4812 results about "Sound wave" patented technology

The invention discloses a portable unmanned aerial vehicle sound wave countering system and method, and relates to the field of radio countermeasure and sound wave interference, and the system comprises a multi-source sensing module which is used for obtaining a multi-modal feature data set of a target unmanned aerial vehicle; the signal processing module is used for carrying out space-time correlation analysis on the multi-modal characteristic data set and generating a threat evaluation parameter set containing an unmanned aerial vehicle type identification result, communication protocol analysis characteristics and navigation mode discrimination indexes; the countering strategy generation module is used for generating a sound wave modulation parameter set, a radio frequency interference waveform sequence and a positioning deception signal template; the cooperative interference module is used for generating a multiband joint countering instruction set according to the improved phase cooperative control strategy; and the execution monitoring module is used for executing the multiband joint countering instruction set in the preset airspace range. According to the portable unmanned aerial vehicle sound wave countering system and method provided by the invention, the multi-level countering capability for different threat levels is improved.

The invention relates to the technical field of coal mine safety monitoring, in particular to an underground coal mine dust concentration monitoring method and system based on multi-modal data fusion, and the method comprises the steps: synchronously collecting dust concentration time sequence data, dust image data, sound wave signal data and environmental parameters through a multi-modal sensor array deployed in an underground coal mine; carrying out preprocessing and feature extraction on the collected data; predicting the decomposed high-frequency and low-frequency component signals by adopting a long short-term memory neural network and a grey Markov model; when the environment humidity is greater than 80%, carrying out light scattering compensation on the predicted value; inputting various predicted values into an improved D-S evidence theory fusion device, and outputting a fusion dust concentration monitoring value; and when the threshold value is exceeded or the temperature and humidity composite condition is reached, an acousto-optic alarm is triggered and a spraying dust-settling device is started. According to the method, the problems of low precision of a single sensor, multi-source data conflict, high-humidity environment measurement deviation and insufficient time sequence and fusion precision in underground coal mine dust concentration monitoring can be solved.

The invention relates to the technical field of data acquisition and data analysis, in particular to a hydraulic fracturing test crack propagation monitoring system which comprises a monitoring system and is used for monitoring crack propagation behaviors in the hydraulic fracturing process in real time. Constant or dynamically changing confining pressure and axial pressure are provided; the pore pressure dynamic regulation and control unit is used for accurately controlling the injection pressure, flow and saturation of fracturing fluid and simulating fracture propagation behaviors under different seepage conditions; the multi-physics field sensing monitoring network unit is used for collecting strain, sound wave signals and a surface displacement field in the crack expansion process in real time; full-scale monitoring of rock fracture propagation is realized, an influence mechanism of pore pressure and time effect on fracture evolution is disclosed, an accurate prediction means is provided for shale gas development and deep engineering, and the fracturing effect and engineering safety are remarkably improved.

The invention discloses a rock multi-field coupling test system and a damage evaluation and prediction method thereof, belongs to the technical field of geotechnical engineering and geomechanics experiments, and solves the problem that the rock state under the multi-field coupling condition cannot be monitored in real time in the prior art. The system comprises a multi-physics field coupling loading system, a multi-field data monitoring system and a multi-field data acquisition system. The system can simulate and apply axial pressure, confining pressure, pore pressure and temperature of an in-situ environment to a rock sample, integrates an acoustic monitoring system, a deformation monitoring system and a CT scanning system to realize multi-dimensional real-time monitoring of rock sample damage, overcomes the limitation of a single monitoring method, realizes multi-system electrical connection by taking an upper computer as a data acquisition center, and realizes multi-dimensional monitoring of rock sample damage. And a multi-parameter model is constructed by fusing sound waves, deformation and CT data, so that the whole-process quantitative evaluation from microcrack initiation to macroscopic fracture is realized, and the damage evaluation is comprehensively performed.

The invention provides an expectoration auxiliary control method and device. The method comprises the steps that audio signals of breathing sounds and cough sounds at multiple positions of the chest of a patient are collected through an audio sensor array, and a standardized audio feature data set is obtained through noise reduction, segmentation and feature extraction; a dynamic weighted graph model is constructed, and a random edge sampling algorithm and a parallel batch processing dynamic algorithm are combined to analyze and obtain a sputum viscosity index and a sputum distribution position map; based on the result, mapping the vibration parameter space into an unweighted disk diagram, and obtaining a personalized vibration treatment scheme by using a shortest path algorithm; in combination with the real-time breathing cycle of the patient, working parameters of the sound wave vibrator and the negative pressure suction device are synchronously controlled, and a coordinated and consistent multi-mode treatment execution instruction is obtained; and collecting real-time feedback data in treatment, and dynamically adjusting working parameters through reinforcement learning to obtain an optimized expectoration adjuvant therapy scheme. Intelligent adjustment can be achieved based on the real-time breathing state and sputum characteristics of the patient, the expectoration efficiency is improved, and discomfort of the patient is reduced.

The invention provides a defect positioning accurate identification method considering the change of the section of a pile body, and the method comprises the steps: carrying out the simulation comparison of a constructed sound wave signal feature vector and a pre-constructed sound wave feature vector of a defect-free pile foundation, calculating the feature difference degree of the two, setting a difference degree threshold value, and if the feature difference degree exceeds the threshold value, carrying out the recognition of the defect-free pile foundation. If yes, preliminarily determining that the pile body has defects; on the basis of the adjusted sound wave detection, sound wave signals are collected again, signal processing and feature extraction are conducted through a self-adaptive path tracking algorithm, and the three-dimensional coordinates of the defect in the pile body are calculated according to the time delay and the propagation speed of reflected waves by combining the established multi-reflection model and an inversion sound wave propagation path; and according to the calculated three-dimensional coordinates of the defect reflection points, the influence degree of the defects on the bearing capacity of the pile foundation is calculated by combining the defect size estimated through the amplitude of reflected waves, the defect type recognized through waveform features and the strength parameter of a pile body material through a finite element method.

The invention relates to an algorithm fusion and fault early warning system in a battery safety management platform, and aims to realize multi-dimensional risk identification and active response control in a battery operation process. The system comprises a multi-source data acquisition module, a fusion calculation engine, a fault early warning response module and an execution interface module. By collecting voltage, current, temperature, humidity, sound wave signals, electrochemical impedance spectroscopy, gas concentration and other multi-mode operation data, a fusion calculation engine completes time sequence correlation modeling, causal feature recognition and risk trend deduction on the basis of a constructed hierarchical algorithm fusion structure, and a risk response signal reflecting a safety state is generated; the fault early warning response module generates hierarchical control instructions corresponding to risk levels according to the signals, the hierarchical control instructions comprise virtual fusing, cooling scheduling, BMS refreshing and the like, the execution interface module issues the control instructions to a physical controller of the battery system, response behaviors are driven, and therefore intelligent, closed-loop and dynamic battery safety management is achieved.

The invention provides a real-time deep sea subsurface buoy monitoring system, which belongs to the technical field of deep sea measurement, and comprises a control chip, a multi-parameter sensor array, a data storage device and a power supply, high-precision monitoring is realized through the following steps: recording ocean data acquired by a multi-parameter sensor; correcting the sound wave propagation model and calculating the relative position of the subsurface buoy; constructing a three-dimensional data matrix and realizing data compression; identifying abnormal data by using a matrix disorder index; processing the time sequence data by adopting a sliding window weighted average method and eliminating jump; carrying out distributed preprocessing on the multi-source heterogeneous data and carrying out parallel processing by applying a matrix partitioning technology; accumulated errors are corrected by combining historical data and applying a Kalman filtering algorithm, and the problems of environment interference and sensor drift are solved through a deep sea environment disturbance compensation network model and a self-adaptive weighted error optimization function.

The invention relates to the technical field of unmanned aerial vehicle detection and recognition, in particular to an unmanned aerial vehicle detection system and method based on vision, laser radar and sound waves, and the system comprises a sensor unit, a data processing fusion unit and a target recognition tracking unit. The data processing fusion unit performs multi-source data alignment and weighted fusion to extract texture, shape and color features of the image, point cloud generates a depth map, sound wave signals are mapped into a two-dimensional feature map, and weights are dynamically distributed based on sensor reliability to generate a unified feature map; the target identification and tracking unit identifies the type of the unmanned aerial vehicle by using the optimized deep learning model and realizes accurate prediction and updating of position and speed states in combination with Kalman filtering, and the decision response unit triggers sound-light alarm and wireless alarm for the non-cooperative unmanned aerial vehicle in real time and transmits target dynamic information to the ground station. And the target detection accuracy of the unmanned aerial vehicle in a complex environment is improved.

The invention discloses a fan blade diagnosis method and system based on voiceprint perception, and relates to the technical field of intelligent monitoring of wind power equipment, and the method comprises the steps: carrying out frequency domain compensation processing, stripping environment wind noise components, and generating a pure voiceprint signal based on a wind flow characteristic parameter set; based on the pure voiceprint signal, identifying a sensitive acoustic frequency band of the fan blade, exciting a sound wave phase synergistic effect in the sensitive acoustic frequency band, and generating an enhanced voiceprint signal; quantifying the internal damage depth of the fan blade material according to the enhanced voiceprint signal, and calculating a sound energy flow disorder degree index; according to the sound energy flow disorder degree index, the blade health state of the fan is judged through a multi-dimensional acoustic characteristic state space mapping mechanism, and a three-dimensional health assessment report is generated; through a physical level wind noise stripping technology, a transfer function matrix is generated based on vortex interference modeling and sound wave-airflow phase offset correction, frequency domain energy reweighted decoupling is realized, and environmental wind noise is accurately stripped in a strong turbulence environment.

The invention provides an underwater target identification method and system based on multi-source sensor data fusion. The method comprises the following steps: firstly, synchronously acquiring an original sound wave reflection signal and original optical image data of a target underwater area, then carrying out acoustic compensation processing on the original sound wave reflection signal to generate a target sound wave reflection signal, and meanwhile, carrying out optical compensation processing on the original optical image data to generate target optical image data; performing dynamic weighted fusion on the reliability measurement of the target sound wave reflection signal and the reliability measurement of the target optical image data through an adaptive fusion module to generate an enhanced multi-modal data set; and finally identifying the specific category of the underwater target through a multi-modal feature matching and decision-making mechanism. According to the technical scheme provided by the invention, the limitation of a single sensing mode is overcome, the quality of collected information is improved, the direct confirmation efficiency of common target recognition is also improved, and high accuracy and data reliability of target category recognition in a complex scene are ensured.

The invention discloses an intelligent pipe network leakage detection method based on edge calculation, and relates to the technical field of pipe network monitoring and leakage detection, and the method comprises the steps: carrying out the feature extraction of a preprocessing data set through an edge analysis terminal, and obtaining a pressure stability coefficient, a sound wave correlation coefficient and a flow mutation coefficient; inputting the pressure stability coefficient, the sound wave correlation coefficient and the flow abrupt change coefficient into a leakage probability evaluation model, and outputting a leakage risk index; and the edge decision terminal generates a leakage level signal according to the leakage risk index and triggers a positioning instruction. The pressure stability coefficient, the sound wave correlation coefficient and the flow abrupt change coefficient are extracted through the edge analysis terminal and input into the leakage probability evaluation model, the leakage risk index is output, accurate quantitative evaluation of the leakage risk is achieved, the model can adapt to changes of different working conditions through introduction of the dynamic weight, and the reliability of the model is improved. And the adaptability and accuracy of evaluation are improved.

The invention relates to the technical field of furnace tube intelligent diagnosis, in particular to a big data AI-based furnace tube leakage intelligent diagnosis method, which comprises the following steps: collecting boiler operation data in real time through a multi-source sensor, including temperature, pressure, vibration, sound wave and flue gas component data; performing time sequence alignment, abnormal value elimination and standardized preprocessing on the collected original data; inputting the preprocessed data into a pre-trained deep neural network model, wherein the deep neural network model is obtained through comparison training of historical normal data and leakage accident data; generating a real-time diagnosis result and an early warning level based on the leakage probability value and the feature contribution degree analysis output by the model; through multi-source sensing, AI deep analysis, dynamic early warning and predictive maintenance closed loop, early, accurate and automatic diagnosis of boiler tube leakage is realized, and the safety and operation and maintenance efficiency of industrial equipment are remarkably improved.

The invention provides an oil and gas equipment leakage acoustic characteristic intelligent identification early warning method and system, and relates to the technical field of oil and gas equipment safety monitoring, and the method comprises the steps: arranging a sonic sensor array to collect signals, calculating a wavefront propagation path, optimizing the wavefront curvature, carrying out the multi-scale decomposition of a sonic signal, and extracting time-frequency characteristics; and reconstructing a sound field by using an acoustic propagation function to determine a leakage point space coordinate. The method can accurately identify the acoustic characteristics of oil and gas equipment leakage, improves the leakage point positioning precision, achieves the early warning of leakage, and guarantees the safe operation of oil and gas equipment.

The invention relates to the technical field of intelligent health, and discloses an intelligent health middle-aged and elderly people behavior data real-time monitoring and analysis method and system, and the method comprises the steps: collecting a biological resonance signal, a gas spectrum and a sound wave signal of middle-aged and elderly people; constructing a resonance characteristic spectrum of the middle-aged and elderly people, and analyzing physiological feedback characteristics of the middle-aged and elderly people by using the resonance characteristic spectrum; constructing an odor-behavior association map of the middle-aged and elderly people, and identifying gas feedback characteristics of the middle-aged and elderly people by using the odor-behavior association map; performing frequency domain decomposition on the sound wave signal to obtain an infrasonic frequency band and an ultrasonic frequency band, identifying internal organ vibration characteristics and body surface action characteristics of the middle-aged and elderly people, and analyzing voiceprint feedback characteristics of the middle-aged and elderly people; the physiological feedback features, the gas feedback features and the voiceprint feedback features are used for conducting behavior analysis on the middle-aged and elderly people, and a behavior monitoring report is obtained. The method can improve the reliability of behavior abnormality analysis of the elderly in smart health.

The invention relates to the technical field of water supply pipeline detection, and discloses an underground water supply pipeline health grade evaluation and risk prediction method, which comprises the following steps: sensor arrangement: arranging a flow sensor, a pressure sensor and a sonic sensor at key positions of an underground water supply pipeline; and data acquisition: acquiring signals of the sensor in real time through a data acquisition module, wherein the signals comprise flow, pressure and sound wave signals. Preprocessing the data: carrying out preprocessing such as denoising and normalization on the collected signals; and multi-source data fusion: inputting flow, pressure and sound wave signals into a deep neural network model, and performing feature extraction and fusion analysis. And health level assessment: assessing the health level of the pipeline based on the output result of the deep neural network model. And risk prediction: predicting abnormal working conditions possibly occurring in the future and risk levels of the abnormal working conditions by analyzing the current pipeline state and historical data. And abnormal positioning: accurately positioning an abnormal position in combination with the propagation time of the sensor signal and a positioning model of the deep neural network.

The invention relates to an unmanned aerial vehicle detection countering method, which comprises the following steps of: deploying a multi-dimensional sensing network to carry out full-band instantaneous scanning, synchronously acquiring noise characteristics of radio signals, material characteristics of a terahertz wave band and Doppler effect characteristics, mapping to a virtual twinborn body, reconstructing physical parameters, a communication protocol and a behavior mode of a target unmanned aerial vehicle, performing depth comparison by combining a disguise sample library generated by the antagonism generation network, and generating a holographic target file containing a three-dimensional motion vector, an energy feature and a potential threat intention; a target quantum encryption communication link is interfered through a quantum entanglement signal, a controllable plasma cloud cluster is generated, navigation and image transmission signals of the controllable plasma cloud cluster are selectively attenuated, the controllability of the target is judged, and an uncontrollable target is repelled through coded sound waves; and sending a control instruction containing a biological heuristic obstacle avoidance algorithm in stages, and guiding the control instruction to an intelligent recovery cabin. According to the invention, accurate detection, intelligent identification, safe countering and reliable takeover of the unmanned aerial vehicle are realized, and the airspace safety guarantee capability is improved.

The invention discloses a noise reduction method and system for a high-performance TWS Bluetooth audio chip, and relates to the technical field of acoustoelectric integration, and the method comprises the steps: collecting an environment noise signal, synchronously obtaining a Bluetooth radio frequency interference signal, converting the Bluetooth radio frequency interference signal into a digital signal through a 24-bit analog-to-digital converter, and outputting a multi-modal noise feature matrix; performing noise separation and reverse sound wave generation on the multi-modal noise characteristic matrix by using a pre-trained lightweight CycleGAN model, and outputting a reverse sound wave signal; monitoring auditory meatus tightness parameters in real time, dynamically adjusting phase offset and frequency response balance parameters of the mixed noise reduction signals, and outputting optimized noise reduction signals adaptive to the current wearing state; and performing parameter calibration on the optimized noise reduction signal through anechoic chamber scene simulation and laser interferometer phase detection to generate a noise reduction control instruction set. According to the invention, the noise interference problem of the TWS earphone in a complex environment is effectively solved.

The invention discloses a dynamic noise analysis method and device for an urban elevated road and a medium, and belongs to the technical field of noise monitoring and analysis. The method comprises the following steps: acquiring a three-dimensional geographic information model and real-time traffic flow data of a target elevated road; processing the three-dimensional geographic information model based on a sound wave propagation attenuation model to generate a noise propagation path grid; fusing the real-time traffic flow data with a noise source intensity calculation model to generate a dynamic noise source intensity sequence; calculating a real-time equivalent sound pressure level of each grid unit through a noise superposition algorithm in combination with the dynamic noise source intensity sequence and the noise propagation path grid; performing rolling prediction on the real-time equivalent sound pressure level based on a time window sliding mechanism to generate a noise space-time distribution thermodynamic diagram; and according to the noise space-time distribution thermodynamic diagram, identifying a noise exceeding area, and outputting a dynamic noise evaluation report. According to the method, the technical effect of dynamically predicting and analyzing the noise of the elevated road is achieved.

The invention discloses a three-dimensional noise mapping method and system based on point cloud, and belongs to the technical field of urban environmental noise prediction.The method comprises the steps that point cloud data driven three-dimensional modeling and parameter extraction are carried out, an urban three-dimensional model with a geometric topological structure is constructed through laser scanning point cloud data, and related parameters are extracted; based on sound field calculation of ISO 9613 standards, a multi-path propagation model including an atmospheric effect, a ground effect and obstacle diffraction is established, a green infrastructure noise reduction effect is especially considered, and propagation attenuation of noise in a three-dimensional space is simulated; and performing visual expression of three-dimensional noise, and constructing fusion expression of a sound pressure level contour surface and a three-dimensional city model. In view of difficulty in simulation of three-dimensional reconstruction and sound waves in a spatial propagation process in the prior art, the method improves the precision of quantitative prediction, and fuses a green infrastructure noise reduction effect to realize rapid and efficient regional three-dimensional reconstruction and noise mapping.

The technology described herein is directed towards one or more modular metasurfaces arranged with unit cells for sound absorption, in which the unit cells are based on Helmholtz resonators that are deployed proximate to a server, without substantially obstructing airflow from the server fan(s). Each sound absorbing unit-cells is designed and constructed based on a specific resonance frequency, and includes a neck portion and air chamber dimensioned to resonate at the desired resonance frequency and thereby inverse phase cancel corresponding narrowband frequencies of incoming sound waves. The metasurfaces can be arranged in a modular frame structure of surrounding unit cells, positioned behind the server's fan(s), with an opening in the frame structure to facilitate airflow therethrough, without obstructing or substantially obstructing the airflow to the surrounding environment.

The technology described herein is directed towards modular metasurfaces arranged with unit cells for narrowband, wideband and / or multiband sound absorption, in which the unit cells are based on Helmholtz resonators that are deployed in a server rack, proximate to a server, without substantially obstructing airflow from the server fan(s). Each sound absorbing unit-cells is designed and constructed based on a specific resonance frequency, and includes a neck portion and air chamber dimensioned to resonate at the desired resonance frequency and thereby inverse phase cancel corresponding narrowband frequencies of incoming sound waves. Differently-dimensioned unit cells can be built into a single metasurface module, thereby facilitating wideband and / or multiband sound absorption with that metasurface. The modular metasurfaces can be attached to the sidewalls of a server rack, or positioned in otherwise empty slots above and below a server, optionally coupled to blanking panels for covering the openings of those slots.

The invention provides a machine tool machining parameter adjustment method and system, relates to the technical field of machine tool machining parameter adjustment, and aims to infer local acoustic impedance of a workpiece material in real time by emitting sound waves and receiving echo signals on the workpiece material in front of cutting of a cutter and extracting acoustic features. And when the local acoustic impedance is continuously higher than a preset normal reference value and exceeds a preset threshold value, determining that a local abnormal region exists, and generating an early warning signal. And before the tool makes contact with the local abnormal area, the machining parameters are adjusted in advance according to the early warning signal. And real-time feedback signals in the cutting process are continuously monitored, and the machining parameters are further adjusted based on the real-time feedback signals and the early warning signals. The problems that in the prior art, due to uneven local hardness of a workpiece material, a tool is unexpected to be abraded, and machining size deviation is accumulated can be effectively solved.

The invention relates to the technical field of submarine cable fault positioning, in particular to a submarine cable fault positioning system based on underwater beacons. The method comprises the steps that an underwater acoustic beacon array unit monitors and collects submarine cable fault sound wave signals in real time based on underwater acoustic beacons; the fault signal processing analysis unit extracts submarine cable fault sound wave signal parameters based on a non-point source space sound source waveform analysis model, and generates multi-parameter fault sound source feature vectors; the sound source positioning analysis algorithm unit performs three-dimensional inversion positioning based on the multi-parameter fault sound source feature vector and the arrival time difference of the submarine cable fault sound wave signal to the underwater acoustic beacon, and obtains the spatial position information of the submarine cable fault point; and the data communication alarm management unit sends the spatial position information of the submarine cable fault point to a shore-based operation and maintenance system. The submarine cable fault positioning method is used for realizing a submarine cable fault positioning technology for quickly and accurately positioning and identifying spatial extension characteristics in a complex submarine environment.

The invention relates to the technical field of transformer detection, and discloses a nondestructive testing optimization method and system for an oil-immersed power transformer, and the method comprises the steps: building a three-dimensional dielectric response coordinate system, and generating a preliminary defect positioning map; aging-dominated and damp-dominated defects are detected and identified through spiral frequency sweep excitation; establishing a temperature gradient excitation scheme based on the defect type to generate a defect degree quantitative evaluation index; designing a sound wave modulation excitation scheme to generate a high-resolution defect characteristic spectrum; constructing a multi-mode intelligent sensor network, and combining a defect development trend prediction model to realize defect evolution prediction and generate a graded early warning signal; according to the method, the whole-process accurate detection of the insulation defect of the transformer from positioning, classification and quantitative evaluation to evolution prediction is realized, and a reliable basis is provided for operation and maintenance.

The invention discloses a drilling construction intelligent decision support and risk early warning method, device and equipment, and the method comprises the steps: collecting drilling sound wave response data, carrying out harmonic analysis to recognize a reservoir resonance node, and constructing a reservoir energy conduction network; a fluctuation propagation path is tracked based on an energy conduction network, and a construction risk area is identified through stress wave spectrum features; porosity and permeability data are extracted for phase difference analysis, a seepage-construction mismatch factor is generated, and a construction optimization window is actively generated; constructing a geological potential energy gradient field to determine an overflow influence range, and performing tensor coupling on multi-dimensional risk factors to generate a three-dimensional construction decision matrix; an optimal construction scheme is generated through energy minimization and risk-efficiency dynamic balance processing; construction parameter response changes are monitored in real time, abnormal parameter combination symptoms are recognized, a grading risk early warning instruction is generated, and intelligent decision support and risk early warning of drilling construction are achieved.

The invention discloses an optical cable fitting fatigue damage detection method and system, and relates to the technical field of power transmission line state monitoring, and the method comprises the following steps: S1, constructing a wind field and structure coupling observation baseline, obtaining a full-time-domain vibration response signal of an optical cable fitting in a non-uniform wind field environment, generating a phase consistency distribution map, and obtaining a phase consistency distribution map; establishing a corresponding relation between the multi-path reflection source group and the time correlation sequence as a traceable reference for phase analysis; and S2, based on the phase consistency distribution map, performing causal beam demixing processing, performing arrival time difference densification calculation and curvature spectrum separation analysis on each sound wave propagation path, and extracting crack propagation pointing data. According to the method, through wind field-structure coupling observation, path unmixing, phase regression and error checking, multi-path propagation recognition and correction are achieved, crack propagation topology is reconstructed, polarization rotation and a phase suppression mechanism are combined, and direction recognition stability and detection adaptivity are improved.

The invention discloses a smart bracelet earthquake early warning service system and method, and the method comprises the steps: collecting user physiological parameters and earthquake early warning signals, building a coupling relation between earthquake intensity parameters and user physiological features, and forming an individualized disaster bearing reference; performing gravity sensing detection in combination with the geographic position of the user and the anti-seismic grade of the building, analyzing the spatial relationship between the micro-inclination angle of the building and the position of the user, and generating gravity field risk features; analyzing a group movement mode through relative position changes among multiple bracelets, extracting movement coordination characteristics and direction coordination coefficients, and forming a displacement coordination early warning sequence; a disaster emergency coefficient is generated based on user motion coordination efficiency and protection response behaviors, a sound wave positioning network of a multi-device linkage trigger area is constructed, collaborative alarm and personalized evacuation guidance are realized, differentiated earthquake early warning services can be provided according to individual physiological features and environmental conditions, and the pertinence and effectiveness of early warning are improved.

The invention relates to the technical field of sewage detection, provides an intelligent detection method for suspended particles in coking sewage based on image deep learning, and aims to solve the problems of detection lag and low precision in the prior art. According to the technical scheme, the method comprises the following steps: synchronously acquiring sound wave and image data through a high-frequency ultrasonic transducer and an industrial camera, and extracting acoustic parameters and optical characteristic parameters; using a dual-channel convolutional neural network to fuse features, and combining a cross-modal attention mechanism to perform dynamic weighting; and outputting a particle concentration classification result and a particle size distribution prediction value through a multi-task learning model, and performing online learning to compensate environmental interference factors. The device is mainly used for realizing real-time and high-precision suspended particle monitoring and automatic process adjustment of coking sewage treatment.

The invention discloses a circuit breaker fault sensing method and system based on multi-source information fusion, belongs to the technical field of power equipment state monitoring and fault diagnosis, and aims to solve the problems of high false report and missing report rate, insensitive early fault and incomplete diagnosis of a single monitoring means. Comprising the following steps: synchronously acquiring multi-modal data such as vibration, sound wave, infrared, current, displacement and the like through a multi-source sensor array; performing signal preprocessing and feature extraction; performing preliminary intelligent diagnosis by adopting a three-layer fusion model comprising a data layer, a feature layer and a decision layer; performing verification and confidence fusion on the preliminary result by utilizing an expert knowledge base to generate a final diagnosis conclusion; triggering early warning according to the confidence level; according to the method, a complete circuit breaker state sensing system is constructed by fusing various physical information such as mechanical vibration, acoustics, thermotics and electricity, mutual verification can be realized, the one-sidedness of a single information source is overcome, the accuracy of fault diagnosis is greatly improved, and false alarm and missing alarm are remarkably reduced.