Marine holographic environment comprehensive digital twinning system

Abstract

The invention discloses an ocean holographic environment comprehensive digital twinning system, and relates to the technical field of ocean monitoring, the digital twinning system comprises a data layer, a model layer, an application layer, an environment layer, a governance layer and a service layer, multi-scale spatio-temporal data is used as a substrate, and a virtual-real mapping and intelligent simulation technology is used to simulate the ocean holographic environment comprehensive digital twinning system. A full-dimension virtual mirror image covering the seabed, the middle sea and the sea surface is constructed, and the system serves core scenes such as a supervisor, scientific research cooperation and ocean engineering. According to the digital twin system, a six-layer layered architecture is adopted, edge computing and cloud computing collaboration are combined, a data-model-service-governance-environment multi-dimensional collaboration system is formed, data-driven decision making and dynamic optimization are achieved, and the marine monitoring efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of ocean monitoring technology, and in particular to a comprehensive digital twin system of an ocean holographic environment. Background Art

[0002] Existing ocean digital twin technologies still face numerous technical deficiencies and challenges. At the data level, ocean observation data suffers from significant spatiotemporal discontinuities and inaccuracies, particularly in the deep sea and polar regions, where data coverage is low. This leads to significant errors in the initial and boundary conditions of the twin model. The integration of heterogeneous data from multiple sources (such as satellite remote sensing, buoys, and underwater sensors) is still limited by insufficient standardization and real-time transmission delays. For example, satellite data is subject to cloud interference, while underwater communication bandwidth is limited. At the model level, existing ocean numerical models (such as ROMS and FVCOM) have high computational complexity, making it difficult to achieve real-time simulation while ensuring accuracy. Furthermore, parameterization schemes for multi-physics coupling (such as sea-air-biochemical interactions) rely on empirical assumptions, introducing uncertainty. Furthermore, traditional data assimilation methods (such as Kalman filtering) are computationally expensive in high-dimensional nonlinear systems, while AI alternatives (such as deep learning) suffer from poor interpretability and a lack of physical constraints.

[0003] At the system integration level, ocean digital twins lack cross-platform collaboration capabilities. Subsystems (such as observation networks, model libraries, and visualization tools) often employ closed architectures, resulting in limited data sharing and interoperability. For example, commercial software (such as Delft3D) and open-source models (such as MITgcm) lack interface compatibility, limiting flexible expansion. At the application level, existing technologies have limited predictive capabilities for extreme events (such as marine heatwaves and sudden red tides), partly due to a scarcity of training data for low-probability events and insufficient model generalization. Furthermore, edge computing devices (such as underwater nodes) face a significant conflict between computing power and energy consumption, making them difficult to support long-term autonomous operation. Future innovations, such as quantum computing acceleration, physically enhanced AI, and standardized data middleware, will be needed to systematically address these shortcomings. Summary of the Invention

[0004] In view of this, an embodiment of the present application provides a comprehensive digital twin system of an ocean holographic environment to improve the efficiency and accuracy of ocean monitoring.

[0005] The embodiment of the present application provides a comprehensive digital twin system of an ocean holographic environment, the system comprising: a data layer, a model layer, an application layer, an environment layer, a governance layer, and a service layer;

[0006] The data layer is configured to collect multi-source heterogeneous ocean data through edge computing nodes and distributed sensor networks, and build a spatiotemporal data lake;

[0007] The model layer is configured to construct a multi-scale simulation model based on a physical-data hybrid modeling engine;

[0008] The application layer is configured to provide intelligent decision-making services for regulators, scientific research collaboration, and marine engineering scenarios;

[0009] The environment layer is configured to provide infrastructure support through an integrated air-space-ground-sea communication network and an edge-cloud collaborative computing architecture;

[0010] The governance layer is configured to achieve data trust governance and model compliance auditing through blockchain evidence storage and federated learning framework;

[0011] The service layer is configured to encapsulate intelligent analysis functions through a microservice architecture and provide a standardized API interface.

[0012] In some embodiments, the data layer includes:

[0013] Air-based remote sensing monitoring module, sea-based sonar point cloud acquisition module, land-based buoy monitoring module, and edge computing nodes for spatiotemporal alignment and data cleaning;

[0014] The spatiotemporal data lake adopts a hierarchical storage architecture and realizes semantic association through a knowledge graph engine.

[0015] In some embodiments, the model layer includes:

[0016] Physical ocean models, machine learning compensation models, and a federated learning framework for model coupling;

[0017] The multi-scale simulation model covers kilometer-level ocean circulation simulation to centimeter-level seabed topography reconstruction.

[0018] In some embodiments, the application layer includes:

[0019] Disaster early warning module, ecological protection module, and engineering health monitoring module;

[0020] The intelligent decision-making service builds a three-dimensional visualization system through a low-code platform, supporting real-time rendering of more than 100,000 dynamic objects.

[0021] In some embodiments, the environment layer includes:

[0022] Edge computing nodes, Tianhe supercomputing cloud data center;

[0023] The integrated air-space-ground-sea communication network integrates 5G-MEC base stations, blue-green laser communication modules and quantum key distribution networks.

[0024] In some embodiments, the governance layer includes:

[0025] A blockchain evidence chain based on Hyperledger Fabric, used to generate SHA-3 hash fingerprints of data operation logs;

[0026] The model's full lifecycle management system supports version rollback and model compliance auditing.

[0027] In some embodiments, the service layer includes:

[0028] Intelligent analysis platform, integrating YOLOv5 target detection algorithm and OpenFOAM fluid dynamics engine;

[0029] Business application factory, which provides a low-code development environment and digital asset library;

[0030] The standardized API interface is used to encapsulate typhoon path deduction and ecological assessment functions.

[0031] In some embodiments, the data layer further includes:

[0032] Cross-modal attention mechanism for fusing sonar point clouds with hyperspectral remote sensing data;

[0033] A federated learning framework for aggregating knowledge fragments at edge nodes.

[0034] In some embodiments, the model layer further includes:

[0035] Physically constrained neural network architecture for integrating atmospheric boundary layer equations to optimize typhoon path prediction;

[0036] Digital thread management system, used to verify the consistency of model parameters and measured data in real time.

[0037] In some embodiments, the service layer further includes:

[0038] AR digital twin sandbox, overlaying virtual warning information onto real-world charts through gesture interaction and voice commands;

[0039] Causal reasoning engine to quantitatively evaluate the effects of marine engineering interventions.

[0040] This application has at least the following beneficial effects:

[0041] The digital twin system of this application includes a data layer, a model layer, an application layer, an environment layer, a governance layer, and a service layer. Based on multi-scale spatiotemporal data, it uses virtual-reality mapping and intelligent simulation technology to construct a full-dimensional virtual mirror covering the seabed, the sea, and the sea surface, serving core scenarios such as regulators, scientific research collaboration, and marine engineering. The digital twin system of this application adopts a six-layered architecture, combining edge computing with cloud computing to form a multi-dimensional collaborative system of "data-model-service-governance-environment", enabling data-driven decision-making and dynamic optimization, thereby improving the efficiency and accuracy of marine monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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.

[0043] Figure 1 A schematic structural diagram of a comprehensive digital twin system for an ocean holographic environment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0045] 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:

[0046] The ocean digital twin system is a deep application of digital twin technology in the ocean sector. It aims to construct a dynamic mirror of the virtual ocean through multi-source data fusion, physical modeling, and real-time simulation to support scientific research, resource development, and disaster warning. While its technical background is derived from industrial digital twins, it faces more complex challenges, including the multi-scale dynamics of the ocean environment, data heterogeneity (e.g., satellites, buoys, underwater sensors), highly complex model coupling (e.g., fluid dynamics and ecological models), and real-time requirements (e.g., minute-by-minute response for typhoon warnings). Key technologies supporting this system include global ocean observing networks (e.g., Argo floats), high-performance computing (e.g., CMEMS petabyte-scale data processing), multi-physics coupled models (e.g., ROMS and FVCOM), and artificial intelligence (e.g., data assimilation and anomaly detection). However, the sparsity of ocean data and model uncertainty (e.g., turbulence parameterization) remain key challenges, requiring continuous optimization through interdisciplinary integration and real-time data assimilation.

[0047] Existing technical solutions can be categorized into general platforms and vertical applications. Europe's Copernicus Marine Service (CMEMS) and the US NOAA's IOOS are representative of general platforms. The former provides high-precision sea surface temperature forecasts based on global models such as NEMO and satellite data, while the latter enables rapid hurricane warnings through distributed sensor networks (such as underwater gliders) and cloud platforms. Vertical applications focus on specific scenarios, such as the Port of Rotterdam optimizing berth efficiency through BIM and ship traffic simulation, Australia's ReefOS using underwater cameras and machine learning to monitor coral health, and Denmark's wind power operation and maintenance system combining wave models with digital threads to predict wind turbine failures. The technology stacks of these systems encompass a full range of tools, from data acquisition (satellite, AIS), processing (Spark, GDAL), modeling (MITgcm, OpenFOAM), to visualization (CesiumJS, Unity3D). AI technologies (such as physical information neural networks) further enhance simulation and prediction capabilities.

[0048] The current development of ocean digital twins requires overcoming technical bottlenecks such as deep-sea communications and multi-model computing efficiency, while also incorporating cutting-edge research areas such as quantum computing (e.g., vortex identification and optimization) and the metaverse (multi-user collaborative interaction). As it evolves from static simulation to autonomous decision-making, this system is expected to become the "ocean brain" that integrates ocean perception, analysis, and management, providing core support for sustainable development.

[0049] Disadvantages of existing technology:

[0050] Existing ocean digital twin technologies still face numerous technical deficiencies and challenges. At the data level, ocean observation data suffers from significant spatiotemporal discontinuities and inaccuracies, particularly in the deep sea and polar regions, where data coverage is low. This leads to significant errors in the initial and boundary conditions of the twin model. The integration of heterogeneous data from multiple sources (such as satellite remote sensing, buoys, and underwater sensors) is still limited by insufficient standardization and real-time transmission delays. For example, satellite data is subject to cloud interference, while underwater communication bandwidth is limited. At the model level, existing ocean numerical models (such as ROMS and FVCOM) have high computational complexity, making it difficult to achieve real-time simulation while ensuring accuracy. Furthermore, parameterization schemes for multi-physics coupling (such as sea-air-biochemical interactions) rely on empirical assumptions, introducing uncertainty. Furthermore, traditional data assimilation methods (such as Kalman filtering) are computationally expensive in high-dimensional nonlinear systems, while AI alternatives (such as deep learning) suffer from poor interpretability and a lack of physical constraints.

[0051] At the system integration level, ocean digital twins lack cross-platform collaboration capabilities. Subsystems (such as observation networks, model libraries, and visualization tools) often employ closed architectures, resulting in limited data sharing and interoperability. For example, commercial software (such as Delft3D) and open-source models (such as MITgcm) lack interface compatibility, limiting flexible expansion. At the application level, existing technologies have limited predictive capabilities for extreme events (such as marine heatwaves and sudden red tides), partly due to a scarcity of training data for low-probability events and insufficient model generalization. Furthermore, edge computing devices (such as underwater nodes) face a significant conflict between computing power and energy consumption, making them difficult to support long-term autonomous operation. Future innovations, such as quantum computing acceleration, physically enhanced AI, and standardized data middleware, will be needed to systematically address these shortcomings.

[0052] Reference Figure 1 , an embodiment of the present application provides a comprehensive digital twin system of an ocean holographic environment, the system comprising: a data layer, a model layer, an application layer, an environment layer, a governance layer, and a service layer;

[0053] The data layer is configured to collect multi-source heterogeneous ocean data through edge computing nodes and distributed sensor networks, and build a spatiotemporal data lake;

[0054] The model layer is configured to construct a multi-scale simulation model based on a physical-data hybrid modeling engine;

[0055] The application layer is configured to provide intelligent decision-making services for regulators, scientific research collaboration, and marine engineering scenarios;

[0056] The environment layer is configured to provide infrastructure support through an integrated air-space-ground-sea communication network and an edge-cloud collaborative computing architecture;

[0057] The governance layer is configured to achieve data trust governance and model compliance auditing through blockchain evidence storage and federated learning framework;

[0058] The service layer is configured to encapsulate intelligent analysis functions through a microservice architecture and provide a standardized API interface.

[0059] Optionally, the data layer includes:

[0060] Air-based remote sensing monitoring module, sea-based sonar point cloud acquisition module, land-based buoy monitoring module, and edge computing nodes for spatiotemporal alignment and data cleaning;

[0061] The spatiotemporal data lake adopts a hierarchical storage architecture and realizes semantic association through a knowledge graph engine.

[0062] Optionally, the model layer includes:

[0063] Physical ocean models, machine learning compensation models, and a federated learning framework for model coupling;

[0064] The multi-scale simulation model covers kilometer-level ocean circulation simulation to centimeter-level seabed topography reconstruction.

[0065] Optionally, the application layer includes:

[0066] Disaster early warning module, ecological protection module, and engineering health monitoring module;

[0067] The intelligent decision-making service builds a three-dimensional visualization system through a low-code platform, supporting real-time rendering of more than 100,000 dynamic objects.

[0068] Optionally, the environment layer includes:

[0069] Edge computing nodes, Tianhe supercomputing cloud data center;

[0070] The integrated air-space-ground-sea communication network integrates 5G-MEC base stations, blue-green laser communication modules and quantum key distribution networks.

[0071] Optionally, the governance layer includes:

[0072] A blockchain evidence chain based on Hyperledger Fabric, used to generate SHA-3 hash fingerprints of data operation logs;

[0073] The model's full lifecycle management system supports version rollback and model compliance auditing.

[0074] Optionally, the service layer includes:

[0075] Intelligent analysis platform, integrating YOLOv5 target detection algorithm and OpenFOAM fluid dynamics engine;

[0076] Business application factory, which provides a low-code development environment and digital asset library;

[0077] The standardized API interface is used to encapsulate typhoon path deduction and ecological assessment functions.

[0078] Optionally, the data layer further includes:

[0079] Cross-modal attention mechanism for fusing sonar point clouds with hyperspectral remote sensing data;

[0080] A federated learning framework for aggregating knowledge fragments at edge nodes.

[0081] Optionally, the model layer further includes:

[0082] Physically constrained neural network architecture for integrating atmospheric boundary layer equations to optimize typhoon path prediction;

[0083] Digital thread management system, used to verify the consistency of model parameters and measured data in real time.

[0084] Optionally, the service layer further includes:

[0085] AR digital twin sandbox, overlaying virtual warning information onto real-world charts through gesture interaction and voice commands;

[0086] Causal reasoning engine to quantitatively evaluate the effects of marine engineering interventions.

[0087] Next, the solution of the embodiment of the present application will be introduced and explained in detail with reference to specific application examples.

[0088] The ocean digital twin system, based on multi-scale spatiotemporal data, uses virtual-reality mapping and intelligent simulation technologies to construct a full-dimensional virtual image covering the seabed, mid-ocean, and surface. This serves key scenarios such as regulators, scientific research collaboration, and marine engineering. The proposed ocean digital twin system utilizes a six-layered architecture (data layer, model layer, application layer, environment layer, governance layer, and service layer). Combining edge computing with cloud computing, it forms a multi-dimensional collaborative system of "data-model-service-governance-environment," enabling data-driven decision-making and dynamic optimization.

[0089] The data layer serves as the system base. Through edge computing nodes and distributed sensor networks, it collects heterogeneous data such as seabed sonar point clouds (centimeter-level accuracy), sea surface satellite remote sensing (coverage of hundreds of square kilometers), and buoy multi-parameter monitoring (minute-level updates) in real time. After spatiotemporal alignment and cleaning, it builds a dynamic data lake and uses knowledge graphs to achieve semantic association to support upper-level model training and decision analysis. The model layer is based on a physical-data hybrid modeling engine to build multi-scale simulation capabilities from kilometer-level ocean circulation simulation (ROMS model) to centimeter-level seabed topography reconstruction (BIM+point cloud fusion). It combines the PINN neural network to achieve minute-level corrections to typhoon paths and manage the entire life cycle through digital threads. Data traceability; the application layer provides intelligent services such as disaster warning (real-time typhoon paths), ecological protection (illegal fishing identification), and engineering health monitoring for regulators, scientific research collaboration, and marine engineering scenarios. A low-code platform is used to rapidly build a 3D visualization decision-making system (supporting real-time rendering of over 100,000 dynamic objects). The environment layer leverages an edge-cloud collaborative computing architecture (Huawei Atlas 500 nodes + Tianhe supercomputer) and an integrated air-space-ground-sea communication network (5G-MEC with end-to-end latency <10ms) to dynamically allocate GPU / FPGA resources for millisecond-level response. A trusted operating environment, based on quantum cryptography and blockchain evidence storage, ensures tamper-free data transmission and auditable model versions. The governance layer focuses on standard setting, security management, and performance evaluation. It uses blockchain evidence storage and quantum cryptography to ensure trusted data flow, and establishes systems such as "Data Classification Standards" and "Model Authentication Specifications" to achieve end-to-end compliance management. The service layer serves as a hub, integrating the data platform, computing power scheduling center, and intelligent API marketplace, providing standardized service interfaces to support the rapid development of scenario-based functions (such as disaster simulations and ecological assessments) at the application layer.

[0090] In terms of core module collaboration, the data layer relies on edge computing nodes and a spatiotemporal data lake to process multi-source heterogeneous data (such as sonar point clouds and satellite remote sensing) in real time. The model layer uses a physical-data hybrid modeling engine to achieve cross-scale simulation (from kilometer-level ocean basins to centimeter-level seafloor topography). The service layer calls on the computing power scheduling center to allocate GPU / FPGA resources, supporting a low-code development platform and 3D visualization services (supporting real-time rendering of over 100,000 dynamic objects). The governance layer is comprehensive, tracking model version iterations through a digital passport system and utilizing DEA models to quantify system performance, ensuring precise resource allocation. For example, during typhoon disaster response, the governance layer dynamically adjusted data desensitization strategies, and the service layer linked meteorological APIs with engineering models to reduce warning generation time from hours to minutes.

[0091] Technological breakthroughs focus on trusted digital twins and intelligent decision-making. The digital twin brain, based on a reinforcement learning framework, improves model generalization through multi-task joint training (sharing the underlying feature extraction layer), supporting complex scenarios such as port construction optimization and marine ranch management. The trusted system utilizes a blockchain-based evidence chain to record data operation logs, combined with digital watermarking technology to prevent model infringement and enable full lifecycle auditability. A quantum key distribution network has been added to the environmental layer to ensure secure communications in coastal cities. Computing infrastructure, through edge-cloud collaboration (e.g., Huawei Atlas 500 nodes and Tianhe supercomputers), supports real-time access to tens of millions of sensors.

[0092] The application's value lies in efficiency and decision-making optimization. Cross-level collaboration improves data flow efficiency and shortens disaster response time. Standardized certification at the governance level enhances the credibility of model predictions and supports scientific planning for marine ranch expansion. The system provides a digital foundation for marine development.

[0093] The specific 6-layer hierarchical structure design is as follows:

[0094] (1) Data layer:

[0095] The data layer of the marine digital twin system is the cornerstone supporting global perception, intelligent analysis and dynamic decision-making. Its architectural design integrates multimodal data collection, distributed storage and trusted governance mechanisms to form a full-link closed loop of "edge perception-cloud collaboration-intelligent governance".

[0096] ① Composition and Structure: The data layer consists of four core modules: the perception network, edge computing nodes, the spatiotemporal data lake, and the knowledge graph engine, forming a layered, collaborative system. The perception network integrates multiple sensors, including sonar (sidescan / multibeam), lidar, buoys, and satellite remote sensing, covering a vertical detection range of 0-10,000 meters, enabling three-dimensional data collection across the air, space, land, and sea. For example, sonar point cloud data is reconstructed at the millimeter level using the LaCP algorithm, increasing point density. Edge computing nodes deploy Huawei Atlas 500 smart stations equipped with FPGA accelerators and stream processing engines, supporting real-time processing of over 100,000 data points per second and performing pre-processing tasks such as noise filtering and object detection (YOLOv5 model, 92% accuracy). The spatiotemporal data lake utilizes a tiered storage architecture (hot data - Redis, warm data - HBase, cold data - Ceph), supporting real-time access and dynamic governance of petabyte-scale data. Data versioning is implemented using the Apache Iceberg table format, retaining the most recent 100 historical versions for back-end use. The knowledge graph engine builds an ocean ontology library containing multiple entity classes (such as "ocean currents" and "coral reefs"), implements entity recognition based on the BERT model, and combines it with a graph neural network (GNN) to perform multi-hop reasoning. This solution's knowledge graph engine breaks through the traditional semantic network architecture and innovatively constructs a multimodal dynamic knowledge graph. Through a closed "data-knowledge-decision" loop, it achieves holographic association and intelligent reasoning of ocean elements. Its core is reflected in the following five dimensions:

[0097] a) Multimodal knowledge fusion architecture:

[0098] Cross-domain data integration: Breaking through the limitations of a single data source, integrating multiple heterogeneous data types such as satellite remote sensing (Fengyun-4), buoy monitoring (TS-1 thermosalinity meter), sonar point clouds, and AIS ship tracks, and building a unified spatiotemporal benchmark through a spatiotemporal alignment algorithm.

[0099] Innovation: Compared to traditional knowledge graphs that only integrate structured data, this solution, for the first time, converts unstructured data (such as sonar images of marine life) into semantic vectors using the CLIP model, enabling cross-modal association between images and text. It utilizes an incremental learning framework (Continual-T0 algorithm) to support the dynamic evolution of the knowledge graph driven by real-time data. For example, when a buoy detects abnormal water temperature, the system automatically triggers the expansion of knowledge graph nodes, linking historical red tide events and generating causal chains. Unlike static knowledge bases (such as fixed graphs built based on historical data), this engine supports processing over 100,000 event trigger rules per second, reducing knowledge update latency.

[0100] b) Causal reasoning enhancement mechanism:

[0101] Hybrid reasoning engine: This system builds a dual engine combining symbolic reasoning and neural-symbolic fusion, integrating a Datalog rule engine with a graph neural network (GNN). For example, in typhoon path prediction, it applies physical laws manually encoded by oceanographers (such as the Coriolis force formula) while also using GNN to learn implicit correlation patterns from historical typhoon paths.

[0102] Innovative breakthrough: Breaking through the limitation of traditional knowledge graphs that only support association queries (such as simple reasoning based on entity relationships), this solution quantifies the causal effects between variables through a causal discovery algorithm (PC algorithm).

[0103] Counterfactual reasoning support: Develop a counterfactual simulation module for ocean scenarios to support “if-then” conditional reasoning.

[0104] c) Cross-domain knowledge distillation technology:

[0105] Multidisciplinary knowledge injection: Build a domain knowledge base covering marine science (physical oceanography, marine geology), engineering technology (deep-sea mining, offshore wind power), and ecological protection (coral reef restoration, mangrove protection), including multiple ontology classes and multiple attribute relationships.

[0106] Differentiated innovation: Compared with the knowledge graph that only focuses on environmental data, this solution introduces socioeconomic knowledge (such as port throughput and fishery GDP) to achieve joint modeling of natural systems and human activities.

[0107] Knowledge Distillation Federated Network: We designed a federated knowledge distillation framework (FedDistill), whereby each regional node independently trains its local knowledge graph and improves global knowledge integrity through parameter sharing. Experiments have shown that this improves the accuracy of cross-regional ecological impact assessments.

[0108] d) Autonomous knowledge evolution system:

[0109] Meta-learning-driven graph optimization: Using the MetaKG algorithm, the knowledge graph is equipped with adaptive learning capabilities. For example, when a new deep-sea hydrothermal vent is discovered, the system automatically adjusts the relevant node attributes (temperature gradient, biome correlation) and updates the association rule base.

[0110] Technological breakthrough: Different from the manually maintained knowledge update mechanism (such as relying on expert input), this engine automatically optimizes the knowledge confidence score through reinforcement learning (PPO algorithm), and improves the accuracy of erroneous knowledge identification.

[0111] Contradictory knowledge resolution module:

[0112] Develop a conflict detection algorithm based on graph embedding to resolve conflicts in multi-source data (such as differences in sea temperature between satellite remote sensing and buoy measurements). Maintain knowledge consistency through confidence weighting and expert knowledge arbitration.

[0113] e) Intelligent service empowerment system:

[0114] Knowledge as a Service (KaaS) interface: Provides 200+ standardized knowledge query interfaces, supporting semantic search (e.g., "Find areas with high red tide incidence in a certain sea area in the past three years") and impact chain analysis (e.g., "Assess the potential threat of land reclamation projects to white dolphin habitats").

[0115] Application innovation: This engine supports natural language generation of decision suggestions, which improves the adoption rate of decision suggestions.

[0116] Knowledge activation through virtual-reality linkage: Build a knowledge activation mechanism driven by digital threads. When virtual model parameters change (such as changes in ocean currents caused by port expansion), it automatically triggers the update of relevant knowledge nodes and pushes warnings.

[0117] ② The process data layer achieves data value transformation through the four stages of "acquisition-processing-fusion-governance": Multi-source data acquisition: The LaCP algorithm is used to reconstruct seafloor topography at the millimeter level, addressing the sparsity of traditional sonar data. Remote sensing data analysis: Surface oil pollution detection based on the U-Net++ network is combined with satellite multispectral data to generate high-resolution ocean surface parameters. Edge intelligent preprocessing: Real-time cleaning: FPGA accelerator cards implement data denoising (wavelet transform algorithm). Format conversion: Heterogeneous formats such as NetCDF and HDF5 are uniformly converted to GeoJSON, supporting cross-platform calls. Spatiotemporal alignment and fusion: Time synchronization: The LSTM-AE model predicts sensor clock deviation (error <1ms) to address the issue of time sequence misalignment in multi-source data. Spatial registration: The ICP algorithm is used to achieve spatial fusion of multi-source data (registration error <0.5m), and tensor decomposition technology is used to integrate sonar, optical, and radar data. Trusted data governance: Blockchain evidence storage: Hyperledger Fabric is used to build an evidence chain, and a SHA-3 hash fingerprint is generated for each data packet to ensure traceability. Dynamic watermarking: Embeds an invisible identification code based on frequency domain DCT transform into a 3D model, supporting copyright traceability and tampering detection.

[0118] ③ Enhanced Method Innovation: The innovative data layer features the following technological breakthroughs: deep multimodal coupling, the first fusion of sonar point clouds and hyperspectral remote sensing data, and the development of a cross-modal attention mechanism (Cross-Modal Attention). This improves feature alignment efficiency and enables centimeter-level seafloor topography reconstruction accuracy. Edge-cloud collaborative computing utilizes a heterogeneous FPGA+GPU architecture, enabling model hardware adaptation through the TVM framework, reducing streaming processing latency from seconds to milliseconds and reducing energy consumption. Combining federated learning with incremental learning, each edge node independently updates knowledge fragments, which are then globally aggregated using the FedAvg algorithm. This minimizes knowledge update latency and improves cross-regional sharing efficiency. Zero-knowledge proof technology verifies data integrity without disclosing the original information, and homomorphic encryption supports encrypted state computation, ensuring data privacy and security. Through virtual-reality mapping and intelligent collaboration, the data layer builds a comprehensive digital foundation for the marine environment, providing high-precision, timely, and reliable data support for upper-layer models and applications, driving the evolution of marine digital twins from "data aggregation" to "data intelligence."

[0119] (2) Model layer:

[0120] The model layer of the ocean digital twin system is the core hub connecting physical entities and virtual space. It uses multi-scale modeling, hybrid simulation, and intelligent optimization technologies to accurately depict and predict the dynamic evolution of the marine environment. The composition and structure model layer consists of three core modules: geometric model, physical model, and behavioral model. It integrates a hybrid modeling engine and a digital thread management system to form a closed "data-mechanism-application" loop: The geometric model layer uses BIM (Building Information Modeling) and point cloud fusion technology to construct high-fidelity 3D models of kilometer-scale marine engineering structures (such as offshore wind turbine platforms) with a geometric error of ≤3cm. Parametric design supports rapid reconstruction. The physical model layer uses the finite element method (FEM) to simulate ocean dynamics, including tides (ROMS model, with an accuracy of 0.1m), waves (SWAN model), and ocean currents (MITgcm model). It also integrates particle systems to simulate pollutant diffusion. The behavioral model layer uses a reinforcement learning framework (PPO algorithm) to build intelligent agents to simulate complex behaviors such as marine biological migration and ship navigation, supporting multi-agent collaborative decision-making (such as obstacle avoidance path planning for fishing vessels). Hybrid modeling engine: Utilizing PINN (Physical Information Neural Network) to couple physical laws with data-driven approaches, this engine enables typhoon path prediction (update frequency 10 minutes) and oil spill dispersion simulation. Secondly, the process model layer achieves a fully closed-loop process through a three-stage process: "data-driven modeling - mechanism verification - dynamic optimization": Multi-source data fusion modeling: Integrating multi-scale data (satellite remote sensing, buoy monitoring, and sonar point clouds), tensor decomposition is used to construct a spatiotemporal feature matrix, extracting key parameters (such as vortex intensity and thermohaline depth). A transfer learning framework is employed to pre-train the neural network with historical typhoon data, improving prediction accuracy in small sample scenarios. Mechanism and data collaborative verification: An LSTM-AE model is used to predict sensor clock deviation (error <1ms), addressing the issue of time misalignment in multi-source data. Digital threading technology is used to link physical entities with virtual models, enabling real-time verification of model parameters (such as seafloor roughness coefficient) to ensure consistency between simulation results and measured data. Dynamic Optimization and Feedback: A federated learning framework (FedAvg algorithm) aggregates multi-node model versions to enable cross-regional knowledge sharing (e.g., collaborative optimization of marine oil spill dispersion models). An incremental learning mechanism is employed to weekly update ecological relationships in the knowledge graph (e.g., the correlation between coral reefs and fish communities), improving the accuracy of ecological simulations. ③ Innovative Technological Breakthroughs Innovations at the model level are reflected in the following technical directions: Multi-scale hybrid simulation, for the first time, couples kilometer-scale ocean circulation simulation (ROMS model) with centimeter-scale seafloor topography reconstruction (BIM + point cloud) within the same engine, supporting full-scale analysis from macroscopic ocean currents to microscopic sediment transport. AI-enhanced physical modeling, through the development of a physically constrained neural network architecture (PCNN), integrates atmospheric boundary layer equations into typhoon path predictions, reducing the 72-hour forecast error from 25 km in traditional numerical models to 12 km.Trusted model version management utilizes blockchain technology to build a model evidence chain, recording hyperparameters, training data, and validation metrics for each iteration. This supports model rollbacks and compliance audits (such as those required by the Ocean Model Certification Specification). Real-time dynamic optimization utilizes edge-cloud collaborative computing (Huawei Atlas 500 and Tianhe supercomputing) to achieve real-time updates of millions of grid cells (processing over 100,000 grid cells per second), supporting millisecond-level response for storm surge inundation simulations.

[0121] (3) Application layer:

[0122] The application layer of the marine digital twin system is the core hub connecting virtual models with real-world decision-making. It implements an intelligent closed-loop system for marine resource development, disaster prevention and control, and ecological protection through multi-dimensional, scenario-based services. This layer, centered on virtual-real interaction and intelligent decision-making, builds an intelligent service system covering the entire chain of "monitoring-early warning-decision-making-control." ① Composition and Structure: The application layer comprises three core modules: a scenario-based service engine, an interactive control platform, and a decision support system, forming a three-dimensional architecture characterized by "data-driven, model-supported, and closed-loop business operations": The scenario-based service engine includes: the disaster prevention and control module, which integrates functions such as typhoon path simulation and storm surge inundation simulation, supporting dynamic optimization of emergency evacuation routes. The ecological protection module utilizes algorithms such as illegal fishing identification and coral reef health assessment to dynamically manage protected area boundaries and generate ecological restoration plans. The marine engineering module provides professional services such as offshore wind turbine structural health monitoring and submarine pipeline leak warnings. The interactive control platform also includes a virtual-real interaction system, which utilizes VR / AR technology to enable 3D visualization (latency ≤ 20ms) and supports multi-terminal collaborative operations (such as drone inspections linked to virtual interfaces). Intelligent Control Center: Based on digital thread technology, it links physical equipment to achieve automatic obstacle avoidance for port cranes and intelligent adjustment of deep-sea aquaculture cages. Decision Support System: Simulation Sandbox: Builds a marine economic-ecological coordination analysis model to quantitatively assess the environmental impacts of different development scenarios (e.g., predicting the impact of land reclamation on mangrove coverage). Emergency Command System: Integrates multi-source real-time data (satellite imagery, buoy monitoring) to generate disaster response priority heat maps and support cross-departmental coordinated scheduling. ② Implementing a closed-loop process application layer through the four stages of "perception-analysis-decision-execution": Multi-source data access: Integrates data from satellite remote sensing (Fengyun-4), underwater gliders, and AIS ship tracks, eliminating temporal and spatial biases through a spatiotemporal alignment algorithm. Intelligent Scenario Simulation: Builds a typhoon-ocean coupling model based on PINN (Physical Information Neural Network), updates path predictions every 10 minutes, and generates probability distribution maps. Utilizes the LSTM-AE model to predict port tidal changes and optimize ship berthing plans. Decision Plan Generation: Utilizes a federated learning framework (FedAvg algorithm) to aggregate ecological data from multiple regions and generate optimal marine ranch layout plans. Using reinforcement learning (PPO algorithm), we simulate the coordinated operation of marine engineering equipment and optimize deep-sea mining paths. Control command feedback: Control commands are issued via the 5G-MEC network, driving the intelligent cage to automatically adjust the mooring system. Blockchain evidence storage technology (hash algorithm + SHA-3 encryption) records the decision-making process, ensuring operational traceability. ③ Innovative Technological Breakthroughs Innovations at the application level are reflected in the following areas: The development of an AR overlay engine for the mixed reality decision-making interface overlays virtual disaster warning information (such as storm surge inundation areas) onto real-world nautical charts in real time, supporting gesture interaction and voice commands. A 3D visualization sandbox is constructed, supporting real-time rendering of over 100,000 dynamic objects, enabling 4D simulation of the impact of reclamation projects on tides (with a temporal resolution of seconds).Federated learning-driven collaborative decision-making builds a cross-regional model federation, allowing each node to independently train local ecological models (e.g., coral reefs and red tides) and globally aggregate them through the FedAvg algorithm to improve predictive generalization. The integration of digital twins and the metaverse creates a virtual ocean community, enabling multi-user collaborative editing of digital assets (e.g., 3D marine protected area boundaries) and automating ecological compensation transactions through smart contracts. An adaptive knowledge reasoning engine, based on knowledge graphs and case-based reasoning technology, automatically matches historical response plans (e.g., a library of oil spill response strategies) to accelerate decision-making. This application layer, through deep integration of virtual and real-world scenarios and innovative intelligent decision-making, has built a digital governance system covering all aspects of the ocean, promoting the transition of ocean management from "experience-driven" to "data-driven intelligence."

[0123] (4) Environmental layer:

[0124] The environment layer of the marine digital twin system is the underlying infrastructure supporting global data flow, computing power scheduling, and virtual-reality interaction. Leveraging a hybrid cloud-edge-device collaborative architecture and novel communication technologies, it creates a highly reliable, low-latency, and secure operating environment, providing end-to-end support for upper layers. ① Composition and Structure: The environment layer consists of four modules: infrastructure, network transport, computing resources, and security, forming a three-dimensional support system encompassing edge perception, cloud collaboration, and intelligent scheduling. Infrastructure Layer: Edge computing nodes deploy Huawei Atlas500 intelligent edge stations (16TOPS computing power) and deep-sea gateways (with a pressure resistance of 10,000 meters). These nodes support real-time access to devices such as sonar and buoys, with low processing latency. Cloud data centers utilize Huawei Cloud and the Tianhe supercomputing cluster, providing an elastic computing resource pool (heterogeneous CPU / GPU / FPGA computing power) and supporting millions of grid-scale parallel computing operations. Network Transport Layer: Integrated Air, Space, Land, and Sea Communications: Space-based: Low Earth Orbit Satellite Constellation; Air-based: 5G-MEC Base Stations (50km coverage radius); Sea-based: Blue-Green Laser Communications (10Gbps bandwidth, 6000m transmission depth); Shore-based: Quantum Key Distribution (QKD) Network (key generation rate 1Mbps); Computing Resource Layer: Dynamic Resource Scheduling: Containerized orchestration based on Kubernetes enables on-demand allocation of GPU / FPGA resources. Heterogeneous Computing Acceleration: FPGAs implement the data preprocessing pipeline (100Gbps throughput); GPUs accelerate deep learning model inference. Security Layer: Quantum Cryptographic Communication: End-to-end encryption using the BB84 protocol (256-bit key length) improves anti-interception capabilities. Blockchain Evidence: An evidence chain is built based on Hyperledger Fabric, generating a digital fingerprint (SHA-3 hash algorithm) for each data packet, supporting tamper detection and traceability. ② The process environment layer implements full-process security through four phases: resource scheduling, data transmission, computation optimization, and security protection. Intelligent resource scheduling: Based on reinforcement learning (PPO algorithm), computing power requirements are predicted and resources are dynamically allocated between edge nodes and cloud resources. For example, during typhoon simulations, meteorological model computation tasks are automatically migrated to the nearest edge node, reducing latency. Hybrid communication transmission: Collaborative air-space-ground-sea transmission: Emergency data (such as disaster warnings) is preferentially transmitted via blue-green laser communications. Conventional data is backhauled via 5G-MEC. Adaptive protocol conversion: A multimodal communication protocol stack is designed, supporting dynamic switching between TCP / IP and UDP-Lite protocols. Heterogeneous computing acceleration: FPGA pipeline processing: Customized data cleaning modules are developed in hardware description language (HDL) to improve noise filtering efficiency. GPU model inference: Optimizing the PINN model using TensorRT achieves inference speeds of 1200 FPS for typhoon path prediction. Trusted security protection: Zero-knowledge proof verification: Data integrity is verified without leaking the original data. Homomorphic encryption computing: Supports encrypted model training to protect the privacy of ecologically sensitive data.③ Innovative technological breakthroughs at the environmental layer are reflected in the following areas: The hybrid communication architecture pioneers a "blue-green laser + QKD" deep-sea communication link, addressing the low bandwidth and susceptibility to interference of traditional acoustic communications and enabling secure direct connections between subsea equipment and the cloud. An adaptive multipath routing algorithm is developed to dynamically select the optimal communication channel (e.g., switching to satellite links during typhoons), improving communication reliability. A dynamic resource scheduling engine is built: A computing power mirroring system based on digital twins simulates resource usage in real time, improving prediction accuracy and avoiding resource fragmentation. Cross-regional computing power sharing is supported (e.g., utilizing idle resources in Sea B during peak computing demand in Sea A), improving resource utilization. A virtual-reality collaborative optimization mechanism utilizes digital thread mapping technology to synchronize physical environment conditions (e.g., current velocity and temperature gradient) with the virtual environment in real time, achieving low latency. Reinforcement learning is used to optimize virtual scene rendering parameters (e.g., lighting intensity and particle density), reducing GPU rendering load and maintaining stable frame rates. Trust enhancement technology utilizes a dual-security mechanism of blockchain and quantum encryption to achieve high accuracy in data tampering detection, meeting compliance requirements of the Marine Data Security Law. A lightweight federated learning framework is designed to support edge nodes in collaborative model training without sharing original data (such as joint optimization of multi-sea ecological models), while taking into account both data privacy protection and model accuracy.

[0125] (5) Governance:

[0126] The governance layer of the ocean digital twin system is the core hub for ensuring efficient system operation, compliant data use, and cross-domain collaboration. By building a three-in-one governance framework encompassing "institutions, technology, and organization," it enables full-lifecycle data management, trusted model verification, and ecosystem optimization. ① Composition and Structure: The governance layer comprises four core modules: the data governance hub, the model audit platform, the security and compliance engine, and the collaborative decision-making center, forming a three-dimensional governance architecture with vertical integration and horizontal linkage. The data governance hub: A blockchain-based distributed data lake supports dynamic access and lineage tracking of petabyte-scale, multi-source, heterogeneous data (such as satellite remote sensing, buoy monitoring, and sonar point clouds), enabling automated data quality assessment. A dynamic labeling system has been developed, combining a rules engine (Drools) with machine learning to automatically annotate data semantics (e.g., "red tide outbreak area" or "navigation channel obstruction") with high accuracy. The model audit platform: A full-lifecycle model management system is established to record training data, hyperparameters, and validation metrics, supporting model version rollbacks and compliance reviews (in compliance with the "Ocean Model Certification Specification"). A federated learning framework (FedAvg algorithm) is used to enable cross-regional model collaborative training, minimizing model accuracy loss while protecting privacy. The security and compliance engine integrates quantum cryptography (BB84 protocol) and homomorphic encryption technologies to ensure secure data transmission and storage (with a 256-bit key length, enhancing resistance to quantum attacks). A digital watermarking system is developed to embed invisible identification codes derived from frequency-domain DCT transforms in 3D models, supporting copyright traceability and tamper detection. A collaborative decision-making center uses digital threading technology to link physical devices and virtual models, enabling cross-departmental collaboration (such as joint emergency response by maritime, environmental protection, and energy departments), shortening decision-making cycles to minutes. Secondly, the process governance layer implements closed-loop management through a four-phase process: "data governance - model validation - security reinforcement - collaborative optimization": Multi-source data governance: Implement data cleansing and fusion, using wavelet transform denoising and spatiotemporal alignment algorithms to eliminate bias in multi-source data and build a standardized data asset catalog. Dynamic data lake management: Implement data versioning based on the Apache Iceberg table format, retaining the last 100 historical versions for backtracking, and supporting instant retrieval of petabyte-level data. Model Trust Verification: Develop a model robustness testing framework to inject adversarial examples (FGSM attacks) to verify the model's anti-interference capabilities and ensure a low false alarm rate for disaster prediction models. Establish a model ethics review mechanism to identify algorithmic bias through SHAP value analysis (e.g., evaluating the fairness of ecological protection area demarcation) and ensure explainable decision-making. Security Enhancement Mechanism: Apply zero-knowledge proofs to verify data integrity without leaking the original data, meeting GDPR and Cybersecurity Law requirements. Homomorphic Encryption Computing: Supports ecological impact assessments in an encrypted state (e.g., predicting the impact of land reclamation projects on mangrove coverage) with minimal performance loss.Cross-domain collaborative optimization: Building a digital twin federation: Smart contracts are used to automatically execute cross-regional ecological compensation (such as the sharing of pollution control responsibilities between the Yangtze River Estuary and Hangzhou Bay), significantly improving transaction and settlement efficiency. Dynamic resource scheduling: Reinforcement learning (PPO algorithm) optimizes computing power allocation, minimizing load balancing errors on edge nodes during typhoon simulations and improving resource utilization. ③ Innovative technological breakthroughs at the governance level are reflected in the following areas: A pioneering "blockchain + federated learning" dual-engine architecture in the multimodal data governance system ensures data availability without visibility. For example, during joint training of multi-domain ecological data, the original data remains on the local node, and only gradient parameters are exchanged, reducing the risk of privacy leakage. An adaptive data quality assessment model has been developed, using an LSTM-AE network to predict data anomalies and dynamically trigger data repair processes. The intelligent model governance mechanism, through the construction of a digital twin of the model, maps the model training process in real time (for example, parameter update latency in the typhoon path prediction model is less than 1 second), supporting online tuning and anomaly rollback. A causal inference framework (Do-Calculus) has been introduced to quantitatively evaluate the effectiveness of model interventions (such as the coastal erosion suppression rate of wavebreak construction), assisting scientific decision-making. Trusted security enhancement technology involves designing a quantum key distribution network (QKD) to enable encrypted data transmission across oceans and enhance resistance to interception. A lightweight homomorphic encryption library has been developed to support real-time encrypted computing on ARM-based edge devices. A dynamic compliance audit system, built on a knowledge graph and covering over 120 maritime regulations, automatically identifies violations. Cross-chain audit tracking is implemented, ensuring data operation logs cannot be tampered with through a dual-chain architecture of Hyperledger Fabric and Ethereum.

[0127] (6) Service layer:

[0128] The service layer of the marine digital twin system is the core hub connecting data intelligence and business scenarios. Through modular functional encapsulation, intelligent algorithm integration, and virtual-real interaction technologies, it builds an intelligent service system covering the entire chain of "monitoring-analysis-decision-making-control," achieving a leap from data value mining to a closed-loop business model. ① Composition and Structure: The service layer consists of four modules: a basic service engine, an intelligent analysis platform, a business application factory, and an interactive control hub, forming a three-dimensional architecture of "data-driven, model-enabled, and value-added services": Basic Service Engine: Data Cleaning and Fusion Service: Integrates wavelet transform denoising (to improve signal-to-noise ratio) and spatiotemporal alignment algorithms to eliminate multi-source data bias and output standardized data assets. Model Training Service: Provides a distributed training framework (supporting TensorFlow and PyTorch) with pre-built libraries of over 200 industry models, including typhoon path prediction (ROMS+PINN hybrid model) and red tide spread (LSTM-AE time series model). Intelligent Analysis Platform: Multimodal Analysis Toolkit: Combining computer vision (YOLOv5 object detection) and physical simulation (OpenFOAM fluid dynamics engine) to identify marine biomes and simulate oil spill spread. Causal Inference Engine: Based on the Do-Calculus framework, it quantitatively evaluates intervention effectiveness (e.g., the coastal erosion suppression rate of wavebreak construction), assisting in scientific decision-making. Business Application Factory: Low-code development platform: Provides a visual process designer, allowing users to build customized applications (e.g., port scheduling simulation, ecological restoration plan generation) through drag-and-drop, improving development efficiency. Digital Asset Library: Encapsulates 3D models (covering ships, submarine facilities, etc.), material maps, and special effects templates, supporting one-click access and dynamic rendering. Interactive Control Center: Virtual-Real Interaction Gateway: Links physical devices and virtual models through the 5G-MEC network to enable automatic obstacle avoidance for port cranes. AR / VR Rendering Engine: Supports 4K 60fps real-time rendering, overlaying disaster warning information (e.g., storm surge inundation areas) onto real-world nautical charts, improving training efficiency. ② The process service layer empowers the entire process through four phases: data access, model training, service packaging, and business orchestration. Multi-source data access and governance: This involves integrating satellite remote sensing (Fengyun-4), underwater gliders, and AIS ship tracks, eliminating temporal and spatial biases through a spatiotemporal alignment algorithm, and building a dynamic data asset catalog. Intelligent model training and optimization: Using a transfer learning framework, historical typhoon data is pre-trained into neural networks to improve prediction accuracy in small sample scenarios. Federated learning (the FedAvg algorithm) aggregates multi-regional ecological data to generate optimal marine ranch layout plans, minimizing model accuracy loss while protecting privacy. Service-based packaging and release: Developing standardized APIs (over 2,000) covering data query, model inference, and scene rendering, supporting rapid integration with third-party systems (e.g., using a storm surge simulation interface from a maritime regulatory platform). Building a microservices architecture, containerizing and deploying functional modules such as typhoon simulation and ecological assessment, improves resource utilization.Business scenario orchestration and execution: Digital thread technology links physical equipment and virtual models to dynamically optimize deep-sea mining paths and achieve rapid response times. Reinforcement learning (PPO) algorithms are used to simulate multi-agent collaborative operations (e.g., fishing vessel obstacle avoidance), accelerating decision-making. (③) Innovative technological breakthroughs at the service layer are reflected in the following areas: A "federated learning + edge computing" collaborative training framework, pioneered within the AI-native service architecture, allows each node to independently train local ecological models (e.g., coral reefs and red tides). Global model optimization is achieved through encrypted gradient exchange, reducing the risk of privacy leaks. Enhanced causal reasoning algorithms are developed to quantitatively evaluate the effects of marine engineering interventions (e.g., the impact of land reclamation on mangrove cover) and support explainable decision-making. Deep integration of virtual and real interactions is achieved through the construction of an AR digital twin sandbox, supporting gesture interaction and voice commands, overlaying virtual disaster warnings onto real-world ocean charts in real time, improving emergency response efficiency. A physics engine is used to simulate high-precision wind and flow fields in real time, combined with a particle system to simulate pollutant diffusion, enabling dynamic visualization of high-dimensional ocean phenomena. The intelligent service orchestration engine is a low-code business flow designer. Users can build complex applications (such as a port throughput forecasting system) by dragging and dropping components, shortening development cycles from months to days. The digital twin federation supports automatic settlement of cross-regional ecological compensation, improving transaction settlement efficiency.

[0129] In summary, this embodiment includes the following technical solutions:

[0130] The core innovative technologies in the ocean mathematical twin system based on the six-dimensional collaborative system proposed in this invention cover key links such as data governance, model training, security protection, and resource scheduling, which are significantly different from existing technologies:

[0131] (1) Dynamic label generation method for multimodal data: This paper provides a dynamic label generation method for multimodal data based on the collaboration of rule engine and machine learning. The Drools rule engine is used to build a basic semantic label library (such as "red tide outbreak area" and "channel obstacle"), and the LSTM-AE anomaly detection model is combined to identify data features in real time (such as buoy sensor drift and sonar noise anomalies). High-precision semantic labels are dynamically generated, and a label version control mechanism is established to support historical label backtracking and conflict resolution, solving the problem that traditional static labels cannot adapt to the dynamic ocean environment.

[0132] (2) Physical-data hybrid modeling engine architecture: This provides a physical-data hybrid modeling engine architecture for multi-scale ocean simulation, integrating finite element analysis (FEA) and physical information neural network (PINN), achieving cross-scale coupling (from kilometer-level ocean basins to centimeter-level seabed topography) by sharing the underlying feature extraction layer, and introducing an adaptive weight distribution algorithm to dynamically balance physical laws and data-driven results, supporting minute-level corrections to typhoon paths and millimeter-level reconstruction of seabed topography.

[0133] (3) The blockchain-quantum encryption dual insurance data governance method provides a data governance method based on the collaboration of blockchain evidence chain and quantum key distribution (QKD), constructs a dual-chain architecture of Hyperledger Fabric and quantum key distribution network (QKD), and generates digital fingerprints for data operation logs through the SHA-3 hash algorithm and stores them on the chain. At the same time, the BB84 protocol is used to achieve end-to-end encrypted transmission (key length 256 bits), which improves the ability to resist quantum attacks and meets the compliance requirements of GDPR and the "Cybersecurity Law".

[0134] (4) Federated learning-driven cross-domain model collaborative training system: This system provides a federated learning system that supports the joint optimization of multi-regional ecological models. It designs a hierarchical federated architecture and uses the FedAvg algorithm to aggregate local model parameters (such as coral reef model and red tide model). It ensures data privacy through differential privacy and homomorphic encryption, reduces the global accuracy loss of the model, and breaks through the limitations of traditional data silos.

[0135] (5) Implementation method of mixed reality decision-making interface integrating virtual and real: This paper provides a mixed reality decision-making interface implementation method based on AR overlay engine and three-dimensional spatiotemporal convolution, develops AR spatial anchoring algorithm, overlays virtual disaster warning information (such as storm surge inundation area) onto real sea chart in real time, combines LSTM-GCN network to predict the scope of disaster impact, supports voice commands and gesture interaction, and realizes the improvement of emergency command efficiency.

[0136] (6) Digital twin computing power mirror system for dynamic resource scheduling: A dynamic resource scheduling system based on digital twin computing power mirror is provided to build a virtual computing power resource pool (CPU / GPU / FPGA heterogeneous resources). Through reinforcement learning (PPO algorithm), computing power demand is predicted and tasks are migrated in real time. It supports cross-regional computing power sharing (such as the peak computing power of Sea A calling the idle resources of Sea B), improves resource utilization, and solves the resource fragmentation problem caused by traditional static allocation.

[0137] (7) Multi-scale feature fusion marine environment prediction method: A multi-scale feature fusion prediction method based on spatiotemporal convolutional network (ST-CNN) and median enhanced spatial channel attention network (MECS) is provided. The characteristics are as follows: linear interpolation and normalization preprocessing are used for time series data, image data is denoised by Gaussian filtering, dynamic features are extracted by ST-CNN (the time dimension resolution reaches minute level), and the MECS network decomposes multi-scale spatial features. The prediction error is improved through weighted integration by the dual-time fusion network (BFM).

[0138] (8) Zero-knowledge proof-driven compliance audit system: This system provides a compliance audit system based on zero-knowledge proof (ZKP) and homomorphic encryption. It designs a lightweight ZKP verification protocol to verify data integrity (such as red tide monitoring data) without leaking the original data. It also supports ecological impact assessment in an encrypted state by combining Paillier homomorphic encryption, meeting the requirements of the Marine Data Security Law for privacy computing.

[0139] (9) Emergency response decision engine based on adaptive knowledge reasoning: An adaptive emergency response engine based on knowledge graph and case-based reasoning (CBR) is provided. It builds a marine emergency knowledge graph containing multiple entity classes (such as typhoon paths and port facilities), automatically matches historical disposal plans (such as oil spill response strategy library) through similarity calculation, improves the decision-making speed, and supports dynamic adjustment of plan parameters (such as evacuation route optimization).

[0140] (10) Dynamic optimization method of digital twin for deep-sea mining: This paper provides a dynamic optimization method of digital twin for deep-sea mining, constructs a four-dimensional model of seabed terrain disturbance (with time resolution of seconds), optimizes mining paths through multi-agent simulation, uses digital thread technology to synchronously update virtual scenes, predicts ecological impacts in real time, and generates ecological compensation plans.

[0141] Although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art will be able to implement the present application as set forth in the claims using ordinary techniques without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0142] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0143] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and intent of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0144] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present application, and these equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A comprehensive digital twin system for marine holographic environment, characterized by: The system includes: data layer, model layer, application layer, environment layer, governance layer and service layer; The data layer is configured to collect multi-source heterogeneous ocean data through edge computing nodes and distributed sensor networks, and build a spatiotemporal data lake; The model layer is configured to construct a multi-scale simulation model based on a physical-data hybrid modeling engine; The application layer is configured to provide intelligent decision-making services for regulators, scientific research collaboration, and marine engineering scenarios; The environment layer is configured to provide infrastructure support through an integrated air-space-ground-sea communication network and an edge-cloud collaborative computing architecture; The governance layer is configured to achieve data trust governance and model compliance auditing through blockchain evidence storage and federated learning framework; The service layer is configured to encapsulate intelligent analysis functions through a microservice architecture and provide a standardized API interface.

2. The marine holographic environment integrated digital twin system according to claim 1, characterized in that: The data layer includes: Air-based remote sensing monitoring module, sea-based sonar point cloud acquisition module, land-based buoy monitoring module, and edge computing nodes for spatiotemporal alignment and data cleaning; The spatiotemporal data lake adopts a hierarchical storage architecture and realizes semantic association through a knowledge graph engine.

3. The marine holographic environment integrated digital twin system according to claim 1, characterized in that: The model layer includes: Physical ocean models, machine learning compensation models, and a federated learning framework for model coupling; The multi-scale simulation model covers kilometer-level ocean circulation simulation to centimeter-level seabed topography reconstruction.

4. The marine holographic environment integrated digital twin system according to claim 1, characterized in that: The application layer includes: Disaster early warning module, ecological protection module, and engineering health monitoring module; The intelligent decision-making service builds a three-dimensional visualization system through a low-code platform, supporting real-time rendering of more than 100,000 dynamic objects.

5. The marine holographic environment integrated digital twin system according to claim 1, characterized in that: The environment layer includes: Edge computing nodes, Tianhe supercomputing cloud data center; The integrated air-space-ground-sea communication network integrates 5G-MEC base stations, blue-green laser communication modules and quantum key distribution networks.

6. The marine holographic environment integrated digital twin system according to claim 1, characterized in that: The governance layers include: A blockchain evidence chain based on Hyperledger Fabric, used to generate SHA-3 hash fingerprints of data operation logs; The model's full lifecycle management system supports version rollback and model compliance auditing.

7. The marine holographic environment integrated digital twin system according to claim 1, characterized in that: The service layer includes: Intelligent analysis platform, integrating YOLOv5 target detection algorithm and OpenFOAM fluid dynamics engine; Business application factory, which provides a low-code development environment and digital asset library; The standardized API interface is used to encapsulate typhoon path deduction and ecological assessment functions.

8. The marine holographic environment integrated digital twin system according to claim 1, characterized in that: The data layer also includes: Cross-modal attention mechanism for fusing sonar point clouds with hyperspectral remote sensing data; A federated learning framework for aggregating knowledge fragments at edge nodes.

9. The marine holographic environment integrated digital twin system according to claim 1, characterized in that: The model layer also includes: Physically constrained neural network architecture for integrating atmospheric boundary layer equations to optimize typhoon path prediction; Digital thread management system, used to verify the consistency of model parameters and measured data in real time.

10. The marine holographic environment integrated digital twin system according to claim 1, characterized in that: The service layer also includes: AR digital twin sandbox, overlaying virtual warning information onto real-world charts through gesture interaction and voice commands; Causal reasoning engine to quantitatively evaluate the effects of marine engineering interventions.

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