172 results about "Drift detection" patented technology

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 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 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 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 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.

The invention discloses a cloud platform automatic inspection method and system based on multi-dimensional intelligent analysis. By collecting resource metadata, performance indexes, logs and change events, a unified event object containing resource identifiers, event timestamps, association keys and credibility scores is generated, and out-of-order rearrangement and layered missing compensation are completed based on event time water level lines. Second-level and minute-level multi-granularity dynamic baselines are constructed for the key indexes, drift detection is carried out, and the exception score is calculated by fusing the super-boundary amplitude, the change rate, the duration and the cross-index consistency. And constructing a resource dependence topology with credibility and aging attenuation, introducing a time-delay consistency constraint to carry out contribution degree attribution and cut candidate root causes, fusing a Bayesian network and a fault knowledge graph and combining a historical fault library to update a prior probability positioning root cause on line, and outputting an interpretable link. Implempotent self-healing execution, acceptance and rollback closed loop are realized according to risk access control, the positioning accuracy is improved, and the fault recovery time is shortened.

The invention provides a self-adaptive weighted hybrid modeling method and system for data drift sensing. The method comprises the following steps: acquiring multi-dimensional time series data and performing feature interaction analysis and screening to generate a fusion factor set; inputting the fusion factor set into a mixed structure comprising at least one ensemble learning model and at least one sequence model for training; based on the verification set and the dynamic evaluation indexes, determining fusion weights of all models in the mixed structure, and constructing a dynamic weighted mixed model; and setting a data distribution drift detection mechanism, comparing the distribution difference between a current data window and a historical reference data set, and automatically starting a retraining process of the hybrid structure to update the model when the difference reaches a trigger condition. According to the method, through cooperation of data drift perception and an adaptive weighting mechanism, the problems that an existing hybrid model is rigid in fusion strategy and lags behind a retraining mechanism are solved, and the robustness and long-term effectiveness of the model in a non-stationary data stream are remarkably improved.

The invention belongs to the technical field of vehicle networking safety, and discloses a vehicle-mounted CAN network ECU identification and intrusion detection method based on incremental online learning, and the method comprises the steps: S1, collecting a differential voltage signal on a vehicle-mounted CAN bus; s2, preprocessing and obtaining time domain features and frequency domain features of the differential voltage signals to obtain a CAN voltage data set, and constructing a data set with labels; s3, constructing an incremental learning classification model based on the initial data; s4, inputting streaming data to be detected to the incremental learning classification model, and monitoring ECU classification performance indexes of the streaming data through a drift detection algorithm; and S5, if the data distribution drift of the streaming data is detected, updating the incremental learning classification model and returning to the step S4. According to the method provided by the invention, the problem that the ECU recognition accuracy is reduced due to data drift in a dynamic environment is effectively solved, and the vehicle-mounted network security is improved.

The invention belongs to the technical field of malicious traffic detection and network security, and provides a malicious traffic detection model robustness enhancement method and system based on reinforcement learning and incremental learning. The method mainly solves the problems that an existing malicious traffic detection model is insufficient in robustness, poor in confrontation sample compliance, prone to catastrophic forgetting in a dynamic threat environment, weak in adaptability and the like. According to the main scheme, the method comprises the following steps: constructing state action space joint modeling, a three-order composite reward function and a compliance action mask mechanism through reinforcement learning in combination with a Transform strategy network, and generating an adversarial sample with high concealment and high compliance; an MMD concept drift detection mechanism is used for sensing attack mode evolution, a hierarchical EWC parameter protection strategy is combined, drift sensing-parameter protection collaborative incremental learning is achieved, and self-adaptive updating of a detection model is completed; an intelligent malicious traffic defense framework with high adversarial robustness and continuous environmental adaptability is constructed through the process, and meanwhile, the model performance can be verified through a related mechanism, so that the defense effect is ensured.

The embodiment of the invention provides an atmospheric pollutant early warning method based on multi-source isomerism, which belongs to the technical field of data processing, and specifically comprises the following steps: step 1, multi-source isomerism data encryption access and transmission; step 2, data quality control and missing measurement processing; step 3, performing space-time alignment and scale transformation; 4, feature engineering and variable construction; 5, carrying out fusion modeling and pollutant prediction; step 6, dynamic threshold and risk scoring; 7, carrying out online learning and drift detection; step 8, uncertainty evaluation and interpretability output are carried out; and step 9, early warning release and closed loop iteration. Through the scheme of the invention, the response capability and the early warning accuracy are improved.

The invention discloses a multi-dimensional fusion sensor data anomaly detection method based on a hydrogen-based shaft furnace, and belongs to the technical field of metallurgical equipment monitoring. The method comprises the steps of data preprocessing, single-variable anomaly detection, multivariable consistency detection, anomaly type judgment and alarm output. The single variable detection adopts a rolling median absolute deviation method, drift detection, continuous tiny variable threshold and border crossing detection; the multivariate detection calculates reconstruction residuals of each sensor through principal component analysis (PCA) and identifies overall consistency anomalies. And the system judges the fault type according to the comprehensive score of the multiple detection results and outputs a detailed alarm record and a statistical report. According to the method, multi-algorithm fusion analysis is carried out on time sequence data collected by a plurality of sensors in the operation process of the shaft furnace, so that the abnormal states of the sensors are accurately recognized. The method can be widely applied to real-time monitoring of the hydrogen-based shaft furnace smelting process, the accuracy and robustness of anomaly detection are improved, and false alarms and missing alarms are reduced.

The invention discloses a molded case circuit breaker fault prediction and alarm method based on artificial intelligence, and the method comprises the following steps: S1, collecting current, voltage, temperature and contact resistance signals, constructing multi-channel time sequence observation data, and generating a fusion observation vector; s2, inputting the fusion observation vector to a noise estimation sub-network, and generating a dynamic covariance parameter; s3, inputting the fusion observation vector, the dynamic covariance parameter and a previous state estimation value into a KalmanNet structure, and outputting a current state estimation value; s4, a physical prior regularization module is introduced in the training stage, a constraint loss function is constructed, and network parameters are jointly optimized; s5, executing drift detection in an operation stage, and extracting historical window data to perform incremental updating when conditions are met; s6, pruning and quantifying the trained KalmanNet structure, and generating a lightweight model; and S7, deploying to a monitoring system, and predicting the state in real time for alarm judgment. According to the invention, the accuracy and deployment efficiency of circuit breaker fault prediction are improved.

Techniques for detecting machine learning model drift are described. Model drift can result in the model misclassifying inputs. A system for detecting drift in natural language processing (NLP) models involves determining high-dimensional embeddings of inputs and high-dimensional embeddings of training samples, reducing the high-dimensional embeddings to low-dimensional embeddings, and comparing the low-dimensional embeddings to determine whether the inputs are statistically different than the training samples. When the inputs are statistically different than the training samples, model drift is detected, and retraining of the model may be performed. The system can detect drift in other classification models as well and can process with respect to other types of inputs (e.g., audio, image, etc.).

The invention relates to the technical field of wastewater treatment management, and discloses an industrial domestic wastewater fine treatment process management method, which comprises the following steps of: accessing an online / offline data source, and finishing time synchronization and data cleaning; state estimation and soft sensing are carried out; constructing a data quality score and anomaly / drift detection; describing a process topology and a material / energy flow by using a directed graph, defining a unified constraint template, and compiling compliance knowledge into executable constraints; mechanism and data driving are coupled at a unit level, combined propagation is carried out at a process line level, and federated migration and parameter aggregation are carried out at a plant station group level; a Pareto strategy is optimized and generated under the goals of effluent indicator, energy consumption, chemical consumption, sludge, carbon strength and maintenance risk. Under the condition that hardware transformation is not needed, operation toughness and performability can be enhanced, the risk that manual adjustment participates in misoperation is reduced, trans-factory multiplexing and governance transparency is improved, and continuous adaptation and compliance closed loop of a strategy are kept under disturbance.

A post-deployment drift detection monitoring of a predictive model is described. The method includes accessing a predictive performance metric of a machine learning model that is deployed at a server, the machine learning model being trained with an initial set of training data containing historical data, the predictive performance metric being based on the initial set of training data and an additional set of training data, the additional set of training data containing training data collected since training the machine learning model, detecting a drift based on the predictive performance metric exceeding a drift detection threshold, generating a drift warning notification to a client device, the drift warning notification indicating that the predictive performance metric exceeds the drift detection threshold, receiving a user feedback from the client device, and adjusting one of the machine learning model or the drift detection threshold based on the user feedback.

The application discloses an online ecological observation data anomaly detection method and system, and comprises the following detection process: learning a data drift detection method from historical ecological observation data, establishing a historical data anomaly detection model and a data drift segmentation list required by data drift detection; based on the historical data anomaly detection model, fine-tuning is performed to obtain an online anomaly detection model of a current drift segmentation, and the online anomaly detection model is used for online anomaly detection; when the training data is insufficient, similar data is matched from the drift segmentation list of the historical ecological observation data to enhance the fine-tuning data training amount. The model learned by the historical data and the historical data enhanced training sample are used for drift detection on online observation data, and an online drift online data anomaly detection model is obtained through fine-tuning training, so that the online data anomaly detection accuracy is improved.

This invention relates to the field of stock index volatility prediction technology, specifically a LightGBM-LSTM hybrid model construction system for stock index volatility prediction. The system includes a data acquisition and processing layer, a multimodal feature processing layer, a dual-branch model training layer, a model fusion and prediction layer, and an application visualization layer. This LightGBM-LSTM hybrid model construction system for stock index volatility prediction employs an offline static screening mechanism to significantly reduce feature dimensionality while retaining effective information. It introduces a dynamic feature weighting and feature drift detection mechanism to dynamically adjust feature weights based on the predictive power of each feature group under different market regimes. A structured feature processor and a temporal feature builder are designed to generate optimal input feature sets for different model branches. A hybrid market regime identification algorithm identifies the current market regime and probability distribution in real time, training LightGBM and LSTM sub-models for the three market regimes and integrating a dynamic fusion strategy to achieve optimal model weight allocation.

The application discloses a dynamic enhanced multi-modal hypergraph retrieval enhancement generation method and system, belonging to the technical field of information retrieval and knowledge engineering, comprising: in the offline stage, cross-modal encoding is performed on multi-source heterogeneous data to generate cross-modal vector index and candidate theme / entity set; knowledge drift detection is performed on the cross-modal vector index and the candidate theme / entity set, if it is determined that there is drift, then local subgraph incremental hypergraph update is triggered, and after multi-expert voting alignment verification, it is written into a multi-modal double hypergraph index library; in the online stage, the user request is subjected to semantic analysis, theme words and entity words are extracted; according to the theme words and the entity words, coarse retrieval and fine retrieval are performed based on the multi-modal double hypergraph index library, evidence subgraphs are extracted, multi-modal context is obtained; the user request and the multi-modal context are input into a multi-modal large language model to generate an answer and feedback the user; the timeliness, stability and reliability of retrieval are improved, and the integrity of complex knowledge reasoning is improved.

Techniques are disclosed for detecting a drift experienced by computing system(s). The system generates multiple snapshots as part of a drift detection process. Each snapshot contains state information of a computing system. Based on the snapshots, the system generates metrics sets according to a general specification. The general specification defines metrics generally suitable for detecting drift in the computing system(s). Based on the circumstances of the drift detection process, the system generates a custom specification. The system optionally employs trained machine learning model(s) for custom specification generation. The custom specification defines modifications to the metric sets designed to make the metric sets more suitable for the circumstances of the drift detection process. The system modifies the metric sets according to the custom specification. Subsequently, the system generates flattened vectors based on the modified metric sets, and the system performs a cluster analysis on the flattened vectors to detect any drift.

The present disclosure relates to a drift detector to detect a drift in features of a diagnosis, the drift detector comprising a first collecting device configured to collect relevant explanations and raw data, a first database compiling the relevant explanations and raw data collected by the first collecting device, a second collecting device configured to collect relevant available expert knowledge from reliable sources, a second database compiling the relevant available expert knowledge from reliable sources collected by the second collecting device, a learned feature extractor configured to group initial data of the second database into a characteristic feature pattern for different diagnoses and to subsequently group the data from the first database into first feature patterns by diagnosis and from the second database into second feature patterns by diagnosis, a comparator configured to compare the empirical distributions of each of the grouped feature patterns in the databases with each other, and a processor configured to generate a report if results of the comparison do not comply with a predefined condition. Applications include medical applications such as recognizing new diseases.

The application relates to the technical field of financial big data management, and discloses a financial big data management system based on a time sequence neural network, wherein the system comprises the following steps: each jurisdictional edge node generates a dynamic transaction directed graph based on a real-time transaction event stream, and extracts a local graph topology difference sequence containing a boundary node in-out degree change vector; a lightweight deep separable causal convolution encoder encodes the sequence, generates a local context-aware node representation through attention-weighted fusion; a boundary embedding vector is uploaded to a coordination node after being anonymized, cross-jurisdiction embedding space progressive unification is realized based on a federal-level contrast learning loss and Fisher information matrix weighted aggregation; asynchronous federal synchronization is triggered based on distribution drift detection; cross-jurisdiction candidate link logical splicing and distributed verification are completed through density clustering and cosine similarity matching; and finally, a multilayer perceptron classification network outputs a risk level and generates a cross-jurisdiction money laundering risk report.

The application discloses a data-driven perfusion process automatic adjustment method based on machine learning, comprising the following steps: collecting and preprocessing multi-source time series data to generate an aligned feature vector sequence; performing perfusion stage division and stage encoding vector generation based on the aligned feature vector sequence; constructing a three-layer liquid state machine model to determine the stage liquid pool activation sequence; performing stage gating encoding to drive the liquid pool to generate a dynamic state, complete cross-stage migration; integrating the dynamic state sequence to generate a joint state, input the joint state into a multi-task readout layer to output an adjustment parameter; performing distribution drift detection, topology update and parameter calibration to output a final perfusion adjustment parameter. Through the data-driven method based on stage topology modeling, liquid state machine dynamic evolution and multi-task readout mechanism, the application realizes accurate prediction, risk identification and adaptive adjustment of the perfusion process, and improves perfusion quality and long-term operation stability.

The invention relates to the technical field of biological information, and discloses a multi-element detection data drift calibration method and system, and the method comprises the steps: analyzing the distribution difference of multi-element detection data, and obtaining a drift detection result; performing statistics on correlation strength values among elements in the multi-element detection data to obtain element correlation; key parameters influenced by drifting in the element correlation are identified, the relative importance of the key parameters in the element correlation is analyzed, and optimized parameters are obtained by increasing the weight ratio of the high-importance key parameters; performing benchmark correlation analysis on the multi-element detection data to obtain an element adjustment priority; according to the element adjustment priority, generating a calibration parameter by combining drift degree information in the drift detection result; performing collaborative transformation processing on the multi-element detection data, and checking the association stability among elements in the processed multi-element detection data to obtain calibration data; according to the invention, the efficiency of multi-element detection data drift calibration can be improved.

The present application relates to the technical field of communication detection, and discloses a training method and device of a border gateway protocol anomaly detection model and computer equipment, wherein the historical route statistical features and historical graph topology features corresponding to historical BGP data are extracted, and the features are trained by using the border gateway protocol anomaly detection model, the drift detection score between the historical anomaly detection result and the historical BGP data is calculated in the training process, and the drift detection threshold is used as a reference benchmark to adaptively update the multiple parameter values of the border gateway protocol anomaly detection model in combination with the drift detection score. Therefore, even if the BGP abnormal condition caused by the dynamic change of the network environment is faced, the BGP abnormal data can be accurately detected, and the high efficiency of the BGP abnormal data anomaly detection can be ensured.

The invention discloses an intelligent tunnel supporting method and system based on data drift detection. The method comprises the steps that actual monitoring data in the tunnel supporting process are obtained; determining a preliminary support scheme according to the actual monitoring data and the support intelligent decision model; calculating a drift index between the actual monitoring data and the reference monitoring data; judging whether the actual monitoring data drifts or not according to the drifting index; when it is judged that the actual monitoring data drifts seriously, the conservative support emergency plan is used as a final support scheme, and the support intelligent decision model and the reference monitoring data are updated, so that the accuracy of the support scheme is ensured, and the safety of support engineering is improved.

The application discloses a temperature drift detection method and a depth camera. The temperature drift detection method is applied to the depth camera, the light emitting direction of the depth camera is provided with a to-be-detected plate, the to-be-detected plate has a to-be-detected surface facing the depth camera, and the temperature drift detection method comprises the following steps: adjusting the position of the depth camera so that the light emitting surface of the depth camera is parallel to the to-be-detected surface; controlling the depth camera to project an image on the to-be-detected surface; acquiring a first depth map of the image at an initial time when the depth camera is turned on, and obtaining a first normal vector of the plane where the first depth map is located; acquiring a second depth map of the image after a preset time of the initial time, and obtaining a second normal vector of the plane where the second depth map is located; determining a temperature drift angle according to the first normal vector and the second normal vector; and detecting whether the temperature drift of the depth camera is qualified according to the temperature drift angle. The technical scheme of the application can complete temperature drift detection, and then judge whether the temperature drift of the depth camera is qualified.

The invention discloses a new energy power prediction model concept drift detection method and system, and belongs to the technical field of new energy power prediction and data flow mining, and the method comprises the steps: collecting high-availability basic operation data of a new energy station in real time, and carrying out the time alignment processing; a preset three-channel collaborative drift detection module is adopted for detection; the three-channel collaborative drift detection module comprises three monitoring channels which run in parallel, and alarm signals corresponding to the channels are output through prediction residual behavior monitoring, actual power distribution drift detection and physical consistency deviation detection. And calculating the confidence coefficient of each channel, carrying out weighted average on the confidence coefficients of the channels to obtain a total drift score, and judging whether the new energy power prediction model generates concept drift according to the total drift score and the corresponding duration. The method has high availability and strong robustness, can realize high-precision and low-false-alarm detection of concept drift, and provides technical support for optimization of a power prediction model.