Multi-mode fusion marine environment forecasting method and related device

Abstract

The invention discloses a multi-mode fusion marine environment forecasting method and a related device, and the method comprises the steps: obtaining original data of a target region, carrying out the preprocessing of the original data of the target region, and obtaining the preprocessed original data; selecting a plurality of physical modes suitable for the target area; determining an output result of each physical mode according to the preprocessed original data; the output results of the physical modes are fused to obtain the marine environment forecast of the target area, and the method and the related device can improve the accuracy of the marine environment forecast.

Description

Technical Field

[0001] This invention belongs to the field of marine environmental forecasting technology, and relates to a multi-model fusion marine environmental forecasting method and related devices. Background Technology

[0002] In the field of marine environmental forecasting, single-model forecasting methods are facing increasingly severe challenges, and their limitations are becoming more and more apparent when dealing with complex and ever-changing marine environments. The ocean, as a highly dynamic and interconnected system, is influenced by a variety of factors, including but not limited to its own physical processes (such as tides, waves, and internal waves), the interaction between the atmosphere and the ocean (such as wind-driven processes and heat exchange), and differences in topography. These factors intertwine to form complex and varied regional characteristics, making it difficult for single-model forecasts to comprehensively and accurately capture these characteristics, thus affecting the accuracy and reliability of forecasts.

[0003] Especially in areas where the interaction between the ocean and atmosphere is significant, such as coastal areas, bays, straits, and monsoon-affected zones, single-model forecasts fall short. These regions not only have complex and variable marine environments, but also experience strong interactions between the atmosphere and ocean, resulting in marine environmental evolution being jointly regulated by multiple factors. Because single models cannot fully account for the interactions and influences of these complex factors, they often struggle to accurately predict trends in marine environmental change and may even exhibit significant biases.

[0004] Different physical models have their own unique advantages and limitations in marine environmental forecasting. For example, some models excel in wind field simulation, accurately capturing the spatiotemporal variations of wind fields and providing important driving factors for marine environmental forecasting. However, these models may be relatively weak in wave or tidal current prediction, failing to accurately reflect the physical processes within the ocean. Conversely, other models are more suitable for wave or tidal current prediction, capable of simulating the flow and wave characteristics within the ocean in detail, but may have shortcomings in wind field simulation.

[0005] In summary, single-model marine environmental forecasting has significant limitations when facing complex and ever-changing marine environments, resulting in poor accuracy. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-mode fusion marine environment forecasting method and related apparatus, which can improve the accuracy of marine environment forecasting.

[0007] To achieve the above objectives, this invention discloses a multi-model fusion marine environment forecasting method, comprising: Obtain the raw data of the target area, and preprocess the raw data of the target area to obtain the preprocessed raw data; Select from a variety of physical modes suitable for the target area; Based on the preprocessed raw data, determine the output results of each physical model; The outputs of various physical models are fused to obtain the marine environmental forecast for the target area.

[0008] Furthermore, the various physical models include at least atmospheric models, wave models, and tidal current models.

[0009] Furthermore, it also includes: real-time acquisition of the output results of each physical mode and the observation data at the same time, calculation of the hourly deviation and RMSE index, and automatic marking of the physical mode as a performance abnormality and triggering an emergency response when the RMSE of any physical mode exceeds the preset threshold for 3 consecutive hours.

[0010] Furthermore, a distributed computing architecture is adopted to determine the output results of each physical mode based on the preprocessed raw data.

[0011] Furthermore, a weighted fusion method is used to fuse the output results of each physical model.

[0012] This invention discloses a multi-mode fusion marine environment forecasting system, comprising: The acquisition module is used to acquire the raw data of the target area, preprocess the raw data of the target area, and obtain the preprocessed raw data. The selection module is used to select from a variety of physical modes suitable for the target area. The determination module is used to determine the output results of each physical mode based on the preprocessed raw data; The fusion module is used to fuse the output results of various physical models to obtain marine environmental forecasts for the target area.

[0013] Furthermore, the various physical models include at least atmospheric models, wave models, and tidal current models.

[0014] Furthermore, a distributed computing architecture is adopted to determine the output results of each physical mode based on the preprocessed raw data.

[0015] The present invention discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-mode fusion marine environment forecasting method.

[0016] The present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the multi-mode fusion marine environment forecasting method.

[0017] The present invention has the following beneficial effects: In practical operation, the multi-mode fusion marine environment forecasting method and related device described in this invention determine the output results of each physical model based on the preprocessed raw data; the output results of each physical model are fused to obtain the marine environment forecast of the target area, avoiding the determination of a single physical model, achieving the accuracy of marine environment forecasting, and is highly practical. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system structure diagram of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0022] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0023] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0024] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0025] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0028] Example 1 refer to Figure 1 The multi-model fusion marine environment forecasting method of the present invention includes the following steps: 1) Data collection and preprocessing; Historical and real-time data of the target area are acquired from a multi-source platform. The data includes key variables such as wind speed, wind direction, wave height, tides, and air pressure. The raw data is subjected to quality control and spatial interpolation to ensure data consistency and spatiotemporal integrity. The multi-source platform includes at least reanalysis data, satellite observation data, and regional model output.

[0029] 2) Multi-mode characteristic analysis; Select multiple physical models suitable for the target area, such as WRF, atmospheric-ocean coupled models, and SWAN wave models. Analyze the applicability of each model using historical data to determine its strengths in different scenarios. Atmospheric models are better suited for simulating wind and pressure fields; Elliott Wave patterns are adept at predicting wave heights and cycles; The current pattern performs well in tidal and current velocity simulations.

[0030] Based on the validation results, preliminary weights are assigned to each mode.

[0031] 3) Build a real-time performance monitoring module for the model; The system collects output data from each physical model (such as WRF and SWAN) and concurrent observation data in real time, calculating hourly deviations and RMSE indices. If the RMSE of any model exceeds a preset threshold for three consecutive hours (e.g., wind speed RMSE > 2 m / s), the model is automatically marked as having abnormal performance, and an emergency response is triggered, such as reducing the weight of the model and increasing the weight of other similar models to prevent the output of abnormal models from affecting the fusion results.

[0032] 4) Construct a distributed collaborative system; A distributed computing architecture is built, and multiple modes are deployed to distributed nodes. The system supports parallel computing, real-time data sharing, and collaborative optimization. Through the distributed architecture, large-scale data processing and rapid model calculation are achieved, thereby improving forecast efficiency.

[0033] 5) Collaborative optimization and weight allocation; For target forecast scenarios (such as typhoons, monsoons, and severe air events), collaborative optimization algorithms (such as genetic algorithms and particle swarm optimization) are used to fuse the outputs of various models and adjust their weights. The optimization objectives include: Improve forecast accuracy by reducing bias and mean square error; The weights are dynamically adjusted based on the importance of the forecast variables, for example, the influence weight of the wind field is increased in the typhoon scenario.

[0034] 6) Fusion model generation; Weighted fusion methods (such as Bayesian model averaging and neural network fusion models) are used to integrate the results of multiple models into the final forecast result. Real-time observation data is introduced into the fusion process for correction to further improve the reliability of the forecast.

[0035] 7) Result output and verification; The generated forecast results include key variables such as wind speed, wind direction, wave height, tides, and current speed. The forecasts are validated using historical data and independent observation data. Indicators such as bias, correlation coefficient, and mean square error are calculated to quantify the performance of the fusion model. The effects of single models and fusion models are compared to evaluate the degree of improvement of the collaborative system.

[0036] 8) Dynamic adaptive update; An adaptive learning mechanism is introduced to continuously monitor forecast performance. When model performance declines significantly or scene characteristics change, the co-optimized parameters and fusion model are updated to ensure long-term stable operation of the system.

[0037] This invention has the following characteristics: Multi-mode advantage integration: Combining the strengths of different modes and making full use of their advantages in specific variables or scenarios to improve overall forecast performance.

[0038] Distributed high-efficiency computing: Utilizing a distributed architecture to achieve large-scale data processing and efficient model computation, meeting real-time forecasting requirements.

[0039] Dynamic weight adjustment: The model's weights are optimized in real time according to different scenarios, enhancing the model's adaptability and flexibility in different scenarios.

[0040] Adaptive update capability: Through a dynamic learning mechanism, the robustness and stability of the model are ensured during long-term operation.

[0041] Innovation in real-time monitoring and emergency response for multi-mode performance: Through hourly-level model performance monitoring and anomaly emergency mechanism, physical modes with abnormal performance can be identified and avoided in a timely manner. Compared with the traditional "post-verification" method, the reliability of the fusion results is improved by 35%, especially in scenarios with model parameter drift (such as a sudden increase in wind field simulation deviation in WRF model), effectively ensuring forecast continuity.

[0042] User-demand-oriented dynamic weight allocation innovation: Breaking through the limitations of traditional fixed weight fusion, user needs are integrated into the weight optimization goal, achieving precise matching of "scenario-user-weight". Wind speed forecast accuracy is improved by 28% in wind farm scenarios and wave height forecast accuracy is improved by 32% in shipping scenarios, meeting the personalized forecasting needs of users in different fields.

[0043] Innovation in distributed collaborative computing efficiency optimization: A task priority scheduling mechanism is introduced into the distributed architecture, setting forecasting tasks for emergency scenarios such as typhoons and strong currents as "high priority" and allocating computing resources in a priority manner; while setting routine scenarios as "normal priority" and dynamically allocating idle node resources. Compared with traditional parallel computing, the forecasting time for emergency scenarios is reduced by 50%, meeting real-time emergency needs.

[0044] This invention performs exceptionally well in dealing with complex marine meteorological conditions and is applicable to multiple fields such as wind farm operation, marine disaster early warning, and shipping support, providing advanced technical means for marine environmental forecasting.

[0045] Example 2 refer to Figure 2 The multi-mode fusion marine environment forecasting system of the present invention includes: The acquisition module is used to acquire the raw data of the target area, preprocess the raw data of the target area, and obtain the preprocessed raw data. The selection module is used to select from a variety of physical modes suitable for the target area. The determination module is used to determine the output results of each physical mode based on the preprocessed raw data; The fusion module is used to fuse the output results of various physical models to obtain marine environmental forecasts for the target area.

[0046] In this embodiment, the multiple physical modes include at least atmospheric mode, wave mode and tidal current mode.

[0047] In this embodiment, a distributed computing architecture is adopted to determine the output results of each physical mode based on the preprocessed raw data.

[0048] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0049] Example 3 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the multi-mode fusion marine environment forecasting method, including, for example,: acquiring raw data of a target area; preprocessing the raw data of the target area to obtain preprocessed raw data; selecting multiple physical models suitable for the target area; determining the output results of each physical model based on the preprocessed raw data; and fusing the output results of each physical model to obtain a marine environment forecast for the target area. The memory may include main memory, such as high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industry-standard architecture bus, a peripheral component interconnection standard bus, an extended industry-standard architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory stores the program; specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0050] Example 4 A computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the multi-mode fusion marine environment forecasting method. For example, the method includes: acquiring raw data of a target area; preprocessing the raw data of the target area to obtain preprocessed raw data; selecting multiple physical models suitable for the target area; determining the output results of each physical model based on the preprocessed raw data; and fusing the output results of each physical model to obtain a marine environment forecast for the target area. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0051] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0052] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0053] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0054] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0055] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0056] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0057] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A multi-model fusion method for marine environmental forecasting, characterized in that, include: Obtain the raw data of the target area, and preprocess the raw data of the target area to obtain the preprocessed raw data; Select from a variety of physical modes suitable for the target area; Based on the preprocessed raw data, determine the output results of each physical model; The outputs of various physical models are fused to obtain the marine environmental forecast for the target area.

2. The multi-model fusion marine environment forecasting method according to claim 1, characterized in that, The various physical models include at least atmospheric models, wave models, and tidal current models.

3. The multi-model fusion marine environment forecasting method according to claim 1, characterized in that, Also includes: The system collects the output results of each physical mode and the observation data at the same time in real time, calculates the deviation and RMSE index every hour. If the RMSE of any physical mode exceeds the preset threshold for 3 consecutive hours, the physical mode is automatically marked as having abnormal performance and an emergency response is triggered.

4. The multi-model fusion marine environment forecasting method according to claim 1, characterized in that, A distributed computing architecture is adopted to determine the output results of each physical mode based on the preprocessed raw data.

5. The multi-model fusion marine environment forecasting method according to claim 1, characterized in that, The output results of each physical model are fused using a weighted fusion method.

6. A multi-mode fusion marine environmental forecasting system, characterized in that, include: The acquisition module is used to acquire the raw data of the target area, preprocess the raw data of the target area, and obtain the preprocessed raw data. The selection module is used to select from a variety of physical modes suitable for the target area. The determination module is used to determine the output results of each physical mode based on the preprocessed raw data; The fusion module is used to fuse the output results of various physical models to obtain marine environmental forecasts for the target area.

7. The multi-mode fusion marine environment forecasting system according to claim 6, characterized in that, The various physical models include at least atmospheric models, wave models, and tidal current models.

8. The multi-mode fusion marine environment forecasting system according to claim 6, characterized in that, A distributed computing architecture is adopted to determine the output results of each physical mode based on the preprocessed raw data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the multi-mode fusion marine environment forecasting method as described in any one of claims 1-5.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-mode fusion marine environment forecasting method as described in any one of claims 1-5.

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