179 results about "Data access layer" patented technology

The invention relates to a synchronous line loss intelligent diagnosis and analysis system based on an electric power knowledge graph. The synchronous line loss intelligent diagnosis and analysis system comprises a data acquisition layer, a data access layer, a data storage layer, a data analysis layer and a visualization layer, the data acquisition layer is used for acquiring power grid node data; the data access layer is used for carrying out dynamic synchronization on power grid node data based on Apache Kafka; the data storage layer comprises a time sequence database and a graph database; the data analysis layer comprises a detection module and an inference engine; and the visualization layer is used for dynamically displaying a propagation path and node characteristics of an abnormal region based on a topological structure of the knowledge graph, and querying an abnormal reason of the node and the influence on the whole system by clicking the node of the device. According to the invention, intelligent detection and diagnosis of the line loss abnormal area are realized, and the problem root cause is rapidly identified.

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.

The invention relates to the field of software development application data processing, and discloses a software development application data processing method based on an AI large model, which comprises the following steps: deploying an anomaly detection model in a data access layer, and carrying out dynamic anomaly detection and real-time cleaning on software development application data, based on historical security event feedback, optimizing the scoring model and grading the software development application data; anti-association desensitization training is carried out on the software development application data, homomorphic encryption is carried out, association attack simulation testing is carried out regularly, and parameters are adjusted; carrying out distributed management on the key, and carrying out audit tracking based on a block chain; accessing a regulation database based on a dynamic compliance engine, analyzing a regulation to generate a rule base, deploying check points in a data key link for real-time detection and blocking, and performing secure erasure verification; dynamic scheduling resources are predicted in combination with a large model, and the cost is monitored in real time through a monitoring system; nodes are deployed on multiple platforms, an aggregation parameter updating model is encrypted, and vulnerabilities are scanned regularly.

The invention relates to an enterprise data operation multi-source data virtualization access and index automatic management and control method and system, belongs to the technical field of computers, and aims to solve the problems of index definition fragmentation, frequent logic conflicts, dependency static management and verification rule rigid coupling in the prior art. Comprising the following steps: constructing a unified virtualized data access layer to integrate heterogeneous data sources; the indexes are disassembled into semantic atoms capable of being managed in a versioning mode and registered to an atom knowledge base; dynamically constructing an index dependency relation directed acyclic graph based on semantic atom combination; dynamically binding the data quality rules with semantic atoms; and analyzing an index request through a virtualized query engine, reversely traversing atlas aggregation dependency and rules, generating and executing local query, and automatically checking result data quality at the same time. According to the method, unified index definition, dependence on automatic conduction, quality embedded verification and multi-source transparent access are realized, and the data consistency, the treatment efficiency and the system flexibility are remarkably improved.

The invention belongs to the technical field of data processing, and particularly relates to a medical big data processing method, device and equipment based on lake and warehouse integration and a medium, the method comprises the following steps: collecting original medical data, and uniformly accessing the original medical data to a data access layer of a lake and warehouse integrated architecture in an original format and full-amount information to form a source pasting library; preprocessing the original medical data in the source library, performing unified coding and format conversion according to the medical data standard set, generating a standardized data set, and storing the standardized data set in a normalization library; on the basis of data in the normalization library and the source pasting library, multi-modal medical data are associated and integrated through cross-source federated query and computational flow technologies according to business scene requirements of clinical diagnosis and treatment, scientific research analysis or management decision, a special topic library oriented to a specific business topic is constructed, and a special topic library view is generated; and providing a query interface and an analysis service for the data application platform through the thematic database. The data processing speed and efficiency are improved, delay is reduced, and the data throughput efficiency is ensured.

The invention provides a multi-node dynamic switching method and a multi-node dynamic switching system for a MongoDB (MongoDB). The method comprises the following steps: acquiring configuration information of a plurality of MongoDB nodes, and establishing a data access layer based on the configuration information; through a data access layer, acquiring index data marks of the MongoDB nodes about a plurality of operation indexes according to a hierarchical frequency acquisition task configured by each MongoDB node; in combination with a node weight preset value of the MongoDB node, converting the node performance index into a weight value of the MongoDB node; based on a connection pool management mechanism and the node available state data, a request distribution strategy is determined according to the weight value, and the request distribution strategy is used for indicating the distribution proportion of the MongoDB requests distributed to the MongoDB nodes; and performing state detection on the MongoDB node through a data access layer, marking the detected MongoDB node as an unavailable state when detecting that the MongoDB node meets an abnormal state condition, and updating available state data of the node. The problems that in the prior art, manual intervention is needed, the real-time load condition of the node cannot be sensed, and the code coupling degree is high are solved.

The invention relates to the technical field of big data management and artificial intelligence application, in particular to an automatic data quality inspection system and method based on a big model and data flow orchestration, and the method comprises the following steps: system architecture construction: employing a three-layer architecture design which comprises a data access layer, an intelligent quality inspection layer and a flow orchestration layer; the method has the beneficial effects that the full-link automatic quality inspection of structured and unstructured data is realized by integrating the intelligent analysis capability of a large language model (LLM) and the data processing pipeline of a low-code process arrangement tool. The system combines a rule engine and large model dynamic reasoning, supports customized quality inspection rule generation, abnormal data intelligent identification and quality inspection process adaptive optimization, improves the automation level and complex scene adaptability of data quality detection, and is suitable for data governance scenes of multiple industries such as government, finance, medical treatment, e-commerce and the like.

The invention relates to the technical field of artificial intelligence and data engineering, in particular to an intelligent data development treatment system and method based on a large model, the system is composed of a data access layer, a large model treatment engine, an intelligent feature factory, a compliance control layer and an application interface layer, and all the modules cooperate to achieve intelligent transformation of the whole data development process; the method has the beneficial effects that the potential mode of the multi-source heterogeneous data can be automatically identified, for example, transaction records, user behavior data and system monitoring information are distinguished from mixed log files, and manual rule pre-definition is not needed. And dynamically adjusting the protection intensity according to the data use scene.

The invention relates to the technical field of network security, and discloses a data desensitization and access control method based on data consanguinity and sensitivity identification, which comprises the following steps of: constructing a data consanguinity graph for describing derivation, circulation and dependency relationships among fields; identifying a sensitivity level of each field based on a field consanguinity context provided by the graph; in response to a data access request of a user, dynamically generating an access control strategy for the target field in combination with a service scene, a user role and the sensitivity level of the target field, the strategy specifying access permission, access denial or access after desensitization; and executing the strategy through a data interceptor deployed in a data access key layer, releasing and shielding the data or outputting the data after real-time desensitization by calling a specified algorithm. According to the invention, accurate identification and dynamic and consistent fine-grained access control of the sensitivity of the network data are realized, and the security protection capability of the network data is improved.

The invention relates to the field of power distribution network graph optimization, in particular to a power distribution network planning graph visibility optimization method, which comprises a layered architecture module, a technical module, a data integration module, a data processing module, a data evaluation module, a visualization algorithm module and a user interaction module. The layered architecture module is divided into a presentation layer, a control layer, a business logic layer and a data access layer through a modern software system; the presentation layer is responsible for interacting with a user and providing a user interface; the control layer is used for processing user input, the processing speed and the response time of the system can be effectively improved and the maintenance cost can be effectively reduced by setting the modules to be mutually connected for use and improving the system performance and architecture, and the maintenance cost of the system is reduced and the availability of the system is enhanced through modular design and automatic deployment. The stability and reliability of the system are improved, uninterrupted operation of key services is ensured, technical innovation is promoted, and a theoretical basis and practical guidance are provided for technical innovation in related fields.

The present invention relates to the field of software development application data processing, and discloses a software development application data processing method based on an AI large model, including: deploying an anomaly detection model at the data access layer to perform dynamic anomaly detection and real-time cleaning on software development application data, optimizing the scoring model based on historical security event feedback and classifying the software development application data; performing anti-correlation desensitization training on the software development application data, performing homomorphic encryption, regularly conducting association attack simulation tests and adjusting parameters; managing keys in a distributed manner and performing audit tracking based on the blockchain; accessing a regulation database based on a dynamic compliance engine, parsing regulations to generate a rule library, deploying checkpoints at key data links for real-time detection and blocking, and performing secure erasure verification; dynamically scheduling resources in combination with large model prediction, and monitoring costs in real time through a monitoring system; deploying nodes on multiple platforms, encrypting and aggregating parameter update models, and regularly scanning for vulnerabilities.

The invention relates to the technical field of enterprise ESG digital management, in particular to a sustainable management ESG platform. The platform comprises a data access layer, a twin modeling layer, an intelligent analysis layer, a report generation layer and a collaborative interaction layer. The data access layer is used for standardized acquisition, cleaning and block chain evidence storage of multi-source ESG data; the twinborn modeling layer constructs enterprise ESG digital twinborn bodies through index mapping and scene simulation; the intelligent analysis layer realizes index dynamic monitoring, abnormity early warning and risk quantitative evaluation based on time sequence prediction and causal reasoning; the report generation layer outputs a compliance report through a multi-template automatic adaptation and natural language generation technology; and the collaborative interaction layer realizes multi-role data sharing, task tracking and strategy optimization. According to the method, multi-modal data fusion and an intelligent algorithm are fused, the efficiency of ESG data integration, risk response, report generation and collaborative management is comprehensively improved, and the problems of data isolation, response lag and manual dependence in a traditional mode are effectively solved.

The invention provides a big data integration and distribution unit component of a hierarchical architecture and a use method, relates to the technical field of data integration and processing, and solves the problems of real-time integration, unified processing and safe distribution of multi-source heterogeneous data. The device adopts a five-layer modular architecture; a data access layer supports multi-source data access and is buffered through Kafka; the cleaning conversion layer performs data cleaning and standardization by using a FlinkSQL, an ETL engine and a rule engine; the intelligent distribution layer judges a data distribution path in combination with a rule engine and a machine learning model; the storage calculation layer adopts a cold and hot separation storage strategy and supports parallel operation of Spark batch processing and Flink stream processing; and the service and security layer provides an interface through an API gateway, and guarantees data security in combination with AES-256 encryption and RBAC access control. Besides, dynamic resource scheduling is realized through Kubernetes, cross-domain model cooperative training is supported by utilizing a federated learning technology, the intelligent shunting decision-making capability is improved, various industrial protocols such as MQTT and Modbus are compatible, and VXLAN tunnel transmission is supported.

The invention discloses a big data governance method and system based on a rule engine, belongs to the technical field of big data processing, and aims to solve the technical problems of low efficiency, slow response and rule stiffness in the current data governance process. Comprising a data access layer used for collecting data from each data source; the metadata management module is used for constructing a knowledge graph based on the metadata; the rule engine module is used for constructing a rule base; the data quality evaluation module is used for analyzing and evaluating the multi-source heterogeneous data based on the rule base; the authority control and compliance auditing module is used for performing authority control and performing compliance analysis on data operation through a preset compliance scale board based on an auditing log; the governance engine module is used for carrying out governance operation on the multi-source heterogeneous data based on the data quality analysis result to form a data governance execution result; and the feedback optimization module is used for optimizing the AI large model based on the data governance execution result.

The invention relates to the technical field of power system data processing, and discloses a meteorological data collection method based on a power grid prediction demand. The problems that existing meteorological data sources are high in isomerism, the preprocessing process is repeated, the labor amount is large, unified standards are lacked, and the self-repairing capacity is insufficient are solved. The method comprises the following steps: constructing a multi-source meteorological data access layer and carrying out unified space-time reference conversion; analyzing a power grid prediction task demand, and dynamically matching and splicing the preprocessing sub-architecture units; executing the customized preprocessing assembly line to generate a numerical mapping graph; performing logic consistency repair on missing or abnormal data; and outputting the numerical mapping graph after integrity restoration to a power grid prediction model interface. According to the technical scheme, unification and adaptation of multi-source heterogeneous meteorological data at the acquisition end according to needs can be achieved, data processing flexibility and expandability are improved, data integrity and physical consistency are guaranteed, and power grid prediction precision and response speed are improved.

The invention belongs to the technical field of automatic testing, and particularly relates to a web automatic testing method and system.The method comprises the steps of automatic script recording and dynamic adaptation, layered framework collaborative testing and data verification and continuous integration testing and test report generation. Wherein automatic script recording is dynamically adapted: recording a page operation script through a playwright tool and applying the page operation script to a UI layer, and when the page UI changes, recording the script again and replacing the original script without element-by-element positioning and code modification; in the hierarchical framework collaborative testing and data verification, a data access layer inputs test data, a UI layer executes page operation and captures display data, and a business logic layer calls an API interface to obtain real data and compares the real data with the display data for verification. According to the method, a web automatic test framework is constructed in combination with the advantages of playwright, a structured engineering project is more suitable for project operation and maintenance, and a sustainable integrated test environment is constructed by utilizing jenkins, so that test input, execution and output are automated.

The invention relates to the technical field of power construction safety management and control, discloses a safety management and control detection method and system based on multi-modal data comprehensive analysis, and is suitable for safety monitoring and management of an overhead line construction scene. The system adopts a layered architecture and comprises a data access layer, a data analysis and processing layer and a service display layer, the data access layer collects data, and the data analysis and processing layer fuses and processes the data. The process comprises sensor calibration, time-space synchronous alignment, point cloud preprocessing, image feature enhancement, visual image and point cloud preliminary fusion, image defect identification and complementation, data fusion in BEV space, warning information generation and sending, timely reception and display of background warning information by a business display layer, and man-machine interaction display. According to the method, feature fusion of multi-modal heterogeneous data from different sensors can be realized, the safety problem of the construction process is efficiently identified, and the method has universality.

The invention provides a construction system for realizing a business intelligent data question-answering system based on semantic recognition, which belongs to the technical field of information and comprises a user interaction layer, a semantic analysis layer, a data access layer, a data analysis layer and a visual presentation layer. A user can input a query request through a natural language, and the system analyzes the intention of the user by using an advanced semantic recognition technology and quickly extracts related information from mass business data. And then, the system visually presents an analysis result to the user in forms of charts, reports and the like through a visual tool, so that a business department is assisted to quickly understand a data trend and make an efficient decision. According to the system, the data query and analysis process is simplified, the dependence on professional data analysts is reduced, the working efficiency is greatly improved through automatic and intelligent means, the operation and maintenance cost is reduced, good expansibility and integration are achieved, and diversified business requirements of business departments are met.

The invention discloses a lake and warehouse integrated Internet of Things time series data storage architecture system and method in the crossing field of Internet of Things time series data storage and engineering machinery intelligent technology. The system comprises a data source layer, a data access layer, a data access and preprocessing layer, a lake and warehouse integrated storage layer, a data fusion and analysis layer and an application layer. The data source layer collects multi-source heterogeneous time sequence data, after the multi-source heterogeneous time sequence data is buffered and temporarily stored by the data access layer, an adaptation module of the data access and preprocessing layer analyzes and converts the data, and a preprocessing module performs quality cleaning to form standardized data; the lake-warehouse integrated storage layer carries out classified storage according to data types; the data fusion and analysis layer extracts fusion features and constructs an equipment health state evaluation model; and the application layer realizes the functions of equipment monitoring, fault prediction, construction optimization and the like according to an analysis result, effectively solves the problems of storage, fusion and query optimization of multi-source heterogeneous time series data, improves the storage efficiency and the query speed, and assists in intelligent operation, maintenance and management of equipment.

The invention discloses an industry operation permission intelligent approval system based on Java and an implementation method, and belongs to the technical field of computer application. The system adopts a four-layer architecture including a presentation layer, a business logic layer, a data access layer and a data storage layer. The core of the system is that the business logic layer integrates an intelligent auditing engine, a rule engine and a workflow engine; the intelligent auditing engine automatically analyzes and extracts key information in the application material through NLP and OCR technologies; a rule system based on industry classification is built in the rule engine, three-dimensional automatic verification of subject qualification, material integrity and condition conformity is executed, and regulatory terms can be mapped into executable logic; and the workflow engine automatically triggers and manages the approval process according to the verification result. According to the invention, full-process automation and intellectualization of industry operation permission approval are realized, the approval efficiency, accuracy and dynamic adaptation capability to policies and regulations are greatly improved, and the problems of low efficiency, different standards, lagging and the like existing in a traditional manual approval mode are effectively solved.

The invention discloses an enterprise data asset right confirmation traceability method based on a data-real fusion technology, and particularly relates to the technical field of enterprise data asset management. The core structure of the method comprises a data access layer, a data processing layer, an identification management module, a right confirmation registration module and a traceability tracking module. The data access layer receives multi-modal original data; the data processing layer performs standardized preprocessing and generates data fingerprints; the identifier management module generates a globally unique digital identifier based on a composite coding rule; the right confirmation registration module binds the ownership information with the identifier and performs block chain evidence storage through a multi-chain architecture; and the traceability tracking module analyzes the data full-life-cycle operation log based on the graphical database. Uniform identification, credible right confirmation and deep traceability of the multi-source heterogeneous data assets are realized, and the core problems of unclear ownership and difficulty in traceability of the data assets are solved.

The invention aims to provide the intelligent switching operation ticket forming system and method based on the large model, which are high in efficiency, high in accuracy and low in cost. The system comprises a data access layer (1), a knowledge support layer (2), an intelligent agent execution layer (3), an application layer (4) and a self-learning optimization layer (5). The method comprises the following steps: S1, a data access layer obtains a scheduling instruction and equipment real-time data and transmits the scheduling instruction and the equipment real-time data to an intelligent agent execution layer; s2, an instruction analysis agent (31) analyzes the scheduling instruction to generate structured feature parameters; s3, the statement generation agent (32) generates an operation ticket first draft in combination with the feature parameters, the topological data and the finished ticket knowledge base; s4, performing logic, normative and multi-ticket comparison verification on the first draft by the verification agent (33), and outputting a verification result; and S5, the application layer displays the operation order passing the verification, and feeds back the data to the self-learning optimization layer to update the system. The method is applied to the technical field of power system operation and maintenance intelligentization.

The invention discloses a report management system and a report making method. The report management system comprises a user interface layer, a data processing layer, a data access layer and a data storage layer, the user interface layer is used for constructing a visual report template design interface and receiving filling data input by a user, so that the user can freely drag various report elements on the report template design interface and set attributes of the elements, and a corresponding report template is generated; the data access layer is used for connecting a data source and extracting filling data; the data processing layer is used for processing and converting the filling data according to the page content in the report template to generate a report file; and the data storage layer comprises a database and a third-party system interface and is used for providing a data source and sharing the report file into a third-party system. According to the method, the user can complete generation of the report template through simple dragging operation, and the filling data is automatically processed according to the report template to generate the report file.

A transaction system receives a transaction request for a database service. The transaction system retrieves, by a first microservice, a set of data from a data access layer of the database service in response to receiving the transaction request. The transaction system performs a set of computations based on the set of data retrieved from the data access layer. The transaction system stores the set of computations in one or more cache nodes of a data access service and accesses, by a second microservice, the set of computations, performed in response to retrieving the set of data by the first microservice, from the one or more cache nodes.

The invention belongs to the field of artificial intelligence, particularly relates to an integrated artificial intelligence analysis and modeling platform supporting large-scale data processing, and aims to solve the problems of low resource scheduling efficiency, model training reasoning splitting, data algorithm coupling, limited cross-modal modeling and the like under high-dimensional heterogeneous mass data. The platform comprises a distributed data access layer, a unified metadata governance engine, a heterogeneous computing resource scheduling center, a model life cycle management module, a multi-modal fusion modeling framework and a dynamic service orchestration interface, and realizes automatic data access governance, dynamic resource scheduling, model full-cycle management and control and cross-modal intelligent fusion. And the resource utilization rate, the modeling accuracy and the service deployment efficiency are remarkably improved.

The invention relates to an intelligent data asset management system. The system comprises a data access layer, a data processing layer, an intelligent analysis layer and an application service layer, the intelligent analysis layer comprises an intelligent label recommendation unit, an intelligent cataloguing unit, an intelligent management unit and an intelligent housekeeper unit; the data access layer is used for receiving the multi-modal data assets and performing format standardization conversion to obtain standardized multi-modal data assets; the data processing layer is used for preprocessing the standardized multi-modal data assets and extracting modal features of each modal data; the intelligent housekeeper unit is used for inputting a question of a user into the intelligent question-answering big language model, performing intelligent question-answering according to an intelligent directory and a knowledge graph and outputting answer content; and the application service layer is used for calling the output of the intelligent analysis layer and providing a data asset query service, a quality monitoring service and a decision support service for a user. By adopting the method, a professional, accurate and efficient data asset data analysis report can be output.

The invention discloses a data interaction management and control system and method for judicial and administrative business resource integration. The system comprises a presentation layer, an application layer, a service layer, a data access layer and an infrastructure layer, wherein the presentation layer is used for providing a visual interface of a judicial administration data collaborative sharing portal and an interactive management and control portal, and supporting data collaborative sharing and management and control operation of a plurality of business scenes; the application layer is used for combining functions of the service layer, realizing a business scenarized application module and recording an operation behavior log in real time; the service layer is used for packaging preset business logic and providing an atomization service interface; the infrastructure layer comprises network resource load balancing, an API gateway, a hardware security device, a computing resource server cluster, a message queue, a log storage, a relational database, a distributed file storage and a block chain platform node. The block chain is utilized to realize on-chain data access, behavior data is prevented from being tampered, traceability and truth restoration are facilitated, and a person in charge is positioned.

The invention discloses a multi-strategy fusion file data desensitization management and control system, and relates to the technical field of information security and data processing, a system architecture adopts a hierarchical design and comprises a presentation layer, a business logic layer and a data access layer, the interface design of the presentation layer is simple, and a user can easily operate the presentation layer; the business logic layer is responsible for processing various rules and ensuring that data can be correctly processed; the data access layer is responsible for interacting with a database to realize secure storage and quick reading of data, and the architecture design ensures the expandability and maintainability of the data desensitization file security management system; system functions are realized in two ways: a common user can perform desensitization operation on sensitive data and download a file; an administrator can modify configuration and manage the desensitization rule besides the functions of a common user; the system can effectively protect sensitive data and reduce the risk of data leakage, and has high practical value.

The invention discloses a model-based micro-service multi-source heterogeneous unified query system and method, and the system comprises a unified data access layer abstract interface which provides a standardized API for a developer to call; according to the model abstraction layer driven by metadata, a three-level metadata structure comprising a BizModel, a BizDataTable and a BizField is adopted for decoupling service logic and physical storage, and multi-source heterogeneous data association crossing MySQL / MongoDB / Elasticsearch / NoSQL is supported through RelativeSource metadata; the declarative query interface is combined with the dynamic SQL generator, provides a chain type API to construct a cross-service query condition, and automatically generates a query statement meeting a target database standard according to a preset data model; and the routing decision module distributes a query request to local execution and remote calling, and supports a plug-in expansion data source dialect generator.

The invention discloses a data warehouse intelligent construction and management method and system, and belongs to the technical field of data warehouse construction and management, and the data warehouse intelligent construction and management method comprises the steps: obtaining an enterprise data source, and extracting the feature parameters of the data source; constructing a data warehouse logic model; self-adaptive ETL configuration parameters are defined; constructing a three-layer physical architecture comprising a data access layer, a data storage layer and a data service layer; intelligent cache strategy configuration is executed based on a deep learning algorithm, a three-level cache architecture is established, and a pre-cache rule and a failure strategy are defined; and running management optimization is implemented through a multi-objective optimization function. According to the method, the construction automation level is remarkably improved, the cache hit rate is increased, the average query response time is shortened, the data consistency and the system performance are effectively balanced, the elastic adaptive capacity of the system is enhanced, the operation and maintenance complexity and cost are reduced, and meanwhile efficient sharing of cache resources is achieved on the premise that data security isolation is guaranteed.