1476 results about "Data classification" patented technology

The invention relates to the technical field of intelligent traffic systems, and discloses an expressway multi-source traffic data grading and classification processing system, which comprises a data acquisition standardization module for acquiring and preprocessing multi-source traffic data; the data source quality evaluation module is used for evaluating the credibility of the data source; the scene emergency degree calculation module is used for calculating the flow anomaly degree, the vehicle speed anomaly degree and the meteorological risk score and identifying an emergency event; the data grading module is used for calculating a comprehensive priority score of the data and obtaining a grading data set by adopting a threshold segmentation method; the data classification module is used for carrying out self-adaptive classification on the data; the data fusion module identifies the same traffic parameter, performs conflict detection and performs fusion processing on conflict data; the result output module is used for obtaining a processing result by adopting a grading and classification output and quality feedback mechanism; according to the invention, intelligent refined processing of the highway multi-source traffic data is realized, and the emergency response speed and the data processing accuracy are improved.

The invention relates to the technical field of artificial intelligence and remote sensing image processing, and particularly provides a hyperspectral image and laser radar data classification method based on a dynamic fusion network. The method comprises the following steps: preprocessing acquired multi-modal data, and constructing multi-scale input; a dual-scale local attention module is designed, and context information of different scales is fused in a self-adaptive weighted mode through gating soft pooling; a dynamic down-sampling feature enhancement module is designed, the down-sampling rate is dynamically adjusted according to the complexity of the feature map, and deep multi-scale interaction is carried out based on a Mama backbone; constructing a directional interactive attention module, extracting features in horizontal, vertical and diagonal directions through directional gating convolution, and capturing an anisotropic structure of a linear ground feature; through the design of a double-path classifier, fusing shallow space details and deep semantic information; and the model is trained, optimized and reasoned to obtain data classification, and the method improves the classification precision and the calculation efficiency.

The invention relates to a flying dust monitoring data processing and classifying method based on multi-source sensing fusion, and the method specifically comprises the following steps: firstly, deploying multi-source flying dust monitoring sensor nodes in a target region to collect data, carrying out the marking, and generating a data set; performing continuous wavelet transform on the acquired data, extracting a wavelet energy spectrum and a Shannon entropy, and splicing to obtain an enhanced feature tensor; secondly, through a two-stage fusion and coding strategy, frequency band energy features are extracted through wavelet packet decomposition, multi-channel cross-correlation, statistical moment and ratio features are calculated to form time sequence mode coding features, and multi-source heterogeneous feature fusion is achieved in combination with a local time sequence feature matrix; then constructing a deep learning model containing a multi-scale time sequence feature extraction and dynamic fusion module, and inputting a fusion feature matrix for training; and finally, inputting the preprocessed new monitoring data into the trained model, and outputting a dust source and pollution level classification result. The dust monitoring data classification accuracy and the dust source identification precision can be effectively improved.

The invention relates to the technical field of medical data management, and discloses a medical data classification management system based on big data. A data integration and classification module of the system receives a historical medical data set and a real-time medical data stream, performs feature fusion and classification prediction through a deep learning model, and generates a theoretical classification result. And the deviation quantitative analysis module performs multi-dimensional deviation calculation on the theoretical classification result and the clinical actual classification result to generate a patient-level multi-dimensional deviation tensor. And the graph network anomaly positioning module inputs the multi-dimensional deviation tensor into a graph neural network for anomaly propagation simulation based on a medical institution topological structure, and generates an anomaly probability distribution diagram to identify a physical region where an anomaly occurs. And the adaptive decision control module dynamically allocates monitoring resources according to the abnormal probability distribution diagram, including starting high-frequency data acquisition for a high-probability abnormal region and executing an intervention test on adjacent nodes.

The invention relates to the technical field of embedded data storage, and discloses a method and a system for prolonging the service life of a FLASH simulation EEPROM (Electrically Erasable Programmable Read-Only Memory) in a single chip microcomputer, and the method comprises the following steps: firstly, initializing the FLASH, dividing the FLASH into a main data area, a mapping table area, a backup area and a transaction log area, selecting a physical block through a dynamic wear leveling algorithm when data is written, and writing the data into a database; the method comprises the following steps of: starting a backup area or a transaction log area, safely updating a mapping table by using a transaction mechanism, performing cyclic redundancy check when reading data, if the cyclic redundancy check fails, starting cascade recovery from the backup area or the transaction log area, and finally, updating a transaction log to record operation so as to form a closed-loop management process. According to the invention, through a set of dynamic wear leveling and data classification management mechanism, global and uniform distribution of the write load in the storage array is realized, the physical block with the least erasing times is selected during writing, the access frequency of the data can be identified, and cold data which is not changed frequently can be actively migrated.

The invention provides an agricultural product supply chain traceability data integrity verification method based on machine learning, and belongs to the technical field of agricultural product supply chains. An agricultural product supply chain data acquisition system is established, layering is carried out according to timeliness, a data classification processing framework is constructed, and original data is classified according to complexity; establishing an agricultural product damage prediction model to identify a vulnerable link, and designing a double-layer game optimization model consisting of an upper layer model taking data integrity maximization as a target and a lower layer model taking calculation efficiency optimization as a target, a liquid neural network is adopted to construct a data fusion verification model, liquid neuron state parameters are dynamically adjusted according to the number of supply chain nodes, data timeliness and other parameters, the parameter range of each evaluation matrix is determined through a small-scale verification experiment, and finally integrity verification is executed to calculate and output a comprehensive score result. The technical problems that the agricultural product supply chain traceability data integrity verification accuracy is not high and the calculation efficiency is low are solved.

The invention discloses a deep well rock mass instability failure time accurate forecasting and early warning system, and relates to the technical field of deep well rock mass failure early warning, and the deep well rock mass instability failure time accurate forecasting and early warning system comprises the following steps: collecting a plurality of deep well rock samples to carry out a uniaxial compression experiment, and collecting acoustic emission signals in real time based on an acoustic emission sensor to obtain original acoustic emission signal data; carrying out self-adaptive preprocessing, and carrying out data classification and integration to obtain rock sample acoustic emission parameter data; constructing an original damage forecasting model, and performing model training and verification optimization based on the rock sample acoustic emission parameter data to obtain a rock damage forecasting model; collecting an acoustic emission signal of the deep well rock mass, and predicting the instability failure time of the deep well rock mass in real time based on the rock failure forecasting model; the method is used for solving the problems that when an existing deep well rock mass damage early warning technology carries out early warning on rock mass instability damage through acoustic emission monitoring, single or few characteristic parameters are manually processed and analyzed, and the early warning reliability and timeliness are not high.

The invention discloses an incomplete multi-view multi-label data classification method based on semantic enhancement and pseudo-label uncertainty perception, and the method comprises the steps: employing a dual-channel feature extraction and decoupling module to obtain the shared semantic representation and specific representation of each view in each sample for a constructed incomplete multi-view multi-label data classification network model; performing cross-view fusion on the shared semantic characterization and the specific characterization, obtaining a unified shared characterization and a unified specific characterization corresponding to each sample, performing feature fusion, obtaining a fusion characterization of each sample, inputting the fusion characterization of the sample output by the dual-channel feature extraction and decoupling module into a classifier for multi-label prediction, and performing multi-label prediction on the fusion characterization of the sample. Therefore, a multi-label classification prediction result is obtained, and model training is carried out based on a total contrast learning loss function and a joint supervision classification loss function. According to the method, the classification performance and the model robustness on incomplete multi-view multi-label data are remarkably improved through training learning under the guidance of semantic enhancement and uncertainty.

The invention belongs to the technical field of computer storage, and particularly relates to a solid state disk data management and efficient storage distribution method, which comprises a data characteristic sensing and classifying unit, a data characteristic sensing and classifying unit, a data processing unit, a data storage unit and a data distribution unit, wherein the data characteristic sensing and classifying unit is configured for analyzing write-in request data flow entering a solid state disk in real time or periodically, extracting various characteristic parameters associated with data blocks and storing the data blocks; classifying the data on the basis of the characteristic parameters, and distributing one or more data classification labels for each data block to be written or stored; and the self-adaptive storage allocation strategy unit is configured to be used for dynamically selecting an optimal physical block for data writing according to the data classification label and in combination with the physical block state information of the current flash memory medium. According to the invention, the solid state disk can better adapt to various dynamic and variable workloads, and especially shows more excellent continuous write-in performance, lower delay jitter and obviously prolonged service life when processing a large amount of random write-in, small block write-in and write amplification sensitive applications.

The invention discloses a communication method between graphics processors, a product, equipment and a medium, relates to the technical field of high-performance calculation and artificial intelligence acceleration, and is applied to a stand-alone system comprising a plurality of graphics processors and a central photoelectric hybrid switching chip constructed based on interconnection of an electric switching matrix and an optical switching matrix. The graphics processor is connected with the central photoelectric hybrid switching chip through an optical link and an electric link; the method comprises the following steps: performing data classification on to-be-transmitted data of a source graphics processor; if the to-be-transmitted data is a control flow, the source graphics processor is controlled to send the to-be-transmitted data to an electric switching matrix through an electric link, and then the to-be-transmitted data is routed to a target graphics processor in the graphics processors; and if the to-be-transmitted data is a data stream, the source graphics processor is controlled to send the to-be-transmitted data to the optical switching matrix through the optical link, and then the to-be-transmitted data is routed to the target graphics processor. Interconnection between graphics processors is optimized to improve communication efficiency between graphics processors.

Disclosed in the present invention is a knowledge graph-based unified fault search method for a specialized vehicle device. The method comprises: acquiring device maintenance related data, and taking a device as a core to establish association relationships between the device maintenance related data; acquiring a fault report, and parsing the fault report to obtain parsing result data; constructing a device fault graph on the basis of the device maintenance related data and the parsing result data; integrating the device fault graph into a knowledge graph platform; receiving a search term entered by a current user in a search box, acquiring search results corresponding to the search term, and displaying the search results on a search result page, wherein the search results comprise a result list, data classification statistics, search result sorting, a knowledge cloud picture, and associated recommendations; and providing in the search result page an operation entry for knowledge exploration, and viewing related knowledge exploration of the current knowledge by the user by triggering the operation entry for knowledge exploration. The method solves the problem in the prior art that the search results are not accurate enough.

The invention discloses a graphical file editing system and method. The system is deployed in the form of an IDE plug-in, and comprises an editor extension module of a JBCefBrowser embedded browser, an editor extension module of a JBCefBrowser embedded browser, and an editor extension module of a JBCefBrowser embedded browser, the file identification module is used for scheduling a corresponding editor based on a file suffix; the graphical engine is used for performing classified rendering on the JSON / XML data based on the component metadata registry; the event processing module supports three-stage dragging and eight-direction zooming; an event driving communication module based on window.post Message, and an event driving communication module based on window.post Message; the operation module is used for synchronizing files and codes through a PSI interface; and a file template management module based on a. Ft template. The method comprises the steps of editor registration and embedding, file identification and loading, content analysis and rendering, interaction event processing, data persistence synchronization and template file creation. According to the method, graphical dragging and editing of the structured file are realized in the IDE, and the development efficiency and the normalization are remarkably improved.

The embodiment of the invention discloses an air conditioner maintenance data classification method and system based on machine learning, and the method comprises the steps: integrating multi-source heterogeneous maintenance records generated in the maintenance process of air conditioner equipment, building a correlation index through a common identification field, and fusing dispersed data into a maintenance data set in a uniform format; performing hierarchical semantic analysis on unstructured texts in the set to generate structured semantic features, and performing time sequence feature extraction on structured data; then constructing a hybrid classification model training framework fusing semantic and time sequence features, and generating a maintenance data classification model through feature space alignment, dynamic weight distribution, hyper-parameter optimization and an early stop strategy; and finally, classifying newly-added maintenance records by applying the model, checking by combining an expert knowledge base, manually rechecking conflict results, and returning corrected data as an incremental sample back to the model to realize continuous optimization.

The invention discloses a multi-tenant data isolation and resource sharing method and device and a medium, and relates to the technical field of software architecture. The method comprises the steps of obtaining tenant data, and verifying the tenant data to allocate a unique tenant identifier corresponding to a tenant; based on the unique tenant identifier, the tenant permission and the resource allocation condition are initialized, and tenant data are classified, encrypted and stored in a public data layer, a tenant shared data layer and a tenant private data layer; based on a preset permission allocation model, allocating the initialized tenant permission, and performing data isolation on private data in the tenant data; and monitoring the resource use condition of each tenant in real time, and adjusting the resource allocation condition based on a load balancing algorithm. According to the method, the unique tenant identifier is allocated to each tenant, and the data is classified, encrypted and stored, so that logic isolation of the data is realized, and accurate allocation of resources is ensured through a dynamic resource adjustment mechanism.

The invention discloses a scene adaptive recommendation method in smart home based on multi-modal data, and relates to the technical field of multi-modal data processing. According to the method, firstly, scene classification is carried out based on multi-mode sensing data for a smart home scene, a current life scene of a user is accurately recognized, meanwhile, an equipment linkage state is analyzed in combination with a classification result, whether equipment operation is matched with a scene requirement or not is judged, and then a scene mode is recognized according to a linkage analysis result to generate preliminary recommendation; whether the dynamic weight space relation model of the user is updated or not is judged, finally, the recommendation effect is quantified according to the weight adjustment judgment result, the recommendation strategy is optimized through two-way feedback, the adaptability to the user requirement is improved, and the user experience is improved. A full-process closed loop of multi-modal data classification, equipment linkage analysis, mode recognition recommendation, weight dynamic updating and effect quantitative feedback in a smart home scene is realized, and the problem that the intelligent degree of scene recommendation in smart home is not high in a user behavior dynamic change scene is effectively solved.

The invention discloses a multi-agent collaborative application big data management system and method. The system comprises a data acquisition unit, a data preprocessing unit, a multi-agent collaborative processing unit, a data storage unit and a data scheduling unit. The data acquisition unit is used for acquiring multi-source heterogeneous original big data and transmitting the multi-source heterogeneous original big data to the data preprocessing unit; and the data preprocessing unit is used for executing preprocessing operation including de-duplication and format standardization on the original big data. The invention relates to the technical field of big data management. According to the multi-agent collaborative application big data management system and method, through a multi-agent collaborative algorithm, the system can realize a self-adaptive data classification and optimization strategy, a data processing mode is dynamically adjusted, the processing efficiency problem of a traditional static rule during data mode fluctuation is improved, and the data processing efficiency is improved. The system adopts a dynamic resource scheduling strategy based on a data access demand and a system load, efficiently distributes calculation and storage resources, and facilitates priority processing of high-priority tasks.

The invention relates to the technical field of big data management, in particular to a bioinformatics data classification method and system based on big data, and the method comprises the following steps: obtaining time sequence recognition trend reversal and positioning fragments, extracting recognition difference positions inside and outside a frequency band data division region, screening samples with consistent features, and rearranging path labels; connecting nodes are cut off to generate fracture indexes, and label states are updated and written into sample fields to form a classification result set. According to the method, a labeling area is constructed by extracting trend inversion points in a time sequence, sample fragments are divided by combining data fluctuation positions in a disturbance frequency band, label numbers are arranged according to the fluctuation sequence of samples in a path, a corresponding sequence of a label chain connection relation and the sample positions is established, and label section boundaries are positioned and limited by fracture nodes. And the updated label state is synchronously written into a sample field, and the path label is bound according to a chain sequence, so that the sample identifier is kept coherent in sequence change, and the continuous coverage capability of the path information in classified output is improved.

The invention relates to the technical field of remote sensing agricultural data management, and discloses a remote sensing agricultural big data management system based on a block chain. According to the system, image data is subjected to radiation, atmosphere and geometric correction preprocessing to generate a standardized remote sensing image; the feature extraction module performs multi-scale segmentation on the crop feature region, identifies the crop feature region, extracts texture, spectrum and shape feature values, and fuses the feature values to form a multi-dimensional feature vector; the block chain storage module performs hash operation on the vector to generate a feature fingerprint, constructs a new block in combination with a timestamp and a preorder block hash value, and forms a non-tampering agricultural data chain after verification of a consensus mechanism; a path analysis module traverses the block sequence, extracts a storage path and screens a high-relevance feature path set; and the intelligent classification module extracts feature tags, and generates an agricultural data classification structure through a multi-dimensional matching algorithm. The system realizes standard processing, safe storage and intelligent application of remote sensing agricultural data, and meets the requirements of agricultural modernization development.

The invention relates to the technical field of data classification, in particular to a hospital intelligent inquiry log analysis method and system.The method comprises the following steps that medical concept depth is counted, symptom keywords are extracted, statistical information gain and semantic consistency scores are calculated, split decision-making indexes are generated by combining the gain and the scores to construct an intention classification tree, and an intention classification result is obtained; inputting a log to generate an inquiry intention identifier, recognizing an error node based on diagnosis and treatment correction feedback and updating a punishment value, establishing a virtual error correction link, calculating a path confidence fusion node, and establishing a physical branch to generate a reconstruction classification tree when a guide weight exceeds a limit. According to the method, keyword statistics is upgraded into three-dimensional feature analysis by introducing medical concept hierarchies, a classification tree following a medical system is constructed, a reverse penalty and virtual link mechanism is constructed by using feedback, node fusion and reconstruction are executed based on confidence, classification logic self-adaptive evolution is realized, and intention recognition interpretation and robustness are improved.

The invention discloses an assembly process knowledge graph construction and updating method based on graph attention network mapping, and the method mainly comprises the following steps: standardization system design construction, data collection, data classification, multi-modal data preprocessing, multi-source information extraction and graph node feature initialization. Semantic modeling and vector space mapping driven by a graph attention network, knowledge storage, construction of an assembly process knowledge graph by using stored knowledge data, and semantic complementation and dynamic updating of a process information model. Through the unique self-attention mechanism of the GAT, the weight can be dynamically distributed for the relationship among different information nodes, so that knowledge reasoning and expression are more accurately carried out, and the problems of knowledge fragmentation, management rigidity, semantic understanding deficiency, low knowledge utilization efficiency and the like in the prior art are systematically solved.

The invention discloses a customer service information generation method and system based on multi-modal intention recognition, and the method comprises the steps: obtaining customer service session data, and carrying out the heterogeneous data classification operation of the session data; performing feature extraction operation on each piece of parting modal information; establishing a semantic bridging matrix among the modal features, performing hierarchical attention routing, and outputting an intention label; a knowledge graph is injected, the knowledge graph comprises a static knowledge base, a real-time service flow and a user historical portrait, and three-source dynamic knowledge is output; constructing a decision tree based on the intention label, the three-source dynamic knowledge and the space-time identifier; and executing a corresponding text generation operation, a visual mark generation operation or a voice synthesis operation based on a response action corresponding to the leaf node of the decision tree, and generating customer service information. According to the method and the system, the buyer intention recognition accuracy of the multi-modal session data can be greatly improved, and the response delay time of generating the customer service information is shortened.

The invention discloses a data storage method and device, electronic equipment and a storage medium, and relates to the technical field of computers.The current application mode information of the storage equipment of any piece of storage data is firstly obtained, and the access feature information of the to-be-stored data is determined according to the target write-in address of the to-be-stored data; then, according to the current application mode information and the access feature information, determining a heat value of the to-be-stored data, when the heat value is represented as hot data, writing the to-be-stored data into an auxiliary memory, and when the heat value is represented as cold data, writing the to-be-stored data into a main memory; the hot value determined according to the current application mode information and the access feature information can flexibly adapt to the current application mode of the storage device, accurate cold and hot division of the to-be-stored data can be achieved even if the threshold value is fixed, the accuracy of a cold and hot data classification result is improved, and then the storage resource utilization rate of the storage device is improved.

The invention provides an event processing method, system and equipment based on Internet of Things equipment and a medium, and belongs to the technical field of Internet of Things. The method comprises the following steps: receiving an Internet of Things equipment message through an MQTT Broker, identifying an event type according to a theme, and distributing the event type to a corresponding thread pool; concurrently analyzing the JSON load in each thread pool, and converting the JSON load into a structured data object; caching the data in a batch processing queue in a classified manner, and triggering batch processing based on a queue state; extracting batch data, generating an optimized SQL statement, executing writing, and processing exceptions; screening data based on a routing rule and asynchronously forwarding the data to message middleware; meanwhile, processing parameters are dynamically adjusted according to the equipment state data and the system load. According to the invention, efficient processing of Internet of Things equipment events is realized, and the system throughput, the resource utilization rate and the data processing real-time performance are improved.

The invention relates to the technical field of nuclear power data asset automatic management, in particular to a nuclear power digital twinning data warehouse structure system, which comprises a data processing layer, and is characterized in that the data processing layer reads original data of a nuclear power industrial internet platform, performs data standardization processing on the original data to obtain standard data, and sends the standard data to the nuclear power industrial internet platform; constructing a nuclear power industry theme library according to the standard data; the data integration layer performs data coding mapping, data classification and data fusion on the data of the nuclear power industry theme library to form a thematic library; the semantic enhancement layer marks the data of the nuclear power industry theme library and the data in the thematic library formed by the data integration layer, and constructs a structured feature tag library for the data associated with feature tags; the analysis application layer reads data of the feature tag library, and constructs a digital twinborn algorithm library and a digital twinborn sample library through algorithm analysis and sample training. The system supports equipment operation and maintenance from regular maintenance to predictive maintenance based on bottom support and four-layer progressive architecture of a nuclear power industrial internet platform.

The invention discloses a data classification and grading method and device based on rule engine and artificial intelligence fusion, and belongs to the technical field of Internet, and the method comprises the steps: obtaining source data needing to be classified and graded, and processing the source data into source data in a uniform format; training the extracted useful features to obtain a trained artificial intelligence model, and predicting the preprocessed source data through the trained artificial intelligence model to obtain a data classification and grading result output by the artificial intelligence model; the rule engine performs preliminary classification and grading on the preprocessed source data according to a set data classification and grading rule to obtain a preliminary classification and grading result; and fusing a data classification and grading result output by the artificial intelligence model with a preliminary classification and grading result output by the rule engine to generate a final accurate and reliable data classification and grading result. According to the invention, interaction between the user and the advertisement content through voice or gestures is supported, so that the attraction and effect of the advertisement are improved.

The invention discloses an overhead transmission line point cloud classification method and system based on rule improved RandLA-Net, and a storage medium, and belongs to the field of three-dimensional laser point cloud processing and power system automation. The classification method comprises the following steps: collecting original point cloud data of the overhead transmission line, and performing preprocessing after data conversion and data classification to obtain a standardized point cloud data set; constructing and training an improved RandLA-Net point cloud classification model, performing feature enhancement through a channel attention mechanism, and outputting a preliminary classification result; performing optimization processing on the preliminary classification result based on a prior rule to obtain a final classification result; and outputting the final classification result and displaying the final classification result through a visual tool for supporting power transmission line inspection. Through improvement of a channel attention mechanism, data preprocessing optimization, combination of four types of prior rules and the like, high-precision classification of power transmission equipment is realized, and reliable data support is provided for power inspection.

The invention discloses a data use control method and system based on a smart contract, and relates to the technical field of data use control, and the system comprises an identity authentication module, a data classification and grading module, a data sandbox module and an evidence storage traceability module. Performing identity authentication on a user request of a data user based on an authentication protocol; performing classification rating on the accessed data; the data user and the data provider negotiate a contract signing detailed rule; the data user and the data provider sign a block chain smart contract; deploying the intelligent contract to a data sandbox, performing authority evaluation and distribution on the user, and performing behavior monitoring on the user; when the data user violates the signed smart contract, terminating the data access permission of the user, and calling the data sandbox to execute operation according to the contract signing content; logs are automatically generated, data access and use circulation are traced in the whole process, and it is ensured that data can be traced, audited and managed from authorization to use. According to the invention, the security and credibility of data circulation are guaranteed in a data transmission use control mode with decentration and multi-party cooperative participation.

The invention discloses a new energy station operation index multi-dimensional comparative analysis display method and system, and belongs to the field of new energy station operation management and data analysis. The method comprises the following steps: fusing and standardizing multi-source heterogeneous data, collecting and cleaning multi-station data, and classifying and storing the multi-station data as a standardized data pool; rasterized multi-dimensional indexes are calculated, grids are divided, data are mapped, and grid-level and hierarchical multi-dimensional indexes are generated; performing dynamic weight and TOPSIS evaluation, constructing an evaluation model to calculate the score of a single station, extracting a core index intersection, and transversely comparing the ranking of the stations; the evaluation result is subjected to instantiation storage and hierarchical storage to guarantee data query and tracing; and performing multi-dimensional visual display, displaying the data by multiple terminals, and generating a decision report. The system comprises corresponding function modules. According to the method, the problems of data fragmentation, evaluation system deficiency, non-visual display and the like are solved, multi-station data integration, quantitative evaluation and visual decision making are realized, and the operation management efficiency is improved.

The invention discloses a shipborne communication data encryption transmission method and system based on a satellite 5G fusion network, and relates to the technical field of satellite communication encryption. Data classification and priority division: dividing the data into five types of navigation instructions according to service types, marking P1-P4 priorities, and adding information; encryption strategy selection: adding random salt values by using algorithms such as hybrid encryption, AES, DES, TEA and the like according to priorities; network routing is fused, satellite and 5G state parameters are collected, and paths are selected according to priorities; real-time encryption transmission, fragmentation and information addition are carried out, FEC is used for satellites, and HARQ is used for 5G; the transmission state is monitored and dynamically adjusted, and the parameters are synchronously encrypted during over-limit time adjustment and switching; decrypting and verifying, namely decrypting and verifying after verifying and recombining; secret key updating management, life cycle setting, KDC distribution and emergency updating are carried out. According to the method, the shipborne data transmission security and network adaptability are improved, the encryption efficiency and continuity are optimized, the cost is reduced, multiple scenes are adapted, and the security and high efficiency of maritime communication are ensured.

The invention discloses a geological data classification method and device based on deep learning, and relates to the technical field of data classification. The method comprises the following steps: constructing a geological data classification model by using a deep learning algorithm based on a double-layer coevolution framework; the method comprises the following steps: collecting multi-source geological data, and preprocessing the multi-source geological data to obtain preprocessed standard geological data; and inputting the preprocessed standard geological data into the geological data classification model to generate a classification result. The problems that in the prior art, multi-source heterogeneous data fusion is difficult, the model optimization efficiency is low, and the model generalization ability is insufficient are solved.