1671 results about "Big data processing" patented technology

The invention provides an AI-based post competency and recruitment post matching method, and relates to the technical field of big data processing, and the method comprises the steps: obtaining multi-source heterogeneous data, and extracting dominant skill entities and implicit ability entities of candidates; constructing a candidate ability evolution graph; analyzing a post dynamic demand by combining a time sequence prediction model, and forming a dynamic post portrait including a current demand, a hidden demand and a future evolution demand; calculating the structural similarity between a candidate map and a post portrait through a map neural network matching algorithm, and generating and sorting comprehensive matching scores in combination with adaptive dimensions such as core capability difference, future integrating degree and team integration potential; team cooperative effect simulation is introduced, the influence of candidate addition on a team capability structure and a cooperative network is predicted, and the model is optimized through a closed-loop feedback mechanism; the talent recognition accuracy and coverage range are remarkably improved, prospective strategic talent matching is achieved, and the matching efficiency is improved.

The invention discloses a multi-modal data fusion method and system based on a large-model intelligent agent, belongs to the technical field of artificial intelligence, big data processing and intelligent agents, and aims to solve the technical problem of how to effectively integrate data of different modalities such as texts, images and voices by using the large-model intelligent agent. According to the technical scheme, the method comprises the steps that data collection and preprocessing are conducted, specifically, various modal data of texts, images and voice are collected through a web crawler, an API interface, a camera and microphone equipment, the collected data are preprocessed, the preprocessed multi-modal data are obtained, and the data quality is ensured; feature extraction and mapping: extracting corresponding modal features from the preprocessed multi-modal data through CNN and Transform models, mapping the different modal features to the same space, and combining the aligned features to form comprehensive feature representation; carrying out multi-modal fusion processing; and performing intelligent decision and feedback.

The invention relates to the technical field of big data processing, and provides a mass data storage optimization method which comprises the following steps: analyzing target data according to access frequency and / or access time of the data, and dividing the target data into hot data, warm data and cold data; aiming at the hot data, the temperature data and the cold data, performing compression processing by adopting corresponding compression algorithms; before the target data is stored, performing duplicate removal on the target data according to the fingerprint of the target data; monitoring the use condition of the memory, the access frequency of the target data and the performance of the compression algorithm in real time, and deploying system resources according to a monitoring result; and setting a multi-level cache, and caching different types of target data to the multi-level cache according to the divided types of the target data. According to the technical scheme, mass data can be efficiently stored and rapidly accessed, and the storage space utilization rate and the overall system performance are remarkably improved.

The invention discloses an enterprise scientific and technological achievement adaptation method based on big data accurate retrieval and query, and relates to the technical field of big data processing and knowledge maps, and the method comprises the steps: constructing an enterprise technical field knowledge map based on a standardized multi-modal feature matrix, receiving incremental technical data in real time by adopting an Apache Flink streaming processing framework, and carrying out the real-time retrieval and query of the enterprise technical field knowledge map. Dynamically updating a graph node relationship, removing expired nodes through a pruning algorithm, and outputting a dynamic knowledge graph with a timestamp; and receiving a query request of a user, extracting a query vector by utilizing the cross-modal embedding model, retrieving a Top-N candidate node list through a dynamic knowledge graph, calculating a comprehensive similarity score in combination with a graph relation weight, and outputting a sorted technical achievement list. Multi-source heterogeneous data is mapped to a unified semantic space through a cross-modal embedding model, and the intelligent level and the actual application effect of scientific and technological achievement adaptation are comprehensively improved in combination with construction and updating of a dynamic knowledge graph.

An intelligent design method for a support scheme of a rock burst section in a TBM tunnel, relating to the field of tunnel engineering. The intelligent design method for the support scheme of the rock burst section in the TBM tunnel comprises: determining a surrounding rock class on the basis of TBM mainframe operation data, surrounding rock feedback evaluation, and a tunnel engineering geological survey report, and using a rock burst intensity assessment method to predict a rock burst grade; and on the basis of the surrounding rock class and the rock burst grade, using a computer deep learning technology to determine an initial surrounding rock support method. The method effectively avoids the impact of subjective factors, actively utilizes TBM mainframe operation data and surrounding rock feedback evaluation, utilizes big data processing and an artificial intelligence technology to propose an intelligently designed support scheme for a rock burst section in a TBM tunnel, and can provide an efficient support scheme for field construction workers for a rock burst section in a tunnel.

The invention discloses a private domain data-based user portrait generation method and a private domain data-based content generation and touch method, and relates to the technical field of big data processing and pushing. Analyzing user behavior characteristics and interest preferences based on the collected data; marking static tags for the corresponding users based on the basic attributes; according to the behavior characteristics and interest preferences, constructing a primary dynamic tag; through user behavior data collected in real time, a time decay weighting algorithm is adopted to improve recent behavior weight and filter abnormal click data, a time sequence cross attention network is adopted to model a behavior feature sequence, interest preference and probability are output, and a dynamic threshold value is set to trigger dynamic label updating; and if the key behavior data of the user is collected, recalculating the dynamic tag based on the key behavior data and performing full-amount refreshing. According to the invention, a data-content-touch real-time linkage closed-loop system is constructed.

The invention discloses an off-site derivative cross-border supervision intelligent adaptation system based on heterogeneous data fusion, and relates to the technical field of big data processing. A data acquisition module is set to obtain off-site derivative transaction data, and an original data set is generated; setting a data structured processing module to perform semantic analysis on the original data set by adopting a financial text-oriented text model to generate a transaction information table, setting a transaction type classification module to judge whether cross-border transactions are involved or not according to transaction types in the transaction information table, and if the cross-border transactions are involved, setting a cross-border compliance evaluation module to perform cross-border compliance evaluation on the cross-border transactions. Performing dynamic rule matching on the transaction information table to generate a compliance report; otherwise, a risk prediction module is set, risk prediction is conducted on the transaction information table through a pre-trained transaction graph neural network model, a risk early warning signal is generated, and corresponding early warning measures are executed. The compliance cost is reduced, and the risk management and control capability is improved.

The invention relates to the technical field of photovoltaic power generation, in particular to a flexible photovoltaic intelligent monitoring and management system and method based on the Internet of Things, multi-source heterogeneous data are comprehensively collected through deployed multiple types of Internet of Things sensor nodes, the data are uploaded to a cloud platform after being cleaned and standardized through edge nodes, a big data processing architecture integrated with flow and batch is adopted, and the intelligent monitoring and management system and method based on the Internet of Things are established. The method comprises the following steps: performing real-time analysis and state judgment on a real-time data stream, performing deep batch processing and feature mining on historical data, extracting high-order features such as a performance attenuation trend and an abnormal mode, fusing real-time and historical features, and realizing comprehensive scoring of a health state of a component and accurate prediction of residual life by utilizing a machine learning model. And based on an evaluation result and a preset knowledge base, automatically generating a differentiated precise operation and maintenance instruction, and issuing and executing the differentiated precise operation and maintenance instruction to form closed-loop management. According to the invention, the monitoring depth and breadth of the flexible photovoltaic system are effectively improved, the conversion from passive alarm to active predictive maintenance is realized, and the operation reliability of the system is significantly enhanced.

The invention relates to the technical field of big data processing and analysis, in particular to a cross-border payment path optimization system and method based on multi-dimensional data analysis. Payment transaction data and exchange rate data are collected, a multi-dimensional payment path evaluation model is constructed from the angles of payment cost, payment timeliness, payment risk, payment availability and the like, and each payment path is scored in real time; constructing a payment path optimization model, dynamically selecting an optimal path and performing intelligent scheduling; a machine learning algorithm is introduced, an abnormal transaction mode is automatically identified from mass payment data, and a real-time anomaly detection and risk prevention and control strategy is established; according to the method provided by the invention, the success rate of cross-border payment can be improved, the cost is reduced, the payment time is shortened, and the payment risk is prevented, so that the cross-border payment service quality and the user experience are comprehensively improved.

The invention provides an intelligent building construction management system and method based on BIM scene construction, and relates to the technical field of building construction management. In the construction process, the management system collects mass data in real time by means of sensing layers spread all over key nodes of a construction site; the method comprises the following steps of: transmitting operation parameters of construction equipment, operation dynamics of constructors, consumption rate of materials and change of a field environment to a data processing center, accurately positioning a low-efficiency waiting problem existing in a material transfer link through deep analysis of a big data processing engine and an artificial intelligence algorithm library, recommending an optimization scheme, and improving the efficiency of material transfer. According to the method, the hoisting sequence is adjusted, unnecessary working hour loss is reduced, by means of a BIM model platform, a construction plan is presented in a visual mode, and the management system can rapidly generate and recommend and flexibly deploy construction team personnel, optimize the construction process sequence, ensure construction coherence, reduce construction delay and shorten the construction period.

The invention relates to the technical field of transformer monitoring, and discloses a comprehensive online transformer monitoring system which comprises a sensor layer, an edge computing layer, a cloud platform layer and a communication module. The cloud platform layer comprises a data warehouse, a big data processing engine, a comprehensive diagnosis engine, a model training engine and a system management module, the edge calculation layer is in communication connection with the cloud platform layer through a communication module, the comprehensive diagnosis engine integrates a deep neural network and a knowledge graph inference engine, and cross validation of data driving and knowledge guiding is achieved; a model training engine utilizes historical data and online incremental data to continuously optimize a model, and a dynamic knowledge graph updates a fault rule through a real-time diagnosis result and expert feedback, so that the system can adapt to novel faults and complex working conditions, and the diagnosis accuracy and adaptability are greatly improved.

The invention relates to the technical field of big data processing, in particular to an express logistics path planning system based on topology analysis. The invention discloses an express logistics path planning system based on topology analysis. The system comprises a node load feature extraction module, a space-time cross correlation screening module, a road contour dynamic correction module, a node optimization sorting module and a path continuous connection reconstruction module. According to the method, the node load change trend is extracted, the node state evolution is dynamically mastered, the load change response precision is improved, the accuracy of correlation identification between the nodes is enhanced based on space-time joint screening, the road boundary is dynamically corrected, the geometric precision of the topological structure is improved, and the node sorting is optimized in combination with the passing time and the communication continuity; connection errors are inhibited through continuous offset detection, the path communication precision is improved, comprehensive control over node dynamics, space-time correlation, road structures and path precision is achieved on the whole, and the real-time performance, flexibility and accuracy of logistics path planning are enhanced.

The invention discloses an electronic product personalized marketing method based on a big data accurate portrait, and relates to the technical field of big data processing, and the method comprises the following steps: multi-source data collection and cross-domain fusion processing: obtaining a multi-source data set related to an electronic product from each platform of the Internet by using a web crawler and a streaming processing engine, the multi-source data set is subjected to cleaning classification and alignment fusion processing to generate multi-modal data, and then the multi-modal data is updated in real time to dynamically reflect data changes, so that a foundation is laid for subsequent data analysis; according to the method, multi-source data is collected by using a web crawler and a streaming processing engine, quantitative analysis is performed on the data by using a Hofscode culture dimension model and a Maslow demand level theory, a user portrait is constructed by using a distributed data flow engine Apache Flink to dynamically update a label, and a GAN (GAN Adversarial Generative Network) anomaly detection and evaluation analysis method is applied, so that the user portrait is obtained. And comprehensive and accurate data acquisition and processing are realized.

The invention discloses a geological disaster monitoring system and method based on iron tower big data, and relates to the technical field of geological disaster monitoring. Comprising an iron tower monitoring node module, an edge calculation module, a data transmission module, a cloud big data processing and analysis module, a visualization and early warning issuing module and an emergency response and decision support module. According to the invention, communication iron tower resources are provided and a plurality of sensing devices are arranged, so that low-cost and large-scale deployment is realized, the monitoring coverage range is expanded, and the accuracy and reliability of geological disaster monitoring are improved at the same time; local preprocessing is carried out on the collected data through the edge computing module, and the data are transmitted to a cloud end in real time through a 4G / 5G network, so that real-time monitoring and quick response of the data are realized, and the timeliness of the monitored data is greatly improved; an intelligent analysis early warning system is established through a cloud big data processing and analysis module, and machine learning and an artificial intelligence algorithm are integrated to carry out multi-source data analysis and risk assessment, so that the early warning accuracy is improved.

The invention relates to the technical field of agricultural product distribution, and discloses an agricultural product distribution system based on the Internet of Things, and the system comprises an Internet of Things sensing layer which is provided with various sensors, including but not limited to a temperature sensor, a humidity sensor, an infrared sensor and an RFID tag; the data transmission layer is used for transmitting the data acquired by the sensing layer to a cloud server in real time by utilizing a 4G / 5G and Wi-Fi wireless communication technology; and the cloud computing and big data processing layer adopts a high-performance cloud computing platform and integrates a big data analysis engine. According to the invention, the accuracy and real-time performance of original data are ensured by optimizing the sensor configuration and the data acquisition frequency of the Internet of Things sensing layer and adopting an advanced wireless communication technology, so that the accuracy of agricultural product quality prediction, freshness evaluation and transportation path optimization is remarkably improved; mass data can be processed and analyzed by means of a high-performance platform of cloud computing and a big data processing layer.

The invention discloses a big data resource service method and system based on multi-modal data fusion, belongs to the technical field of big data processing, and solves the problems that a scheduling scheme is formulated only by calculating an energy consumption value and a resource utilization rate of a server in an existing method, the adaptability to dynamic changes is poor, and the overall performance and the resource utilization rate of the system are reduced. The method comprises the steps that a multi-modal data sequence is preprocessed, a shared database is created, multi-modal data are encrypted and distributed to a distributed hash table of distributed nodes, a resource retrieval instruction is recognized based on a resource scheduling model, and the resource scheduling model dynamically deploys a data transmission task based on target data attributes and real-time transmission path information; according to the method, the resource scheduling model considering the multi-modal data distribution and transmission characteristics in the distributed environment is constructed, collaborative learning and optimization among distributed nodes are realized, the transmission path is dynamically optimized, the delay and energy consumption of data transmission are reduced, and the intelligent level of scheduling is improved.

The invention discloses a goaf collapse risk assessment data fusion system based on big data processing, and particularly relates to the technical field of geological disaster assessment, and the system comprises three core modules: a multi-source data adaptive weighted fusion module which establishes a unified space-time coordinate system, converts non-raster data into a continuous field through Kriging interpolation, and performs data fusion on the continuous field; combining the information entropy and the correlation coefficient to dynamically distribute weights, and generating an enhanced feature field through self-supervised pre-training; the physical-space-time neural network dynamic prediction module is integrated with elastic-plastic mechanical constraint loss and multi-task learning, and outputs a future multi-time step risk probability field and a deformation prediction field through a space-time convolution-memory network; and the risk field three-dimensional subdivision and emergency response module is used for clustering three-dimensional voxels in a high-risk area, automatically calculating risk body parameters, generating an emergency scheme in combination with DEM data and an A * algorithm, and improving evaluation accuracy and emergency scheme practical operability through digital twinborn deduction evaluation.

The invention relates to the field of geological data acquisition, and particularly discloses an intelligent acquisition method for surveying in a plateau bare area based on an unmanned aerial vehicle, which aims at performing surveying according to a preset route through the unmanned aerial vehicle in route surveying work of a plateau bare area geological acquisition industry. And the identified information is subjected to big data processing and related data acquisition operation is executed. The method mainly comprises the steps that the flight route of the unmanned aerial vehicle is preset, the unmanned aerial vehicle carries out multi-position data collection at related positions through carried peripherals in cooperation with images and videos in an auxiliary mode, and after route exploration is completed, data are returned; and analyzing related route data, extracting related entity data by using a professional geological big data model, and performing accuracy correction and complementation on the extracted related entity data, so as to finally generate accurate route acquisition data. After acquisition is completed, the data can be automatically displayed on a mobile terminal web terminal according to related acquisition data for field acquisition personnel to provide related reference.

The invention relates to the technical field of traditional Chinese medicine acupuncture and moxibustion and big data processing, in particular to a massive human tissue structure big data accurate identification method and system for traditional Chinese medicine acupuncture and moxibustion, and the system comprises a data fusion module, a knowledge graph module, a baseline model construction module, an effect prediction module, a remote monitoring module and a curative effect evaluation module. Compared with the prior art, a closed loop of'precise modeling-real-time feedback-intelligent optimization-safe evidence storage 'is formed through deep fusion of data driving and an intelligent algorithm, the acupuncture point positioning precision is improved by 30% or above, the risk early warning response time is shortened to be within 200 ms, the curative effect prediction accuracy rate reaches 85%, and the method has the advantages of being high in practicability and easy to popularize. Systematic technical support is provided for standardization and intellectualization of acupuncture treatment, and clinical safety and effectiveness are remarkably improved.

The invention discloses a marketing content generation method and system based on big data, and relates to the technical field of big data processing and artificial intelligence, and the system comprises a multi-source data collection and fusion module which is used for receiving original multi-source data from different behavior sources, content sources and business scene sources, and performing structured processing, time relationship coordination and feature fusion on the original multi-source data to generate a fused portrait data set for describing the relationship between the user and the product. In the invention, through the multi-source data acquisition and fusion module, deep integration of heterogeneous multi-source data and unified representation of multi-modal features are realized, a high-quality data basis is provided for subsequent knowledge graph construction and content generation, and the problems of isolated island and insufficient feature utilization of traditional marketing data are solved; through a ternary knowledge graph construction module, information such as users, products, scenes and the like is constructed into a structured knowledge graph, and the semantic understanding ability and relevance of content generation are greatly enhanced.

The invention discloses a big data processing system based on AI cloud computing training, and the system comprises a data collection module which collects multi-source image data through a cloud interface, and generates a standardized data set after distributed cleaning and feature extraction; the data prediction module constructs a resource prediction model, and outputs a resource availability prediction vector in combination with a federated average algorithm and a space-time diagram neural network; the dynamic scheduling module adopts a double-queue architecture and an NSGA-II algorithm to optimize resource allocation and reduce task delay and energy consumption; the fusion decision module integrates the data and the scheduling result, generates a decision through an AI model, dynamically adjusts task migration, and improves the cloud image processing efficiency; therefore, the number of the image data fragments can be automatically adjusted according to the real-time load and the storage capacity of the cloud computing nodes, load imbalance among the nodes is avoided, the efficiency of image data cleaning, normalization and feature extraction is greatly improved, the data processing time delay is reduced, and the overall throughput is improved.

The invention discloses a classification model training method based on big data distributed computing, and relates to the technical field of big data processing and machine learning, and the method comprises the steps: S1, obtaining communication delay data between distributed computing nodes, and analyzing delay characteristics to generate regulation and control parameters, the method comprises the steps of S1, dynamically determining the batch size of each training batch based on regulation and control parameters, and ensuring the optimization of the synchronization efficiency, S3, distributing the adjusted batch size to each node, and executing the forward and reverse propagation calculation process of the model, and S4, collecting the calculation result of each node to perform parameter aggregation updating, and completing the iterative training of the classification model. According to the classification model training method based on big data distributed computing, the batch size is regulated and controlled according to the communication delay between the nodes so as to solve the problem that the distributed synchronization efficiency is low.

The invention discloses a multi-source power distribution network energy-saving optimization scheduling method, system, equipment and storage medium based on big data processing, and relates to the technical field of power systems and big data processing. After power distribution network multi-source data is collected, through preprocessing and feature extraction, loads are predicted and classified through a long and short-term memory network; the load dynamic optimization module combines cost, network loss and photovoltaic consumption, generates a scheduling instruction through a mixed integer programming model, compares power loss before and after scheduling after CPLEX solution, and generates a difference thermodynamic diagram. According to the scheme, accurate load prediction and dynamic optimization scheduling of the multi-source power distribution network are realized through big data processing, the cost and energy efficiency are effectively improved, the consumption capability of photovoltaic energy is enhanced, the energy-saving effect is visually displayed through the power loss difference thermodynamic diagram before and after scheduling, and powerful support is provided for intelligent management of the power distribution network.

The invention relates to the technical field of data processing, in particular to a business process dynamic optimization decision-making system based on AI industrial big data processing, and the system comprises the steps: obtaining stress-strain transmission data of a petrochemical pipeline network in different operation states and corresponding pipe network operation parameters, and constructing a historical decision-making case library; adaptively determining a core transfer feature set from stress-strain transfer data, and iteratively optimizing a feature screening weight through historical data; inputting the transfer feature set and the pipe network operation parameters into a dynamic correlation modeling engine, generating a self-updating transfer-operation correlation model, and correcting correlation coefficients according to real-time data deviation; and based on the transfer-operation association model and the historical decision case library, executing a dynamic adjustment decision of the pipe network operation parameters. Stress-strain and operation parameters are collected through distributed sensing, space-time deviation is eliminated, a space-time association data set is formed, screening weight is optimized through historical feature importance iteration, and a key transmission path is automatically identified.

The invention discloses a zero-carbon park energy supply optimization method and system based on big data analysis. The method comprises the following steps: S1, constructing a unified park energy operation data set; s2, performing feature extraction and data fusion on the park energy operation data set by using a big data processing technology to generate an energy supply and demand prediction result at a future moment; s3, constructing a distributed energy storage regulation and control optimization model of the zero-carbon park; s4, solving the distributed energy storage regulation and control optimization model of the zero-carbon park by using an improved hybrid optimization algorithm, and generating a plurality of distributed energy storage regulation and control strategy candidate solutions; and S5, evaluating the distributed energy storage regulation and control strategy candidate solution, selecting an optimal distributed energy storage regulation and control strategy which is most matched with the current park energy supply and demand state according to a preset fitness evaluation index, and taking the optimal strategy as a regulation and control scheme. According to the method, the energy utilization efficiency can be effectively improved in a complex dynamic environment, the energy storage loss and carbon emission are reduced, and the intelligent energy supply optimization target of the zero-carbon park is achieved.

The invention provides a big data intelligent diagnosis system based on a large model, and belongs to the field of big data processing and diagnosis operation and maintenance, and the big data intelligent diagnosis system based on the large model is based on technologies of a large language model, vector recall and the like and comprises modules of error information preprocessing, large model intelligent diagnosis, vector recall, RAG enhancement, rule engine, solution generation, feedback optimization and the like. Efficient processing and intelligent diagnosis of error information of the big data system are realized, a reliable solution is quickly generated, the diagnosis capability is continuously optimized, and the operation and maintenance efficiency and accuracy of the big data system are remarkably improved.

The invention provides a big data processing method and system and a storage medium, and belongs to the technical field of big data processing and analysis. The method comprises the following steps: acquiring original data and classifying the original data through a data type identification module to generate a data type tag sequence; based on the data type label sequence, performing preliminary cleaning on original data to form a data set after preliminary cleaning; based on the data set after preliminary cleaning, a multi-dimensional data quality evaluation model is adopted to generate a data quality scoring sequence; according to the data quality scoring sequence, abnormal values and noise are identified and repaired, and a repaired data set after quality evaluation is generated; and based on the repair data set, executing multi-source data fusion and deduplication operation to form a fusion data set after deduplication. The method improves the quality and accuracy of the medical big data, realizes efficient data processing and unified storage, and is suitable for processing scenes of complex multi-source data.

The invention relates to the field of artificial intelligence and big data processing, and discloses a professional field agent construction method and system based on a multi-modal hybrid expert model and a knowledge graph. The method comprises the following steps: collecting multi-source heterogeneous data such as user behaviors, texts, images, structured data and voices, and vectorizing and coding the multi-source heterogeneous data; performing feature extraction on different modal data and fusing to form unified representation; constructing a hybrid expert network based on a gating mechanism to perform task specialization processing on the fusion representation; introducing a domain knowledge graph and performing semantic enhancement through a graph neural network; on the basis of fusion representation, tasks such as recommendation, intention recognition and question and answer are executed at the same time through a multi-task learning framework; and an output result is fed back through the multi-modal interaction interface, and user feedback is collected to update the model. According to the method, the semantic understanding, task prediction and personalized recommendation capabilities of the model in a complex scene can be improved.

The invention provides an unstructured data synchronization method and system based on Flink, and relates to the technical field of big data process.The method comprises the steps that fragmentation processing is conducted on unstructured data, hierarchical storage of the data in a heap memory layer and a RocksDB layer is achieved on the basis of access feature scores, data partition optimization is conducted in combination with deep reinforcement learning, and the unstructured data are synchronized. And the parallelism degree parameter and the resource quota are dynamically adjusted by calculating the back pressure index value, and the data synchronization reliability is ensured by adopting a check point snapshot mechanism, so that the data processing efficiency and the system throughput are effectively improved.

Along with increasingly important global marine resources, the problems of marine pollution, ecological damage, natural disasters and the like are increasingly severe, which proposes higher requirements for monitoring and managing the marine environment. A traditional marine monitoring method has a certain effect in practice, but the coverage range is limited, data acquisition is difficult, the processing efficiency is low, and comprehensive monitoring and timely response to a dynamic marine environment cannot be realized. In order to solve the problem, the invention provides the ocean multi-modal data processing method, device and equipment for ocean monitoring and the storage medium, the satellite remote sensing technology, the underwater sound telemetering technology, the buoy observation technology and the shipborne observation technology are integrated, and the advanced big data processing and heterogeneous data fusion method is combined, so that the multi-modal data processing of the ocean is realized. And efficiency is provided for comprehensive monitoring and early warning of the marine environment.