4341 results about "Data ingestion" patented technology

The present invention provides a system and method for causality-augmented generative intelligence capable of autonomously discovering non-obvious actionable insights from heterogeneous and multimodal data sources. The system integrates a data ingestion unit for semantic and temporal harmonization of structured and unstructured datasets, a causal inference processor for constructing a dynamically evolving directed causal knowledge representation using perturbation-based validation, a latent representation processor that combines multimodal semantic embeddings with causal parameters to generate fused latent vectors, and a generative insight processor utilizing causally constrained generative reasoning to synthesize hypotheses anchored to verified cause-effect dependencies. A validation processor performs counterfactual assessment and observational verification to ensure retention of only those insights that remain consistent with causal ground truth.

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

The invention discloses a three-dimensional Gaussian sputtering scene reconstruction method based on structure perception refined Gaussian, and aims to solve the problems of Gaussian drift, edge blur, structure artifacts and the like of a reconstruction model due to the fact that sparse point cloud contains outliers, Gaussian morphology and normal are mismatched and a multi-dimensional optimization target is lacked in an existing three-dimensional Gaussian sputtering reconstruction method. A key frame is extracted by collecting target scene video data, sparse three-dimensional point clouds are reconstructed by using an SfM algorithm, a depth map and a normal map are generated through a Lotus model, three-dimensional Gaussian distribution is initialized after the sparse point clouds are filtered, a Gaussian covariance matrix is adjusted by using a normal consistency regular term, and the sparse point clouds are extracted. And after structure attribute analysis is carried out, a comprehensive scoring function is constructed to screen Gaussian points, and finally, a combined training framework including luminosity, normal consistency and structure continuity loss is adopted to optimize and generate a three-dimensional Gaussian scene model. The method is mainly applied to the field of three-dimensional reconstruction and multi-view rendering, and scene reconstruction precision and geometric consistency can be improved.

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The present disclosure provides a method and system for orchestrating automated data processing and transformation. A centralized orchestrator receives a request to process a client dataset and initiates a data ingestion process to obtain sample data. A semantic analysis module analyzes the sample data to determine semantic types of data fields. A transformation module generates data transformation instructions based on the determined semantic types. The orchestrator deploys a data processing pipeline to a client-controlled environment and configures privacy preservation parameters to identify and obfuscate potential personally identifiable information. The pipeline applies the transformation instructions and privacy parameters to the dataset. A configuration module determines data storage configurations for the transformed dataset. The transformed dataset is stored according to the configurations in a client-controlled or cloud environment. A machine learning module generates a model based on the transformed dataset, which is stored in a model repository accessible to the client.

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

A system and a method for automatically discovering and managing actions in an application is disclosed. The system includes a data ingestion layer for receiving application data from multiple sources, a scanning and systematic traversal engine for interacting with UI elements and capturing network calls, an action mapping and generation module for correlating UI actions with API calls and categorizing actions, an AI-driven icon and description generator for creating visual representations and textual descriptions of actions, a user interface for displaying and modifying discovered actions, and a continuous monitoring component for triggering re-scanning based on coverage metrics, error detection, or version updates. The system employs synthetic data generation and AI-driven exploration to uncover hidden or undocumented APIs, enabling comprehensive mapping of an application's capabilities at the API level.

The invention relates to a power adapter charging protocol identification and compatibility self-learning optimization method. After the adapter establishes physical connection with a target terminal, a voltage signal output by the target terminal is collected, a handshake data sequence is formed, feature extraction is performed on the handshake data sequence, and a first protocol feature vector is generated. And performing similarity matching on the feature vectors in a protocol feature library to determine a historical protocol category and extract a corresponding historical charging parameter template. And during voltage and current step-by-step adjustment, collecting target terminal load impedance change data, extracting impedance spectrum features, and fusing the impedance spectrum features with the first protocol feature vector to form an enhanced protocol feature vector. And based on the enhancement protocol feature vector and the historical parameter template, predicting negotiation parameters supported by the target terminal, generating a predicted negotiation parameter set, and applying the predicted negotiation parameter set to a charging negotiation process, thereby realizing stable charging output with the target terminal. According to the method, the compatibility and the self-adaptive capability of the power adapter to various fast charging protocols can be improved.

The present invention relates to a system and method for cross-domain predictive modeling using Bedrock-based foundation models and blockchain-anchored data. The invention integrates large-scale foundation model reasoning with distributed ledger-based data provenance to enable verifiable, secure, and explainable predictive analytics across heterogeneous domains such as finance, healthcare, logistics, and environmental systems. The system comprises a data ingestion unit for receiving and normalizing multi-domain datasets, a blockchain anchoring unit for generating cryptographic hashes and recording data provenance into a distributed ledger, a cross-domain harmonization processor for aligning heterogeneous feature representations into a unified latent space, a foundation model processor configured to execute Bedrock-based predictive inference with adaptive domain contextualization, a verification processor for validating predictions against blockchain-anchored ground truths, and a governance processor for maintaining immutable audit trails of model evolution.

The invention discloses an anomaly detection and intelligent diagnosis method, system and device based on digital power grid multi-source data and a medium, and belongs to the technical field of anomaly detection, and the method comprises the steps: obtaining multi-source operation data from a power grid operation process, carrying out the preprocessing, and generating a standardized data set; time sequence features are extracted based on historical data, a power grid state reference model is constructed, and normal operation states in different load scenes are represented; on the basis of deviation calculation of the standardized data set and the power grid state reference model, abnormal candidate signals are detected, and high-confidence-coefficient abnormal signals are screened and generated; determining an abnormal source based on the high-confidence abnormal signal in combination with a power grid topological structure, and performing analysis to obtain fault type information; and generating a control instruction according to the fault type information and issuing the control instruction to a power grid control system. According to the method, a complete technical scheme of multi-dimensional data fusion, dynamic deviation detection, high-confidence anomaly screening, anomaly source accurate positioning and fault type rapid diagnosis is realized.

The invention relates to the technical field of energy storage battery diagnosis, and discloses an energy storage battery fault diagnosis method and system based on a data fusion algorithm. The method comprises the following steps: acquiring historical operation data and real-time operation data of an energy storage battery in a preset operation period, and generating a historical fault data set according to the historical operation data; performing multi-source feature fusion processing on the historical fault data set to generate a fusion feature parameter set integrating voltage, current, temperature and impedance parameter joint change features; a multi-dimensional fault space is constructed based on the parameter set, coordinate axes of the multi-dimensional fault space correspond to different parameter dimensions, and spatial position coordinates represent parameter change characteristic values; calculating the fault correlation degree of the historical fault event in the multi-dimensional fault space, and determining a fault early warning index set; and extracting real-time characteristic parameters based on the real-time operation data to form a state vector, carrying out space mapping correlation calculation on the state vector and the fault early warning index set in a multi-dimensional fault space, and outputting a real-time fault correlation factor.

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

The invention provides an intelligent event identification method and system based on a high-speed camera, and the method comprises the steps: setting the collection parameters of the high-speed camera, and triggering the camera to collect a target scene video stream. And hardware acceleration decoding processing is carried out on the collected original video data stream, real-time environment illumination information of the environment illumination sensor is obtained, and dynamic brightness equalization processing is executed. And performing motion adaptive denoising processing on the video sequence. Geometric distortion correction is carried out on the image sequence through camera calibration parameters, sub-pixel-level displacement vectors and dense optical flow field data of a moving object are extracted, and multi-scale morphological features are extracted. And the features are fused to generate motion feature data, the data are processed through a spatio-temporal joint event classification model, an event identification result is output, the result is bound with a high-precision timestamp, and event identification information is output to an industrial control system display device in real time. According to the invention, the accuracy and real-time performance of event identification can be improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a decision-making method and device guided by a multi-modal semantic map, equipment and a medium. Extracting a visual feature vector, a language feature vector and an action feature vector, splicing to generate a multi-modal initial feature, mapping the multi-modal initial feature to a shared semantic space, constructing a multi-modal semantic map, and inputting a map-guided attention mechanism to generate a cross-modal alignment feature; the cross-modal alignment features and task targets are input into a meta-learner to generate task adaptability features, the task adaptability features are input into a parallel reasoning network to execute subtasks in parallel, and a gating fusion network integrates output results to generate a global decision. According to the method, cross-modal semantic association and task adaptability are enhanced through the combination of shared semantic space mapping, map guiding attention and a meta learning device, and the accuracy and efficiency of multi-modal decision making are improved through the combination of parallel reasoning and gating fusion.

The invention discloses an anomaly detection model selection method and system based on index portraits, and relates to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: collecting historical data of a target monitoring index, extracting multi-dimensional features to construct an index portrait, and classifying the index portrait; screening candidate anomaly detection models from the matching rule base, performing adaptation degree scoring in combination with a model compatibility evaluation mechanism, determining an optimal anomaly detection model to perform anomaly detection, and outputting an anomaly judgment result; when a plurality of models exist, generating a final abnormal result through a confidence-driven arbitration mechanism; for multi-index abnormity, causal reasoning is carried out in combination with an electric power knowledge graph, main alarm indexes are determined, and secondary indexes are processed according to a delay strategy; meanwhile, incremental updating of index portrait features, adaptive adjustment of model parameters and dynamic optimization of matching rules are supported, and a whole-process closed-loop mechanism covering'portrait construction-model matching-result fusion-alarm decision-feedback updating 'is constructed.

The embodiment of the invention provides a multi-modal fusion deep learning analysis method and system. The method is applied to the technical field of multi-modal learning, and comprises the following steps: obtaining multi-modal original data, sequentially processing image, text, audio and video data, and extracting visual features of the image, semantic features of the text, frequency spectrum and time sequence features of the audio, image features and time sequence features of a video frame and time domain features of an audio sequence; and then, according to the complementary information of the multi-source features, fusion processing is carried out to form a unified multi-modal feature representation, the unified multi-modal feature representation is input to a preset deep learning analysis model, and finally a multi-modal analysis result of comprehensive expression is obtained. According to the scheme, information complementarity and robustness are enhanced through multi-modal feature fusion, the comprehensive analysis capability of the model on semantic understanding, behavior recognition and state judgment in a complex scene is remarkably improved, and a more accurate, efficient and stable decision basis is provided for a multi-modal intelligent sensing system.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

The invention relates to the technical field of data monitoring, in particular to a battery production whole-process data monitoring system and method. The method comprises the following steps: acquiring real-time multi-source data of a battery production line and an assembly station image; analyzing the current abrupt change amplitude and the regional temperature difference standard deviation of the real-time multi-source data of the battery production line; identifying a production stage boundary point of the assembly station image, and performing production process division on the battery by using the current abrupt change amplitude and the regional temperature difference standard deviation to generate battery production process data; and an electrode slurry preparation stage of extracting battery production process data, and performing quantum dot sensor array deployment on the slurry stirring kettle based on the electrode slurry preparation stage to obtain sensor array deployment data. According to the invention, multi-source real-time data fusion, quantum dot sensor array spectrum decoding, three-dimensional particle state reconstruction and digital twinning technologies are utilized, so that the monitoring precision, the abnormity identification capability and the intelligent collaborative response level of the whole battery production process are improved.

The invention discloses a power transmission line fault diagnosis and operation and maintenance scheduling method and system based on networking learning, and relates to the technical field of power transmission line fault diagnosis operation and maintenance scheduling. Related data is extracted to construct a high-risk equipment area and a visual high-risk area thermodynamic diagram, a visual risk grading diagram is constructed in combination with electrical quantity data, and meanwhile, an intelligent recognition storage network and a fault type classification recognition model are constructed in combination with a convolutional neural network-long and short-term memory network hybrid model; the model is optimized through networking learning and an attention mechanism, maintenance teams and resources are autonomously allocated in combination with an operation and maintenance management system, then autonomous optimization and closed-loop operation are achieved, full-process coverage of fault sensing, intelligent decision making and efficient response is achieved, the response time after a line fault occurs is remarkably shortened, and the maintenance efficiency is improved. And the fault handling and operation maintenance capabilities of the power grid system are comprehensively enhanced.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a multi-source data-based customer portrait generation method, device and equipment and a medium, and the method comprises the steps of obtaining structured data and unstructured data of a target customer in a data source; separately performing privacy desensitization processing on the structured data and the unstructured data to obtain structured desensitization data and unstructured desensitization data; extracting multi-modal features and time sequence features of the structured desensitization data and the unstructured desensitization data, and performing feature fusion on the multi-modal features and the time sequence features to obtain fusion features; constructing a target label set according to the fusion features, and analyzing real-time label weight distribution of the label set by using a federal learning model; and updating the target label set according to the real-time label weight distribution to obtain a real-time label set, and generating a real-time portrait according to the real-time label set. The accuracy of a customer portrait generation result can be improved.

The invention relates to the technical field of metasurface antennas, and provides a metasurface antenna parameter optimization method and system based on a convolutional neural network, and the method comprises the steps: collecting metasurface sample data; extracting a comprehensive electromagnetic feature set, and establishing a nonlinear mapping relation model; constructing an antenna performance comprehensive evaluation function, inputting the nonlinear mapping relation model and the antenna performance comprehensive evaluation function into a hybrid optimization algorithm to generate a parameter candidate set, and performing local optimization on the parameter candidate set by using a particle swarm algorithm to obtain a metasurface antenna parameter combination; and extracting electromagnetic characteristics of the metasurface antenna parameter combination, iteratively adjusting the height parameter of the resonant cavity until the height parameter meets a threshold value to obtain an electromagnetic simulation verification result, and feeding back the electromagnetic simulation verification result to the deep Q neural network model for parameter updating to obtain an optimal metasurface antenna parameter combination. According to the method, the optimization of antenna parameters is realized, the design efficiency of the metasurface antenna is improved, and the consumption of electromagnetic simulation calculation resources is reduced.

The invention relates to the technical field of remote sensing recognition, in particular to a remote sensing recognition method and system applied to ecological system investigation. The method comprises the following steps: acquiring multi-temporal remote sensing image data of a target area; extracting a land cover type of the multi-temporal remote sensing image data, and performing dominant human activity area identification on the multi-temporal remote sensing image data according to the land cover type to generate dominant human activity area data; acquiring night light data of the target area; performing space-time registration on the night light data of the target area and the multi-temporal remote sensing image data to generate fused night light remote sensing data; performing boundary region extraction on the multi-temporal remote sensing image data through the land cover type to obtain edge region data; and calculating a vegetation index and a noctilucence index of the marginal region data based on the fused noctilucence remote sensing data. According to the method, through multi-temporal and multi-source data fusion and multi-index time sequence analysis, the accuracy and comprehensiveness of ecological system investigation remote sensing recognition are improved.

The invention relates to the technical field of semiconductor manufacturing, and discloses an edge breakage defect prediction and compensation method in a wafer cutting process, and the method comprises the steps: obtaining a wafer cutting initial scheme and historical cutting data, extracting edge breakage key parameters, defining an edge vulnerability function, and building a high-risk region space mapping model; according to the real-time cutting position, multi-dimensional data are collected through a three-level monitoring mode, the abnormal deviation state of the track is judged, and an edge breakage risk assessment model is constructed and early warning is carried out; a compensation scheme is formulated after early warning is triggered, cutting is implemented, and the effect is verified; according to the method, multi-dimensional data are fused, the risk area is dynamically delimited, self-adaptive adjustment of the cutting parameters is achieved, and the wafer cutting yield is remarkably improved.

The invention discloses a switch loop data extraction method, device and equipment based on phase locking, and the method comprises the steps: carrying out the interval change analysis of operation data in a ring main unit, and recognizing a communication abnormal node through a phase locking technology; associating a mechanical vibration source based on the communication abnormal nodes, detecting a vibration conduction path, extracting current fluctuation characteristics, and determining position deviation; the operation current intensity is adjusted in combination with fluctuation characteristic parameters, and accurate off-position control is achieved through reverse braking force; constructing a dynamic threshold reference based on frequency domain analysis, and generating a complete operation record according to the dynamic threshold reference and the accurate parking data; performing typed grouping and transmission optimization on the data, and controlling data switching by adopting a gradually migrated beat sequence; and finally, through signal reconstruction and data fusion technologies, a switch loop data set with continuous time sequence and complete space is generated. The method can effectively cope with communication interference, mechanical vibration and other complex environmental factors, and improves the integrity and accuracy of data acquisition.

The invention discloses an intelligent digital human training method and system based on multi-modal interaction, and belongs to the technical field of semantic indexing.The method specifically comprises the steps that voice, vision and text data are analyzed and converted into high-dimensional feature vectors through a modal exclusive encoder, the high-dimensional feature vectors are projected to a unified semantic space through a cross-modal semantic mapping model, and the high-dimensional feature vectors are obtained; generating a semantic primitive containing a modal identifier, a core semantic tag and a feature weight; semantic primitives are used as nodes, directed edges and edge weight table association strength are established based on semantic similarity, typical scene node connection weights are strengthened, and a mesh map containing intra-modal hierarchy and inter-modal cross association is formed; constructing a double-layer index on the basis of the mesh map; semantic primitives are extracted from newly added data, the position of a new node in an association graph is determined through a graph matching algorithm, an association edge with an existing node is automatically established, and a lower-layer modal exclusive index is synchronously updated.