A typical marine ecological disaster remote sensing monitoring method, device, equipment and medium

Abstract

This application discloses a typical marine ecological disaster remote sensing monitoring method, apparatus, equipment, and medium, relating to the field of remote sensing monitoring technology. The method includes: obtaining marine algae ecological disaster monitoring data from multi-source land and sea satellite remote sensing data; using a pre-established marine algae ecological disaster identification algorithm to classify marine algae ecological disasters in the marine algae ecological disaster monitoring data; performing three-dimensional quantitative measurement of the classified marine algae ecological disasters to obtain the characteristics and height of the marine algae ecological disasters; and predicting the displacement trajectory of the marine algae ecological disasters based on the characteristics and height of the marine algae ecological disasters. This application fully utilizes multi-source land and sea satellite remote sensing data to monitor marine algae ecological disasters, providing more accurate, intelligent, comprehensive, and effective information support for marine algae ecological disaster monitoring and supervision.

Description

Technical Field

[0001] The present application relates to the field of remote sensing monitoring technology, and in particular to a method, device, equipment and medium for remote sensing monitoring of typical marine ecological disasters. Background Art

[0002] Remote sensing monitoring solutions for marine algae blooms continue to advance, from early reliance on medium-resolution satellite imagery to the gradual adoption of high-resolution drone imagery and now, deep learning-based satellite data processing. These new technologies are continuously improving the accuracy and efficiency of marine algae bloom monitoring products. However, current intelligent marine ecological satellite remote sensing monitoring solutions and information application services still face challenges such as a single data source, limited data coverage, and data accuracy that fails to meet practical application requirements. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to propose a typical marine ecological disaster remote sensing monitoring method, device, equipment and medium to improve the monitoring accuracy of marine algae ecological disasters.

[0004] To achieve the above objectives, one aspect of the present invention provides a method for remote sensing monitoring of typical marine ecological disasters, comprising the following steps:

[0005] Obtain marine algae ecological disaster monitoring data from multi-source land and sea satellite remote sensing data;

[0006] Using a pre-built marine algae ecological disaster identification algorithm to classify the marine algae ecological disasters in the marine algae ecological disaster monitoring data;

[0007] Conduct three-dimensional quantitative measurement of the classified marine algae ecological disaster to obtain the characteristics and height of the marine algae ecological disaster;

[0008] According to the characteristics and height of marine algae ecological disasters, the deviation trajectory of marine algae ecological disasters is predicted.

[0009] In some embodiments, the method of obtaining marine algae ecological disaster monitoring data from multi-source land and sea satellite remote sensing data includes the following steps:

[0010] Obtain the first marine algae ecological disaster monitoring data from ocean satellite remote sensing data;

[0011] Obtain the second marine algae ecological disaster monitoring data from the high-resolution image data in the Landsat remote sensing data;

[0012] The third marine algae ecological disaster monitoring data is obtained from the satellite laser altimetry data in the land satellite remote sensing data.

[0013] In some embodiments, before using the pre-built marine algae ecological disaster identification algorithm to classify the marine algae ecological disaster in the marine algae ecological disaster monitoring data, the method further includes the following steps:

[0014] The data from different data sources in the marine algae ecological disaster monitoring data are integrated.

[0015] In some embodiments, the step of pre-building the marine algae ecological disaster identification algorithm includes the following steps:

[0016] Obtain prior information on the ocean water environment, marine meteorology, satellite spectrum, and texture before and after marine algae ecological disasters;

[0017] Using a time series correlation analysis method to obtain target indicative parameters based on the ocean water environment, the ocean meteorology, the satellite spectrum and the texture information;

[0018] The target indicative parameters and the XGBOOST machine learning algorithm are combined to construct the pixel-by-pixel marine algae ecological disaster identification algorithm.

[0019] In some embodiments, the three-dimensional quantitative measurement of the classified marine algae ecological disaster to obtain the characteristics and height of the marine algae ecological disaster includes the following steps:

[0020] The spectral characteristics and index characteristics of the marine algae ecological disaster obtained by optical data classification were extracted, and the first texture characteristics of the marine algae ecological disaster were extracted using the first three components of the principal component analysis of the original image;

[0021] extracting a backscatter coefficient of the marine algae ecological disaster obtained by classification of the SAR data and extracting a second texture feature of the marine algae ecological disaster from the backscatter coefficient;

[0022] Extracting the elevation characteristics of marine algae ecological disasters obtained by classification of elevation data;

[0023] Performing feature selection and dimensionality reduction on the spectral feature, the index feature, the first texture feature, the second texture feature, and the elevation feature by using a random forest algorithm;

[0024] Laser altimetry satellites are used to monitor and extract the growth range of classified marine algae ecological disasters and the surrounding sea level height.

[0025] In some embodiments, the method of predicting the offset trajectory of the marine algae ecological disaster based on the characteristics and height of the marine algae ecological disaster includes the following steps:

[0026] A prediction model for the trajectory of marine algal ecological disasters was established based on ocean satellite remote sensing data using a mesoscale meteorological model, an unstructured grid nearshore ocean model, and Monte Carlo random statistical theory.

[0027] The marine algae ecological disaster trajectory prediction model is used to calculate the wind-induced drift of the marine algae ecological disaster based on the drift motion of the marine algae ecological disaster under the action of ocean surface fluid and wind, and the velocity decomposition method is used;

[0028] The marine algae ecological disaster trajectory prediction model is used to predict and generate a statistically significant optimal trajectory area that evolves over time based on multiple drifting particles deployed in the area and time period where the marine algae ecological disaster is first observed and various random and uncertain factors in the drift process using the Monte Carlo method;

[0029] The marine algae ecological disaster trajectory prediction model is used to calculate the shore-impact information of marine algae ecological disaster candidates according to specific ocean and meteorological conditions.

[0030] In some embodiments, the method further comprises the following steps:

[0031] Monitoring and early warning information is output according to the offset trajectory of the marine algae ecological disaster.

[0032] To achieve the above objectives, another aspect of the present application provides a typical marine ecological disaster remote sensing monitoring device, the device comprising:

[0033] A data acquisition unit is used to obtain marine algae ecological disaster monitoring data from multi-source land and sea satellite remote sensing data;

[0034] an algae classification unit, configured to classify the marine algae ecological disasters in the marine algae ecological disaster monitoring data using a pre-built marine algae ecological disaster identification algorithm;

[0035] Algae measurement unit, used to perform three-dimensional quantitative measurement of the classified marine algae ecological disasters to obtain the characteristics and height of the marine algae ecological disasters;

[0036] The marine algae ecological disaster trajectory prediction unit is used to predict the deviation trajectory of the marine algae ecological disaster according to the characteristics and height of the marine algae ecological disaster.

[0037] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method when executing the computer program.

[0038] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above-mentioned method when executed by a processor.

[0039] The embodiments of the present application include at least the following beneficial effects:

[0040] This application can obtain marine algae ecological disaster monitoring data from multi-source land and sea satellite remote sensing data; use a pre-built marine algae ecological disaster identification algorithm to classify marine algae ecological disasters in the marine algae ecological disaster monitoring data; perform three-dimensional quantitative measurement of the classified marine algae ecological disasters to obtain the characteristics and height of the marine algae ecological disasters; and predict the displacement trajectory of the marine algae ecological disasters based on the characteristics and height of the marine algae ecological disasters. This application fully utilizes multi-source land and sea satellite remote sensing data to monitor marine algae ecological disasters, providing more accurate, intelligent, comprehensive, and effective information support for marine algae ecological disaster monitoring and supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0042] Figure 1 A flow chart of a typical marine ecological disaster remote sensing monitoring method provided in an embodiment of the present application;

[0043] Figure 2 This is an example flow chart of a typical marine ecological disaster remote sensing monitoring method provided in an embodiment of the present application;

[0044] Figure 3 A true color image spatial distribution map of marine algae ecological disasters provided in an embodiment of the present application;

[0045] Figure 4 A distribution map of marine algae ecological disasters provided in the embodiments of this application;

[0046] Figure 5 Another true color image spatial distribution map of marine algae ecological disasters provided in an embodiment of the present application;

[0047] Figure 6 A schematic structural diagram of a typical marine ecological disaster remote sensing monitoring device provided in an embodiment of the present application;

[0048] Figure 7A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0050] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0051] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0053] Before describing the embodiments of the present application in detail, some of the related technologies involved in the embodiments of the present application are first described as follows:

[0054] Marine remote sensing ecological monitoring, including marine algal blooms, involves multiple aspects, including monitoring and detection, precise measurement, trajectory prediction, and intelligent monitoring information services. For this purpose, medium-resolution satellites, such as MODIS, Sentinel-3, and HY-1C / D, offer more sensitive band responses, larger swath widths, and higher revisit periods, making them one of the most effective means of monitoring large-scale marine algal blooms. Methods for extracting floating algal bloom information using satellite remote sensing technology include supervised classification, the large floating algae index, the sea surface algal bloom index, the floating algae index, the virtual baseline floating algae index, the water color index algorithm, the normalized vegetation index, the discriminant index algorithm, the marine algal bloom index algorithm, the green light algal body index, single-band extraction, the two-band ratio method, the two-band difference method, and a combination of true color and band ratio methods. The identification of marine algal blooms using medium-resolution remote sensing primarily relies on spectral-derived index threshold segmentation. For example, Related Technology 1 proposed using the maximum chlorophyll index (CHI) to identify marine algal blooms in the Gulf of Mexico and the North Atlantic based on MERIS (Medium Resolution Imaging Spectrometer) data, promoting a systematic understanding of the subsequent development trends of marine algal blooms in these waters. Related Technology 2 proposed an Enteromorpha marine algal bloom index and a marine algal bloom index algorithm based on MODIS satellite data to distinguish Enteromorpha from marine algal blooms. Related Technology 3 extracted algal spectral curves and calculated the AFAI index based on MODIS data, extracting the spatiotemporal coverage of marine algal blooms and mapping their drift paths. Regarding the fine-grained measurement of marine algal blooms, researchers have used medium- and low-resolution satellite data (such as MODIS and MERIS) to detect large-scale marine algal blooms over large areas. However, due to limited spatial resolution, they cannot effectively detect small patches of marine algal blooms, making it difficult to track and monitor the temporal changes of marine algal blooms. The advent of high-spatial-resolution imagery has made up for the inability to effectively monitor small marine algae disasters. However, high-spatial-resolution imagery suffers from insufficient spectral resolution, making it difficult to effectively distinguish between marine algae disasters and other large algae when multiple large algae occur simultaneously in the same ocean. Furthermore, in the area of ​​early warning and monitoring of marine algae disaster trajectories, researchers utilized the advanced ROMS model and Lagrangian method to deeply study the characteristics of the Kuroshio Current's path and validated the method's effectiveness by comparing it with various data sets.Since the 1990s, Lagrangian methods based on high-resolution numerical models have gradually matured and have been widely applied in the study of ocean circulation, ocean fronts, and pollutant dispersion. Related article 4 uses the ROMS model and Lagrangian methods to study the complete Kuroshio Current path and its characteristics. Related article 5 uses Lagrangian methods to investigate potential risk areas for the occurrence of Alexandria toxic algal blooms on the northern continental shelf of Chilean Patagonia. Related article 6 compares the results of passive Eulerian tracers and Lagrangian particle trajectories in the migration of cod eggs and larvae. The Lagrangian model allows for parameterization of behavioral characteristics in a variety of ways, offering great flexibility. In the development of intelligent marine ecological satellite remote sensing monitoring products and information application services, research institutions both domestically and internationally are actively exploring the application value of marine ecological satellite remote sensing monitoring data. Through in-depth mining and analysis of remote sensing data, it is possible to monitor the changing patterns of the marine environment and promptly detect and warn of marine pollution and ecological disasters. The technology for developing remote sensing monitoring products such as marine algae ecological disasters is also continuously improving. From the early reliance on medium-resolution satellite imagery, to the gradual adoption of high-resolution drone imagery, and now to the processing of satellite data based on deep learning models, the application of new technologies has continuously improved the accuracy and production efficiency of marine algae ecological disaster monitoring products. However, in the current development of marine ecological satellite remote sensing intelligent monitoring products and information application services, there are still problems such as a single data source, limited data coverage, and data product accuracy that cannot meet actual application needs. Through multi-sensor and multi-source satellite remote sensing data fusion monitoring, and the application of technologies such as big data and artificial intelligence, the potential for mining satellite remote sensing marine ecological monitoring data can be further stimulated, and more accurate and intelligent marine ecological satellite remote sensing intelligent monitoring products and information application services can be developed and constructed, providing more accurate, intelligent, comprehensive and effective information support for marine ecological monitoring and supervision.

[0055] However, the existing technology has technical problems:

[0056] Technical issue 1: Research on key technologies for precise identification and monitoring of marine algae ecological disasters.

[0057] To address the current issues of inaccurate identification of marine algal ecological disasters and high boundary misjudgment rates using traditional ocean remote sensing satellites, this study is investigating key technologies for the precise identification and monitoring of marine algal ecological disasters using medium-resolution satellite remote sensing, specifically exploring the processes of formation, development, migration, and extinction of different marine algal ecological disasters.

[0058] The relationship between it and different marine environmental parameters (such as temperature, salinity, nutrient concentration, flow rate, light, etc.) is clarified, its internal influencing mechanism is selected, and key influencing parameters are selected. The marine algae ecological disaster identification algorithm is constructed in combination with the spectral and texture information of the ocean satellite. At the same time, the research on the refined identification of the boundaries of marine algae ecological disaster areas and the fusion technology of single-day multi-phase and multi-region disaster extraction results is carried out, so as to make the best use of the advantages of multi-satellite high-frequency revisits to realize the timely identification and discovery of marine algae ecological disasters.

[0059] Technical issue 2: Research on three-dimensional quantitative measurement technology of marine algae ecological disasters.

[0060] In response to the problem that the monitoring of marine algae ecological disasters by ocean remote sensing satellites has a relatively large spatial scale, based on the use of medium-resolution ocean satellite remote sensing to achieve fine identification of marine algae ecological disasters, research is carried out on the use of high-resolution multi-source remote sensing images from land satellites combined with feature optimization to accurately identify marine algae ecological disasters, further improving the precision of identification of marine algae ecological disasters. Specifically, the study uses multi-source high-resolution remote sensing data to obtain multi-dimensional feature information of marine algae ecological disasters, including thickness, texture and morphological characteristics, and then studies feature optimization technology to screen and optimize feature information. Research is also conducted on the construction of an identification model for marine algae ecological disasters through machine learning or deep learning models, realizing the construction based on the optimal feature set from multiple sources. In addition, based on the high-precision extraction of marine algae ecological disasters, combined with satellite laser altimetry and ocean dynamics, research is conducted on the three-dimensional quantitative calculation technology of marine algae ecological disasters. The research uses the data obtained by laser altimetry technology to process and analyze, establish a three-dimensional distribution model of marine algae ecological disasters, and realize the precise quantitative calculation of the vertical distribution of marine algae ecological disasters in water bodies.

[0061] Technical issue three: Research on marine algae ecological disaster trajectory prediction technology.

[0062] In view of the problem of trajectory drift caused by the influence of marine environmental factors such as marine meteorology, waves, and flow fields in the trajectory prediction of marine algae ecological disasters, this study uses ocean dynamics and numerical simulation methods, applies Monte Carlo random statistical theory, fully considers the randomness and possible uncertainties in the drift process of drifting objects, and constructs a new Lagrangian method for predicting the trajectory of marine algae ecological disasters. This model provides an important scientific basis for predicting the drift behavior of marine disaster organisms such as marine algae ecological disasters, accurately simulates and predicts the drift trajectory and dynamic changes of marine algae ecological disasters, and provides support for relevant marine environmental monitoring and early warning as well as marine ecological disaster prevention and control.

[0063] Therefore, the embodiments of the present application provide a typical marine ecological disaster remote sensing monitoring method, device, equipment and medium. The technical solution of the present application includes: obtaining marine algae ecological disaster monitoring data from land and sea multi-source satellite remote sensing data; using a pre-built marine algae ecological disaster identification algorithm to classify the marine algae ecological disasters in the marine algae ecological disaster monitoring data; performing three-dimensional quantitative measurement on the classified marine algae ecological disasters to obtain the characteristics and height of the marine algae ecological disasters; and predicting the offset trajectory of the marine algae ecological disasters based on the characteristics and height of the marine algae ecological disasters. The present application makes full use of land and sea multi-source satellite remote sensing data to monitor marine algae ecological disasters, which can provide more accurate, intelligent, comprehensive and effective information support for the monitoring and supervision of marine algae ecological disasters.

[0064] The embodiments of the present application provide a typical marine ecological disaster remote sensing monitoring method, device, equipment and medium, which relate to the field of remote sensing monitoring technology. The typical marine ecological disaster remote sensing monitoring method, device, equipment and medium provided in the embodiments of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements a typical marine ecological disaster remote sensing monitoring method, etc., but is not limited to the above forms.

[0065] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0066] Reference Figure 1 The present application embodiment provides a typical marine ecological disaster remote sensing monitoring method, which may include but is not limited to S100 to S130, as follows:

[0067] S100: Obtain marine algae ecological disaster monitoring data from multi-source satellite remote sensing data on land and sea;

[0068] S110: using a pre-built marine algae ecological disaster identification algorithm to classify the marine algae ecological disaster in the marine algae ecological disaster monitoring data;

[0069] S120: Conduct three-dimensional quantitative measurement of the classified marine algae ecological disaster to obtain the characteristics and height of the marine algae ecological disaster;

[0070] S130: Predicting the deviation trajectory of the marine algae ecological disaster based on the characteristics and height of the marine algae ecological disaster.

[0071] Optionally, obtaining marine algae ecological disaster monitoring data from land and sea multi-source satellite remote sensing data includes the following steps:

[0072] Obtain the first marine algae ecological disaster monitoring data from ocean satellite remote sensing data;

[0073] Obtain the second marine algae ecological disaster monitoring data from the high-resolution image data in the Landsat remote sensing data;

[0074] The third marine algae ecological disaster monitoring data is obtained from the satellite laser altimetry data in the land satellite remote sensing data.

[0075] Optionally, before using the pre-built marine algae ecological disaster identification algorithm to classify the marine algae ecological disaster in the marine algae ecological disaster monitoring data, the method further includes the following steps:

[0076] The data from different data sources in the marine algae ecological disaster monitoring data are integrated.

[0077] Optionally, the step of pre-constructing the marine algae ecological disaster identification algorithm includes the following steps:

[0078] Obtain prior information on the ocean water environment, marine meteorology, satellite spectrum, and texture before and after marine algae ecological disasters;

[0079] Using a time series correlation analysis method to obtain target indicative parameters based on the ocean water environment, the ocean meteorology, the satellite spectrum and the texture information;

[0080] The target indicative parameters and the XGBOOST machine learning algorithm are combined to construct the pixel-by-pixel marine algae ecological disaster identification algorithm.

[0081] Optionally, the three-dimensional quantitative measurement of the classified marine algae ecological disaster to obtain the characteristics and height of the marine algae ecological disaster includes the following steps:

[0082] The spectral characteristics and index characteristics of the marine algae ecological disaster obtained by optical data classification were extracted, and the first texture characteristics of the marine algae ecological disaster were extracted using the first three components of the principal component analysis of the original image;

[0083] extracting a backscatter coefficient of the marine algae ecological disaster obtained by classification of the SAR data and extracting a second texture feature of the marine algae ecological disaster from the backscatter coefficient;

[0084] Extracting the elevation characteristics of marine algae ecological disasters obtained by classification of elevation data;

[0085] Performing feature selection and dimensionality reduction on the spectral feature, the index feature, the first texture feature, the second texture feature, and the elevation feature by using a random forest algorithm;

[0086] Laser altimetry satellites are used to monitor and extract the growth range of classified marine algae ecological disasters and the surrounding sea level height.

[0087] Optionally, the predicting of the offset trajectory of the marine algae ecological disaster according to the characteristics and height of the marine algae ecological disaster comprises the following steps:

[0088] A prediction model for the trajectory of marine algal ecological disasters was established based on ocean satellite remote sensing data using a mesoscale meteorological model, an unstructured grid nearshore ocean model, and Monte Carlo random statistical theory.

[0089] The marine algae ecological disaster trajectory prediction model is used to calculate the wind-induced drift of the marine algae ecological disaster based on the drift motion of the marine algae ecological disaster under the action of ocean surface fluid and wind, and the velocity decomposition method is used;

[0090] The marine algae ecological disaster trajectory prediction model is used to predict and generate a statistically significant optimal trajectory area that evolves over time based on multiple drifting particles deployed in the area and time period where the marine algae ecological disaster is first observed and various random and uncertain factors in the drift process using the Monte Carlo method;

[0091] The marine algae ecological disaster trajectory prediction model is used to calculate the shore-impact information of marine algae ecological disaster candidates according to specific ocean and meteorological conditions.

[0092] Optionally, the method further comprises the following steps:

[0093] Monitoring and early warning information is output according to the offset trajectory of the marine algae ecological disaster.

[0094] Next, the solution of the embodiment of the present application will be introduced and explained in detail with reference to specific application examples.

[0095] Illustratively, this embodiment may include the following technical solutions:

[0096] Optionally, the marine algae ecological disaster studied in this application may include Sargassum, and the monitoring of Sargassum may be carried out with reference to the following examples.

[0097] For example, an optional Sargassum monitoring flow chart based on the embodiment is as follows: Figure 2 shown.

[0098] First, the technical solution studied in this embodiment is described.

[0099] Research content 1: Research on key technologies for precise identification and monitoring of Sargassum using ocean remote sensing satellites.

[0100] The research aims to address the complex coupling relationships between multiple environmental factors in the Sargassum marine disaster, building an efficient remote sensing identification algorithm based on big data machine learning to accurately monitor and promptly identify the disaster. Specific work includes collecting disaster-related data and integrating data from different environmental factors; designing and implementing a disaster identification algorithm based on the coupling of multiple environmental factors; identifying the main influencing factors and mechanisms; verifying the accuracy and reliability of the algorithm; and developing the optimal extraction method and parameters for Sargassum disasters.

[0101] Research content 2: Research on key technologies for three-dimensional quantitative measurement of Sargassum using ocean land satellites.

[0102] This study addresses the limitations of traditional Sargassum monitoring methods, which are limited to two-dimensional area monitoring and ignore three-dimensional features, as well as data redundancy and potential "dimensionality disasters" caused by multi-source and multi-feature fusion. This study investigates the precise area calculation of Sargassum using multi-source remote sensing and three-dimensional quantitative measurement using laser altimetry. By analyzing the correlation between different data sources and their features, and applying feature selection or machine learning methods to determine the optimal feature set, this simplifies the data processing process, reduces redundant information, and lowers computational costs, improving remote sensing image processing accuracy and enhancing remote sensing image classification and recognition performance, thus providing data and technical support for the precise extraction of Sargassum. Furthermore, by utilizing remote sensing technology and digital image processing methods, the precise calculation and quantitative analysis of the three-dimensional structure of Sargassum can be achieved. Combining laser altimetry data with multi-source remote sensing imagery and using three-dimensional reconstruction technology, three-dimensional quantitative calculation of Sargassum can be carried out, comprehensively and accurately obtaining information on its three-dimensional structure, growth status, and spatial distribution, providing refined measurement data and information services for the early warning and prevention of Sargassum disasters.

[0103] Research content three: Research on key technologies for marine Sargassum monitoring trajectory prediction.

[0104] The operating principles of numerical forecast models are studied, and forecast data processing is carried out. By analyzing historical numerical forecast data, necessary preprocessing and corrections are performed on ocean satellite monitoring data. Based on this, the application of numerical forecast data to ocean convection-diffusion equations is studied, providing a dynamic basis for simulating the spread of marine hazards such as Sargassum. Furthermore, through research on the modeling and solution of unsteady ocean convection-diffusion equations, unsteady ocean convection-diffusion equations with source terms are constructed to simulate the movement and diffusion of Sargassum in the ocean. A complete Lagrangian simulation model for Sargassum is developed, coupling high-precision long-term numerical forecast data results from ocean meteorology, waves, flow fields, etc., and applying them to the precise prediction of Sargassum trajectory changes. Ultimately, detailed Sargassum forecast distribution results for the simulation area are generated, and the simulation results are analyzed.

[0105] Next, the specific research method of this embodiment is described.

[0106] Research method 1: Fine-grained identification method of Sargassum based on ocean remote sensing satellite.

[0107] A medium-resolution ocean remote sensing satellite Sargassum fine-grained identification algorithm was constructed. Taking the prior Sargassum disaster event as the research object, the ocean water environment, marine meteorology, satellite spectrum, and texture information before and after the disaster were collected. The time series correlation analysis method was used to obtain the main indicative parameters. Combined with the XGBOOST machine learning algorithm, a pixel-by-pixel Sargassum disaster classification algorithm was constructed. At the same time, mixed response decomposition was carried out on the pixels in the disaster boundary area, and disaster identification was performed again to obtain refined disaster classification results.

[0108] Research method 2: A precise measurement method of Sargassum based on multi-source land satellite high-resolution remote sensing images and satellite laser altimetry data.

[0109] On the one hand, it involves the extraction and optimization technology of Sargassum features from multi-source remote sensing images: in order to better highlight the features of Sargassum and reduce data redundancy, this embodiment quantitatively evaluates the contribution of each feature to the Sargassum extraction results by means of feature optimization. For optical data, spectral features and index features are extracted, and the first three components of the principal component analysis of the original image are used to extract texture features. For SAR data, backscatter coefficients and texture features extracted from the backscatter coefficients are extracted. For elevation data, Sargassum elevation features are extracted. In order to reduce feature redundancy and avoid the "curse of dimensionality" while optimizing multi-source features, the classification of remote sensing images is achieved through the random forest algorithm, while supporting feature selection and dimensionality reduction, and realizing the construction of the optimal feature set for multi-source remote sensing images.

[0110] Another aspect involves extracting Sargassum altitude from laser altimetry data. This example utilizes laser altimetry satellites such as the Gaofen-7 and ICESat-2 to monitor and extract sea level elevation within and around the Sargassum growth area. The Gaofen-7 satellite's dual-beam laser altimetry system acquires high-precision elevation control points, improving the elevation accuracy of optical camera stereo mapping. ICESat-2, equipped with a photon-counting LiDAR (Light Detection and Ranging) system and ATLAS (Advanced Terrain Laser Altimeter System), primarily uses sea level data to design a Gaussian fitting algorithm to identify the peaks and valleys of Sargassum.

[0111] Research method three: Sargassum trajectory prediction method based on ocean remote sensing satellite.

[0112] This example uses ocean satellite remote sensing data to develop a Sargassum trajectory prediction model system using the mesoscale weather model WRF (Weather Research and Forecasting Model), the unstructured grid nearshore ocean model FVCOM (Finite-Volume Coastal and Ocean Model), and Monte Carlo stochastic statistics. The model primarily considers the drifting motion of Sargassum under the influence of ocean surface fluids and wind. It also employs a velocity decomposition method, unlike the wind angle method widely used in existing prediction models, to calculate wind-induced drift. Furthermore, the model design considers the complexity and dynamics of drifting objects. By deploying a large number of drifting particles within the region and time period where the Sargassum was first observed, it also accounts for various random and uncertain factors in the drift process. Using the Monte Carlo method, the model predicts and generates statistically significant, time-evolving optimal trajectory regions. Furthermore, it calculates potential Sargassum landing information based on specific oceanographic and meteorological conditions.

[0113] By building a complete business process for data processing and application service development for coordinated monitoring of marine ecology by land and sea satellite remote sensing, relying on big data and cloud services, we will develop an intelligent information service system for coordinated monitoring of marine ecology by land and sea remote sensing satellites, and construct a quantitative monitoring plan for Sargassum.

[0114] Next, exemplary implementations of this embodiment will be described with reference to the accompanying drawings.

[0115] For the tracking and monitoring of the outbreak of Sargassum in Haikou Bay, by applying the Sargassum remote sensing monitoring method of this embodiment, refer to Figure 3 , extracting satellite image remote sensing data, found that there are still large strips of Sargassum floating in the waters north of Haikou Bay, with a distribution length of about 34km and the shortest distance from the shore of about 4km. It is expected that in the next 48 hours, the Sargassum will drift towards Haikou Bay, and a large amount of Sargassum will accumulate on the Haikou beach (see Figure 4 Satellite remote sensing interpretation and analysis showed that Sargassum was distributed in a strip-like pattern about 4 km south of a certain place, with a range of about 8 km in length and an area of ​​about 0.09 km. 2 The initial judgment is that it is the source of the recent landing of Sargassum in Haikou Bay (see Figure 5 It is expected that after this round of Sargassum floating ashore, there may not be further large-scale Sargassum landings in the future.

[0116] The beneficial effects of this embodiment include:

[0117] 1. Marine remote sensing satellites that take into account all factors such as environment and spectrum to provide refined identification of Sargassum disasters.

[0118] On the basis of using the traditional satellite's own spectrum and texture information to carry out disaster identification, a variety of marine environmental parameter information (such as temperature, salinity, nutrient concentration, flow rate, light, etc.) is introduced to improve the accuracy of the corresponding Sargassum algorithm recognition. In the face of the problem of high misclassification rate of disaster boundaries, hybrid pixel decomposition technology is used in the boundary area to improve the accuracy of the disaster boundary extraction range, and a single-day disaster extraction product fusion algorithm is developed to form a standardized output product. The advantage of high-frequency revisits of high-resolution satellites is maximized to form a high-precision medium-resolution ocean remote sensing satellite Sargassum disaster monitoring product.

[0119] 2. Three-dimensional quantitative monitoring of Sargassum using multi-source remote sensing images and laser altimetry data.

[0120] Overcoming the limitations of traditional Sargassum monitoring methods that are limited to two-dimensional area monitoring and ignore three-dimensional features, as well as three-dimensional quantitative monitoring technology, by fusing multi-source remote sensing images and satellite remote sensing laser altimetry data, and combining ocean dynamics to construct a three-dimensional height calculation model, we break through the limitations of traditional monitoring methods and provide a new solution for the three-dimensional quantitative calculation of Sargassum.

[0121] 3. Research on the establishment of Sargassum regional forecast model and numerical forecast data assimilation and processing technology.

[0122] Further development of simulation data from ocean satellite remote sensing numerical forecasts has enabled the application of high-precision, long-term numerical forecast data for ocean meteorology, waves, and current fields to the simulation of Sargassum trajectories. Furthermore, an innovative combination of Lagrangian methods and Monte Carlo stochastic statistics has been employed to simulate the corresponding uncertainties through random sampling, providing a probabilistic estimate of possible trajectory predictions under different scenarios.

[0123] Reference Figure 6 The present application also provides a typical marine ecological disaster remote sensing monitoring device, which can implement the above-mentioned typical marine ecological disaster remote sensing monitoring method. The device includes:

[0124] A data acquisition unit is used to obtain marine algae ecological disaster monitoring data from multi-source land and sea satellite remote sensing data;

[0125] an algae classification unit, configured to classify the marine algae ecological disasters in the marine algae ecological disaster monitoring data using a pre-built marine algae ecological disaster identification algorithm;

[0126] Algae measurement unit, used to perform three-dimensional quantitative measurement of the classified marine algae ecological disasters to obtain the characteristics and height of the marine algae ecological disasters;

[0127] The marine algae ecological disaster trajectory prediction unit is used to predict the deviation trajectory of the marine algae ecological disaster according to the characteristics and height of the marine algae ecological disaster.

[0128] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0129] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method of the present application. The electronic device can be any smart terminal, such as a tablet computer or an in-vehicle computer.

[0130] It can be understood that the contents of the above method embodiments are all applicable to the embodiments of the present device, the functions specifically implemented by the embodiments of the present device are the same as those of the method of the present application, and the beneficial effects achieved are also the same as those achieved by the method of the present application.

[0131] See also Figure 7 , Figure 7The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0132] The processor 701 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0133] The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called by the processor 701 to execute the methods of the embodiments of this application.

[0134] Input / output interface 703, used to implement information input and output;

[0135] Communication interface 704, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0136] Bus 705 , which transmits information between various components of the device (e.g., processor 701 , memory 702 , input / output interface 703 , and communication interface 704 );

[0137] The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0138] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method of the present application is implemented.

[0139] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0140] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0141] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0142] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0143] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0144] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0145] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0146] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0148] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0150] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0151] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A typical marine ecological disaster remote sensing monitoring method, characterized in that: The method comprises the following steps: Obtain marine algae ecological disaster monitoring data from multi-source land and sea satellite remote sensing data; Using a pre-built marine algae ecological disaster identification algorithm to classify the marine algae ecological disasters in the marine algae ecological disaster monitoring data; The marine algae ecological disaster obtained by classification is measured in a three-dimensional quantitative manner to obtain the characteristics and height of the marine algae ecological disaster; specifically, the method includes: extracting spectral features and index features of the marine algae ecological disaster obtained by classification based on optical data, and extracting the first texture features of the marine algae ecological disaster using the first three components of the principal component analysis of the original image; extracting the backscattering coefficient of the marine algae ecological disaster obtained by classification based on SAR data, and extracting the second texture features of the marine algae ecological disaster from the backscattering coefficient; extracting the elevation features of the marine algae ecological disaster obtained by classification based on elevation data; performing feature selection and dimensionality reduction on the spectral features, the index features, the first texture features, the second texture features and the elevation features through a random forest algorithm; monitoring and extracting the growth range of the classified marine algae ecological disaster and the surrounding sea level using a laser altimetry satellite; wherein the height is the thickness of the marine algae obtained by satellite laser altimetry; and the features include texture features and morphological features of the marine algae. According to the characteristics and height of the marine algae ecological disaster, the deviation trajectory of the marine algae ecological disaster is predicted; The method of predicting the displacement trajectory of the marine algae ecological disaster based on the characteristics and height of the marine algae ecological disaster includes the following steps: A prediction model for the trajectory of marine algal ecological disasters was established based on ocean satellite remote sensing data using a mesoscale meteorological model, an unstructured grid nearshore ocean model, and Monte Carlo random statistical theory. The marine algae ecological disaster trajectory prediction model is used to calculate the wind-induced drift of the marine algae ecological disaster based on the drift motion of the marine algae ecological disaster under the action of ocean surface fluid and wind, and the velocity decomposition method is used; The marine algae ecological disaster trajectory prediction model is used to predict and generate a statistically significant optimal trajectory area that evolves over time based on multiple drifting particles deployed in the area and time period where the marine algae ecological disaster is first observed and various random and uncertain factors in the drift process using the Monte Carlo method; The marine algae ecological disaster trajectory prediction model is used to calculate the shore-impact information of marine algae ecological disaster candidates according to specific ocean and meteorological conditions.

2. A typical marine ecological disaster remote sensing monitoring method according to claim 1, characterized in that: The method of obtaining marine algae ecological disaster monitoring data from land and sea multi-source satellite remote sensing data includes the following steps: Obtain the first marine algae ecological disaster monitoring data from ocean satellite remote sensing data; Obtain the second marine algae ecological disaster monitoring data from the high-resolution image data in the Landsat remote sensing data; The third marine algae ecological disaster monitoring data is obtained from the satellite laser altimetry data in the land satellite remote sensing data.

3. A typical marine ecological disaster remote sensing monitoring method according to claim 1, characterized in that: Before classifying the marine algae ecological disasters in the marine algae ecological disaster monitoring data using the pre-built marine algae ecological disaster identification algorithm, the method further includes the following steps: The data from different data sources in the marine algae ecological disaster monitoring data are integrated.

4. A typical marine ecological disaster remote sensing monitoring method according to claim 1, characterized in that: The steps of pre-constructing the marine algae ecological disaster identification algorithm include the following steps: Obtain prior information on the ocean water environment, marine meteorology, satellite spectrum, and texture before and after marine algae ecological disasters; Using a time series correlation analysis method to obtain target indicative parameters based on the ocean water environment, the ocean meteorology, the satellite spectrum and the texture information; The target indicative parameters and the XGBOOST machine learning algorithm are combined to construct the pixel-by-pixel marine algae ecological disaster identification algorithm.

5. A remote sensing monitoring method for typical marine ecological disasters according to any one of claims 1 to 4, characterized in that: The method further comprises the following steps: Monitoring and early warning information is output according to the offset trajectory of the marine algae ecological disaster.

6. A typical marine ecological disaster remote sensing monitoring device, characterized in that: The device comprises: A data acquisition unit is used to obtain marine algae ecological disaster monitoring data from multi-source land and sea satellite remote sensing data; an algae classification unit, configured to classify the marine algae ecological disasters in the marine algae ecological disaster monitoring data using a pre-built marine algae ecological disaster identification algorithm; An algae measurement unit is used to perform three-dimensional quantitative measurement on the classified marine algae ecological disaster to obtain the characteristics and height of the marine algae ecological disaster; specifically comprising: extracting spectral features and index features from the marine algae ecological disaster classified according to optical data, and extracting the first texture features of the marine algae ecological disaster using the first three components of the principal component analysis of the original image; extracting backscattering coefficients from the marine algae ecological disaster classified according to SAR data, and extracting the second texture features of the marine algae ecological disaster from the backscattering coefficients; extracting the elevation features of the marine algae ecological disaster classified according to elevation data; performing feature selection and dimensionality reduction on the spectral features, the index features, the first texture features, the second texture features, and the elevation features using a random forest algorithm; and monitoring and extracting the growth range of the classified marine algae ecological disaster and the surrounding sea level using a laser altimetry satellite; wherein the height is the thickness of the marine algae obtained by satellite laser altimetry; and the features include texture features and morphological features of the marine algae. a marine algae ecological disaster trajectory prediction unit, configured to predict a deviation trajectory of the marine algae ecological disaster based on the characteristics and height of the marine algae ecological disaster; The method of predicting the offset trajectory of the marine algae ecological disaster based on the characteristics and height of the marine algae ecological disaster includes the following steps: A prediction model for the trajectory of marine algal ecological disasters was established based on ocean satellite remote sensing data using a mesoscale meteorological model, an unstructured grid nearshore ocean model, and Monte Carlo random statistical theory. The marine algae ecological disaster trajectory prediction model is used to calculate the wind-induced drift of the marine algae ecological disaster based on the drift motion of the marine algae ecological disaster under the action of ocean surface fluid and wind, and the velocity decomposition method is used; The marine algae ecological disaster trajectory prediction model is used to predict and generate a statistically significant optimal trajectory area that evolves over time based on multiple drifting particles deployed in the area and time period where the marine algae ecological disaster is first observed and various random and uncertain factors in the drift process using the Monte Carlo method; The marine algae ecological disaster trajectory prediction model is used to calculate the shore-impact information of marine algae ecological disaster candidates according to specific ocean and meteorological conditions.

7. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

Images