941 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 discloses a self-adaptive power grid state court intelligent fusion terminal and a control method thereof, relates to the technical field of intelligent power grids, and solves the defects that an existing fusion terminal is insufficient in self-adaptive capability and limited in data processing and analysis capability. Learning power grid state change in real time through a deep power grid dynamic control algorithm and a prediction analysis engine combining LSTM and random forest, and autonomously adjusting a control strategy; the edge calculation and distributed processing module is utilized, so that the data processing flow is optimized, and the data processing speed and efficiency are greatly improved; the application of time sequence analysis and a machine learning model further excavates and understands a complex data mode; the resource configuration efficiency is improved through an intelligent scheduling algorithm, and a cross-layer coordination mechanism is established through a distributed coordination algorithm, so that rapid issuing and feedback collection of a control instruction are ensured; according to the invention, the intelligent degree of the existing fusion terminal is greatly improved.

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 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 privacy information storage and transmission security situation intelligent prediction method, which comprises the steps of constructing a multi-source data fusion basis, extracting and updating dynamic features, constructing a hybrid model, performing anomaly detection and adaptive learning, and performing real-time monitoring and feedback adjustment, and comprises the following steps of: firstly, constructing the multi-source data fusion basis, collecting and integrating multi-channel data; the method comprises the following steps of: performing dynamic feature extraction and updating, extracting and optimizing features, constructing a hybrid model, combining different types of models, applying a fusion strategy, performing anomaly detection, adaptive learning and multi-dimensional anomaly detection, adjusting the model according to the anomaly detection and the multi-dimensional anomaly detection, and finally performing real-time monitoring, feedback adjustment, continuous data acquisition and result comparison to optimize the model. According to the method, through close cooperation of the steps, prediction can be carried out without being limited to existing data modes and trends, and effective prediction and prevention can be carried out on newly occurring threats or attack modes.

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.

Computerized systems and methods are described for executing personalized bundling processes. Methods include receiving user inputs specifying preferences for product bundles and utilizing a Real-Time Data Mesh (RTDM) to retrieve relevant real-time data. An Advanced Analytics and Machine Learning (AAML) Module analyzes these inputs alongside market data to generate personalized bundle recommendations. Recommendations are then displayed to the user via a Single Pane of Glass User Interface (SPoG UI) and, upon user confirmation, transferred as orders to a vendor system. Validation steps use algorithms within the AAML Module to ensure accuracy and relevance of the bundles. Real-time reports on user engagement and bundle success rates are generated. The system, accessible on multiple devices, integrates machine learning models that continually refine the bundling process based on evolving data patterns and user feedback, enhancing personalization and efficiency.

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 provides a multi-platform collaborative data acquisition and analysis method, and relates to the technical field of data processing and analysis. The method comprises the following steps: receiving and converting data from different platforms into a unified intermediate format through a data adaptation intermediate layer, and processing a data structure and an interface of a specific platform by using a plurality of adapters; the data abstraction layer defines a standardized interface, so that multiple platforms request data through a unified interface, and a mapping rule converts the request into calling of the adaptation layer; the data integration engine preprocesses the data to ensure data consistency and accuracy; the data warehouse adopts a distributed architecture to ensure the service continuity through load balancing, data partitioning, a caching mechanism and a redundant storage and fault transfer mechanism; and the data processing and analysis module converts the data into decision suggestions by using a visual tool and a data mining algorithm. According to the method, manual intervention is reduced, the data processing speed and accuracy are improved, the data mode and trend are deeply mined, and service decision is promoted.

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.

The invention relates to the technical field of sports events, in particular to a sports event data analysis system and method.The system comprises a real-time data analysis module, a technical action evaluation module, a dynamic competition prediction module and a decision support module, based on a real-time competition data stream, the positions, scores and instant statistical information of players are received, time sequence analysis is executed, and the real-time competition data stream is analyzed; key data changes are recognized, scoring opportunities and defense position points are extracted, and a data pattern analysis result is obtained. According to the invention, through integration of real-time and historical data, accurate capture and deep analysis of key information in a sports event are realized, through instant receiving and processing of player positions, scores and statistical information, a data mode is identified by using time sequence analysis, dynamic understanding of the event is enhanced, more progress data about the event is allowed to be obtained, and the progress of the event is improved. And through comparison of the time convolution network and the action database, the accuracy of technical action evaluation is improved.

Systems and methods involve an automated risk assessment platform server that receives data regarding a potential fraudulent account event from a fraud detection platform processor and a data mining function that extracts additional data related to the potential fraudulent account event from a plurality of data sources. An artificial intelligence engine function enhances the additional data for further processing, and a pattern recognition function searches the data for one or more data patterns that indicate whether or not the account event is fraudulent and generates a treatment recommendation when at least one data pattern is found that indicates that the account event is fraudulent.

The invention provides a quality data online management method and system in the processing process of traditional Chinese medicine decoction pieces, and relates to the technical field of data processing, and the method comprises the steps: carrying out the real-time monitoring of key quality indexes in the processing process of the traditional Chinese medicine decoction pieces through the final fusion data; when the abnormal data mode is detected, preset early warning is automatically triggered according to key features in abnormal data; using an ant colony optimization algorithm to optimize parameters of the neural network model used for quality prediction; searching a final solution in the parameter space by simulating the foraging behavior of ants and a pheromone updating mechanism so as to obtain optimized parameters; retraining the neural network model by using the optimized parameters to obtain a trained neural network model; and predicting the quality trend of the traditional Chinese medicine decoction pieces according to the trained neural network model. According to the invention, the prediction precision and real-time performance of the model are improved, and the production cost is reduced.

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.

The present invention discloses a system for monitoring and alert, implemented by plurality of modules comprising one or more processors, operatively coupled to non-transitory computer readable storage devices, comprising the steps of: —analysis module configured for aggregating and / or synchronizing, filtering and calculating intensity changes and or creating data patterns template for each data type from data meters of electricity, waterflow gas data and all types of data communication flow: —data pattern analyse module, configured to identify anomaly between the current measured pattern data and compare it with the template saved in the memory, on the same time and day of the week, two weeks or three weeks. —alert module configured for determining alerts based on identifying anomaly in data patterns.

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.

The invention provides a digital twin city construction and operation optimization method based on cloud computing and a big data technology. The method comprises the following steps: optimizing layout of various sensor devices through a particle swarm optimization algorithm and an adaptive sampling algorithm, and adjusting acquisition frequency; establishing a distributed database, and realizing uniform distribution and efficient access of data in the distributed database through a load balancing algorithm; mining a time rule of urban operation by using a time sequence model, and discovering association between different data; a city model is constructed by using a city three-dimensional live-action fusion modeling method which takes unmanned aerial vehicle oblique photography as a main part and takes ground laser radar as an auxiliary part, different areas in a city are identified by applying a machine learning method, and a refined model of each field of the city is constructed; real-time simulation of a city operation state is realized through virtual reality and augmented reality technologies; the system is continuously optimized by using an online learning algorithm, so that the system continuously learns and adapts to a new data mode in the operation process, and the construction and operation optimization capability of the system is improved.

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.

A system and method for data compaction optimization which leverages a neural network to predict optimal block sizes for data encoding, enhancing efficiency and adaptability in various applications. It begins with data preprocessing, extracting features, and creating labeled datasets for training. The neural network architecture is carefully designed, allowing it to learn complex relationships between data characteristics and optimal block sizes. During training, the network is fine-tuned and optimized using appropriate loss functions and regularization techniques. Once deployed, it continuously monitors incoming data streams for shifts in data patterns and adapts predictions accordingly. By predicting multiple block sizes, the system accommodates diverse compression needs. This versatile system offers real-time adaptability, ensuring optimal encoding performance as data patterns evolve over time.

A video analysis system trains and uses a machine-learned adaptive thresholding model configured to receive a query (e.g., text, image, video), and generate a predicted threshold value for a corresponding video retrieval model that indicates what value the filtering threshold should be for the query. The video analysis system filters video segments for the query that are associated with relevance scores above the predicted threshold generated by the adaptive thresholding model. In one instance, the adaptive thresholding model is configured as a machine-learned model, including neural networks, embedding models, transformer-based architectures, and the like that are capable of generating a predicted threshold value given a query that includes, for example, text, images, videos, audio, or any other appropriate data modality.

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.