308 results about "Drift detection" patented technology

The invention discloses an AI agent design platform decision optimization method based on big data, and particularly relates to the field of artificial intelligence, comprising multi-modal data sensing layer construction, a streaming feature calculation engine, a dynamic index fusion center and an adaptive decision matrix. According to the method, accurate synchronous monitoring of the utilization rate of hardware resources and dynamic collaborative optimization of heterogeneous computing units are achieved, and the resource scheduling efficiency in a complex computing scene is remarkably improved; knowledge system degradation caused by long-term learning is effectively prevented, and the continuous reliability of a cognitive system is ensured. The provided multi-dimensional decision credibility verification system is fused with interpretability penetration analysis, environment coupling modeling and logic drift detection technologies, the limitation of a traditional single credibility index is broken through, the risk prediction and fault-tolerant capability of the decision process is remarkably enhanced, and a full-dimensional safety decision guarantee system is constructed for an intelligent agent.

Systems / techniques that facilitate data drift detection for unstructured texts via deep learning autoencoders are provided. In various embodiments, a system can access a pre-trained natural language model and a set of unstructured texts on which the model is to be executed. In various aspects, the system can determine, via execution of a deep learning autoencoder, how different a first set of reconstruction errors associated with the set of unstructured texts are from a second set of reconstruction errors associated with a set of training unstructured texts on which the pre-trained natural language model was trained. In various instances, the system can generate, in response to a determination that the first and second sets of reconstruction errors differ by more than a threshold margin, a first alert indicating that data drift has occurred and that the model is thereby unable to confidently analyze the set of unstructured texts.

The invention discloses a circuit board AOI detection result analysis method based on an intelligent algorithm, and the core of the method is to carry out the high-precision synchronous collection, denoising normalization processing and time sequence alignment of a detection image and multi-dimensional process parameters, and extract joint features through an attention mechanism. A process parameter-detection result correlation distribution model under each process scene is established, model output abnormal drift detection and tracing reasoning are realized, the detection model and the process parameters are dynamically optimized in combination with interpretability AI, the method realizes efficient tracking, attribution and adaptive optimization of detection misjudgment caused by process parameter variation, and the detection accuracy is improved. And the stability of the production process of the SMT detection system is improved.

The invention discloses a federated learning and drift detection-based network abnormal flow detection method and system, and the method comprises the steps: carrying out the data preprocessing and spatial-temporal feature extraction on a plurality of edge nodes, and generating feature data; and each edge node independently detects whether the flow has concept drift or not, and if drift is detected, incremental learning is started and only changed data is updated. The updated local model parameters are uploaded to a central server, aggregated and generated into a global model, and the global model is issued to an edge node for real-time flow detection after evaluation. According to the method, federated learning and drift detection technologies are combined, data privacy can be protected, the accuracy and real-time performance of network flow detection can be improved, and the method is suitable for flow monitoring and anomaly detection in a large-scale distributed network. And a graph neural network model and a bidirectional long-short term memory network model are adopted to improve the precision of feature extraction and model training, so that the stability and reliability in a complex network environment are ensured.

The invention discloses a transistor high-power electromagnetic heat model optimization system based on deep learning, and relates to the technical field of electromagnetic heat. Comprising an input module, a deep learning module, a global optimization module, an online adaptive module and a real-time simulation module. The input module obtains various key parameters and constructs a high-dimensional input space; the deep learning module constructs a deep learning model by embedding a physical constraint and uncertainty quantization mechanism; the global optimization module is used for carrying out global search on design parameters, rapidly evaluating a target function by utilizing an agent model, and searching a Pareto optimal solution in a design parameter space; the online adaptive module is used for drift detection and updating; the real-time simulation module simulates an electromagnetic field and a thermal field in real time, and data exchange ensures timely synchronization of boundary data through dynamic scheduling and load balancing; all the modules cooperate with one another through data and feedback, and a complete, closed-loop and continuously self-adaptive updating optimization system is achieved.

The invention relates to the technical field of smart factory decision-making, and discloses a digital twin-driven smart factory AI intelligent decision-making system, which comprises a data acquisition and preprocessing module used for acquiring and preprocessing real-time and historical data of a production line; the twinborn modeling identification module is used for constructing a twinborn body and carrying out parameter identification and uncertainty quantification; the alignment evaluation credible module is used for comparing twin prediction data with real data and generating availability marks and trust scores; the strategy simulation optimization module is used for generating candidate strategies and risk evidences based on the constraints and the key performance indicators; the grayscale online publishing module is used for screening and grayscale publishing a strategy according to the trust score and the performance result; the operation evaluation auditing module is used for collecting operation data and generating an auditing packet; and the drifting detection updating module is used for detecting distribution drifting and recalibrating and updating the twinborn body and the strategy library. According to the invention, the reliability, robustness and continuous optimization of the production decision of the smart factory are realized.

The invention discloses a self-adaptive calibration system and method for a combustible gas sensor. The self-adaptive calibration system and method are suitable for a catalytic combustion type combustible gas sensor. The system comprises a basic calibration unit, a real-time monitoring unit, a drift detection and compensation unit and an output adjustment unit. The basic calibration unit is used for carrying out basic calibration on the sensor and determining an initial sensitivity coefficient, zero offset and response time of the sensor; the real-time monitoring unit is used for monitoring real-time parameters of the sensor under different environmental conditions; the drift detection and compensation unit is used for establishing a sensor performance parameter drift model and calculating a calibration compensation coefficient; the output adjusting unit is used for adjusting the voltage output value of the sensor in real time according to the calibration compensation coefficient; the method can adapt to environment change for real-time calibration, effectively compensates sensor drift, improves measurement precision and reliability, prolongs the service life of the sensor, reduces maintenance cost, and improves system safety.

The invention relates to the technical field of electronic data processing in pipe gallery diagnosis, and discloses an underground comprehensive pipe gallery fault self-diagnosis system based on AI multi-source data identification, and the system comprises a data collection module which is used for collecting multi-type environment parameter data in real time; the environment fingerprint construction module is used for learning multi-dimensional environment parameter characteristics in a normal operation state by using an AI algorithm and constructing a dynamically updated normal operation environment fingerprint baseline; the real-time environment fingerprint extraction module is used for extracting environment fingerprints at the current moment in real time; the environment fingerprint drift detection module is used for comparing the difference degree between the real-time environment fingerprint and the baseline, and judging the drift if the difference degree exceeds a threshold value; and the intelligent alarm module generates a fault alarm in combination with the context information. According to the invention, the environment fingerprint base line of the underground comprehensive pipe gallery is constructed, and the drifting state of the underground comprehensive pipe gallery is monitored, so that the environment abnormity can be found in time, a brand new visual angle is provided for early fault early warning, maintenance personnel can intervene earlier, and the safety and stability of the operation of the pipe gallery are obviously improved.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

The invention relates to the technical field of transformer substation intelligent inspection, in particular to a meta-learning-based small-sample transformer substation equipment state adaptive inspection system, which comprises a state acquisition module for acquiring the current feature vector and environmental parameter data of a target node; the drift detection module is used for comparing environment parameters to judge data drift and dynamically adjusting a confidence coefficient threshold value; the risk assessment module inputs the feature data into a meta-learning model to output an initial risk probability, and generates an effective risk probability based on threshold filtering; the blind area measurement module is used for acquiring unobserved nodes and calculating system state blind area entropy; the scheduling decision-making module is used for comparing the blind area entropy with a threshold value and generating an entropy reduction bottom instruction or a self-adaptive routing inspection distribution instruction; the strategy updating module is used for extracting an actual inspection result and feeding back to the model for parameter updating; according to the invention, the scheduling difficulty when the resources are limited is solved, and the self-adaptive capability of the system under different environment interferences is improved.

The invention discloses a key sensor short-time abnormal distribution drift detection method in a unit start-stop process, and belongs to the technical field of gas turbine power plant financial supervision and artificial intelligence, and the method comprises the steps: synchronously triggering multi-channel signal collection through a main clock, and achieving noise suppression and data pre-screening through the combination of first-order difference and threshold filtering; constructing a nonlinear weighted feature matrix, and fusing a time attenuation coefficient and a shafting acceleration factor to enhance the transient feature expression capability; generating a sensor association graph based on double-threshold determination of weighted Pearson's correlation coefficients and mutual information, and dividing stable subgroups by using an incremental label propagation algorithm; designing a double-layer Cluster-GCN model, aggregating subgroup internal characteristics in the first layer, introducing a fuel valve position-acceleration comparison gating mechanism in the second layer to correct a global edge weight, and generating a node embedding vector sensitive to working condition change; gaussian kernel density estimation and an instantaneous deviation index of embedding similarity are fused, and a historical sliding mean value and subgroup connectivity analysis are combined, so that sensor faults and working condition abrupt changes are distinguished.

The invention provides an AI-driven equipment health state assessment method and system, and relates to the technical field of intelligent operation and maintenance. The method comprises the following steps: acquiring equipment operation data, and performing time and dimension unification and quality control to form a multi-source operation data set and an environment context; generating an initial state feature based on the mechanism feature library, and obtaining a general representation through self-supervised pre-training; executing calibration learning by using a preset health label, and establishing a fusion evaluation model containing time sequence consistency and physical boundary constraint; carrying out distribution alignment and uncertainty estimation on the basis of scene differences to obtain alignment characterization and credibility scores so as to optimize a model gating strategy; performing joint mapping on the new data, outputting health index, fault probability and residual life estimation, and generating a root cause clue; lightweight online updating is executed under drifting detection, health indexes and root cause clues are written back to a mechanism feature library, early warning levels and maintenance suggestions are generated, and therefore complete-cycle intelligent sensing and self-adaptive optimization of the equipment state are achieved.

The invention discloses a version knowledge graph reasoning method and system based on large language model enhancement, and relates to the technical field of dynamic knowledge graphs, and the method comprises the steps: employing a semantic drift detection and compensation mechanism, comparing context coding differences of same entities of different versions in an initial knowledge graph, recognizing drift, and dynamically adjusting entity embedding vectors, outputting a compensation update atlas; an LLM enhanced multi-hop reasoning algorithm is adopted, multi-hop reasoning is carried out on the compensation updating map, symbol reasoning, vector reasoning and context reasoning are carried out in parallel in each hop, and an entity relation reasoning result is obtained through dynamic weight fusion; and superposing an entity relationship reasoning result to the compensation updating graph through a cloud collaborative node, adding an entity edge and automatically maintaining a version history log, and obtaining a reasoning fusion version knowledge graph. Through a multi-hop reasoning algorithm enhanced by a large language model, the deep semantic mining capability and reasoning accuracy of a cross-version entity relationship are effectively improved.

The invention relates to the technical field of remote education, and discloses a monitoring device and method for network remote education, and the method comprises the steps: collecting micro-motion data, and generating an original data sequence for concentration analysis; micro-action feature vectors are extracted, and time sequence feature vectors are generated; constructing a personalized concentration scoring model, and generating a concentration score and a personalized threshold value based on the time sequence feature vector and the time decay function; behavior drift detection and model adaptation: monitoring a change trend of a learner behavior mode along with time based on a time sequence feature vector, executing behavior drift detection, and updating concentration scoring model parameters according to the behavior drift detection; generating a concentration state evaluation report, performing multi-level classification on the concentration state of the learner, and generating a real-time evaluation report; according to the method, personalized concentration evaluation is realized, and customized concentration monitoring strategies are provided for different learners by constructing an individual historical behavior mode database and a personalized threshold adjustment mechanism.

The invention discloses a deep learning correction system for an MEMS sensor, and relates to the technical field of sensor correction, and the system comprises a multi-source data collection module which collects various data in real time, verifies and caches the data, and transmits the data; the feature extraction and analysis module processes the data and extracts features, and transmits the features to the drift detection modeling and motion impact discrimination module; modeling, calculating, monitoring sudden change and synchronizing information; the event type output result is judged; the correction decision execution module executes correction accordingly, and is internally provided with self-checking and fine tuning functions to guarantee the stability of the system; according to the invention, technologies of multi-source data acquisition, multi-dimensional feature analysis, coupling dynamic regression, deep convolutional neural network and the like are fused, so that comprehensive sensing and accurate drift detection of the sensor are realized; and through collaborative operation of an attention fusion joint discrimination algorithm and the like, event types are accurately distinguished and targeted correction is performed, the system stability is maintained, and the measurement precision and the practical value are improved.

The invention provides an AI knowledge base construction method and system for multi-source data integration, and belongs to the technical field of knowledge base construction. The method comprises the following steps: adaptively pulling unstructured data at an API-free site through a reversible crawler of a heterogeneous data access framework; the mode drift detector senses field change in real time and updates a protocol in a hot plug mode within 500ms, so that zero-stop synchronization is realized; carrying out weighted resolution on conflicts according to source credibility index attenuation, and generating version stamps with digital watermarks and consanguinity URI (Uniform Resource Identifier) for records; uniformly mapping to a shared semantic space through cross-modal comparative learning, and constructing an incremental hypergraph knowledge graph which can be evolved by probability weight; and extracting an optimal sub-graph by using generative adversarial reinforcement learning, writing the optimal sub-graph into a time sequence knowledge warehouse by using a zero knowledge evidence chain and carrying out block chain solidification, and outputting a verifiable certificate. By the adoption of the AI knowledge base construction method and system for multi-source data integration, end-to-end automatic, high-credibility and traceable large-scale knowledge base construction is achieved.

The invention belongs to the technical field of data processing, and discloses a multi-source heterogeneous data fused remote sensing map dynamic database construction method and system, and the method comprises the steps: firstly obtaining multi-source heterogeneous remote sensing data of a target area, extracting remote sensing time sequence data of a land parcel after preprocessing, and obtaining a remote sensing time sequence; constructing a time sequence semantic track containing a key semantic feature time sequence for each land parcel; identifying a semantic evolution trend by using a semantic drift detection model, and generating an abandoned suspicious score; the static factors, the dynamic factors and the prior knowledge of the land parcels are fused, a structure causal graph is constructed by means of a causal discovery algorithm, and abandoned causal credibility is reasoned based on a graph neural network model; and automatically generating abandoned land labels for the land parcels of which the double scores exceed the threshold value, and finally writing the land parcel information into a dynamic database. And the problems of construction lag and information incompleteness of the dynamic database are solved.

A system, method, and computer-readable medium performing a drift detection of a data center asset, comprising receiving an encrypted desired state configuration of the data center asset; receiving an encrypted current state configuration of the data center asset; performing a fully homomorphic encryption (FHE) analysis on the encrypted desired state configuration and the encrypted current state configuration to determine drift detection, wherein drift detection includes identifying differences between the encrypted desired state configuration and the encrypted current state configuration; providing an encrypted drift detection result that includes recommended changes to configuration of the data center asset.

The invention discloses a machine learning-based flight wheel block removal time prediction method, and relates to the technical field of flight prediction, and the method comprises the steps: collecting flight preorder state data, airport resource distribution data and meteorological data in real time, and generating an original data set through multi-source heterogeneous data fusion; constructing spatio-temporal features including a preorder flight delay propagation chain, a stand-vehicle conflict energy matrix and a meteorological attenuation factor, and screening and optimizing a feature set through distribution drift detection; training and verifying the Bayesian depth quantile regression model, and outputting a prediction result with a confidence interval; and combining the airport Internet of Things positioning feedback optimization feature set and parameters to generate a prediction deviation diagnosis report. According to the method, a preorder flight delay propagation chain and a stand-vehicle conflict energy matrix are constructed, flight dynamics, resource allocation and weather attenuation factors are embedded into a unified spatial-temporal feature space, and the problem of feature information loss caused by data isolation is solved.

The invention relates to the technical field of industrial data processing, in particular to an industrial quality monitoring and analyzing system based on industrial Internet of Things, which comprises a data acquisition module, a drift detection module, a countermeasure compensation module, a dynamic optimization module and a quality monitoring module. According to the method, multi-dimensional parameters are collected through the industrial Internet of Things nodes and the sensors, real-time consistency and comprehensiveness of parameter data are ensured in combination with timestamp alignment and data verification, and fine recognition of parameter fluctuation in a dynamic environment is realized based on distribution deviation quantization and fluctuation value evaluation. By combining an optimization strategy of adjusting compensation factor weights in real time, adaptive adjustment of process parameters is more flexible and efficient, a dynamic analysis and matching strategy is utilized, a product quality fluctuation rate and a key parameter deviation value are evaluated in real time, machining quality is accurately controlled, product consistency and production efficiency are improved, and resource loss and adjustment delay caused by abnormities are reduced.

The invention discloses a data-driven perfusion process automatic adjustment method based on machine learning, and the method comprises the steps: collecting and preprocessing multi-source time sequence data, and generating an alignment feature vector sequence; performing perfusion stage division and stage coding vector generation based on the aligned feature vector sequence; constructing a three-layer liquid state machine model, and determining a stage liquid pool activation sequence; stage gating coding is executed, the liquid pool is driven to generate a dynamic state, and cross-stage migration is completed; integrating the dynamic state sequence, generating a joint state, and inputting a multi-task readout layer to output adjustment parameters; and executing distribution drift detection, topology updating and parameter calibration, and outputting final perfusion adjustment parameters. Through a data driving method based on stage topology modeling, liquid state machine dynamic evolution and a multi-task readout mechanism, accurate prediction, risk identification and self-adaptive adjustment of the perfusion process are achieved, and the perfusion quality and long-term operation stability are improved.

The invention relates to the technical field of data processing, and discloses a model dimension-based new energy vehicle value retention rate evaluation method and system, and the method comprises the steps: constructing three-table data, and carrying out the preprocessing; and fusing the static features and the time sequence features according to the type identifier to generate a feature sequence. And inputting the static features into a static feature sub-network to obtain a static representation, inputting the time sequence features into a time sequence feature sub-network to obtain a hidden representation of each time step, and obtaining a time sequence representation based on attention weighting. And performing drift detection on the input distribution and the model residual distribution. And splicing the static representation and the time sequence representation, inputting the spliced representation into a quantile prediction sub-network, outputting two value retention rate prediction results of different quantiles, and obtaining a value retention rate quantile set. And obtaining an insurance amount evaluation suggestion value based on the value retention rate quantile set and the new vehicle purchase price. According to the method, fine-grained description of different type feature differences is ensured, and the accuracy and reliability of a prediction result are improved.

The invention relates to a business data processing method and system, equipment and a medium. The method comprises the following steps: preprocessing multi-source heterogeneous cross-domain economic data to generate a standardized data stream; semantic drift in the standardized data flow is detected in real time, and a detection result is generated; semantic alignment judgment is carried out based on the detection result, and a dynamic alignment signal is generated; performing incremental training of a mapping model by using the dynamic alignment signal to generate an updated cross-domain mapping model; and finally, performing mapping conversion on the data stream based on the updating model, and outputting service data with unified semantics. By adopting the method, the semantic change in the economic data can be responded in real time, the problems of semantic drift detection lag, long model updating period and high maintenance cost in the traditional technology are effectively solved, and the accuracy and timeliness of cross-domain economic data processing are remarkably improved.

The invention discloses a button switch adaptive optimization method and a storage medium. The method comprises the following steps: acquiring a tactile signal through a high-density piezoelectric sensor, and extracting pressure distribution, gradient change rate and energy concentration degree characteristics in combination with a space-time convolution kernel and a dynamic baseline calibration algorithm; integrating time-frequency domain analysis of the historical operation sequence and neural differential equation modeling, predicting an operation probability and marking an anomaly; and an anti-interference control instruction is generated by fusing environmental parameters, and multi-source data are dynamically weighted by adopting a gating attention mechanism. The system constructs a multi-stage reliability protection mechanism, and fault immunity is realized through feature drift detection, digital twin residual verification and hybrid control switching. And responding to a real-time demand, dynamically optimizing heterogeneous computing resource allocation, executing three-dimensional storage reconstruction and precision bit width switching, and forming a closed-loop cognitive evolution link.

A computer-implemented method according to one embodiment includes identifying an occurrence of accuracy drift by a trained model; identifying data associated with the accuracy drift, utilizing a drift detection model (DDM) constructed for the trained model; applying the data associated with the accuracy drift to a decision tree to determine a feature space and specific subset of the data causing the accuracy drift; analyzing a distribution of features within the feature space for the specific subset of the data causing the accuracy drift to determine specific features of the data causing the accuracy drift; and returning the specific features of the data causing the accuracy drift.

Investigation systems and methods can investigate a target host computer. A reference computer investigation of the reference host computer can be performed by the investigation system sending at least one investigative module to a reference host computer, and the at least one investigative module performing the at least one investigative function and returning reference investigation data. A target computer investigation of the target host computer can be performed by the investigation system sending the at least one investigative module to the target host computer, and the at least one investigative module performing the at least one investigative function and returning comparison investigation data. A reference data fingerprint of the reference investigation data is compared to a comparison data fingerprint of the comparison investigation data to determine if the reference and comparison data fingerprints are identical.

A method may include: (1) receiving reference data comprising input texts and corresponding labels; (2) training a covariate drift detector comprising a syntactic drift detector and a semantic draft detector with the reference data; (3) training a concept drift detector comprising a plurality of classifiers with the reference data; (4) receiving production data comprising a plurality of instances; (5) determining that the production data has drifted; (6) calculating similarity scores between each instance of the production data and the reference data; (7) detecting concept drift by generating a predictive distribution using the plurality of classifiers and calculating an entropy of the predictive distribution; (8) identifying final drifted instances from the covariate drifted instances and the concept drifted instances; and (9) receiving updated labels for the final drifted instances.

The invention discloses a configuration drift detection and consistency coordination method during operation of a Changsafety chain, and relates to the technical field of distributed system configuration management, the method comprises the following steps: deploying a main controller and a query-side double container in an Operator controller Pod, setting a client tool and a hierarchical cache in the query-side, and obtaining on-chain configuration by the main controller through a localhost interface; in the Recencile cycle, configuration drift is judged through standardization processing and hash comparison, and a change source is recognized in combination with self-defined resource metadata and chain side change metadata; coordination is executed according to four synchronization modes of CR forcing, chain observation, bandwidth period CR forcing and manual arbitration; according to the scheme, the problems of state fuzziness, error coverage and the like caused by dual fact sources are solved, low-delay detection and flexible coordination are achieved, and configuration consistency during running of the long safety chain is guaranteed.

The invention discloses an AI-based online data analysis method for medical molecular sieve oxygen production equipment. The method comprises the following steps: step 1, collecting multi-source operation monitoring data of the medical molecular sieve oxygen production equipment; 2, constructing a task prompt vector to obtain a normalized feature vector; 3, inputting the normalized feature vector into an improved TabPFN model to obtain a hidden space representation, and generating an enhanced representation in combination with a retrieval result of the prototype memory bank; 4, performing drift detection on the enhanced representation; 5, inputting the robust representation into a multi-task decoder; and 6, executing event label judgment, and outputting online analysis result data. According to the invention, real-time analysis of multi-source data and advanced identification of abnormal trends are realized, and the method is suitable for equipment state monitoring and risk early warning in hospital wards, rehabilitation centers and long-term oxygen supply scenes.

The invention discloses an intelligent and automatic test case generation method based on a large language model, and relates to the technical field of large language model application, and the method comprises the following steps: collecting a software demand description file and an interface standardization file of a project, carrying out semantic analysis and mapping, generating a logic constraint set, and establishing a path mapping table; inputting the path mapping table into a large language model, generating a test scene and assertion, and executing semantic drift detection; when semantic drift is detected, a drift report is generated, a reverse correction process is executed, a patch prompt is generated, and the generation chain is executed again; and after the semantic distance of the prompt chain is detected to reach a convergence state, outputting a stable test case set. According to the method, the generation structure is controlled through the deterministic prompt sequence, so that the stability and controllability of test scene and assertion generation are realized; and through semantic drift detection and a reverse correction mechanism, real-time correction of semantic offset of the prompt chain is realized, and the accuracy and stability of the test case are improved.