531 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.

Systems, apparatus, articles of manufacture, and methods to utilize cached generative artificial intelligence responses are disclosed. An example apparatus includes interface circuitry to access a request to a generative artificial intelligence model, computer readable instructions, and programmable circuitry to at least one of execute or instantiate the instructions to replace a named entity within the request with a generic tag to generate a modified request, tokenize the modified request to create an array of tokens, detect a similar prior request based on the array of tokens, and after detection of the similar prior request, cause output of a cached response to the request without execution of the generative artificial intelligence model.

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 provides a linear dimension measurement method and device for the total length of a road guardrail stand column, and relates to the field of linear dimension measurement, the device is deployed on a detection vehicle, and comprises a control module, a ground penetrating radar measurement module, a 3D camera measurement module, a sensor module, a lower computer and an upper computer; according to the invention, accurate data acquisition is realized through multi-module cooperative control, and the lower computer is combined with the AI image recognition technology to realize accurate acquisition of contour data; the lower computer transmits radar original data (reflected waves and diffracted waves) and 3D contour data (column cap space coordinates) to the upper computer, the upper computer achieves data fusion through a hierarchical velocity model (time-depth conversion) and a coordinate system mapping algorithm (3D-road coordinate system), and the accuracy of the total length measurement error is greatly improved compared with that of single module detection. The method solves the problems of low efficiency, large destructiveness and poor accuracy of traditional manual detection, and has the advantages of method innovation and engineering landing.

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 utility model provides a detection device suitable for wafer bearing piece levelness detection, the detection device is arranged on the periphery of a wafer bearing piece in the horizontal direction so as to detect the levelness of the wafer bearing piece, the detection device comprises at least one detection assembly, the detection assembly comprises a laser emitting element and a laser receiving element which are oppositely arranged, and a detection light beam emitted by the laser emitting element is propagated above the wafer bearing piece along the horizontal direction. Based on the detection assembly in the detection device, whether the levelness of the wafer bearing part meets the requirement can be rapidly and conveniently measured according to whether the laser receiving element can receive the detection light beam emitted by the laser emitting element, and the levelness detection and calibration efficiency of the wafer bearing part is effectively improved. And the bearing assembly and the multiple groups of detection assemblies are arranged, and / or the detection device and the wafer bearing piece are rotatably arranged, so that the detection efficiency is further improved.

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.

The invention relates to the technical field of USB data line testing, and discloses a USB data line testing device for protecting a connector, the USB data line testing device comprises a supporting bottom plate, a testing assembly used for testing a USB data line is arranged on the supporting bottom plate in a turnover manner, and the testing assembly comprises a detection unit and a detection head slidably connected in the detection unit; a clamping assembly is arranged at one end, far away from the turnover shaft, of the detection unit, and is used for clamping and fixing a connector of the USB data line to be tested; the bottom of the detection unit is provided with a vibration assembly used for knocking the clamping assembly and the connector of the USB data line in the clamping assembly. According to the invention, the screw rod and the guide shaft are arranged, the adjacent moving seats are linked through the driving of the screw rod and the connecting rod, the multiple groups of detection heads are driven to move and slide in the detection unit until the detection heads are plugged with the USB data line connector for testing, the plugging of an operator is not needed, and the damage of the connector caused by violent plugging of the operator is effectively avoided.

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.

The invention relates to the technical field of blast pump detection, in particular to an air suspension blast pump convenient and rapid detection box device which comprises a test box body, a bottom plate, an eccentric wheel set, a switching mechanism, a transmission mechanism and a blast pump mounting mechanism, a box cover is rotatably hinged to the outer side of the test box body, and an operation plate is fixedly connected in the test box body; hydraulic rods are fixedly connected to the inner wall of the box cover. By integrating the test box body, the switchable eccentric wheel group and the sound insulation cover mechanism, multi-working-condition dynamic simulation and noise isolation are realized in single equipment, the problems of low detection efficiency, high cost and data deviation in the prior art are effectively solved, two groups of detection stations are arranged, a control group is conveniently provided, and the detection efficiency is improved. According to the air pump data detection device, the detected air pump data are more accurate and reliable, the detection devices are modularly combined in the test box body, the detection steps are more convenient, the detection efficiency is improved, and the use requirements of people are met.

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 utility model discloses an aviation part defect detection recording workbench, and relates to the technical field of part defect detection. The device comprises a base body, and further comprises a conveying belt arranged in a working table, and two groups of detection frames are arranged on the upper end face of the working table at equal intervals; the visual detection equipment is arranged in the center of one group of detection frames, and an ultrasonic detection module is arranged at the lower end of the other group of detection frames; the recording plates are arranged on the left side and the right side of the connecting plate, and recording mechanisms are arranged on the inner sides of the recording plates. According to the utility model, the visual detection device and the ultrasonic detection module are arranged, so that the same group of aeronautical parts can be respectively detected, the equipment has two detection modes, the detection error of the aeronautical parts can be reduced, the detection efficiency of the aeronautical parts can be ensured, and if the aeronautical parts have defects, the equipment can be used for detecting the aeronautical parts. And the arranged micro push rod can push the marking pen to make contact with the aviation parts, and then the defective aviation parts can be marked and recorded.

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.

The present invention relates to the field of micro-displacement detection technology, and specifically to a micro-displacement radar detection method, system, equipment and storage medium, and the steps are as follows: the radar transmits multiple groups of linear frequency modulation signals with different carrier frequencies for detection, and each group of signals includes multiple pulses; the echo received by each pulse is subjected to FFT transformation, and the results after FFT processing of all pulse groups are accumulated to obtain the range Doppler matrix of any pulse group; the range Doppler matrix of the first pulse group is subjected to target detection to obtain the detection target position, and the radar is repeatedly transmitted, and the fuzzy displacement is obtained by solving the phase difference of the detection target position; the phase difference between the detection target position and the detection target position of the subsequent pulse group is solved to obtain the rough displacement; the fuzzy displacement is solved based on the rough displacement to obtain the real displacement of the target. The present invention can increase the target signal-to-noise ratio, improve the detection capability and stability, and can accurately solve the target displacement even if the target displacement is greater than the radar wavelength.

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 embodiment of the invention provides a battery abnormal connection detection method, electronic equipment, a storage medium and a program product. The method comprises the steps that detection information of a battery cluster is acquired, the battery cluster comprises a plurality of temperature to-be-detected points, and the detection information is used for indicating detection temperatures and current information of the plurality of temperature to-be-detected points in a first preset time period; based on the current information, determining a state distribution condition of the battery cluster in a first preset time period, and based on the state distribution condition, determining target detection data; performing grouping processing on the target detection data, and constructing feature parameters based on multiple groups of detection data; and performing abnormal connection detection on the battery cluster based on the feature parameters corresponding to the multiple groups of detection data to obtain abnormal points in the battery cluster. According to the method, feature extraction is performed on the detection data, abnormal points are accurately positioned, and the problem of battery connection looseness of the energy storage system is recognized in advance, so that the safety and the use performance of the battery cluster of the energy storage system are improved.

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 provides a user message acquisition method and apparatus, an electronic device, a storage medium and a product, and relates to the technical field of edge data transmission, blind detection is performed on a public search space through an RA-RNTI in a system message, DCI of an RA message is acquired, and then the RA message is acquired. According to the method, the detection interval of the NID is obtained through the C-RNTI interval in the RA message, so that the detection range of the NID is greatly reduced, the detection quantity of the CCE group of the non-public search space is reduced, and the efficiency of obtaining the user message based on blind detection of the NID is improved. And the C-RNTI value is calculated according to the NID value, so that the detection accuracy of the matching CCE group of the non-public search space is realized, and the accuracy of obtaining the user message is improved. And the next time period is used as the current time period, so that periodic updating of the user messages is realized.