14 results about "Operational database" patented technology

In at least one aspect, a computer-implemented method includes: accessing, by an agricultural management system, first data from an operational database; accessing, by the agricultural management system, second data from a real-time data server; determining a metric based on the first data and the second data; generating a first graphical user interface (GUI) for display on a client device, where the first GUI includes at least one data visualization based on the metric; and adjusting, by the agricultural management system, a setting of a piece of equipment.

The invention discloses a customer service optimization method and system based on generative artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: reading the retention parcel attribute and retention duration, and setting a reminding short message sending frequency and configuring a sending time zone in combination with the site load and the historical average pickup duration of a user; retrieving a sample data set from a parcel operation database based on the user portrait and a preset parcel taking association factor; training a generative artificial intelligence model by using the sample data, and constructing a pickup probability predictor; and in the short message sending time zone, a predictor is used for optimizing and determining an optimal time node corresponding to the maximum predicted pickup probability, and a reminding short message is sent to a user. Therefore, the technical effects of accurate reminding, improvement of processing efficiency and improvement of user experience are achieved.

Embodiments of the present disclosure relate to metadata-driven analytical data modeling. Systems and methods are provided for converting data stored in an operational database to a format / structure optimized for a data lake bin. The data conversion is metadata-driven, where metadata characterizing the data conversion can be automatically generated via various denormalization / modeling techniques, including: path / edge / tree / log denormalization, and state machine / aggregation / adaptive modeling.

A system and method for individualized airline passenger disembarkment integrates with Passenger Service Systems (PSS) and Airport Operational Databases (AODB) using IATA New Distribution Capability (NDC) standards for real-time data exchange. Passengers book priority disembarkment options during reservations, with availability determined via priority queues in distributed software modules interacting with cloud-based hardware to allocate limited slots efficiently. Upon detecting delays, the system identifies at-risk passengers for missed connections by computing gate-to-gate travel times using graph data structures, assigns priority disembarkment passes (physical or virtual) with verification elements such as QR codes, NFC, or RFID, and deactivates passes post-disembarkment via geo-fencing or hash-based structures. This reduces airline costs, enhances operational efficiency by minimizing latency and optimizing resources, and complies with aviation regulations across multiple jurisdictions while improving passenger satisfaction.

Described herein are systems and techniques to facilitate a rapid database system that automates estimating a database footprint for a database implemented across multiple environments. The system may receive inputs from a user and / or automatically generate inputs. The system may determine database size using various logic-based extraction methods. For instance, operational database resources may be sized using logic-based extraction to search a database catalog of a production database, non-production databases may be sized using logic-based extraction to search database catalog metadata, development database resources may be sized using fuzzy logic queries generated based on production database information (e.g., since development database names are likely to resemble, but be different than, production database names), and associated mirror databases are also determined and measured during these steps. Outputs from the queries are used to generate a database footprint estimate and cost information, which may be transmitted to a user device for display.

This invention relates to a data-driven method for estimating the driving range of new energy vehicles, belonging to the field of battery technology. The method includes: collecting operational data of new energy vehicles of corresponding models to establish a new energy vehicle operational database; establishing an estimation model based on charging data and data augmentation technology; calculating the vehicle's current available total capacity based on the estimation results and the ampere integral method; extracting charging and driving feature sets correlated with the driving range from the driving range estimation feature set based on the Pearson correlation coefficient index; constructing a driving range estimation model based on XGBoost based on the selected charging and driving feature sets; and estimating the driving range using the driving range estimation model. This invention utilizes historical vehicle operating data to calculate the current available total battery energy, effectively considering the impact of battery aging on driving range, extracting features from battery status and driving behavior, and comprehensively reflecting the actual operating conditions of the vehicle.

A server to manage a plurality of robots, where each robot performs a specified set of tasks, includes a robot management table, an operational database, a preprocessor and an initializer. The robot management table lists per robot tasks and associated scripts according to a unique robot ID for each of the plurality of robots. The operational database stores operational data required to run the scripts. The preprocessor runs at least one algorithm to obtain and update the operational data. The initializer initializes a particular robot of the plurality of robots according to its unique robot ID with its associated the scripts when the server is in network communication with the particular robot.

The application discloses a customer service optimization method and system based on generative artificial intelligence, and relates to the technical field of artificial intelligence. The method comprises the following steps: reading the attributes of a retained parcel and the retention duration, combining the site load and the historical average parcel pickup duration of a user to set the sending frequency of a reminder message and configure the sending time zone; retrieving a sample data set from a parcel operation database based on a user portrait and a preset parcel pickup correlation factor; training a generative artificial intelligence model with the sample data to build a parcel pickup probability predictor; and in the message sending time zone, determining the optimal time node corresponding to the maximum predicted parcel pickup probability by using the predictor to optimize, and sending a reminder message to the user. The technical effects of accurate reminding, improved processing efficiency and improved user experience are achieved.

Systems and methods are provided for transforming data stored in an operational database to a format / structure optimized for use in a data lakehouse. The data transformation is metadata-driven, where the metadata characterizing the transformation of data may be automatically generated via the performance of various denormalization / modeling techniques, including: path / edge / tree / log denormalization, and state machine / aggregate / adaptive modeling.

The present disclosure provides an IoT system and a method for managing smart gas-integrated data center. The method includes obtaining comprehensive gas enterprise data through a smart gas company management platform; generating a tagging parameter based on the comprehensive gas enterprise data; generating target gas data based on the comprehensive gas enterprise data and the tagging parameter; storing the target gas data into a corresponding gas database, the gas database includes at least one of a basic information base, an operational database, a specialized database, a supervisory database, and a shared database; in response to receiving a data sharing request, retrieving, from at least one of the gas database, the target gas data, through a data switch, based on a data sharing priority and the estimated transmission time; and sending the target gas data satisfying a preset requirement to a gas user platform or a residential user platform to enable the gas user platform and the residential user platform to share data based on the target gas data.

This invention discloses a BMS fault analysis method, apparatus, medium, and vehicle based on a large language model. The method includes: triggering a BMS fault cause analysis process after detecting a BMS operational fault event; using a large language model to dynamically generate query questions and incremental data query mechanisms to obtain BMS domain knowledge corresponding to the BMS operational fault event from a pre-built BMS domain knowledge base and real-time operational data corresponding to the BMS operational fault event from a BMS operational database; and performing fault analysis on the BMS operational fault event based on the BMS domain knowledge and real-time operational data using the large language model to generate a fault analysis report corresponding to the BMS operational fault event. Thus, by using a large language model, the knowledge-guided and data-verified analysis process in BMS operational fault analysis is integrated, thereby improving the intelligence level of fault cause localization and achieving comprehensive diagnosis of diverse BMS faults and unknown sudden faults.

Data storage management is carried out by an Internet of Things platform having access to operational and long-term databases carrying tables for storing device-related data emitted from connected devices. Device-related data is received and stored in operational database table entries. Tables are associated with devices. Table entries are associated with entry dates. A table in the operational database is monitored to obtain load statistics. Data from the operational database is offloaded by replication to the long-term database. Queries against operational database tables are tracked to obtain query statistics. Cleanup rules determining timeframes within which data stored in operational database table entries are to be deleted are set up, based on a date of data entry. Cleanup rules currently set up are carried out as a retention run, based on the load statistics and / or query statistics.

In at least one aspect, a computer-implemented method includes: accessing, by an agricultural management system, first data from an operational database; accessing, by the agricultural management system, second data from a real-time data server; determining a metric based on the first data and the second data; generating a first graphical user interface (GUI) for display on a client device, where the first GUI includes at least one data visualization based on the metric; and adjusting, by the agricultural management system, a setting of a piece of equipment.

Techniques for embodiments generating ETL code for transforming normalized database tables, i.e., snowflake schema, and metadata from an operational database into star schema denormalized dimensions are disclosed. The system accesses metadata associated with the normalized database tables and analyzes the metadata to identify tables and relationships between the tables. Identifying tables includes identifying fact tables and dimension tables referenced by the fact tables. Pattern matching may be used to identify the tables within the normalized dimensions. The tables and the relationships between the tables are parsed to generate an abstract representation, i.e., abstract syntax tree, of the normalized database tables. The system generates an intermediate representation from the abstract representation that defines operations for denormalizing the normalized dimensions. The system renders the operations defined in the intermediate representation into ETL code for creating denormalized dimensions from the normalized dimensions in the operational database.