411 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 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.

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.

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 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.

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.

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.

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.

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.

A vehicle recommendation system comprising: a specialized data normalization architecture comprising a semantic understanding to automatically map diverse data schemas to a unified vehicle representation model; a specialized transformer architecture associated with a graph-based data representation to capture complex multi- stakeholder relationships associated with vehicle transactions and track the temporal evolution of the complex multi-stakeholder relationships; and a multi-objective optimization sub-system comprising reinforcement learning architecture to balance vehicle transactions constraints in real-time by generating optimal transaction parameters.

The invention relates to the technical field of data cleaning, in particular to an automatic heterogeneous data cleaning method and system based on a configurable rule engine. The method comprises the following steps: acquiring a heterogeneous data source, uniformly converting a data mode and an instance into a multi-dimensional heterogeneous fact graph, calculating an execution weight for each conflict rule according to the authority of the data source and the priority of the rule when a write operation conflict is detected, segmenting data partitions by utilizing the betweenness centrality of a connected component and a node, the method comprises the following steps of: generating a plurality of data partitions, forming rule subsets corresponding to the data partitions, creating isolated Drools session instances to perform parallel cleaning, generating traceability logs in the parallel cleaning process, aggregating the traceability logs after all the data partitions are cleaned, and re-evaluating and updating the authority of each data source to solve rule conflicts in the next cleaning period. According to the scheme, the writing conflict can be precisely processed, the safety of parallel cleaning is improved, the processing period is shortened, and a continuously optimized processing closed loop is formed.

Systems and methods directed to validating an address extracted from a telephone conversation. An artificial intelligence (AI) engine is operable to utilize natural language processing and large language model (LLM) capabilities to generate and analyze a transcript of the telephone conversation in real-time. The AI engine is operable to extract an address from the telephone conversation, populate a custom data schema, request more information, and compare the extracted address to a database of addresses to ensure the accuracy of the extracted address.

The technology disclosed relates to detecting a data attack on a local file system. The detecting includes scanning a list to identify files of the local file system that have been updated within a timeframe, reading payloads of files identified by the scanning, calculating current content properties from the payload of the files, obtaining historical content properties of the files, determining that a malicious activity is in process by analyzing the current content properties and the historical content properties to identify a pattern of changes that exceeds a predetermined change velocity. Further, the detecting includes determining that the malicious activity is in process by analyzing the current content properties and known patterns of malicious metadata to identify a match between the current metadata and the known patterns of malicious metadata, determining a machine / user that initiated the malicious activity, and implementing a response mechanism that restricts file modifications by the machine / user.

A method for verifying a low-density parity-check (LDPC) unit capable of being applied in a memory system can include receiving original data corresponding to a memory device, encoding the original data by the LDPC unit to be verified, injecting errors into the encoded original data by a data pattern for generating verifying data, and verifying a soft decode capability of the LDPC unit by utilizing the verifying data. The data pattern can include the errors generated by threshold voltage (Vth) distributions interlaced between two neighboring logic states of 2n logic states of the memory device. The method and system can provide an error injection to accurately and efficiently verify a LDPC soft decode capability of the LDPC unit, decrease errors, increase error correction accuracy and efficiency, more accurately model actual threshold voltage (Vth) distributions, increase flexibility, increase speed, increase performance, and reduce firmware overhead.

The invention provides a big data compliance risk control method and system, and relates to the technical field of big data processing and risk control. By obtaining the original business data and adding the metadata information for packaging, standardization and traceability of the data source are realized. Then, the metadata information is utilized to carry out preliminary matching in the data mode dictionary, and the potential mode of the data can be quickly identified. The data structure and the field type are inferred through the probability model, and the inference confidence is output, so that the problems of analysis errors and label noise caused by nonstandard data format and update lag of the analyzer in the prior art are effectively solved.

The invention discloses a data simulation method based on a database function and a timed task. The method comprises the following steps: S1, acquiring source data based on a multi-modal lightweight intelligent acquisition strategy; s2, performing data cleaning and standardization so as to ensure engineering processing of simulation data quality and consistency; s3, creating a user-defined function in the database to package intelligent logic of data simulation; according to a time condition and a data mode in the timed task, intelligently performing simulation trigger judgment and simulation data generation through the user-defined function; s4, performing efficient data injection based on a buffer and batch optimization mechanism of the intermediate table; and S5, performing timed task period regulation and control. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The invention relates to the technical field of medical information, in particular to an interventional operation patient perioperative period nursing information intelligent interaction and management system which comprises the steps that through an integrated data collection module, a timeline generation module, a risk identification module, a nursing intervention module and an interaction management module, time axis alignment and structured coding are conducted on scattered physiological data and diagnosis and treatment events; and a dynamic comprehensive nursing timeline is formed. And performing real-time analysis on the timeline based on a preset risk knowledge graph, identifying an abnormal data mode and generating an early warning. The system automatically matches a nursing rule base, pushes individualized intervention suggestions including specific operations, priorities and bases, synchronizes information and collects feedback through a multi-role interaction channel, and realizes closed-loop management. According to the system, fragmented nursing information can be integrated into a continuous dynamic view, and risk early warning and structured nursing actions are directly linked, so that the collaboration, accuracy and timeliness of nursing management are improved.

The invention provides a risk identification method for a wind power system, and relates to the technical field of electric power safety supervision data processing, and the method comprises the steps: collecting control instruction sending and receiving timestamp data from a wind power system network communication link, recognizing a transmission time consumption value of each control instruction, and building a communication performance data set; based on the communication performance data set, obtaining timestamp records of equipment response and adjustment completion from the execution module, calculating a time difference value between instruction issuing and equipment adjustment completion, and forming a system response efficiency index library; and establishing an attack risk classification model based on the network security abnormal mode library, analyzing the offset of the power set value of the wind turbine generator and the data mode of the change frequency of the control instruction through the attack risk classification model, identifying different types of network security threats, and generating a security risk classification file.

The invention discloses an online diagnosis and treatment data supervision method and system based on artificial intelligence, which is used for carrying out real-time intelligent supervision on free text diagnosis and treatment records generated by an online medical platform, and specifically comprises the following steps: carrying out semantic coding on text fields such as basic information, chief complaint, present medical history, past history and diagnosis of a patient; executing necessary filling and logic consistency verification based on a predefined multi-level rule base; clustering analysis is carried out on the semantic vectors by adopting a density clustering algorithm and the like, and semantic abnormity or duplicate records are identified; filtering junk texts and test data by using keyword extraction and named entity recognition technologies; and finally, outputting a structured supervision report and adding a quality label to each record. Through the method, various quality and compliance problems in the diagnosis and treatment data can be found and labeled in real time, and powerful support is provided for compliance supervision of an internet medical platform, improvement of service quality and guarantee of data credibility; meanwhile, a rule-driven and AI model-driven'double-track 'framework is adopted in the method, and an active learning mechanism is combined, so that the system can continuously perform self-learning and iterative evolution and continuously adapt to a new data mode and a new supervision requirement.

The invention relates to the technical field of wind power plant short-term wind power prediction, in particular to a wind power plant short-term wind power prediction method based on data mode pre-judgment. Comprising the following steps: collecting historical multi-dimensional feature data, dividing the data into a core group and an edge group according to frequency, generating fine-grained codes by the core group through a sliding window, generating sparse codes by the edge group based on events, and constructing a time-space coding matrix with balanced frequency in combination with an adaptive weight algorithm; after the matrix is segmented, an intensive mode and an envelope mode are extracted, and meteorological type, unit topology and time dynamic three-level indexes are constructed and stored in a historical mode library; extracting a corresponding mode from the real-time data, matching a historical mode library through three-level indexes, and classifying the historical mode library into four association modes; based on association mode grading weight fusion prediction, forming a composite prediction model; and comparing prediction and actual measurement results in real time, and triggering an adaptive learning updating model. The method effectively balances the influence of high-frequency and low-frequency events, and improves the prediction precision and model adaptability.

Methods, systems, and computer-readable storage media for receiving a user query provided in natural language and requesting data stored in a resource according to a data schema, determining a set of relationship and context data represented in a knowledge graph, the set of data schema and context data determined to be relevant to the user query from a super-set of data schema and context data stored in a graph database, generating a prompt using the user query and the set of data schema and context data, receiving a data query from a LLM that is responsive to the prompt, the data query being in a structured format, processing the data query to provide a query result, and displaying the query result to a user.

The invention relates to the technical field of power grid monitoring, in particular to a digital power grid hidden asset detection and anomaly modeling method, system and device based on a variational auto-encoder and a storage medium. The method comprises the following steps: acquiring multi-dimensional operation data such as active power, reactive power, a voltage effective value, a current effective value, frequency, a power factor and third harmonic content in a digital power grid, constructing a variational auto-encoder model comprising an encoder, a decoder and a loss function module, and carrying out unsupervised training only by using normal operation data; the method automatically learns the potential distribution characteristics of the normal operation mode of the power grid, builds an anomaly discrimination mechanism based on reconstruction errors, achieves the active recognition of private connection equipment, temporary loads and other hidden assets through the characteristic that VAE is poor in reconstruction capability of an unseen data mode, and solves the technical problem that traditional static management cannot dynamically sense illegal access. And establishing a four-level grading alarm mechanism and time window consistency discrimination, and providing quantitative abnormal evaluation and false alarm suppression.

According to embodiments, a memory system includes a data management circuit, a memory, a write control circuit, and a data determination circuit. The data management circuit manages data, which is received from a host on a first data size basis, on a second data size basis. The second data size is greater than the first data size. The data determination circuit determines whether or not data of the second data size received by the write control circuit matches a data pattern set in advance. In a case where first data received by the write control circuit matches the data pattern, the data management circuit sets a flag indicating that the first data matches the data pattern in an entry of a management table corresponding to the first data, and the write control circuit discards the first data without writing the first data to the memory.