579 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

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.

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 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.

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.

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.

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.

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.

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.

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 for a latent transformer-based machine learning core. A hierarchical supervisory network, comprising low-level, mid-level, and high-level nodes, monitors local neural network regions, performing real-time statistical analysis and implementing structural modifications. The system efficiently handles multi-modal data, capturing complex relationships between input types. An adaptive codebook generation method, coupled with the supervisory architecture, ensures responsiveness to evolving data patterns and task requirements. This approach provides accurate and timely forecasts by leveraging diverse data types in a sophisticated, integrated manner, while continuously adapting its structure during operation to maintain optimal performance.

The invention relates to the technical field of large models, and particularly provides a large model data enhancement method and device, and the method comprises the following steps: S1, collecting original data through a sensor cluster, grouping the data through a Pearson's correlation coefficient, and for each group, determining a head sensor through employing a minimum spanning tree algorithm, and building a generation sequence; s2, starting a generator based on a long short-term memory (LSTM) network, and training data of a head sensor by using the LSTM generator; s3, starting a ridge regression generator, and training data of other sensors by using the ridge regression generator; and S4, generating corresponding data by using the model obtained by training according to a specified demand of a user, and increasing the data volume through interpolation. Compared with the prior art, the method has the advantages that inherent complex time behaviors in the industrial production process can be learned, the relation between sensors which is crucial to accurate simulation process conditions is kept, and it is ensured that the generated data mode has high fidelity while the original process characteristics are kept.

Disclosed is a method, performed by an electronic device, for conflict process control. The method comprises obtaining a first data set from one or more systems; determining, based on the first data set and a predictive model having one or more parameters, one or more conflict data patterns indicative of a conflict; and predicting, based on the one or more conflict data patterns, a conflict result parameter and a confidence score associated with the conflict result parameter.

The present invention relates to an automated travel planning data processing system and method that leverages advanced artificial intelligence (AI) and machine learning (ML) algorithms to generate personalized travel itineraries in real-time. The system comprises a central server with one or more processors, memory, and a machine learning module, as well as a travel database that stores aggregated data from multiple sources. Users interact with the system through a natural language processing-based user interface, which receives inputs comprising travel dates, destinations, and preferences. An AI-powered itinerary generation engine processes user inputs and aggregated data to create personalized travel plans, utilizing a multithreading module for simultaneous data retrieval and processing. The machine learning module continuously optimizes the itinerary generation process by analyzing user preferences and travel data patterns. The invention also provides a method for automated travel itinerary planning using the data processing system.

Prompt injection attack detection in responses from large language models includes receiving, at a server from a user device, a user prompt segment to an LLM, generating a LLM prompt from the user prompt segment, sending the LLM prompt to the LLM, and receiving a response from the LLM. Prompt injection attack detection further includes comparing the response to a structured data schema for the response to validate the response, and sending, responsive to validating the response, the response to the user device.

The invention relates to a method for displaying sixteen gray scales by an electronic paper price tag type display module, which comprises the following steps of: performing refreshing by utilizing a condition that at most four waveforms are controlled during refreshing each time in a new and old data comparison mode by utilizing a 1-bit data IC (Integrated Circuit) of electronic paper, identifying the first three gray scales and pure white gray scales in sixteen gray scale data during the first refreshing, and displaying the sixteen gray scales in the sixteen gray scale data; converting into data of corresponding waveforms, and brushing four corresponding gray scales through the debugged gray scale waveforms; in the second to fifth refreshing, three pieces of gray scale data are identified each time, and the debugged corresponding gray scale waveform is called to brush out three gray scales; the method is used for sixteen-gray-scale display through five times of superposition of different-gray-scale pictures.

Some embodiments include apparatuses and methods using a clock generator to generate clock signals, the clock signals being out of phase with each other; a transmitting circuit to provide patterns of data at an output of the transmitting circuit responsive to timing of the clock signals; and calculation and control circuitry to calculate an integral nonlinearity vector that represents offsets of transitions of the patterns from respective target positions, and to generate control information based on the integral nonlinearity vector to adjust phases of the clock signals based on the control information.

The invention discloses an intelligent data quality repairing method based on dynamic rule evolution, which belongs to the technical field of data quality management, and comprises the following steps: constructing a knowledge graph based on physical storage structure information and service logic of structured data; performing deep learning on the knowledge graph by using a graph neural network, and dynamically generating a global topology view based on a learning result; in combination with a historical damage mode and a global topology view, an optimal structured data scanning path is generated by utilizing reinforcement learning, and association anomalies of abnormal partitions in an optimal path scanning result are identified based on a graph neural network; the method comprises the following steps: constructing a multi-modal association sub-graph based on association anomaly, repairing structural defects in the multi-modal association sub-graph through a correct physical structure reversely deduced by a graph neural network, and carrying out credibility scoring on a repairing result to form a structured data management closed loop. The method can adapt to continuously evolved data modes and novel anomalies, continuously improves the robustness and autonomy of the system to deal with complex data problems, and reduces the long-term operation and maintenance cost.

A method for converting unstructured data. The method includes receiving a custom-defined data schema that is constructed according to a structured data syntax. The method includes converting the custom-defined data schema into a language model modifier that restricts outputs based on preceding outputs and integrating the language model modifier with an autoregressive machine-learned language model (LLM) to modify output scores of the autoregressive LLM. When receiving a data file that includes unstructured data, the method includes generating a first output from the autoregressive LLM and receiving a set of tokens representing candidates of a second output succeeding the first output. Each token is associated with a score. The method further includes identifying a rule in the language model modifier using the first output, modifying scores of the tokens that violate the rule and selecting one of the tokens as the second output based on the modified scores.

A cybersecurity system provides the ability to detect security risks in a cross-platform cloud solution. A unified data schema is used to abstract resources, principals and others across multiple platforms. A security graph is generated to present a unified view of cloud environments, which are then easily queried using the structure of the data schema. The solution allows a compact representation of cloud environments, which is scalable and multi-layered. The security graph allows for representation of production environments, staging environments, as well as code for deploying workloads in the cloud environment. Thus the solution is also able to present a complete picture of a user's entire cloud environment. The solution further allows to generate subgraph views, by associating a tag to certain nodes, then rendering a view based on nodes which include the tag, and all children nodes thereof.

The invention provides an intelligent test data generation and verification method and system based on a hierarchical architecture, and relates to the technical field of data generation, and the method comprises the steps: constructing a cross-layer constraint dependency graph by analyzing each layer of data mode and constraint and conversion logic of a hierarchical architecture software system; generating candidate test data meeting the constraint based on the dependency graph in a back-stepping manner, and verifying the candidate test data through projection; and iteratively optimizing the dependency graph and the test data by monitoring the difference between the actual execution and the prediction form. According to the method, cross-layer data consistency verification is realized, the test coverage rate and the defect detection efficiency are improved, and the quality risk of a software system is reduced.

A monitoring server is provided for learning event patterns in pharmaceutical order processing and identifying anomalous events based on learned event patterns. The monitoring server is configured to define a plurality of feeds of monitoring data from the monitored nodes using a monitoring link. The feeds of monitoring data are defined at least partially based on the monitoring vector definition. Each feed of monitoring data is associated with pharmaceutical order processing. The processor is additionally configured to determine a set of monitoring vector data for each of the plurality of feeds of monitoring data. The processor is also configured to identify a monitoring vector signature for each of the plurality of feeds. The processor is also configured to identify an anomalous data pattern. The processor is also configured to transmit an alert indicating that pharmaceutical order processing for the associated feed of monitoring data is anomalous.

The invention relates to the technical field of highway traffic, and discloses a digital management and control method and system for highway engineering test detection. The method comprises the following steps: acquiring highway engineering test detection data, preprocessing original detection data in real time by using edge computing nodes, and generating a standardized detection data stream; based on the data stream, a dynamic window segmentation algorithm is adopted to extract time domain features of detection data, and a multi-level data quality evaluation model is established. And performing exception marking on the detection data through the model, matching exception types in combination with a historical exception data pattern library, and outputting an exception detection result and confidence. And triggering a self-adaptive sampling strategy according to an abnormal detection result, adjusting the detection data acquisition frequency and the sampling precision, and generating an optimized detection task instruction. And finally, issuing an instruction to an edge computing node, synchronously updating a parameter threshold value of the multi-level data quality evaluation model, and forming a closed-loop feedback control mechanism.