531 results about "Datawarehouses" patented technology

The invention relates to a building module data interface intelligent monitoring system based on the Internet of Things and a deployment method, and belongs to the technical field of building information monitoring. The system is composed of a distributed sensing unit, an edge computing gateway, a cloud platform and a visual terminal, a self-adaptive filtering algorithm and an abnormal mode recognition model are built in the edge computing gateway, the cloud platform adopts a time sequence database to construct a multi-dimensional data warehouse, and structural health degree evaluation is carried out by fusing an LSTM neural network and a random forest algorithm. The deployment method comprises the steps of optimizing a sensor distribution strategy based on a BIM model, establishing a wireless Mesh ad hoc network communication architecture, and configuring a grading early warning mechanism and a fault tracing function. The innovation point is that a dynamic threshold adjustment algorithm and an interface performance degradation prediction model are provided, and real-time monitoring of the connection state of the building module and life prediction are realized. The system has the advantages of flexible deployment, high detection precision and low maintenance cost, and the intelligent level of building structure safety monitoring is effectively improved.

The invention relates to the technical field of smart city traffic, and discloses a smart city traffic dynamic optimization system and method based on digital twinning. The system obtains urban traffic network multi-dimensional data from a plurality of heterogeneous data sources through a traffic multi-dimensional data acquisition module and integrates the urban traffic network multi-dimensional data into a traffic related data warehouse; a traffic digital twinning model construction module extracts features from the data warehouse to generate a traffic related feature matrix, and a digital twinning traffic dynamic model is constructed according to the traffic related feature matrix to output a theoretical traffic state value; the traffic flow map construction module determines a dimension link map of each dimension and constructs a traffic flow link map; the traffic core feature screening module screens a traffic core feature sequence based on the map; the traffic multi-dimensional optimization analysis module performs multi-dimensional difference analysis on the theoretical traffic state value and real-time actually measured traffic data, and generates a region-level difference coefficient matrix in combination with the core feature sequence; and the traffic event association positioning module can realize accurate management and dynamic optimization of urban traffic.

The invention relates to the technical field of transformer monitoring, and discloses a comprehensive online transformer monitoring system which comprises a sensor layer, an edge computing layer, a cloud platform layer and a communication module. The cloud platform layer comprises a data warehouse, a big data processing engine, a comprehensive diagnosis engine, a model training engine and a system management module, the edge calculation layer is in communication connection with the cloud platform layer through a communication module, the comprehensive diagnosis engine integrates a deep neural network and a knowledge graph inference engine, and cross validation of data driving and knowledge guiding is achieved; a model training engine utilizes historical data and online incremental data to continuously optimize a model, and a dynamic knowledge graph updates a fault rule through a real-time diagnosis result and expert feedback, so that the system can adapt to novel faults and complex working conditions, and the diagnosis accuracy and adaptability are greatly improved.

The invention discloses a supply chain collaborative management method and system for a whole-industry link platform, and the method comprises the steps: constructing a distributed supply chain data warehouse through employing a block chain and lightweight encryption; generating a multi-dimensional matching degree score table to recommend suppliers; decrypting and storing the encrypted bidding file, and calculating a comprehensive score according to a quantitative index and an expert qualitative index; constructing a hierarchical prediction model, and outputting a three-dimensional prediction result; calculating a supply-demand gap according to a supply-demand elastic coefficient coupling algorithm, automatically generating an order, and synchronizing the order to each end in real time; building a supply chain digital twinborn model, and dynamically planning an optimal transportation route through an intelligent fusion road condition algorithm; early warning information is automatically sent to generate a coping strategy, and full-link operation data are integrated for visual display; the objective of the invention is to solve the problems of existing supply chain data splitting, low supplier matching efficiency, low demand prediction precision, rigid logistics planning, risk monitoring lagging and poor collaboration of all links.

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.

According to an embodiment of the present invention, a method for using information in conjunction with a data repository includes encrypting data associated with the information with an encryption key, sending at least the encrypted data to the data repository, and possibly deleting the information. The method also includes receiving a request for the information from a remote device, and sending a request for the encrypted data to the data repository. The method further includes receiving the encrypted data from the data repository, decrypting the encrypted data using the encryption key, and sending the information to the remote device.

A generative artificial intelligence (AI) system includes a generative AI model configured to generate outputs based on a training data set. The AI system additionally includes a secure data vault system. The secure data vault system additionally includes a sandbox system storing the generative AI model and operatively coupled to the generative AI model to send inputs to generate the outputs from the generative AI model, wherein the sandbox system comprises an execution environment configured to restrict execution of the generative AI model to a predefined memory address range. The secure data vault system further includes a secure network service communicatively coupled to the sandbox system and configured to authenticate a connection to an external system and to download from the external system an update package for the generative AI model when the connection is authenticated.

Mechanisms are provided for dynamically generating workspaces and provisioning them with datasets. The mechanisms store datasets in a data vault for provisioning to dynamically generated workspaces associated with users. The dynamically generated workspaces are computer environments through which the users can perform operations on the one or more datasets. The mechanisms receive a request, from a user, for access to a specified dataset, and retrieve a data usage agreement (DUA) corresponding to a pairing of the user with the specified dataset. The DUA specifies a level of obfuscation to be applied to the specified dataset when provisioning a workspace associated with the user, with the specified dataset. The mechanisms dynamically generate, on-demand, the workspace associated with the user based on the retrieved DUA. The mechanisms also automatically provision, on-demand, the dynamically generated workspace with a version of the specified dataset corresponding to the level of obfuscation specified in the DUA.

A server computer is programmed to: generate a first plurality of encrypted records partitioned into groups, each including encrypted records whose corresponding input records share a prefix; receive, from a client device, a second plurality of encrypted records, each accompanied by a prefix; encrypt the second plurality of encrypted records to create a second plurality of doubly encrypted records, query, using the prefix, a data warehouse to fetch a group of encrypted records; generate a data structure encoded to indicate a presence of each encrypted record of the group fetched from the data warehouse; and transmit the second plurality of doubly encrypted records and the data structure to the second device, the second device using the second plurality of doubly encrypted records and the data structure to determine whether at least one of the second plurality of encrypted records is included in one group of encrypted records.

The invention relates to the technical field of information processing and artificial intelligence, and discloses a multi-source fusion maintenance work order association early warning method and system. The method is used for solving the problem that the accuracy and timeliness of early warning are insufficient due to comprehensive modeling of multi-modal data in a traditional method. The method comprises the steps that firstly, a work order, a log event, a monitoring index and asset information are accessed, and after time caliber and field caliber unification and quality labeling are completed, the work order, the log event, the monitoring index and the asset information are written into a real-time data lake and a batch processing data warehouse respectively; normalizing the work order text, extracting a key identifier, and generating a feature index bound with the work order number; aligning the log and the index data according to a work order time window to form an event fragment and an index fragment, and associating an asset node; and constructing a dependency graph based on an asset ledger and a deployment link, forming a work order candidate pair, carrying out classification and hierarchical pushing, and recording feedback and version information for tracing.

The invention relates to the field of traffic hub intelligent agents, is used for solving the problems of insufficient management and control advance and insufficient management and control effect caused by dependence on passive response management and control and lack of active management and control during traffic hub management and control, and particularly relates to a traffic hub state monitoring system based on multi-source data fusion analysis. According to the method, three-dimensional fusion is carried out by combining a knowledge graph and multi-source data, the relevance between different data is judged according to a fusion result, a related data warehouse is created as a prediction rule, personnel and traffic flow of a traffic center are subjected to regional analysis, occurrence and occurrence point locations of abnormal events are automatically judged through intelligent analysis, and the prediction accuracy is improved. Active early warning is carried out, managers are assisted in finding abnormal events in time, when the managers carry out dredging decision making on the abnormal events, effect prediction analysis can be carried out in combination with the created data warehouse, the effect of the dredging decision making is dynamically evaluated according to the effect prediction analysis, and the decision making efficiency is improved based on active feedback. Management personnel are assisted to better manage the traffic center.

The invention provides a dynamic optimization and visual decision-making system for port and navigation multimodal transport knowledge graph collaboration, and solves the problems of high transport mode conversion cost, difficult information sharing and the like. Aiming at the defects that a traditional static data warehouse (such as Hadoop) is insufficient in real-time performance, a general data fusion framework (such as Apache NiFi) is poor in field adaptability and the like, the system innovatively constructs a multi-source heterogeneous database, integrates port production, economic association, infrastructure and freight data, and realizes accurate path planning and personalized logistics scheme recommendation through an intelligent algorithm. The system adopts a React + ECharts + ShadcnUI technology stack to realize knowledge graph visualization, practice shows that the operation efficiency can be remarkably improved, the transportation cost is reduced, and technical support is provided for sustainable development of ports.

The invention provides an encryption access control method and platform for industrial security data, and relates to the technical field of data encryption access, and the method comprises the steps: carrying out the attribute classification and code identification of an industrial system data set; performing security level division on the industrial system data set according to the data security quantitative index system; performing matching analysis on the multi-level industrial security data set based on the data encryption algorithm list; encrypting and storing the industrial system data set based on the industrial data identification code set by adopting a multi-stage data encryption algorithm to generate an industrial encrypted data warehouse; and establishing a fine-grained access mechanism and a decryption interaction mechanism according to the user role library, and performing access authority distribution and encryption access control on the industrial encrypted data warehouse based on the fine-grained access mechanism and the decryption interaction mechanism. According to the invention, the technical problem that the comprehensive requirements of high safety, high precision and high flexibility in a modern industrial system are difficult to meet in the prior art can be solved.

The invention discloses a composite stress test method and device for a solid state disk, equipment and a medium, and the method comprises the steps: loading a test configuration parameter, and building communication connection with a to-be-tested solid state disk which is preset in a preset test environment; performing performance baseline calibration and preprocessing on the to-be-tested solid state disk; executing a composite stress performance test, and dynamically coupling at least two of temperature stress, capacity occupation stress, power interruption stress, garbage collection induced stress and mixed load stress; synchronously acquiring test data and constructing a time sequence data warehouse; and performing intelligent deep analysis based on the time sequence data warehouse, wherein the intelligent deep analysis comprises performance consistency deep analysis and health degree and risk prediction analysis. According to the method, a real complex environment is simulated through the composite stress test, the problem that a traditional test method is single in scene is solved, meanwhile, the problem that data analysis is shallow is solved through intelligent deep analysis, and the reliability of the solid state disk can be comprehensively and accurately evaluated.

The invention relates to the technical field of artificial intelligence and engineering project management, provides a full-cycle engineering project management system and method, and aims to solve the problems of data fragmentation, decision-making dependence on manpower and low execution collaboration efficiency in traditional engineering project management. The system comprises a data layer, a processing layer and an application layer. The data layer integrates a historical change data warehouse, dynamic market data, a supply chain collaborative database and BIM model full life cycle management, and multi-source heterogeneous data association storage is achieved. The processing layer extracts changed text key information through an NLP semantic analysis module, judges the priority, outputs a quantitative decision basis in combination with an intelligent decision support module (risk prediction, multi-source data fusion and BIM simulation), and realizes task optimal allocation and progress monitoring through an automatic execution module; and the application layer provides an interactive interface to support full-process visual collaboration. The method comprises the four stages of change request submission, NLP semantic analysis, approval decision making and automatic execution, and closed-loop management is formed.

The invention relates to a multi-hospital-area manpower dynamic allocation decision support system based on a hierarchical response mechanism, which integrates hospital information system, electronic medical record and office automation data in real time through a multi-source data fusion module, and constructs a distributed data warehouse through standardized cleaning. During operation of the system, the data acquisition layer captures abnormal events of the courtyard area in real time, and drives the prediction engine to output early warning after cleaning and verification. And when the response threshold value is reached, the decision-making unit automatically matches the optimal scheduling scheme and synchronizes the task instruction through the mobile terminal. Full-chain tracking is implemented in the execution process, and the resource state is dynamically updated. And afterwards, multi-index scoring is carried out based on dimensions such as human efficiency and business load, strategy library iteration and model weight adjustment are driven, and a'monitoring-prediction-decision-execution-evaluation 'closed loop is formed. According to the invention, intelligent collaboration and elastic scheduling of human resources in multiple courtyards are realized, and the emergency scene response efficiency and the medical resource utilization rate are remarkably improved.

The invention discloses an electric power skill training and scoring evaluation method based on a large model algorithm, and belongs to the technical field of electric power skill training and scoring evaluation, and the method comprises the steps: collecting and processing student information data and historical operation behavior data, and integrating the processed data into a unified data warehouse for storage; analyzing the information data and the power skill post demand of each student by using a natural language processing technology to generate a demand analysis result; generating a training content scheme according to the demand analysis result based on a pre-selected large model; by analyzing learning behavior data, constructing a student learning style model, and further optimizing a training content scheme to obtain an optimal training scheme; a three-dimensional electric power working scene model is constructed by using computer graphics and VR / AR technologies; through establishing an operation evaluation model, real-time scoring evaluation is carried out on electric power skill operation of students, and an evaluation result of each student is obtained.

The invention relates to the technical field of data warehouses, and particularly provides a statistical aperture generation method and device, a storage medium and computer equipment. The large aperture generation model can automatically understand the complex data structure of the data warehouse through the physical table building statement of the data warehouse. When the initial statistical caliber is obtained, mutual exclusion verification, grammar compliance verification and data source matching verification can be performed on the initial statistical caliber so as to verify whether the statistical caliber generated by the large model has the problems of logic conflict, grammar non-compliance or data source inconsistency and the like which influence the reliability of the statistical result; and judging whether the statistical caliber generated by the large model can be directly put into use or not. And if yes, setting a final statistical caliber corresponding to the target data index according to the initial statistical caliber. Therefore, by introducing the large model technology, the technical threshold is greatly reduced, the generation efficiency of the statistical caliber is improved, and the reliability of data analysis can also be ensured.

The invention discloses a dialogue type business intelligent analysis method based on a natural language, and relates to the technical field of business management systems, and the method mainly comprises the steps: recognizing the intention type of a user intention contained in a natural language request through a general large language model; according to the identified specific intention type, calling a corresponding special analysis module to perform semantic analysis and parameter extraction on the natural language request so as to generate an operable analysis instruction; calling a business intelligent system data interface to execute the analysis instruction, and obtaining corresponding structured data from a business intelligent system data warehouse; and generating a comprehensive analysis report which is matched with the identified intention type and the corresponding structured data and comprises visual elements and analysis explanations based on the identified intention type and the corresponding structured data. According to the method, the intention type of the natural language request of the user is understood through the large language model, so that the user with a non-professional background can easily perform complex data exploration, and the use threshold is reduced.

The embodiment of the invention discloses a product pushing method, electronic equipment, a storage medium and a program product, and relates to the technical field of financial science and technology, and the method comprises the steps: obtaining behavior data of a user in a target application before the current moment from a distributed publishing and subscribing system through a distributed data flow engine, obtaining real-time behavior data, and transmitting the real-time behavior data to a server; obtaining offline behavior data of the user from the data warehouse to obtain historical behavior data; fusing the real-time behavior data and the historical behavior data to obtain user behavior data; determining a pushing rule satisfied by the user behavior data from a preset pushing rule base to obtain a plurality of candidate rules; obtaining a static feature and a dynamic feature of each candidate rule, and determining a priority score of the corresponding candidate rule based on the static feature and the dynamic feature; and the optimal rule is determined based on the priority score of each candidate rule, and the target product corresponding to the optimal rule is pushed to the user terminal of the user, so that the timeliness and accuracy of product pushing are improved.

The invention discloses a data warehouse table structure generation method and device, a medium and a product, and belongs to the technical field of data warehouses. According to the method, the Excel template configured with multiple pieces of source database table structure information is obtained, table field definition is automatically analyzed, the source data type is intelligently mapped and converted into the target data type according to the source database type, meanwhile, partition key fields are automatically recognized based on the preset strategy, and finally a complete and accurate data definition language (DDL) statement is generated. According to the method, remarkable technical breakthrough and value are achieved in practical application, the table building efficiency can be improved by 90% or above, batch processing of hundreds of tables is supported, and manual errors are reduced by 95% or above; mainstream database type conversion is comprehensively supported, and various data models such as Dores are adapted; the method has the capabilities of automatic type detection and conversion, intelligent key field selection and partitioning and bucket dividing strategy optimization.

The invention relates to the technical field of freight station scheduling, and particularly discloses an aviation freight station TV vehicle dynamic scheduling method and system based on multi-dimensional data, and the method comprises the steps: obtaining a multi-dimensional data source in real time, building a unified real-time data warehouse through the processing of a platform in the Internet of Things, and obtaining the load of a beating platform through a machine learning prediction model based on fusion data. A task sequence is generated in combination with a sorting plan, a dynamic scheduling algorithm is adopted to dispatch tasks, plan an optimal path, distribute instructions in real time and monitor the state of the TV vehicle, deviation is calculated based on feedback data, and parameters are dynamically adjusted to form a closed loop. According to the method, a unified real-time data warehouse is constructed, data islands of all systems are broken, seamless real-time sharing of data is achieved, accurate data support is provided for subsequent scheduling, task allocation and paths are optimized through multiple targets, the total empty driving distance and task delay of the TV vehicles are reduced, the resource utilization rate is increased, the TV vehicle scheduling efficiency is finally improved, and the scheduling efficiency of the TV vehicles is improved. And key indexes such as the empty driving rate and the punctuality rate are optimized.

Systems and methods are disclosed for converting natural language queries to a query instruction set for searching a data warehouse. To generate a query instruction set from a natural language query, a system iteratively uses a generative artificial intelligence (AI) model and database query tools to generate a query instruction set in a stepwise manner. The system and generative AI model do not require a priori knowledge of data table contents in the data warehouse, which may include sensitive information. In addition, the system does not require access to the data warehouse to generate the query instruction set. Instead, the system is implemented to use structure information from the data warehouse, including table lists (such as table names) and table format information (such as column names) of tables in the data warehouse, and the generative AI model is a generally trained model to generate the query instruction set.

The invention provides a data analysis method. The data analysis method comprises the steps of obtaining query information for data; according to the method, the query information is analyzed, the query requirement of the user is determined, the pre-constructed data warehouse is searched based on the query requirement, the target query content is determined, and the pre-constructed data warehouse is associated with databases storing different types, so that cross-database query can be performed based on the query requirement of the user, and the query efficiency is improved. Therefore, various types of data query of texts, images, videos and the like can be realized, and multi-dimensional and complex-dimensional data analysis can be realized. According to the query information, the generation template of the data is determined, and the analysis report is generated according to the generation template based on the target query content, so that automatic report generation is realized, the generated report conforms to the identity of the user, and the working efficiency of the user is improved.

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.

The application belongs to the technical field of real-time data collection, and discloses a method, system and medium for detecting data collection abnormity in a real-time data transmission process. Business data is obtained by analyzing database operation logs. The business data is stored in a distributed publish-subscribe message system to obtain a real-time data warehouse. A detection time interval is set, and offset information of the real-time data warehouse is obtained according to the time interval. Offset information obtained at a current time node is compared with offset information obtained at a previous time node to obtain a comparison result. If the comparison result is that the offset information has changed, the database operation logs are continuously analyzed to obtain the cause of data collection abnormity. Otherwise, it is determined that data collection is normal, thereby realizing detection of data collection abnormity in the real-time data transmission process. The application can also call a preset intelligent script to repair a data collection program when the collection program is dead.

The invention discloses a WeChat applet JS-SDK code defect detection method, system and device and a medium, and the method specifically comprises the steps: based on an instrumentation framework, carrying out API calling through a wx proxy object of a WeChat applet, recording an API calling path, a parameter combination and an execution state, and generating API-level fine-grained coverage rate data; the API-level fine-grained coverage rate data, API calling tracking data and code version library change records are stored in an associated mode, and a time-space associated data warehouse is constructed; based on the data warehouse, modeling the historical coverage rate sequence by using an LSTM neural network, predicting the risk level of uncovered codes, and generating targeted test case suggestions; based on the data warehouse, uncovered codes and recent code changes are matched through an Szz algorithm, potential defect positions are positioned, and defect confidence is marked. The WeChat applet JS-SDK code quality guarantee method provides comprehensive and accurate support for WeChat applet JS-SDK code quality guarantee, and the development efficiency and the code reliability are improved.

The invention relates to the technical field of charging pile operation and maintenance, and discloses a charging pile intelligent early warning operation and maintenance method and system based on big data. The method comprises the following steps: acquiring multi-dimensional operation data of the charging pile, and integrating to generate an operation data warehouse; extracting features from the warehouse to obtain a feature parameter set, and building a trend change feature matrix according to the time change trend of the parameter set; and constructing a multi-dimensional state space according to the matrix, and calculating the state aggregation degree of historical fault events in the space to determine a feature early warning index set. Acquiring real-time operation data, extracting parameters, establishing a real-time feature state vector, and calculating the spatial position correlation between the real-time feature state vector and the early warning index set to obtain a real-time risk correlation factor; and generating a risk probability prediction model in combination with the association factor and the trend matrix, predicting a fault probability, and judging whether to early warn and generate a maintenance scheduling suggestion according to the fault probability. According to the method, real-time monitoring, accurate early warning and efficient operation and maintenance of the charging pile are realized through big data, and reliable operation of equipment and charging experience of a user are guaranteed.

The invention discloses a bucket dividing regulation and control method and device for a financial data warehouse and electronic equipment, and relates to the technical field of artificial intelligence or other related fields, and the method comprises the steps: collecting a time sequence performance set of the financial data warehouse through a distributed collection system; multi-level feature extraction is carried out on the time sequence performance set by adopting a sliding window mechanism to obtain a time sequence feature set, and the categories of time sequence features comprise instantaneous fluctuation features, periodic trend features and steady state baseline features; performing association analysis on the time sequence feature set based on a decision tree integration algorithm by taking an expected query performance requirement as a target to obtain a bucket-dividing adjustment strategy; and bucket index reconstruction is carried out on the financial data warehouse based on the bucket division adjustment strategy, and the financial data warehouse after index updating is obtained. Through the method and the device, the technical problem that the system performance is degraded due to the fact that a static bucket dividing mechanism of a real-time data warehouse cannot dynamically adapt to space-time imbalance of data loads in a financial service scene in the related technology is solved.

Methods and systems for minimizing disruption when changes to a data graph are detected are disclosed. A data graph is continuously monitored for one or more changes to entities or relationships within a data warehouse. Based on a detection of the one or more changes, each of the one or more changes is categorized as either breaking or non-breaking based on one or more criteria pertaining to stability or data integrity. One or more modifications to the data graph or the data warehouse are executed to accommodate the one or more identified changes, wherein the one or more modifications are executed using an algorithm optimized to minimize disruption or enhance data processing efficiency.