1258results about "Multi-dimensional databases" patented technology

The invention relates to the field of digital twinborn modeling, and discloses a digital twinborn visual modeling method and system based on a neural network, and the method comprises the steps: collecting the multi-dimensional perception data of a target physical system, carrying out the format unification and normalization processing of the original data of a sensor through a heterogeneous data fusion module, and obtaining a data fusion module; constructing a high-dimensional input feature set for neural network modeling by combining a structured embedding algorithm; carrying out stability pre-evaluation on the constructed input feature set, and screening core features by adopting a disturbance sensitivity analysis mechanism; a dynamic residual feedback mechanism is introduced to carry out enhanced training on the preliminary twin model, and modeling is carried out on the space-time dependency relationship of different components through a graph neural network; tracking a stability index of visual representation in real time in a model training process; and according to a multi-dimensional visualization result output by the final twin model, performing interpretation in combination with an industrial scene semantic rule base. The method has the advantage of improving the practicability of the twin model in the industrial scene.

The invention relates to a method convenient for data consanguinity collection and analysis, which comprises the following steps of: obtaining original consanguinity data comprising a task execution log, application metadata and a cross-system dependency relationship, and carrying out standardization processing on the original consanguinity data to generate structured consanguinity information comprising an asset unique identifier, an upstream and downstream association relationship and a data operation type; structured consanguinity information is synchronously written into a graph database and a distributed data warehouse, the graph database stores real-time association topology, the distributed data warehouse stores full-amount historical versions, and a transaction consistency algorithm is adopted to ensure the atomicity of double-write operation, so that the data storage efficiency is improved. Single-asset-level consanguinity tracking is performed based on real-time topology of a graph database, global consanguinity analysis is performed based on batch computing power of a distributed data warehouse, a direct dependence path, a deep association network and a closed-loop link detection result are generated, and a closed-loop management mechanism from data acquisition, analysis to optimization is formed. And the problem that an analysis result is disjointed from an acquisition end in a traditional scheme is solved.

The invention relates to the technical field of data processing, and discloses a cross-border e-commerce commodity recommendation system and method based on multi-source data fusion. The system comprises an acquisition module for performing data acquisition to obtain a cross-border unified data warehouse and federal learning cooperation data; the classification module performs text classification processing to obtain a user preference analysis result and an interpretable attention mark; a quantization module carries out quantization processing to obtain a cross-border selection feature matrix; the fusion module carries out weighted fusion processing to obtain a basic comprehensive score and a weight convergence detection result; the evaluation module performs risk evaluation processing to obtain risk probability distribution and cross-border compliance evaluation results; and the sorting module carries out real-time processing through lightweight preprocessing and a flow-type calculation pipeline to obtain a comprehensive score sorting list of cross-border selected products. The problem that a traditional cross-border e-commerce product selection method cannot effectively integrate multi-source heterogeneous data and cannot analyze user feedback text semantic information is solved.

The invention relates to an enterprise-level simulation knowledge graph construction method based on multi-modal data integration, and belongs to the technical field of knowledge graphs. The method comprises the following steps: integrating structured data, semi-structured data and unstructured data through a multi-modal data warehouse; performing knowledge extraction on the semi-structured data and the non-structured data to obtain entities and relationships, and performing knowledge fusion; storing the fused entities and relationships by using a graph database, and constructing a simulation knowledge graph; a vector database is embedded in combination with a simulation knowledge graph, semantic extension search is realized through multi-modal joint search, similar cases are searched through a simulation result graph, and a simulation scheme comparison matrix is automatically generated. The multi-modal data is effectively integrated, the comprehensiveness and accuracy of knowledge graph construction are improved, more powerful, efficient and intelligent support is provided for simulation analysis of enterprises, and the enterprises can be assisted in rapidly making scientific decisions in complex and changeable business scenes.

The invention discloses a tunnel blasting quality evaluation and optimization method based on multi-source data fusion, and belongs to the field of tunnel blasting quality evaluation, and the method comprises the steps: collecting and preprocessing multi-source data of a tunnel blasting region; based on the preprocessed multi-source data, performing blasting quality evaluation according to local back break, a blasting contour line, average linear back break and point cloud extraction to obtain a blasting quality evaluation result; according to the blasting quality evaluation result, the blasting quality is graded, and a comprehensive blasting quality score is calculated and graded; and establishing a database containing geological parameters, surrounding rock response parameters and blasting process parameters, training through a convolutional neural network model to generate a blasting parameter optimization scheme, and dynamically adjusting blasting parameters of the next cycle according to the comprehensive blasting quality score. According to the method, the blasting parameter optimization and the quality evaluation process are closely combined to form a closed-loop system, the specific situation in the construction can be reflected in real time, and the accuracy of the blasting effect is ensured.

The invention relates to the technical field of data processing, and discloses a financial compliance risk assessment method and system based on big data, and a storage medium. The method comprises the following steps: collecting and standardizing multi-source data to form a structured data set; constructing a three-level evaluation system comprising risk fields, subclasses and indexes; training the stacked fusion network to obtain an evaluation model; analyzing and generating a risk report in real time; identifying risk points and performing quantitative evaluation; and an early warning threshold value is set, and an early warning signal is generated and sent to a risk management department when the threshold value is exceeded. According to the invention, efficient identification, accurate evaluation and timely early warning of compliance risks are realized, and the automation level and scientific decision-making ability of compliance risk management of financial institutions are improved.

The invention discloses a manufacturing enterprise data center system and method based on a large model, and the system comprises a data collection layer which is used for constructing a multi-source heterogeneous data collection system; the data storage layer is used for constructing a data warehouse system comprising multiple layers and realizing data lake storage; the data processing layer and the service layer are used for deploying a model and integrating an anomaly detection module, a multi-modal fusion engine and a prediction model; the service layer is used for an interface group, integrating a Drive / ClickHouse query engine and a Redis cache, and supporting multi-condition combined query and millisecond response; deploying a visual service module and an RBAC / ABAC authority control system to realize data grading sharing; and the application layer is used for developing a production optimization application, a supply chain management application and a CRM application. Through a uniform data acquisition interface and an efficient data acquisition tool, mass data inside and outside an enterprise can be integrated, data islands are broken, centralized management and sharing of the data are realized, and availability and value of the data are improved.

Optimizing data warehouse utilization by a data ingestion pipeline, including: gathering information describing usage of a plurality of data warehouses; receiving, from a data ingestion pipeline, a request for a data warehouse to receive data from the data ingestion pipeline; selecting, based on the information, the data warehouse from the plurality of data warehouses; and providing, to the data ingestion pipeline, an indication of the data warehouse.

Systems and methods are described for a connected AI agent for managed multidimensional search based on an electronic message and management policies. A messaging application at a client device can send a new electronic message, such as an email, to a connector service. An attachment can be ingested and stored in a vector database. Then one or more artificial intelligence (“AI”) agents can be selected for responding to the body of the email, such as a query in the body. The responses can be formatted and sent to multiple parties, such as a sending user of the electronic message and a recipient that was copied or also sent the new electronic message. The AI agents can use different AI models, prompts, and vector databases depending on user permissions. This allows for building up vector databases with relevant content items and answering user questions based on those vector databases.

Systems and methods are described for obtaining accurate responses from large language models (LLMs) and chatbots, including for question and answering, exposition, and summarization. These systems and methods accomplish these objectives via use of noun phrase avoiding processes such as a noun phrase collision detection process, a query splitting process, and a topical splitting process as well as by use of formatted facts, formatted fact model correction interfaces (FF MCIs), bounded-scope deterministic (BSD) neural networks, processes and methods, and intelligent storage and retrieval (ISAR) systems and methods. These systems and methods avoid and bypass noun phrase collisions and correct for errors caused by noun phrase collisions so that hallucinations are eliminated from LLM responses.

The invention discloses an equipment data acquisition and query optimization method based on a large model enabling ERP-MES system, and relates to the technical field of integrated enterprise resource planning and monitoring management systems, and the method comprises the following steps: carrying out the dynamic adaptive data acquisition and preprocessing of equipment: employing an edge node to deploy a lightweight noise detection model, identifying an electromagnetic interference mode in sensor data collected by the equipment in real time, and dynamically compensating and correcting by combining environment temperature and humidity data; according to the method, an edge node lightweight noise detection model is combined with environmental data compensation and correction, and a BERT large model is utilized to analyze a protocol document design middleware, so that accurate acquisition and efficient processing functions of dynamic self-adaptive data of equipment are realized; preprocessed data are mapped and fused through a multi-modal large model to construct a unified data warehouse, and a mapping strategy is optimized through reinforcement learning, so that the equipment data quality is improved, and the data fusion and utilization efficiency is enhanced while heterogeneous data fusion is achieved.

The invention discloses a data production and application method based on index management, and the method comprises the steps: receiving an index query request containing a target index identifier, a dimension constraint condition and a time range parameter, carrying out the analysis and semantic verification of the target index identifier based on a global index asset library, and obtaining the index definition information; generating a standardized query statement according to the index definition information, and selecting an optimal data calculation engine to execute query; performing multi-level cache optimization and parallel computing acceleration on the query process to obtain an original data set; processing the original data set in real time according to a preset analysis model to generate structured index data containing trend analysis, anomaly detection or attribution inference results; and filtering and desensitizing the data based on a fine-grained permission control strategy, only returning contents in a permission range and recording an audit log. By means of the method, unified management, efficient query and intelligent analysis of the index data are achieved, the automation level and query performance of data production are improved, and meanwhile data safety and access controllability are guaranteed.

The invention discloses a chain drugstore purchasing scheme generation method and system based on big data, and relates to the field of purchasing management. The method comprises the following steps: firstly, collecting historical sales data, real-time inventory data and external environment data, and constructing a multi-dimensional database; performing standardization processing on the data, and constructing a medicine classification matrix through a data mining model; identifying key factors influencing drug requirements by using a causal analysis method, and establishing a requirement influence factor weight matrix; based on the weight matrix, calculating purchase parameters such as a safe inventory level, an economic purchase batch, a purchase trigger point and a purchase period; and finally, in combination with a supplier evaluation system, generating a purchasing scheme including a purchasing medicine list, a purchasing quantity, purchasing time and supplier selection. According to the method, through multi-dimensional data analysis and causal relationship mining, scientization and accuracy of purchasing decision are realized, the purchasing efficiency is improved, and the inventory cost is reduced.

The disclosed embodiments provide systems and methods for performing queries via an intelligent query engine. Various embodiments include receiving an input query and parsing the input query into a query representation object; generating an evaluation plan based on the query representation object, wherein the evaluation plan comprises a graph of computation nodes, each computation node specifying a granularity for grouping, associated filters, an aggregation schema, and join dependencies required for node computation, and wherein the evaluation plan is ordered to account for hierarchical dependencies among the computation nodes; translating the evaluation plan into an executable query optimized for a specific target data store, wherein the translation adapts query syntax and join structures to capabilities of a target data store; and executing the translated query on the target data store, resolving dependencies and aggregations according to the evaluation plan to generate query results satisfying the input query.

The invention relates to the technical field of information and data processing, in particular to an enterprise digitalization-oriented data management and application method, which comprises the following steps of: acquiring multi-source heterogeneous data and recording metadata; establishing a cross-department management system to standardize the treatment process; building an asset catalog based on the business section and the data field classification; formulating unified data description of a multi-level data standard system; designing a quality control rule to filter low-quality data; constructing a business index system and embedding the business index system into a business system; identifying a cross-system and cross-type association mode through a metadata association technology and a rule engine; and dynamically adjusting the treatment process by adopting a multi-objective optimization algorithm in combination with service feedback and treatment parameters. According to the method, the defects of existing data governance in comprehensiveness, depth and business adaptability are overcome, standardized governance and potential value mining of multi-source data are achieved, and effective data support is provided for intelligent decision making of enterprises.

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a digital data visualization from data of an analytics database. In particular, in some embodiments, the disclosed systems receive a digital text prompt that includes a target digital data visualization description. Furthermore, in some embodiments the disclosed systems generate visualization token predictions from the digital text prompt utilizing a language machine learning model. Moreover, in some embodiments the disclosed systems modify the visualization token predictions to generate refined visualization tokens and generates the digital data visualization based on the refined visualization tokens.

The invention discloses a lithium battery full life cycle quality tracing system based on digital twinning, and relates to the technical field of lithium battery quality tracing, and the system comprises a physical entity layer which integrates multiple types of sensors and detection equipment to collect full-link data; the digital twinning modeling layer is used for constructing a multi-scale model to map a battery state; the data acquisition and transmission layer is used for realizing data real-time transmission preprocessing through an edge gateway and an encryption protocol; the data storage and management layer is used for establishing a data warehouse by using a distributed database and performing backup; the intelligent analysis and decision-making layer integrates an algorithm to realize anomaly detection, performance prediction and tracing; the visualization and interaction layer is used for providing a multi-terminal visualization and interaction function; and the block chain evidence storage layer constructs an alliance chain to ensure that the data cannot be tampered. According to the invention, full-life-cycle data connection and credible tracing of the lithium battery are realized, the quality problem positioning efficiency is improved, intelligent early warning and optimization capabilities are provided, the operation threshold is reduced, and industrial quality control and green development are facilitated.

The invention discloses a data warehouse multi-hop relationship quick retrieval method and system based on a graph embedded index, and relates to the technical field of multi-hop relationship retrieval data processing. The quick retrieval method for the multi-hop relationship of the data warehouse based on the graph embedded index comprises the following steps of: monitoring data flow interference; monitoring index updating timeliness; monitoring the query accuracy of the multi-hop relationship; and timely monitoring of data query response. According to the method, whether to perform graph model modeling to obtain the graph model is determined by judging whether to trigger the interference suppression mechanism, then the multi-level index is constructed based on the graph model, and whether to trigger the index step length updating mechanism is determined based on the index updating timeliness judgment result; and finally, based on a multi-hop relationship result query accuracy verification result, determining whether to trigger a candidate node screening range shrinkage mechanism, thereby achieving the effect of improving the multi-hop relationship retrieval efficiency of the data warehouse, and solving the problem of low multi-hop relationship retrieval efficiency of the data warehouse caused by data flow interference in the prior art.

The invention provides a data driving and multi-element cloud edge cooperative computing method for intelligent workshop construction, which comprises the functions of multi-protocol heterogeneous equipment sensing, edge node adaptive load balancing, lightweight real-time edge computing processing, cloud centralized management control, industrial intelligent application service deployment scheduling, cloud edge task unloading and the like. The system comprises a data acquisition and transmission middleware, an edge stream processing engine and a cloud service center. The method has the characteristics of real-time performance, low delay, flexibility, expandability, loose coupling, low resource occupancy, high availability and privacy security.

The invention discloses a cultural heritage file multi-dimensional storage system, which relates to the field of cultural heritage digital protection and intelligent storage and comprises a data acquisition and processing module, a double-library management module, a multi-dimensional index retrieval engine, a security and convergence module and a backup and disaster recovery module. According to the method, structured texts and unstructured files are uniformly accessed through various interfaces, a special tool chain is adopted for format standardization, audios and videos are transcoded into an H.264 / AAC format through FFmpeg, images are subjected to resolution adjustment and compression through ImageMagick, plain texts are extracted from texts through ApacheTika and are converted into UTF-8 codes, standardized data are uniformly converted into an intermediate format, and the format of the intermediate format is converted into the intermediate format. According to the technical scheme, the method comprises the following steps of: standardizing input data by adopting a three-layer duplicate removal strategy, facilitating other modules to receive data, performing refined redundancy processing on the standardized input data by adopting a three-layer duplicate removal strategy, remarkably reducing storage redundancy, partitioning a cultural heritage data main library according to three layers of ''project type-medium type-age'', and partitioning an inheritor information library according to two levels of ''province-inheritor number prefix''.

The invention discloses a control and alarm method and device for multi-parameter grain condition data and a medium, and relates to the technical field of granary management. The method comprises the following steps: acquiring grain depot data and grain pile data through a sensor, and synchronously acquiring warehouse in-out receipt information; the grain depot data and the grain pile data are compared with a historical database, a dynamic threshold value model is established, and the dynamic threshold value model adjusts a parameter deviation range based on climate data and a storage period of the grain depot in the same region; according to the dynamic threshold model, obtaining a corresponding boundary value, generating a 3D distribution diagram of the grain pile, and calculating an interlayer difference; if the grain depot data and the grain pile data reach boundary values or the interlayer difference exceeds a first preset threshold value, an alarm signal is triggered, and the stock quantity is verified by associating the in-out stock receipt information. According to the method, real-time collection of multi-source data and application of the nano biosensor are achieved, and comprehensive and accurate monitoring of internal and external environmental parameters and grain physiological indexes of the grain depot is achieved through the dynamic threshold value model.

The invention relates to the technical field of data processing, in particular to a main data management method and system based on enterprise data governance, and the method comprises the steps: defining a basic model structure based on an enterprise general main data object, and storing the basic model structure in a metadata form; obtaining a business system change event and a manual expansion demand and converting the event and the demand into a structured description; zero-coding extension configuration of attributes, classification and association relationships is completed through a visual interface; executing interface adaptation, data mapping adaptation and rule conflict detection; and synchronizing the effective extension model to a service layer, a cleaning layer and a monitoring layer. The system comprises a demand awareness module, a dynamic modeling module, an extension configuration module, an adaptation engine module and a life cycle management module. According to the method, full-process management of the master data model from demand capture to business landing is realized, and the model expansion efficiency and the business adaptation capability are improved.

The invention relates to a medical health big data exploration system, and the system comprises a demand understanding module which is used for converting a data exploration demand into a structured exploration task; the compliance review module is used for performing compliance review on the probing task; the code generation module is used for generating a query code according to the probing task and the target database characteristics; the query execution module is used for querying query result data corresponding to the query code from a target database according to the query code; the query result analysis module is used for performing multi-dimensional analysis on the query result data to obtain a multi-dimensional analysis result; a report generation module; and the result presentation module is used for presenting the exploration report in an interactive mode in a user interface and supporting iterative improvement of the report by providing a feedback mechanism, so that the problems of data isomerism, difficulty in quality evaluation, low exploration efficiency, high professional threshold, insufficient privacy protection and the like in the existing medical data exploration process are solved.

The invention relates to the technical field of environment monitoring, in particular to a sediment heavy metal resuspension pollution evaluation system based on multi-dimensional data, which comprises a parameter acquisition module, a coupling resuspension simulation module, a heavy metal form migration calculation module and an ecological risk entropy evaluation module. The parameter acquisition module is used for acquiring hydrodynamic parameters, sediment heavy metal occurrence forms and biological disturbance intensity indexes; the coupling resuspension simulation module fuses hydrodynamic shear stress and biological irrigation flux and simulates resuspension concentration distribution; the heavy metal form migration calculation module combines the redox sensitive factor field and the exchangeable heavy metal concentration to calculate the dynamic release flux of the dissolved heavy metal; the ecological risk entropy evaluation module introduces an acute and chronic toxicity dual evaluation mechanism, constructs a pollution risk entropy matrix and generates a three-dimensional pollution risk entropy cloud picture. The method is suitable for dynamic evaluation and early warning of resuspension pollution risks in water bodies such as offshore water bodies, river mouths and lakes.

The invention discloses a dynamic data docking method based on a rule engine, which comprises the following steps: S1, data source registration and automatic detection: a user inputs various heterogeneous data source information through a system console, and a system automatically detects the data source type, the data structure and the connection availability and generates an adaptive connection configuration file; according to the method, a dynamic data docking method with the rule engine supporting hot updating as the core is introduced, seamless integration and flexible conversion of multi-source heterogeneous data are achieved, and the problem of business interruption caused by code modification and service restarting due to rule change in a traditional scheme is thoroughly solved; through visual rule management, a unified data model and a closed-loop verification feedback mechanism, the development and maintenance cost of the system is remarkably reduced, and meanwhile, the flexibility, reliability and traceability of data processing and the continuous self-adaptive optimization capability of the system are greatly improved.

The invention discloses a settlement monitoring system based on computer vision, and relates to the technical field of infrastructure monitoring, the settlement monitoring system comprises a settlement monitoring platform, the settlement monitoring platform is in communication connection with an image acquisition module, a sensor network module, a data fusion analysis module, a settlement abnormity identification module and a settlement early warning module, the modules are in electric signal connection; and the image acquisition module is used for monitoring a foundation area of the infrastructure by using a plurality of cameras deployed in a monitoring area to obtain image data of the infrastructure. Through combination of the image acquisition module and the sensor network module, comprehensive coverage and accurate monitoring of an infrastructure foundation area are realized, a plurality of cameras are utilized to carry out omnibearing and multi-angle image acquisition, dead-corner-free monitoring is ensured, physical parameter data of a foundation are acquired in real time through multiple types of intelligent sensors, and the monitoring precision is improved. Monitoring comprehensiveness and accuracy are obviously improved, and potential safety hazards caused by monitoring blind areas are effectively avoided.

Systems and processes are disclosed for processing and integrating multinational data into data lakes. Utilizing a generative AI engine, data schema templates can be dynamically generated to standardize diverse data streams from various sources. The systems and processes incorporate edge computing for localized preprocessing, ensuring data quality and compliance with international regulations. Innovative features include real-time data routing and prioritization algorithms for efficient hyper-ingestion, and an embedded livestream for instantaneous business decision-making. The disclosed cutting-edge approaches address the complexities of modern data integration challenges.

The invention relates to the technical field of data governance, and provides a data platform metadata annotation completion method, system and equipment, and the method comprises the steps: collecting metadata basic annotations in a multi-source heterogeneous database through a standard application program interface; collecting a logic model definition document and a data standard specification document, and constructing a local knowledge base through vectorization processing; when a user triggers annotation completion, calling a local knowledge base through a large language model, and generating metadata intelligent annotations; and returning the collected basic annotation of the metadata and the generated intelligent annotation of the metadata to the user as annotation completion results. According to the data platform metadata annotation completion method, system and equipment, metadata collection normalization, multi-source knowledge fusion, knowledge base construction and intelligent annotation completion can be achieved, cost reduction and efficiency improvement are achieved, the metadata annotation quality is remarkably improved, and the use threshold is lowered.

The invention discloses a role playing dimension implementation method and system, and relates to the technical field of data processing. Comprising the steps that a meta-model of a dimension model related to a role playing dimension is created, the meta-model comprises five types of data tables, a role playing dimension name is selected from a role playing dimension name source according to requirements, when the role playing dimension name is different from a physical dimension table name, it is considered that the dimension corresponding to the role playing dimension name is the role playing dimension, and the role playing dimension is selected from a physical dimension table name; when the related dimension of one index is displayed, the role playing dimension name is used for replacing the physical dimension name to display the actual business meaning of the dimension, when the dimension is selected to summarize the indexes, the role playing dimension name is used for constructing a dynamic SQL, and when the composite index is analyzed, the actual business meaning of the dimension is displayed. When multiple indexes are analyzed in parallel, if the facts where the polyatomic indexes forming the composite indexes are located are all associated with the same physical dimension, the composite indexes are summarized through the physical dimension, and when the multiple indexes are analyzed in parallel, if the facts where all the indexes participating in analysis are located are all associated with the same physical dimension, the multiple indexes are summarized through the physical dimension; and the associated dimension list is displayed by using the physical dimension name, so that confusion caused by different role names is avoided.