Ocean storm early warning method, device and equipment based on remote sensing data

Abstract

The embodiment of the invention discloses an ocean storm early warning method, device and equipment based on remote sensing data, and relates to the technical field of ocean risk early warning. According to the scheme, the method comprises the following steps: acquiring ocean data in an ocean monitoring area, wherein the ocean data at least comprises remote sensing data and actual measurement data; fusing the first feature parameter of the remote sensing data and the second feature parameter of the measured data to obtain a target feature parameter; according to the target characteristic parameters, the ocean storm prediction model is utilized to obtain ocean early warning information, the accuracy of ocean forecasting is improved, and powerful support is provided for ocean management and offshore operation.

Description

Technical Field

[0001] The present application relates to the field of marine risk warning technology, and in particular to a method, device and equipment for marine storm warning based on remote sensing data. Background Art

[0002] Ocean forecasting is mainly based on the research methods of physical ocean, including data analysis methods based on ocean observation data and methods combining data analysis with mechanistic research. Ocean data represents and implies various ocean phenomena and can provide storm warnings for the ocean.

[0003] There are currently many sources of ocean data, such as remote sensing data, observation data, etc. In particular, remote sensing technology has been used in the ocean field with remarkable results in recent years. However, due to the complexity of ocean data, the multi-source nature of data sources, heterogeneous data platforms, and non-standard data formats, the efficiency of ocean data use has been reduced, resulting in low accuracy in ocean forecasts.

[0004] Therefore, how to improve the accuracy of ocean forecasts has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The embodiments of this specification provide an ocean storm warning method based on remote sensing data to solve the problem of low accuracy of ocean forecasting in the prior art.

[0006] To solve the above technical problems, the embodiments of this specification are implemented as follows:

[0007] In a first aspect, an ocean storm warning method based on remote sensing data is provided in an embodiment of this specification, comprising:

[0008] Acquiring ocean data within the ocean monitoring area, wherein the ocean data at least includes remote sensing data and measured data;

[0009] Fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data to obtain a target characteristic parameter;

[0010] According to the target characteristic parameters, the ocean storm prediction model is used to obtain ocean warning information.

[0011] In a second aspect, an ocean storm warning device based on remote sensing data is provided in an embodiment of this specification, comprising:

[0012] An acquisition module, used to acquire ocean data in an ocean monitoring area, wherein the ocean data includes at least remote sensing data and measured data;

[0013] A fusion module, used for fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data to obtain a target characteristic parameter;

[0014] The determination module is used to obtain ocean warning information based on the target characteristic parameters using an ocean storm prediction model.

[0015] In a third aspect, an embodiment of the present specification provides an ocean storm warning device based on remote sensing data, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the ocean storm warning method based on remote sensing data in scheme one.

[0016] An embodiment of the present specification can achieve the following beneficial effects: the present application fuses the characteristic parameters of remote sensing data and measured data, inputs the fused ocean data into an ocean storm prediction model, and obtains ocean warning information, thereby improving the accuracy of ocean forecasts and providing strong support for ocean management and offshore operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 A schematic diagram of a process flow of an ocean storm warning method based on remote sensing data provided in an embodiment of this specification;

[0019] Figure 2 A schematic diagram of the structure of an ocean storm warning device based on remote sensing data provided in an embodiment of this specification;

[0020] Figure 3 A schematic diagram of the structure of an ocean storm warning device based on remote sensing data provided in an embodiment of this specification. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of one or more embodiments of this specification clearer, the technical solutions of one or more embodiments of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of one or more embodiments of this specification.

[0022] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0023] In order to solve the defects in the prior art, this solution provides the following embodiments:

[0024] A method for early warning of ocean storms based on remote sensing data provided in an embodiment of the specification is specifically described in conjunction with the accompanying drawings.

[0025] Figure 1 A schematic flow chart of an ocean storm warning method based on remote sensing data provided in an embodiment of this specification.

[0026] like Figure 1 As shown, the method may include:

[0027] Step 110: Acquire ocean data in the ocean monitoring area, where the ocean data at least includes remote sensing data and measured data.

[0028] In the embodiments of this specification, ocean data may include remote sensing data, measured data, basic spatial data, numerical products, and the like.

[0029] The data includes meteorological data and ocean data. The meteorological data includes visibility, wind speed and direction, temperature, dew point temperature, relative humidity, station air pressure, sea level air pressure, potential height, pressure trend, 3-hour pressure change, precipitation, current weather phenomena, past weather phenomena, total cloud cover, high, medium and low cloud types, maximum temperature, minimum temperature, sunshine, temperature, air pressure, cloud supplementary observation, 24-hour rainfall, special weather phenomena, etc.; the ocean data includes surface sea temperature, wave cycle, wave height, wind and wave cycle, wind and wave height, first / second swell system wave direction, cycle, height, shipboard icing conditions, sea ice density, ice thickness, ice speed, development conditions, sea ice edge direction, high-precision wave height, etc.

[0030] Step 120: Fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data to obtain a target characteristic parameter.

[0031] In the embodiments of this specification, characteristic parameters of remote sensing data and measured data are extracted, and the first characteristic parameters may include sea surface temperature, sea surface wind speed, sea surface roughness, etc., and the second characteristic parameters may include water temperature, flow velocity, wave height, etc.

[0032] By fusing the first characteristic parameter with the second characteristic parameter, a target characteristic parameter combining remote sensing data and measured data can be obtained.

[0033] In practical applications, the acquired basic spatial data and numerical product data can also be integrated with remote sensing data and measured data.

[0034] Step 130: Obtain ocean warning information based on the target characteristic parameters using an ocean storm prediction model.

[0035] In the embodiments of the present specification, the target characteristic parameters are input into the ocean storm prediction model, and the ocean storm prediction model outputs ocean warning information, which may include the probability, intensity, path, etc. of the occurrence of ocean storms.

[0036] In practical applications, based on the output results of the ocean storm prediction model, thresholds and judgment rules can be set to determine the warning level of the ocean storm, and the generated ocean warning information can be promptly released to relevant departments, the public and offshore workers so that they can take appropriate response measures.

[0037] In practical applications, ocean storm prediction models can also be built based on machine learning (such as neural networks, random forests, support vector machines, etc.) or statistical methods (such as time series analysis, regression analysis, etc.).

[0038] It should be understood that the order of some steps in the methods described in one or more embodiments of this specification can be interchanged according to actual needs, or some steps can be omitted or deleted.

[0039] This application integrates the characteristic parameters of remote sensing data and measured data, inputs the integrated ocean data into the ocean storm prediction model, and obtains ocean warning information, thereby improving the accuracy of ocean forecasts and providing strong support for ocean management and offshore operations.

[0040] based on Figure 1 The method in this specification also provides some specific implementation plans of the method, which are described below.

[0041] In order to ensure data quality, optionally, before fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data in the embodiment of this specification, the following may be included:

[0042] Preprocessing the remote sensing data, wherein the preprocessing includes data synchronization and message decoding;

[0043] The preprocessed remote sensing data is stored in the database in grid format.

[0044] In the embodiments of the present specification, preprocessing may include data synchronization and message decoding, wherein data synchronization is to automatically synchronize message data files from a dedicated server via FTP; message decoding is to decode the file, determine and process the required message codes according to the message type, extract the encoded data, and store it for use.

[0045] The steps for data synchronization are as follows:

[0046] 1) Read parameter configuration, including FTP parameters, comparison time selection, time threshold parameters, start interval, log records, etc., which are stored and managed in configuration mode;

[0047] 2) Obtain the directory structure corresponding to the date through the time of each startup, compare the last modification time of the server message file in the directory that meets the requirements, retrieve the message file that meets the conditions and execute the download;

[0048] 3) The downloaded message files are merged, a separator identifier is added to the end of each message file, and the files are renamed and stored in the specified location.

[0049] The steps for message decoding are as follows:

[0050] 1) When message decoding is executed, the data file between two timestamps is processed each time;

[0051] 2) Read the message file, separate the data first, obtain the message block, determine the message type according to the message header, and transfer to the corresponding processing program;

[0052] 3) In each type of processing program, according to the encoding rules, first divide the paragraphs, then match the message groups, and extract the encoding results;

[0053] 4) If the decoding is successful, it will be transferred to data verification. If the decoding fails, it will be transferred to message failure processing and recorded in the log. In the data verification stage, the records that pass the inspection are stored in the warehouse, and the records that fail are marked for later manual review.

[0054] The specific method of message decoding is determined according to the message format, which can be roughly divided into two parts and processed independently. One is observation messages. The paragraph format of this type of message in international exchange is relatively standardized. The type can be directly determined by the simplified header, and then decoded in groups after segmentation. The other type is tropical cyclone warnings. This type of message needs to be processed separately according to the customized rules of the issuing agency.

[0055] The steps for decoding observation-type messages are to extract the simplified header code from the message segment, match the corresponding type, jump to the decoding process, segment and process it according to the message segment, and then match and decode each message segment in groups to obtain a set of decoding results for each type of message in a processing process, perform a preliminary check on the set content data, and store it in batches to complete a processing process.

[0056] The steps of tropical cyclone warning decoding are to extract the simplified header code and issuing agency code from the message segment, match the corresponding type, jump to the decoding process, first segment according to the warning message format, and then process each message segment according to keyword matching, obtain the decoding result set of tropical cyclone warning messages from each agency in one processing process, perform preliminary verification on the collection content data, and store it in batches to complete one processing process.

[0057] In order to effectively store and manage data, a grid format can be used to organize the data by voyage, batch, and time, and store the data at the corresponding spatial location of the grid.

[0058] Optionally, before fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data in the embodiments of this specification, the following steps may also be included:

[0059] Based on the field object-oriented ocean spatiotemporal data model, construct the ocean storm prediction model;

[0060] The ocean storm prediction model is trained using an existing ocean data set to obtain an optimized ocean storm prediction model.

[0061] In the embodiments of the present specification, an ocean storm prediction model is constructed based on an improved field object-oriented ocean spatiotemporal data model. The model as a whole is divided into two major parts, namely a data module and an algorithm module. The field object data organization is replaced by an ocean phenomenon data set, and the analysis and identification of field objects is replaced by ocean phenomenon analysis and identification. It can accurately grasp the overall dynamic changes of data and processes at a higher level, and the system is more integrated and standardized.

[0062] In practical applications, historical ocean data (including remote sensing data and measured data) are preprocessed, including geometric correction, atmospheric correction, radiation correction and spatial registration, to ensure the accuracy and consistency of the data. Feature parameters are extracted and a target feature parameter data set is constructed. The ocean storm prediction model is trained by constructing a target feature parameter data set. The target feature parameter data set is divided into a training set, a validation set and a test set. The training set is used to train the model, the validation set is used to adjust the model parameters and prevent overfitting, and the test set is used to evaluate the final performance of the model.

[0063] Optionally, the fusing of the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data to obtain the target characteristic parameter in the embodiments of this specification may specifically include:

[0064] The first characteristic parameter and the second characteristic parameter are input into a neural network model to obtain the target characteristic parameter output by the neural network model.

[0065] In the embodiments of this specification, remote sensing data is obtained through sensors carried by platforms such as satellites or aircraft, and mainly includes parameters such as spectrum, temperature, salinity, ocean current, wind speed, etc. The original remote sensing data is preprocessed using remote sensing image processing technology, such as radiation correction, geometric correction, atmospheric correction, etc. Subsequently, the required characteristic parameters, such as sea surface temperature, chlorophyll concentration, sea surface roughness, etc., are extracted from the preprocessed remote sensing data through a specific algorithm or model.

[0066] Measured data are usually directly measured by ships, buoys, submersibles and other equipment, and contain more accurate physical or chemical parameters. Depending on the measurement equipment, measured data may include seawater temperature, salinity, flow rate, dissolved oxygen content, etc. After quality control and calibration of the measured data, characteristic parameters corresponding to or complementary to the remote sensing data are extracted.

[0067] Select the parameters that are useful for subsequent analysis from the extracted feature parameters, and remove redundant or irrelevant parameters. The selected feature parameters are further processed and optimized, such as data standardization, normalization, dimensionality reduction (such as principal component analysis PCA), etc., to improve the effectiveness and fusion effect of the features.

[0068] In practical applications, a neural network model is used to take the characteristic parameters of remote sensing data and measured data as input. Through the learning and reasoning process of the model, the fused characteristic parameters are output. The characteristic parameters of remote sensing data and measured data can also be directly combined into a larger characteristic vector or data set. Different weights can be assigned to each characteristic parameter based on the importance or credibility of the characteristic parameters of different data sources, and then weighted summation or weighted average are performed to obtain the fused characteristic parameters or comprehensive indicators.

[0069] The fused feature parameters are further processed and analyzed, such as spatiotemporal interpolation, data assimilation, error estimation, etc., to improve the spatial resolution and temporal continuity of the data.

[0070] The present invention fuses the characteristic parameters of remote sensing data and measured data, inputs the fused ocean data into an ocean storm prediction model, and obtains ocean warning information, thereby improving the accuracy of ocean forecasts and providing strong support for ocean management and offshore operations.

[0071] Figure 2 This is a schematic diagram of the structure of an ocean storm warning device based on remote sensing data proposed in an embodiment of this specification.

[0072] The ocean storm warning device based on remote sensing data recorded in the embodiments of this specification may include:

[0073] An acquisition module 202 is used to acquire ocean data in the ocean monitoring area, wherein the ocean data at least includes remote sensing data and measured data;

[0074] A fusion module 204 is used to fuse the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data to obtain a target characteristic parameter;

[0075] The determination module 206 is used to obtain ocean warning information based on the target characteristic parameters using an ocean storm prediction model.

[0076] Optionally, before fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data in the embodiments of this specification, the following may be included:

[0077] A preprocessing module, used for preprocessing the remote sensing data, wherein the preprocessing includes data synchronization and message decoding;

[0078] The storage module is used to store the pre-processed remote sensing data in the database in the form of a grid.

[0079] Optionally, before fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data in the embodiments of this specification, the following steps may also be included:

[0080] A model building module, used for building the ocean storm prediction model based on the improved field object-oriented ocean spatiotemporal data model;

[0081] The training module is used to train the ocean storm prediction model using the existing ocean data set to obtain an optimized ocean storm prediction model.

[0082] Optionally, the fusing of the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data to obtain the target characteristic parameter in the embodiments of this specification may specifically include:

[0083] The first characteristic parameter and the second characteristic parameter are input into a neural network model to obtain the target characteristic parameter output by the neural network model.

[0084] Based on the same idea, the embodiments of this specification also provide a device corresponding to the above method.

[0085] Figure 3 A schematic diagram of the structure of an ocean storm warning device based on remote sensing data provided in an embodiment of this specification.

[0086] like Figure 3As shown, an ocean storm warning device based on remote sensing data provided in an embodiment of this specification includes a memory 330, a processor 310 and a computer program 320 stored in the memory, and the processor 310 executes the computer program 320 to implement the ocean storm warning method based on remote sensing data described in any of the above embodiments.

[0087] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0088] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0089] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0090] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0091] For the convenience of description, the above device is described in terms of functions and is described separately in various units. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0092] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0097] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0098] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0099] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0100] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0101] The present application may 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, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0102] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for early warning of ocean storms based on remote sensing data, characterized in that: include: Acquiring ocean data within the ocean monitoring area, wherein the ocean data at least includes remote sensing data and measured data; Fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data to obtain a target characteristic parameter; According to the target characteristic parameters, the ocean storm prediction model is used to obtain ocean warning information.

2. The method according to claim 1, characterized in that Before fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data, the method includes: Preprocessing the remote sensing data, wherein the preprocessing includes data synchronization and message decoding; The preprocessed remote sensing data is stored in the database in grid form.

3. The method according to claim 1, characterized in that Before fusing the first characteristic parameter of the remote sensing data with the second characteristic parameter of the measured data, the method further includes: Based on the improved field object-oriented ocean spatiotemporal data model, construct the ocean storm prediction model; The ocean storm prediction model is trained using an existing ocean data set to obtain an optimized ocean storm prediction model.

4. The method according to claim 1, characterized in that The fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data to obtain the target characteristic parameter specifically includes: The first characteristic parameter and the second characteristic parameter are input into a neural network model to obtain the target characteristic parameter output by the neural network model.

5. An ocean storm warning device based on remote sensing data, characterized in that: include: An acquisition module, used to acquire ocean data in an ocean monitoring area, wherein the ocean data includes at least remote sensing data and measured data; A fusion module, used for fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data to obtain a target characteristic parameter; The determination module is used to obtain ocean warning information based on the target characteristic parameters using an ocean storm prediction model.

6. The device according to claim 5, characterized in that Before fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data, the method includes: A preprocessing module, used for preprocessing the remote sensing data, wherein the preprocessing includes data synchronization and message decoding; The storage module is used to store the pre-processed remote sensing data in the database in the form of a grid.

7. The device according to claim 5, characterized in that Before fusing the first characteristic parameter of the remote sensing data with the second characteristic parameter of the measured data, the method further includes: A model building module, used for building the ocean storm prediction model based on the improved field object-oriented ocean spatiotemporal data model; The training module is used to train the ocean storm prediction model using the existing ocean data set to obtain an optimized ocean storm prediction model.

8. The device according to claim 5, characterized in that The fusing the first characteristic parameter of the remote sensing data and the second characteristic parameter of the measured data to obtain the target characteristic parameter specifically includes: The first characteristic parameter and the second characteristic parameter are input into a neural network model to obtain the target characteristic parameter output by the neural network model.

9. An ocean storm warning device based on remote sensing data, comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 4.

Images