686 results about "Data patterns" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention belongs to the technical field of financial data management, and particularly discloses a financial multi-source protocol self-adaptive fusion system based on AI semantic understanding and a knowledge graph, which comprises the steps of avoiding fusion errors caused by semantic misunderstanding through deep semantic analysis and a conflict resolution decision based on the knowledge graph; when protocol version updating is detected, incremental learning and model adjustment are carried out based on a newly added sample and historical experience, and a fusion protocol standard is dynamically updated, so that the high adaptive capacity to dynamic change of a financial protocol is realized; an exception monitoring and repairing mechanism is introduced, data missing, format errors and other exceptions are found in time and repaired online based on historical data modes and business logic, and negative influences of abnormal data on downstream risk control, transaction decision making and other key business links are avoided; through deep collaboration and information feedback among the intelligent modules, an intelligent system capable of self-learning and evolution is constructed.

A method and system of the device may include obtaining hardware constraints of an FHE accelerator configured to execute the FHE program. In addition, the device may include selecting an optimal bootstrapping configuration that corresponds to the hardware constraints. The device may include identifying repetitive data patterns in the auxiliary data to be used in the bootstrapping process. Moreover, the device may include reducing the auxiliary data by applying at least one auxiliary data optimization technique based on the repetitive data patterns. Also, the device may include modifying the FHE program to include an instruction to load at least a portion of the reduced auxiliary data into an internal memory of the FHE accelerator, where the at least a portion of the reduced auxiliary data is loaded to the internal memory once prior to the execution of the plurality of bootstrapping processes.

The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokens—or fingerprints—that encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation

The invention discloses a construction method of an aerospace manufacturing knowledge graph based on a heterogeneous data joint extraction mode. The construction method comprises the following steps: designing and constructing a knowledge model of the knowledge graph by using an automatic modeling tool based on a top-down theory; performing knowledge graph bottom layer storage in a manner of combining a table structure and a graph structure; knowledge extraction and knowledge mining are carried out from structured, semi-structured and unstructured data; carrying out data mode layer fusion and data layer fusion on data from different sources; knowledge discovery and knowledge graph completion are carried out based on rapid knowledge reasoning of a graph neural network; and carrying out intelligent question answering and visual decision support based on semantic search in combination with a specific application form of the knowledge graph and aerospace manufacturing field data and business scenes. According to the method, challenges of various knowledge sources and data formats on knowledge extraction can be handled, so that the aerospace manufacturing knowledge graph can be applied to the ground, and the method has mobility, scientificity and seriousness.

A method, belonging to the field of road disease prevention technologies, for constructing a simulation model for roadbed diseases, a test method, and a system, where a simulation model for roadbed diseases is first constructed, and based on the model, an experimental method is performed to simulate diseases such as roadbed settlement, cracks, loosening, and pipeline breakage. Data of multi-sensing devices is associated with different diseases in different control groups, data statuses of various roadbed diseases in complex scenarios are acquired, and data patterns are summarized through control tests, to obtain sensor thresholds when the diseases occur in actual roadbed conditions, thereby providing a basis for road disease prevention.

A method is provided that includes: obtaining first urban data of a first sample urban region; inputting the first urban data into a multi-modal foundation model to obtain respective predicted vector representations of a plurality of first data segments; obtaining a plurality of general-purpose foundation models that are pre-trained; for each general-purpose foundation model: generating a vector representation label of a first data segment of a corresponding data modality by using the general-purpose foundation model; and determining a knowledge distillation loss of the general-purpose foundation model based on the vector representation label and a predicted vector representation of the first data segment; and adjusting parameters of the multi-modal foundation model based on at least respective knowledge distillation losses of the plurality of general-purpose foundation models.

A system and method for real-time time series forecasting using a compound large codeword model with integrated supervisory neurons. The system processes diverse inputs through adaptive codebook generation and codeword allocation. A projection network fuses different data types, creating unified representations for a latent transformer-based machine learning core. The core contains local neural network regions of interconnected operational neurons, monitored by supervisory neurons. These supervisory neurons receive activation data from operational neurons, perform real-time statistical analysis, determine necessary structural modifications, and initiate their implementation during operation. This architecture enables efficient handling of multi-modal data, capturing complex relationships between different input types. The combination of adaptive codebook generation and the supervisory neuron system ensures responsiveness to evolving data patterns and task requirements. This approach provides more accurate and timely forecasts by leveraging diverse data types in a sophisticated, integrated manner, while continuously adapting its structure to maintain optimal performance.

The invention discloses an AI-based network security risk identification and management and control system, which is characterized by comprising network security equipment and a server, the network security equipment is used for monitoring flow data flowing into / out of the server, and the AI-based network security risk identification and management and control system also comprises an interface conversion module which is used for connecting different network security equipment, original operation logs of different network security devices are collected; data collection: collecting multi-source data from multiple channels of network traffic, server logs, application program logs and user behavior records; preprocessing the data; preprocessing the data, removing noise data, filling missing values, and extracting useful features from the original data; simulation training: training the model to identify normal and abnormal behaviors through machine learning by utilizing supervised learning, unsupervised learning or semi-supervised learning methods; the deep neural network is used for modeling a complex data mode, and the detection precision is improved.

The invention discloses a wind driven generator anomaly detection method and system based on SCADA periodic data. The method comprises the steps of obtaining operation data collected by an SCADA system of a wind driven generator in actual operation; according to the method, a normal data set is constructed besides the abnormal data of SCADA alarm, and the abnormal data collected by an SCADA system comprises a periodic type, a stable type and an irregular fluctuation type; performing preliminary labeling on three types of data in the abnormal data; performing data pattern recognition on the obtained data by using a neural network model to recognize periodic data; performing feature engineering processing on the data of which the output result is periodic, and extracting corresponding data features; enhancing the features in a mode of calculating abnormal scores by using an isolated forest; and performing anomaly classification on the feature vectors formed in the time window by using a machine learning model, and detecting abnormal data in the periodic data. According to the method, the accuracy of abnormal data detection of the wind driven generator can be improved.

Various aspects of the disclosure relate to automated testing for application programming interfaces (APIs). A dynamic API validation computing system leverages a generative AI model to dynamically generate a multitude of data sets and rules for use during API validation activities. A federated byzantine agreement mechanism performs the validation of each API using the generated test cases and test data. A generator engine incorporates a generative AI model that may be trained on a large corpus of API metadata and / or data characteristics to predict data patterns and / or validation rules for each of the APIs under test. The generator engine may also predict a structure and format of requests based on the training model inputs. Multiple Test cases for an API created through use of the generative AI model may be distributed and executed on different testing nodes that reach a consensus about whether each API has passed or failed.

Systems and methods for Artificial Intelligence (AI)-driven computer system utilities for generating dynamic User-Interface (UI) elements and loading parsed semi-structured data into a structured data schema, which may then be readily queried utilizing known end-user tools.

The invention relates to the field of intelligent fault diagnosis, and particularly discloses an intelligent fault diagnosis method for a buried pipeline cathode protection system, which comprises the following steps of: capturing a data mode hidden in multiple dimensions by fusing information (time characteristics) of test pile parameters changing along with time and position topology and spatial correlation (spatial correlation) among test piles; therefore, the identification capability of the difference between similar representation fault types is effectively improved. The space-time collaborative analysis mode provides a solid foundation for realizing intelligent fault diagnosis with high confidence, high precision and high robustness, and is an important technical path for promoting intelligent operation and maintenance management of the buried pipeline cathode protection system.

An AI-driven system (100) for a personalized chatbot for financial advice using LLMS and real-time data integration, comprising the following: a chatbot module based on a large language model (LLM) that is configured to process user requests and generate personalized financial recommendations by using natural language understanding and historical data patterns; a real-time data processing module configured to retrieve and process financial data from stock exchanges, banking systems, government economic reports and financial news sources, continuously updating relevant financial information such as stock prices, interest rates and economic indicators; a risk assessment module configured to analyze a user's financial profile, including their risk tolerance, investment history and financial objectives, to ensure regulatory compliance and the suitability of investment recommendations in accordance with relevant financial regulations; an adaptive module based on machine learning that is configured to refine and optimize financial recommendations based on user interactions, financial trends, historical data and evolving market conditions, learning from past user behavior and adapting to changing market dynamics; a natural language processing module that facilitates interactive interaction with the user through text, speech, and graphical financial reports, thus enabling multimodal interaction to improve user experience and understanding of complex financial information; a proactive warning module configured to inform users about investment opportunities, market risks, and necessary portfolio adjustments based on real-time market data, trend analyses, and predictive models; a security and compliance module that ensures encrypted data transmission, user authentication and compliance with financial data protection regulations, including the secure storage of sensitive financial data and adherence to industry standards such as GDPR and PCI-DSS.

A system, method, and program product are presented for causing a processor of a computing device to receive content, determine a context of the content, and when the determined context is relevant for obfuscation, scan the content to identify information that matches, within the context, one of a plurality of data patterns indicative of being sensitive information. The processor further executes to assign a confidence value to the identifying process. When the sensitive information is identified with the confidence value above a confidence threshold, the processor executes to generate an obfuscated version of the sensitive information within the content, replace the sensitive information with the obfuscated version to form updated content, and output the updated content. The context may include at least one of a feature or a subject, a manner of use, and / or a determination of a user accessing, transmitting, or receiving the received content.

The invention belongs to the technical field of power natural language data questions and answers, and particularly relates to a multivariate knowledge link driven NL2SQL question enhancement method. The method comprises the following steps: constructing an electric power data mode by utilizing database table information, and carding electric power domain knowledge; constructing a problem analysis Prompt template, analyzing the structure of an original problem by using a large language model, and extracting a key entity from the original problem; on the basis of the sorted electric power field knowledge and key entities extracted from the original questions, retrieving the electric power field knowledge by adopting a hybrid similarity retrieval mode; obtaining a database table and a data mode related to the original problem through a multi-stage mode linking method; based on the retrieved power field knowledge and the obtained data mode, knowledge standardization is carried out, a problem enhancement Prompt template is designed, reconstruction enhancement is carried out on an original problem by using a large language model, confusion and interference factors are eliminated, and the question and answer accuracy is improved.

Apparatus and method for generating an output using an AI-PII model. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor receive personally identifiable information (PII) data, receive one or more model constraints, map, using an AI-PII model, the PII data to at least a data schema as a function of the one or more model constraints by identifying at least a PII datum of the PII data, categorizing the at least a PII datum to one or more categories of a plurality of categories, and mapping the PII data to the at least a data schema, modify the data schema based on a refinement datum, wherein the refinement datum is generated based on a temporal datum of the one or more model constraints, and generate an output as a function of the refinement datum and data schema.

The present invention extracts entity data from a plurality of related data objects from an external multi-tenant SaaS environment. The system extracts metadata related to an entity's data records and uses the metadata to identify a data schema associated with the entity's data. The system provides an interface for enabling a user to create an extraction tree that includes the data objects in the data schema from which data will be extracted. Enabling a user to create an extraction tree includes enabling a user to add a root node to the extraction tree, add any parent and child nodes of the root node in the data schema to a next level of the extraction tree, and add multiple additional levels of the extraction tree. The system creates an extraction plan based on the extraction tree and extracts data from the multi-tenant SaaS environment based on the extraction plan.

A federated system and method for data compression optimization in distributed device networks. The system comprises multiple edge devices that analyze local data patterns to generate device characteristic profiles while performing local compression optimization and maintaining data privacy. Edge devices contribute to collaborative learning by generating privacy-preserved updates without transmitting raw data. A central coordination system aggregates encrypted contributions using secure multi-party computation protocols, identifies device groups based on data pattern similarities, and generates optimized compression parameters for each group. The system coordinates collaborative training of data reconstruction models across device groups and deploys group-optimized reconstruction capabilities. Device grouping is performed by calculating similarity scores between device characteristic profiles and clustering devices with scores above predetermined thresholds. The system dynamically adapts compression and reconstruction parameters through federated learning while preserving individual device data privacy, enabling efficient data compression and near-lossless recovery across heterogeneous Internet-of-Things networks.

Mechanisms are provided for automatically detecting data leakages and generating data leakage detection rules for a rules engine. The rules engine is configured with rules for identifying first sensitive data patterns in input data, and a large language model (LLM) is trained to identify second sensitive data patterns in input data. New input data is processed via the rules engine to determine whether it comprises any of the first sensitive data patterns. In response to the rules engine making a negative determination, the LLM is executed on the new input data to determine whether the new input data comprises any of the second sensitive data patterns. Responsive to a positive determination by the LLM, the rules engine is updated with a new rule based on the at least one second data pattern.

Systems and methods for detecting patterns in data from a time-series and for detecting outliers in network data in an unsupervised manner are provided. In one implementation, a method includes the steps of obtaining network data from a network to be monitored and creating a window from the obtained network data. The method also includes the step of detecting outliers of the obtained data with respect to the window using an unsupervised deep learning process (e.g., using a Generalized Adversarial Network (GAN) learning technique and / or a Bidirectional GAN (BiGAN) learning technique) for enabling the learning of a data distribution. The unsupervised process, for example, does not require manual intervention.

The invention provides a distributed photovoltaic energy storage method and system based on a deep learning model, and the system comprises a data collection and preprocessing module, a deep learning model construction module, a model training and optimization module, an energy storage control strategy module, a cooperative work and optimization module, and a system monitoring and feedback module. A deep learning model can accurately predict photovoltaic power generation capacity and load requirements, realizes optimized charge and discharge control of energy storage equipment, maximally utilizes photovoltaic energy and reduces energy waste, and meanwhile, the deep learning model can quickly learn and adapt to a new operation scene and a data mode, so that the energy storage efficiency is improved. The system can cope with various complex and changeable environments and load conditions, and through accurate prediction and an intelligent control strategy, the fluctuation of photovoltaic power generation is effectively stabilized, the stable power supply of the micro-grid under different working conditions is ensured, and the fluctuation of voltage and frequency is reduced.

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.

According to the method, thermal management of the wind side data center is focused, and an AI optimization wind side data center CFD order reduction prediction system using Transform is constructed. Environment and server data are acquired and processed through a multi-source data acquisition and preprocessing module, and a data rule is mined through a Transform-based AI optimization module in combination with a traditional CFD reduced-order model based on POD. The system realizes real-time accurate thermal environment prediction, can quickly respond to the working condition change of a data center, and effectively overcomes the defects of a traditional reduced-order model. The system has excellent adaptability and stability, ventilation equipment can be regulated and controlled according to needs, energy conservation and consumption reduction are remarkably achieved, the service life of the equipment is prolonged, expansion and transformation of the data center can be flexibly supported, and powerful guarantee is provided for efficient and stable operation of the data center.

Disclosed is a method comprising accessing, by a machine learning system, a set of data records for a plurality of users, the data records representative of physical statistics measured for each of the plurality of users over a time period. At least a subset of the data records comprises patterns of missing data for at least a portion of the time period. The method also comprises generating a set of masked data records by masking a subset of the data records in accordance with a pattern of natural missingness from a data record. The method also comprises generating, by the machine learning system, a set of learned representations from at least the set of masked data records. Finally, the method comprises fine tuning, by the machine learning system, a machine learning model using the set of learned representations, the machine learning model configured to perform a downstream machine learning task.

The present invention belongs to the technical field of power natural language data question answering, and specifically relates to a method for enhancing NL2SQL questions driven by multi-source knowledge links. The method includes: constructing a power data schema using database table information and sorting out power domain knowledge; constructing a question parsing Prompt template and using a large language model to analyze the structure of the original question to extract key entities from the original question; retrieving power domain knowledge in a hybrid similarity retrieval manner based on the sorted out power domain knowledge and the key entities extracted from the original question; obtaining database tables and data schemas related to the original question through a multi-level schema linking method; standardizing knowledge and designing a question enhancement Prompt template based on the retrieved power domain knowledge and the obtained data schema, and using a large language model to reconstruct and enhance the original question to eliminate confusion and interference factors and improve the accuracy of question answering.

Systems and methods for intelligent data sanitization employing PCM and AI / ML are provided. The idea uses AI / ML to detect specific facts that needs sanitization rather than full properties in incoming records. Data sanitization is optimized using this focused method, saving computational resources. To properly manage changing data volumes, PCM shifts between Logical 0 and Logical 1 states. Logical 0 processes smaller volumes with high resistance and low conductivity, while Logical 1 processes large volumes with low resistance and high conductivity. The AI / ML module organizes and directs data to maximize resource and processing efficiency. The PCM processes data in-memory and directly overwrites, eliminating erasure. AI / ML and PCM integrate to sanitize data quickly, efficiently, and securely, improving system performance and data integrity without a central repository. The system dynamically adjusts to changing data patterns, protecting and optimizing data.

The invention discloses a multi-agent collaborative application big data management system and method. The system comprises a data acquisition unit, a data preprocessing unit, a multi-agent collaborative processing unit, a data storage unit and a data scheduling unit. The data acquisition unit is used for acquiring multi-source heterogeneous original big data and transmitting the multi-source heterogeneous original big data to the data preprocessing unit; and the data preprocessing unit is used for executing preprocessing operation including de-duplication and format standardization on the original big data. The invention relates to the technical field of big data management. According to the multi-agent collaborative application big data management system and method, through a multi-agent collaborative algorithm, the system can realize a self-adaptive data classification and optimization strategy, a data processing mode is dynamically adjusted, the processing efficiency problem of a traditional static rule during data mode fluctuation is improved, and the data processing efficiency is improved. The system adopts a dynamic resource scheduling strategy based on a data access demand and a system load, efficiently distributes calculation and storage resources, and facilitates priority processing of high-priority tasks.

The present invention discloses an automatic matching method, device, computer equipment and storage medium for data migration, the method includes building a universal data processing professional knowledge base; building a personalized data processing auxiliary knowledge base; building a source data model table to be migrated; reading environmental information from a target database system, and using a probability deduction algorithm to calculate the target data model based on the universal data processing professional knowledge base and the personalized data processing auxiliary knowledge base. The method realizes the automatic mapping of field types and names involved in heterogeneous source and target database systems; realizes the automatic generation of a new target database system based on the source database system; realizes the automatic mapping to an existing target database system based on the source database system; realizes the automatic mapping to multiple target database systems based on multiple source database systems; and greatly improves the efficiency of database system matching by leveraging the general industry experience and the experience of actual work of unit entities.

Systems, devices and methods for optimising and managing distributed energy storage and flexibility resources on a localised and group aggregation basis, particularly around the determination, analysis and predictive learning of local data patterns, scoring availability for flexibility and risk profiles, to inform the optimisation of energy supply and behind the meter storage resources and local clusters of co-located or close resources within a community, low voltage network, feeder, neighbourhood or building. Said optimisation to involve scheduled, reactive and active management of data sources and local clusters of resources, for a range of goals such as price, energy supply, renewable leverage, asset value, constraint or risk management. Or where said optimisation achieves a local objective such as providing resources to off-set, aid local balancing or constraint management of larger local supplies and loads, or to aid active management of local energy demands and renewable supplies, storage resources, electric heat resources, electric vehicle charging resources or clusters of electric vehicle chargers, flexible loads in buildings.