3224 results about "Data detection" patented technology

The invention provides a metro driver abnormal behavior recognition method based on multi-modal data fusion. The method comprises the steps that metro driver abnormal behaviors are recognized based on multi-modal data fusion, track positioning and platform screen door sensor data are fused to detect the position of a train, and a station entering confirmation instruction is generated; according to the data of the seat pressure distribution sensor, driver off-seat inspection is judged; generating an outbound completion event in combination with a track circuit signal, an ATO instruction and the like; analyzing console touch pressure, voice instructions, hand actions and the like to verify operation compliance, and triggering an alarm when the operation is abnormal; aggregating results at edge computing nodes, encrypting and transmitting the aggregated results to a central scheduling system, dynamically mapping driver behaviors in a digital twin cockpit, and automatically triggering hierarchical alarms and associating data snapshots when the driver behaviors are abnormal; according to the invention, accurate recognition and real-time early warning of subway driver behaviors can be realized by means of multi-modal data fusion and intelligent analysis, and the subway operation safety and the emergency response efficiency are greatly improved.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention provides a network security multi-mode intelligent detection system and method, relates to the technical field of network security management, and is applied to a power distribution system which comprises a communication device and a power distribution device. The network security multi-modal intelligent detection system comprises a multi-modal data acquisition module used for acquiring network flow data, equipment characteristic data and behavior data of a power distribution system; the detection engine module is used for analyzing and detecting the network flow data, the equipment characteristic data and the behavior data to obtain a detection result; and the decision and response module is used for performing risk assessment according to the detection result based on a preset risk assessment model to obtain a risk grading result and a response strategy. According to the invention, comprehensive monitoring and deep analysis of the network security condition of the power distribution system are realized. By integrating multi-modal data, the limitation of a traditional single data detection mode is effectively overcome, and the accuracy and timeliness of threat detection are improved.

The invention relates to the field of data detection, in particular to a network traffic anomaly detection method based on aggregated mimicry distillation. According to the method, protocol level analysis is carried out on traffic through a multi-branch feature extraction network, and a unified representation vector is generated through a cross-layer fusion mechanism; calculating a first abnormal score based on a protocol perception weighted confrontation soft contrast mechanism; a heterogeneous teacher model is constructed and dynamically weighted and aggregated, and the student model generates a second abnormal score through knowledge distillation learning; forming a heterogeneous redundant detection pool by the student model, the teacher model and the rule detector, dynamically selecting the detector and applying adaptive disturbance to obtain a third abnormal score; and dynamically fusing the three types of abnormal scores to output a detection result. According to the method, the problems of protocol semantic segmentation, weak boundary sample discrimination, knowledge migration simplification and defense path predictability are effectively solved, and the detection accuracy, robustness and dynamic defense capability are remarkably improved.

Techniques for determining a presence of an object, especially an object such as animal or debris, in a path of a vehicle, are discussed herein. For example, sensors of various modalities, which may include multispectral sensors, may capture data representing an environment the vehicle is traversing. In examples, one or more trained machine learned (ML) models, operating on a vehicle computing system, may detect and / or classify objects in the environment, based on input data of one or more modalities or spectral bands. The ML models may be pre-trained using training data including real sensor data, synthetic data, and / or augmented data, along with auto-generated annotations. In some examples, hyperspectral data may be used to identify materials associated with detected objects. A confidence score associated with the detection of the object may also be computed. The vehicle may be controlled based on detection of the object and its classification.

A control system for controlling a second unmanned aerial vehicle that flies while holding a first cable connected to a first unmanned aerial vehicle that performs work and a second cable extending from a cable reeling machine, includes a sensor to sense a surrounding environment and output sensor data, and a controller configured or programmed to control operation of the unmanned aerial vehicle and, during flight of the second unmanned aerial vehicle, detect the first cable and the second cable based on the sensor data, and upon predicting that at least one of the first cable and the second cable will contact the ground or an obstacle on the ground, change a trajectory of the second unmanned aerial vehicle to avoid the contact.

The invention relates to the technical field of mechanical equipment state monitoring, in particular to a method and sensor for detecting temperature and vibration data of dynamic equipment, and the method comprises the steps: collecting a temperature signal and a vibration signal of the dynamic equipment, and carrying out the fusion processing, thereby obtaining a fusion feature set; establishing a feature distribution baseline based on a Gaussian mixture model, calculating a relative entropy of the fusion feature set and the feature distribution baseline, and generating a dynamic threshold sequence; constructing a detection model according to the fusion feature set and the dynamic threshold sequence, and outputting to obtain an abnormal mode label; performing time serialization processing on the abnormal mode label, predicting a fault probability in a future time period according to the abnormal mode label subjected to time serialization, and generating a fault prediction result; and performing priority ranking on the fault types in the fault prediction result, and generating a maintenance report according to a priority ranking result. The reliability and practicability of temperature vibration data detection of the dynamic equipment are comprehensively improved, and an efficient solution is provided for health management of the dynamic equipment.

Method and system for analyzing at least one computing system for supply chain vulnerabilities of at least one machine learning model include configuring machine learning model and optionally additional data; decomposing operations of the machine learning model's computational graph into smaller decomposed components; associating properties of each decomposed component with properties of the original operations and associating additional data; detecting the semantic similarity of decomposed component and previously encountered decomposed components; converting decomposed components into a standardized representation; calculating signature of decomposed components; evaluating whether portions of the machine learning model are similar to previously calculated signature; testing for supply chain and model vulnerabilities that exist based on previous signatures; identifying vulnerabilities that persist and correlating defenses; storing the generated signatures, identified vulnerabilities, and identified defenses; and generating report detailing the machine learning model's supply chain, vulnerabilities, and defenses.

The invention discloses a multi-modal abnormal data detection and restoration method and system oriented to a power business scene. The method comprises the following steps: collecting multi-source heterogeneous power data, abstracting a power system into a weighted undirected graph, uniformly mapping the multi-source heterogeneous data into a graph signal, and preprocessing the collected data; extracting spatial features of nodes in a topological structure by adopting a graph convolutional network, and capturing a time dependency relationship in combination with a time sequence encoder; identifying various types of data abnormal points through an abnormal scoring function fusing the time sequence prediction error and the neighborhood consistency; a prediction-reconstruction combined repair strategy is adopted, time sequence prediction and neighborhood diffusion estimation are fused, and a preliminary repair value is generated; a lightweight parameter adapter is introduced, a scene feature vector is used as input, a repair weight and a regularization coefficient are dynamically generated, and a repair strategy is automatically adjusted; and performing physical consistency verification on a data result, wherein the physical consistency verification comprises power injection conservation constraint, voltage amplitude range constraint and time sequence continuity constraint.

The invention provides a data detection method and system, a detection device, computer equipment and a storage medium, and the method comprises the steps: obtaining a detection signal of each wafer surface in a planetary reaction cavity, and cutting the detection signal based on a trigger signal to obtain a first signal sequence and a second signal sequence; processing the first signal sequence by adopting a classification model to obtain a material type on each wafer; querying a corresponding optical model based on the material type; and performing iterative optimization on the optical model, the first signal sequence and the second signal sequence by adopting a decoupling algorithm to obtain a temperature detection value and a reflectivity detection value. According to the method, the original detection signal is segmented into the independent signal sequences based on the trigger signal, then the material type is determined according to the independent signal sequences, the corresponding optical model is selected according to the material type, and finally the temperature detection value and the reflectivity detection value with high accuracy are obtained through decoupling according to the optical model. Accurate analysis of a single wafer signal is realized, and process repeatability and stability are greatly improved.

Techniques and apparatuses for enhanced text suggestions are described. An example method includes detecting a user gesture performed by a user of the computing system based on data from one or more neuromuscular sensors and identifying a set of text characters corresponding to the user gesture. The method further includes causing display of the set of text terms in a user interface and determining whether a cognitive load of the user meets one or more criteria. The method also includes providing a text suggestion to the user based on the set of text characters in accordance with a determination that the cognitive load of the user meets the one or more criteria, and forgoing providing the text suggestion to the user based on the set of text characters, in accordance with a determination that the cognitive load of the user does not meet the one or more criteria.

The invention relates to the technical field of cable aging diagnosis, in particular to a cable aging diagnosis monitoring method and system. The method comprises the following steps: acquiring cable operation log data and cable internal structure design data, extracting a cable structure distribution condition in combination with an operation log, and evaluating cable operation state data; electric power sudden demand characteristics are identified, and then a non-uniform thermal coupling unbalance state is determined; based on the thermal coupling unbalance and the operation state data, detecting the coupling failure condition of the cable multilayer structure, and further judging the structural integrity damage condition; deducing a load stability disintegration situation according to a structure damage and insulation strength reduction trend; further detecting the decline of the quality of transmitted electric energy, analyzing the increase degree of abnormal voltage fluctuation, and identifying the fatigue trend of the metal material of the cable; through the above cable aging diagnosis, the cable aging diagnosis data is output. According to the cable aging diagnosis method and device, cable aging diagnosis is diagnosed, so that cable aging diagnosis is more accurate and efficient.

The invention provides a nuclear power safety report data extraction method and system based on multi-modal feature fusion, and belongs to the technical field of data processing, and the method comprises the steps: obtaining a nuclear power safety report document; carrying out OCR (Optical Character Recognition) and paragraph segmentation on the report document to obtain first text data; performing text semantic understanding on the first text data by adopting a dynamic template matching and semantic driving extraction mechanism to obtain a text feature vector; segmenting pages of the report document and identifying fonts and fonts to generate first format data; adopting a cross-page table reconstruction algorithm to detect table cells of the first format data and splicing a cross-page table to obtain format feature vectors; performing feature alignment on the text feature vector and the format feature vector, and inputting the text feature vector and the format feature vector into a large pre-training language model to obtain a semantic vector; and fusing the text feature vector, the format feature vector and the semantic vector to form a composite expression unit, and intelligently extracting structured data based on the composite expression unit. The semantic entity and structural relationship in the nuclear power report can be effectively identified.

The invention relates to the technical field of financial risk management, in particular to a big data-driven financial risk early warning system, which comprises a data detection module, a behavior risk rating module, a dynamic risk monitoring module, a behavior pattern clustering module and a financial risk early warning module. According to the method, the sensitivity and accuracy of risk detection are improved by analyzing small deviation between transaction behaviors and historical data, the dynamic risk monitoring function can realize quick response to abnormal transactions by continuously tracking the fund flow direction and the transaction frequency, and the risk detection accuracy is improved by analyzing the relationship between the fund flow direction and past risk events. According to the method, normal financial activities and potential risk behaviors can be effectively distinguished, an enterprise can take prevention measures before the risk actually occurs, and the clustering analysis of the risk behaviors can help to more accurately position a risk source, so that the measures are taken in a targeted manner, financial resources are optimized, and the risk prevention and control capability and decision-making efficiency are enhanced.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

The invention relates to the technical field of cold chain product traceability, and discloses a cold chain product whole-process wireless traceability system based on the Internet of Things, and the system comprises an acquisition module which is used for collecting environment data of a cold chain product; the state matrix construction module is used for constructing an observation state matrix; the feature extraction module is used for extracting observation state matrix features and constructing transportation abnormity and risk grade indexes; the risk evaluation module is used for evaluating whether environment abnormity and state mutation exist or not at present; the data uploading control module is used for sending alarm information to the cloud; a state graph modeling and visualization module; and constructing a state graph based on the alarm information. State estimation is carried out through the state estimation unit, reconstruction compensation can still be carried out on key data when the nodes are in the dormant state, the integrity and continuity of a data chain are guaranteed, a data blind area is effectively filled, the integrity of data detection is improved, and the accuracy of cold chain product traceability is improved.

The invention discloses an optical surface defect data detection method based on deep learning, and relates to the technical field of optical defect detection, and the method comprises the following steps: constructing an optical scattering physical model, inputting a collected optical surface image into the optical scattering physical model for multi-modal data synthesis, and generating multi-modal image data; constructing a deep learning feature extraction network, inputting multi-modal image data, and performing multi-scale feature fusion and enhancement through a bidirectional attention feedback mechanism to generate a deep feature map; performing spatial domain analysis on the depth feature map by using a deep learning region generation method, positioning coordinates of potential defect regions, and generating a candidate defect region coordinate set; through multi-modal data synthesis driven by an optical scattering physical model, the limitation of a single imaging mode is broken through, the scattering characteristics of defects under multi-physics field coupling are dynamically analyzed, the recognizable degree of weak defects in a complex scattering environment is enhanced, and the problem of defect missing detection is solved.

The invention provides a marine meteorological data quality control method based on a cross-parameter correlation network, which belongs to the technical field of marine meteorology, and comprises the following steps: constructing a cross-parameter correlation network model comprising an air temperature and air pressure correlation rule, a humidity and air temperature linear correlation rule and a wind speed and air pressure gradient extraction correlation rule; a sliding window algorithm is adopted to calculate a correlation coefficient in real time and identify correlation abnormity, a multivariate abnormity detection mechanism of four dimensions of parameter threshold overrun, spatio-temporal change rate abnormity, probability density distribution offset and correlation verification failure is established, a fuzzy comprehensive evaluation method is adopted to calculate a comprehensive abnormity index, and data abnormity is judged. And the authenticity of the abnormal data is confirmed in combination with a multi-sensor cross validation mechanism, and finally the abnormal data is restored by adopting a virtual sensor data reconstruction algorithm based on correlation network reverse calculation, so that the technical problem of poor abnormal data detection effect in the prior art is solved.

The invention belongs to the technical field of financial risk control systems, and particularly relates to an isolated forest abnormal data detection method and system based on clustering enhancement and a medium. S2, a K-Means clustering algorithm is adopted to screen out data clusters needing to be further detected; s3, constructing an isolated forest model according to the screened data clusters, and performing model training through a training set; s4, predicting the model through the test set, and calculating an initial abnormal score; the class density is calculated to serve as a weight to weight the initial abnormal score, and a final abnormal score is obtained; s5, judging the final abnormal score according to a preset threshold value, and outputting an abnormal data detection result; and S6, evaluating the isolated forest model, adjusting model parameters according to an evaluation result, and continuously optimizing the model until a preset requirement is met. The problems that abnormal data detection is complex in calculation and low in efficiency are solved.

The invention discloses a pilot valve control method and device based on data detection and automatic pressure regulation, and relates to the technical field of pilot valves. The method comprises the steps that the medium pressure of a piston containing cavity is obtained in real time through a pressure sensor, and the displacement amount of a piston shaft is obtained in real time through a displacement sensor; querying a pre-stored pressure-displacement correlation curve based on the medium pressure to determine a target displacement amount; generating an adjusting signal according to the deviation data of the real-time displacement and the target displacement; and the pressure regulating component is controlled to dynamically regulate the pretightening force of the spring to realize pressure closed-loop control. The device comprises a mechanical execution unit, a data detection unit and a control processing unit, all the units work cooperatively, closed-loop control over pressure adjustment is achieved by detecting medium pressure and piston shaft displacement in real time and dynamically adjusting spring pre-tightening force, and the device has the advantages of automatic calibration, manual intervention reduction and control precision improvement.

The invention relates to the technical field of elevator energy consumption analysis, and discloses an elevator energy consumption analysis method and system.The elevator energy consumption analysis method comprises the steps that elevator operation data are obtained, anchor point events are detected based on the elevator operation data, time alignment processing is conducted on the elevator operation data, and a synchronous data sequence with a unified time axis is generated; based on the synchronous data sequence, elevator running states are recognized, regeneration running stages are marked, and time segments corresponding to the elevator running states are output; carrying out integral calculation on the active power of the line in a time segment, realizing bottom consumption stripping in combination with a standby power reference, and outputting segment energy consumption through positive and negative power decomposition; and in the regeneration operation stage, feedback energy and braking energy are calculated according to the elevator operation data, feedback efficiency is calculated, and an energy consumption analysis result is output. According to the method, a reliable basis is provided for energy efficiency evaluation, abnormal early warning and operation optimization of the elevator system.

Embodiments relate to a method and apparatus for determining lane level localization of a vehicle with global coordinates. An automotive vehicle includes a vehicle navigation system, a database comprising standard-definition map data corresponding to road level latitude and longitude coordinates for a plurality of roadway features, at least one image sensor, and a processor in communication with the vehicle navigation system, the database, and the at least one image sensor. The processor is configured to determine an initial location of the vehicle, estimate a current location of the vehicle based on a dead reckoning process, detect, based on image data from the at least one image sensor, a lateral position of the vehicle within a lane of a road, and update the current location of the vehicle based on a difference between the estimated current location and the lateral position of the vehicle within the lane.

The invention discloses a photoelectric fusion beam control method and device based on a reconfigurable intelligent reflecting surface. The method comprises the following steps: acquiring internal and external parameter matrixes of a binocular camera and a binocular-RIS coordinate transformation matrix; capturing a binocular image stream of a detection target by using a binocular camera and executing stereo matching to obtain a disparity map and depth data of a binocular image; detecting a target image in the binocular image and outputting bounding box information of a center point of the target image; calculating target image coordinates through the depth data and the bounding box information, and converting the target image coordinates into three-dimensional space coordinates under an RIS coordinate system so as to determine the spatial position of the target; generating an RIS phase codebook according to the target spatial position, wherein the RIS phase codebook comprises a near-field phase compensation item; and loading the phase codebook to the RIS unit to form a target beam so as to perform directional tracking on a target. According to the technical scheme of the invention, high-precision beam control and stable positioning and tracking of the target can be realized in a complex multi-target environment.

The invention discloses a power system multi-source data intelligent fusion method based on a soft merging D-S evidence theory, and relates to the technical field of power system industrial multi-source heterogeneous data usage, comprising: acquiring multi-source data, and calculating a change index of the multi-source data; performing data detection on the multi-source data to obtain a detection result; performing aggregation processing on the multi-source data based on the change index and the detection result to obtain data features; and fusing the data features to obtain a data fusion result. According to the method, abnormal data is detected by aiming at the volatility and change characteristics of the power grid data and combining a Z-score method, an aggregation mode is flexibly selected, and the accuracy of data processing is improved; multi-source heterogeneous data are fused by using a D-S evidence theory, and in the face of evidence conflicts, a soft merging method is used for introducing regulatory factors to relieve conflict influences, so that the problem that time scales are not matched in the use process of industrial source data is effectively solved.

Techniques and apparatuses for enhanced text suggestions are described. An example method includes detecting a user gesture performed by a user of the computing system based on data from one or more neuromuscular sensors and identifying a set of text characters corresponding to the user gesture. The method further includes causing display of the set of text terms in a user interface and determining whether a cognitive load of the user meets one or more criteria. The method also includes providing a text suggestion to the user based on the set of text characters in accordance with a determination that the cognitive load of the user meets the one or more criteria, and forgoing providing the text suggestion to the user based on the set of text characters, in accordance with a determination that the cognitive load of the user does not meet the one or more criteria.

Certain embodiments provide a power tool comprising a motor including a flywheel, an inertial measurement unit (IMU) configured to output IMU data, and a controller coupled to the motor and the IMU. The controller is configured to process the IMU data to generate processed IMU data, detect, based on the processed IMU data, whether the power tool has been moved in a pickup motion that is associated with an imminent use of the power tool, and increase a speed of the motor to a target speed in response to the detection.

The present invention relates to the technical field of intelligent out-of-distribution fault detection for construction machinery, and discloses an out-of-distribution fault detection method and system based on energy propagation and graph learning, and the method includes: acquiring vibration acceleration signals in typical fault states, carrying out similarity calculation to obtain an adjacency matrix composed of the maximum mutual information coefficients, and taking the adjacency matrix as input in a graph neural network; carrying out feature extraction on the adjacency matrix through adopting a GraphSage graph convolution method, and generating each node representation; calculating an energy score of each node, and distinguishing between in-distribution data and out-of-distribution data; and enhancing out-of-distribution data confidence estimation for each node, and carrying out out-of-distribution data identification and out-of-distribution data detection under different working conditions of a rolling bearing.

A method may include receiving, from a safety system coupled to an infusion system, repair data associated with a repair event, during which a user accesses the infusion system. The method may also include detecting, based on the repair data, the repair event is unauthorized. The method may also include causing, based on the detecting, at least one safety adjustment to operation of the infusion system. Related methods and articles of manufacture, including apparatuses and computer program products, are also disclosed.

The invention relates to the technical field of data detection, in particular to a three-dimensional multi-parameter real-time monitoring system and method for a floating roof of a crude oil storage tank. The multi-mode sensor array is arranged on the surface of a floating roof and the inner wall of a storage tank and used for collecting displacement, deformation, pressure and environmental parameters of the floating roof in real time; the data acquisition and preprocessing module is connected with the sensor array and is used for signal conditioning, analog-to-digital conversion and noise suppression; the edge calculation unit is deployed on a storage tank site and used for processing sensor data in real time and executing initial analysis; the three-dimensional dynamic modeling module is used for constructing a floating roof three-dimensional attitude model based on multi-source sensor data; the cloud analysis platform is connected with the edge computing unit through the industrial Internet of Things and is used for deep data analysis and early warning decision making; according to the scheme, the technical problems of insufficient parameter coverage, difficult data fusion, one-sided state evaluation and the like can be solved.

The invention relates to the field of industrial data detection, in particular to an industrial production data anomaly detection system, which comprises a production monitoring module used for detecting a production control difference parameter and a set drift parameter and determining a production data state of a time sequence training data set; the window analysis module is used for responding to the window evaluation condition to determine a window setting strategy of the time sequence training data set; the extraction execution module is used for performing training execution window extraction based on the window setting strategy determined by the window analysis module; the training execution module is used for training the anomaly analysis model based on the acquired training execution windows; and the anomaly evaluation module is used for carrying out reconstruction anomaly analysis on each window analysis data of the to-be-evaluated data to determine a weighted anomaly index of each window analysis data, and responding to the early warning evaluation condition to determine anomaly detection data, and the anomaly detection efficiency of the multivariate time series data is improved.