47results about How to "Reliable identification" patented technology

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 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 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 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.

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.

This invention discloses a method for rapidly detecting conventional and hybrid varieties of industrial chili peppers and its application, relating to the field of molecular breeding technology. The method includes: extracting DNA from fresh leaves of the industrial chili pepper to be tested and performing whole-genome resequencing at a sequencing depth of 8×–12×; aligning the sequencing data to the chili pepper reference genome (Capsicum chinense cv. 'PBC932') to screen for high-quality heterozygous SNP loci; using a 1 Mb sliding window to statistically analyze the distribution density of heterozygous SNPs across the entire genome; and determining the variety type based on density thresholds: an average density <300 SNPs / Mb indicates a conventional variety, and >2000 SNPs / Mb indicates a hybrid variety. This invention eliminates the need for phenotypic observation in subsequent generations, requiring only leaves from current-generation plants to rapidly and accurately distinguish conventional and hybrid varieties at the molecular level. It features a short detection cycle, objective and reliable results, and is not limited by the number of molecular markers, providing strong technical support for efficient identification and early screening of industrial chili pepper germplasm resources.

This invention discloses a lightweight method and system for identifying smoke areas in photovoltaic (PV) plants based on an improved YOLO framework. It relates to the field of image recognition technology and includes: multi-point monitoring feature mining based on historical smoke detection logs of the PV plant; real-time monitoring of a multi-sensor array controlled by a monitoring attention-configured graph network to obtain visual and thermal image streams of the plant area; introducing an improved YOLO multi-level learning architecture for visual and thermal smoke probability detection; interference tracing correction and time-dependent aggregation of the first and second smoke detection graph networks; and performing smoke area identification based on the reliable smoke detection graph network to obtain a smoke area identification graph network. This invention solves the technical problems of low accuracy, poor real-time performance, and insufficient reliability in smoke area identification in complex and vast PV plant environments, achieving accurate, reliable, and adaptively focused real-time smoke identification in PV plant areas.

The invention discloses a voice interaction graphene AI intelligent tea table control method, and relates to the technical field of home voice interaction, and the method comprises the control steps: S1, monitoring a surrounding user instruction based on a built-in microphone of a tea table, and after the tea table receives the voice instruction, synchronously carrying out the preliminary noise reduction processing, and retaining the voice segment of the instruction; s2, training a data set containing a noise scene, enabling a built-in model to learn features for distinguishing voice and noise, and filtering non-voice signals in real time; and S3, for dialects and accents in different regions, collecting a scale data set, training a dialect recognition model, and inputting the obtained voice signal into the dialect recognition model to recognize and judge the current dialect and accents. Through preliminary noise reduction and microphone array layout, the voice acquisition quality is improved, and instruction omission is reduced; secondly, by training a multi-noise scene data set, the adaptability of the model to a complex environment is enhanced, and reliable recognition is guaranteed; and aiming at dialects and accent optimization, the product universality is improved.

This invention provides an open-set encrypted traffic classification method and system based on a structured fine-tuning pre-trained model, comprising: preprocessing the original encrypted traffic into a lexical sequence that can be processed by a language model; fine-tuning the pre-trained language model using the lexical sequence: performing structured guidance operations on the lexical sequence such as header field rearrangement and / or flow packet segmentation and representation to guide the model to learn the structured semantics of the traffic; extracting aggregated feature vectors of the traffic sequence to be classified using the fine-tuned model; and based on the feature vectors, using an open-set classification algorithm based on distance metrics to determine whether it belongs to an unknown category, and if not, further determining its specific known category. This invention can guide the model to deeply understand the protocol and packet sequence structure of encrypted traffic, thereby achieving both high-precision classification of known traffic and effective identification of unknown traffic in open-set scenarios.

The application provides a computer vision defect detection system and method on an electric control board production line, and relates to the technical field of data processing.The method comprises the following steps: according to collected multi-angle images, combining illumination conditions and visual angle information, the overall area of the electric control board is partitioned; image block processing is performed to generate a plurality of image sub-areas, and texture features, brightness distribution features and geometric edge features are extracted in each image sub-area; rare defect feature enhancement processing is performed, low-frequency abnormal textures are repeatedly superimposed in multiple scales, and the edge fracture is directionally extended; the corresponding normal area combined features are compared and differenced, and normalized correction is performed in combination with the illumination conditions and the visual angle information; multi-angle recurrence degree analysis is performed, when the same suspected defect is detected under different angles, a reliable defect area is formed, otherwise, an interference area is removed, and an electric control board defect detection result is generated; and the application improves the accuracy of defect detection.

The application discloses a photovoltaic module defect detection method and a detection device, and the method comprises the following steps: receiving a current environment signal detected by an environment monitoring module; controlling a power inverter and an image acquisition module to work according to the current environment signal, so that the power inverter applies a preset mode of power disturbance to the photovoltaic module, and the image acquisition module scans the photovoltaic module according to a preset cruise route when the power inverter applies the disturbance to the photovoltaic module, and a plurality of thermal imaging images of the surface of the photovoltaic module are acquired; the starting time of the power inverter and the image acquisition module is less than 1ms; each thermal imaging image is received, and the defects of the photovoltaic module are determined according to each thermal imaging image. By using the above method, the temperature signal of the early defect can be actively enhanced, the subtle defects such as hidden cracks and virtual welding can be reliably identified, the millisecond-level accurate synchronization of excitation and acquisition is realized, the interference such as environmental temperature drift is effectively inhibited, and the signal-to-noise ratio of the detection signal and the result repeatability are greatly improved.

The invention provides a vehicle braking intention grade determination method, electronic equipment and a storage medium, and belongs to the technical field of vehicle braking control. The control method comprises the steps that a displacement signal reflecting the motion state of a brake pedal is obtained; based on the displacement signal, the movement speed and the movement acceleration of a brake pedal are determined; according to the displacement signal, the movement speed and the movement acceleration, a braking intention intensity quantized value is generated through a preset fusion rule; the braking intention intensity quantized value is compared with a plurality of preset grade threshold values, and the current target braking intention grade is determined; and outputting the target braking intention level to a vehicle control system for vehicle control. The target braking intention level is determined by fusing the pedal displacement, the speed and the acceleration, early, accurate and reliable recognition of the braking intention level is achieved, and therefore the real braking intention intensity of a driver can be accurately distinguished.

A bus ring roll ratio measuring device belongs to the field of bus ring performance testing technology. A base is detachably fixed to an optical platform, and a drive mechanism is fixed to the base. The bus ring rotor is fitted against the outer circumferential surface of the drive mechanism's rotating shaft. One end face of the bus ring rotor is fitted against the stepped surface of the rotating shaft, and the other end face is pressed by a rotor positioning assembly, which is screwed onto the rotating shaft. The bus ring stator is positioned and fixed by two adjustable stator clamps, which are symmetrically arranged and fixed to the base. Two high-speed cameras are slidably connected to vertically arranged straight slots on the base via two camera support frames. The two high-speed cameras are located above and below the bus ring, respectively. Marker points are set in the mounting groove on the inner ring surface of the flexible bus ring and in the non-central area of ​​the rotor end face. The signal output terminals of the high-speed cameras are connected to the signal input terminals of a data processing unit. This invention is used for measuring the roll ratio of bus rings.

The application provides a buoy cooperative underwater positioning system based on multipath suppression and sound velocity profile correction, and has the characteristics that the system comprises an intelligent buoy network module, a sound velocity measurement module, a signal suppression module and a cooperative positioning module; the intelligent buoy network module is used for constructing a positioning reference network through a plurality of buoys distributed in space in a target water area; the sound velocity measurement module is used for obtaining sound velocity distribution information of an underwater environment; the signal suppression module is used for identifying and suppressing multipath propagation interference in an underwater acoustic channel; and the positioning solution module is used for obtaining an accurate three-dimensional space position of an underwater target; through the cooperative positioning mechanism of multipath inversion suppression and trajectory prediction constraint, the underwater target positioning system achieves the technical effects of high positioning accuracy, strong stability and good robustness in a complex underwater acoustic environment.

The invention discloses a reliable receiver subset selection method and system under a non-line-of-sight condition, belongs to the technical field of time difference of arrival positioning, and solves the problem of false low residual but positioning errors caused by selection of subsets only depending on minimum fitting residual under a non-line-of-sight (NLOS) condition in the prior art. The method comprises the following steps: constructing a candidate receiver subset, and determining a traversal sequence; a balance parameter is introduced to form an extended set, screening is carried out, and a reliable newly-added receiver set is determined; and combining the candidate receiver subset with the reliable newly-added receiver set to obtain a reconstructed receiver set, and performing reference consistency check on the reconstructed receiver set to obtain a final reliable receiver set. The method is suitable for a time difference of arrival positioning scene.

The invention discloses a curve burr visual detection method based on main body contour reconstruction and residual analysis, and belongs to the technical field of machine visual detection. The method comprises the following steps: acquiring a workpiece image containing a to-be-detected curve contour, and performing region-of-interest extraction and preprocessing on the workpiece image; extracting a curve initial edge point set based on the preprocessed image, and carrying out iterative reconstruction on a curve contour by adopting a robust fitting mode to obtain a smooth main body contour without burr influence; comparing the smooth main body contour with the original image, calculating normal geometric deviation of pixels or sampling points relative to the main body contour and gray level distribution abnormity along the main body contour direction, generating multi-scale residual error features and forming a residual error feature map; and extracting an abnormal candidate region based on the residual feature map, identifying and quantifying the candidate abnormal region, and outputting size parameters and position information of burrs on the curve. The method is suitable for visual detection of burrs of curve workpieces.

The application relates to the technical field of stress monitoring, in particular to a kind of optical fiber stress monitoring method and system.It includes: preprocessing original optical signal data to form initial signal sequence;Based on frequency separation, extract polarization state fluctuation characteristics, determine polarization response mode;According to polarization response mode analysis coupling effect, extract low-frequency component to obtain purified signal sequence, calculate polarization elliptical parameter change rate to determine the principal stress direction coordinate of target material;Combining cluster analysis to identify optical signal cluster, quantize irreversible change between clusters to determine the plastic deformation starting point;Based on phase shift integral value, establish deformation quantization model, calculate plastic deformation degree index;Update polarization response mode and correct coupling effect parameter, output real-time deformation monitoring result of target material.The application solves the problem that signal coupling interference is large and plastic deformation is difficult to accurately identify in the existing optical fiber sensing technology, and realizes high sensitivity, high reliability and real-time monitoring of material stress.

The invention discloses a lightweight scene text recognition method for a low-quality image, and relates to the technical field of scene text recognition, and the method comprises the steps: 1, calculating the quality perception domain distance of each sample image in a collected training data set, and dividing the training data set into a plurality of batches of data sets according to the quality perception domain distance; 2, constructing a lightweight scene text recognition model, wherein the lightweight scene text recognition model comprises a lightweight feature extraction backbone network and a sequence decoding module which are connected in series; step 3, training the lightweight scene text recognition model by using the plurality of batches of data sets and adopting a hierarchical training strategy to obtain a trained lightweight scene text recognition model; and step 4, inputting the collected to-be-recognized image into the trained lightweight scene text recognition model to generate a recognition result. According to the method, lightweight feature extraction, quality perception domain adaptation and progressive layered fine tuning are integrated, so that the efficiency, robustness and generalization ability of image text recognition are improved.

The invention provides a power converter and a power supply system, which are applied to the technical field of power electronics, the power converter comprises a main circuit, a fault detection circuit, a signal processing circuit and a control unit, the fault detection circuit performs fault detection on the main circuit and outputs a detection result, and when the detection result represents that the main circuit has a fault, the signal processing circuit outputs the detection result. The signal processing circuit continuously outputs a fault signal so that the control unit can control the operation of the power converter according to the fault signal, the power converter provided by the invention comprises the signal processing circuit, and the signal processing circuit continuously outputs the fault signal under the condition that the fault detection circuit detects that the main circuit has a fault. The output of the fault signal is not influenced by the disappearance of the fault of the main circuit, so that the control unit can reliably identify and process the fault signal, the repair probability of the corresponding fault is improved, and the safety and reliability of the power converter are improved.

The invention discloses a man-machine on-screen behavior monitoring and identity authentication system based on multi-modal perception, and relates to the technical field of identity authentication. The man-machine on-screen behavior monitoring and identity authentication system based on multi-modal perception comprises an intelligent perception unit used for collecting visual image data of a target area; the security authentication unit is used for acquiring two-factor authentication information of the hardware encryption token, performing password verification processing and generating a hardware identity authentication result; the identity authentication unit is used for fusing the authentication result and the visual data to perform identity authentication and generate an unlocking instruction; and the behavior monitoring unit is used for continuously collecting the connection state information of the visual image data and the hardware encryption token after the screen is unlocked based on the unlocking control instruction, carrying out cooperative continuous trust evaluation processing and generating a permission judgment value. Therefore, identity compliance of terminal operation is guaranteed, and safety control is improved.

The application relates to an interactive method and system based on a handheld smart device and a head-mounted display device, wherein the handheld smart device is provided with an image acquisition device, the method comprises the following steps: collecting a head image of a user wearing the head-mounted display device through the image acquisition device of the handheld smart device; analyzing the head image; determining the relative position between the handheld smart device and the head-mounted display device according to the analysis result of the head image; tracking the position change of the handheld smart device and the posture change of the head-mounted display device, and determining the real-time relative position between the handheld smart device and the head-mounted display device; and presenting virtual content associated with the handheld smart device on a corresponding area on the display medium of the head-mounted display device according to the real-time relative position between the handheld smart device and the head-mounted display device.

The invention relates to the technical field of detection devices, in particular to a method and system for detecting foreign matter between a movable workbench and a lower cross beam of a hydraulic machine. Comprising the following steps of S1, reference distribution model establishment, S2, detection parameter threshold setting, S3, real-time collection and detection, S4, characteristic value calculation, S5, state judgment and S6, execution control. And the detection system consists of a displacement sensor, a data acquisition and preprocessing module, a calculation and control module, an execution module and a self-adaptive updating unit. Through the detection system and the detection method, tiny foreign matters which are difficult to find by human eyes or foreign matters with colors similar to the background can be rapidly detected and recognized, and the probability of missed detection is greatly reduced; core structural parts such as the movable workbench, the lower cross beam and the machine body are protected against damage of abnormal stress, so that the service life of the whole hydraulic machine is prolonged, and long-term precision and stability of equipment are kept.

This specification provides a method, apparatus, electronic device, and medium for implementing a mail packaging damage attribution model. The method includes: collecting multi-source data related to mail and preprocessing the collected multi-source data; extracting basic features based on the preprocessed multi-source data; constructing higher-order features based on the basic features; selecting and concatenating features from the basic and higher-order features to construct a unified feature space; and constructing and training a multi-feature interaction-based mail packaging damage attribution model based on the feature space. The technical solution of this application effectively solves the problem inaccurate attribution of mail packaging damage caused by fragmented multi-source data and insufficient mining of feature interaction relationships in existing technologies.

The application relates to the field of face recognition technology and discloses a dynamic face recognition device for a smart construction site based on a BIM model, which comprises a perception terminal integrated on a standard safety helmet and comprises an internal miniature camera, an external wide-angle camera and a controller. The internal miniature camera is fixed to the inner side of the brim of the safety helmet and captures stable eyebrow-eye and forehead area images of a wearer in a non-invasive manner, so as to collect a first biological feature image of a worker. The external wide-angle camera is installed on the forehead of the safety helmet and is used for collecting a first environment image of the visual angle of the worker. The first environment image is collected by the external wide-angle camera to determine the space-time anchor point of the worker, a BIM real-time interaction engine infers an expected worker based on the space-time anchor point, and then the unobstructed local first biological feature image of the eyebrows and eyes is collected by the internal miniature camera, and the first biological feature image is compared with a preset biological feature model of the expected worker, so that the stability and accuracy of identity recognition are ensured.

This invention discloses a knowledge graph-based interference relationship identification method, comprising: real-time acquisition of at least one first parameter of a first device to be identified, and at least one second parameter of a second device to be identified; constructing a first triplet for the first device and a second triplet for the second device based on the first parameter of the first device and the second parameter of the second device, respectively; inputting the first triplet and the second triplet together into a trained interference identification network, wherein the trained interference identification network preprocesses each input first triplet and second triplet to obtain preprocessed first triplets and preprocessed second triplets; and outputting an identification result indicating whether there is interference between the first device and the second device based on the preprocessed first triplet and preprocessed second triplet. This invention can efficiently analyze interference relationships within a large-scale system with low computational cost.

The invention discloses an RFID-based device anti-substitution method and device, and relates to the technical field of energy power equipment, and the method comprises the steps: controlling an RFID reader to read the identification information stored by an RFID tag disposed on each power module in the equipment before the equipment is started; comparing all the read identification information with all legal identification information pre-stored in the control panel; if the comparison result shows that all the read identification information is matched with all the pre-stored legal identification information one by one, the control equipment is normally started; and if the comparison result shows that any identification information is not matched or missing, the control equipment terminates the starting process. According to the invention, access of a non-authentication module is completely eradicated from the source, electrical potential safety hazards caused by mismatching of module parameters and quality defects are directly eliminated, and the reliability and the service life of equipment operation are guaranteed. Meanwhile, the mandatory identity verification effectively prevents the behavior of replacing the module privately by the user.

The application provides a digital management and control platform and method for service areas, belonging to the technical field of data management, specifically comprising: dividing the charging piles into different combinations by using the models of the charging piles, determining the reliable combinations in the combinations based on the distribution data of the charging piles in the reliable service areas, determining the analysis and processing strategy of the operation data of the charging piles in the service areas based on the distribution data of the charging piles in the reliable combinations in the service areas and the distribution data of the charging piles in the reliable service areas in the same combination, determining the suspected abnormal data of the charging piles in the combinations in the different service areas based on the analysis and processing strategy, and determining the intelligent management and control method of the charging piles based on the suspected abnormal data and the reliable service areas in the service areas, thereby improving the reliability of the operation and maintenance processing of the charging piles.

A rapid identification method for solar array mode types for on-orbit identification includes the following steps: (1) Processing the free vibration time-domain information of the measuring points distributed on the solar array using the FFT method to obtain the vibration spectrum of each measuring point in three orthogonal motion directions. (2) Identifying in-plane and out-of-plane modes based on the spectrum calculation results. (3) Using the ERA method, after the flexible structure of the solar array enters the free vibration state, extracting the free vibration response data of each measuring point in the out-of-plane motion direction for a certain period of time to obtain the system state matrix and observation matrix reflecting the discrete state equation of each measuring point's out-of-plane motion. (4) Calculating the real mode shapes of the flexible structure. (5) Completing the out-of-plane mode type identification based on the obtained real mode shapes. This invention enables rapid identification of the types of each obtained mode when performing on-orbit identification of large space solar array structures, thereby achieving the goal of rapid analysis and comparison with ground finite element models.

The application discloses a kind of fast identification method and system of high-risk local power grid facing extreme disaster driving, method includes: the power grid topology of preset voltage level is as identification reference layer, the power grid in reference layer is carried out connected subgraph identification;When the number of external tie line of connected subgraph satisfies first preset constraint condition, the connected subgraph is determined as preliminary candidate local power grid area;With preliminary candidate local power grid area as reference, respectively to upper level voltage level and lower level voltage level of preset voltage level is topologically traversed, the total connection channel number of preliminary candidate local power grid area between corresponding voltage level and main grid is determined;When total connection channel number satisfies second preset constraint condition, then it is judged as easy-to-isolate local power grid;Risk quantification evaluation index system of easy-to-isolate local power grid under extreme disaster is constructed, and the comprehensive risk value of easy-to-isolate local power grid area is calculated based on risk quantification evaluation index system.