73 results about "Data patterns" patented technology

An apparatus and method for generating at least a data connector. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive input data from an external source, wherein the input data comprises a plurality of source parameters, identify a first data schema associated with the external source and a second data schema associated with an internal system, instantiate a runtime instance of a data connector associated with the external source as a function of the first data schema and the second data schema, configure the data connector by binding the data connector to a web service protocol, registering the data connector to the internal system, and validating the data connector using at least a compliance protocol, and deploy the data connector to a service-based orchestration layer of the internal system.

A diagnostic system for communication behavior is provided. The diagnostic system includes a wireless device configured to communicate with an intranet, and a diagnostic device. The diagnostic device includes a data receiving module, a pattern identification module, an event detection module, and a processing module. The data receiving module is configured to receive at least one log data from the wireless device. The pattern identification module is configured to receive the log data and identify whether the log data has at least one key pattern of data. The event detection module is configured to obtain an event corresponding to the key pattern of data. The event represents a communication behavior of the wireless device. The processing module is configured to perform a specific operation corresponding to the event.

This invention provides a multi-model fusion method, device, electronic device, and system for debris flow early warning. The method includes: collecting geographical and geological data, real-time rainfall, runoff, and auxiliary monitoring data; fusing a shallow flow numerical model, a moving least squares machine learning model, and a generalized regression neural network model, and obtaining a comprehensive runoff prediction through weighted least squares calculation; constructing critical runoff indicators based on the safety requirements of disaster-bearing bodies such as roadbeds, retaining walls, and culverts, and inversely calculating critical rainfall thresholds, then making differentiated adjustments based on the average gradient of the gully; generating a debris flow early warning report by comparing real-time predictions with thresholds, achieving final graded and zoned early warning for the warning area. This invention improves the accuracy and engineering relevance of early warnings by integrating physical mechanisms and data patterns, achieving refined early warning from "whether it will occur" to "when, where, and what the impact will be," while reducing the error of a single model and adapting to different terrains and disaster-bearing bodies along highways.

The embodiment of the application discloses a data conversion method, system and device, the method comprises the following steps: when a trigger condition corresponding to a scheduling task is met, triggering the scheduling task, and transmitting task parameters to a push task main program; through the push task main program, target data in a database is queried based on the task parameters in a stream mode, the target data is parsed according to a template file, and the target data is written into a landing shard data file; after the target data in the database is queried and written into the landing shard data file, the total number of the target data under each data mode is determined, a text file corresponding to each data mode is generated according to the total number of the target data under each data mode and each landing shard data file under each data mode; and the text file corresponding to each data mode is compressed, and the compressed text file is uploaded to a target server. The method can ensure the timeliness and accuracy of data pushing, and realizes decoupling of the database and a data consumer.

A memory device operates with multiple internal ranks that enable concurrent access. The memory device receives multiple commands from a memory controller, which are assigned to different individual ranks and executed concurrently. Data is transferred between the memory device and the memory controller in an interleaved data pattern that includes interleaved data associated with multiple commands for multiple different ranks.

In some implementations, a memory apparatus may receive a command to write data to volatile memory of the memory system. The memory apparatus may compare the data to one or more data patterns to identify whether the data matches a data pattern of the one or more data patterns. The memory apparatus may increment a reference counter associated with the data pattern based on identifying that the data matches the data pattern. The memory apparatus may write, based on identifying that a measure flag associated with the data pattern is set, the data to the volatile memory.

To accurately detect read errors in postcodes. [Solution] The magnetic disk device of the embodiment comprises a magnetic disk having a plurality of tracks, a magnetic head for reading and writing data to the magnetic disk, a plurality of servo regions provided on each of the plurality of tracks and including at least a portion of a first physical pattern which is the physical pattern after writing of a first data pattern including position information for each of the plurality of tracks, and a control unit for controlling the magnetic head. Each of the plurality of servo regions includes either a first servo region including the first physical pattern, or a second servo region including a second physical pattern which is the physical pattern after writing of a second data pattern different from the first data pattern, for each of the plurality of tracks, and the control unit determines whether the third servo region among the plurality of servo regions of the plurality of tracks has the first or the second physical pattern.

An error correction circuit includes an error processing circuit, a poison processing circuit, and a data pattern conversion circuit. The error processing circuit receives data, generates delayed data, a parity signal, and syndrome information, and outputs error correction data generated by correcting errors in the data according to the syndrome information. The poison processing circuit outputs one of a write poison flag and a read poison flag as a poison pattern control signal in accordance with a write command. The data pattern conversion circuit converts the delayed data and the parity signal into a poison pattern according to the poison pattern control signal and outputs the poison pattern.

A system and method are provided for artificial intelligence (AI) model selection and loading. The method includes receiving an input data stream for an AI application and analyzing the stream to determine temperature scores based on topic frequency, importance weightage, and access frequency. The method also includes mapping these temperature scores to relevant expert AI models and predicting the temperature and context of the next prompt using historical data patterns through a sliding window that smooths temporary anomalies. The system dynamically selects AI models based on temperature scores and predicted contexts, then pre-loads these models into a memory hierarchy where higher-temperature models are placed in faster memory. The method processes incoming prompts using these pre-loaded models and outputs responses, improving processing efficiency through predictive model management. The system addresses challenges of managing large-scale AI models by implementing intelligent pre-loading mechanisms that anticipate and prepare for upcoming processing needs.

The present application relates to a kind of time series prediction methods under multi-scale double clustering, the present application can simultaneously capture the data pattern and relevance under different time scales by multi-scale input mechanism, improve the modeling ability to complex real-world signal.Effective decoupling and modeling TDS and CCT: by parallel patch intra-domain clustering module and mask attention module, respectively, special modeling is carried out on two core challenges of time distribution drift and channel correlation conversion, avoids causal confusion, and improves the robustness and generalization ability of the model.The model carries out twice dynamic, hybrid expert-based dependency filtering at patch level, can accurately capture instantaneous and time-varying correlation, avoids the information loss or noise interference caused by traditional coarse-grained or static dependency modeling.

A system and method for evicting a resource from a unified network topology (UNT) of a computing environment, is disclosed. The method includes: generating a representation of a UNT for a network topology of a computing environment, the representation based on a unified data schema, wherein the unified data schema is mapped to a plurality of data schemas, each corresponding to a unique data source; receiving an event record at a first time, the event record including a resource identifier; updating in the UNT a representation corresponding to the resource based on extracted data from the event record at the first time; determining that a time period has lapsed from the first time which is greater than a threshold; detecting that a number of event records which include the resource identifier is less than a threshold value; and removing the representation of the resource from the unified network topology.

A coding / decoding method and apparatus are disclosed, relating to the multimedia field. The method includes: dividing an 11B7T sequence into four groups, namely 3B2T, 3B2T, 3B2T, and 2B1T; using a 2-bit data pattern, setting feature combinations in specific regions of the 7 symbols mapped to the 11 bits; using these feature combinations as identifiers to indicate the encoding method; and at the decoding end, mapping the 7 symbols to 11 bits according to the encoding method indicated by the feature combinations of the specific regions. This divides a long sequence into multiple short sequences, performs PAM3 coding / decoding on these short sequences, and combines the feature combinations of the specific regions to indicate the encoding method, reducing the complexity of long-sequence PAM3 coding / decoding, thereby reducing the circuit implementation complexity of long-sequence PAM3 coding / decoding and improving PAM3 coding / decoding performance.

The invention provides a time-space spectrum strong correlation optimization method based on water ecological characteristic elements, and relates to the technical field of water ecological environment monitoring and simulation. Comprising five steps of synchronous acquisition and processing of multi-source water ecological data, spatial-temporal spectrum three-dimensional feature primitive structured construction, spatial-temporal spectrum strong association rule analysis based on a graph model, parameter optimization of a coupling physical mechanism and an association rule, and model closed-loop optimization under dual verification. According to the method, a standardized data set is generated by fusing multi-source data, space-time spectrum feature primitives are constructed, then space-time spectrum strong association rules among elements are mined by utilizing a graph attention network, then the rules are embedded into a mechanism model in a constraint form, parameter optimization is performed by adopting a hybrid optimization algorithm, dual driving of a physical mechanism and a data rule is realized, and the method is suitable for the spatial-time spectrum strong association rules. Finally, the optimization model is output through closed-loop verification iteration, and therefore the precision, rationality and generalization ability of water ecological simulation are effectively improved.

A system and method are provided for artificial intelligence (Al) model selection and loading. The method includes receiving an input data stream for an Al application and analyzing the stream to determine temperature scores based on topic frequency, importance weightage, and access frequency. The method also includes mapping these temperature scores to relevant expert Al models and predicting the temperature and context of the next prompt using historical data patterns through a sliding window that smooths temporary anomalies. The system dynamically selects Al models based on temperature scores and predicted contexts, then pre-loads these models into a memory hierarchy where higher-temperature models are placed in faster memory. The method processes incoming prompts using these pre-loaded models and outputs responses, improving processing efficiency through predictive model management. The system addresses challenges of managing large-scale Al models by implementing intelligent pre-loading mechanisms that anticipate and prepare for upcoming processing needs.

This invention discloses a high-value data mining method based on retail scenarios, belonging to the field of data mining technology. It includes parallel processing of original transaction records and customer trajectory data to extract two dimensions: product value and customer behavior. By constructing a cross-time, cross-category product value transmission link and quantifying customer behavior density and transition strength, the two are merged to form a multi-dimensional value field. A business focus, including rules such as value concentration thresholds, is injected into this value field to dynamically guide the aggregation direction of the data flow. Based on the guided data flow, the dynamic relationship network between customers and products is reconstructed. Through pruning and reinforcement operations, high-value data clusters are extracted, and these clusters are used as the core to iteratively evolve the final data pattern. This method effectively characterizes the dynamic value transmission process and can proactively focus the mining process according to business intent, thereby improving the accuracy and business orientation of pattern discovery.

Disclosed are systems and methods for intelligently transforming data to generate improved output data, including, for example, for use in a multi-application network with disparate parties. The systems and methods include transforming data using a probabilistic network and a knowledge base generated using historic data to generate improved output data and include the steps of receiving first data associated with a first user and associated with a first incident object. In some embodiments, the systems and methods include generating a first computing object, transmitting the first computing object, receiving a first selection, transmitting a data collection computing input tool, receiving a second selection, receiving second data comprising a first image of a first incident object, transmitting the second data, transforming the second data using a probabilistic network, a machine learning model, a knowledge base, and a data group associated with patterns of processed historic data, and generating improved output data.

The invention relates to an error correction circuit and a semiconductor device including the same. The error correction circuit includes an error processing circuit, a poisoning processing circuit, and a data pattern conversion circuit. The error processing circuit receives data, generates delay data, a parity signal, and syndrome information, and outputs error correction data generated by correcting an error in the data according to the syndrome information. The poisoning processing circuit outputs one of a write poisoning flag and a read poisoning flag as a poisoning pattern control signal in accordance with the write command. The data pattern conversion circuit converts the delay data and the parity signal into a poisoning pattern according to the poisoning pattern control signal and outputs the poisoning pattern.

A display device can include a display panel, a data driver, and a timing controller that transmits various signals through an interface connected to the data driver, analyzes a pattern of input data, and controls output strength of at least one of VOD or PE. The timing controller includes a data storage that distributes and stores the input data and then outputs pattern data, a pattern detector that detects whether the pattern data includes data of 0 after consecutive 1s, and detects whether the pattern data includes data of 1 after consecutive 0s, a pattern determination circuit that decodes first pattern detection signals and second pattern detection signals and outputs VOD control signals and PE control signals, and a driver that controls the output strength of at least one of the VOD or PE based on the VOD and PE control signals.

This invention provides a method for scanning a laser to generate three-dimensional information of an object without requiring high-precision timing control. [Solution] The scanning mechanism 10 scans the object with a laser according to pattern information indicating a projection pattern. The projection pattern indicated by the pattern information includes a clock line in which bright areas irradiated by the laser and dark areas not irradiated by the laser are arranged alternately, and a data line adjacent to the clock line in which data patterns representing numerical values ​​are arranged according to the arrangement of bright / dark areas. The data line has a sequence of data patterns in which, when a predetermined number of consecutive data patterns are viewed as one data, the data are different from each other. In the projection pattern, all bright areas are connected.

A system and method for generating a cybersecurity policy for a computing environment is presented. The method includes generating a representation of a computing environment in a security database having a predefined data schema; receiving a natural language query; matching the natural language query to a preexisting policy of a policy engine, the policy engine configured to apply a policy on the representation; generating a prompt for a large language model (LLM) based on the natural language query and the preexisting policy; applying a first policy to the representation, the first policy extracted from a result of executing the prompt utilizing the LLM.

A system and method for agentless detection of sensitive data in a cloud computing environment is presented. The method includes inspecting a disk for a cybersecurity object, the cybersecurity object indicating sensitive data, wherein the disk is deployed in a cloud computing environment; extracting a data schema from the cybersecurity object, in response to detecting the cybersecurity object on the disk; generating a classification of the data schema; determining that the data schema corresponds to sensitive data based on the generated classification; detecting in the disk a plurality of data files, each data file including the data schema; generating in a security database: a representation of the data schema, and a representation of each data file; and rendering a visual representation of the cloud computing environment including a representation of the data schema based on a query result of the security database.

This application discloses a cross-data space data synchronization method based on connector construction, belonging to the field of data integration. The method includes: determining a source connector and a target connector based on a user-submitted task definition file; the source connector is the connector corresponding to the source data space, and the target connector is the connector corresponding to the target data space; each connector includes a connection configuration unit, a data pattern descriptor, semantic mapping rules, a data reading interface, and a data writing interface; determining a data synchronization task graph based on the semantic mapping rules of the source connector and the target connector; reading the original data from the source data space through the source connector; mapping the original data pattern and original semantics corresponding to the original data to a general data pattern and general semantics; mapping the general data of the general data pattern and general semantics to the target data of the target data pattern and target semantics through the target connector; and writing the target data into the target data space.

Monitor the plant's operating status. [Solution] The plant system includes a generation unit that generates a first data pattern by operating a tuned physical model that reproduces the behavior of the plant's processing steps and a trained prediction model that predicts the process values ​​of the plant using the calculation results of the tuned physical model, using a dataset of the plant in a predetermined state, modifying a portion of the data, and operating the model using a dataset that includes the modified data; and an output unit that compares a second data pattern generated by operating the tuned physical model and the trained prediction model using measured values ​​taken in the plant's processing steps with the first data pattern and outputs the comparison result.

This invention provides a real-time software defect prediction system and method based on data heterogeneity, belonging to the interdisciplinary field of software engineering and artificial intelligence. The system includes: a code submission input module for receiving code submission data; a feature extraction module for extracting multidimensional features from the code submission data; a data heterogeneity awareness controller for identifying the data sub-distribution to which the code submission belongs; a basic prediction model module for providing general defect prediction capabilities and selecting whether to enable a lightweight adapter to obtain prediction results; and a result output module for outputting the defect prediction results. This invention adopts a "centralized basic model + lightweight adapter" architecture, eliminating the need to maintain a complete prediction model for each data pattern; it also significantly improves prediction performance, making it suitable for real-time defect prediction scenarios in actual software development processes, and has high engineering practical value; it enhances robustness under abnormal samples and optimizes the engineering input-output ratio.

In example embodiments, a semantic multimodal search function is provided in an engineering application for searching engineering documents. In an indexing phase, the application indexes a library of engineering documents to build an embedding database. For each of the engineering documents, a region detector extracts regions that each correspond to a different mode of technical or engineering data, and a set of ML models is used to generate embeddings that represent the semantic significance of technical or engineering data in each of the regions. In a query phase, the application receives search input and uses the region detector to extract regions that each correspond to a different mode of input. The application uses the ML models to generate embeddings that represent the semantic significance of input in each of the regions, and then performs a vector search between these embeddings and those in the embedding database.

The invention provides a self-adaptive blade deicing system and method based on multi-modal perception and AI decision, and is applied to the technical field of wind power generation, the system comprises an acquisition device used for acquiring blade state data; the mode selection module is used for adopting different operation modes according to the external environment state data, correspondingly starting acquisition devices in different areas on the blade, and acquiring real-time blade state data; the data processing and communication module is used for performing primary feature extraction on the real-time blade state data to obtain primary feature data and synchronously transmitting the primary feature data to the AI decision module; the AI decision module is used for generating a deicing measure of the blade based on the primary characteristic data, performing ice melting process prediction to obtain a secondary icing risk, and generating an enhancement strategy in combination with the secondary icing risk; and the partition execution module is used for performing partition deicing according to the enhancement strategy. The problems that a traditional deicing system is high in energy consumption, fuzzy in perception and prone to causing secondary icing are effectively solved.

A glucose data analysis system includes an on-body unit and a processor. The on-body unit is configured to measure glucose levels of a user. The processor is configured to receive therapy data of the user including glucose data monitored by the on-body unit, analyze daily glucose profiles of the glucose data over an analysis period by a machine learning model, group the daily glucose profiles into two or more patterns based on the analysis performed by the machine learning model, and output a report including identification of the two or more patterns associated with each daily glucose profile. Advantageously the system may identify and visualize patterns of glucose data, identify recurring patterns, identify correlations between patterns and external events (e.g., medication, meals, exercise, sleep, stress, etc.), reduce glycemic variability, streamline analysis with unsupervised machine learning (e.g., no training), and provide recommendations for user intervention or actions based on identified patterns.