230 results about "Data view" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal content compliance auditing method, device, equipment and medium, and the method comprises the steps: receiving a multi-modal content set, and generating to-be-audited content; converting the multi-source content into unified semantic representation through an analysis unit; calling the judgment model based on the auditing strategy library and the configured auditing engine to perform semantic comparison, and generating an auditing processing result; outputting an abnormal level result according to the processing result; a rectification suggestion is generated and pushed based on the abnormal level result, and feedback data is received at the same time; recording feedback data and auditing tracking records, and generating a compliance data view and an auditing conclusion; and updating the auditing strategy library and the configured auditing engine according to the feedback data. According to the method, cross-modal check is realized through unified semantic representation, the check flexibility is improved in combination with a strategy library and a configuration engine, rectification and feedback are driven by using an abnormal level result, and the accuracy and efficiency of compliance check are improved.

The embodiment of the invention provides a dynamic knowledge graph-driven cross-department government affair data collaboration method and a dynamic knowledge graph-driven cross-department government affair data collaboration system. The method is applied to the technical field of computer science and technology, and comprises the steps of obtaining cross-department government affair service data and corresponding policy rule data, constructing a data knowledge graph and a policy knowledge graph, performing incremental updating and consistency verification on the knowledge graphs based on preset event information, and extracting a newest service state and a newest policy rule. A minimum necessary data view and an authorization range are generated through joint reasoning, efficient and safe collaboration of cross-department government affair data is achieved on the basis, and accurate authorization and compliance management of data access are achieved. According to the scheme, only necessary data is provided and the access permission is strictly controlled in cross-department government affair data collaboration, so that the data sharing efficiency, security and compliance are remarkably improved, and meanwhile, the risks of unauthorized access and redundant data transmission are reduced.

The invention relates to the technical field of textile production scheduling and resource allocation planning, in particular to a textile equipment scheduling management and optimization system of a textile factory, which comprises a data perception and integration module, a scheduling optimization decision module, a plan execution and equipment control module and a closed-loop feedback and self-learning module. The data sensing module collects and fuses order data, equipment operation state data and production environment data in real time, and a unified real-time data view is generated through cleaning and alignment processing; the scheduling module runs a mixed integer programming dynamic model, and minimizes the comprehensive cost and synchronously optimizes the equipment utilization rate and energy consumption in combination with rolling horizon optimization under the condition of meeting the process constraints of order delivery time limit and process dependency matrix representation; the plan execution module analyzes the scheduling instruction into an equipment executable instruction, drives equipment operation and collects execution deviation; and the closed loop module triggers rescheduling when the deviation exceeds the limit or the order is plugged. The scheduling accuracy and adaptability are improved, the cost is reduced, and efficient and stable production is guaranteed.

The invention relates to the technical field of government affair information management, in particular to a cloud-based government affair information management method, which comprises the following steps of: S1, adding a sensitivity label for each piece of government affair data in a cloud storage layer; s2, extracting data from the government affair database and generating a dynamic data view with an authority mark; s3, establishing a mapping relation table between routing keys and data source addresses; s4, acquiring a requester department function code, a business scene code and a target data identifier; s5, matching a target data source, and based on the authority mark in the dynamic data view, executing a desensitization operation on the sensitive field; and S6, if the genetic marker bit of the original data is true, superposing the data security classification in the original tag and the current security classification to form a new tag. According to the method, by constructing the sensitivity label and the dynamic authority control mechanism, safe inheritance and accurate access of government affair data in cross-department circulation are achieved, and the safety and compliance of data management are remarkably improved.

The invention belongs to the technical field of office park electric load prediction, and relates to an office park electric load prediction and scheduling method for a novel electric power system. The method comprises the steps that S1, multi-source data fusion and panoramic view construction are carried out, historical load data of a park are collected, and park operation data, meteorological data and a special event calendar are obtained; s2, data preprocessing and feature directional extraction; s3, building and cooperating short-term, medium-term and long-term prediction models on the basis of multi-time scale prediction of model cooperation to form a hierarchical prediction system; s4, optimizing and evaluating a prediction result, and performing uncertainty evaluation on the prediction result in the step S3; and S5, generating and executing a hierarchical scheduling instruction based on hierarchical scheduling decision and execution of multi-objective optimization. According to the method, the problem of full-chain link splitting is solved, panoramic data view, collaborative prediction model, quantitative risk assessment and multi-target optimization scheduling are realized, prediction accuracy and scheduling flexibility are improved, and active participation in energy management of the office park is supported.

The invention discloses a financial abnormal event analysis method, electronic equipment, a medium and a product. The method comprises the steps of obtaining at least one financial clue event, and performing structured element extraction and standardization processing on each financial clue event to obtain a standardized event behavior sequence; performing semantic feature and behavior pattern analysis based on the normalized event behavior sequence, performing event reasoning rule recall in combination with a pre-constructed event reasoning library, and determining a recalled rule set; generating an abnormal event analysis execution plan based on at least one inference rule in the rule set; querying original data in the data system according to an inference rule included in the abnormal event analysis execution plan, generating a data view, executing the abnormal event analysis execution plan based on the data view, and generating an inference execution result set and execution path information; and an event reasoning analysis result is generated according to the reasoning execution result set and the execution path information, and the problems of complex abnormal event analysis modeling, low interpretability and the like are solved.

The invention relates to the technical field of big data, in particular to a warehouse management analysis method and system based on big data and a medium, comprising heterogeneous data real-time acquisition, stream batch fusion processing, AI intelligent decision making, digital twinborn verification and hierarchical storage scheduling. Compared with the prior art in which a distributed data acquisition and delay batch processing scheme is adopted, which leads to serious data islands and lagging of decision basis, the scheme adopts an edge protocol conversion gateway and flow batch fusion processing architecture, uniformly accesses real-time data flow of industrial equipment through MQTT, captures service system changes in combination with DebeziumCDC, realizes millisecond data integration through Kafka, and realizes real-time data flow access through MQTT. Dynamic feature calculation and deep model training are synchronously completed by means of a Flink-Spark hybrid engine; the method has the advantages of total-factor real-time mapping and cross-domain data collaboration, gets through information barriers of an equipment layer, a business layer and an environment layer, and provides millisecond-level fresh global data views for storage decisions.

The invention discloses an industry and trade enterprise public health service resource optimization decision support system, and relates to the technical field of public health management, and the system comprises a data perception and access layer, a semantic fusion and modeling layer, a knowledge graph and risk calculation layer, an intelligent early warning and decision layer, and a feedback optimization closed loop. Multi-source heterogeneous data are collected through a protocol adapter, a unified data view is constructed through industry and trade public health field ontology semantic fusion, a space-time knowledge graph is dynamically generated, risks are deduced in combination with an improved SEIR space-time propagation model and a Monte Carlo method, visual early warning and interactive decision making are achieved based on digital twinborn bodies, and the method is applied to the field of industry and trade public health. According to the method, data islands can be broken, accurate advanced early warning of public health risks is realized, resource allocation is optimized, and the method is suitable for industry and trade enterprises with dense personnel such as machine manufacturing and electronic processing.

The invention relates to the technical field of invoice risk control management, and discloses an invoice risk control management method and system based on multi-source data linkage. According to the method, multi-source data of the whole invoice business process is collected in real time through a multi-source data fusion gateway. And performing streaming ETL processing and encryption integration on the multi-source data to generate an encrypted unified data view. And based on the view, constructing an association hypergraph by utilizing a dynamic graph association technology, analyzing an entity relationship by virtue of a graph neural network, and identifying false invoice making gang and repeated financing risks. And calculating a dynamic risk score by adopting a risk quantification and traceability technology, and outputting a risk level, a responsibility node identifier and a capital flow thermodynamic diagram. When the risk score exceeds a threshold value, risk gradient information is safely shared among the group headquarters, the molecular companies and the tax-bank institutions, and linkage processing actions such as invoice freezing, red character notification or credit adjustment are triggered. According to a manual reexamination result, the risk model is finely adjusted through a knowledge self-updating technology, and the rule base is hot-updated.

The invention provides a building intelligent operation and maintenance agent cluster construction method and equipment, and the method comprises the steps: carrying out the integration and cleaning of building data, and obtaining a cleaned and repaired data view; on the basis of the cleaned and repaired data view, a Model Context Protocol protocol is adopted to construct a unified tool calling interface; constructing the following representative operation and maintenance agents, including a building supervisor leader agent and other expert agents; based on an A2A protocol, a building supervisor leader agent and other expert agents are enabled to automatically discover, distribute, cooperate, pass back and trace back through a standardized interface to form a decoupling and safe multi-agent collaborative operation and maintenance mechanism; according to the method, the building operation and maintenance response speed and the task processing efficiency can be remarkably improved, and manual intervention and misjudgment are reduced; on the premise of ensuring comfort and safety, energy consumption and sudden failure occurrence frequency are effectively reduced.

The invention discloses a three-dimensional visualization head and neck tumor preoperative imaging system based on deep learning. The method comprises the following steps: image analysis and preprocessing; carrying out image denoising and spatial-temporal feature extraction based on a deep learning convolutional neural network architecture, and introducing a u-Net model to realize image segmentation and semantic annotation; performing three-dimensional reconstruction and surface rendering by adopting a Marking Cubes algorithm, and realizing visual interaction image presentation and personalized operation by combining volume rendering and surface rendering; and data is output through multiple ports, and data viewing and interactive operation of a web terminal and a mobile terminal are supported. The method has the advantages that according to the technical scheme, through fusion of a three-dimensional attention mechanism and multi-phase data, the tumor segmentation Dice coefficient reaches 0.92 + / -0.03, and the method is superior to a traditional threshold segmentation method.

The invention discloses a big data measurement asset supply and demand matching and inventory optimization method, and relates to the technical field of big data processing and supply chain management. The method comprises the following steps: segmenting supply chain asset data through a hash function according to multi-dimensional attributes to generate a data fragment set, and constructing a global index tree according to the data fragment set; and monitoring the load state of the leaf nodes of the global index tree in real time, and migrating and verifying data during unbalance. And querying the global index tree, positioning fragment data associated with the query request from the uniformly distributed leaf nodes, generating a preliminary matching asset set, calculating the matching degree of each asset and the query request, and generating a resource allocation result. And updating the global index tree, and generating the latest data view representation. And adjusting the attribute weight coefficient of each dimension in the hash function, and generating optimized storage layout configuration. Irrelevant data are filtered through a neighbor algorithm, and a final matching result set is generated. Balanced storage, efficient query and accurate matching of asset data are realized, and the overall response speed and the resource utilization rate are improved.

The embodiment of the invention discloses an intelligent number asking method and system based on dynamic feedback, a storage medium and electronic equipment. The method comprises the steps that a subject domain and a semantic model are constructed, a data view is established, data mapping is configured, and a natural language problem and a standardized index are analyzed; generating an SQL (Structured Query Language) logical expression based on the Few-shot example and the large model, and carrying out semantic correction on the SQL logical expression; sQL query is executed, dynamic feedback storage is carried out based on an execution result, and SQL generation parameters are updated by utilizing a self-adaptive learning mechanism. According to the method, through semantic modeling and data mapping, precise alignment of a natural language and a database structure is achieved; few-shot examples and large model generation are combined with semantic correction rules, so that the accuracy and stability of SQL generation are improved; and a continuously optimized intelligent number-asking closed loop is constructed by executing a feedback and adaptive learning mechanism, so that the self-learning and evolution capabilities of the system are improved.

The invention relates to the technical field of software, and discloses a nursing shift change system based on homologous heterogeneous data fusion and a large language model, and the system comprises a shift change generation module which comprises a first generation unit and a second generation unit, calling a large language model to generate global summary information of nursing shift change; and the second generation unit is used for calling a large language model to generate corresponding detailed handover information only based on the original data of the key event. And the report synthesis module is used for integrating the global summary information and the detailed handover information according to a preset structure to generate a nursing handover report. According to the method, the panoramic data view of the patient is constructed based on the unified time axis, so that structured data and unstructured data which are originally dispersed in different systems can be organized and called in the same semantic space, and a consistent data basis is provided for subsequent data analysis and processing.

The invention relates to the technical field of information, and discloses a hotel resource digital management method and system and a storage medium. The method comprises the following steps: constructing a unified semantic data model; multi-source data fusion and real-time state perception visualization are realized; cross-department collaborative arrangement is carried out; and intelligent decision support and optimization are realized. According to the invention, a comprehensive digital management platform based on a unified semantic data model, multi-source data fusion, real-time state perception, cross-department collaborative workflow arrangement and intelligent decision support and optimization is constructed. According to the platform, global visualization and real-time dynamic management of each core resource of the hotel are realized, and the operation efficiency and the fine management level of the hotel are remarkably improved. The departments of guest rooms, catering, conference rooms, spare part inventory, human resources, finance and the like do not combat each other, but perform cooperative work based on a unified data view.

The invention discloses an electroencephalogram signal clustering method based on a Mama framework and comparative learning, and belongs to the crossing field of engineering application and information science, and the method comprises the following steps: collecting and preprocessing label-free electroencephalogram data; the method comprises the following steps of: constructing a Mama-based feature extractor, dividing an input electroencephalogram signal into a plurality of slices by adopting an electroencephalogram signal slice embedding strategy so as to obtain fine-grained local information, and modeling a potential context relationship by utilizing a slice perception scanning mechanism; the method comprises the following steps: constructing an original data view and an enhanced data view, and respectively inputting samples of the two data views into two Mamba feature extractors with shared network parameters for feature embedding; processing the feature pairs of the two views using an instance projector, constructing weighted instance level contrast learning wherein the distance in the original data space provides weight information; processing feature pairs of the two views by using a clustering projector, and constructing a clustering distribution discrimination contrast learning branch and a semantic perception contrast learning branch; and finally, performing joint training by using the comparative learning branches, and outputting a high-quality clustering result by ensuring feature, clustering distribution and semantic consistency.

The invention relates to a data-in-channel-based cross-source data joint query method, which comprises the following steps of: S1, constructing a data-in-channel access strategy model, and automatically generating a most suitable access mode; s2, constructing a power data knowledge graph, and analyzing a dependency relationship, a traceability path and a blood relationship among the data by using a graph neural network; s3, seamless access is achieved through natural language understanding, natural language query translation is adopted, and the natural language query of the user is converted into a corresponding SQL statement; s4, according to the SQL statements, similarity is calculated through the embedded vectors, user problems and data models are matched, and according to historical query modes and service requirements, a virtual data view is generated; s5, based on the generated virtual data view, sensitive information fields possibly contained in the data set are recognized through a natural language model and regular pattern recognition, and automatic labeling and classification are carried out. According to the method, the technical threshold of data fusion and sharing is remarkably reduced, and the intelligence of a data platform system in the power industry is improved.

The invention discloses a digital operation management platform oriented to enterprise transformation and upgrading and a construction method thereof, and belongs to the field of enterprise digital transformation. The platform comprises a front-end system cluster which comprises a safety management module, a production management module, an asset management module, a material management module, a collaborative office module, a production real-time module, a public foundation module, a system management module and a data governance module. The construction method comprises the following steps: S1, constructing a unified data interface architecture in a data governance server, accessing a multi-source heterogeneous data source and caching an original data packet; s2, cleaning and converting data based on the unique field identifier, and generating a structured data table; s3, transmitting the data to a data fusion center node, performing partition storage, deconstructing semantics, and generating an index metadata set; s4, constructing a multi-path service route in the data service distribution server, and generating a static index table and a dynamic index flow; and S5, pushing the data to a front-end system cluster through an interface service layer, and calling the data by a user terminal to realize index-driven data view construction. The method has the beneficial effects that the data integration efficiency and the operation intelligence level are improved.

The invention discloses a credit rating system based on multi-source data fusion and an enterprise knowledge graph. The credit rating system comprises the following modules: a data acquisition module, a data preprocessing module, a data fusion module, a knowledge graph construction module and a credit rating module. The data collection module is configured to collect enterprise-related data from a plurality of heterogeneous data sources. By integrating multi-source data fusion and an enterprise knowledge graph technology, comprehensive and dynamic evaluation of enterprise credit is realized, the system can acquire data from multiple channels and construct a unified enterprise data view through preprocessing and fusion, and the enterprise knowledge graph constructed on the basis not only contains attributes of enterprises, but also can construct a unified enterprise data view. Various relationships between enterprises are also disclosed, enabling credit rating to take into account associated risks.

The invention discloses a lithium battery remaining service life prediction method based on multimode collaborative compressed sensing and IDMamba, and relates to the technical field of batteries. According to the method, a multi-mode cooperative compressed sensing (MCS) method and an IDMamba neural network model method are adopted, the advantage of data denoising is achieved, the MCS is adopted for processing sequence data, mapping the data to a low-dimensional measurement space, removing redundant information and noise and then conducting data reconstruction, in the process, a self-attention mechanism is added to pay more attention to key features, and the accuracy of data reconstruction is improved. And the stability is improved through layer normalization, so that redundant components of the original data can be removed, a clearer data view is provided, and the nature of the data can be better understood.

The invention discloses a cross-platform teaching tool data collaboration and personalized resource recommendation system based on an MCP protocol, and belongs to the technical field of education. According to the system, a multi-platform teaching tool is in unified butt joint through the MCP protocol adaptation layer, and the problem of data splitting is solved; the data co-processing layer cleans and fuses multi-tool data, generates a teacher / student / parent data view and stores an evidence through a block chain; the personalized recommendation engine generates role-based resource recommendation based on a teaching knowledge graph and multi-dimensional behavior analysis; the user interaction layer supports multi-terminal access, and the data security module guarantees compliance. According to the system, the cross-platform data collaboration efficiency is improved by 70% or above, the resource recommendation matching degree reaches 92%, the system is suitable for K12 education, higher education and vocational education scenes, teaching tool data barriers are effectively broken through, and the teaching data utilization rate and the resource recommendation accuracy are improved.

The invention discloses an enterprise digital management system based on multi-source data fusion, and the system comprises a data collection module which is used for constructing a multi-source data view from internal and external data sources of an enterprise; the embedding mapping module is used for generating a feature domain embedding vector set; the improved extremely deep factorization machine model module is used for feature extraction and explicit high-order interactive modeling and outputting a basic matching score; the triple generation module is used for constructing a positive and negative sample triple and calculating a business importance coefficient and an exposure probability; the Bayesian personalized sorting module is used for constructing weighted sorting loss and updating model parameters; and the online recommendation module is used for calculating matching scores of the candidate schemes and sorting and outputting a recommendation management list. According to the method, accurate and intelligent recommendation and sorting decision of candidate schemes in an enterprise management scene are realized by improving an extremely deep factorization machine model and Bayesian personalized sorting.

The invention relates to the technical field of data noise reduction, and discloses a safety alarm noise reduction method and system based on a data weaving technology, and the method comprises the steps: building a unified logic data view based on a data weaving architecture; deploying an AI-driven data intelligent agent component system, automatically identifying and adapting a changing API and a new data source, and fusing metadata, threat intelligence and infrastructure operation state information; constructing an attack chain panorama and a behavior causal chain model; a semantic noise reduction algorithm, time sequence clustering analysis and a confidence scoring mechanism are adopted to perform de-duplication alarm, false alarm identification and low-risk event filtering on the security alarm; and alarm de-duplication and priority adjustment are carried out to generate a linkage processing strategy. According to the invention, the problems of alarm flooding, high false alarm rate, low processing efficiency and the like in the existing security alarm system are solved.

The invention belongs to the technical field of data processing, and discloses an enterprise-oriented AI cross-data integration service architecture design method and system, and the method comprises the steps: building a semantic connection layer of a cross-enterprise data source, penetrating bottom layer heterogeneous metadata in a layering and non-isolation manner to carry out logic unification, combining hotspot perception and a self-evolution mechanism, and carrying out the self-evolution of the semantic connection layer. Forming a cross-source semantic ontology of data weaving; based on the cross-source semantic ontology, generating a virtualized data view by adopting a zero-migration architecture; an optimization mechanism with query delay and a data filtering rate as reward signals is introduced to dynamically adjust a mapping strategy, and a virtual data product is output in combination with semantic mapping; low cost, high efficiency and sustainable evolution of enterprise cross-data integration are realized, and a solid support is provided for deep application of AI in a multi-enterprise cooperation scene.

The application relates to the computer technical field, in particular to a data display method and device, electronic equipment and storage medium, to improve the data viewing efficiency. The method comprises the following steps: presenting a video display interface containing an access record video of a first object and a data display control; in response to a selection operation of a second object on a target video frame in the access record video, displaying the target video frame in the video display interface; and in response to a trigger operation on the data display control, drawing a corresponding heat map based on an operation behavior data set of the first object in a target operation page displayed by the target video frame, and superimposing and displaying the heat map on the target operation page. In the application, the second object can freely select the target video frame to generate the heat map according to the demand, which has high flexibility, and a large number of page snapshots do not need to be saved to meet the object demand, so that the data viewing efficiency is improved and the storage space occupation is reduced.

The application discloses an interactive system for exploring video set by using embedded visualization, comprising a video view module, a query view module and a data view module; wherein the video view module displays videos and extracted data, provides interactive functions with the videos, and carries out interactive query by specifying attributes; the query view module carries out hierarchical division according to video interactive query, and displays the division results; and the data view module displays detailed information of video data selected from the query view module. Through the interactive system, a user can interactively manipulate video elements, specify query conditions, generate query results, select video data meeting the conditions from the data set, and finally quickly locate a target typical video through a series of embedded visualization interactions designed for different video elements.

The application belongs to the technical field of intelligent security and protection home, and relates to a smart floor system capable of identifying falling behaviors, which comprises a smart pressure sensing floor, a data acquisition system and an upper computer, the smart pressure sensing floor is a composite structure of a solid floor and a flexible sensing fabric, the flexible sensing fabric contains 40 electrodes and 40*40 array distributed sensors, and a piezoresistive sensing layer is constructed by a graphene-carbon black mixed filler to form a conductive network; the data acquisition system collects signals through FPC flat cable, transmits data to the upper computer through a sensor router and a public network router; the upper computer adopts a "cloud-edge-end" architecture, carries a machine learning identification algorithm, can accurately identify falling and intrusion behaviors, the falling identification accuracy is more than 98%, and the response time is less than or equal to 1 second; the terminal side is associated with a cloud program to realize alarm pushing and data viewing functions. The disclosed system simultaneously integrates two core functions of old people falling monitoring and home safety protection, and meets the diversified needs of users living alone.

The invention provides a heterogeneous data source processing method and apparatus, and an electronic device. The method comprises the steps of accessing a relational database corresponding to a heterogeneous data source; mapping the physical table in each data source into a business-oriented logic data model; based on the logic data model borne by each product, defining an association relationship, a service capability parameter and a business rule for a main table and a plurality of levels of sub-tables of a group of hierarchical association data structures corresponding to each product; generating a user interaction interface matched with each product in combination with the logic data model and the association relationship of each product, the service capability parameter and the business rule; associating the logic data model of each product with a user interaction interface based on a business topic, and providing a visual arrangement canvas to define an association configuration relationship between product data from different data sources; and weaving the cross-product data configured based on the association configuration relationship into a unified virtual data view, and packaging the virtual data view into callable data service.

A big-data view integration platform generates integration guided user interfaces (GUIs). A first edge node ingests push-based and pull-based data from a plurality of platform services, which include legacy and non-legacy services having incompatible communication protocols. An event-based queue receives from the first edge node a plurality of queue events as indirect push-based data. A second set of queue events includes direct push-based data as received directly from a non-legacy platform service. A conformity component integrates the push-based data, the pull-based data, and the plurality of queue events into integration data having an enhanced integration format. A view integration component generates a plurality of data views from the integration data. A second edge node exposes the plurality of data views via an access services application programming interface (API). A new service execution component accesses the access services API to generate integration GUIs based on the data views.

The invention provides a full-link analysis early warning method based on transaction and grain depot data fusion, and relates to the technical field of artificial intelligence and big data analysis, and the method comprises the steps: forming a multi-dimensional data set based on grain depot data, transaction data and own data; through unique identifier matching, an analysis rule and a mapping table are self-defined, transverse and longitudinal association is carried out on the multi-dimensional data set, and a full-link service data view is constructed; and analyzing the full-link service data view according to different service scenes to obtain an early warning result.