1685 results about "Anti jamming" patented technology

The invention relates to the technical field of power distribution network fault positioning, in particular to a power distribution network fault positioning method based on traveling wave detection, and the method comprises the following steps: S1, distributed detection network construction and signal collection: collecting a three-phase current traveling wave signal in real time; s2, time-frequency joint analysis and feature extraction: extracting time-frequency energy distribution features; s3, intelligent screening of effective fault wave heads: screening out the effective fault wave heads; s4, initial arrival time parameter determination: determining the initial arrival time of each detection point; s5, according to topology self-adaptive error correction, transmission path wave velocity deviation is corrected through line parameter dynamic compensation, and clock errors of the asynchronous detection device are eliminated through adjacent node clock difference estimation; and S6, multi-source data fusion and fault positioning: resolving space coordinates of fault points. According to the invention, the method improves the recognition accuracy of fault features, and solves the problems of poor anti-interference capability and high misjudgment rate of a single feature in a conventional method.

The invention provides a multi-target tracking and interference cooperation method and system based on a millimeter wave radar. The method comprises the following steps: firstly, acquiring an original detection signal in real time by using a millimeter-wave radar carried by an unmanned aerial vehicle, establishing a track correlation sequence of each moving target, extracting interference waveform characteristics formed by signal coupling among a plurality of moving targets in the original detection signal, and then, according to a spatial distribution parameter of the interference waveform characteristics, determining a track correlation sequence of each moving target; correcting the space-time continuity judgment rule of the trajectory correlation sequence, and finally outputting an anti-interference cooperative tracking result based on the corrected space-time continuity judgment rule. According to the technical scheme provided by the invention, the industry pain point that waveform distortion interference of the millimeter wave radar in a dense target scene cannot be eradicated is solved, and the error correlation rate in a dense formation scene is also reduced.

The invention discloses a traveling wave fault positioning system and method based on Beidou synchronization and intelligent filtering, and belongs to the technical field of power system fault detection. The system comprises a Beidou synchronization module used for acquiring a high-precision time service signal and realizing multi-terminal clock consistency; the traveling wave sampling module is used for synchronously collecting multi-point electric power signals and filtering the multi-point electric power signals; the wave head extraction module realizes wave head identification through a sliding window and dynamic threshold judgment; the time coding module constructs a time difference vector and maps a position relation in combination with a power grid topology; the fault positioning module estimates an initial position based on a nonlinear propagation model, and the intelligent filtering module carries out dynamic correction through state prediction and unscented Kalman filtering. The method realizes rapid and accurate positioning of fault points, has the advantages of high synchronization precision, strong anti-interference capability, small positioning error and the like, and is suitable for traveling wave fault positioning requirements in various electric power scenes.

The invention discloses a photoelectric event identification optimization method and system for a single photoelectron detector, and the method comprises the steps: collecting environment noise data, carrying out the modeling of environment noise, and generating a noise processing model; real-time operation monitoring data of the target single photoelectron detector are acquired, noise processing is carried out, and time-frequency domain waveform decoupling is carried out after processing is completed; performing pulse accumulation detection through a time-frequency domain decoupling result, marking an accumulation event, and separating accumulated pulses by using a pre-trained pulse separation network to obtain pulse separation information; generating a floating judgment boundary parameter according to the pulse separation information, and carrying out photoelectric event judgment to obtain a photoelectric event judgment list; and obtaining a historical photoelectric event discrimination list of the target single photoelectron detector, extracting an abnormal discrimination event, analyzing whether maintenance is needed, generating a maintenance self-inspection report, and pushing the maintenance self-inspection report. The detector keeps high robustness and anti-interference capability in the whole life cycle, and the identification error rate improvement caused by aging change is avoided.

The invention discloses an intelligent film-drawing and unwinding control method and system based on real-time tension, and relates to the field of automatic control and intelligent manufacturing, and the method comprises the following steps: ensuring normal starting of equipment and system states through equipment self-inspection and technological parameter input; tension and speed data are collected in real time and preprocessed, and support is provided for fuzzy PID control; the parameters of the fuzzy PID controller are dynamically adjusted by calculating the tension error and the change rate of the tension error so as to adapt to different membrane material characteristics and unwinding working conditions; an improved genetic algorithm is used for optimizing parameters of a PID controller, overshoot is minimized, the adjusting time is shortened, and the robustness and the anti-interference capability of the system are improved; a closed-loop control system is constructed, and the stability of unwinding tension is ensured through real-time feedback signals; and the system state is monitored in real time in combination with an anomaly detection algorithm, and a control strategy is automatically adjusted or fault protection is performed. The control method disclosed by the invention can be widely applied to the fields of film unwinding and automatic production lines, and has a relatively high intelligent level.

The invention discloses an acoustic emission intelligent detection method and system for hydrogen-induced damage of a high-pressure hydrogen system, and the method comprises the steps: collecting a system operation signal through an acoustic emission sensor, carrying out the combined preprocessing of variational mode decomposition and adaptive wavelet threshold noise reduction, restraining noise, constructing a lightweight MobileNet-TCN network, carrying out the deep feature extraction, and carrying out the detection of the hydrogen-induced damage of the high-pressure hydrogen system. GRU and a three-dimensional point cloud technology are fused to realize submillimeter-level positioning of an acoustic emission source, a big data damage case library is associated based on an acoustic emission parameter accumulation model, dynamic assessment and early warning of damage risks are realized, multi-physics field monitoring data are combined, damage classification is optimized through a GCNs (Graph Convolutional Networks), and the above processes are systematically integrated. And full-chain intelligent processing from signal acquisition to risk early warning is completed. The scheme has the advantages of strong anti-interference capability, submillimeter positioning precision, high edge end reasoning efficiency, high damage classification accuracy and dynamic early warning capability, and is suitable for safety monitoring of hydrogen energy storage and transportation equipment.

The invention provides a frequency division multiplexing multi-beam anti-interference method for a shaped antenna, and the method comprises the steps: obtaining the signal data of each frequency band according to the multi-beam spatial domain data received by the shaped antenna and a filter of a frequency domain corresponding to the working frequency band of each beam; performing high-resolution spatial spectrum estimation on the signal data of each frequency band by adopting a MUSIC algorithm to obtain an incoming wave space direction of an interference signal; according to the incoming wave space direction of the interference signal, on the basis of a preset multi-beam zeroing anti-interference strategy, self-adaptive weights of all same-frequency beams are obtained in a spatial domain and frequency domain two-dimensional combined processing mode; the adaptive weights of the same-frequency beams are combined, a comprehensive directional diagram is obtained, and a multi-beam interference zeroing result is obtained, according to the method, the whole airspace is covered with the beams of different frequency points, and space-frequency combined suppression of airspace complex interference is achieved.

The invention discloses a high-low voltage switch cabinet feeder fault positioning method and system based on transient traveling waves, and belongs to the technical field of power system fault detection and positioning, and the method comprises the steps: synchronously collecting electric and acoustic multi-mode signals, and generating a weighted transient synchronization feature matrix; performing time-frequency transformation and feedback optimization on the matrix, and outputting a multi-scale time-frequency feature set; analyzing the feature set by using an integrated learning network, and outputting a layered preliminary positioning result; convergence to an accurate fault section is carried out through iteration calibration; and finally, fusing multi-model calculation and outputting a comprehensive fault positioning report. According to the method, a technical path of combining multi-modal signal fusion and a physical model is adopted, convergence from fuzzy region division to an accurate position can be realized in stages, and the accuracy, the speed and the anti-interference capability of switch cabinet feeder line fault positioning are remarkably improved.

The invention relates to the technical field of wireless communication, and particularly discloses a self-adaptive anti-interference communication method and system based on star flash-Bluetooth dual-mode, and the method comprises the steps: monitoring channel interference parameters in real time through a dual-mode radio frequency module on a communication terminal, including the out-of-band radiation power and the pulse interference duty ratio at the switching moment; instantaneous interference intensity features are extracted by using synchronous compression wavelet transform and sparse time-frequency analysis, periodic pulse interference features are identified by combining Hawkes process modeling, and a comprehensive anti-interference feature vector is constructed by fusion; the feature vectors are input into a pre-trained gradient boosting tree model for communication quality evaluation, an optimal communication mode is dynamically selected according to a scoring result, intelligent response to a complex interference environment is achieved, the stability and reliability of a communication system in a high-interference scene are improved, energy efficiency optimization and resource scheduling are considered, and the method is suitable for large-scale popularization and application. The method is suitable for industrial, medical, Internet of Things and other application scenes with strict requirements on communication quality.

The invention discloses a building facility detection data intelligent analysis and report system and method, and relates to the technical field of building structure health monitoring, and the method comprises the steps: achieving the high-precision data capture in a strong light dust raising environment through a laser radar and a dual-spectrum camera of a multi-mode anti-interference collection module; a millimeter wave radar array and an IMU inertial unit of the high-risk area strengthening module penetrate through severe working conditions to monitor key structure displacement; the credibility management engine is provided with an NFC timestamp chip and a double-chain block chain, and data judicial-level credibility and operation traceability are ensured; through humidity response gel packaging and Peltier semiconductor refrigeration of the self-maintenance sensing network, node environment self-adaption and drift suppression are achieved; and the multi-stage analysis center fuses edge-cloud computing power based on federal learning, generates a dynamic risk map and automatically outputs a compliance report. Accurate and timely reference is provided for operation and maintenance decisions, and the safety management level of building facilities is effectively improved.

The invention relates to the technical field of hydrogen production, in particular to a quick response system of a double-probe hydrogen leakage explosion detector and a grading early warning method. Comprising a first hydrogen concentration detection probe and a second hydrogen concentration detection probe which are respectively arranged at two different positions of a monitored area to form a space complementation detection network; the signal processing unit is electrically connected with the two probes and is used for receiving and processing the concentration signals output by the probes; the quick response control module is used for calculating the hydrogen concentration change rate in real time based on an output result of the signal processing unit; the grading early warning module is used for generating different grades of early warning signals according to the hydrogen concentration change rate and the absolute concentration value; the communication interface module is used for transmitting the early warning signal to a remote monitoring terminal; the multi-physical-field parameter change of the electrolyte can be sensed in real time, the control instruction is dynamically corrected, and the anti-interference capability is high.

The invention relates to the technical field of performance testing, and discloses a multi-parameter insulating sleeve aging performance testing method and system, and the method comprises the steps: carrying out the multi-parameter detection signal collection of an insulating sleeve, and obtaining standardized signal data; inputting the standardized signal data into a coupling recognition network for feature analysis to obtain a classification recognition result; performing aging factor threshold adjustment on the rising edge slope of the discharge pulse of the insulating sleeve based on the classification identification result to obtain a dynamic separation parameter; performing feature space matching and differential identification on the partial discharge signal according to the dynamic separation parameter to obtain feature classification data; phase clustering analysis and step response characteristic analysis are carried out based on the characteristic classification data to obtain the aging state variable coefficient of the insulating sleeve, the problem that the recognition precision of a traditional fixed parameter method is reduced in different aging states is solved, and high recognition accuracy and anti-interference robustness can still be kept in a complex electromagnetic environment.

The invention provides an anti-interference and multi-target positioning method for a vehicle-mounted millimeter-wave radar, and the method comprises the steps: constructing a third-order cumulant interference suppression module, carrying out the processing of a signal received by the vehicle-mounted millimeter-wave radar through a third-order cumulant algorithm, suppressing the mutual interference between radars, and generating a high-resolution distance-angle thermodynamic diagram; constructing a dual-channel adaptive edge detection module, dividing the generated thermodynamic diagram into an original channel and an edge detection channel, dynamically allocating weights based on local gradient intensity, and fusing and outputting an enhanced thermodynamic diagram; and constructing a target identification and positioning module, performing target detection and positioning on the enhanced thermodynamic diagram through an improved YOLOv8 neural network, and outputting distance and angle information of the target. According to the method, the problems of mutual interference between radars, difficulty in detection of weak and small targets, low separation precision of overlapped targets and the like in a complex traffic scene are solved by combining high-order statistical signal processing and deep learning technologies, and the robustness and the real-time performance of target positioning are improved.

The invention discloses an energy router inversion grid-connected port control method for optimizing a VSG, and the LADRC can dynamically compensate the influence of external disturbance on a system through the real-time observation of the state of the system, especially the monitoring of the disturbance and the change of the system, thereby enhancing the anti-interference capability of the system. When the load is suddenly changed or is externally disturbed, the LADRC can quickly identify the disturbance and generate an adaptive control signal, so that the inverter system is quickly recovered to a stable state. And the fuzzy adaptive PI controller adaptively adjusts the proportionality coefficient and the integral coefficient of the PI controller according to the real-time deviation and the change rate of the system, so that the parameters of the controller can be automatically adjusted according to the state of the system, and the limitation of a fixed parameter controller under complex working conditions is avoided. The self-adaptive adjustment not only improves the control precision, but also greatly improves the dynamic response speed of the system, so that the inverter can maintain an efficient and stable working state under the conditions of power grid disturbance, load change and the like.

The invention relates to a tunnel lining deformation real-time monitoring system and method, and relates to the technical field of tunnel engineering health monitoring, and the system comprises a dynamic sensing layer which is provided with a self-adaptive sensor array composed of magnetic type sensor capsules deployed by an unmanned aerial vehicle cluster and double-helix anti-shearing optical fibers, and covers a traditional monitoring blind area; the edge processing layer carries a lightweight AI chip to process laser point cloud and image data in real time so as to realize disease grading response; and the cloud decision-making layer generates a three-dimensional health map through a digital twin engine, and ensures data credibility in combination with block chain evidence storage. The method comprises the steps of dynamic networking, multi-source acquisition, edge cloud collaborative analysis and intelligent decision making. The problems of large monitoring blind area, high response delay and weak anti-interference capability in the prior art are solved, the early recognition precision and operation and maintenance efficiency of tunnel lining diseases are remarkably improved, and the method is suitable for full-life-cycle health management of tunnels in complex environments such as railways and highways.

The invention discloses a multi-modal fusion green port digital management and control system and method, and relates to the technical field of port management and control, and the method comprises the following steps: collecting the signal-to-noise ratio data of a radio channel of a video link, and generating a port area electromagnetic interference thermodynamic diagram through spatial interpolation; and dynamically adjusting a synchronous clock of each camera according to the electromagnetic interference thermodynamic diagram, mapping the frame triggering offset into a pixel compensation matrix, and performing sub-pixel-level space-time correction on the image frame to obtain a corrected video frame sequence. According to the method, target credibility judgment and path optimization under multi-modal perception are realized through construction of an interference thermodynamic diagram, space-time correction, artifact recognition and point cloud fusion, an interference model and a recognition threshold are dynamically regulated and controlled through closed-loop feedback, and an end-to-end self-adaptive port digital management and control method is constructed. The artifact identification accuracy and scheduling stability are significantly improved, and the anti-interference and green efficient operation capabilities of the port management and control system are enhanced.

The invention relates to the technical field of target recognition, and discloses an improved YOLOv8-based small target detection and recognition method and system, and the method comprises the steps: obtaining a small target defect image of an industrial detection object, and constructing a small target defect image data set; the method comprises the following steps: on the basis of a YOLOv8 network model, replacing a C2f module in a backbone network of the YOLOv8 network model with a dynamic snakelike convolution module containing a four-branch cavity convolution group, deploying a dual-stage screening mechanism behind a detection head, and constructing an improved YOLOv8 model; training the improved YOLOv8 model by using the small target defect image data set to obtain a trained improved YOLOv8 model; and inputting an optical image of a to-be-detected object into the trained improved YOLOv8 model to carry out small target defect detection and identification. According to the scheme, the technical problems of low tiny target identification precision and weak anti-interference capability in industrial detection can be solved.

The invention provides an unmanned aerial vehicle navigation signal interference method and system based on an electromagnetic shielding array. According to the method, firstly, a navigation signal sent by a target unmanned aerial vehicle is monitored, positioning information transmission characteristics in the navigation signal are identified, then a periodic dynamic interference mode synchronous with a navigation signal transmission period is generated according to the positioning information transmission characteristics, and then, through spatial gradient barrier distribution of an electromagnetic shielding array, a dynamic interference mode is generated. Converting the periodic dynamic interference mode into directional space interference energy, and finally controlling the directional space interference energy to be continuously applied to a navigation signal receiving path of the unmanned aerial vehicle so as to complete navigation signal interference of the unmanned aerial vehicle. According to the technical scheme provided by the invention, a frequency hopping anti-interference mechanism of the unmanned aerial vehicle is effectively cracked, invalid energy consumption is remarkably reduced, the interference efficiency is improved, and error interference on non-target equipment is avoided.

The invention provides a transformer substation switch cabinet partial discharge detection method and system based on a sensor, and relates to the technical field of discharge detection, and the method comprises the steps: deploying an ultrasonic sensor according to a sensing collection channel, accessing a signal processing terminal, and configuring a sensor sensitivity threshold value and a signal sampling frequency; adjusting the signal gain of each monitoring node coordinate corresponding to the sensing acquisition channel, synchronously recording the monitoring time, the monitoring node coordinates and cabinet body temperature parameters, and setting a monitoring association control matrix in combination with a sensor sensitivity threshold and a signal sampling frequency; determining a partial discharge type and discharge intensity based on the signal processing terminal; and generating a discharge characteristic distribution map, and generating a partial discharge detection report in combination with the insulation performance index of the substation switch cabinet, a safe discharge threshold and a pulse frequency threshold. The technical problems that in the prior art, a transformer substation switch cabinet partial discharge detection method is poor in anti-interference capacity and low in recognition precision are solved.

The invention relates to the technical field of information engineering, in particular to an electronic communication anti-interference system for information engineering, which comprises the following modules: a signal acquisition module, an interference monitoring module, an interference identification module, an anti-interference strategy generation module, an anti-interference execution module, a feedback optimization module and a data storage and management module. Through cooperative work of the signal acquisition module and the interference monitoring module, the interference condition in a communication signal can be monitored in real time, and the interference type and intensity are accurately identified through the interference identification module, so that an accurate basis is provided for subsequent anti-interference processing; the anti-interference strategy generation module can dynamically generate a proper anti-interference strategy according to the specific situation of interference, the limitation of a traditional fixed strategy is avoided, and the adaptability of the system to a complex interference environment is improved.

The invention provides a radar anti-interference strategy generation method based on reinforcement learning by introducing a reinforcement learning technology into a radar anti-interference strategy generation problem on the basis that a radar anti-interference decision generation process has an obvious Markov decision property. And an optimization solution process generated by the radar anti-interference strategy is converted into an optimization solution process of reinforcement learning. According to the invention, reinforcement learning is introduced, and mechanisms such as a dynamic learning rate, action exploration, advantage normalization, reward scaling and hyperbolic tangent activation are combined, so that the radar anti-interference strategy generation method for near-end strategy optimization of asynchronous advantage actors and commentators and normalized scaling tangent based on dynamic learning exploration is provided. According to the method, high-dimensional features of different types of interferences can be accurately extracted in the time-frequency domain, the precision of anti-interference pattern selection and parameter selection and the environmental adaptability are improved, and thus the anti-interference capability and the information acquisition capability of the radar are improved.

The invention provides a point cloud data processing method and device of a laser radar, equipment and a medium, and the method comprises the steps: carrying out the adaptive local neighborhood construction of each query point cloud, and determining a local neighborhood point set of the query point cloud; determining a plurality of geometric features of each query point cloud based on the local neighborhood point set; performing normalization processing and dynamic weight optimization on the plurality of geometric features of each query point cloud, constructing a comprehensive feature of each query point cloud, dividing the plurality of original point cloud data based on the plurality of comprehensive features, and determining a plurality of point cloud categories; and carrying out noise removal processing on the original point cloud data in each point cloud category, determining updated point cloud data, repeatedly carrying out local neighborhood point set determination and point cloud division processing on the updated point cloud data until a convergence condition is met, and outputting the denoised point cloud data. Effectiveness and anti-interference capability of feature fusion are improved, and intelligent identification and denoising of the point cloud and accurate retention of the effective point cloud are realized.

The invention discloses an anti-attack interleaving method and device for communication information. The method comprises the following steps: acquiring an information sequence to be transmitted; performing statistical analysis processing on the to-be-transmitted information sequence to obtain a statistical information set; carrying out detection modeling processing on the communication channel of the information sequence to be transmitted to obtain channel dimension information; and performing interleaving parameter calculation processing on the statistical information set and the channel dimension information to obtain an interleaving parameter information set. According to the invention, the problems of anti-interference effect and influence on the security of the communication system caused by fixed parameters of the interleaving method in the current communication system are solved.

The invention discloses an unknown interference efficiency evaluation method and system for an irregular electronic countermeasure scene, in the electronic countermeasure scene, a deep reinforcement learning model based on a MuZero algorithm is introduced, and the greatest advantage of the MuZero algorithm is that decision optimization and efficiency evaluation in a complex environment can be completed without explicit rule modeling. By learning environment dynamics in a potential state space, the model can adapt to dynamic changes of an interference mode and a radar anti-interference strategy, and dependence of a traditional method on rule hypothesis is avoided. Besides, real-time strategy optimization of the model is realized in combination with Monte Carlo tree search (MCTS) and deep learning, the optimal interference strategy can be quickly generated in confrontation scenes with unknown rules and complex environments, and the intelligence and efficiency of the electronic confrontation system are remarkably improved.

The invention relates to the related technical field of dust detection, and provides a mining induced charge and light scattering fusion dust detection device and method.The dust detection device comprises a workbench, a sampling assembly and a detection assembly, and the sampling assembly comprises a sampling air pump, a conical airflow homogenizer and a closed sampling cavity; the detection assembly comprises an annular sliding rail, a light scattering detection module and an electrostatic induction detection module; the dust detection method comprises the following steps: S1, device initialization and zero calibration; s2, sampling preparation of dust-containing airflow; s3, dual-module synchronous signal acquisition; s4, data processing and temperature and humidity compensation; s5, carrying out anti-interference protection assembly; and S6, data fusion and result output. S7, device cleaning and standby; through cooperative work of the double detection modules, dust detection signals in different concentration intervals are accurately captured, wide-range coverage from low concentration to high concentration is achieved, and synchronous monitoring of dust concentration and particle size characteristics is achieved.

The invention belongs to the technical field of intelligent hydropower, and discloses a hydropower station operation period dam safety classification and grading early warning system and method, through man-machine collaborative inspection of an anti-interference optical fiber sensor and an unmanned aerial vehicle, in combination with edge calculation filtering, YOLO model detection and multispectral analysis, data acquisition precision and preprocessing quality are improved; dynamic risk assessment is realized by adopting a fusion formula, a finite element and LSTM time sequence prediction, and the limitation of a fixed threshold value is broken through; according to safety index grading early warning, the Mesh ad hoc network and the GIS system are linked, and it is ensured that response is timely and accurate; self-evolution optimization is achieved through digital twin iteration and sensor and equipment health management, the false alarm rate and maintenance cost are reduced, information isolated island is eliminated, the accuracy and reliability of dam safety early warning are improved, and the problems that in the prior art, a dam early warning system is single in monitoring means and insufficient in data analysis, multi-parameter coupling risks are difficult to dynamically evaluate, and the safety early warning effect is poor are solved. And missing report or false report is easy to occur.

The invention belongs to the technical field of audio and video fusion transmission, and discloses an audio and video signal fusion transmission method and device, equipment and a medium, and the method comprises the steps: extracting 512-dimensional semantic feature vectors of an audio and a video, and generating a joint feature matrix through a cross-modal attention mechanism; calculating a fusion feature flow based on a coefficient alpha output by the scene classification model; a quantum random number generator is used for generating a session master key Kmain, the session master key Kmain is packaged into an encrypted session key Kenc through a Kyber-768 algorithm, and frame-level dynamic encryption is carried out on the fusion feature flow; and selecting a GPMI Type-B wired protocol or wireless transmission based on QUIC according to the transmission environment. According to the invention, the transmission efficiency, the anti-interference capability and the anti-quantum-attack security are improved, and multi-scene requirements are met.

The invention discloses a cable accessory nondestructive testing method based on image recognition, and relates to the technical field of data encryption, and the method comprises the steps: extracting a photon signal from an anti-noise coding control template, generating an anti-interference partial discharge light spot image in combination with a compressed sensing reconstruction algorithm, and carrying out the alignment fusion of the anti-interference partial discharge light spot image and LiDAR point cloud data of a cable accessory, generating spatial registration feature data; based on the neural radiation field network, constructing a three-dimensional radiation field model, inputting spatial registration feature data, generating time-space domain deformation field data and partial discharge hot spot distribution data, and generating a defect candidate region by using a spatial weighted fusion algorithm; a deep residual network is used as a framework, a transfer learning strategy is combined, a cable accessory defect model is constructed, a defect candidate area is input, and a cable accessory health state report is generated. According to the method, the three-dimensional radiation field model is constructed by using the neural radiation field network as a framework, accurate positioning and characterization of defects are realized, the detection reliability is improved, and the method is suitable for micro-defect identification.

The invention relates to the technical field of arc fault diagnosis, and provides an energy storage system arc fault diagnosis method and system based on voiceprint detection, and the method comprises the following steps: collecting a voiceprint signal in real time, and carrying out the anti-interference preprocessing, and obtaining a time-frequency domain spectrogram; identifying the time-frequency domain spectrogram based on a multi-branch convolutional neural network and weighting the time-frequency domain spectrogram to obtain a time sequence feature sequence; inputting the time sequence feature sequence into a time sequence convolutional network model, and outputting global context features; performing dynamic weighted pooling on the global context features to obtain key frame feature vectors representing the arc fault; and inputting the key frame feature vector into an arc fault diagnosis classification model to obtain an arc fault type, an arc fault severity grade and sound source positioning information, and generating a corresponding alarm signal and a control instruction based on the arc fault severity grade. According to the invention, the detection sensitivity and the anti-interference capability are improved, and the safety protection level and the fault early warning capability of the energy storage system are improved.

The invention discloses a Mini LED lamp panel needle mark defect detection method and system, and the method comprises the following steps: S1, carrying out the preprocessing of a Mini LED lamp panel surface image, suppressing high-frequency noise, and enhancing the defect features; s2, extracting defect features by adopting a self-adaptive convolution adjacency analysis method: S21, dynamically adjusting the shape and size of a convolution kernel through a deformable self-adaptive convolution kernel, and matching the local form of the needle mark defect; and S22, carrying out correlation analysis on the pixel points and neighborhoods thereof, and eliminating isolated noise interference. According to the method, defect forms are dynamically matched through the self-adaptive convolution kernel, isolated noise is eliminated through adjacency analysis, defects of different sizes are covered through multi-scale fusion, wavelet filtering and a parallel computing architecture are combined, the detection precision and the anti-jamming capability are remarkably improved, stable recognition of the tiny needle marks in the complex environment is achieved, meanwhile, high-speed real-time processing is guaranteed, and the detection accuracy and the anti-jamming capability are improved. The method is suitable for diversified industrial scenes and material requirements, reduces the false detection and omission ratio, and promotes the intelligent upgrading of precision manufacturing quality inspection.