2235 results about "Signal characteristic" patented technology

The invention discloses a data driving-based power battery safety risk assessment method and system, particularly relates to the technical field of battery safety monitoring, and is used for solving the problems of false alarm and missing detection caused by confusion of electrochemical relaxation effect and real fault signal characteristics in the existing standing stage fault detection method. Through multi-source data acquisition based on voltage, temperature and state of charge in a standing stage, a current slope extreme point is identified through charge and discharge mode weighted correction, a time-delay mapping relation with a voltage curve geometrical morphology abrupt change point is established, abnormal inflection points are screened, and fractal dimension attenuation characteristics are quantified; according to the method, abnormal distribution density and material damage indexes are fused to generate a dynamic threshold value, composite criterion analysis is carried out by combining frequency domain energy offset characteristics extracted by time-frequency transformation and heat conduction time-lag phase difference, relaxation interference signals are effectively stripped, hidden danger characteristics are locked, full-dimension safety state evaluation from microdefect evolution to macroscopic parameter abnormity is realized, and the safety state evaluation accuracy is improved. And the reliability of power battery fault detection under complex working conditions is improved.

The invention discloses a power distribution network line fault positioning detection system, and relates to the technical field of power distribution network fault detection. The system comprises a mixed information acquisition layer, a fault feature extraction layer, an intelligent diagnosis layer and a fault positioning layer. The mixed signal acquisition layer comprises a high-frequency transient wave recording unit, a power frequency measurement unit, a wireless pulse sensor and a distributed optical fiber temperature measurement unit; the fault feature extraction layer comprises a time-frequency analysis module, a preprocessing module and a three-dimensional feature vector module; the intelligent diagnosis layer comprises a convolutional attention network, a space-time diagram neural network and a transfer learning module; the fault positioning layer comprises a particle swarm module and a fuzzy reasoning module. According to the invention, data information of the cable is acquired through the mixed information acquisition layer, a video analysis window function is dynamically matched with signal characteristics, a time domain graph scale, a frequency domain resonance component and a space field intensity gradient are constructed, fault diagnosis and positioning are carried out by using the intelligent diagnosis layer, and the fault positioning detection efficiency of the power distribution network is improved.

The invention provides a power grid fault detection and positioning method and system for an intelligent power grid system. The method comprises the following steps: collecting a fault traveling wave signal when a power distribution line has a fault; preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; carrying out traveling wave front edge detection on the target fault traveling wave signal, and determining the signal arrival time of the fault traveling wave signal; fault signal features are extracted from the target fault traveling wave signals through a frequency domain analysis method and a time domain analysis method; estimating the fault line characteristics of the fault line with the fault in combination with the fault signal characteristics and the signal arrival time; outputting the line type and the fault type of the fault line through a feature recognition model; selecting an optimal fault positioning algorithm by using a decision tree algorithm based on the line type and the fault type; and determining the line position of the fault line according to the target fault traveling wave signal by adopting an optimal fault positioning algorithm. The method has the effect of intelligently selecting the optimal positioning algorithm according to the line type.

The invention provides an encryption transmission method and system of a wireless information transmission communication base station, relates to the technical field, discloses the encryption transmission method of the wireless information transmission communication base station, and belongs to the technical field of wireless communication. The method comprises the following steps: receiving a communication signal and extracting identity authentication information and a signal characteristic parameter; performing identity authentication scoring based on a deep neural network, performing signal feature scoring through spectral analysis, and constructing a communication security assessment model; a key pair is generated by adopting an elliptic curve cryptographic algorithm, and data is encrypted after being subjected to confusion transformation and diffusion transformation; transmitting a key by using a quantum key distribution technology; and selecting an optimal transmission channel based on the operation state data of the communication base station, and performing data transmission by adopting interleaving coding and adaptive power control technologies. According to the invention, the security and transmission reliability of wireless communication are improved, and the anti-interference capability is enhanced.

The invention provides a circuit board impedance matching and signal integrity optimization method and system, and relates to the technical field of circuit board design, and the method comprises the steps: dividing a signal integrity analysis region, and setting a signal test point; signal characteristic parameters are collected and classified and analyzed; identifying a signal quality abnormal region and establishing a multi-dimensional evaluation index system; constructing a signal compensation model and a recursive filter; constructing a deep reinforcement learning model, and dynamically adjusting a compensation coefficient and a filtering coefficient; carrying out processing and real-time evaluation on the signal quality abnormal region; and when the score continuously reaches a preset threshold value, determining that optimization is completed.

The invention relates to the technical field of optical signal processing, in particular to an optical signal processing algorithm based on biological agent fluorescence detection, which comprises the following steps: S1, collecting fluorescence signals with different wavelengths emitted by each probe, and generating a multi-channel fluorescence spectrum data set; s2, carrying out spectral de-mixing on the fluorescence signals in the fluorescence spectrum data set, separating a plurality of overlapped fluorescence signals, and decoupling the fluorescence signals with different wavelengths; s3, for highly overlapped fluorescence signals, carrying out secondary overlapping separation treatment on the fluorescence signals by using a nonlinear optimization method; s4, extracting the intensity, wavelength and time change characteristics of each fluorescent probe signal, and constructing a multi-dimensional fluorescent signal characteristic vector; and S5, by amplifying the characteristic peak value of the weak fluorescence signal, it is ensured that the signal is not covered by strong signals of other probes in multi-probe detection. According to the invention, the resolution of fluorescence detection is improved, so that the high-precision detection effect can be maintained under complex experimental conditions.

The invention provides a welding defect identification method, system and equipment for an automobile part and a medium, and relates to the technical field of automobile welding.The method comprises the steps that a structured light vision sensor and a high-frequency ultrasonic sensor are used for obtaining three-dimensional structured light data of a to-be-welded area of the automobile part and sound wave signals generated in the welding process; obtaining characteristic frequency information used for identifying welding internal defects; the method comprises the following steps: capturing a heat distribution image of a to-be-welded area of an automobile part through a thermal imaging camera to obtain area feature information used for identifying welding defect thermal anomaly, and obtaining magneto-optical signal feature information used for identifying welding defects; performing defect identification and classification on the fused feature representation by training a deep learning model to obtain a defect identification result; and welding parameters are adjusted through a simulated annealing algorithm, so that the welding defects of the automobile parts are recognized. According to the invention, various welding defects can be identified, so that the accuracy and efficiency of welding quality detection are remarkably improved.

The invention relates to the technical field of low-altitude safety emergency, and discloses a low-altitude safety emergency system based on large model application. The system comprises a low-altitude situation awareness module which constructs a dynamic situation prediction model based on historical flight data, collects a low-altitude target track coordinate sequence, radar echoes and communication signal characteristics in real time, and outputs a theoretical security situation value. The multi-source data fusion analysis module performs three-dimensional difference analysis on theoretical and actually measured situation values, which relates to time domain trend deviation, space domain coverage offset and event sequence matching degree, and generates a target-level difference feature matrix; and the risk propagation path deduction module inputs the matrix into a geographic topology analysis network, generates a probability thermal distribution diagram of an abnormal risk propagation path in combination with airspace node communication parameters and a target position, and locates a risk influence area. And the intelligent disposal strategy generation module configures parameters according to the graph, starts high-frequency monitoring for a high-probability area, and performs communication disturbance test on an adjacent target.

The invention provides a positioning and tracking early-warning method and system for a Bluetooth signal, and relates to the technical field of positioning and tracking, and the method comprises the steps: receiving a Bluetooth signal of a target Bluetooth device through a Bluetooth receiver, correcting a signal feature parameter through a signal attenuation compensation model, and combining the position coordinate of the Bluetooth receiver, the real-time position of the target equipment is calculated through a positioning algorithm, and the real-time position and the corrected signal characteristic parameters are stored in a track database; based on historical trajectory data, a space-time diagram convolutional neural network model is constructed and trained to predict a target trajectory, and an electronic fence is dynamically generated in combination with target activity rule parameters; and generating fence early warning information by comparing the prediction track and the electronic fence boundary, extracting a target behavior feature vector, identifying an abnormal behavior by using a behavior identification model, and generating comprehensive early warning information in combination with the fence early warning information.

The invention relates to the technical field of computer-aided communication, in particular to a wireless mouse strong interference source avoiding method based on a wireless frequency hopping model, which comprises the following steps: acquiring multi-band signal characteristics by monitoring electromagnetic spectrum data of an environment where a wireless mouse is located in real time; constructing an interference source analysis model by using an improved weighted fuzzy clustering algorithm, and accurately identifying the type and intensity distribution of an interference source; according to an analysis result, dynamically generating a frequency hopping strategy by using a channel state prediction model based on reinforcement learning, and determining priority sequences of a main channel and a standby channel; when the interference on the main channel exceeds a preset threshold value, performing real-time evaluation by means of a composite quality index and switching to an optimal standby channel; meanwhile, through a closed-loop feedback mechanism, according to the difference between actual channel quality and expected channel quality, a stochastic gradient descent method is adopted to optimize frequency hopping strategy parameters, and dynamic self-adaptive adjustment of interference avoidance is achieved. Complex electromagnetic environment interference can be effectively dealt with, and the communication stability of the wireless mouse and the use experience of a user are remarkably improved.

The invention provides a civil construction monitoring intelligent sensor information detection method and system under signal mismatch, and the method comprises the steps: carrying out the time-frequency analysis of temperature and strain fundamental frequency components, selecting a wavelet basis function adaptive to the concrete setting and strength increasing process, carrying out the self-adaptive threshold processing of a wavelet coefficient, removing high-frequency noise, and carrying out the detection of the temperature and strain fundamental frequency components. Low-frequency signal characteristics reflecting concrete strength increase are reserved; performing feature extraction on the denoised temperature and strain signals by adopting a singular value decomposition algorithm, detecting internal defects of the concrete through abrupt change of a singular value spectrum, and if crack and hole defects exist, causing strain signal abrupt change by stress concentration; if it is detected that defects exist in the concrete, that is, spatial heterogeneity of temperature and stress distribution exists, temperature and strain monitoring data of multi-point arrangement and a defect detection result serve as boundary conditions and constraints, and a temperature field and a stress field in the whole concrete structure are obtained through numerical simulation.

The invention discloses an electromagnetic compatibility testing method and an electromagnetic compatibility testing system for an industrial control power supply, which are used for evaluating conducted interference, radiated interference and immunity performance. The system comprises a load module, a signal connection module, an electromagnetic interference measurement module, an immunity test module, a data processing module and a hardware acceleration module. The load module simulates the actual working state of the industrial control power supply through resistive, inductive, capacitive and dynamic loads; the signal connection module adopts high-frequency shielding and grounding optimization design, so that external interference is reduced; the electromagnetic interference measurement module is combined with the frequency domain analysis and time domain response module to capture interference signal characteristics and power supply dynamic output; the data processing module generates an electromagnetic compatibility report through a comprehensive evaluation algorithm; and the hardware acceleration module realizes real-time processing of high-frequency sampling data by utilizing the FPGA. The method is suitable for the electromagnetic compatibility test of the industrial control power supply in the fields of industrial automation, communication equipment and power electronics, and has wide engineering application value.

The invention provides a mobile phone signal blocking, shielding and self-adaptive identification method and system, and relates to the technical field of mobile phone signal identification and shielding, and the method comprises the steps: collecting signals through a distributed signal collector, obtaining signal features and positions through time-frequency domain analysis and triangulation positioning, inputting a convolutional neural network to extract communication protocol and behavior pattern features, and carrying out the blocking, shielding and self-adaptive identification. And predicting a signal movement track and a communication behavior. And calculating a minimum interference power threshold based on a channel capacity theory, iteratively optimizing interference power by adopting a multi-objective optimization algorithm, calculating a beam direction and phase delay according to optimal power configuration parameters and signal movement track prediction data, and generating a blocking control signal. A directional interference beam is compensated by adopting a digital pre-distortion technology, an interference effect is monitored in real time, evaluation data is fed back to a convolutional neural network to update signal identification data, and closed-loop control is formed, so that accurate and efficient mobile phone signal blocking, interference power reduction and anti-interference capability improvement are realized.

The invention relates to the technical field of electric energy metering, in particular to an intelligent correction method, device and equipment for charging pile electric energy metering and a medium, and the method comprises the steps: collecting the current and voltage of the output end of a charging pile, and decomposing the characteristics of a current signal into a DC signal and an unsteady state signal; respectively carrying out electric energy metering on the direct-current signal and the unsteady-state signal, and adding the electric energy to obtain a preliminary electric energy metering result; establishing a temperature error correction model; establishing a self-adaptive correction model; and correcting the initial electric energy metering result through a temperature error model, a self-adaptive correction model, the obtained charging electric energy data recorded by the electric vehicle and the obtained standard load electric energy metering result in the charging pile to obtain a final electric energy metering result. The charging pile electric energy metering result can be intelligently corrected, the fault report rate of electric energy metering is reduced, the regular checking period is prolonged, the service life of an electric energy metering module electronic component in the charging pile is prolonged, and the metering precision is improved.

The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; a neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and a digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

The invention relates to the technical field of gallium nitride gate drivers, and discloses a design method and system of a gallium nitride gate driver, and the method comprises the steps: carrying out the working signal collection of the gallium nitride gate driver, obtaining original waveform sampling data, and building a signal integrity model under the action of electromagnetic interference; separating the original waveform sampling data into an effective driving signal, a low-frequency interference signal and a high-frequency noise signal, and extracting characteristic parameters to obtain a signal characteristic parameter set; switching time and circuit power consumption are set as optimization variables, constraint conditions are set according to the signal integrity model, and an optimization objective function is obtained; performing online identification and optimization calculation on the driving parameters to obtain a driving control parameter sequence; according to the method, the adaptive capacity of the driver to the change of the working condition is improved, and meanwhile, the stability and the reliability of the driving waveform are ensured.

The invention discloses an online multi-channel rapid gas chromatographic analysis system and method, particularly relates to the technical field of gas chromatographic analysis, and is used for solving the problems of baseline noise rise and trace component detection precision reduction caused by physical coupling and signal interference when multiple detectors work in parallel in the prior art. Interference signal features of adjacent detection channels are extracted, a chaotic or linear compensation signal is dynamically generated in combination with harmonic primary and secondary sorting and recursive quantization factor analysis, and common-mode interference of a target channel is suppressed. Electromagnetic isolation parameters of a shielding structure are adjusted in real time based on the baseline signal after noise suppression, and accurate matching of impedance and interference spectrum is achieved; further separating time domain overlapped signals and analyzing asymmetric distortion characteristics, and eliminating system errors through normalization calibration; dynamic interference suppression, electromagnetic shielding optimization and signal distortion traceability are combined, the anti-interference capability and analysis precision of multi-channel detection are remarkably improved, and the method is suitable for high-throughput rapid detection scenes of complex samples.

The invention belongs to the technical field of signal acquisition and processing, and discloses a low-noise biopotential signal acquisition system and a low-noise biopotential signal processing method. An original bioelectricity signal of a human body is collected through an electrode array, and analog-to-digital conversion is carried out after the signal is processed by a multi-stage self-adaptive filtering and amplifying circuit. A digital signal is sequentially subjected to adaptive wavelet transform denoising and empirical mode decomposition to obtain a multi-level intrinsic mode function set, an intrinsic mode function of a characteristic frequency band is extracted from the multi-level intrinsic mode function set, and the signal is reconstructed. Introducing a dual noise reduction mechanism combining adaptive wavelet transform and empirical mode decomposition; a signal quality real-time evaluation system is established, and system parameters including the gain of a variable gain amplifier and the bandwidth of a dynamic band-pass filter are dynamically adjusted through closed-loop feedback. The self-adaptive optimization of the whole biopotential signal acquisition process is realized, various interferences are effectively inhibited, the signal characteristics are reserved, and the signal-to-noise ratio is remarkably improved.

The invention relates to a GIS partial discharge digital twinborn identification method based on a simulation fault database and a storage medium. The problems that in the prior art, due to the fact that GIS partial discharge signal features have large randomness, the accuracy and precision of algorithm recognition and positioning are not high, meanwhile, manpower and material resources are high in cost, complex operation conditions are difficult to simulate, and the defect type coverage range is not wide are solved, and the adopted technical scheme is that a GIS three-dimensional geometric model is constructed based on a GIS true model; setting a digital discharge source in the GIS three-dimensional geometric model, discharging through the digital discharge source, obtaining UHF signals in batches, and extracting time domain pulse waveform characteristic parameters in the UHF signals; constructing a simulation fault database based on the time domain pulse waveform characteristic parameters; constructing a fault inversion network based on the simulation fault database; recognizing and positioning a fault through a fault inversion network; based on the GIS partial discharge digital twinborn model, a fault identification result and a positioning process are arranged on the GIS partial discharge digital twinborn model, and fault diagnosis and fault location positioning can be accurately and visually carried out.

The invention discloses a head-mounted brain body workability assessment method based on a space-time dynamic fusion network, belongs to the technical field of artificial intelligence and human body signal acquisition, and solves the problems that an existing method cannot comprehensively reflect the comprehensive state of an individual, and interaction and potential risks among multiple factors are difficult to reveal. The method comprises the steps that physiological parameter signals are collected based on wearable equipment, and an attention and fatigue prediction result is output by an LSTM prediction model; according to the method, the graph structure data packet is analyzed and processed based on the pre-constructed space-time dynamic fusion network model, the working state signal characteristics of the plateau high-risk operating personnel can be accurately evaluated, the space-time graph neural network (STGNN) can capture the complex relation, the high-dimensional topological characteristics are extracted, and the high-risk working personnel can be accurately evaluated. The dynamic event-driven pulse neural network (deSNN) can convert the pulse sequence features into pulse sequence features so as to describe feature values more accurately, iterative optimization enables the pulse sequence features and the pulse sequence features to cooperate, and the accuracy and reliability of time-space dynamic fusion network model evaluation are improved.

The invention relates to a UWB-based man-machine real-time positioning method in a long-distance tunnel hole. The method comprises the following steps: S1, collecting original ranging information of integrated signal characteristics; s2, correcting an original distance based on signal quality; s3, carrying out NLOS signal identification based on SVM (Support Vector Machine); s4, NLOS ranging error elimination based on an error distribution fitting model; s5, preliminarily resolving a tag position stake number; s6, label position correction based on Kalman filtering; and S7, determining a man-machine position based on label matching. According to the invention, the UWB positioning system is used for positioning and tracking personnel machinery in a hole, so that real-time transmission and analysis of man-machine position pile number information in the hole are realized, and management personnel can timely and accurately master man-machine operation positions; according to the man-machine real-time positioning method, the influence of the complex construction environment in the tunnel hole is reduced, and the man-machine positioning precision and stability in the tunnel hole are effectively improved.

Accordingly, there are disclosed herein receivers and receiving methods that provide a graceful transition from PAM2 to PAM4 signaling. One illustrative method includes: negotiating a link speed having PAM4 signaling; performing adaption of at least one gain or filter coefficient during PAM2 signaling; switching to PAM4 detection before receiving PAM4 signaling; disabling said adaptation before said switching to PAM4 detection; detecting PAM4 signaling using at least one statistic of detected PAM4 symbols; and enabling said adaptation after PAM4 signaling is detected. Another illustrative method includes: negotiating a link speed having PAM4 signaling; adapting at least one of gain and filter coefficients during PAM2 signaling; monitoring for a change in at least one signal characteristic while performing PAM2 detection; and transitioning to PAM4 detection after detecting said change.

The invention relates to the technical field of ultrasonic flaw detection of titanium alloy bars, in particular to an ultrasonic flaw detection method and system for titanium alloy bars, and the method comprises the following steps: obtaining ultrasonic signals of all positions; obtaining a receiving duration sequence of each position; according to the difference between adjacent peak values in the ultrasonic signal of each position, the dispersion degree of all the peak values, the average level of the receiving duration sequence and the difference between the receiving duration sequence of each position and the receiving duration sequence of the adjacent position, the real defect of each position is obtained; according to the dispersion degree of the amplitude average level of all the modal components of each position, the total number of the modal components and the energy concentration degree and the bandwidth of the ultrasonic signal of each position in the frequency domain, obtaining a signal abnormal factor of each position; defect factors of all the positions are obtained, and then whether defects exist in all the positions or not is judged. By analyzing the signal characteristics of the real defects in the titanium alloy bar, the accuracy of ultrasonic flaw detection of the titanium alloy bar is improved.

The invention relates to the technical field of circuit boards, and discloses a circuit board interlayer connection detection method, device and equipment, and the method comprises the steps: carrying out the electrical characteristic parameter measurement of a multilayer flexible circuit board, and obtaining the characteristic parameter data of the circuit board; designing and arranging a multi-test-point detection structure on the multi-layer flexible circuit board, and performing signal excitation to obtain test point position information; applying a differential excitation signal, and collecting a differential response waveform between the test points to obtain a time domain reflection signal feature; impedance spectrum conversion and parameter extraction are carried out on the time domain reflection signal features, and an interlayer connection impedance distribution diagram is obtained; and establishing an interlayer connection defect characteristic model aiming at nickel-based alloy wire fracture, interface cracking and liquid metal fiber felt delamination in various bending states by utilizing the interlayer connection impedance distribution diagram, so that accurate classification of defect types and severity is realized, and interlayer connection faults and surface welding quality problems can be distinguished; and the misjudgment rate is greatly reduced.

The invention relates to an adaptive feature weighted fusion fault diagnosis method for multi-source heterogeneous data of a motor, and the method comprises the steps: constructing a multi-head deep convolutional neural network, designing a plurality of parallel convolutional channels in the neural network, deeply mining the abnormal features of original signals of current and vibration, and focusing on the signal features in different dimensions, thereby achieving the fault diagnosis of the multi-source heterogeneous data of the motor. And comprehensive capture of key feature information is ensured. And adaptive weighted fusion is performed on the extracted multi-dimensional features by adopting a correlation analysis method to highlight abnormal key features, so that the feature fusion effect of the multi-source heterogeneous data and the accuracy of anomaly detection are improved. An SELU activation function is adopted and a hyper-parameter optimization strategy of a dynamic attenuation learning rate is combined so that an overfitting problem which is easy to occur in a model training process can be effectively solved and convergence stability of the model and high precision of fault identification can be enhanced. Through adaptive fusion of multi-source heterogeneous data features, key fault features are effectively extracted, and the diagnosis accuracy in an electromechanical coupling fault scene is improved.

The invention discloses a brain-computer interface control system and method based on multi-mode fusion, and relates to the technical field of brain-computer interface control. Comprising a microelectrode array management module which is responsible for arranging a microelectrode array, collecting initial signals and monitoring electroencephalogram signal characteristics in real time; the parameter optimization module is used for dynamically adjusting sampling parameters of the microelectrode array so as to optimize signal quality; the multi-channel signal processing module is used for carrying out weighted fusion and noise suppression on a plurality of electrode channel signals; the self-calibration feedback module is used for optimizing acquisition parameters of the microelectrode array according to feedback self-calibration electrode parameters; the control instruction generation module is used for generating an instruction; and the output adjusting module is used for dynamically adjusting output. According to the invention, accurate acquisition and processing of the electroencephalogram signals are realized, so that the flexibility and accuracy of the system in real-time application are improved.

The invention provides a transformer on-load tap-changer state evaluation method and system, electronic equipment and a medium, and belongs to the field of fault diagnosis of transformer on-load tap-changers. The method comprises the following steps: acquiring electrical and mechanical performance parameters of the on-load tap-changer of the transformer, and generating an oil chromatographic data characteristic matrix according to oil chromatographic data; for the vibration signals, generating a vibration signal feature matrix; generating a driving motor current signal characteristic matrix according to the driving motor current signal; fusing the three feature matrixes to generate a multi-dimensional information feature matrix; and calculating correlation among data by using an attention mechanism, endowing a multi-dimensional information feature matrix with a weight, and inputting the multi-dimensional information feature matrix into a BIGRU model for state evaluation. The characteristic data of the electrical characteristics and the mechanical characteristics of the on-load tap-changer are comprehensively selected, and the evaluation model is used for fault diagnosis, so that the diagnosis accuracy and adaptability are improved.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

A distributed optical fiber sensing event classification and identification method based on mask auto-encoder self-supervised learning belongs to the technical field of distributed optical fiber sound and vibration sensing, on one hand, a pre-training network does not need label data, a downstream classification and identification network only needs a small amount of labeled data, and the demand of the labeled data is reduced; and on the other hand, in the pre-training process, the model extracts DAS signal features and compresses the calculation amount of the DAS signals, so that pixel-level reconstruction of DAS signal reconstruction is changed into feature reconstruction, a large amount of time is saved, and the accuracy is ensured.

The invention provides a rotary mechanical equipment fault identification method and device based on vibration data, and belongs to the field of vibration monitoring. The method comprises the following steps: acquiring a component information library of to-be-monitored rotary mechanical equipment; fault information corresponding to the component information base is determined, the fault characteristic frequency of each fault is calculated, and a fault characteristic frequency model corresponding to the component information base is established; collecting a vibration signal characteristic value of each component in the component information base in real time; extracting a vibration signal characteristic value, and matching the frequency domain characteristic value with the fault characteristic frequency based on a component information base and a fault characteristic frequency model to obtain a matching result; positioning an abnormal feature in the signal based on the time domain feature value and the geometric feature value; and identifying the fault type and degree of the mechanical equipment according to the matching result and the abnormal features. According to the mechanical equipment vibration monitoring data processing method and device, potential faults of the equipment can be found as soon as possible, and the equipment can be maintained in time.