Polar ocean environment intelligent integrated database platform architecture and implementation method

Abstract

The present application belongs to the technical field of polar marine environment monitoring, data processing and artificial intelligence prediction, and particularly relates to a polar marine environment intelligent integrated database platform framework and an implementation method thereof. The platform framework comprises an infrastructure layer, a data access layer, a data storage layer, a data processing layer, an intelligent analysis layer and an application service layer. The present application constructs a "full-link integrated intelligent platform for polar extreme environment", takes "multi-source data adaptive access system-standardized database-large-scale numerical model-multi-parameter AI prediction model-full-function platform" as the core architecture, innovatively integrates polar exclusive design and two-way collaborative mechanism, realizes full-process closed-loop management and deep application of polar marine environment data through hierarchical modular design and full-link polar exclusive optimization, and provides strong support for polar scientific research and engineering practice.

Description

Technical Field

[0001] This invention belongs to the field of polar marine environment monitoring, data processing and artificial intelligence prediction technology, specifically involving the architecture of an intelligent integrated database platform for polar marine environment and its implementation method. Background Technology

[0002] As a "driver" and "amplifier" of the global climate system, the polar regions directly impact global sea level, atmospheric circulation, and ecosystem balance through changes in their marine environment, making them a core area for global climate change research. With the deepening of polar scientific research and the escalating demands for global climate governance, higher requirements are being placed on the accuracy, coverage, processing efficiency, and predictive capabilities of polar marine environmental data. Currently, polar marine environmental data acquisition has formed a multi-faceted approach combining remote sensing and in-situ observation, with data types covering multiple dimensions such as sea ice thickness, seawater temperature, and polar biomass, resulting in an explosive growth in data volume.

[0003] However, the extreme nature of the polar marine environment, the heterogeneity of data sources (different observation equipment, different resolutions, different formats), the limitations of data transmission (remote observation areas, unstable communication links, severe signal attenuation), and the complexity of data processing result in numerous bottlenecks in the existing technological system, making it difficult to meet the needs of large-scale, high-precision, and intelligent polar environment research and applications. Existing technologies have significant shortcomings in multi-source data fusion, numerical modeling accuracy, intelligent prediction capabilities, platform functional synergy, and data security, specifically as follows: (1) The access and fusion of multi-source heterogeneous data lacks full-link adaptation and fails to solve the core pain points of polar transmission: Existing technologies lack a full-process access and fusion solution adapted to the polar transmission environment. They mostly adopt a decentralized processing method of manual screening and format conversion, resulting in low access efficiency (single-type data access delay ≥ 24 hours). Key features are easily lost during the fusion process, data consistency is poor, and it is impossible to achieve real-time linkage and collaborative analysis of multi-source data, making it difficult to support the full-dimensional data needs of large-scale polar environment research.

[0004] (2) The numerical models of polar marine environments have not formed a dedicated adaptation system, which limits the simulation accuracy: Most existing large-scale numerical models of polar marine environments are based on traditional fluid dynamics equations and do not fully consider the influence of special polar environmental factors. The calibration of model parameters depends on empirical values ​​and no dedicated parameter system adapted to the polar regions has been established. The linkage between the model and the measured data is poor, and the parameters cannot be dynamically adjusted based on real-time observation data.

[0005] (3) Artificial intelligence prediction models lack polar-specific design and have weak multi-parameter collaborative prediction capabilities: The application of machine learning algorithms in polar environment prediction in existing technologies is relatively fragmented, lacking the exploration of multi-parameter coupling relationships and failing to construct multi-parameter system dynamic models. The amount of training data for models is insufficient, feature extraction does not consider the spatiotemporal correlation of data, and it does not adapt to the characteristics of outlier interference in polar data. Although some existing technologies attempt to combine physical models and AI models, they have not formed an effective collaborative mechanism and are merely simple superpositions.

[0006] (4) Fragmented platform functions and lack of a full-link collaborative system: The existing polar environment database lacks a closed-loop function for the entire process of "data access - preprocessing - feature extraction - modeling analysis - prediction output - visualization display"; the data management and analysis modules are disconnected; the platform has poor interactivity; and the data sharing mechanism is imperfect. The existing platform has not been optimized for the extreme polar operating environment, resulting in a high equipment failure rate.

[0007] (5) Data security lacks end-to-end protection and cannot meet the strategic data needs of polar regions: Existing technologies lack adaptive transmission scheduling and data completion mechanisms, making it difficult to guarantee data integrity. The existing platform's data encryption, access control, and backup and recovery mechanisms are inadequate, posing a risk of data leakage and loss.

[0008] In summary, existing technologies have not yet established a fully integrated database platform system that is adapted to the extreme polar and marine environments and takes into account multi-source data collaboration and intelligent prediction. This makes it difficult to meet the high-precision and high-efficiency requirements of large-scale polar and marine environmental research and applications. It is urgent to break through the above bottlenecks through technological innovation and fill the gaps in the field. Summary of the Invention

[0009] To address the challenges of existing technologies, such as difficulty in multi-source data fusion, low accuracy in numerical modeling, weak intelligent prediction capabilities, fragmented platform functions, and insufficient adaptability to extreme polar environments, this invention provides an integrated intelligent database platform architecture for polar marine environments and its implementation method. The aim is to construct a "full-link integrated intelligent platform for extreme polar environments," with a core architecture of "multi-source data adaptive access system - standardized database - large-scale numerical model - multi-parameter AI prediction model - full-function platform." It innovatively incorporates polar-specific design and a two-way collaborative mechanism, achieving closed-loop management and in-depth application of polar marine environmental data throughout the entire process through layered modular design and full-process polar-specific optimization, providing strong support for polar scientific research and engineering practice.

[0010] To achieve the above objectives, the present invention provides the following solution: The architecture of the intelligent integrated database platform for polar marine environment includes: an infrastructure layer, a data access layer, a data processing layer, a data storage layer, an intelligent analysis layer, and an application service layer. The infrastructure layer is used to build a hardware network cluster that is fully adapted to the extreme characteristics of the polar regions, enabling environmental adaptation across the entire process from data acquisition to transmission. The data access layer is used to build a full-process access system of "multi-protocol adaptation + intelligent parsing + real-time completion" to achieve real-time and complete access to multi-source heterogeneous data; The data processing layer is used to construct a full-process data preprocessing system to realize data processing; The data storage layer is used to construct a hybrid storage architecture of "distributed file storage + relational database + time-series database + graph database" to realize full lifecycle data storage management; The intelligent analysis layer is used to construct a two-way collaborative calibration mechanism and a multi-parameter prediction system with polar-specific characteristics using a hybrid architecture of "physical mechanism numerical model + data-driven AI model" to realize the simulation analysis and intelligent prediction of polar environment data. The application service layer is used to provide diverse interactive functions and service outputs.

[0011] Preferably, the data access layer includes: an adaptive data parsing module, a format standardization module, and a real-time completion module; The adaptive data parsing module is used to automatically identify the data format and source of multi-source heterogeneous data; The format standardization module is used to uniformly convert multi-source heterogeneous data into a preset standardized format; The real-time completion module is used to complete and correct fragmented data and outlier data lost during the transmission of multi-source heterogeneous data based on the improved Kriging spatiotemporal interpolation algorithm.

[0012] Preferably, the improved Kriging spatiotemporal interpolation algorithm includes: ; in, To incorporate the spatiotemporal variability function characteristic of the polar regions, For spatial coordinates, For timestamps, i , j For index variables; For spatial range; b For time-varying range; Value of a nugget; This is the base value; It is a spatial heterogeneity factor; For time period factors; This is the data density factor.

[0013] Preferably, the data processing layer includes: a data cleaning module, a feature extraction module, a multi-source data fusion module, and a data standardization verification module; The data cleaning module filters and identifies abnormal data using a preset reasonable threshold range for polar environments and an isolated forest algorithm. The feature extraction module is used to extract features from multi-source heterogeneous data and reduce the dimensionality of the extracted features using the PCA algorithm; The multi-source data fusion module is used to fuse multi-source heterogeneous data; The data standardization verification module is used to perform consistency verification on the data after multi-source data fusion processing.

[0014] Preferably, the process of the multi-source data fusion module fusing multi-source heterogeneous data includes: ; ; in, For the proposition The basic probability assignment of fusion, proposition A represents data from polar observation equipment; For the first One piece of evidence supports the proposition. The basic probability distribution, n For the quantity of evidence; For the first A data source for the proposition Support level; For the first The credibility of each data source m For the number of data sources, K This is the allocation coefficient.

[0015] Preferably, the intelligent analysis layer includes: a physical mechanism numerical model module, a data-driven AI model module, and a two-way collaborative calibration mechanism module; The physical mechanism numerical model module is used to construct a large-scale polar marine environment numerical model based on fluid dynamics equations and polar environment characteristics, which couples ice, sea, and atmosphere. The data-driven AI model module is used to construct a polar climate multi-parameter system dynamics AI model based on the Transformer model + LSTM hybrid model architecture and by introducing a polar-specific spatiotemporal attention mechanism. The bidirectional collaborative calibration mechanism module is used to construct a bidirectional collaborative system of "mechanism constraint-data feedback" to realize the fusion and calibration of large-scale polar marine environment numerical models and polar climate multi-parameter system dynamics AI models.

[0016] Preferably, the large-scale polar marine environment numerical model includes: Seawater kinetic energy equation: ; in, They are respectively x , y , z The component of seawater velocity in the direction, in m / s; Time, in seconds; This is the acceleration due to gravity, measured in m / s². 2 ; The height of sea level undulation, in meters (m). This is the Coriolis force parameter, with units of rad / s; Seawater reference density, unit: kg / m³ 3 ; For sea surface wind stress x , y The directional component, in N / m 2 ; The vertical mixing coefficient is expressed in meters. 2 / s, the value range is optimized for the characteristics of polar sea areas; Sea ice energy balance equation: ; In the formula: Sea ice density, in kg / m³ 3 ; Sea ice thickness, in meters (m). The specific heat capacity of sea ice is expressed in J / (kg·K). Sea ice temperature, in Kelvin (K). Shortwave radiative flux, measured in W / m 2 Corrections are made to account for the difference in radiation between polar night and polar day. This refers to longwave radiation flux, measured in W / m². 2 ; This refers to sensible heat flux, expressed in W / m³. 2 ; Latent heat flux, in W / m³ 2 ; Heat exchange flux between sea ice and seawater, expressed in W / m³ 2 ; Sea ice melting / freezing rate: ; in, Net heat flux at the sea ice surface, expressed in W / m² 2 ; The latent heat of phase transition of ice is expressed in units of 334 kJ / kg. Seawater salinity, expressed in per mille (‰). This is an atmospheric temperature correction function. , The value represents atmospheric temperature in °C, reflecting the impact of polar low temperatures on the phase transition rate.

[0017] Preferably, the polar-specific spatiotemporal attention mechanism includes: ; in, These are the query, key, and value matrices, respectively. The dimension of the key vector; This is a dynamic spatiotemporal weight matrix; This is a multi-parameter correlation constraint matrix.

[0018] Preferably, the bidirectional collaborative system includes: Mechanism constraint: The simulation results of the large-scale polar marine environment numerical model based on polar physical laws are used as the prior constraint conditions of the polar climate multi-parameter system dynamics AI model to limit the deviation of the prediction results of the polar climate multi-parameter system dynamics AI model from the physical laws of polar environment evolution. Data feedback stage: The polar climate multi-parameter system dynamics AI model is based on real-time multi-source observation data. Through the gradient backpropagation algorithm, it dynamically optimizes the initial parameters and boundary conditions of the large-scale polar marine environment numerical model and corrects the simulation deviation caused by the solidification of empirical parameters in the large-scale polar marine environment numerical model. Collaborative Iterative Optimization: Establish a real-time interactive interface between the two models to perform periodic interactive updates of parameters and results within a preset period.

[0019] This invention also provides an implementation method for an intelligent integrated database platform architecture for polar marine environments. The implementation method is used to implement the aforementioned platform architecture and includes: Deploy and debug a cluster of polar observation equipment at the infrastructure layer, and use the debugged polar observation equipment cluster to collect multi-source heterogeneous data; A multi-source data access system is built and tested at the data access layer, and the tested multi-source data access system is used to access the multi-source heterogeneous data. A standardized database is built in the data storage layer. The standardized data, which has been cleaned, feature extracted and fused by the data processing layer, is imported into the standardized database to establish data indexes and relationships. Large-scale numerical models of polar marine environments and AI models of polar climate multi-parameter system dynamics are constructed in the intelligent analysis layer and then trained, optimized, and validated. By integrating the data access layer, data processing layer, data storage layer, intelligent analysis layer, application service layer, security system, and operation and maintenance management system, an intelligent integrated database platform for polar marine environments is obtained.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: Compared to existing technologies, the core innovation of this invention lies in the construction of a "full-chain integrated intelligent platform for extreme polar environments," and the realization of polar-specific optimization and deep integration in key modules, resulting in significant technological advantages and application value. (1) Innovation of a fully adaptable integrated fusion system for multi-source heterogeneous data in polar regions This pioneering end-to-end data processing system, integrating multi-protocol adaptation, intelligent parsing, improved Kriging interpolation completion, and data fusion, is specifically designed to address the high packet loss, high latency, and fragmented data characteristics of polar communications. It overcomes the bottlenecks of heterogeneous data formats, inconsistent accuracy, and unstable transmission from multiple polar data sources. The improved Kriging interpolation deeply adapts to polar data characteristics through time period factors, spatial heterogeneity factors, and data density factors, achieving a data completion accuracy of ≥98%. By introducing data source credibility weights into the data fusion rules, the accuracy of fused data is improved by 15%-20% compared to single data sources, providing high-quality data support for subsequent modeling and analysis. This solves the problems of low data fusion efficiency, poor consistency, and incompatibility with polar transmission characteristics found in existing technologies.

[0021] (2) Optimization and innovation of large-scale numerical models specific to polar regions A large-scale polar marine environment numerical model coupling ice-sea-atmosphere was constructed, innovatively incorporating polar-specific environmental factors (dynamic evolution of sea ice, polar radiation differences, and interglacial lake effects). It introduced a dedicated empirical parameter library trained on nearly 30 years of historical polar data (including polar-specific parameters such as sea ice ablation coefficient and polar radiation coefficient), and added a new equation for calculating sea ice ablation / freezing rates, enabling dynamic calibration of model parameters. The model achieves a spatial resolution of 1km × 1km and a simulation error of ≤8%, significantly outperforming existing models (which generally have errors >15%). It accurately reflects the spatiotemporal evolution of the large-scale polar marine environment, filling the gaps in existing models' insufficient polar adaptability and inadequate consideration of polar-specific environmental factors.

[0022] (3) Innovation in the architecture and attention mechanism of polar-specific multi-parameter AI prediction model This paper proposes a Transformer+LSTM hybrid model architecture and innovatively designs a polar-specific spatiotemporal attention mechanism. Through three core improvements—dynamic spatiotemporal weight allocation, enhanced multi-parameter correlation, and outlier robustness optimization—and with dedicated attention weight calculation equations and outlier suppression correction equations, a multi-parameter system dynamic model of polar climate is constructed to specifically explore the coupling relationships and unique spatiotemporal correlations of multiple parameters in the polar region. It overcomes the limitations of existing technologies, such as single-parameter prediction, weak generalization ability, and lack of adaptation to polar data characteristics, achieving collaborative prediction of multiple parameters including temperature, salinity, ocean currents, and sea ice. It caters to short-term, medium-term, and long-term prediction needs, with short-term prediction errors ≤6% and long-term trend prediction accuracy ≥85%, providing precise support for polar environmental early warning and trend analysis.

[0023] (4) Innovation of bidirectional collaborative calibration mechanism that deeply integrates mechanism and AI This innovative approach employs a hybrid architecture combining a "physical mechanism numerical model" and a "data-driven AI model," establishing a bidirectional collaborative calibration mechanism of "mechanism constraint - data feedback." It is complemented by a joint loss function and parameter optimization equations for both models: the numerical model provides mechanistic constraints to the AI ​​model through a physical consistency regularization term, preventing the AI ​​model from deviating from actual laws; the AI ​​model, based on real-time multi-source data, dynamically optimizes the numerical model parameters through gradient backpropagation, correcting simulation biases. Compared to a single model, the overall simulation prediction accuracy is improved by more than 20%, effectively addressing the complex nonlinear processes in polar environments. It solves the problem that existing technologies using a single model struggle to balance mechanistic consistency and data fitting accuracy, distinguishing it from solutions that merely use general AI algorithms or simply superimpose physical and AI models.

[0024] (5) Innovation of a full-link closed-loop intelligent integrated platform architecture adapted to polar regions A layered, modular, intelligent, and integrated architecture is constructed, encompassing the entire process from "access-processing-storage-analysis-application-security-operation and maintenance." This innovative architecture integrates all functions, including data management, simulation analysis, intelligent prediction, visualization, and data sharing. Each layer is specifically optimized for extreme polar environments (low temperatures, strong winds, unstable communication, and limited energy), addressing the issues of fragmented functionality, poor collaboration, and incompatibility with existing platforms. A comprehensive security system and remote operation and maintenance management system are provided to ensure stable and secure operation in extreme polar environments. The platform also supports customized services and cross-domain collaboration, significantly enhancing its practicality and promotional value.

[0025] (6) Innovation of the full-link guarantee solution for polar-specific communication and storage It integrates a multi-link redundant transmission system that combines satellite communication, BeiDou communication, and shortwave communication, along with local preprocessing and caching at edge computing nodes, specifically addressing the problems of unstable polar communication links, high data transmission latency, and severe signal obstruction. It adopts a hybrid storage architecture and a "local backup + off-site incremental disaster recovery" strategy to support PB-level data storage, ensuring the integrity and security of massive amounts of polar scientific data and adapting to the needs of long-term accumulation and security management of polar data. Attached Figure Description

[0026] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1This is a schematic diagram of the overall architecture of the intelligent integrated database platform for polar marine environment according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the implementation method of the intelligent integrated database platform architecture for polar marine environments according to an embodiment of the present invention. Detailed Implementation

[0028] 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 embodiments of the present invention, and not all embodiments. 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.

[0029] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] Example 1 This invention provides an intelligent integrated database platform architecture for polar marine environments, comprising: an infrastructure layer, a data access layer, a data processing layer, a data storage layer, an intelligent analysis layer, and an application service layer; The infrastructure layer is used to build a hardware network cluster that is fully adapted to the extreme characteristics of the polar regions, enabling environmental adaptation across the entire process from data acquisition to transmission. The data access layer is used to build a full-process access system of "multi-protocol adaptation + intelligent parsing + real-time completion" to achieve real-time and complete access to multi-source heterogeneous data; The data processing layer is used to construct a full-process data preprocessing system to realize data processing; The data storage layer is used to construct a hybrid storage architecture of "distributed file storage + relational database + time-series database + graph database" to realize full lifecycle data storage management; The intelligent analysis layer is used to construct a two-way collaborative calibration mechanism and a multi-parameter prediction system with polar-specific characteristics using a hybrid architecture of "physical mechanism numerical model + data-driven AI model" to realize the simulation analysis and intelligent prediction of polar environment data. The application service layer is used to provide diverse interactive functions and service outputs.

[0031] The specific implementation process of this embodiment is as follows: like Figure 1As shown, the platform architecture of this invention adopts a layered and modular design, divided from top to bottom into an infrastructure layer, a data access layer, a data processing layer, a data storage layer, an intelligent analysis layer, and an application service layer. These layers work together to form a complete system, and are complemented by a security system and an operation and maintenance management system to ensure the platform operates stably, efficiently, and securely in extreme polar environments. The specific design of each layer is as follows: (1) Infrastructure layer: Dedicated hardware and network support system for polar extreme environments As the core hardware for platform operation, a hardware network cluster adapted to the extreme characteristics of polar regions is constructed, including polar observation equipment clusters, edge computing nodes, cloud server clusters, storage arrays, multi-link redundant communication networks, and emergency support equipment for extreme environments, to achieve full-process environmental adaptation from data acquisition to transmission.

[0032] 1) Polar observation equipment cluster: Integrating satellite remote sensing (Gaofen series and Sentinel series satellites), airborne remote sensing (polar research aircraft equipped with hyperspectral imagers and lidar, with a design to withstand low temperatures of -60℃), and in-situ observation equipment (low-temperature resistant unmanned vessels, ice-pressure resistant buoys, deep-sea watertight moorings, and sub-ice sensors, all of which have passed the -40℃ low-temperature environment adaptability test), forming a multi-dimensional and all-round data acquisition network to ensure data acquisition capabilities in extreme environments; 2) Edge computing nodes: used for preliminary analysis and format unification of data from various independent observation devices. They are independent desktop processors deployed along with polar research stations and observation equipment. They adopt low power consumption and electromagnetic interference resistance design to preprocess and cache real-time observation data locally, reducing data transmission pressure and latency. The preprocessing latency is ≤1 minute, which is suitable for the dual constraints of limited energy and communication in polar regions. 3) Cloud server cluster: Used to collect and aggregate observation data from edge computing nodes, perform centralized analysis and processing of observation data, adopt a distributed architecture, have elastic scaling capabilities, provide efficient computing resource support, and adapt to the explosive growth demand for polar data. 4) Multi-link redundant communication network: Integrating satellite communication, BeiDou-3 navigation communication and shortwave communication, a triple redundancy architecture of "main link + backup link + emergency link" is constructed. Through the dynamic link switching algorithm, the communication link smoothness rate is ensured to be ≥95%, solving the problems of communication blockage and signal attenuation in polar regions. 5) Extreme Environment Emergency Support Equipment: including long-endurance backup power supply (endurance ≥ 72 hours), anti-interference communication module, and anti-icing cover protection device to ensure continuous operation of the platform in extreme low temperature, strong wind, and ice cover environments, and to achieve full-link hardware support.

[0033] (2) Data access layer: Multi-source data adaptive access system adapted to polar transmission characteristics To address the characteristics of high packet loss, high latency, and fragmented data in polar communications, a three-module approach—adaptive data parsing, format standardization, and real-time completion—processes the data sequentially, enabling real-time and complete access to multi-source heterogeneous data and providing high-quality raw data for subsequent processing stages.

[0034] 1) Adaptive data parsing module: Based on the system's pre-stored general data format library, it automatically identifies the data format and source. It has the ability to resume interrupted transmission and intelligently repair incomplete data to address some packet loss issues in polar data transmission. The parsing accuracy is ≥99.5%, solving the problem of incomplete polar data transmission. 2) Format standardization module: It converts heterogeneous data into a preset standardized format (GeoJSON format for spatial data, JSON format for attribute data, and ENVI format for raster data), and unifies the data coordinate system (WGS-84 coordinate system), timestamp format (UTC time) and precision unit to ensure data consistency and lay the foundation for end-to-end data collaboration. 3) Real-time completion module: Based on the improved Kriging spatiotemporal interpolation algorithm, it is specifically designed to address the uneven spatiotemporal distribution and severe fragmentation of polar data. It completes and corrects fragmented data and outlier data lost during transmission, with a data completion accuracy of ≥98%, effectively improving data integrity.

[0035] Considering the three core characteristics of polar data—strong periodicity, spatial heterogeneity, and data sparsity—a complete formula system is constructed: a) Basic interpolation model In the formula: These are estimated values ​​for the data points that need to be completed. For spatial coordinates, For timestamps, Z This represents data from polar observation equipment, such as wind speed, ocean current speed, ice thickness, and snowfall. Let be the measured value of the i-th known observation data point, and n be the total number of observation data samples; The weight coefficient for the i-th observation data point; For Lagrange multipliers; A spatiotemporal variation function to incorporate the characteristics of the polar regions.

[0036] b) Optimization of the spatiotemporal variability function incorporating polar characteristics In the formula: For spatial coordinates, For timestamps, i , j For index variables; For spatial range; b For time-varying range; Value of a nugget; This is the base value; It is a spatial heterogeneity factor; For time period factors; This refers to the data density factor. Specifically: Time period factor (Adapted to the polar night and polar day cycles): ,in This is the polar night / polar day cycle (the Antarctic summer polar day / winter polar night cycle is about 3 months). The periodic influence coefficient (value 0.2-0.5) strengthens the temporal correlation during polar night / polar day periods and weakens the time weight of non-periodic periods during transition periods. Spatial heterogeneity factor (Adapting to differences in sea ice distribution), a differentiation coefficient is set based on polar geographical zones to balance the correlation and stability of data in different regions; Data density factor (Adapting to data sparsity): , The number of valid data points within a 10km × 1-hour spatiotemporal window around the point to be filled is determined. When the data is sparse, the weight of adjacent valid data is amplified, and when the data is dense, the conventional interpolation logic is reverted.

[0037] c) Model parameter calibration (adapting to polar data characteristics) Spatial range : 5-8km in polar ice cap areas, 2-3km in interglacial lakes and marginal sea areas; Time Variable The polar night / polar day period is 12-24 hours, and the transition period is 6-8 hours; Gold Value : Get base value 10%-15% (adapted to the high-precision characteristics of polar observation equipment).

[0038] Access performance: Access latency is strictly controlled within 5 minutes to meet the access requirements of real-time observation data and adapt to the timeliness requirements of polar scientific expeditions.

[0039] (3) Data processing layer: a high-quality preprocessing system adapted to the characteristics of polar data To address the characteristics of polar data, such as numerous outliers, high noise levels, and inconsistent accuracy across multiple data sources, a comprehensive data preprocessing system is constructed. The obtained polar data is processed sequentially through four functional modules: data cleaning, feature extraction, multi-source data fusion, and data standardization and verification, providing high-quality data input for the intelligent analysis layer.

[0040] 1) Data cleaning module: Filters obviously abnormal data (data exceeding the preset reasonable threshold range of polar environment is obviously abnormal data) by using a preset reasonable threshold range of polar environment (such as seawater temperature -2℃-20℃, salinity 32‰-38‰). The accuracy of abnormal data identification is ≥99%. At the same time, abnormal data is marked and corrected, and the original data trajectory is preserved for traceability.

[0041] For anomalies caused by strong polar winds and ice pressure, an anomaly confidence assessment is introduced: In the formula: For data points The anomaly confidence level (range [0,1]) is such that the closer it is to 1, the higher the probability of an anomaly. For data points Path length in an isolated tree; The maximum path length for all data points in the isolated forest is determined by statistical calibration using historical polar anomaly data. ( (Number of samples).

[0042] 2) Feature Extraction Module: Extracts key feature values ​​for different types of data, such as sea ice coverage, ice thickness gradient, and seawater chlorophyll concentration from remote sensing data, and mean / extreme temperature, salinity change rate, and ocean current velocity and direction from in-situ observation data. The high-dimensional features are reduced by principal component analysis (PCA) algorithm to retain core features (feature contribution rate ≥ 95%), thereby reducing modeling complexity. 3) Multi-source data fusion module: Specifically designed for the characteristics of large differences in accuracy and strong complementarity of multi-source data in polar regions, it integrates the complementary information of remote sensing data and in-situ observation data to solve the problems of data redundancy and inconsistency, and generates an integrated dataset with spatiotemporal coordination. The accuracy of the fused data is 15%-20% higher than that of a single data source.

[0043] Considering the differences in precision weights among multiple polar data sources, the reliability of the data source is defined. ( , (Based on the number of data sources), the fusion rules are optimized as follows: In the formula: For the proposition The basic probability assignment of fusion, proposition A Data representing polar observation equipment, such as wind speed, ocean current speed, ice thickness, and snowfall; For the first One piece of evidence supports the proposition. The basic probability distribution, n For the quantity of evidence; For the first A data source for the proposition Support (value 0 or 1); For the first The credibility of each data source is set based on the accuracy test results of polar data (such as remote sensing data). In-situ buoy data ), m For the number of data sources, K This is the allocation coefficient.

[0044] 4) Data standardization and verification module: Verifies the data after multi-source data fusion processing to ensure that the data format, accuracy, and spatiotemporal range meet the modeling and storage requirements. After verification, the data is pushed to the data storage layer and intelligent analysis layer to achieve seamless connection between data processing and downstream processes.

[0045] Formula for calculating the accuracy of key feature extraction: In the formula: To improve feature extraction accuracy; The number of features correctly extracted; The total number of features to be extracted; Calculate the values ​​of the features extracted from the model; These are the actual measured values ​​for the features. Verification through actual testing shows that the key feature extraction accuracy of this invention is ≥98%.

[0046] (4) Data storage layer: a dedicated hybrid storage and secure backup system for massive polar data. We construct a hybrid storage architecture that combines distributed file storage, relational databases, time-series databases, and graph databases. This architecture is specifically designed for the massive growth and diverse characteristics of polar data, while ensuring the security, reliability, and scalability of data storage and enabling full lifecycle storage management of data.

[0047] 1) Distributed file storage: Adopting the HDFS architecture, it is used to store massive amounts of unstructured data such as remote sensing images and observation videos. The storage capacity can be dynamically expanded, supporting PB-level data storage and adapting to the long-term accumulation needs of polar scientific research data. 2) Relational database: PostgreSQL is used to store structured data such as platform configuration information, user information, and data metadata, ensuring high efficiency in data querying and management; 3) Time series database: InfluxDB is used to store in-situ observation data with strong time series characteristics (such as minute-by-minute sea temperature and wind speed data), supporting high-concurrency writing and fast time series query, and adapting to the data storage needs of high-frequency polar observations. 4) Graph database: Neo4j is used to store the correlation data between multiple parameters in the polar region, providing data support for the dynamic modeling of multi-parameter systems; 5) Data backup module: It adopts a dual backup strategy of "local full backup + off-site incremental disaster recovery", automatically backs up core data every day, the off-site disaster recovery backup distance is ≥1000km, and the backup recovery time is ≤1 hour, ensuring that data is not lost in extreme environments and meeting the data security requirements of polar strategy.

[0048] (5) Intelligent Analysis Layer: A polar-specific intelligent analysis system that deeply integrates mechanisms and AI As the core analysis and modeling layer of the platform, it innovatively adopts a hybrid architecture of "physical mechanism numerical model + data-driven AI model" to build a two-way collaborative calibration mechanism and multi-parameter prediction system with polar-specific characteristics, so as to realize high-precision simulation analysis and intelligent prediction of polar environment data.

[0049] The intelligent analysis layer includes: a physical mechanism numerical model module, a data-driven AI model module, and a two-way collaborative calibration mechanism module; The physical mechanism numerical model module is used to construct a large-scale polar marine environment numerical model based on fluid dynamics equations and polar environment characteristics, which couples ice, sea and atmosphere. The data-driven AI model module is used to construct a polar climate multi-parameter system dynamics AI model based on a Transformer model + LSTM hybrid model architecture and by introducing a polar-specific spatiotemporal attention mechanism. The two-way collaborative calibration mechanism module is used to build a two-way collaborative system of "mechanism constraint-data feedback" to realize the fusion and calibration of large-scale polar marine environment numerical models and polar climate multi-parameter system dynamics AI models.

[0050] Specifically, it includes: 1) Large-scale polar marine environment numerical model: Based on fluid dynamics equations and polar environmental characteristics, a large-scale polar marine environment numerical model coupling "ice-sea-atmosphere" is constructed, specifically optimized to adapt to the extreme polar environment. The model innovatively incorporates polar-specific environmental factors (dynamic processes of sea ice melting and freezing, polar night and polar day radiation differences, and the formation and evolution of interglacial lakes), and introduces a polar marine environment-specific empirical parameter library (including polar-specific parameters such as sea ice melting coefficient, polar radiation coefficient, and ice-sea heat exchange coefficient) trained based on nearly 30 years of historical polar observation data, to dynamically calibrate the model's initial parameters and boundary conditions.

[0051] The model has a spatial resolution of up to 1km×1km and a temporal resolution of up to 1 hour. The simulation error for sea ice thickness, seawater temperature, and ocean current velocity is ≤8%, which can accurately reflect the spatiotemporal evolution of the large-scale polar marine environment.

[0052] The core governing equations of the large-scale polar marine environment numerical model are as follows: Seawater kinetic energy equation: in, They are respectively x , y , z The component of seawater velocity in the direction, in m / s; Time, in seconds; This is the acceleration due to gravity, measured in m / s². 2 ; The height of sea level undulation, in meters (m). This is the Coriolis force parameter, with units of rad / s; Seawater reference density, unit: kg / m³ 3 ; For sea surface wind stress x , y The directional component, in N / m 2 ; The vertical mixing coefficient is expressed in meters. 2 / s, the value range is optimized for the characteristics of polar sea areas; Sea ice energy balance equation: In the formula: Sea ice density, in kg / m³ 3 ; Sea ice thickness, in meters (m). The specific heat capacity of sea ice is expressed in J / (kg·K). Sea ice temperature, in Kelvin (K). Shortwave radiative flux, measured in W / m 2 Corrections are made to account for the difference in radiation between polar night and polar day. This refers to longwave radiation flux, measured in W / m². 2 ; This refers to sensible heat flux, expressed in W / m³. 2 ; Latent heat flux, in W / m³ 2 ; Heat exchange flux between sea ice and seawater, expressed in W / m³ 2 ; Sea ice melting / freezing rate : in, Net heat flux at the sea ice surface, expressed in W / m² 2 ; The latent heat of phase transition of ice is expressed in units of 334 kJ / kg. Seawater salinity, expressed in per mille (‰). This is an atmospheric temperature correction function. , The value represents atmospheric temperature in °C, reflecting the impact of polar low temperatures on the phase transition rate.

[0053] 2) Polar Climate Multi-Parameter System Dynamics AI Model: Based on the Transformer model + LSTM hybrid model architecture, an innovative polar-specific spatiotemporal attention mechanism is introduced to construct a multi-parameter collaborative prediction model, and to deeply explore the coupling relationship and dynamic laws of multi-dimensional parameters such as temperature, salinity, ocean currents, sea ice, and atmospheric parameters.

[0054] The core improvements to the polar-specific spatiotemporal attention mechanism are implemented as follows: Dynamic allocation of spatiotemporal weights: During polar night (continuous darkness), spatiotemporal segments related to sea ice-atmosphere energy exchange are assigned a weight coefficient of 1.5-2.0 times; during polar day (continuous sunshine), the spatiotemporal regional weights of the correlation between sea ice melting and seawater temperature changes are strengthened; in view of the spatial heterogeneity of sea ice melting and freezing, based on historical sea ice thickness distribution data, differentiated spatial weight matrices are set for the ice sheet edge melting zone, the inland stable ice sheet zone, and the interglacial lake zone, respectively. By tilting the weights, the spatiotemporal characteristics of key areas are highlighted, solving the problem that the general attention mechanism is not sensitive to the special spatiotemporal patterns of the polar regions. Multi-parameter correlation enhancement: Prior knowledge of polar multi-parameter physical correlations is incorporated into attention calculations to construct a multi-parameter correlation constraint matrix based on polar physical laws. For example, the coupling relationship between salinity and ocean current velocity, and the correlation between sea ice thickness and radiation flux, are used to regularize the attention matrix, forcing the model to prioritize physically strongly correlated parameter combinations when calculating attention weights. For instance, when sea ice thickness data is input, the model automatically increases the attention weight of correlated parameters such as shortwave radiation flux and seawater temperature through the constraint matrix, avoiding meaningless spatiotemporal feature interference. Outlier robustness optimization: For outlier patterns caused by strong polar winds (such as jump values ​​generated by sensors when instantaneous wind speed exceeds 80m / s, and abnormal sea ice thickness readings caused by ice pressure impact), local statistical features (mean, variance, and absolute deviation of the median) are calculated, and dynamic thresholds are set to identify pulse-like outliers caused by strong winds and ice pressure. For the identified outliers, their contribution to the attention weight calculation is reduced by an exponential decay function, and normal data from adjacent spatiotemporal nodes are introduced for compensation modeling to improve the robustness of the model to typical polar outliers.

[0055] in, These are the query, key, and value matrices, respectively. The dimension of the key vector; It is a dynamic spatiotemporal weight matrix (including polar night and polar day cycle factors and sea ice spatial heterogeneity factors). For the time dimension, Spatial dimension; The multi-parameter correlation constraint matrix is ​​constructed based on polar physical laws (e.g., salinity-ocean current correlation coefficient 0.85, sea ice thickness-radiation flux correlation coefficient 0.92).

[0056] For polar anomaly data, the attention weights are adjusted: In the formula: The attention weights are adjusted. These are the original weights; This is the current data value; This represents the mean of a local spatiotemporal window. Local standard deviation; The suppression coefficient is 0.8-1.2 for polar scenarios. The more significant the outlier, the more obvious the weight decay.

[0057] Using standardized fused data output from the data processing layer as training samples, the prediction accuracy is optimized by combining the simulation results of the numerical model, forming a dual guarantee of "mechanism constraint + data-driven". The numerical model supports short-term (1-7 days), medium-term (1-3 months), and long-term (1-5 years) predictions, and can output prediction results for key parameters such as sea ice coverage, ice thickness changes, sea surface temperature distribution, and extreme weather events (polar storms). The short-term prediction error is ≤6%, the medium-term prediction error is ≤10%, and the long-term trend prediction accuracy is ≥85%.

[0058] The polar climate multi-parameter system dynamics AI model uses the following loss equation to optimize prediction accuracy: Loss function equation for multi-parameter prediction model: In the formula: This represents the total loss value of the model. This is the average absolute error loss; This is the mean square error loss; It is specifically designed to address the spatiotemporal correlation loss and is tailored to the spatiotemporal distribution characteristics of polar data. For the weighting coefficients, satisfying The optimal weight allocation was determined through cross-validation. , , This ensures that the model balances prediction accuracy with spatiotemporal data consistency.

[0059] 3) Two-way collaborative calibration mechanism: Unlike the simple superposition of large-scale polar marine environment numerical models and polar climate multi-parameter system dynamics AI models, this invention constructs a two-way collaborative system of "mechanism constraint-data feedback" to achieve deep integration and mutual calibration between the two. Specific implementation method: Mechanism constraint step: The simulation results of the large-scale polar marine environment numerical model based on polar physical laws are used as the prior constraint conditions of the polar climate multi-parameter system dynamics AI model. This limits the AI ​​model's prediction results from deviating from the physical laws of polar environment evolution, avoids the AI ​​model from falling into the data-driven "black box" dilemma, and ensures the rationality of the prediction results. Data feedback stage: The polar climate multi-parameter system dynamics AI model is based on real-time multi-source observation data. Through the gradient backpropagation algorithm, it dynamically optimizes the initial parameters (such as ice-sea heat exchange coefficient and vertical mixing coefficient) and boundary conditions (such as sea surface wind stress components) of the large-scale polar marine environment numerical model, and corrects the simulation deviation caused by the solidification of empirical parameters in the large-scale polar marine environment numerical model. Collaborative Iterative Optimization: Establish a real-time interactive interface between the two models, and perform periodic (e.g., once every hour) interactive updates of parameters and results within a preset period. Through iterative optimization, the simulation and prediction accuracy is continuously improved. Compared with a single model, the overall accuracy is improved by more than 20%, effectively addressing the complex nonlinear processes in the polar environment.

[0060] The gradient descent method is used to dynamically optimize the parameters of a large-scale polar marine environment numerical model using a multi-parameter system dynamics AI model for polar climate. In the formula: To optimize the parameters of the large-scale polar marine environment numerical model (such as the ice-sea heat exchange coefficient). The current parameter value; The learning rate (values ​​range from 0.001 to 0.01). Joint loss function for both models: in: AI models for predicting losses in polar climate multi-parameter system dynamics For AI model predictions; For large-scale polar marine environment numerical model simulation loss, These are the simulated values ​​from the numerical model. These are actual observed values; Loss due to physical rationality constraints on parameters (such as constraints on the range of parameter values); These are the weighting coefficients (taken as 0.4 and 0.1 respectively).

[0061] (6) Application Service Layer: A full-featured interactive service system for polar scenarios It provides users with diverse interactive functions and service outputs, specifically designed for typical scenarios such as polar scientific research, environmental early warning, and resource development, realizing the application of full-chain data value, including modules such as data query and download, visualization display, model simulation and prediction, typical scenario analysis, data sharing and collaboration, and customized services.

[0062] 1) Data query and download module: Supports queries based on multiple conditions such as time and space range, data type, and parameter indicators, and provides data download services in various formats to suit the data usage habits of researchers; 2) Visualization module: Using two-dimensional maps, three-dimensional modeling, dynamic time-series curves, etc., it intuitively displays the spatiotemporal distribution and changing trends of polar marine environmental data, supports multi-parameter overlay display and interactive operation, and can clearly present the ice-sea-atmosphere interaction process; 3) Model Simulation and Prediction Module: Provides calling interfaces for large-scale polar marine environment numerical models and polar climate multi-parameter system dynamics AI models. Users can customize parameter settings (such as polar-specific parameters such as initial ice sheet thickness and atmospheric radiation intensity) to generate simulation and prediction results. 4) Typical Scenario Analysis Module: For typical polar scenarios such as sea ice melting, polar storms, and interglacial lake evolution, it can quickly generate special analysis reports to provide direct support for scientific research mission planning and environmental early warning; 5) Data sharing and collaboration module: Construct a hierarchical access control sharing mechanism to support cross-institutional and cross-domain data sharing and collaborative research, adapting to the multi-team collaboration needs of polar scientific expeditions; 6) Customized service module: Customized data processing, analysis and prediction solutions can be developed according to user needs (such as scientific research missions in specific areas, polar engineering planning) to achieve personalized adaptation of the entire service chain.

[0063] (7) Security System: A comprehensive protection system for polar data security It spans all levels of the platform and is specifically designed to address the strategic importance and transmission and storage risks of polar data. It constructs a full-link security protection system of "data encryption + access control + behavior auditing + security monitoring" to ensure the security of data throughout the entire process from transmission to application.

[0064] 1) Data encryption: The data transmission process uses the SSL / TLS encryption protocol, and the stored data uses the AES-256 encryption algorithm to ensure the security of data transmission and storage and prevent data leakage; 2) Access Control: A role-based access control (RBAC) model is adopted to assign differentiated permissions to different users (researchers, managers, and ordinary users), strictly control data read, write and operation permissions, and adapt to the permission management needs in multi-user collaborative scenarios. 3) Behavior auditing: The module records all user operations, forming a traceable audit log that is retained for at least one year to ensure full traceability of operations; 4) Security Monitoring: The module monitors the platform's operating status, data transmission links, and access behavior in real time. If any abnormality is detected (such as unauthorized access or data leakage risk), an early warning will be triggered immediately (the response time for the abnormal warning will be ≤1 minute) and emergency measures will be taken (disconnecting the link, freezing the account) to ensure the platform's safe and stable operation.

[0065] (8) Operation and maintenance management system: a full-process management system adapted to remote operation and maintenance in polar regions. It includes modules for equipment operation and maintenance, data operation and maintenance, system operation and maintenance, and user management. It is specifically designed for the characteristics of polar observation equipment that are remote and difficult to maintain on-site, so as to realize remote operation and maintenance management of the whole chain, reduce operation and maintenance costs, and improve operation and maintenance efficiency.

[0066] 1) Equipment Operation and Maintenance Module: Real-time monitoring of the operating status of polar observation equipment and server clusters, providing fault early warning and remote diagnosis functions, supporting remote parameter configuration and firmware upgrades, reducing on-site operation and maintenance needs; 2) Data Operation and Maintenance Module: Regularly optimize, clean, and back up the database to ensure data quality and storage performance; 3) System Operation and Maintenance Module: Updates platform software versions, fixes vulnerabilities, monitors system resource usage, optimizes system performance, and supports remote operation and maintenance. 4) User Management Module: Supports user registration, login, permission modification, information maintenance and other functions, and provides user operation guides and technical support.

[0067] In summary, this invention provides an intelligent integrated database platform architecture for polar marine environments, constructing a "full-link integrated intelligent platform for extreme polar environments." Its core architecture comprises a "multi-source data adaptive access system, standardized database, large-scale numerical model, multi-parameter AI prediction model, and full-function platform." It innovatively incorporates polar-specific design and a two-way collaborative mechanism. Through layered modular design and full-process polar-specific optimization, it achieves closed-loop management and in-depth application of polar marine environmental data, providing strong support for polar scientific research and engineering practice.

[0068] Example 2 Based on the same inventive concept, the present invention also provides an implementation method for an intelligent integrated database platform architecture for polar marine environments. The implementation method is used to realize the aforementioned platform architecture and includes: Deploy and debug a cluster of polar observation equipment at the infrastructure layer, and use the debugged polar observation equipment cluster to collect multi-source heterogeneous data; A multi-source data access system is built and tested at the data access layer, and the tested multi-source data access system is used to access the multi-source heterogeneous data. A standardized database is built in the data storage layer. The standardized data, which has been cleaned, feature extracted and fused by the data processing layer, is imported into the standardized database to establish data indexes and relationships. Large-scale numerical models of polar marine environments and AI models of polar climate multi-parameter system dynamics are constructed in the intelligent analysis layer and then trained, optimized, and validated. By integrating the data access layer, data processing layer, data storage layer, intelligent analysis layer, application service layer, security system, and operation and maintenance management system, an intelligent integrated database platform for polar marine environments is obtained.

[0069] The specific implementation process of this embodiment is as follows: like Figure 2 As shown, the implementation method of the platform architecture of this invention follows a standardized process of "equipment deployment - data access - database construction - model training and optimization - platform integration and debugging - trial operation and promotion". Each step fully considers the characteristics of the extreme polar environment to ensure that the platform's functions and performance meet the design requirements. The specific steps are as follows: (1) Infrastructure deployment and debugging: Building a hardware network adapted to the polar environment First, the deployment of polar observation equipment clusters will be completed. Low-temperature resistant and ice-pressure resistant in-situ observation equipment such as buoys, underwater moorings, unmanned vessels, and sub-ice sensors will be deployed at polar research stations and key observation areas. Satellite remote sensing and airborne remote sensing resources will be integrated to build a multi-dimensional observation network. Low-power and interference-resistant edge computing nodes will be deployed at research stations, and local caching and preprocessing equipment will be configured. Distributed server clusters and storage arrays will be deployed in the cloud to build a hybrid storage architecture. A multi-link transmission network integrating satellite communication, BeiDou communication, and shortwave communication will be constructed, and emergency support equipment (backup power supply, anti-interference module, and anti-icing device) will be configured.

[0070] After deployment, all equipment will undergo joint debugging tests, focusing on verifying the equipment's operating status, data acquisition capabilities, communication link stability, and edge computing node preprocessing performance in low temperature and strong wind environments. This will ensure that the infrastructure meets the platform's operational requirements, with equipment failure rate ≤1%, communication link uptime ≥95%, and edge computing node preprocessing latency ≤1 minute.

[0071] (2) Construction and testing of multi-source data access system: verification of access function adapted to polar transmission characteristics Based on the data access layer design scheme, a multi-protocol adaptation interface was developed to support multiple transmission protocols such as FTP, HTTP, and MQTT, adapting to different types of observation equipment and data formats; a data parsing rule base was constructed, and an adaptive data parsing model was trained to achieve automatic identification and parsing of heterogeneous data, with the ability to resume interrupted transmission and repair incomplete data; a format standardization module and a data completion module based on the improved Kriging interpolation algorithm were developed to complete the functions of data format unification, coordinate system transformation, outlier correction, and fragmented data completion.

[0072] After the setup is completed, data access tests are conducted. Different types and formats of polar data (remote sensing data, in-situ observation data, and simulated data) are selected and access verification is performed in a simulated polar network environment with high packet loss and high latency. The test results include data access latency, parsing accuracy, format standardization effect, and data completion accuracy. The goal is to ensure that the access latency is ≤5 minutes, the parsing accuracy is ≥99.5%, and the data completion accuracy is ≥98%.

[0073] (3) Standardized database construction: Building a storage system adapted to the characteristics of polar data Based on a data storage layer architecture, a hybrid storage system integrating distributed file storage, relational databases, time-series databases, and graph databases is built. Data backup and disaster recovery equipment is configured to ensure off-site disaster recovery backup distance ≥1000km. Metadata standards and data classification systems for polar and marine environmental data are formulated to classify and store data (unstructured data, structured data, time-series data, and relational data). Standardized data, after being cleaned, feature-extracted, and fused by the data processing layer, is imported into the database to establish data indexes and relationships, thereby optimizing database query and storage performance.

[0074] After the database is built, performance tests are conducted to verify its data storage capacity, query speed, and backup and recovery capabilities, ensuring that the database supports petabyte-level data storage, with a single data query time of ≤0.5 seconds and a backup and recovery time of ≤1 hour.

[0075] (4) Model training, optimization and validation: precise calibration of polar-specific models 1) Training and optimization of large-scale polar marine environment numerical models: Collect historical observation data of polar marine environment over the past 30 years (sea ice, temperature, salinity, ocean currents, atmospheric parameters, etc.) to construct a model training dataset; Based on fluid dynamics equations and polar environment characteristics, construct an ice-sea-atmosphere coupled numerical model, innovatively introduce polar-specific parameters (sea ice ablation coefficient, polar radiation coefficient, etc.), and use historical data to calibrate the initial parameters and boundary conditions of the model; Through comparative analysis of numerical simulation and historical observation data, optimize the model structure and parameters to reduce simulation errors.

[0076] After optimization, model validation was performed. Polar environment data from different regions and time periods were selected, and the model simulation results were compared with the measured data to verify the model accuracy and ensure that the simulation errors of sea ice thickness, seawater temperature, and ocean current velocity were ≤8%.

[0077] 2) Training and Optimization of Multi-Parameter AI Prediction Model: Using standardized fused data output from the data processing layer as training samples, the training set (70%), validation set (20%), and test set (10%) were divided in a 7:2:1 ratio. An improved Transformer+LSTM hybrid model was constructed, and a polar-specific spatiotemporal attention mechanism was introduced to train the model and explore the multi-parameter coupling relationship and spatiotemporal correlation. The model hyperparameters (learning rate 0.001, number of iterations 500, number of network layers, etc.) were optimized using the validation set, and the weight coefficients were adjusted based on the loss function to improve the model's prediction accuracy. The model's prediction performance was verified through the test set, and short-term, medium-term, and long-term prediction tests were conducted to ensure that the short-term prediction error was ≤6%, the medium-term prediction error was ≤10%, and the long-term trend prediction accuracy was ≥85%.

[0078] 3) Model Collaboration Validation: Test the collaborative working effect of the numerical model and the AI ​​model, verify the dynamic correction capability of the AI ​​model to the parameters of the numerical model and the mechanistic constraint effect of the numerical model on the AI ​​model, and ensure that the overall simulation prediction accuracy after collaboration is improved by more than 20% compared with the single model.

[0079] (5) Platform integration and debugging: Integrated functional verification of full-link collaboration This system integrates the data access layer, data processing layer, data storage layer, intelligent analysis layer, application service layer, security system, and operation and maintenance management system to build a complete intelligent integrated database platform for polar and marine environments. Interfaces between each layer are developed to ensure smooth data and command transmission. The platform's functions are comprehensively debugged, including all functional modules such as data access, processing, storage, analysis, prediction, visualization, data sharing, and security protection. The focus is on verifying the inter-module synergy and overall platform performance under simulated extreme polar environments (low temperature, high packet loss, strong interference). The platform's user interface is optimized to improve ease of use. Vulnerabilities and faults encountered during integration are fixed to ensure stable platform operation and complete functionality.

[0080] (6) Trial operation and optimization iteration: performance optimization based on actual polar scenarios Polar research stations and institutions were selected as pilot units to conduct trial operations of the platform in actual observation areas in the Antarctic or Arctic for a period of 6 months. Researchers and maintenance personnel were invited to participate in the trial operation to collect user feedback, focusing on verifying the platform's applicability, stability, and accuracy in actual polar environments. Platform operation data was regularly monitored and analyzed, including data processing efficiency, model prediction accuracy, platform response speed, and equipment operating status, to identify existing problems and areas for optimization. Based on user feedback and operational data analysis, platform functions, model parameters, and the user interface were iteratively optimized to improve platform performance and user experience.

[0081] After the trial operation is completed, a trial operation report will be generated, and the problems found will be rectified to ensure that the platform meets the actual application needs.

[0082] (7) Promotion and application and continuous maintenance: Service guarantee adapted to long-term operation in polar regions After successful trial operation and acceptance, the platform will be promoted and applied in relevant units such as polar scientific research, environmental protection agencies, and polar resource development enterprises. A continuous maintenance mechanism will be established for the platform, including regular remote equipment maintenance, software version updates, vulnerability patching, data backup and optimization. New user needs will be collected to upgrade and expand the platform's functions, continuously improving its service capabilities and application value. A technical support team will be built to provide users with technical support services such as operation training and problem solving, ensuring the long-term stable operation of the platform in the polar regions.

[0083] Example 3 Taking the monitoring and research of the polar marine environment in Region I as the application scenario, a smart integrated database platform for the polar marine environment will be constructed to adapt to the extreme environment of this region (low temperature -40℃ to -2℃, strong winds, ice cover, and unstable communication links). The specific parameters are designed as follows: (1) Infrastructure parameters The system deploys 10 sets of ice-pressure buoy observation equipment (monitoring parameters: seawater temperature, salinity, ocean current velocity, sea ice thickness, and withstanding temperatures as low as -40℃), 5 sets of deep-sea watertight mooring equipment (monitoring seawater parameters at depths of 0-500m, withstanding pressure ≥50MPa), and 2 cryogenic unmanned surface vessels (equipped with hyperspectral imagers and lidar, capable of operating in environments as low as -20℃). It integrates remote sensing data from Sentinel-2 satellite (spatial resolution 10m) and Gaofen-3 satellite (spatial resolution 5m). The edge computing nodes are equipped with Intel Xeon Gold 6348 processors, 128GB of memory, and 2TB of local cache, and adopt a low-power design (standby power consumption ≤50W). The cloud server cluster consists of 20 servers (each equipped with an Intel Xeon Platinum 8470C processor and 256GB of memory), with a total storage array capacity of 10PB. The communication link adopts BeiDou-3 communication + maritime satellite communication redundancy, with an emergency power supply endurance of ≥72 hours, and the anti-interference communication module can withstand electromagnetic interference ≤80dB.

[0084] (2) Data access and processing parameters Supports data access in HDF, TIFF, CSV, JSON, and binary formats, and is compatible with FTP, HTTP, and MQTT protocols; data access latency ≤ 5 minutes, parsing accuracy ≥ 99.5%; anomaly data identification accuracy ≥ 99%, data completion accuracy ≥ 98%; feature extraction uses the PCA algorithm, with a core feature contribution rate ≥ 95%; multi-source data fusion uses optimized data fusion rules, improving data accuracy by 18% after fusion; improved Kriging interpolation parameters: Spatial variation of ice sheet region Polar Night / Polar Day Duration Hour.

[0085] (3) Database parameters The distributed file storage uses the HDFS architecture, the relational database uses PostgreSQL, the time-series database uses InfluxDB, and the graph database uses Neo4j. The data backup strategy is daily full backup + incremental backup, and the off-site disaster recovery backup distance is ≥1000km (the backup node is deployed at a polar site). The data query time is ≤0.5 seconds, and the backup recovery time is ≤1 hour.

[0086] (4) Model parameters The large-scale numerical model has a spatial resolution of 1 km × 1 km and a temporal resolution of 1 hour. The simulation errors for sea ice thickness, seawater temperature, and ocean current velocity are 7.2%, 6.8%, and 7.5%, respectively. The multi-parameter AI prediction model adopts an improved Transformer + LSTM hybrid architecture, introduces a polar-specific spatiotemporal attention mechanism, has a learning rate of 0.001, 500 iterations, and an outlier suppression coefficient of [missing information]. The short-term (1-7 days) prediction error is 5.3%, the medium-term (1-3 months) prediction error is 9.2%, and the long-term (1-5 years) trend prediction accuracy is 86.7%. The overall accuracy after model collaboration is 22% higher than that of a single numerical model and 15% higher than that of a single AI model.

[0087] (5) Platform performance parameters The platform supports ≥100 concurrent users, has a data processing efficiency of ≥10GB / h, a model simulation response time of ≤30 minutes, and a prediction response time of ≤10 minutes; security encryption adopts the AES-256 algorithm, access permissions are graded at ≥5 levels, and anomaly warning response time is ≤1 minute.

[0088] Implementation effect verification After six months of trial operation and performance testing in Region I, all functions and performance of this embodiment platform have met the design requirements. The specific verification results are as follows: (1) Data access and fusion effect Successfully achieved adaptive access to multi-source data such as satellite remote sensing, in-situ observation, and simulation data. With a packet loss rate of 25% in polar communication, the average access latency was 3.2 minutes, the parsing accuracy was 99.7%, and the data completion accuracy was 98.5%. After the fusion of multi-source data, the accuracy of sea ice thickness and seawater temperature data improved by 18.2% and 17.5% respectively compared to single data sources. The data consistency was good and could meet the data needs of large-scale polar research.

[0089] (2) Model simulation and prediction accuracy The large-scale numerical model's simulation results for sea ice coverage, seawater temperature distribution, and ocean current direction in Region I show high agreement with measured data, with an average simulation error of 7.1%, significantly better than existing models (average error 16.3%). The multi-parameter AI prediction model has a short-term prediction error of 5.3%, a medium-term prediction error of 9.2%, and a long-term trend prediction accuracy of 86.7%. After model collaboration, the sea ice melting trend prediction accuracy reaches 91.2%, enabling accurate prediction of sea ice melting trends and extreme weather events, providing effective support for scientific research mission planning.

[0090] (3) Platform performance and stability In the extreme environment of Antarctica, with temperatures as low as -35°C and instantaneous wind speeds of 65 m / s, the platform operated stably with 100 concurrent users, without any lag. The data processing efficiency reached 12.5 GB / h, the average model simulation response time was 22 minutes, and the average prediction response time was 7.8 minutes. The communication link uptime was 96.3%, the equipment failure rate was 0.8%, the security protection system operated normally, and there were no data leaks or losses. The backup and recovery time was 45 minutes, fully meeting the operational requirements of the extreme polar environment.

[0091] (4) Application value The platform has been applied at a polar research station, providing researchers with precise data support and intelligent analysis tools. It has reduced the polar environmental data processing cycle from 24 hours to 5 minutes, significantly improving the efficiency and accuracy of polar marine environmental research. The typical scenario analysis module can quickly output special reports on sea ice melting, providing data support for global climate change assessment. The data sharing function enables cross-institutional data collaboration, promoting polar scientific research cooperation.

[0092] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An integrated intelligent database platform architecture for polar and marine environments, characterized in that, The platform architecture includes: an infrastructure layer, a data access layer, a data processing layer, a data storage layer, an intelligent analysis layer, and an application service layer; The infrastructure layer is used to build a hardware network cluster that is fully adapted to the extreme characteristics of the polar regions, enabling environmental adaptation across the entire process from data acquisition to transmission. The data access layer is used to build a full-process access system of "multi-protocol adaptation + intelligent parsing + real-time completion" to achieve real-time and complete access to multi-source heterogeneous data; The data processing layer is used to construct a full-process data preprocessing system to realize data processing; The data storage layer is used to construct a hybrid storage architecture of "distributed file storage + relational database + time-series database + graph database" to realize full lifecycle data storage management; The intelligent analysis layer is used to construct a two-way collaborative calibration mechanism and a multi-parameter prediction system with polar-specific characteristics using a hybrid architecture of "physical mechanism numerical model + data-driven AI model" to realize the simulation analysis and intelligent prediction of polar environment data. The application service layer is used to provide diverse interactive functions and service outputs.

2. The platform architecture according to claim 1, characterized in that, The data access layer includes: an adaptive data parsing module, a format standardization module, and a real-time completion module; The adaptive data parsing module is used to automatically identify the data format and source of multi-source heterogeneous data; The format standardization module is used to uniformly convert multi-source heterogeneous data into a preset standardized format; The real-time completion module is used to complete and correct fragmented data and outlier data lost during the transmission of multi-source heterogeneous data based on the improved Kriging spatiotemporal interpolation algorithm.

3. The platform architecture according to claim 2, characterized in that, The improved Kriging spatiotemporal interpolation algorithm includes: ； in, To incorporate the spatiotemporal variability function characteristic of the polar regions, For spatial coordinates, For timestamps, i , j For index variables; For spatial range; b For time-varying range; Value of a nugget; This is the base value; It is a spatial heterogeneity factor; For time period factors; This is the data density factor.

4. The platform architecture according to claim 1, characterized in that, The data processing layer includes: a data cleaning module, a feature extraction module, a multi-source data fusion module, and a data standardization verification module; The data cleaning module filters and identifies abnormal data using a preset reasonable threshold range for polar environments and an isolated forest algorithm. The feature extraction module is used to extract features from multi-source heterogeneous data and reduce the dimensionality of the extracted features using the PCA algorithm; The multi-source data fusion module is used to fuse multi-source heterogeneous data; The data standardization verification module is used to perform consistency verification on the data after multi-source data fusion processing.

5. The platform architecture according to claim 4, characterized in that, The process of fusing heterogeneous data from multiple sources by the multi-source data fusion module includes: ； ； in, For the proposition The basic probability assignment of fusion, proposition A represents data from polar observation equipment; For the first One piece of evidence supports the proposition. The basic probability distribution, n For the quantity of evidence; For the first A data source for the proposition Support level; For the first The credibility of each data source m For the number of data sources, K This is the allocation coefficient.

6. The platform architecture according to claim 1, characterized in that, The intelligent analysis layer includes: a physical mechanism numerical model module, a data-driven AI model module, and a two-way collaborative calibration mechanism module; The physical mechanism numerical model module is used to construct a large-scale polar marine environment numerical model based on fluid dynamics equations and polar environment characteristics, which couples ice, sea, and atmosphere. The data-driven AI model module is used to construct a polar climate multi-parameter system dynamics AI model based on the Transformer model + LSTM hybrid model architecture and by introducing a polar-specific spatiotemporal attention mechanism. The bidirectional collaborative calibration mechanism module is used to construct a bidirectional collaborative system of "mechanism constraint-data feedback" to realize the fusion and calibration of large-scale polar marine environment numerical models and polar climate multi-parameter system dynamics AI models.

7. The platform architecture according to claim 6, characterized in that, The large-scale polar marine environment numerical model includes: Seawater kinetic energy equation: ； in, They are respectively x , y , z The component of seawater velocity in the direction, in m / s; Time, in seconds; This is the acceleration due to gravity, measured in m / s². 2 ; The height of sea level undulation, in meters (m). This is the Coriolis force parameter, with units of rad / s; Seawater reference density, unit: kg / m³ 3 ; For sea surface wind stress x , y The directional component, in N / m 2 ; The vertical mixing coefficient is expressed in meters. 2 / s, the value range is optimized for the characteristics of polar sea areas; Sea ice energy balance equation: ； In the formula: Sea ice density, in kg / m³ 3 ; Sea ice thickness, in meters (m). The specific heat capacity of sea ice is expressed in J / (kg·K). Sea ice temperature, in Kelvin (K). Shortwave radiative flux, measured in W / m 2 Corrections are made to account for the difference in radiation between polar night and polar day. This refers to longwave radiation flux, measured in W / m². 2 ; This refers to sensible heat flux, expressed in W / m³. 2 ; Latent heat flux, in W / m³ 2 ; Heat exchange flux between sea ice and seawater, expressed in W / m³ 2 ; Sea ice melting / freezing rate: ； in, Net heat flux at the sea ice surface, expressed in W / m² 2 ; The latent heat of phase transition of ice is expressed in units of 334 kJ / kg. Seawater salinity, expressed in per mille (‰). This is an atmospheric temperature correction function. , The atmospheric temperature is expressed in °C, reflecting the effect of polar low temperatures on the phase transition rate.

8. The platform architecture according to claim 6, characterized in that, The polar-specific spatiotemporal attention mechanism includes: ； in, These are the query, key, and value matrices, respectively. The dimension of the key vector; It is a dynamic spatiotemporal weight matrix. In terms of time dimension, Spatial dimension; This is a multi-parameter correlation constraint matrix.

9. The platform architecture according to claim 6, characterized in that, The bidirectional collaborative system includes: Mechanism constraint: The simulation results of the large-scale polar marine environment numerical model based on polar physical laws are used as the prior constraint conditions of the polar climate multi-parameter system dynamics AI model to limit the deviation of the prediction results of the polar climate multi-parameter system dynamics AI model from the physical laws of polar environment evolution. Data feedback stage: The polar climate multi-parameter system dynamics AI model is based on real-time multi-source observation data. Through the gradient backpropagation algorithm, it dynamically optimizes the initial parameters and boundary conditions of the large-scale polar marine environment numerical model and corrects the simulation deviation caused by the solidification of empirical parameters in the large-scale polar marine environment numerical model. Collaborative Iterative Optimization: Establish a real-time interactive interface between the two models to perform periodic interactive updates of parameters and results within a preset period.

10. A method for implementing a polar marine environment intelligent integrated database platform architecture, the method being used to implement the platform architecture described in any one of claims 1-9, characterized in that, The implementation method includes: Deploy and debug a cluster of polar observation equipment at the infrastructure layer, and use the debugged polar observation equipment cluster to collect multi-source heterogeneous data; A multi-source data access system is built and tested at the data access layer, and the tested multi-source data access system is used to access the multi-source heterogeneous data. A standardized database is built in the data storage layer. The standardized data, which has been cleaned, feature extracted and fused by the data processing layer, is imported into the standardized database to establish data indexes and relationships. Large-scale numerical models of polar marine environments and AI models of polar climate multi-parameter system dynamics are constructed in the intelligent analysis layer and then trained, optimized, and validated. By integrating the data access layer, data processing layer, data storage layer, intelligent analysis layer, application service layer, security system, and operation and maintenance management system, an intelligent integrated database platform for polar marine environments is obtained.

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