17119results about "Database updating" patented technology

A method to be executed on a computing device comprising (i) accessing and / or modifying a database to be automatically curated, (ii) optionally accessing additional data or information sources for further useful data or information, (iii) using one or more pre-trained large language models (LLMs) accessed via API or other connections, issuing prompts and retrieving prompt-answers and executing database curation requests that specify database curation tasks to be performed on at least one sub-structure of the database, the tasks comprising (a) a database enrichment task to compute new data records to be inserted into the database sub-structure, (b) a database verification task to verify, using the one or more LLMs, data contained in the sub-structure, and identify incorrect data, (c) a database update, and (d) a null-value or a missing value replacement task. The requested tasks are automatically performed via a computation comprising an adaptively generated prompt sequence.

Computer-implemented systems and methods for tokenization. The system includes a network, at least one token database coupled to the network, and a batch tokenization server coupled to the at least one token database via the network, the server having a token table. The server is configured to receive at least one batch request comprising a delta table. In response to the batch request, the server retrieves a plurality of recent tokens from the at least one token database and updates the token table with the plurality of recent tokens. The server determines whether at least one payload to be tokenized from the delta table has a respective at least one corresponding entry in the token table. For each of the at least one corresponding entry that is not found in the token table, the server generates a new token and updates the at least one token database.

Computer-implemented systems and methods for synchronizing data for dataset execution. The system includes a source database that stores a canonical dataset, a secondary database that stores a processed dataset, and a synchronization server that comprises a processor and a memory. The processor is configured to monitor for a publication of one or more source tables and when the publication is detected, identify the processed tables, corresponding to the source tables, to be updated in the processed dataset. The processor determines a tolerance level corresponding to each processed table and updates the processed tables in the processed dataset. The processor 10 determines whether the processed tables in the processed dataset were successfully updated within the tolerance levels and transmits a notification based on determining whether the processed tables in the processed dataset were successfully updated within the tolerance levels.

The invention provides a block chain data management method and system, and relates to the technical field of block chains, and the method comprises the steps: receiving and preprocessing an electronic file; encrypting the archive by using a public key and calculating a hash value; packaging the encrypted file and the hash value into a block chain transaction and broadcasting the block chain transaction; acquiring block information and safely storing the private key; verifying the integrity of the electronic file; recording a full life cycle operation log; the log is stored in an on-chain and off-chain combined mode; auditing the electronic file; and realizing access authority automatic management based on the intelligent contract. According to the method, the security, traceability and management efficiency of the electronic archives are improved.

Systems and methods for generating contextualized data visualizations. A first graphical user interface (GUI) object based on first data is provided, along with a second GUI object based on the second data. A user may select a scenario to be analyzed and, in response to the single selection of the scenario, the system models a first impact of the scenario on the first data to generate predicted first data and separately models a second impact of the scenario on the second data to generate predicted second data. The predicted data may be generated by an artificial intelligence system including a prediction processor. The first and second GUI objects are updated using the predicted first and second data, respectively.

The invention provides a query method and system for generating an SQL statement based on a natural language, and relates to the technical field of programming, and the method comprises the steps: receiving a user query request, obtaining data source configuration information, and obtaining a multi-stage locking identifier through a distributed lock management module to control concurrency; extracting a query mode from the query mode knowledge base to generate a data template based on the data source configuration information and a user historical query record; selecting an optimal query path through query path evaluation, and calling the large model server to analyze the data template to generate an SQL statement; predicting resource occupation and setting a monitoring threshold value according to the SQL statement structure, monitoring a query state in real time in an execution process, and triggering a multi-stage fault-tolerant mechanism to process exceptions; and finally, releasing the locking identifier, returning a query result, and asynchronously updating the query mode knowledge base.

A block chain double-chain cold chain traceability management system for meat quality fluctuation belongs to the technical field of traceability management systems and comprises a data acquisition and preprocessing module, a composite index module, a security verification module, a federated learning and AI resource management module, a fragmentation and verification architecture module and an automatic error correction module. According to the method, data chain storage environment data hash and transaction chain record operation logs are innovatively separated through a block chain double-chain architecture, hash fingerprints are synchronized in combination with an anchoring technology, cross-chain consistency is ensured, long-term non-tampering of data is realized through an anti-quantum hash algorithm, resource allocation is optimized through hierarchical storage design, and the method is suitable for large-scale popularization and application. The composite index technology is fused with hypergraph dynamic index to trace production batches, vehicles, warehouses and space-time quadtree dynamic subdivision areas, complex query response time is remarkably compressed, path deviation real-time alarm is achieved, the edge node cache hit rate is increased, and full-link data safety and query efficiency are remarkably improved.

The invention provides a time sequence knowledge graph federation collaborative optimization method, system and device based on causal inference and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an enhanced knowledge unit with a causal mark through the real-time access of a multi-field heterogeneous data stream and the execution of a space-time alignment operation; by calculating new and old knowledge conflict scores, conflict resolution and version management are realized through a decision tree mechanism, and a time sequence knowledge graph with history tracing is output; node weights are dynamically distributed among distributed nodes based on knowledge entropy, a hierarchical aggregation strategy is adopted to update an entity embedding layer and a relation prediction layer, and a global optimization model is output; the method comprises the following steps: analyzing a natural language query containing an anti-fact condition, extracting a factor sub-graph from a time sequence knowledge graph, executing intervention calculation, and generating an anti-fact influence report, thereby solving the technical problems of a traditional time sequence knowledge graph in the aspects of multi-source heterogeneous data fusion, knowledge conflict resolution and privacy protection; and the accuracy and the interpretability of the knowledge graph are improved.

The present invention relates to a digital twin processing method and system, and a cloud platform. The method comprises: acquiring production system elements, carrying out abstraction definition and parameterization description on the production system elements by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, so as to construct a digital twin ontology model; analyzing and reconstructing model data to obtain a mapping model of which object variables can be directly accessed and operated by a collective motion control method, so that the model is visualized at the cloud; and using an external data source to drive parameter update and operation matching of the model by means of a motion control method, so as to complete cooperative deployment and synchronous evolution of an actual physical device and the model in the production process on a cloud server. According to the present invention, a model is constructed by means of digital-to-analog knowledge fusion of a mathematical model and a mechanistic model, the model is mapped to achieve motion visualization, model parameter update and operation matching on the cloud are achieved, and then cooperative deployment and synchronous evolution of a physical device and the model are completed.

The invention relates to the technical field of artificial intelligence and enterprise decision making, and discloses an intelligent decision making system and method based on an enterprise life index large model, and the system comprises a multi-source heterogeneous data collection module which is used for obtaining and associating various data in real time, and generating target enterprise multi-source heterogeneous data; the dynamic index system generation module is used for sequencing feature importance through a graph neural network and generating a dynamic evaluation index set containing survival, risk and growth indexes; the enterprise life index large model building module is used for fusing index sets, capturing enterprise state evolution, embedding risk factors and outputting multi-dimensional enterprise life indexes; the intelligent decision dynamic generation module is used for inputting the indexes into the decision strategy tree model and matching a strategy library to generate a decision instruction set; and the closed-loop feedback optimization module is used for realizing closed-loop optimization by acquiring execution data and combining Bayesian optimization dynamic correction parameters. According to the invention, the accuracy and timeliness of enterprise intelligent decision making can be improved.

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

The present invention relates to the technical field of data metering management for power distribution networks, and in particular, to a method for constructing a multi-source heterogeneous data knowledge graph of a power distribution network. The method comprises: model layer construction: summarizing and analyzing a power transmission network knowledge content system framework, analyzing multi-source heterogeneous data of a power distribution network, defining an entity type, an attribute type, and an entity relationship, and constructing an organizational framework of knowledge of the power distribution network; data layer construction: multi-source heterogeneous data access and fusion, data preprocessing, knowledge extraction, knowledge integration, and knowledge storage and updating; and metering management application: real-time monitoring and early-warning, abnormal data troubleshooting, data quality management, and statistical analysis and decision support. According to the design of the present invention, data fusion and application integration can be performed with various service systems. The multi-source heterogeneous data of the power distribution network can be effectively integrated, the organization and management of the knowledge of the power distribution network are implemented, and the sustainable development of a power grid is promoted. By means of interactive displaying of a knowledge graph and use of a data quality management platform, abnormal metering data is automatically troubleshot.

The invention discloses a multi-modal bill processing method based on dynamic knowledge enhancement. The multi-modal bill processing method comprises the following steps: S1, constructing a dynamic knowledge base containing an aging weight; s2, synchronously processing text, image and format features of the bill by adopting a multi-modal feature fusion network to generate a composite feature vector; s3, semantic-level, format-level and timeliness three-level fusion retrieval is carried out based on the composite feature vector, and a three-level fusion retrieval engine comprises dynamic weighted sorting with timeliness attenuation, a difference degree triggered artificial review mechanism and a policy sensitive slope adjustment algorithm; s4, setting a multi-expert cooperative verification system, wherein the multi-expert cooperative verification system comprises cooperative work of a rule engine, a large language model and a logical reasoning module; s5, implementing a dynamic knowledge updating mechanism, and automatically triggering incremental learning of the knowledge base when policy change or format update is detected; and S6, outputting structured data, and synchronously generating an auditing traceability chain containing a decision path. According to the method, the key field identification accuracy can be improved, and auditing traceability and non-perceptual increment updating in the whole process are realized.

The invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

A digital content management system may include a set of one or more memory components and a set of one or more processing devices coupled to the set of one or more memory components. The set of one or more processing devices to perform operations comprising: obtaining one or more electronic signatures on a digital document to yield an electronically signed digital document, storing the electronically signed digital document at an off-chain address, using a trained machine learning model to create a smart contract, the smart contract comprising a reference to the off-chain address; and deploying the smart contract to a blockchain.

Techniques are described for partitioning B-tree keys of file systems into key ranges for parallel processing in delta generation during file storage replications between file systems in different cloud infrastructure regions. In certain embodiments, a delta generation processing for cross-region replication may utilize a key-range splitting mechanism involving a recursive algorithm that partitions B-tree keys of a source file system into roughly equal-size key ranges. All the partitioned key ranges may be processed in parallel and concurrently by different processing threads, one thread per key range, to improve the performance of the delta generation and achieve scalability.

The present application discloses a data processing method, device and system, and a storage medium. The method of the present application comprises: constructing a quality problem dictionary on the basis of an experience database, the quality problem dictionary comprising data quality problems and corresponding problem identifiers; acquiring data to be processed, and identifying a target data problem present in the data to be processed; querying the quality problem dictionary for the target data problem to obtain a corresponding target problem identifier; adding the target question identifier into target data by means of a predefined field; reading a plurality of target fields in the target data, determining the plurality of target fields according to a preset fusion rule, and on the basis of a determination result, determining whether there is conflict data; if there is conflict data, identifying a service application scenario of the current data, and selecting an authority source determination strategy on the basis of an identification result; and processing the credible source authority level of each field in the conflict data according to the authority source determination strategy, and updating the database.

A method for electronic processing of user queries maintaining factual consistency during processing includes receiving a user query, executing a hybrid retrieval operation, generating a context information space that includes a set of paragraph output from the paragraph retrieval operation, a set of triplet output from the triplet retrieval operation, and a set of graphs output from the graph retrieval operation. Furthermore, generating a first prompt instruction to generate an intermediate rationale as explanation output and performing an automatic fact-checking operation to verify the explanation output and re-feeding the user query along with the generated explanation output to the generative language model to derive an answer output that is succinct in one or more language parameters as compared to the generated explanation output. The answer output includes a verified and evidence-supported answer and at least one fact reference from first set of factual information with reduced hallucinations and factual inconsistencies.

The invention discloses a distributed storage method and system for data security, and belongs to the technical field of data storage security, the method comprises the following steps: firstly, fragmenting original data, and generating a unique identifier for each data fragment; for each fragment, an encryption algorithm and key credentials are adaptively selected, and the key credentials are stored in a key management system. By performing homomorphic encryption on the key voucher, an encryption handle is generated, and efficient positioning and retrieval of the key are realized. According to the method, a differentiated encryption strategy is adopted, a most suitable encryption scheme is selected according to fragment characteristics, and encrypted data fragments are dispersed and stored in a plurality of distributed storage nodes. A global mapping index is constructed in a metadata management server, and a corresponding relation between an original data global identifier and metadata of each data fragment is recorded, including information such as identifiers before and after encryption, a storage position, an encryption algorithm, a key handle and the like. According to the method, the data leakage risk is effectively reduced, and the safety and reliability of the storage system are improved.

The invention relates to the technical field of task allocation, in particular to a task allocation method for flexible employment. According to the technical scheme, the method comprises the following steps that a database of related information of tasks and employees is constructed, wherein the database comprises detailed information of the tasks and comprehensive information of the employees; respectively constructing a task model and an employee portrait based on the information in the database; and matching the task with the employee through an intelligent matching algorithm, wherein the intelligent matching algorithm comprises matching algorithm selection and optimization, real-time matching and dynamic adjustment, and multi-objective optimization and tradeoff. According to the method, multiple matching algorithms are combined for optimization, weights are selected and adjusted according to tasks and employee characteristics, accurate matching is achieved, a task model and employee portrait multi-dimension consideration adaptability is achieved, adaptive changes are matched in real time, tasks and multiple aspects of employees are optimized and balanced in a multi-objective mode, the development of rights and interests of the employees is considered, and the matching quality, the working enthusiasm and the stability are improved; flexible employment resources are managed more effectively, and the task allocation effect is improved.

The invention relates to the technical field of information retrieval, and discloses a distributed storage and indexing method, which comprises the following steps of: dynamically fragmenting an original file through an improved consistent Hash algorithm to generate a plurality of data blocks with timestamps; constructing a three-dimensional Bloom filter index matrix containing timestamps, data types and content features for the data blocks with the timestamps, associating a B + tree local index with an inverted global index through a hierarchical index structure, and establishing an index update priority queue by adopting a two-channel synchronization mechanism, performing real-time increment synchronization on the hotspot index through a heartbeat mechanism, performing batch synchronization on the cold data layer index according to a cold data synchronization period, predicting a data distribution probability through a distributed query statistics probability table during query, and initiating multi-path query in parallel based on probability weight, and the query path is dynamically optimized according to the node group storage medium type and the inter-node network transmission delay, so that rapid distribution adjustment and access of distributed storage are realized.

The invention provides an intelligent station multi-source heterogeneous data acquisition and remote collaborative analysis method and system, and relates to the technical field of heterogeneous data, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out the standardization processing, constructing a station real-time adaptive three-dimensional model through a self-calibration laser scanning technology, and carrying out the semantic mapping of the model and a BIM model; constructing a self-evolution feature library based on a digital twinborn model, and carrying out space-time correlation analysis by adopting a graph convolutional neural network; a remote cooperation platform is established to realize real-time cooperation of multiple experts, and a diagnosis process is recorded through a block chain technology. According to the invention, efficient acquisition, analysis and remote collaborative diagnosis of the data of the smart station are realized, and the accuracy and efficiency of fault diagnosis are improved.

The invention relates to a work order material intelligent tracking and dynamic updating system based on multi-source data fusion. The system comprises an acquisition module, a tracking module, an updating module, a man-machine interaction module and a storage module, the acquisition module is used for acquiring work order information from a bill of material, scheduling information and inventory and supplier data, and constructing a material data model; the tracking module monitors material inventory, consumption and material shortage risks based on the model, and realizes automatic state updating and graphical path recording in combination with bar code recognition and ambiguity analysis; the updating module predicts material requirements through a machine learning algorithm, optimizes a distribution plan and outputs a dynamic report; the interaction module supports multi-format data import, unstructured field classification and interrupted operation control; the storage module ensures credible synchronization and tamper-proofing performance of data among different systems through a block chain technology; according to the invention, high-efficiency management and dynamic allocation of work order materials can be realized in an environment without an MES system, and the production transparency and the resource utilization efficiency are improved.

The invention relates to the technical field of smart communities, and discloses a smart community resident health management method based on machine learning, and the method comprises the steps: continuously collecting the health feedback data of residents, such as the physical condition change after outdoor activities, carrying out the correlation analysis of the health feedback data with the previous health data and environment data, dynamically updating and optimizing a correlation model, and obtaining the health feedback data of the residents; the prediction precision of the model and the effectiveness of suggestions are improved, the relationship between the use condition of the community public space and the resident health indexes is analyzed, key public facilities and areas influencing resident health are identified, data support and decision basis are provided for community planning and facility reconstruction, and the method is suitable for popularization and application. Community resident health and environment condition analysis reports are generated regularly, and resident health index change trends are visually displayed for reference of community managers and residents. According to the invention, intelligent analysis and application of environment and health data are realized, and technical support is provided for building a healthy community.

The invention provides an electronic certificate management and supply chain quality tracing method and system based on a block chain, and belongs to the technical field of information. According to the method, encryption storage and tamper-proof protection are carried out on the electronic certificate information through the distributed account book of the block chain and the Hash algorithm, and verification, approval and data updating operations are automatically executed based on the intelligent contract. Fine-grained authority management and abnormal access detection of all parties of the supply chain are realized through a user access control module; and through a tracing query module, performing multi-condition combination query on the electronic certificate information stored in the block chain, and generating a quality tracing report meeting supervision requirements. According to the method, through tamper-proofing, distributed storage and intelligent contracts of the block chain, trusted storage, automatic verification and full-chain quality tracing of the electronic certification are realized, data security and supply chain transparency are improved, trust cost is reduced, and supervision and tracing efficiency is improved.

The invention provides a natural language SQL (Structured Query Language) conversion method based on a data platform and a large language model, which relates to the technical field of data processing and comprises the following steps of: performing vectorization storage on metadata, blood relationship, indexes and historical query information, performing intention judgment and vector matching on a user query request, and determining a query condition based on a matching result. An association path is constructed according to data consanguinity, and an optimal SQL statement is generated by utilizing a historical query optimization table connection sequence and combining index matching. According to the method, intelligent conversion from the natural language to the efficient SQL can be realized, the query efficiency is improved, and the use threshold of a user is reduced.

The invention relates to the technical field of geographic information surveying and mapping, in particular to a city-level surveying and mapping geographic information standard system fusion construction method and system, and the method comprises the following steps: obtaining surveying and mapping data space coordinates, extracting terrain boundaries, ground feature distribution and road intersection features, calculating boundary point spacing, and calling a database to divide a topological structure. And detecting precision, spatial offset and overlapping conditions, and generating a topological correlation result. According to the method, by deeply analyzing the space coordinates, the time sequence, the geographic boundary and the classification information of the surveying and mapping data, the organizability and logicality of the data are improved, the tracking and updating capabilities of the data are enhanced through the refinement of the topological structure, the dynamic monitoring of the ground feature change is realized, the regional conflict is reduced through the optimization of the boundary adjustment mechanism, and the accuracy and the reliability of the surveying and mapping data are improved. The accuracy and adaptability of boundary data are improved, the consistency and application value of surveying and mapping data are enhanced through accurate matching of classification information and adaptive weight adjustment of data fusion, and accurate fusion of the data in a multi-source heterogeneous environment is ensured.

This disclosure includes a process for merging legacy databases having different data schema into a principal data store. A computing device retrieves a legacy database from each of a plurality of domains of an organization to form a plurality of legacy databases, where at least two legacy databases in the plurality of legacy databases have a different data schema. For each of the plurality of legacy databases, the computing device retrieves data from the respective legacy database, places the data from the respective legacy database into a respective unstructured database, and merges the respective unstructured database into a principal data store designed to hold data for every domain of the plurality of domains.

The invention discloses an enterprise-level knowledge base construction method based on a large model. The enterprise-level knowledge base construction method comprises the following specific steps: acquiring original data of an enterprise and preprocessing the original data to obtain first target data; performing knowledge extraction and representation based on the first target data to obtain second target data; based on the random-degree large model and the second target data, performing knowledge graph construction and storage to obtain an enterprise-level knowledge base; performing knowledge retrieval and recommendation on the enterprise-level knowledge base by using a modular retrieval enhancement generation method; and periodically updating and maintaining the enterprise-level knowledge base. According to the method, keywords and concepts in various knowledge carriers such as internal documents, reports and communication records of enterprises can be automatically identified based on the strong analysis capability of the random-degree large model, and automatic association among knowledge elements is realized; the enterprise-level knowledge base is quickly constructed, the knowledge base is periodically updated and maintained in time, the content of the enterprise-level knowledge base is enriched, and a foundation is laid for subsequent enterprise knowledge search.

A computerized method is disclosed for automated handling of data ingestion anomalies. The method features operations of detecting a data ingestion anomaly and determining a cause for the data ingestion anomaly. The causal determination may be conducted by at least (i) determining features of an anomalous data ingestion volume, (ii) training a second data model, after a first data model being used to detect the data ingestion anomaly, with data sets consistent with the determined features, (iii) applying the second data model to predict whether a data ingestion sub-volume is anomalous, (iv) obtaining system state information during ingestion of the anomalous data ingestion sub-volume, and (v) determining the cause of the anomalous data ingestion volume based on the system state information.