449 results about "Group detection" patented technology

The invention relates to the technical field of unit detection, in particular to a unit magnetic variable on-line monitoring method and system, and the method comprises the steps: carrying out the multi-source data collection through a sensor, carrying out the correction of the multi-source data through a multi-dimensional calibration mechanism, synchronizing a timestamp through a dual-synchronization system, and carrying out the frequency-band-divided conditioning and standardization processing; environmental noise in the standardized multi-source data is eliminated through an intelligent algorithm, feature vectors are extracted, and purified feature vectors are obtained; screening effective abnormal features through an isolation forest algorithm; and inputting the effective abnormal features into an LSTM prediction model to obtain a corrected prediction value, substituting the corrected prediction value into a logistic regression formula to obtain a fault prediction probability of fault occurrence, calculating a health degree score, and performing graded early warning according to the health degree score. According to the scheme, through multi-dimensional calibration, working condition adaptive feature extraction and graded early warning, the unit monitoring data precision, the fault feature recognition accuracy and the operation and maintenance decision efficiency are improved.

A method includes obtaining a first attack sequence comprising at least a first attack phase, a second attack phase, and a third attack phase. The method further includes determining that a first number of alerts generated for the first attack phase based on a set of detection operations and a second number of alerts generated for the third attack phase based on the set of detection operations satisfy a threshold criterion. The method further includes determining that a third number of alerts generated for the second attack phase based on the set of detection operations fails to satisfy the threshold criterion. The method further includes determining that the set of detection operations is to be modified to detect future malicious activities corresponding to the second attack phase.

The invention relates to the technical field of unit detection, and discloses an intelligent fault self-diagnosis method for an evaporative condensation integrated screw water chilling unit, which comprises the following steps of: decoupling operation data of a compressor through a blind source separation algorithm, performing image processing on operation data of a condenser in combination with a morphological analysis method, and performing fault diagnosis on the operation data of the condenser; the operation characteristics of the compressor and the operation characteristics of the condenser can be extracted respectively, effective decoupling and clear expression of coupling signals are realized, and by constructing a dual-channel LSTM network comprising a gating fusion unit and introducing a cross-channel coupling gating mechanism, the system reliability is improved. The dynamic coupling relation between the compressor and the condenser can be modeled and regulated in the time dimension, the coupling mechanism between the compressor and the condenser is truly reflected, the time sequence parameter sequence is input into the pre-trained deep LSTM network, the fault types such as control lag and performance degradation can be accurately recognized, and the fault diagnosis accuracy is improved. Therefore, the accuracy and real-time performance of fault diagnosis are improved, and the requirement for intelligent fault management of the water chilling unit is met.

The invention provides a low-illumination target detection method and system fusing multi-scale features and local detail enhancement. The system comprises a feature extraction network based on a multi-pooling pyramid and cross-stage double-mixed attention, a dynamic detail semantic fusion pyramid network and double groups of detection heads. The feature extraction network based on the multi-pooling pyramid and the cross-stage double-mixed attention comprises a convolutional layer, a C2PSA module, an MPSPPF module and a CSP-EDHAN module; the dynamic detail semantic fusion pyramid network is used for fusing shallow high-frequency details and deep semantic information through top-down and bottom-up multi-scale feature fusion and introducing a surface detail fusion module into multiple scales, so as to output a fused feature map; the double groups of detection heads adopt a decoupling detection branch design, and the position and the category of a target are directly predicted on a fused feature map. The method can remarkably improve the precision and robustness of target detection in a low-illumination environment, and is suitable for the fields of night monitoring, automatic driving, security and protection and the like.

An embodiment intrusion detection system for detecting intrusions in an in-vehicle network includes a message queue module configured to store network messages collected from the in-vehicle network in a message queue, a memory configured to securely store a ruleset comprising a set of detection rules used in a plurality of detection techniques, a rule engine configured to apply the plurality of detection techniques to the collected network messages to detect security events and configured to determine a severity score and a reliability score for each detected security event, and an interface manager configured to transmit detection reports to a remote backend server in response to detection of the security events.

The invention discloses a nuclear power plant fault diagnosis method and device, and the method comprises the steps: collecting nuclear power plant data, training and constructing an edge end model group and a cloud model group according to the nuclear power plant data, and the nuclear power plant data comprises operation regulations, real-time operation data and historical operation data; deploying the edge end model group to an edge computing node near equipment in a plant, and deploying the cloud end model group to a cloud platform; inputting the real-time operation data of the nuclear power plant into the edge end model group, detecting a fault type, reconstructing sensor parameters if the fault type is sensor abnormality, and uploading an abnormality report to the cloud platform if the fault type is system abnormality, and reasoning and tracing the abnormal report through the cloud model group, and pushing fault information and a decision scheme.

The invention relates to the technical field of impurity gas detection control, and discloses an impurity detection control method and system for high-purity oxygen, and the method comprises the steps: arranging a plurality of groups of TDLAS detection probes and temperature vibration monitoring units, and obtaining a spectrum harmonic signal and temperature and vibration data; the concentration is inverted through wavelength modulation spectrum demodulation and temperature and pressure disturbance compensation; pCA fusion denoising processing is executed through the edge calculation unit, and correction concentration is output; and abnormity is judged through a multi-scale dynamic threshold mechanism and linkage control is carried out. Compared with a common single-point probe or traditional low-pass filtering in the prior art, the technical problem that interference between impurity concentration change and environmental disturbance cannot be effectively handled especially in a high-vibration and high-temperature-difference environment is solved. According to the invention, through multi-channel cooperative weighting, PCA fusion compensation and a dynamic threshold determination mechanism, accurate detection control of the high-purity oxygen conveying pipeline in an industrial environment is realized, and the automation level of pipeline impurity gas detection control is improved.

Multi-timescale Doppler processing and associated systems and methods are provided. In one example, a receiver receives radar return data, where the radar return data is associated with reflections, from a scene, of a plurality of transmitted radar signals. The radar return data is processed to obtain a plurality of sets of detection data, where each set of detection data of the plurality of sets of detection data is associated with a respective processing size. Target data associated with the scene is generated based at least in part on the plurality of sets of detection data. Related systems and methods are also provided.

The invention provides a TBM tunneling tunnel vault settlement automatic measurement alarm device and an operation method, the TBM tunneling tunnel vault settlement automatic measurement alarm device comprises an arch-shaped frame, a plurality of groups of support arms are uniformly distributed on the arch-shaped frame, and each group of support arms extend outwards along the radial direction of the arch-shaped frame; the arched plates are arranged at intervals along the upper circumferential side of the arched frame; the plurality of groups of detection units are arranged on each arched plate at intervals along the length direction of the arched plate, and each detection unit comprises a mounting part detachably connected with the corresponding arched plate, a trigger part movably arranged on the mounting part, and a reference part arranged on the mounting part; and the alarm unit comprises a sensor arranged on the mounting piece, and an alarm and a power supply system which are electrically connected with the sensor. The triggering piece synchronously moves along with the settlement of the vault, the settlement of the vault can be visually observed on the basis of the reference piece, and manual intervention is not needed in the whole process; the sensor senses abnormal movement of the triggering piece in real time, and once the abnormal movement exceeds a safety threshold value, the alarm immediately gives an alarm.

The invention provides a remote sensing building group depth detection and change intelligent evaluation method for urban planning, and the method comprises the steps: carrying out the multi-modal data collection of a target urban region, and carrying out the standardization processing, abnormal value elimination, multi-modal alignment and interference suppression processing, and obtaining a multi-modal data set; constructing a multi-branch hybrid network, and performing triplet feature extraction on the multi-modal data set through the multi-branch hybrid network to obtain a building group fusion feature map; performing change area detection and change type classification on the two building group fusion feature maps with different time phases to obtain building group change information; and constructing a dynamic evaluation index based on the urban planning core demand, and performing building group change evaluation in combination with the building group change information. According to the method, through multi-source and multi-modal data acquisition and processing, multi-branch hybrid network triplet feature extraction and change detection and dynamic evaluation index application, urban planning core requirements are adapted, and building group detection precision and change evaluation effectiveness are improved.

The invention relates to the technical field of electronic control system testing and diagnosis, and discloses a high-precision control module detection and data analysis system and method, and the system comprises a data collection and excitation execution module which is used for carrying out data interaction with a tested control module and applying an excitation signal; the dynamic divergence calculation module is used for calculating the dynamic divergence representing the internal dynamic relation deviation degree of the tested control module according to the real-time state data of the tested control module and by comparing with a preset reference state space tensor; and the predicted deviation degree calculation module is used for calculating the predicted deviation degree between the actual state and the predicted state of the tested control module according to a preset state observer model. According to the method, a closed-loop self-adaptive test framework is adopted, active exploration is carried out by taking maximization of dynamic divergence as a target, deep faults which are difficult to expose in a conventional test and are caused by dynamic coupling failure can be efficiently found, and the depth and coverage rate of fault detection are greatly improved.

A bot detection service associated with an overlay network operates to score traffic as a probability of being a bot, as opposed to returning a binary classification (i.e., bot or human). According to the approach herein, scoring is determined through probability estimates, wherein a score (the probability) is based on considering a set of detections concurrently. In one embodiment, all (or substantially all) triggered (current) threat detections contribute to the score. The preferred approach penalizes requests that fail all (or substantially all) combinations of detection algorithms. According to a further feature, an automated tuning (autotuning) is also applied, e.g., using real-time empirical statistical models, to adapt the measurement of false positive probability for one or more threat detection algorithms to suit customer traffic trends. The approach herein is also extensible to include any number of future threat detection algorithms.

Data platforms described herein are configured to monitor a compute environment and to use machine learning models for managing security threat alerts for the compute environment. Such a data platform may identify, based on event data indicative of events occurring in the compute environment, a set of detected security threats present within the compute environment. Using a machine learning model trained based on previous event data indicative of events that occurred previously, the data platform may reduce the set of detected security threats to form a subset of prioritized security threats. The data platform may then provide security threat alerts for the compute environment in a manner that emphasizes alerts associated with prioritized security threats over alerts associated with other detected security threats of the set of detected security threats. Corresponding methods, systems, and products are also disclosed.

Systems and methods of using sensor sets for detecting multiple tasks are disclosed. An example method includes receiving, via a first sensor of the shared sensor set, first data representative of visual intent and receiving, via a second sensor of the shared sensor set, second data representative of a hand input. The method includes determining, using a contrastively-trained model, third data that describes a relationship between the first data and the second data and determining, using a task-inferring model and the third data, a task to be performed at an XR system. The method further includes providing instructions for causing performance of the task at the XR system.

One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

A method for determining occupied positions in a space starts by receiving detections of object movement from a radar monitoring the space. Each detection is associated with a position in the space and a time point when the detection was made. The method accumulates detections that have a time point within a first time period of predefined duration, and identifies a set of clusters of detections in the space by analyzing similarity in position of the accumulated detections. Any cluster whose detections are temporally distributed within a proportion of the first time period which is below a predefined proportion threshold are then removed from the set of clusters. The positions in the space that correspond to the set of clusters are determined to be occupied during the first time period.

The invention relates to the technical field of glue pudding quality detection, and discloses a glue pudding quality intelligent detection system and method based on multi-sensor fusion. The system comprises a data acquisition module, a stability analysis module, a detection parameter search module, a quality detection execution module and a quality judgment module. The data acquisition module acquires a surface image, weight and texture elasticity data set of the glue pudding sample through a multi-sensor array; the stability analysis module evaluates the acquired data to obtain image, weight and texture elastic stability factors; the detection parameter search module takes each stability factor as a joint index, and determines an optimal detection parameter group through density clustering search in a detection parameter space; the quality detection execution module detects the target glue pudding of the production line according to the optimal parameter group to obtain multi-source detection data; and the quality judgment module inputs the multi-source data into a classifier to obtain a quality grade judgment result. The system realizes comprehensive detection of the glue pudding quality, and improves the detection intelligence and automation level.

The invention, which relates to the technical field of assembling, discloses an automatic detection machine for assembling a snap spring in a connecting piece, comprising an equipment cabinet, and an A material vibration disc, a B material vibration disc, an assembling frame, a detection frame, a primary preassembling module, a secondary assembling module, an insertion and extraction force detection module, a first screening module, a CCD camera, a second screening module and a transfer module which are installed at the upper part of the equipment cabinet. The material A and the material B are supplied by a vibrating disc, the material A and the material B are preliminarily pressed by the primary preassembling module, the material A and the material B are secondarily pressed by the secondary assembling module, the insertion and extraction force is detected by the insertion and extraction force detection module, the insertion and extraction force unqualified products are removed by the first screening module, visual detection is performed by the CCD camera, the products after visual detection are sorted by the second screening module, and the press-fitting body is transferred by the transferring module. The equipment realizes automatic assembly and detection, improves the efficiency and precision, and reduces the labor cost and the rate of unqualified products.

A computer-implemented method for anomaly detection comprising receiving a set of data parameters; retrieving a dataset corresponding to the set of data parameters from a database; analyzing, using a machine learning model trained in real-time, the dataset to detect one or more anomalies in the dataset; selecting a set of anomaly parameters corresponding to the detected one or more anomalies; filtering an output of the machine learning model according to the set of anomaly parameters; generating a set of instructions for identifying one or more anomalous items based on the set of data parameters, the set of anomaly parameters, and a set of detection pattern parameters; executing the set of instructions for identifying anomalous items to identify one or more anomalous items in real-time within the dataset responsive to updates to the dataset; and transmitting information about the one or more anomalous items to a user device or computing device.

The invention relates to the technical field of gear detection, and discloses a precision detection device for gear processing, which comprises an assembly part, a lifting component for controlling the assembly part to lift is arranged above the assembly part, a displacement rotating component is arranged in the assembly part, and an adjusting component is arranged below the displacement rotating component. According to the precision detection device for gear machining, the multiple sets of detection probes for detection are arranged and circumferentially distributed on the outer side of the assembly part, the multiple sets of detection probes jointly detect a circumferential double-tooth gear, the detection efficiency is greatly improved, the multiple sets of detection probes can be synchronously close to or away from each other, and the detection accuracy is improved. According to the gear detection device, gears of different sizes can be flexibly detected, meanwhile, the multiple sets of detection probes can synchronously rotate, the detection probes can rotate while ascending and descending through cooperation with the lifting assembly, the gear detection device can detect horizontal gears and bevel gears, and the flexibility of the gear detection device is greatly improved.

The invention discloses an early warning method and device based on nursing requirements, a medium and a smart wearable product, and relates to the technical field of smart home / smart home, and the early warning method based on nursing requirements comprises the steps: detecting the physiological feature data of a target user through a millimeter wave radar, and obtaining the physiological feature data of the target user; detecting first temperature and humidity data and posture data of the target user through an auxiliary sensor module; and based on the physiological feature data, the first temperature and humidity data and the posture data, predicting a nursing demand value of the target user through a nursing prediction model, generating early warning information when the nursing demand value is greater than a preset threshold, and sending the early warning information to at least one terminal device. The method is different from a traditional single sensor detection scheme, through multi-dimensional data fusion, the prediction accuracy is improved, the false alarm rate is reduced, the problem of nursing response delay existing in a traditional scheme is solved, timely response in a complex environment is ensured, and the use experience of a user is improved.

The invention relates to the field of product identifier identification, and discloses a product identifier attachment identification system and equipment, the equipment comprises an information flow control module, a pre-labeling station, labeling equipment, an identifier printing defect abnormity processing module and an equipment hardware fault detection module; meanwhile, the system is applied to the equipment and comprises a product information acquisition module, a labeling instruction issuing module, a labeling execution module and a detection module; in the working process, after products flow into a pre-labeling station, after detection and information collection, an upper computer is linked with an MES to obtain printing information, a printing system is controlled to print labels, after pre-detection is qualified, the labels are attached through a mechanical arm, then secondary detection is conducted through multiple sets of detection modules, and a repairing mechanism is started when the products are abnormal; according to the invention, full-process automation can be realized, the production efficiency is improved, the detection reliability is enhanced, flexible production requirements can be met, and the technical problems of low efficiency and poor detection quality of a traditional process and insufficient adaptability of existing equipment are solved.

The invention relates to a device and a method for detecting a tobacco tray group conveyed and put in storage, a first detection mechanism is provided with a part located on a detection position, and the first detection mechanism is used for carrying out appearance detection and superelevation detection on an empty tobacco tray group; the second detection mechanism is provided with a part located on the detection position, and the second detection mechanism is used for conducting beam detection and bar code detection on the tray structure of the tobacco empty tray set. On one hand, normal stacking of the tobacco empty tray set by the mechanical arm is facilitated, and the linkage problem caused by abnormal stacking is avoided; normal stacking of all tray structures in the tobacco empty tray set is guaranteed, the situation that the tray structures stacked irregularly collide with tobacco boxes of a goods shelf and the goods shelf during warehousing can be effectively avoided, and the situation that forks collide with the tray structures when a stacking machine takes and places goods due to the fact that the tray structures damage deviated cross beams is prevented. Therefore, loss caused by collapse of the tray structure and the finished cigarettes is avoided, binding of the finished cigarettes and tray information is confirmed through bar code detection, and the stacking and warehousing efficiency of the finished cigarettes is prevented from being affected.

A high-impedance node detection method and system, and a circuit simulation device and a readable storage medium. The high-impedance node detection method comprises: acquiring a conduction matrix generated in a circuit simulation process, wherein each row and each column of the conduction matrix respectively correspond to one node, and when a matrix element is a first identification value, this indicates that a node corresponding to the row where the matrix element is located is connected to a node corresponding to the column where the matrix element is located (S110); on the basis of the conduction matrix, dividing mutually connected nodes into the same group, and respectively performing separate grouping on nodes that do not have connected nodes, so as to obtain a plurality of groups of nodes (S120); and among the plurality of groups of nodes, determining at least one group of nodes comprising a reference node to be a non-high-impedance group, and determining the other groups of nodes to be high-impedance groups (S130). The detection method can quickly identify high-impedance nodes of a simulated circuit at various simulation moments, thereby reducing the computational workload in a high-impedance node detection process, improving the detection efficiency, and shortening the detection time.

The invention discloses a sensing, retrieval and positioning target detection method and system for a synthetic aperture radar remote sensing image, a storage medium and a product, belongs to the technical field of computer vision and remote sensing image processing, and solves the problem of insufficient utilization of group distribution characteristics caused by a single-target independent detection strategy. A target initial small group box is generated through a weak detector based on a synthetic aperture radar remote sensing image, and position correction is carried out; a middle group detection frame and a large group detection frame are generated step by step based on a merged group generation algorithm and a target initial small group frame before correction, and after size processing of each cutting area is completed through a Gaussian size adjustment module, the cut areas are respectively input into a middle group detector and a large group detector for detection. Obtaining a corrected middle group frame and a corrected large group frame; and inputting each corrected group box into a progressive detector based on a box integration fusion algorithm, and processing to obtain a predicted group box. The method is used for target detection in the synthetic aperture radar remote sensing image.

The application discloses a kind of unmanned aerial vehicle group detection and distinguishing method and device based on KT and DeANM, the method includes: the three-dimensional echo signal of digital beam forming transformation is obtained to echo signal in space dimension;According to the acceleration and velocity ambiguity number of three-dimensional echo signal search target, and based on wedge stone conversion algorithm to the three-dimensional echo signal after preliminary filtering compensation;According to the distance, velocity and angle of three-dimensional echo signal after compensation to target rough estimate;According to distance rough estimate value, velocity rough estimate value and angle rough estimate value, the distance dimension and angle dimension of three-dimensional echo signal after compensation are extracted Fourier inverse transform and obtain two-dimensional echo signal;Two-dimensional echo signal is decoupled atomic norm minimization processing and obtains the topley matrix of target, and the distance super-resolution estimate value and angle super-resolution estimate value of target are obtained by Van der Monde decomposition to the topley matrix of target.The present application can realize high-precision detection and distinguish to unmanned aerial vehicle group.

This utility model discloses a capsule testing device, belonging to the technical field of capsule testing machines. Specifically, it relates to a multi-purpose, fully compatible capsule testing machine, comprising a feeding hopper, a rotary feeding device, two sets of testing stations, a conveying device, a synchronization device, a testing system, a rejecting system, a finished product collection device, and a control system. This utility model is compatible with the testing of three major types of materials: empty capsules, filled capsules, and soft capsules, and can accommodate the testing needs of different capsule models. It is easy to clean and maintain, and can operate stably and uniformly, improving the accuracy and stability of testing results. This project adopts machine vision inspection principles and high-speed image processing technology to perform fully automatic, blind-spot-free testing of capsule products. It quickly models and manages different product types, counts and statistically analyzes the test results, and automatically rejects defective products. It features fast testing speed, high testing accuracy, comprehensive technical service, strong upgrade capabilities, and simple and easy-to-use equipment.

A method for execution by a computer includes detecting a set of lighting objects of an environment based on at least one of environment signaling of the environment and premise messages exchanged with another computer to produce a set of detected lighting objects. The method further includes generating processor-executable instructions for use by the computer to subsequently monitor at least one of further environment signaling and further premise messages associated with the set of detected lighting objects. The method further includes storing object monitor information for the set of detected lighting objects within a digital twin memory by applying the processor-executable instructions. The method further includes interpreting a portion of the object monitor information to produce as-is configuration information for the set of detected lighting objects, where the as-is configuration information includes spatial placement information of at least some of the set of detected lighting objects.

Disclosed are a method, a device and a system of a sensor integrated safety system based computing platform. The computing platform is executed on a data processing device and integrated with a set of sensors associated with components of a safety system In accordance therewith, the data processing device collects a number of parameters of the components of the safety system and / or of access thereof through detection of the number of parameters via the set of sensors. The number of parameters includes one or more parameter(s) related to breathable air from a source within the safety system supplied thereacross via a fixed piping system implemented therein and / or access of the breathable air. The data processing device also monitors the safety system and / or one or more components thereof based on the collected number of parameters.

The utility model relates to the technical field of probe position detection, in particular to a detection tool. The detection tool is used for detecting a probe assembly of to-be-tested equipment, the probe assembly comprises a plurality of probes arranged along a first horizontal direction, and the detection tool comprises a support; the detection module is arranged on the bracket and can move along a first horizontal direction relative to the bracket; the driving device is arranged on the support and connected with the detection module so as to drive the detection module to move in the first horizontal direction; the detection module comprises a first sensor, and the first sensor is configured to detect whether the detection end of each probe is located at a first preset height or not in the process of moving in the first horizontal direction. And if the detection module detects that the probe which does not need to be positioned at the first preset height is positioned at the first preset height, or the probe which needs to be positioned at the first preset height is not positioned at the first preset height, judging that the channel switching is wrong. Therefore, the condition of channel switching errors can be found through the detection module.