184results about How to "Reliable identification" patented technology

The invention discloses a method for automatically adjusting a head wave detection voltage threshold value of an ultrasonic flowmeter, relates to the technical field of flow velocity measurement, and ensures that the ultrasonic flowmeter accurately identifies a head wave and measures the flow velocity under a complex working condition by dynamically detecting and adjusting the peak value of a received signal and a wave hopping phenomenon. The method comprises the following steps: S101, initializing a peak acquisition circuit, an adjustable potentiometer, a logic control circuit and a head wave trigger and zero-crossing control circuit of a system, and adjusting the gain of an amplifier; by dynamically monitoring the peak value of the ultrasonic receiving signal and adjusting the head wave triggering voltage threshold value, the accuracy of flow velocity measurement is remarkably improved, false triggering and missing triggering caused by a fixed threshold value are reduced, the measurement error is reduced, the measurement precision is improved, the head wave signal is reliably identified by an introduced front-back jump wave fine tuning mechanism, the measurement failure is prevented, and the accuracy of flow velocity measurement is improved. The ultrasonic flowmeter can stably work in a dynamically changing industrial environment, and the high-precision metering requirement is met.

The invention provides an unsupervised continuous emotion electroencephalogram analysis method and device based on deep reinforcement learning, and the method comprises the steps: in a prototype learning stage, obtaining sample features of time sequence signals corresponding to sample objects, carrying out the clustering processing of the sample features based on a K-Means algorithm, obtaining the global distribution information of emotion information in the time sequence signals, and carrying out the clustering processing of the global distribution information; in the reinforcement learning stage, a distribution-prototype reward function is determined according to the global distribution information, an Actor-Critic framework is adopted, a near-end strategy optimization algorithm PPO is used, an Actor executes a current action for a given time sequence signal based on a current strategy, Critic determines a current score of the current action according to a distribution-prototype reward fed back by the environment, and the current score of the current action is determined according to the distribution-prototype reward fed back by the environment. And the Actor performs strategy learning based on the current score until a preset iteration termination condition is reached, and a model used for determining key signals in the target time sequence signals according to the target time sequence signals is obtained. Therefore, the accuracy and reliability of the finally obtained model are improved.

The invention discloses an arc risk situation awareness prediction method and system based on multi-characteristic dynamic mapping, and belongs to the technical field of electrical fault detection.The method comprises the steps that arc characteristics are extracted according to a current waveform of an electric power system, and the arc generation position is determined by combining the propagation rule of the arc characteristics; predicting the development trend of the arc form image and the energy signal according to the arc form image and the energy signal of the current waveform; according to the development trend of the arc characteristics, taking the arc as a heat source, reflecting the contact area of the arc and air by the arc form image, and combining with the heat transfer rule of the arc to obtain the temperature field distribution of the arc; and according to the arc generation position and the temperature field distribution, in combination with the distance perception between the ignition point of the evaluation object and the arc boundary, predicting the arc risk on the installation line of the evaluation object, and making corresponding emergency prevention measures according to the risk evaluation result. And accurate prediction, reliable identification and rapid cutting-off of the fault arc are realized.

The invention belongs to the field of molecular biological identification, and particularly relates to a specific SNP site combination for identifying a Tonchang pig variety and application. The specific SNP site combination for identifying the tunchang pig variety comprises 62 SNP sites, and the physical position is determined by sequence comparison based on a pig reference genome Sscrofa11.1. The specific SNP site combination is screened based on a method of combining whole genome association analysis with selection signal analysis so as to ensure the accuracy of site selection. The selected specific SNP site combination can rapidly realize accurate identification of the Tonchang pig variety on the gene level, and has significant application value in the aspects of genetic resource accurate protection and variety utilization of Hainan Tonchang pigs.

The invention relates to the technical field of underwater static target object detection and processing, in particular to an unmanned cross-domain positioning and acoustic fingerprint processing method and system for an underwater static target. Comprising the following steps: performing wide-area scanning on a task sea area, and scheduling an AUV and unmanned aerial vehicle cluster to a target area after discovering a suspicious target; the unmanned aerial vehicle cluster receives an acoustic signal of the AUV and transmits the acoustic signal to the cooperative resolving center in combination with self-positioning data so as to provide accurate coordinate guidance for the AUV; the AUV sails to a target area, parallel processing map construction and accurate positioning, target identification and dual-mode fingerprint generation are carried out, and a target task package is packaged; the ROV receives and analyzes the task packet, autonomously plans a path and sails to a target area, a sonar is started to collect data and generate real-time feature fingerprints, and the real-time feature fingerprints are matched with fingerprints in the task packet; and after matching succeeds, a specific job task is autonomously executed. According to the invention, a complete automatic solution is provided for scenes such as deep and far sea detection, emergency salvage, pipeline maintenance and the like.

The invention provides an elevator personnel existence detection method and system based on a millimeter wave radar, and relates to the technical field of detection, and the method comprises the steps: orthogonally arranging a positioning radar and a motion detection radar at the top of an elevator car, collecting radar echo signals, and carrying out baseband demodulation; a positioning baseband signal and a motion detection baseband signal are processed through a dynamic threshold elimination method and a self-adaptive harmonic suppression algorithm respectively, and compensation enhancement is carried out in combination with a geometric structure of the lift car; performing fast Fourier transform on the enhanced signal in the dimensions of distance, speed and height, acquiring three-dimensional coordinate data of a target by adopting an adaptive spatial spectrum decomposition positioning algorithm, and performing non-uniform sampling Doppler processing to obtain target motion track data; a recursive state estimation model is adopted for time sequence tracking, human body height judgment is achieved, and the method is used for safety monitoring in the lift car, and early warning for children and pets entering the lift car independently and early warning for long-time staying in the lift car.

The invention provides a joint design method and system for water flow estimation and ship waterline monitoring, and belongs to the technical field of ship monitoring, and the method comprises the following steps: firstly, a shore-based center deploys an underwater robot and plans a motion path according to the needs of a monitoring task, and builds a water gauge data set for model training; when the underwater robot arrives at the nearby area of the ship, the underwater robot measures the water flow speed and direction by using a Doppler velocimeter, and predicts a future water flow velocity field in combination with the trajectory information of the underwater robot; meanwhile, the underwater robot collects a waterline image through an airborne camera, pre-processes the waterline image, and calculates the credibility of the waterline image; and further, the underwater robot corrects the attitude in real time according to water flow velocity field prediction and waterline image credibility, and joint estimation of the water flow and the ship waterline is realized. According to the invention, water flow estimation and ship waterline monitoring are combined, and stable and efficient omnibearing ship waterline monitoring is realized through the underwater robot.

The invention discloses a vehicle identification method in the technical field of vehicle re-identification, and aims to solve the technical problems that the vehicle identification precision is low and visually similar vehicles cannot be distinguished. The method comprises the following steps: obtaining local fine-grained features by using a graph structure, and recognizing a vehicle through fusion features obtained by fusing the local fine-grained features and global features, thereby improving the recognition capability and robustness of a system, improving the effect of the system in coping with environmental inconsistency in the real world, ensuring reliable vehicle recognition, and improving the vehicle recognition efficiency. The accuracy and the key area identification capability are improved, and visually similar vehicles are distinguished.

The invention relates to the technical field of quality detection, in particular to a quality detection analysis method used in the grinding wheel production process. The method comprises the following steps: acquiring a target surface image of a grinding wheel to be detected and a surface feature value vector corresponding to each pixel point on the target surface image; according to a principal component analysis algorithm and the surface feature value vector, obtaining scatter points in a mapping space corresponding to the target surface image and pixel points corresponding to the scatter points; obtaining an initial bandwidth of a mean shift clustering algorithm, and clustering scattered points in the mapping space according to the initial bandwidth to obtain each target cluster; and obtaining an abnormal cluster according to the distance between the scatter points in the target cluster, obtaining an uneven area on the target surface image according to the pixel points corresponding to the scatter points in the abnormal cluster, and obtaining a quality detection result of the grinding wheel to be detected according to the uneven area. According to the invention, the accuracy of quality detection of the to-be-detected grinding wheel can be improved.

The invention provides a water conservancy safety detection method based on AI edge calculation, and the method comprises the steps: deploying a sensor in a water conservancy system according to the installation and coding of the sensor, coding the sensor, and recording the geographic position information and measurement physical quantity of the sensor; setting measurement parameters, and setting a measurement interval value and a threshold value of each sensor; data linkage setting: defining data linkage selection data, and triggering pump IO output when the data reaches a specific interval threshold value; the method comprises the following steps: monitoring sensor data in real time, if the data is abnormal, starting a video AI to check, comparing whether the result is consistent, if the video AI confirms that the sensor data is abnormal, intercepting a real-time video as sample data, sending the sample data to a cloud server, triggering a corresponding alarm according to the abnormal condition, and notifying related personnel to process. The accuracy and timeliness of abnormal condition detection are improved, and a powerful guarantee is provided for safe operation of a water conservancy system.

According to the abnormal wear monitoring method for the disc cutter of the shield tunneling machine, tunneling parameters closely related to the wear state of the disc cutter are screened according to the abnormal wear monitoring requirement of the disc cutter in the tunneling process of the shield tunneling machine, and data missing caused by the collection problem is filled up. According to the long and short term memory self-encoder model based on the abnormal attention mechanism, prior correlation in an original sequence is obtained through a learnable Gaussian kernel, the distinguishability between normal and abnormal conditions is improved by obtaining correlation differences of the sequence, and accurate and reliable abnormal wear monitoring is conducted on the shield tunneling machine disc cutter in combination with the extreme value theory. According to the method, the tunneling parameters closely related to the running state of the disc cutter are identified more accurately, and the model training precision and the accuracy of abnormal wear monitoring of the disc cutter are improved. The method is of great significance to the application of abnormal wear monitoring of the disk cutter of the shield tunneling machine.

The invention discloses a method and system for identifying seismic liquefaction, and the method comprises the steps: collecting a remote sensing image of a to-be-evaluated region, and extracting the liquefaction related parameters of each pixel point; according to the liquefaction related parameters of each pixel point, determining a classification type representing whether the pixel point belongs to sand liquefaction by using a seismic liquefaction identification model; and forming a complete sand liquefaction pit area according to the classification type of the pixel points. According to the method, high-precision evaluation of the seismic liquefaction area and rapid analysis of sand liquefaction space distribution are realized.

The invention relates to the technical field of plant type identification, and discloses a crop planting type identification method based on a Transform model, and the method comprises the steps: calculating a vegetation index of a crop planting plot, analyzing the spectral characteristics of the crop planting plot, and constructing a spectral label of a planted crop according to the vegetation index and the spectral characteristics; inputting the spectrum label of the planted crop into a Transform model, analyzing the phenological characteristics of the planted crop under the time sequence change by using the Transform model, analyzing the land parcel characteristics of the crop planting land parcel, and evaluating the growth mode of the planted crop according to the phenological characteristics and the land parcel characteristics; and extracting the shape descriptor of the planted crop, analyzing the texture characteristics of the planted crop according to the shape descriptor, and identifying the planting type of the planted crop in combination with the growth mode and the texture characteristics. According to the invention, multi-modal data fusion of the planted crops can be realized, and the type identification efficiency of the planted crops is improved.

The invention discloses a tower climbing obstacle identification and positioning method based on machine vision. The method mainly comprises the following steps: S1, acquiring comprehensive environment and obstacle information and monitoring illumination conditions in real time; s2, developing an adaptive illumination compensation algorithm, and dynamically adjusting image processing parameters according to real-time illumination conditions; s3, performing semantic segmentation on the image, identifying different types of obstacles, and taking different countermeasures according to the characteristics of the obstacles; s4, constructing a high-precision real-time positioning system, accurately positioning the identified obstacle, and generating three-dimensional coordinate information of the obstacle; s5, tracking the position change of the obstacle in real time in the tower climbing process, and continuously updating the position information of the obstacle; and S6, designing an intelligent path optimization algorithm, planning a safe tower climbing path, and dynamically adjusting the tower climbing path in the tower climbing process. According to the method, misjudgment caused by insufficient illumination or overexposure is greatly reduced, and the safety of tower climbing operation is improved.

The invention relates to the technical field of computer vision and image processing. The invention provides a large model-fractal combined assisted mineral image intelligent segmentation and identification method, which comprises the following steps: acquiring mineral image data, and preprocessing to obtain a mineral image data set; constructing an initial joint model based on the SAM and the fractal model, and training the initial joint model to obtain a joint model; performing optimization processing on the joint model through the mineral image data set to obtain an optimized joint model; segmenting the to-be-processed mineral image, and marking boundaries of mineral particles in the to-be-processed mineral image; and fractal analysis is conducted on the labeled mineral particles through the optimized joint model, and segmentation masks and fractal quantitative information of the mineral particles are output. The problems that a traditional mineral image analysis method depends on manual observation and manual labeling, time and labor are consumed, and analysis results are inconsistent and uncertain due to the fact that the traditional mineral image analysis method is prone to being affected by subjective factors are solved.

The invention relates to the technical field of intelligent medical treatment, and mainly relates to a heart disease recognition system and method based on 12-lead electrocardiosignal HRV feature extraction, and the system comprises a 12-lead electrocardiosignal preprocessing module which is used for carrying out denoising preprocessing on electrocardiosignals; the heart rate variation HRV signal extraction module is used for extracting a heart rate variation HRV signal; the HRV feature rule mining and rule base construction module is used for mining HRV signal sequence features and generating a standard rule base; and the multi-classification heart disease identification module is used for classifying and identifying heart diseases. According to the system, the correlation between heart rate variation characteristics and various heart diseases is comprehensively considered, and the accuracy of heart disease recognition and classification is improved through matching calculation of the spectrum amplitude change sequence and the rule base. Moreover, the system is wide in applicability, and can effectively meet the recognition requirements of different types of heart diseases.

The invention relates to the technical field of charging pile load detection, and discloses a charging pile abnormal load detection system comprising a data acquisition module which is responsible for acquiring voltage and current data in real time from an electricity meter integrated with a charging pile and calculating a power factor; and the data analysis and judgment module is used for analyzing and judging whether the current load is an abnormal load or not according to the collected voltage, current and power factor data. According to the method, the existing hardware design does not need to be changed, and the required functions can be realized only by software upgrading, so that the implementation and maintenance cost is greatly reduced, and the performance and the safety of the charging pile can be improved under the condition that an enterprise does not increase extra hardware investment.

The application discloses a baby crying sound recognition method for white noise equipment, relates to the field of audio processing and intelligent acoustic recognition, and comprises the following steps: collecting pure reference signals, first signals and second signals for synchronization and preprocessing, and obtaining mixed audio signals based on the first signals; calculating residual signals through a self-adaptive echo cancellation algorithm; calculating acoustic masking parameters based on the residual signals, the pure reference signals and environmental noise estimation; constructing a three-level recognition processing path comprising light feature detection, registered voiceprint comparison and multi-modal information fusion; selecting a recognition processing path based on the numerical range of the acoustic masking parameters, confirming the crying event of the target baby from the residual signals; and triggering corresponding graded alarms based on the crying event confirmation result. The three-level recognition path is scheduled through the acoustic masking parameters, multi-modal information is fused, and accurate and low-power baby crying monitoring under strong interference is realized.

The present application relates to a photoelectric sensor and a method for detecting an object. A photoelectric sensor (10) for detecting an object in a monitoring area (16, 120, 212) is provided, wherein the sensor (10) has: a laser scanner (110) having a deflection unit (112) rotatable about a rotation axis (118), the deflection unit being used to scan the monitoring area (120) with at least one scanning beam (126, 128); and a first distance measuring unit (140) being used to determine a 3D measurement point of a corresponding object scanned by the scanning beam (126, 128) using a light time-of-flight method; a panoramic camera (210) having a panoramic optical device (216) and an image sensor (218), the image sensor having a plurality of light receiving elements for detecting image points; and a control and evaluation unit (14, 140, 222) configured to fuse 3D measurement points and image points. In this case, the optical axis (220) of the panoramic camera and the rotation axis (118) coincide.

The invention discloses a puncture tissue recognition and transmembrane event control method and system based on force sense and image fusion, and the method comprises the steps: collecting a force signal at the tail end of a puncture needle, a real-time ultrasonic image sequence and the pose data of a mechanical arm, and carrying out the timestamp alignment to construct a synchronous data set; on the basis, tissue stiffness, a force change rate, extreme value features, texture features, envelope boundary features and optical flow displacement features are extracted, the tissue level where the puncture needle is located is recognized through multi-modal fusion, and a membrane penetrating event is detected in combination with the force sense extreme value features and envelope boundary fracture; according to the tissue level and the transmembrane event, event driving control strategies such as constant-speed propulsion, deceleration early warning, rotation assistance, transmembrane braking and path compensation are automatically triggered, so that the puncture process is accurately regulated and controlled. The accuracy of puncture tissue recognition and the reliability of transmembrane event judgment can be remarkably improved, and the safety and stability of puncture operation are improved.

The invention relates to a method and a radar arrangement (12, 18) for radar-based dimensional classification of an object (14). The invention also relates to a correspondingly designed motor vehicle (10). In the method, radar signals (24) reflected by an object (14) are detected in a plurality of receiving channels (20). The detected reflected radar signal (24) is then evaluated with respect to its phase profile over the plurality of receiving channels (20). In the event of a phase jump in the phase curve, the respective object (14) is classified as an extended object (14).

The invention provides a snore source recognition method and device and a pillow system. The method comprises the steps that a pressure signal of a deformation element in a pillow is obtained; acquiring an audio signal; when the number of snore contained in the audio signal within a certain window length is larger than the preset number of snore, acquiring snore peak data within the window length, and acquiring pressure data of the air pressure signal within the window length; based on the snore peak data and the pressure data, source matching is conducted on the snore and the pressure, and if matching is conducted, the source of the snore is recognized as the pillow user. When the wave trough / wave crest of the pressure value of the deformation element and the peak value of the snore meet a certain condition, the source of the snore is identified as the pillow user, and the scene that the pillow intervenes when the pillow user does not snoring while sleeping is avoided.

The invention discloses a vehicle driving road safety monitoring and early warning method and system, a terminal and a medium, and relates to the technical field of intelligent traffic, and the key points of the technical scheme are as follows: after a vehicle-mounted terminal generates and uploads an abnormal trigger signal, positioning information and a driving state vector cached by the vehicle terminal are obtained; and when two or more effective state vectors are obtained at the same positioning information in a preset period, the data volume uploaded by the vehicle-mounted terminal can be reduced, and the abnormal type is identified according to the generality of the two or more effective state vectors, so that the vehicle-mounted terminal can identify the abnormal type after effectively weakening the influence of the road characteristics on the driving state. Therefore, the identification of the abnormal type is more accurate and reliable, and meanwhile, the probability of misjudgment caused by misoperation of a driver can be reduced.

The application provides an anti-malicious anchor node target node positioning method, device and equipment, and belongs to the field of network security and signal processing. The method comprises the following steps: acquiring the received signal strength measurement data between nodes, establishing a received signal strength nonlinear measurement model containing an unknown attack strength parameter, further converting the multiplicative noise item in the established model into an explicit additive equivalent noise item, constructing an optimization model with the weighted residual square sum minimization as an objective function, introducing an L1 norm regularization term for the attack strength parameter, coupling the identification of the malicious anchor node and the position estimation of the target node into the same optimization problem to obtain an initial optimization model, introducing an auxiliary variable for relaxing the non-convex item, converting the non-convex constraint into a convex constraint and solving the same to determine the target node position, and providing a reliable basis for subsequent network security maintenance and node trust management.

This application discloses a method for training a recognition model, a recognition method, a computer device, and a computer-readable storage medium. The training method includes the following steps: controlling a single-photon lidar to emit photon pulses towards a target scene and receiving one-dimensional single-photon echoes reflected from the target scene, where the target scene includes at least one object to be recognized; constructing a training sample set based on the one-dimensional single-photon echoes; constructing an initial model and inputting the training sample set into the initial model for iterative training; validating the initial model after each iteration, marking the validated initial model as the recognition model, and outputting it. The application method involves inputting the echo signal to be recognized into the trained recognition model, and obtaining the recognition result based on the processing of the recognition model. The recognition result includes the pose and type of the object in the target scene. Therefore, this application can achieve high-precision recognition of long-distance targets.

The present invention belongs to the field of molecular biological identification, and specifically relates to a specific SNP site combination and application for identifying Tunchang pig breeds. The specific SNP site combination for identifying Tunchang pig breeds includes 62 SNP sites, and the physical location is based on the reference genome of the pig. Sscrofa11 .1 The specific SNP locus combination was screened based on the whole genome association analysis combined with the selection signal analysis method to ensure the accuracy of the locus selection. The selected specific SNP locus combination can quickly realize the accurate identification of the Tunchang pig breed at the gene level, and has significant application value in the accurate protection of the genetic resources and breed utilization of the Tunchang pig in Hainan.

The present invention discloses an LED flash control circuit and control method capable of detecting water ingress and egress, comprising a power conversion circuit electrically connected to a power source, a single-chip computer system electrically connected to the power conversion circuit, a water ingress and egress detection circuit and a power control circuit, the water ingress and egress detection circuit electrically connected to the single-chip computer system, the single-chip computer system electrically connected to the power control circuit, the power control circuit electrically connected to the light detection circuit, and also comprising a light detection circuit electrically connected to the single-chip computer system, an LED control circuit and a flash frequency setting circuit. The control circuit disclosed in the present invention has the advantages of simple circuit structure, low cost, long effective working time, adjustable LED flash frequency, high reliability, etc. The control method disclosed in the present invention can enable the control circuit to stably and reliably identify water ingress and egress status, thereby extending the service life and effective working time of the equipment.

The invention relates to the technical field of radar calibration, and discloses a radar calibration method in a mixed environment. The method comprises: collecting radar echo signals and filtering the collected echo signals to obtain filtered echo signals, performing adaptive signal separation on the filtered echo signals to identify target echo signals and clutter signals, tracking the identified target echo signals to estimate target states, calibrating target position trajectories through sequential analysis, performing error estimation based on the target position trajectories obtained through analysis and the actual target position estimation, calibrating radar antennas based on the estimated errors, and improving the accuracy of radar calibration.

The embodiment of the invention provides a method for monitoring and diagnosing inner leakage of a main steam valve of a steam turbine. According to the method, the XGBoost gradient elevator algorithm is adopted to establish the inner leakage diagnosis model, and the XGBoost algorithm is adopted to establish the inner leakage diagnosis model, so that the method has remarkable beneficial effects of improving the accuracy and efficiency of diagnosis. The XGBoost is a powerful machine learning algorithm, and can effectively process a complex nonlinear relationship and improve the prediction accuracy by constructing a plurality of decision trees and carrying out integrated learning. Meanwhile, factors which have the most influence on internal leakage diagnosis can be screened out from a large number of features, so that the interpretability and predictive ability of the model are improved; secondly, the high efficiency of the XGBoost algorithm means that the model completes training in a short time, and the XGBoost algorithm is suitable for a real-time monitoring system; through the iterative optimization model, the prediction error is minimized, misdiagnosis and missed diagnosis are reduced, it is ensured that the monitoring system can accurately and reliably recognize the inner leakage fault, and powerful guarantee is provided for safe production of power generation enterprises.

This utility model discloses a beauty head recognition circuit and a beauty device. The beauty device includes a main unit and a beauty head detachably mounted on the main unit. The main unit is equipped with an electroacupuncture assembly for interfacing with the beauty head. The beauty head recognition circuit includes a microcontroller module and several recognition modules. One end of each recognition module is connected to an electroacupuncture pin in the electroacupuncture assembly, and the other end is connected to the input port of the microcontroller module. When no beauty head is installed on the main unit, none of the electroacupuncture pins connected to the recognition modules are grounded. When the beauty head is installed on the main unit, at least one electroacupuncture pin connected to the recognition module is grounded, so that the level signal provided by the recognition module to the microcontroller module is different from that when no beauty head is installed. The microcontroller module can thus reliably identify the access status of the beauty head based on the level signals provided by the several recognition modules.