457 results about "Data resources" patented technology

The invention provides a multi-source heterogeneous data integration and governance-oriented data weaving method, which comprises the following steps of: performing data acquisition from an accessed multi-source heterogeneous data source to generate an original multi-source heterogeneous data stream; performing standardization processing on the original multi-source heterogeneous data stream to generate a standardized multi-source heterogeneous data set; performing active content scanning processing on the standardized multi-source heterogeneous data set to determine business metadata, and performing consanguinity tracking processing on the business metadata to generate enhanced business metadata; calling a domain ontology framework to carry out standardized constraint on the enhanced service metadata to obtain standardized service metadata without cross-data source semantic ambiguity, and carrying out implicit association mining processing on the standardized service metadata based on a graph neural network to generate a semantic knowledge graph containing core entities and relationships; and performing logic abstraction processing on the distributed data resources according to the semantic knowledge graph to generate a unified data access interface.

The invention relates to the technical field of intelligent customer acquisition, in particular to a cross-border supply chain financial service AI intelligent customer acquisition system based on big data analysis. Functional components such as a big data access engine, an artificial intelligence analysis model, a customer portrait modeling engine, a preference setting interface, a public opinion and risk identification module, an intelligent screening engine and a docking output module are integrated; multi-source data such as customs data, trade data, enterprise industry and commerce data, judicial public data, international manufacture and cutting lists and internet public information from all places around the world are widely fused. The system performs modeling calculation on industry categories, regional distribution, export history, transaction records, public opinion trends and compliance risks of import and export enterprises, establishes customizable customer optimization rules, accurately outputs a target customer list which can be covered by credit insurance and financing service in combination with a machine learning algorithm, and attaches contact information. Various import and export related data resources can be accessed, automatic screening and output of credit insurance and account receivable financing adaptive exporters are realized through preference setting and AI analysis, the customer obtaining efficiency is remarkably improved, the marketing and risk control cost is reduced, and the economic benefit is improved. The problems that traditional cross-border supply chain financial service customer obtaining depends on manual recommendation, offline visit and a static customer library, the efficiency is low, and small and medium-sized export enterprises are difficult to cover are solved.

The invention relates to an enterprise multi-dimensional business index analysis data resource optimization method, a medium and equipment, and the method comprises the steps: collecting a multi-dimensional business index analysis request of an enterprise business system, carrying out the mode recognition and feature extraction of the multi-dimensional business index analysis request, and generating business entity features; and inputting the business entity features into the resource allocation model, outputting a priority score and a resource demand prediction result, calculating an optimal resource allocation scheme through an optimization decision model based on the priority score and the resource demand prediction result, and performing automatic resource scheduling according to the optimal resource allocation scheme to generate a business decision instruction. And finally outputting a business decision instruction and a data resource management report. The resource reuse potential among different requests is automatically identified and quantified by adopting a set similarity algorithm, so that the enterprise data resource utilization efficiency is remarkably improved, the redundancy calculation consumption is reduced, and the resource allocation is more reasonable and efficient; manual intervention is reduced, and enterprise data resource management efficiency and response speed are improved.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent pre-auditing system and method for public data resource registration elements with a large model, and the method comprises a management side rule base construction step and an operation side auditing step. The method has the beneficial effects that automatic extraction, structured conversion and compliance pre-auditing of registration elements are realized by applying a multi-modal data analysis engine and a large model technology, and manual intervention links are reduced by more than 40%. A multi-dimensional rule knowledge base containing national standards, industry specifications and local regulations is constructed, dynamic loading and self-adaptive updating of verification rules are supported, and the adaptability to different types of data resources is improved. And a pre-auditing device based on the micro-service architecture is designed, and technical support is provided for marketization configuration of data elements. Through pre-auditing quality problem big data of process precipitation, the standard conformity of a data production end is reversely optimized, and a'registration-feedback-improvement 'closed-loop treatment mechanism is formed.

The invention discloses a low-code platform design system based on modularization and a dynamic extension component, and relates to the technical field of low-code development, and the system comprises a form module which is used for standardly defining a data model and dynamically rendering a user interface, realizing unification of form configuration through a structured metadata specification, and establishing a form module; bidirectional binding of the view and the data model is realized; the process module is used for realizing modeling, execution and state management of a business process, supporting automatic task scheduling and multi-instance management based on standardized process elements, and keeping consistency of a process state through an increment synchronization mechanism; and the data processing module is used for cooperatively managing platform data resources, supporting dynamic expansion and decoupling dependence between the form module and the process module through an event-driven mechanism. According to the method, the strong binding relationship between the decoupling form and the process is established, the reusability of the process module is improved, and the process module is independent of the form and can independently provide API service to the outside, so that the method is adaptive to more business scenes.

The invention discloses a cross-industry data sharing method and system using a trusted data space, and relates to the technical field of data sharing, and the method comprises the steps: obtaining a cross-industry data dimension relation; obtaining a sharing request, and carrying out dimension migration analysis on the sharing request to obtain a dimension migration path set; according to the dimension migration path set, adaptive reorganization operation is executed on the to-be-shared data, and reorganized shared data is obtained; according to the cross-industry data dimension relationship and the dimension migration path set, performing cross-industry confidence evaluation on the recombined shared data to generate a shared decision result; and performing data sharing on the recombined shared data through a secure channel of the trusted data space. The technical problems that existing cross-industry data sharing is difficult, and data resources are wasted are solved.

The invention relates to the technical field of government affair data sharing, in particular to a government affair data security sharing and real-time interaction method and device, and the method comprises the steps: firstly deploying a government affair MCP Server cluster, carrying out the data calling and processing, then encrypting the data, and finally achieving the standardized packaging and convenient calling of government affair data resources. Compared with the prior art, 70% of cross-department API development workload can be reduced through an MCP standardized interface, the data leakage risk is reduced to 0.01% through an authority approval and data encryption mechanism, and the data analysis delay is reduced to the second level from the minute level through streaming communication.

The invention provides a port logistics intelligent simulation and decision optimization platform based on a digital twin technology, and the platform comprises a basic layer which provides calculation, storage, network and basic software support; the resource layer integrates data resources, model resources and algorithm resources and is used for managing port multi-source data, business process models and intelligent algorithms; the digital twinborn layer is used for constructing and maintaining a high-fidelity digital twinborn body synchronous with a physical port and realizing virtual-real mapping, dynamic data fusion and model self-evolution; the simulation layer is used for deducing based on the digital twin and comprises a simulation engine and an event driving and data interaction module; and the application layer is used for providing a user interface, analyzing decision and optimizing functions and supporting scheme simulation, evaluation and closed-loop optimization. According to the method, the high-fidelity digital twinborn body of the port can be constructed, real-time mapping, accurate simulation, prospective prediction and intelligent decision making of the whole process of port logistics are achieved, and a technical closed loop of data acquisition, digital twinborn, intelligent decision making and virtual-real linkage is formed.

A train operation intelligent dispatching system and method are provided, where the system includes: a data acquisition and control module, configured to collect multi-source data affecting train operation; a data processing module, configured to perform data aggregation on the multi-source data, and process the multi-source data to form multi-source data in a unified data format; a multi-disciplinary data resource pool, configured to perform real-time storage and updating of the multi-source data in the unified data format, and store historical multi-source data; an intelligent fault analysis module, having intelligent fault analysis models therein, configured to perform fault analysis based on the multi-source data to obtain fault analysis results of corresponding disciplinary systems, and output fault advance warnings of corresponding disciplinary systems according to the fault analysis results; and a process preplan module, configured to provide preplan handling processes corresponding one-to-one with external multi-disciplinary systems.

The embodiment of the invention provides an enterprise information architecture (4A)-based AI Agent planning enhancement system, a control device and equipment, and the system comprises a Prompt construction module which is used for analyzing an unstructured demand to obtain a structured demand, and cooperating with other modules to generate a standardized Prompt comprising a business process, a data entity and a tool strategy; the knowledge matching module is used for matching related knowledge based on a knowledge graph employment number fusion knowledge base and sorting the related knowledge into a knowledge list containing business processes, practical experience, a data list and a tool list; and the context reasoning module is used for generating a reasoning result based on the historical data and the knowledge list and feeding back the reasoning result to the Prompt construction module so as to assist in generating the normalized Prompt. According to the system, the integration capability of the AI Agent on business logic and data resources in the task planning process is remarkably improved, so that the AI Agent can more accurately understand enterprise-level business scenes, and standardized and intelligent planning support is provided for practical application of the AI technology in enterprise digital transformation.

The invention relates to the technical field of industrial control system security testing, and provides a construction method and device for a dynamic attack and defense testing environment of an industrial control system. The method comprises the following steps: realizing virtualization operation and behavior modeling of industrial control firmware through a hardware system simulation technology based on logic self-learning; constructing a meta-aggregation data resource pool to realize intelligent scheduling and automatic deployment of industrial control component resources; when it is detected that the behavior of the industrial equipment is abnormal, deviation behavior data are complemented based on a causal relationship reasoning mechanism, and a simulation model is driven to be adjusted; constructing a digital-analog fusion model supporting space-time driving and data synchronization, and realizing data consistency and linkage between a virtual environment and a real environment; an AI agent mechanism is introduced, an attack path is automatically deduced, a defense strategy is optimized, and intelligent evaluation of a drilling process is completed; attack protection scene configuration and control are carried out through an interactive user interface, and simulation and verification operations in a complex dynamic attack and defense environment are realized.

The invention discloses an industrial system data resource aggregation utilization platform system and method, and belongs to the field of intelligent platforms. The industrial system data resource aggregation utilization platform system comprises a multi-source heterogeneous data fusion center module, enterprise production data, supply chain data, market dynamic data and policy and regulation data are converted into a mapping data model of a unified structure, and dynamic weight reconstruction is carried out on the correlation degree between nodes through a graph neural network; by setting a multi-source heterogeneous data fusion center module, on the basis of a vector space mapping mechanism and a graph modeling framework, enterprise production data, supply chain data, market dynamic data and policy and regulation data are converted into a graph data model of a unified structure, and a graph neural network is introduced to carry out dynamic weight learning on the correlation degree between graph nodes, so that the dynamic weight learning is realized. Therefore, the problem of data islands existing in a traditional system is effectively solved, and the aggregation utilization efficiency of industrial data resources is improved.

The invention relates to a federated learning-based magnetocardiogram analysis method, device and equipment and a medium, and relates to the technical field of magnetocardiogram analysis, and the method comprises the following steps: aiming at a preset arrhythmia analysis task, configuring a corresponding independent task head on a frozen shared feature backbone network, and caching a corresponding reference logic value; and configuring a corresponding new task head for the new task, and inserting a low-rank adapter module into the frozen shared feature backbone network so as to perform joint optimization on the inserted low-rank adapter module, the new task head and the independent task head, updating parameters of the low-rank adapter module, parameters of the new task head and parameters of the independent task head, according to the method, the limitation of a data island is broken through, data resources of multiple centers are integrated, massive unlabeled data can be fully mined, the model feature extraction capability is improved, the situation that new and old data are used for retraining the model is avoided, and the safety of the federal continuous learning model is improved. And the old category diagnosis capability is ensured.

The invention relates to the technical field of intelligent control of an air-jet vortex spinning yarn-forming process in the textile industry, in particular to an air-jet vortex spinning yarn-forming process control model based on a semi-supervised stacked auto-encoder, which comprises the following steps of: (1) analyzing data hidden variable distribution by adopting a Gaussian mixture model, and marking unlabeled data to expand data resources; (2) constructing a semi-supervised auto-encoder, and synchronously realizing data reconstruction and multi-quality characteristic'balance point 'prediction through weight sharing design; (3) stacking layer by layer to form a depth model, and strengthening multi-characteristic correlation modeling; and (4) realizing automatic disturbance detection in combination with a sequence auto-encoder containing an LSTM layer, an attention mechanism and a Tukey rule. The model reduces data dependence, improves the quality stability of resultant yarn and the anti-interference performance of production, adapts to industrial intelligent requirements, and assists the application upgrade of the air-jet vortex spinning technology.

The invention relates to the technical field of computers, in particular to a query request response method and device, equipment and a storage medium, which are applied to query response for data resources in an enterprise, and the query request response method comprises the following steps: obtaining a query request input by a user, and performing intention understanding and context analysis on the query request; generating an intention analysis result containing the query semantics, the task type and the preliminary subtask disassembly result; generating an agent scheduling instruction based on the intention analysis result; the Agent scheduling instruction is used for calling at least one sub-Agent in a data query Agent, an attribution analysis Agent or an intelligent decision Agent; according to the Agent scheduling instruction, the scheduled sub-Agents execute corresponding data query, root cause analysis or decision suggestion generation operation, and sub-Agent execution results are obtained; whether the sub-Agents need to be scheduled continuously or not is evaluated according to execution results of the sub-Agents, and when the sub-Agents need to be scheduled continuously, at least one Agent in the data query Agent, the attribution analysis Agent or the intelligent decision-making Agent is called circularly until task termination conditions are met, and an intelligent analysis report is obtained; and displaying the intelligent analysis report.

The invention provides a multi-dimensional and dynamic digital assessment system and assessment method. Comprising a support module (comprising an index knowledge base, an algorithm operation base and an evaluation model base), a multi-source index fusion center (for constructing a three-dimensional data model and integrating a multi-dimensional data resource pool), a dynamic rule configuration engine (for realizing flexible configuration through a visual interface, a model adaptation mechanism and double-track verification) and a core business module (for managing assessment indexes, evaluating indexes and evaluating indexes). And evaluating the model and plan). The support module lays a basic resource layer, a fusion center constructs a data base, a configuration engine realizes flexible verification, the business module drives full-process execution, and full-process digital management from index creation, model configuration to examination execution is realized. The problems of iteration lag, multi-scene adaptation redundancy, weak data verification and the like of a traditional system are solved, index legality is guaranteed through static grammar detection and dynamic simulation operation, data traceability is achieved through a temporary database and full-link recording, and the dynamic response capacity, scene compatibility and data reliability of the assessment system are improved.

The invention discloses an intelligent recommendation method, system and device for data resources of a public data open platform and a medium, belongs to the technical field of data mining, information retrieval and intelligent recommendation, and aims to solve the technical problem of how to improve the accuracy of data resource recommendation of the public data open platform and relieve the defect of cold start. According to the technical scheme, the method comprises the steps of collecting and preprocessing multi-source data; constructing a feature extraction layer, and extracting feature vectors from different data sources of user behaviors, data resource attributes and platform operation data; constructing a feature fusion layer, dynamically integrating features of different sources and types by adopting a strategy of combining weighted fusion and an adaptive attention mechanism, and generating a fusion feature vector; constructing a recommendation layer, and learning deep association between a user and data resources by using a recommendation model based on a deep neural network to realize accurate personalized recommendation; real-time recommendation is generated through a recommendation model based on a deep neural network; and feeding back and updating.

Time based file access credentials are disclosed herein. For instance authentication data representing a user identifier and a user password associated with the user identifier is received from user input associated with a user identity. In response to determining that the user identifier and the user password are valid and are associated with the user identity and based on first metadata associated the user identity, it is determined that the user identity is authorized to access storage server equipment associated with the authentication server. Furthermore, based on second metadata associated with data resources stored on the storage server equipment, it is determined that the user identity is permitted access to at least one data resource of the data resources.

The invention discloses a data backup method and device, electronic equipment and a nonvolatile storage medium. The method comprises the following steps: acquiring target equipment state data of resource pool equipment in a first time period; determining an equipment load parameter of the resource pool equipment in a second time period according to the target equipment state data in the first time period; determining a data variable quantity corresponding to the resource pool equipment, and determining a target data backup strategy according to the data variable quantity and the equipment load parameters; and backing up the data resources in the resource pool equipment according to the target data backup strategy. According to the data backup method and device, the technical problem that data backup is not timely or resources are wasted due to the fact that data backup depends on a fixed period or manual triggering and is lack of flexibility in the prior art is solved.

The invention discloses a data security access method, which relates to the technical field of data security, and is characterized by comprising the following steps of: acquiring access request data of a data access subject, and generating a data access scene type according to the access request data; setting a control domain of fine-grained access, an evaluation dimension parameter corresponding to the control domain and a risk weight corresponding to the evaluation dimension parameter according to the data access scene type; obtaining a real-time monitoring factor of the fine-grained access control domain based on the evaluation dimension parameter, and obtaining a dynamic risk coefficient of the control domain according to the real-time monitoring factor, the evaluation dimension parameter and the risk weight; performing comparative analysis on the dynamic risk coefficient and a preset risk threshold value to obtain a high-risk access request; generating an access strategy factor according to the identity attribute of the data access subject and the data resource tag; the method has the effect of realizing balance between data security and business efficiency.

The invention relates to a multi-protocol intelligent conversion method based on large model knowledge distillation, the method is realized based on a cloud-edge collaborative multi-protocol conversion architecture, and a cloud layer executes the following steps: obtaining data resources related to a multi-channel industrial protocol to generate a multi-source heterogeneous protocol data set, thereby generating a structured feature vector; building prompt input based on the structured feature vector, performing preliminary training on the pre-training general large model, adjusting the pre-training general large model based on a hierarchical generation architecture, obtaining an L2-level multi-protocol conversion large model, and training the L2-level multi-protocol conversion large model; and the edge layer executes the following steps: taking the trained L2-level multi-protocol conversion large model as a teacher model, training a student model by adopting a combined strategy of fusing behavior distillation and representation distillation, outputting a protocol conversion result by utilizing the student model, and optimizing model performance in combination with real-time performance feedback during operation. Compared with the prior art, the cross-system and cross-scene real-time adaptive protocol conversion and optimization can be realized.

The invention discloses a development data query management method and system based on data security, and relates to the technical field of computers, the method comprises the following steps: obtaining a query task configured by a user at a client based on a target demand; performing access permission pre-verification from the dimensions of a user, a role and a data resource through a three-level permission model, and performing security processing on a query task and associated data after verification succeeds to generate a standardized query task with a security identifier; extracting multi-dimensional features of request execution time, task emergency degree, data scale and data type; generating a storage node priority sequence; a plurality of mainstream databases are adapted through a unified adaptation interface, and task analysis, conversion and execution are completed; monitoring a task execution state in real time; according to the method, access permission preprocessing can be carried out from multiple dimensions of users, roles and data resources through a three-level permission model and security processing, and the risks of unauthorized access and data leakage are avoided.

The application relates to a blockchain-based vehicle networking security data sharing method and belongs to the technical field of mobile communication. First, a cloud-edge-end three-layer network model of a vehicle networking scene is established. Then, a double-layer blockchain architecture is proposed to enable a data sharing process, and a sharing strategy and an asynchronous partition sharing method are further proposed. The scheme can realize safe and efficient asynchronous model sharing in the vehicle networking scene while protecting the data privacy of user vehicles. The scheme maximizes sharing efficiency in the vehicle moving scene, efficiently utilizes edge network and user data resources, and breaks the bottleneck of artificial intelligence application in the vehicle networking scene.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a target population determination and product matching method and device, equipment and a medium. Establishing a unified data model taking the unique identifier of the user as a core to generate a user data resource library, screening a target population in combination with the user data resource library and a combined circle selection condition, extracting data associated with the target population from the user data resource library to construct a three-dimensional user portrait, and matching candidate target products for the target population in a target product library, and determining a personalized presentation opportunity and a presentation form based on the stereoscopic user portrait, and presenting the candidate target products to the target crowd. According to the method, a unified model is established by integrating heterogeneous data of multiple service plates, efficient data fusion and accurate crowd positioning are realized, the matching precision is improved based on deep user portraits, and the recommendation integrating degree and the conversion rate are improved through a personalized presentation strategy.

The invention provides an SAR three-dimensional enhanced imaging method based on non-local tensor decomposition, and the method comprises the following steps: 1, searching m + 1 SAR images according to a similarity measurement criterion, and constructing a homogeneity data tensor, m being a positive integer; 2, analyzing the low-rank performance of the homogeneous data tensor, and if the low-rank performance requirement is not met, repeating the step 1; if the low-rank requirement is met, the data tensor is a low-rank data tensor; and step 3, for the constructed low-rank data tensor, signal enhancement is carried out by using a nuclear norm optimization method based on tensor Tucker decomposition, and the method can realize high-quality three-dimensional SAR imaging under the condition of limited data resources.

The invention provides a refined calculation method for thermal equilibrium of a secondary pipe network. The refined calculation method comprises the following steps: S1, establishing an indoor comprehensive temperature calculation model of a building; s2, an outdoor comprehensive temperature calculation model is established, and time-average calculation optimization is carried out; s3, optimizing an indoor comprehensive temperature calculation model of the building, and eliminating system delay; s4, the theoretical building indoor comprehensive temperature is calculated, whether the building indoor set target temperature is met or not is judged, and pipe network thermodynamic equilibrium adjustment is conducted; and S5, evaluating the thermal equilibrium effect of the pipe network. Data resources of secondary pipe network supply and return water temperature, heating buildings and outdoor meteorological temperature are fully utilized, an indoor and outdoor temperature dynamic calculation model is established through heat balance theory analysis, thermal delay is considered synchronously, a system cooperative adjustment strategy is achieved, and system stability is improved.

The invention discloses a data resource scheduling method based on multi-agent game collaboration, which comprises the following steps: acquiring operation state data, and generating a scheduling state data set; constructing a hierarchical agent structure based on the scheduling state data set, and generating an initial parameter set; obtaining an initial strategy network set based on the hierarchical agent structure and the initial parameter set; freezing a current strategy parameter based on the initial strategy network set to obtain a local Nash response strategy combination; constructing a joint optimization objective function based on the local Nash response strategy combination and the initial strategy network set; outputting an updated strategy network set by using a multi-agent reinforcement learning algorithm; feedback information is collected, and scheduling execution feedback data is formed; and based on the scheduling execution feedback data, combining with the updated policy network set, and repeatedly executing an iteration process. According to the method, hierarchical agent game reinforcement learning is realized, and collaborative scheduling of data resources is realized.

The invention discloses a heterogeneous data semantic metadata discovery method and system based on big and small model collaboration, and the method comprises the steps: configuring data source information in a visual Web page according to the needs of a service, reading the data source information through a multi-modal data interface, and receiving the original data of the service; according to the type of the original data, automatically selecting and calling a large model and a special small model to perform semantic analysis on the original data, and generating basic semantic metadata of heterogeneous data resources; generating extended semantic metadata; automatically checking the extracted semantic metadata through a metadata quality evaluation module; establishing a dynamic index relationship between the verified semantic metadata and the original data; the system comprises a data access module, a data generation module, a quality evaluation module and a dynamic index module. According to the method, the extracted semantic metadata can be automatically evaluated, the persistence of the mapping relation can be realized, and a user can conveniently mine and utilize data resources.

The invention discloses a power distribution network fault studying and judging method and system based on topological relation and multi-source data checking. The studying and judging method comprises the following steps: acquiring an original signal and topological data; setting an initial confidence coefficient of each device, and taking the initial confidence coefficient as a confidence coefficient threshold value; checking power failure signals in the original signals of the devices according to a confidence threshold, and replacing the power failure signals of the devices of which the confidence is lower than the confidence threshold; selecting equipment i with the initial value of i being 1, and calculating a power failure signal estimated value of the equipment i; performing threshold division and qualification on the power failure signal estimation value to obtain a power failure signal qualitative value; judging whether i exceeds the total number m of equipment; and based on the topological data and in combination with a power failure signal qualitative value, determining a power failure type and determining a power failure range. According to the method, the original signal and the topological data of the power distribution network equipment are divided into four levels, multi-dimensional data resources of different levels and different types of equipment are comprehensively covered, and the limitation that the prior art depends on a single signal is thoroughly changed.

The embodiment of the invention discloses a digital twin data processing method based on AI, electronic equipment and a computer program product, and belongs to the technical field of electrical data processing.The method comprises the steps that multi-source heterogeneous data is obtained and preprocessed to generate a digital twin data resource pool, the method comprises the following steps: constructing a resource-cost association topological graph, dynamically optimizing a cost edge weight through a time sequence diagram neural network, generating an adjacent matrix, carrying out cost clustering analysis, constructing a cost dimension sub-graph based on a clustering result, and fusing through a tensor decomposition algorithm to generate a multi-dimensional cost semantic mapping model. The model and historical data training are utilized to obtain an intelligent quotation generation algorithm, a real-time data resource pool and a semantic mapping model are combined, and a dynamic quotation suggestion and a quotation scheme are generated by fusing cost features through an attention mechanism. According to the embodiment of the invention, virtual-real mapping, dynamic perception and intelligent decision-making of cost management are realized, and the quotation precision and efficiency are effectively improved.