time scales: , as shown in Table 5.

[0181] Table 5 Seawater diffusion matrix group at five time scales in a certain sea area

[0182]

[0183] Then, the environmental change component The seawater diffusion matrix at the above five time scales By performing cascade matrix operations, we can obtain the environmental change trend matrix of 5 time scales. , as shown in Table 6.

[0184] Table 6 Environmental change trend matrix of five time scales in a certain sea area

[0185]

[0186] Next, the environmental change trend matrix of these five time scales is subjected to time series weighted fusion to obtain the comprehensive environmental change trend matrix , as shown in Table 7. Among them, the time scale weight coefficient , , , , , time-space coupling coefficient .

[0187] Table 7 Comprehensive environmental change trend matrix of a certain sea area

[0188]

[0189] According to the comprehensive environmental change trend matrix , calculate the marine environmental risk vector , as shown in Table 8. Among them, the temperature risk weight , Salinity risk weight , Pollutant Risk Weight .

[0190] Table 8 Marine environmental risk vectors for a certain sea area

[0191]

[0192] Next, based on historical monitoring data, a multi-scale risk warning threshold matrix was established. Assuming the threshold adjustment coefficient , , , , and other coefficients. Substituting these parameters into the formula, we can get the multi-scale risk warning threshold matrix group shown in Table 9.

[0193] Table 9 Multi-scale risk warning threshold matrix group for five time scales in a certain sea area

[0194]

[0195] Finally, the marine environmental risk vector Combined with multi-scale risk warning threshold matrix Perform time series matching operations to obtain a comprehensive risk warning index Assuming that the time scale is comprehensive weighted , , , , , early warning dynamic adjustment coefficient , , , , Substituting these parameters into the formula, the comprehensive risk warning index can be calculated .

[0196] According to the early warning judgment formula:

[0197]

[0198] here , and They represent the thresholds of different warning levels. They can be set based on historical data and experience values:

[0199] , represents the threshold of normal warning level , indicating the threshold of the moderate warning level , indicating the threshold of severe warning level

[0200] Substituting these values ​​into the early warning judgment formula, hour, , thus triggering a moderate warning signal, i.e. .

[0201] The basis for setting the warning level threshold is as follows:

[0202] , which means that when the comprehensive risk warning index is less than 1.0, the environmental conditions are good and belong to the normal warning level.

[0203] , which means that when the comprehensive risk warning index is between 1.5 and 2.0, the environmental conditions begin to deteriorate and certain emergency measures need to be taken, which is a moderate warning level.

[0204] , which means that when the comprehensive risk warning index is greater than or equal to 2.0, the environmental conditions have seriously deteriorated and more stringent emergency control measures need to be taken, which belongs to the severe warning level.

[0205] Such threshold settings can better reflect the warning levels under different environmental conditions and provide a basis for subsequent emergency responses.

[0206] According to the early warning judgment formula, when When the moderate warning signal is triggered, This means that the sea area needs to be closely monitored in the future, and there may be more serious environmental problems, which require certain emergency measures.

[0207] Figure 2 The comparison of the effects of the traditional single-source monitoring method and the multi-source fusion method of the present invention on environmental parameter monitoring is shown by time series comparison. The figure contains the actual environmental parameter curve (black dashed line), the monitoring results of the traditional method (red dotted line) and the monitoring results of the present invention method (blue solid line), and the semi-transparent area is used to represent the respective error ranges. It can be clearly seen that the method of the present invention has smaller monitoring error and better stability.

[0208] In summary, the integrated marine environment monitoring and early warning method of the present invention can effectively analyze the changing trend of the marine environment and warn of possible risks in a targeted manner. Through this method, relevant management departments can take timely response measures to ensure the safety and stability of the marine ecological environment. This method has a wide range of application prospects and can play an important role in many aspects such as red tide early warning, hypoxia early warning, pollution early warning, etc.

[0209] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for integrated monitoring and early warning of marine environment, characterized in that: The following steps are involved: S10. Collect ocean remote sensing monitoring data, including seawater temperature data, seawater salinity data, seawater pollutant concentration data, and seawater flow rate data; S20, performing multivariate mixed decomposition calculation on the seawater temperature data, the seawater salinity data, and the seawater pollutant concentration data to obtain an environmental stability component and an environmental change component; S30, setting the number of diffusion steps to N, establishing a seawater diffusion kinetic model of N time scales based on seawater velocity data, and calculating a seawater diffusion matrix group corresponding to the N time scales according to Fick diffusion theorem; S40, performing cascade matrix operations on the environmental change components and the seawater diffusion matrices of N time scales respectively to obtain an environmental change trend matrix of N time scales; S50, performing time series weighted fusion on the environmental change trend matrices of N time scales to obtain a comprehensive environmental change trend matrix; S60, establishing a marine environmental risk vector according to the comprehensive environmental change trend matrix, wherein the marine environmental risk vector includes a temperature risk component, a salinity risk component and a pollutant risk component; S70, establishing a multi-scale risk warning threshold matrix group based on historical monitoring data to characterize the risk level of environmental parameters at different time scales; S80, performing a time series matching operation on the marine environmental risk vector and the multi-scale risk warning threshold matrix group to obtain a comprehensive risk warning index; S90, judging whether to trigger a warning signal according to the comprehensive risk warning index, and outputting a corresponding warning level; Among them, the environmental stability component is: ; Environmental change component: ; In the formula, is the overall environmental component matrix, is the weight coefficient, which is determined by principal component analysis; are the historical average matrices of seawater temperature, salinity, and pollutant concentration respectively; is the spatial weight coefficient; is the spatial variation coefficient; is the error term; is the number of rows of the monitoring area in the collected ocean remote sensing monitoring data; is the number of columns in the monitoring area.

2. The integrated monitoring and early warning method for marine environment according to claim 1 is characterized in that: The seawater diffusion matrix group The calculation is specifically expressed as follows: ; In the formula, is the time scale serial number; is the initial diffusion coefficient; is the time step, is the seawater velocity data matrix; is the characteristic time scale; is the diffusion coefficient; is the Laplace operator; is the error term.

3. The integrated monitoring and early warning method for marine environment according to claim 2 is characterized in that: The environmental change trend matrix The calculation is specifically expressed as follows: ; In the formula, represents the Hadamard product; is the spatial correlation coefficient; is the error term.

4. The integrated monitoring and early warning method for marine environment according to claim 3 is characterized in that: The calculation of the comprehensive environmental change trend matrix is ​​as follows: ; In the formula, is the time scale weight coefficient; is the time-space coupling coefficient; is the error term.

5. The integrated monitoring and early warning method for marine environment according to claim 4 is characterized in that: The marine environmental risk vector is calculated as follows: ; In the formula, are the risk weight coefficients of temperature, salinity, and pollutants respectively; is the error term.

6. The integrated monitoring and early warning method for marine environment according to claim 5 is characterized in that: The multi-scale risk warning threshold matrix group The calculation of is as follows: ; In the formula, are the mean values ​​of historical temperature, salinity, and pollutant risk, respectively; arrive is the threshold adjustment coefficient; is the error term.

7. The integrated monitoring and early warning method for marine environment according to claim 6 is characterized in that: The comprehensive risk warning index is calculated as follows: ; In the formula, is the time scale comprehensive weight; Dynamic adjustment coefficient for early warning; is the error term; The early warning signal judgment formula is: ; In the formula, is the warning level, where 0 means normal, 1 means mild, 2 means moderate, and 3 means severe; is the preset warning level threshold.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the integrated monitoring and early warning method for the marine environment as described in any one of claims 1 to 7.

9. An integrated monitoring and early warning system for the marine environment, characterized in that: Contains the computer-readable storage medium of claim 8.

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