1266 results about "Acoustic sensor" patented technology

The invention provides a home abnormal state signal detection method and system based on multi-modal sensing, and relates to the technical field of detection, and the method comprises the steps: collecting human body motion, acoustics and environment parameters through a millimeter wave radar, an acoustic sensor and an environment sensor, inputting the parameters into a deep fusion network, a spatio-temporal attention mechanism and a multi-scale convolutional neural network are used to extract a spatio-temporal feature sequence, and joint probability features are formed in combination with an acoustic feature spectrum analysis result. And inputting the joint probability features into a time sequence knowledge graph, and obtaining scene adaptive features after processing by a graph attention network and a double-flow auto-encoder network. And finally, the multi-task learning network is combined with the risk propagation neural network and the recursive neural network to carry out anomaly prediction and risk level evaluation, and is combined with historical early warning information to output an early warning strategy. According to the invention, multi-modal data can be effectively fused, the accuracy and reliability of home abnormal state detection are improved, and a more accurate risk assessment and early warning strategy is provided.

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention discloses a server heat dissipation device and a heat dissipation control method.The surface of a server mainboard is divided into a core heat dissipation area, a transition heat dissipation area and an edge heat dissipation area, and the method comprises the steps that real-time temperature data of all the areas are synchronously collected through a temperature detection module; constructing a three-dimensional temperature field distribution map containing temperature gradient and heat flow direction; then thermodynamic parameter calculation is carried out, temperature time sequence change characteristics of all the partitions are extracted, and a thermal diffusion path in a future set time period is predicted by fusing the temperature time sequence change characteristics and the thermodynamic parameters; a control instruction set is generated according to the predicted thermal diffusion path, the actual heat dissipation response is monitored through an acoustic sensor in the execution process, monitoring data and the predicted thermal diffusion path are dynamically compared, and control parameters of the control instruction set are updated till all the partitions reach the target temperature threshold value; and the sudden temperature change is pre-judged, the hot spot elimination response time is shortened, and the collaborative breakthrough of the heat dissipation efficiency and the energy consumption control of the high-density server cluster is realized.

The invention discloses a sound interaction intention recognition and intelligent decision-making method based on an AI large model, and relates to the technical field of intelligent voice interaction, and the method comprises the steps: collecting a voice signal through an acoustic sensor, carrying out the noise reduction processing and acoustic feature extraction, capturing a text instruction, and carrying out the semantic segmentation and text feature extraction, splicing the acoustic feature vector and the text feature vector to form a multi-modal data packet; retrieving a historical memory library based on the enhanced fusion feature vector to generate a memory context vector, identifying the category of a deliberate map through a two-stage intention reasoning model, analyzing operation parameters, and outputting a structured intention instruction; and performing parameter legality verification, equipment state verification and security risk assessment on the structured intention instruction, and packaging the structured intention instruction into an executable instruction set after correcting abnormal parameters. According to the method, through double screening of the frequency band energy ratio and the lexical item importance score, the acoustic-text feature scale difference is reduced.

A method of making an acoustic sensor includes forming or providing a mold having one or more grooves extending in a direction of the length of the mold to a distal end of the mold. The method also includes forming or depositing a structure having one or more piezoelectric layers over the top surface of the mold to define a beam with a proximal portion and a distal portion, the distal portion having a corrugated section including one or more grooves that correspond to the one or more grooves of the mold. The method also includes forming or applying an electrode to the proximal portion of the structure and releasing the structure from the mold to form one or more cantilever beams. The corrugated section inhibits bending of the corrugated section along the length of the distal portion of the structure when the acoustic sensor is subjected to sound pressure.

The invention relates to the technical field of power transformer state monitoring, and discloses a main transformer winding temperature rise on-line monitoring method and system, and the method comprises the steps: obtaining the calibration flight time of ultrasonic waves through an acoustic sensor array disposed on the outer wall of an oil tank in a quasi-isothermal state of a transformer; calculating a structure additional time delay which is caused by an internal fixed structure and does not change along with the temperature, and establishing an acoustic fingerprint for the transformer; during on-line monitoring, oil way net propagation time reflecting temperature change of the insulating oil is obtained from on-line flight time measured in real time; and then, based on the net propagation time of the oil way, an acoustic tomography algorithm is adopted to reconstruct internal two-dimensional or three-dimensional temperature field distribution, and the winding hot-spot temperature is finally and accurately calculated in combination with the real-time load current. According to the invention, through the acoustic fingerprint correction technology, the measurement interference of an internal fixed structure is eliminated, and non-intrusive, high-precision and visual online monitoring of the winding hot-spot temperature is realized.

The invention discloses a transformer fault intelligent detection method and device based on deep learning multi-mode fusion, computer equipment and a readable storage medium. The method comprises the steps that firstly, RGB image data of a target transformer are collected by means of an image sensor, infrared thermal imaging image data are collected by means of an infrared sensor, and sound signals are collected by means of a sound sensor; and preprocessing the data, taking the preprocessed data as multi-modal detection data, and inputting the multi-modal detection data into a pre-trained multi-modal fault recognition model which comprises a sensing layer, a fusion layer, a backbone network and an output layer which are cascaded in sequence, so as to obtain a fault detection result of the target transformer. According to the method, multi-modal data are fused, the deep learning advantage is exerted, the comprehensiveness and accuracy of transformer fault detection are improved, and an effective means is provided for intelligent operation and maintenance of a power system.

The invention relates to the technical field of intelligent travel control of walking-aided robots, and discloses a multi-sensor-based obstacle avoidance control method and system for a walking-aided robot. The obstacle avoidance control method for the walking-aid robot is applied to walking-aid robot equipment, and specifically comprises the following steps: S101, completing the installation of a multi-sensor module on the walking-aid robot, and collecting multi-source data obtained based on multiple sensors in real time, unifying the multi-source data to a base coordinate system of the walking aid robot by adopting timestamp synchronization and a coordinate system conversion matrix, and generating a fused dynamic obstacle sequence map; and S102, on the basis of the dynamic obstacle sequence map, a Kalman filtering and LSTM neural network fusion model is adopted to predict the motion trajectory of the dynamic obstacle. According to the method, the environment perception and decision-making architecture of the walking-aid robot is reconstructed, a multi-sensor space-time cooperation mechanism is matched through cross-modal data of laser radar and binocular vision, and ultrasonic sensor low obstacle special detection is combined, so that the step recognition rate is greatly improved.

The invention relates to a shipbuilding safety monitoring system and a monitoring method thereof, and belongs to the technical field of shipbuilding monitoring, and the system comprises a multi-modal data collection module, an edge computing node, a digital twin platform, a digital twin platform, an analysis module, a block chain traceability module, and a feedback control module. The multi-modal data acquisition module comprises a visual sensor, an acoustic sensor, a thermal imager, a strain sensor, a laser displacement sensor and an environment sensor. The visual sensor, the acoustic sensor, the thermal imager, the strain sensor and the environment sensor are installed in a shipbuilding workshop. According to the invention, by fusing visual, acoustics, thermodynamics and other multi-sensor data and combining edge calculation real-time processing and digital twinning dynamic simulation, process quality prediction, defect detection and carbon emission tracking are realized, and the problems of insufficient real-time performance, difficulty in cross-system collaboration and lack of green manufacturing monitoring in the prior art are solved.

The invention discloses an indoor air quality monitoring method and system based on multi-dimensional analysis, and belongs to the technical field of air quality monitoring. The indoor air quality monitoring method based on multi-dimensional analysis comprises the steps that various data of a chemical sensor, an optical sensor and an acoustic sensor are collected; preprocessing the data, including unifying sampling frequency and normalizing different data formats; performing multi-dimensional feature extraction to obtain a multi-modal feature; carrying out feature fusion by adopting a machine learning algorithm; the air quality state is evaluated based on the fusion features; through multi-source data fusion analysis, the problems of one-sided monitoring result and insufficient reliability of a single sensor are solved, the monitoring accuracy and reliability are improved, and the method can be widely applied to scenes such as smart homes and office buildings.

The invention discloses a battery evaluation computer system and method based on multi-modal characteristics, and belongs to the technical field of battery health management, and the system mainly comprises a multi-modal sensing module, specifically a grating sensor unit, which is used for collecting surface and internal deformation data of a battery, including wavelength offset (delta lambda), strain (epsilon) and deformation characteristics after temperature compensation; the electrochemical sensor unit is used for acquiring voltage, current, internal resistance and temperature data of the battery; and the acoustic sensor unit is used for detecting ultrasonic signals of gas separation or structure abnormity in the battery. By integrating the fiber grating sensor, the electrochemical sensor and the acoustic sensor, deformation, voltage / current, temperature and acoustic signals of the battery are acquired in real time, and high-precision battery state of health (SOH) evaluation and residual life (RUL) prediction are realized in combination with multi-modal data processing and an intelligent algorithm.

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

The invention relates to a multi-defect ultrasonic guided wave nondestructive testing, positioning and recognition method based on a mechanical arm, relates to the technical field of nondestructive testing and automatic testing, and solves the technical problems that in the prior art, time and labor are consumed in detection of large components, and positioning recognition and detection precision is difficult to guarantee in a manual detection mode. The method comprises the following steps of: post-processing signals acquired for N times, and setting a signal amplitude threshold value; performing global scanning on the tested piece; and whether the mechanical arm completes global scanning or not is judged. A mechanical arm is used for carrying an electromagnetic acoustic sensor to scan a large plate and a large curved surface structure, and detection and positioning recognition of various different defects are achieved; using the collected data as an original data set to construct a deep learning model and an integrated CNN model, and realizing adaptive scanning detection of internal defects of a large component and a large curved surface structure; the use of a coupling agent is reduced, the detection time efficiency and the detection precision are improved, and the detection accuracy and the defect positioning accuracy are realized.

The invention relates to the technical field of distributed optical fiber acoustic sensors, in particular to a distributed optical fiber sensor vibration identification method and system, and the method comprises the steps: obtaining original data, and carrying out the preprocessing; inputting the preprocessed vibration time sequence into a Mamba model, and extracting a time domain feature vector reflecting a time evolution rule; after obtaining a time-frequency spectrogram of the preprocessed vibration time sequence, extracting to obtain a frequency domain feature vector representing different frequency components and a global association relationship thereof; inputting the time domain and frequency domain feature vectors into a time-frequency feature fusion model, and outputting a vibration event recognition result of the distributed optical fiber acoustic sensor; training the time-frequency feature fusion model, and storing the optimal model weight for prediction; and performing prediction by using the optimal model, storing a prediction result, performing verification, calculating an evaluation index, and finally performing result comparison. The invention aims to solve the problems of insufficient performance and the like of a distributed optical fiber acoustic sensing technology in the prior art.

The invention proposes an underwater target intelligent recognition system based on multi-sensor fusion, and relates to the technical field of underwater target recognition, and the system comprises an underwater robot which is provided with an optical sensor, an acoustic sensor, a three-dimensional laser sensor, and a water quality environment sensor; the data acquisition module is used for synchronously acquiring original multi-modal data and water quality parameters from the underwater robot; the data preprocessing module is used for generating preprocessed multi-modal data; the multi-modal feature fusion module is used for extracting each modal feature from the preprocessed multi-modal data and generating a fusion feature; the target recognition and positioning module is used for processing the fusion features by adopting a target detection network and outputting the category, the position and the confidence of the underwater target; and the intelligent analysis output module is used for comprehensively analyzing the information of the underwater target and generating an analysis result and visual data. According to the invention, the limitation of a single sensor can be overcome, and the underwater target can be accurately and stably identified by combining an intelligent algorithm.

The invention discloses a gear performance accurate evaluation method based on voiceprint analysis, and relates to the field of gear performance evaluation. Determining the optimal arrangement position of the acoustic sensor according to different gears, and adjusting the sampling frequency and duration in real time according to the operation state change of the gears to collect voiceprint data; de-noising by using a composite de-noising network based on deep learning, and processing data by using a new normalization method; extracting a plurality of innovative features to construct a feature vector, and combining a deep forest with model fusion to establish an evaluation model; and inputting a to-be-evaluated gear feature vector into the model to evaluate the performance, verifying an evaluation result through a virtual verification mode and the like, and optimizing the model based on feedback. The gear performance is accurately evaluated, voiceprint data are intelligently collected, and the evaluation accuracy is improved through feature extraction and model construction; real-time monitoring and early warning are achieved, and different gears are adapted; the block chain ensures data reliability, the evaluation model evolves continuously, the maintenance cost is reduced, and stable operation of equipment is guaranteed.

The invention belongs to the technical field of drainage pipeline detection, and particularly relates to a voiceprint recognition detection method for internal defects of a drainage pipeline, and the method comprises the following steps: S1, collecting sound signals in the pipeline through an acoustic sensor array; s2, preprocessing the collected sound signals, wherein the preprocessing comprises noise filtering, signal enhancement and segmentation processing; s3, extracting time domain, frequency domain and time-frequency domain features of the sound signals, and constructing voiceprint fingerprints; s4, establishing a defect classification model based on the deep neural network, and identifying different types of pipeline defects; s5, carrying out confidence evaluation on the detection result, and determining the position of the defect in the pipeline; s6, outputting a detection result and carrying out graded alarm; according to the method, various defect forms of cracks, blockage, damage and the like of different degrees of the drainage pipeline can be recognized, the recognition accuracy of the internal defects of the drainage pipeline can reach 95% or above through multi-dimensional voiceprint feature extraction and a deep learning algorithm, and continuous monitoring and real-time alarming can be achieved.

The invention relates to the technical field of intelligent detection, and further relates to a non-excavation underground water supply and drainage pipe network leakage detection method based on multi-data fusion. The method comprises the steps that 1, acoustic sensors are arranged along a pipe network at equal intervals, each acoustic sensor collects an acoustic time domain signal generated by pipeline leakage, and then the frequency spectrum intensity of the acoustic time domain signal at the pipeline characteristic frequency is calculated; step 2, calculating a cross-correlation function between two different acoustic sensors according to the frequency spectrum intensity, and determining acoustic delay of the acoustic time domain signal between the different acoustic sensors by searching a peak value position of the cross-correlation function; step 3, according to the acoustic delay, calculating the position of a leakage point by using a sound wave propagation principle; according to the position of the leakage point, the water leakage amount is evaluated in combination with pipeline use data. According to the method, through multi-data fusion and intelligent analysis, effective combination of trenchless, high-sensitivity, accurate positioning and scientific quantitative evaluation is realized.

The invention relates to the technical field of ball valves, provides an intelligent sensing modular ball valve and a control method, and designs a valve body module and a control module. The valve body module is integrated with a multi-mode sensor, comprises a spirally-distributed acoustic sensor array, a physical quantity sensor group and a valve element state detection sensor, and monitors fluid acoustic characteristics, pressure, temperature, flow, liquid level and valve element displacement and torque in real time. The control module analyzes sensor data through a data fusion algorithm, and early identification of cavitation faults and detection of micro-crack faults are achieved. The system adopts an 8-bit dynamic coding mechanism to accurately position the fault type, and adjusts the opening or torque of the actuator in a linkage manner. The method has multi-parameter cooperative sensing, early fault early warning and self-adaptive control capabilities, and the reliability and safety of the ball valve under complex working conditions are remarkably improved.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

The present invention discloses a multi-modal system for real-time plant hydration monitoring and irrigation management. The system comprises at least one acoustic sensor configured to emit controlled sound waves through plant tissues and detect corresponding vibrations and resonance frequencies using sensitive microphones or piezoelectric sensors to assess plant hydration. The system includes at least one bioelectrical sensor for monitoring bioelectrical signals. At least one nanotechnology-based sensor is configured to detect molecular changes in water content within plant cells through embedded or externally applied nanosensors. An aerial imaging device mounted on an unmanned aerial vehicle captures data related to canopy temperature, leaf color, and hydration indicators using infrared, multi-spectral, and thermal cameras. A central processing unit receives and analyzes data using predictive models, integrates the data via data fusion algorithm to generate a plant hydration profile, and controls water delivery through an irrigation management unit based on the hydration profile.

The invention relates to a mechanical arm ultrasonic guided wave multi-defect detection path planning method based on data driving, relates to the technical field of nondestructive detection and automatic detection, and solves the technical problem that automatic defect detection for a large plate structure and a large curved surface structure does not exist in the prior art. A system applicable to the method comprises a control and test system and a tested piece. According to the invention, the mechanical arm is used for carrying the electromagnetic acoustic sensor to scan a large plate and a large curved surface structure, so that detection, positioning and identification of various different defects are realized; the use of a coupling agent can be reduced, the detection time efficiency is improved, the detection precision is improved, the detection accuracy is realized, and the accuracy of positioning and identifying defects is improved; the scanning path of the mechanical arm is optimally designed, so that the detection efficiency is improved; and the collected data serve as an original data set to construct a deep learning model and an integrated CNN model, and mechanical arm scanning detection path planning of multiple defects in a large component and a large curved surface structure is achieved.

The invention discloses a method, a system, equipment and a medium based on a multi-composite ultrasonic penetration technology, and the method comprises the steps: generating high-frequency fundamental ultrasonic waves through a piezoelectric transducer array, and adjusting the frequency range to match the acoustic impedance characteristic of target soil; first, second and third harmonics are generated through the induction of a nonlinear medium in the fundamental wave propagation process, the phases of the harmonics are adjusted to realize coherent superposition, and the fundamental wave and the first, second and third harmonics are guided into a composite waveguide device for focusing and delay compensation to form a high-energy composite wave beam; a pulse interval is adjusted by adopting an intermittent emission strategy so as to inhibit energy attenuation in the soil; reflected wave spectrum components are monitored through an embedded acoustic sensor array, and driving parameters are dynamically adjusted; and residual ultrasonic energy is captured and fed back to a driving circuit to form a closed-loop energy utilization system. By integrating multi-order harmonic resonance and a dynamic frequency tuning technology, efficient transmission and deep penetration of ultrasonic energy in a complex medium are realized.

The invention relates to the technical field of oil and gas exploitation, and discloses an oil and gas well water production position positioning method, device and equipment based on distributed optical fiber sound waves and a medium, and acoustic signals corresponding to a plurality of production profiles can be collected through a distributed acoustic sensor arranged in an oil and gas shaft. And performing preprocessing, time-frequency conversion and normalization on the acoustic signal corresponding to each production profile to obtain a normalized acoustic spectrum corresponding to each production profile. And extracting a characteristic wave crest section from the normalized acoustic spectrum corresponding to each production profile. And carrying out reverse normalization on the characteristic wave crest segment corresponding to each production profile to obtain a reverse normalization wave crest segment. And based on the water phase response frequency band, determining a matched first wave crest section in the anti-normalized wave crest section corresponding to each production profile, and determining the production profile corresponding to each first wave crest section as a water production position. According to the invention, intelligent detection of the water production position of the oil and gas well can be realized, and the detection precision of the water production position is effectively improved.

The invention discloses a fan blade fault diagnosis method based on voiceprint recognition, and the method comprises the steps: collecting a voiceprint signal of a fan blade through an acoustic sensor, carrying out the marking of the voiceprint signal, carrying out the preprocessing of the voiceprint signal, and carrying out the processing of the preprocessed voiceprint signal, and obtaining a Mel time-frequency spectrogram; constructing a hybrid model used for identifying fan blade faults, and training and testing the hybrid model; and finally, fan blade fault detection is carried out by using the hybrid model which is tested to be qualified. The method has the advantages of real-time monitoring, high accuracy and the like, and provides powerful guarantee for stable operation of the wind turbine generator.

A non-intrusive sensing system for monitoring an electrochemical device and a method of operating the non-intrusive sensing system can include multi-static ultrasonic sensors for detecting data indicative of a property of electrolytic media in an electrochemical device, an acoustic sensor for detecting and measuring a signature of electrodes associated with a health condition of the electrochemical device. A temperature sensor can be used to detect surface temperature data and correlate the surface temperature data with the signature identified and extracted by the acoustic sensor and the data indicative of the property of the electrolytic media. The data detected by the multi-static ultrasonic sensors, the signature detected by the acoustic sensor, and the surface temperature data identified can be subject to feature extraction and processing by a detection and prediction model to produce information pertaining to the safety, reliability and operating efficiency of the electrochemical device.

The invention provides an agricultural scene adaptive multi-modal feature extraction and fusion method and system, and belongs to the technical field of agricultural monitoring, and the method comprises the following steps: obtaining multi-modal data of crops through deploying an image sensor, a soil sensor, an environment sensor and an acoustic sensor in a farmland monitoring area; preprocessing the multi-modal data, wherein preprocessing comprises the steps of performing normalization and noise filtering on the data; visual feature vectors are extracted from the image data through an improved ResNet-50 network, time sequence features are extracted from the environment data through 1D-CNN, and acoustic data are input into a lightweight MobileNetV3 network to extract voiceprint features after being subjected to Mel spectrum conversion; calculating a modal weight based on a feature fusion algorithm of an attention mechanism; and outputting the fusion feature vector, inputting the fusion feature vector into a pest classifier and a growth state regression device, and generating a pest prediction result. Through multi-modal data acquisition, preprocessing, feature extraction and adaptive fusion, the modal weight is dynamically adjusted in combination with an attention mechanism, the multi-source heterogeneous data fusion effectiveness is improved, the disease and pest feature sensitivity is enhanced, and the effect of dynamic weight distribution is achieved.