564 results about "Vibration sensing" patented technology

The invention discloses a subway tunnel intrusion monitoring and early warning system and method, and relates to the technical field of subway safety monitoring. The system comprises a vibration monitoring module, a stress monitoring module, a signal analysis unit, an alarm unit and a database module, wherein the vibration monitoring module adopts a distributed vibration sensing optical cable to collect tunnel wall surface vibration signals in real time; the stress monitoring module monitors the stress change of the tunnel structure through a spirally wound and pre-embedded fiber grating array; the signal analysis unit decomposes vibration frequency band energy through wavelet packet transformation, counts a stress standard deviation in combination with a sliding time window, and performs multi-modal feature matching with an abnormal signal database by using a dynamic time warping algorithm; and the alarm unit triggers multi-stage response according to a feature matching degree threshold value or vibration-stress space-time correlation abnormity, and train speed limiting control is linked. The problems that the single-mode false alarm rate is high, multi-source interference cannot be decoupled and stress monitoring coverage is insufficient are solved, recognition and early warning of construction intrusion events are achieved, and tunnel structure safety is improved.

The invention relates to the technical field of intelligent equipment monitoring, and discloses a sandstone separator monitoring control method and system based on multi-source data processing. According to the method, equipment operation parameters, real-time image data and vibration sensing data of the sandstone separator are collected in real time, data preprocessing and feature extraction are carried out, a monitoring model based on a multi-modal deep learning framework is constructed, and accurate joint characterization of the equipment state is achieved. The system can dynamically generate a control instruction according to a monitoring result, adjust equipment operation parameters in time, and avoid production interruption and quality problems. Besides, the multi-modal data fusion technology and the self-adaptive PID control algorithm are adopted, so that the operation efficiency and stability of the sandstone separator are further improved, and powerful technical support is provided for the field of intelligent manufacturing.

The invention discloses a road network abnormal event identification and risk evaluation early warning method based on vibration perception, and relates to the technical field of urban road safety monitoring. Comprising the steps of collecting road vibration data of a road network through a sensor; preprocessing the road vibration data through Kalman filtering and multi-dimensional feature extraction; abnormal event recognition is carried out on the processed road vibration data through a differentiation algorithm, wherein abnormal events comprise vehicle overload, illegal construction and road structure damage; and carrying out risk scoring on the abnormal events through dynamic weight distribution, dividing the risk scores according to the risk levels, and carrying out graded response. According to the invention, through differential algorithm design, a mixed model architecture and a hierarchical response mechanism are adopted, abnormal event identification, risk level quantitative evaluation and differential early warning processing are realized, the safety state of an urban road network is monitored in real time, and early warning information is provided.

The invention discloses a multi-scene positioning and intelligent warning system and method for industrial equipment, and the system comprises a track analysis module which obtains the radar signal data of the industrial equipment, carries out the denoising, extracts a signal intensity change feature, and generates a human body dynamic track when the matching degree of the feature and a preset metal reflection model exceeds a threshold value; the abnormity judgment module is used for acquiring the vibration sensing data of the key position, generating a vibration frequency spectrum by applying wavelet decomposition, and generating abnormal vibration alarm information if the vibration frequency spectrum exceeds a range; the intrusion alarm module is used for acquiring node signal sources, performing weighted fusion to obtain an initial positioning coordinate group, inputting the initial positioning coordinate group into a three-dimensional map model, obtaining a final coordinate result through multi-source fusion, and sending out intrusion alarm information if the result falls into a dangerous fence boundary; and the safety evaluation module is used for performing time alignment and deep fusion on the human body dynamic track, the abnormal vibration alarm information and the intrusion alarm information to generate a safety evaluation report. According to the invention, omnibearing safety monitoring and management of industrial equipment are realized.

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster monitoring method and system based on a distributed optical fiber vibration sensing system, and aims to solve the problem of how to solve the interference of the change of an optical fiber and the change of an external environment on a geological disaster prediction result in optical fiber transformation. In order to solve the problem, the geological disaster monitoring method provided by the invention comprises the following steps: determining an area to be expanded according to a monitoring demand and a system distribution path of an original optical fiber; determining an actual distribution path of newly added optical fibers according to the landform characteristics and the geological risk level of the to-be-expanded area; after the newly-added optical fiber is installed, correcting the characteristic data according to the loss condition of the original optical fiber and the transmission characteristics of the newly-added optical fiber, and constructing a prediction model according to the corrected data; and combining the prediction model with the environment information, and determining the type of a disaster occurring in the target area.

The invention relates to the technical field of optical cable line external force damage monitoring, and discloses an optical cable external force damage intelligent identification system. The system comprises an optical fiber vibration sensing module, a multi-source feature fusion analysis module, an abnormal event association positioning module and an intelligent checking strategy generation module. The optical fiber vibration sensing module constructs a model based on historical data and outputs a reference vibration characteristic value; the multi-source feature fusion analysis module performs three-dimensional fusion analysis of time domain, frequency domain and phase on the reference value and the measured value to generate a feature difference matrix; the abnormal event association positioning module is used for generating an abnormal probability distribution cloud picture and positioning an abnormal area in combination with the topological parameters and the environment information; and the intelligent checking strategy generation module configures checking parameters according to the cloud picture, starts high-frequency acquisition for a high-probability region, and applies an environment disturbance test to adjacent lines. The system can accurately identify and position optical cable external force damage, and the monitoring efficiency is improved.

The invention provides an ocean seismic signal processing method based on distributed optical fiber vibration sensing, and belongs to the technical field of seismic signal processing. The method comprises the following steps: acquiring a marine seismic signal through a distributed optical fiber vibration sensor; performing data preprocessing on the collected seismic signals to construct a seismic oscillogram data set; according to the preprocessed data, feature extraction is carried out on the preprocessed seismic signals through a U-KAN network to obtain feature data; identifying and classifying the feature data to obtain a P wave and an S wave; and calculating the time difference between the P wave and the S wave according to the identified P wave and S wave, and estimating the seismic source distance and magnitude according to the time difference and the seismic wave propagation speed. The earthquake signal processing automation degree is improved, the earthquake wave arrival time is accurately determined, the marine earthquake monitoring response speed and accuracy are improved, and a scientific basis is provided for prevention and reduction of marine earthquake disasters.

The invention discloses a handheld integrated itinerant detector and an itinerant detection method thereof. The itinerant detector comprises a multi-source sensing unit and a magnetic attraction positioning module, the inspection method comprises the following steps: establishing a multi-dimensional calibration parameter of a target device, adsorbing on the surface of the target device based on a magnetic attraction positioning module, loading a spatial layout, and generating an environment calibration model; synchronously operating the far / close distance temperature measurement module and the vibration sensing module, and outputting multi-source fusion data; performing dynamic correlation analysis, analyzing vibration spectrum energy distribution by using a feature extraction algorithm, constructing a vibration-temperature coupling feature vector in combination with temperature gradient change, inputting a fault diagnosis result of a pre-trained fault diagnosis model, generating a visual early warning map, and feeding back the early warning map in real time through an interactive interface; according to the method, the limitation of single parameter monitoring is overcome, the detection precision is remarkably improved, and a real-time, rapid and accurate fault detection result is provided through self-adaptive switching of a temperature measurement mode and a multi-frequency-band vibration acquisition strategy.

The invention discloses a polarization-state-based optical fiber vibration sensing system and application, and relates to the technical field of optical fiber sensing, and the polarization-state-based optical fiber vibration sensing system mainly comprises a transmitting end and a receiving end. The transmitting end comprises a laser diode, a first erbium-doped optical fiber amplifier, a first circulator, a polarization scrambler, a single-mode optical fiber link, a second circulator and a second erbium-doped optical fiber amplifier which are connected in sequence; and the receiving end comprises a polarization beam splitter, a first shunt module, a second shunt module, an optical mixer, a first balanced photoelectric detector, a second balanced photoelectric detector, a third balanced photoelectric detector and a polarization analysis module. According to the optical fiber vibration sensing system based on the polarization state and the application, the vibration monitoring performance and reliability in a long-distance and high-noise environment can be improved.

The invention relates to the technical field of automatic control, and discloses a cable winding and unwinding speed self-adaptive control system under a dynamic working condition, which comprises an endogenous vibration sensing module configured on a cable reel supporting bearing and a system state decoding module, and the decoding module can decode a single original electric signal from a single original electric signal converted by the endogenous vibration sensing module. According to the invention, the core bearing structure of the reel is converted into an information source of a self-state, and multi-dimensional state information of real-time tension, overall rotational inertia of the system, abrupt change of friction and the like after temperature self-calibration is decoded in parallel, so that the self-adaptive control module is driven to match and execute a corresponding speed control strategy. Signal delay and system vulnerability caused by traditional multi-sensor layout are avoided, a control decision is made according to information reflecting the current system state, and the system further has the functions of optimizing a control strategy and compensating temperature influence according to historical data.

The invention discloses a vertical shaft construction measuring device, which relates to the technical field of laser measurement and comprises a laser plumb aligner main body, a chassis, a dial, a pointer, a laser plummet, a base and an adjusting knob. When laser measurement is carried out, a laser plummet is turned on firstly, a focused light spot directly irradiates a well bottom mark point, an initial position P1 is recorded, then a plurality of angles are rotated to record P2, P3 and P4, diagonal lines are connected to obtain a reference point, and the instrument is adjusted to enable the light spot to coincide with the reference point to complete calibration so as to measure shaft deformation. Stepped vibration induction is achieved through cooperation of a trigger block and trigger teeth, manual errors are avoided, meanwhile, friction force is increased through cooperation of a resistance block and a sliding rod, fine adjustment is assisted, in addition, through cooperation of a locking rod and a first connecting groove, the angle is locked under the gravity effect, position movement is prevented, and double-hand unlocking is achieved through cooperation of the connecting rod, a first magnetic block and a second magnetic block; safety specifications are compulsively met, and laser measurement accuracy is guaranteed through multiple measures.

The invention belongs to the technical field of distributed optical fiber sensing and low-altitude security and protection, and particularly relates to an unmanned aerial vehicle long-distance tracking method based on distributed optical fiber vibration sensing. The invention relates to an unmanned aerial vehicle vibration / electromagnetic fusion multi-mode identification method, a special installation formation design, a dispersion compensation and amplitude phase error compensation design, an unmanned aerial vehicle target vibration / electromagnetic fusion multi-mode identification design, a detection capability calculation method of an unmanned aerial vehicle vibration sound pressure-space attenuation law-distributed optical fiber processing equipment detection capability coupling model, a long-distance sensing optical cable and an electromagnetic monitoring node. According to the tracking method, a special optical fiber installation mode, dispersion compensation and phase error compensation of an original phase signal, a vibration / electromagnetic fusion multi-mode identification method, an unmanned aerial vehicle vibration sound pressure-space attenuation model-distributed optical fiber processing equipment detection capability corresponding method and other innovative work are adopted; a mature distributed optical fiber vibration sensing system is utilized to realize long-distance passive detection and target tracking of the low-slow-small unmanned aerial vehicle.

The invention provides an optical cable intrusion detection method and device based on distributed optical fiber vibration sensing, belongs to the technical field of optical fiber sensing and security monitoring, and solves the problems that existing optical cable security monitoring is high in false alarm rate, poor in real-time performance and difficult to meet long-distance optical cable security requirements. The method comprises the following steps: acquiring a first vibration signal in a preset amplitude range; performing frequency filtering processing on the first vibration signal to obtain a second vibration signal; performing feature extraction processing on the second vibration signal to obtain a high-dimensional feature vector; performing multi-scale time sequence classification processing on the high-dimensional feature vector to obtain classification data; and determining the vibration state of the optical cable according to the classification data. According to the scheme, long-distance security monitoring of the optical cable is realized, and the accuracy of a detection result is improved.

The invention provides a highway subgrade void recognition method, device and system and a storage medium, and belongs to the technical field of signal processing, and the method comprises the steps: obtaining corresponding original time domain vibration signals generated when a current vehicle passes through a plurality of grating measurement points in a grating array vibration sensing optical cable arranged below a highway surface layer; extracting dominant frequency features corresponding to each grating measuring point from the original time domain vibration signal; determining a signal fluctuation characteristic value of an area where the plurality of grating measuring points are located based on the dominant frequency characteristic corresponding to each grating measuring point; and determining a roadbed void identification result of an area where the plurality of grating measuring points are located based on the signal fluctuation characteristic values. The void identification is carried out according to the quantized data of the signal energy fluctuation between the grating measuring points, the identification mode is simple and rapid, and the fluctuation value of the dominant frequency characteristic is used as an identification index, so that the high-sensitivity discrimination of the abnormal state in the roadbed structure is realized, and the accuracy and efficiency of the void identification of the highway roadbed are improved.

The invention discloses a dam safety monitoring and crack prediction method and system based on vibration sensing data, relates to the technical field of data processing, and solves the problems of incomplete data acquisition, data processing lag and insufficient prediction capability in existing dam safety monitoring. Adaptive normalization processing based on a dynamic window is carried out on the dam vibration data; a prediction model is constructed, an LSTM network captures time dependence of dam vibration data, an attention mechanism network fuses LSTM network output and external environment variables to extract key time steps, and an output layer predicts the probability of crack generation according to the key time steps; inputting the preprocessed dam vibration data into a prediction model to obtain a crack generation probability; calculating a dynamic threshold according to the external environment factor, and comparing the crack probability to obtain a crack prediction result; by collecting dam vibration data at multiple positions, the crack risk is predicted in advance based on the prediction model.

The invention discloses a cutting fluid drainage nozzle of a high-measurement slicing machine, which belongs to the technical field of cutting processing equipment and comprises a base, a drainage panel, an adjustable composite damping support module, a piezoelectric adjusting strip, a microcirculation flow channel network, a vibration sensing system and a control system. Comprising a high-molecular polymer damping material layer and a high-strength light alloy restraint layer, a piezoelectric ceramic actuator is embedded, passive damping energy absorption and active vibration counteracting are achieved, a piezoelectric adjusting strip is fixed in a drainage groove, and the dual functions of local vibration suppression and flow control are achieved; vibration sensing monitors the vibration state in real time through a plurality of piezoelectric acceleration sensors, the control system achieves working condition self-adaption and frequency division cooperative control through a neural network algorithm, cooperative work is achieved through multiple vibration reduction mechanisms, the vibration suppression effect is improved, the line breakage rate is reduced, meanwhile, the functions of fault diagnosis and predictive maintenance are achieved, and the reliability of the system is improved. And the cutting precision and efficiency and the equipment intelligence level are improved.

The invention relates to the technical field of hydraulic control one-way valve testing, and discloses a hydraulic control one-way valve testing method which is used for solving the problem that the real performance of a hydraulic control one-way valve under actual complex working conditions is difficult to comprehensively reflect due to the fact that an existing testing method is single in working condition, insufficient in data acquisition dimension and lack of dynamic modeling capacity. A programmable working condition loading module is introduced, a hydraulic test environment with dynamic characteristics is constructed according to a pressure and flow change curve in practical application, and high-precision and multi-dimensional synchronous acquisition of the whole opening and closing process of the hydraulic control one-way valve is realized by combining a high-frequency displacement sensor, a micro-flow sensor and a vibration sensing unit; according to the scheme, the defect that a traditional testing method is difficult to restore the real dynamic working condition is effectively overcome, the authenticity of valve performance testing and the detail integrity of response data are remarkably improved, and powerful support is provided for accurately evaluating the dynamic behavior of the hydraulic control one-way valve under the complex working condition.

The invention discloses a rock slope sliding interface identification and degradation monitoring method based on distributed sound wave sensing. The monitoring method comprises the steps that a plurality of vibration sensing optical cables are vertically arranged in a rock slope along a drill hole, multi-channel vibration time sequence data are obtained through a distributed sound wave sensing demodulator, and the multi-channel vibration time sequence data are obtained through detrending and noise suppression of a data preprocessing unit, space-time consistency filtering of apparent velocity constraint and phase weighted stacking. STA / LTA triggering and DBSCAN clustering are utilized to realize single-cable event merging, and unique pairing and three-dimensional positioning of cross-cable events are completed in combination with geometric matching degree and waveform form correlation degree; the sliding interface inversion unit adopts a deep learning model to classify abnormal events, constructs an event density field and an energy intensity field for sliding related events, forms a sliding interface evidence field, and optimizes an inversion sliding interface in a limited solution domain; the degradation state monitoring unit identifies a hot spot area on a sliding interface, tracks intensity and scale evolution, constructs a degradation index, and can realize early warning in combination with multi-source information such as earth surface displacement.

The invention provides a ship collision bridge body vibration event classification method and device, and relates to the technical field of bridge safety. The method comprises the following steps: acquiring vibration data of a ship collision bridge structure through a distributed optical fiber vibration sensing system, and constructing a vibration signal space-time matrix; generating a frequency band sub-band signal by using a preprocessing method combining dynamic segmentation and a sliding window; splicing the space-time matrix and the frequency band sub-band signal; feature extraction is carried out through a deformable convolutional layer; processing through a space-time mixed attention layer and a multi-scale feature aggregation layer to obtain fused features; and converting the fused features into vectors with fixed lengths, and outputting a classification probability through a full connection layer. According to the invention, a complete technical chain from sensing to abnormal event classification is provided for intelligent bridge safety protection, the monitoring efficiency and emergency response capability of bridge ship collision event resistance can be significantly improved, and data support is provided for structural health monitoring and long-term maintenance.

The invention discloses a turnout fault diagnosis system and method based on vibration spectrum analysis, and relates to the technical field of railway signal equipment fault diagnosis, and the system comprises a multi-source vibration sensing module, a mechanical transmission chain signal decoupling module, a dynamic baseline modeling module, a fault decision engine module, and a cross-correlation verification module. Vibration signals of different channels can be collected at a turnout through the multi-source vibration sensing module, and the mechanical transmission chain signal decoupling module decomposes the complex vibration signals into three types of independent components by adopting constrained variational mode decomposition signals, so that the running state of each mechanical part can be analyzed more carefully, and the reliability of the system is improved. The dynamic baseline modeling module dynamically updates a threshold value according to factors of the turnout action times and the environment temperature, the fault decision engine module combines judgment rules and compares feature values of all components with the threshold value, and the fault types of gear tooth breakage and locking oil shortage of the switch machine can be accurately distinguished.

The invention relates to the field of buried facility monitoring, and discloses a piezoelectric cantilever vibration sensing and super capacitor self-powered energy closed-loop operation method and system, and the method comprises the steps: building a star array composed of a master pile and a slave pile, and configuring a TDMA time slot and an uplink message length; the slave pile collects a vibration signal through a piezoelectric cantilever, the vibration signal is rectified by a piezoelectric rectification energy taking module and then charges a super capacitor, and the voltage state of the two ends of the super capacitor is obtained; when the voltage state reaches a preset upper threshold, the boosting module supplies power to the processing circuit and the transmitting circuit of the slave pile, and the processing circuit generates a four-byte feature code; the slave piles send four-byte feature codes in a magnetic induction link according to TDMA time slots, the master pile receives and aggregates the four-byte feature codes sent by the multiple slave piles, completes positioning and fusion according to the arrival time difference, and returns the four-byte feature codes to the platform through LoRa and 4G, and the platform completes storage and output according to the four-byte feature codes and a positioning result. According to the invention, end-side cloud connection and replicable deployment can be realized, and low-frequency vibration acquisition and buried communication are adapted.

The invention discloses a solder paste defect detection method based on machine vision, relates to the technical field of printed circuit board detection, and aims at the challenge of solder paste printing quality detection in superfine-pitch high-reliability manufacturing, high-precision three-dimensional point cloud data is collected and an attitude matrix is marked through laser triangle-grating interference dual-mode imaging and board micro-vibration sensing; constructing an adaptive reference surface based on the point cloud and real-time fitting warping curved surface parameters, executing local height remapping, and eliminating the influence of micro warping; calculating a warping composite slope index and a glare texture robust index by using the height matrix, and adjusting a confidence threshold of the boundary enhanced convolutional network to realize real contour segmentation of adjacent pads; dynamically updating imaging parameters, accumulating real scene samples, and optimizing the adaptive ability of the model; and cross-shift knowledge reuse is realized through the shared model library. According to the invention, the volume measurement consistency and the first pass yield of the ultra-fine pitch bonding pad can be improved, and the process debugging time is shortened.

The invention discloses a 3D (three-dimensional) perimeter security system and an operation method thereof, the system comprises a server terminal, a front-end sensing system, a data transmission system and a central control system, the front-end sensing system is networked in multiple groups of partitions, and each group of partitions of the front-end sensing system is provided with a nine-axis sensor, an edge computing all-in-one machine and a switch; and lighting, wind and rain sensor, broadcasting, microwave radar and camera equipment. The data transmission system transmits data; the central control system comprises an application server and a data server. The system forms a first defense line for active detection of the microwave radar and the camera and a second defense line for vibration sensing of the nine-axis sensor, and a monitoring blind area is eliminated through three-dimensional data fusion. The operation method comprises four groups of subsystem modules. According to the method, 'sky-ground-fence 'dead-corner-free monitoring is realized, the false alarm rate is reduced by combining multi-modal data fusion with an intelligent algorithm, self-evolution is realized by edge-cloud federal learning, and threat disposal efficiency is improved by automatic linkage response.

The invention discloses a dike underwater piping positioning system based on distributed optical fiber vibration sensing, and relates to the technical field of positioning systems, and the dike underwater piping positioning system comprises a distributed optical fiber vibration sensing host which is used for transmitting a laser pulse to a sensing optical cable and demodulating a backward Rayleigh scattering signal; the multi-mode sensing optical cable is arranged along the dike underwater area and comprises a vibration sensing optical fiber and a temperature sensing optical fiber; the data processing module is used for executing time-space synchronous analysis of the vibration signal # imgabs0 # and the temperature signal # imgabs1 #; and the data processing module comprises a multi-modal feature fusion unit, and the multi-modal feature fusion unit is used for generating a combined feature factor # imgabs4 # by fusing the intensity # imgabs2 # and the temperature change rate # imgabs3 # of the vibration signal and carrying out weight distribution. According to the invention, through the technical closed loop of multi-mode sensing, intelligent layout and deep learning, the technical blank of the existing distributed optical fiber monitoring system in the aspects of early recognition, accurate positioning and intelligent early warning of underwater piping is filled.

The invention belongs to the technical field of engineering machinery intelligence and construction quality detection, and provides an intelligent compaction degree detection system and method integrated on a road roller. The detection system comprises a multi-mode sensing unit which comprises a vibration sensing module, a machine vision module, an inertia measurement positioning module and a flatness detection module; the detection method comprises the following steps: S1, synchronously collecting data; s2, vibration signal processing and compaction index calculation; s3, image analysis and apparent feature extraction; s4, a multi-source data fusion and machine learning model; s5, positioning and drawing; in conclusion, the invention provides the intelligent compaction degree detection system and method integrated on the road roller. The method is reasonable in design and simple to operate, can realize real-time, comprehensive, lossless and high-precision detection of the compaction degree, and can evaluate the construction apparent quality at the same time, so that the method is suitable for industrial popularization.

The invention discloses a phi-OTDR (Optical Time Domain Reflectometer) vibration signal classification method based on multi-scale residual convolution and a bidirectional long-short-term memory network, relates to the technical field of phase-sensitive optical time domain reflectometer vibration sensing, and solves the problems of insufficient feature extraction, single model fusion mechanism, poor system real-time performance and the like of an existing phi-OTDR vibration signal classification method. The method is realized through the steps of a multi-scale deep convolutional network, a bidirectional long-short term memory network, a cross attention fusion module, a learnable gating mechanism and the like. A cross attention fusion module is adopted, association between spatial features and time sequence features is dynamically established in a double-path network, feature interaction and information coupling are deepened, and the sensing ability of the model to key information is remarkably improved. Meanwhile, the fusion proportion of the multi-path features is adaptively adjusted according to the actual distribution of the input features, background noise interference is effectively suppressed, and the robustness and discrimination performance of the model are enhanced. Accurate identification and classification of external vibration signals are realized.

The invention provides a vehicle-mounted instrument image anti-shake acquisition system and assembly based on a magnetic control camera. The system comprises S1, a vibration sensing module; s2, a vibration identification module; s3, a vehicle operation sensing module; s4, magnetically controlling an anti-shake control module; s5, an image acquisition module; and S6, an image anti-shake module. The assembly comprises a magnetic field generating device and a suspension magnetic suspension camera, the magnetic field generating device comprises a miniature electromagnetic coil and an integral frame, and the suspension magnetic suspension camera comprises a camera body and a suspension module. According to the invention, the problems of serious image acquisition jitter, inaccurate identification and the like of detection equipment in the driving process of the motor vehicle are solved, and the image stability and the identification precision are remarkably improved.

The invention relates to the technical field of friction power generation, in particular to an energy collection and vibration sensing device and system for a transmission line, which comprises a shell, a vibration sensor arranged in an inner cavity of the shell, and energy collectors arranged at the upper end and the lower end of the vibration sensor respectively. The vibration sensor comprises a balancing weight, and elastic pieces are arranged between the upper end and the lower end of the balancing weight and the adjacent energy collectors respectively. A positive friction piece is installed on the outer ring of the balancing weight, and a negative friction piece is installed on the inner wall of the shell. The positive friction piece can move up and down along with the balancing weight and make contact with the negative friction piece so as to generate electric signals. The energy collector comprises a positive electrode power generation membrane group and a negative electrode power generation membrane group which are oppositely arranged up and down and can make contact with each other and achieve friction power generation, one of the positive electrode power generation membrane group and the negative electrode power generation membrane group abuts against the shell, and the other abuts against the end, away from the balancing weight, of the elastic piece.

The invention relates to the technical field of optical fiber sensing, in particular to a distributed optical vibration sensing early warning verification method for an island multi-source environment. Signals collected in different environments and different states are preliminarily divided into threat signals and non-threat signals, the effectiveness of data is further verified through frequency spectrum transformation, low-frequency noise and natural environment interference signals such as sea waves and wind sound are filtered out from the signals through a 5Hz high-pass filter, high-frequency noise is further suppressed by applying a wavelet denoising technology, and the non-threat signals are obtained. And useful signal features are reserved. A variational mode decomposition technology is adopted to carry out multi-scale decomposition on signals, and multiple classic features of a mode function obtained after decomposition are selected to extract feature vectors. Low-frequency effective signals are extracted, and high-frequency noise is removed, so that essential characteristics of the signals can be better captured. And the data subjected to signal processing is input into a CNN-LSTM-Attention model for deep learning analysis.

The invention discloses a lightweight optical fiber vibration intrusion event identification method and system for perimeter security and protection, and belongs to the technical field of optical fiber sensing technology and mode identification, and the method comprises the steps: collecting an original vibration signal of a perimeter monitoring region through a distributed optical fiber vibration sensing system; performing wavelet threshold de-noising preprocessing on the original vibration signal to obtain a de-noised signal; performing feature extraction on the denoised signal, and constructing a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector by using a linear discriminant analysis method to obtain a low-dimensional classification feature vector; and inputting the low-dimensional classification feature vector into a pre-trained lightweight convolutional neural network model for classification and identification, and outputting a corresponding intrusion event category. According to the method, through cooperation of front-end LDA dimension reduction and a rear-end lightweight network, the model parameter quantity and calculation overhead are greatly reduced while high recognition precision is guaranteed, efficient real-time deployment on edge equipment is achieved, and the method is suitable for intrusion detection in perimeter security and protection.