673 results about "Piezoelectric sensor" patented technology

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

The invention, which relates to the technical field of traffic management, discloses an intelligent detection system for a vehicle parking space in a passenger and goods mail fusion station, and the system comprises a multi-mode sensing module which is configured to collect vehicle data information, the collection mode comprises the steps that vehicle data information is collected through an enhanced geomagnetic array, an adjustable video monitoring set, a lateral laser radar and a piezoelectric sensor; and the edge computing node module is configured to integrate an AI chip, the AI chip comprises a vehicle classification model, an abnormal parking detection algorithm and a data fusion engine, and the collected data is computed and analyzed. The edge computing node is integrated with an AI chip, and data are analyzed and processed through a built-in algorithm; the hybrid communication network transmits data through an optical fiber backbone network and a specific redundant link, self-adaptive visual adjustment and intelligent algorithm optimization are performed, the problem of complex vehicle type detection in a passenger and goods mail fusion station is solved, and the parking space utilization rate and the operation efficiency are remarkably improved.

The invention discloses a sleep state real-time monitoring method and system, and belongs to the technical field of sleep monitoring, and the method specifically comprises the steps: collecting a heart rate variability signal, an electroencephalogram signal and body movement data of a user in real time through a non-invasive sleep pad integrating a piezoelectric sensor, a flexible dry electrode and a pressure sensor; based on the physiological data, whether the user reaches an autonomous sleep state or not is judged through a first algorithm model; if not, starting an active intervention program for playing the adjustable music, and dynamically adjusting the music volume, the playing speed or the track in combination with the physiological data feedback until the user enters an autonomous sleep state; after the user falls asleep, physiological data are continuously collected, and a sober period, a light sleep period, a deep sleep period and a rapid eye movement period are divided through a second algorithm model; if the staging result is a waking period, the autonomous sleep state judgment is executed again; according to the invention, non-intrusive monitoring and personalized intervention are combined, and the monitoring comfort and accuracy are improved.

The invention discloses a rotating machine vibration protection method and system of an adaptive resonance neural network, and the method comprises the steps: collecting a millisecond vibration acceleration signal of a rotating machine through a distributed piezoelectric sensor array, obtaining the real-time working condition parameter of equipment, and generating a time-work synchronous vibration signal flow; inputting the time-work synchronous vibration signal flow into a resonance neural network, and dynamically generating a frequency domain-time varying adaptive threshold group in combination with the working condition parameters; extracting a weak impact resonance characteristic tensor by using a multi-scale resonance kernel of the resonance neural network; projecting a weak impact resonance characteristic tensor to an orthogonal fault subspace through a tensor decomposition algorithm, and separating out a decoupling fault characteristic matrix; and generating a real-time protection decision instruction set based on a comparison result of the decoupling fault feature matrix and the equipment operation historical database. According to the embodiment of the invention, the method can improve the detection sensitivity and positioning precision of a weak fault under an unsteady working condition, and achieves the real-time adaptive optimization of the vibration protection of the rotating machine.

The invention provides a blasting engineering site safety risk real-time assessment and intelligent scheduling method. According to the method, a piezoelectric sensor array is arranged on a blasting engineering site to collect sound wave signals, and dynamic change information of fractures in a rock mass is captured; calculating sound wave propagation time difference data between adjacent nodes, and determining a new fracture space position and an existing fracture expansion trend according to time difference fluctuation characteristics; the fracture position, the expansion trend and the label of the current blasting operation stage are input into a fuzzy logic decision-making system, and a fracture risk distribution diagram reflecting the real-time stability of the rock mass is generated through collaborative analysis; determining a field safety risk level based on the graph, and quantifying the subsequent instability possibility of the rock mass; and a scheduling instruction is generated according to the risk level, the drilling position, the charging sequence and the detonation time sequence are dynamically adjusted, the operation process is optimized, and the instability risk is avoided. According to the method, the dynamic sensing precision and response speed of the blasting engineering field rock mass instability risk are improved.

The invention relates to the technical field of deep learning, in particular to a dialysis hypotension risk prediction system and method based on deep learning, and the system comprises a multi-point signal collection module, a signal processing module, a multi-scale causal expansion convolutional network prediction module, an osmotic pressure correction module and a dialysis plan automatic adjustment module. The method comprises the following steps: synchronously collecting physiological signals of a patient through a finger tip photoelectric volume sensor and a radial artery piezoelectric sensor array, carrying out band-pass filtering and morphological refinement processing on the signals, extracting features, and calculating pulse wave conduction time and a change rate thereof; inputting the characteristic parameters into a multi-scale causal expansion convolutional network to predict the hypotension risk in dialysis; dynamically correcting the prediction result according to electrolyte monitoring data; based on the corrected risk, a hierarchical response strategy is adopted to automatically adjust dialysis parameters, a multi-scale causal expansion convolutional network is adopted to capture physiological parameter changes on different time scales at the same time, and high-precision prediction of hypotension is achieved.

Systems and methods include a sensor device for detecting passing valves. The sensor device includes a housing including a first side configured to contact a wall of a pipe in a pipe system. A cover plate is coupled to a second side of the housing opposite the first side. A piezoelectric sensor is disposed in the housing and configured to detect acoustic emissions from a valve in the pipe system. A sensor holder is disposed within the housing to maintain a position of the piezoelectric sensor. A spring is positioned around a portion of the sensor holder and configured to bias the sensor holder away from the cover plate and toward the first side of the housing. A computer system is mounted to the housing and configured to determine that a valve is a passing valve based on acoustic emissions detected by the piezoelectric sensor.

The invention discloses a micro-seismic monitoring piezoelectric acceleration sensor optimization method based on an NSGA-II algorithm, provides a piezoelectric sensor based on a triangular shear structure, and relates to the technical field of piezoelectric acceleration sensors. According to the micro-seismic monitoring piezoelectric acceleration sensor optimization method based on the NSGA-II algorithm, the NSGA-II genetic algorithm is adopted to optimize the structural size of the designed sensor, and an optimal structural parameter combination is obtained, that is, when the height of a mass block is 10.6 mm, the thickness of a piezoelectric patch is 3.29 mm, the height of the piezoelectric patch is 8.1 mm and the height of a central column is 19 mm, the sensor design is optimal, and the optimal structural parameter combination is obtained. The working frequency of the optimized sensor is improved by 2.68%, and the voltage sensitivity is improved by 8.96%. And finally, respectively carrying out a calibration experiment and a micro-seismic signal test on the designed sensor by building an experiment platform. Experimental results show that the working frequency range of the designed sensor is 0.1-2123 Hz, and the voltage sensitivity is 173.49 mv / g.

The invention discloses a fuzzy theory fused tongue diagnosis characteristic quantitative analysis method and system, and relates to the technical field of traditional Chinese medicine tongue diagnos.The method comprises the steps that a micro piezoelectric sensor array and a hyperspectral imaging module are integrated, tongue surface Young modulus distribution data and hyperspectral reflectivity data are synchronously collected, and a multi-mode tongue picture fusion data set is generated; further performing crack region segmentation and super-resolution reconstruction through a neural network model, outputting a crack network diagram and a tongue papilla form entropy, generating topological fingerprints in combination with continuous homology analysis, identifying a mechanical gradient mutation region and extracting a metabolic index, constructing a mechanical-metabolic associated feature set, and obtaining a mechanical-metabolic associated feature set; topological fingerprints and mechanical-metabolic characteristics are fused in a three-dimensional space-time coordinate system, a dynamic intuitionistic fuzzy decision field is constructed, finally, tongue picture pathology evolution risk indexes and a visual early warning report are output through a pathology prediction model, and the system provides quantifiable decision support for'prevention before illness' in traditional Chinese medicine.

The utility model belongs to the technical field of structural health monitoring, and particularly relates to a clamp for a piezoelectric sensor bonding process. The clamp is characterized in that a force application rod mounting part is arranged on a fixed bracket; one end of the telescopic support frame is connected with the fixed bracket, and the other end of the telescopic support frame is connected with a structural member of a piezoelectric sensor to be bonded through a fixed sucker; the force application rod is mounted on the force application rod mounting part, a force application handle is mounted at one end of the force application rod, and a force sensor is mounted at the other end of the force application rod; the piezoelectric sensor bonding head is mounted on the force sensor, and a piezoelectric sensor is bonded in the piezoelectric sensor bonding head; the sensor output display device is used for displaying and recording measurement data of the force sensor. According to the invention, standardization and uniformity of the installation technology can be realized, difference caused by manual bonding is eliminated, and the bonding performance of the sensor is improved.

The invention discloses a photovoltaic panel disease detection method based on multi-source spectrum feature fusion. The method comprises the following steps: constructing a multi-source spectral data collaborative acquisition system, acquiring spectral characteristics of a 400-2500nm wave band through a hyperspectral imaging module, acquiring a dynamic response spectrum of a 0.1-10kHz frequency band by adopting a distributed piezoelectric sensor array, capturing thermodynamic spectrum characteristics in combination with a thermal infrared imager, and synchronously acquiring IV curve electrical parameters; establishing a cross-spectral domain feature fusion model, extracting multi-scale depth features by using an improved ResNet-50 network, dynamically allocating weights of a spectrum, a dynamic spectrum and a thermal spectrum through an attention mechanism, and constructing a multi-modal association topology in combination with a graph neural network; and a multi-label classifier based on XGBoost is designed. The method breaks through the limitation of a single detection mode, the recognition accuracy of the subfissure disease reaches 96.2%, unmanned aerial vehicle carrying is supported to achieve full-field rapid inspection of the photovoltaic power station, and the detection efficiency is improved by more than 5 times compared with a traditional method.

Sensor modules mountable on a wheel are described with methods of use. A sensor module for a wheel includes a base module mountable to a rim. The sensor module further includes a first set of piezoelectric sensors coupled to the base module at a first side and a second set of piezoelectric sensors coupled to the base module at a second side. A method includes receiving a first force from a tire on a piezoelectric sensor mounted on a rim, the force resulting from an inflation of the tire and causing a first strain on the sensor. The method further includes receiving a second force on the sensor corresponding to a deformation of the tire due to surface contact, where the second force causes a second strain. The method also includes generating wheel data from electrical signals produced by the sensor; and transmitting the wheel data to a remote device.

The invention provides a flange bolt group damage identification method, electronic equipment and a storage medium. The method is used for carrying out damage identification on a bolt on a target flange plate. A piezoelectric driving piece or a piezoelectric sensing piece is arranged between every two adjacent bolts on the target flange plate, and the piezoelectric driving piece and the piezoelectric sensing piece are arranged on the two sides of each bolt on the target flange plate respectively. The method comprises the following steps: controlling each piezoelectric driving sheet to sequentially excite an ultrasonic guided wave signal; receiving the ultrasonic guided wave signal by using each piezoelectric sensing sheet to obtain a real-time ultrasonic guided wave signal; for each real-time ultrasonic guided wave signal, extracting a real-time dominant frequency modal signal in the signal; based on the difference between the real-time dominant frequency modal signal and the reference dominant frequency modal signal, determining the damage condition of the bolt on the target flange plate; wherein the dominant frequency modal signal comprises a preset number of modal components with the maximum energy in the modal components corresponding to the ultrasonic guided wave signal. According to the method, the damage condition of each bolt can be accurately judged.

The present invention relates to the fields of ferroelectric single crystal technology and acousto-optical device technology, particularly to a silicon-based lead magnesium niobate (PbMn) single crystal piezoelectric wafer structure, preparation method, and application. The structure comprises a silicon substrate on which a silicon dioxide layer and a single crystal piezoelectric thin film layer are sequentially disposed; the single crystal piezoelectric thin film layer is a PbMn-PT single crystal, including a binary PMN-PT single crystal thin film or a ternary PIN-PMN-PT single crystal thin film. This piezoelectric wafer structure on a silicon substrate can be used to fabricate various high-performance piezoelectric MEMS sensors, actuators, and electro-optical modulator devices. The resulting surface acoustic wave (SAW) filters and thin film bulk acoustic wave (BAW) filters exhibit a relative bandwidth increase of over 50%, and the resulting electro-optical modulators exhibit lower half-wave voltage and higher electro-optical modulation efficiency. The device volume can be reduced by 10 times, better meeting the future 6G communication requirements for higher efficiency, higher integration, and miniaturization of MEMS devices. This provides a novel multilayer piezoelectric wafer structure for the miniaturization and integration of piezoelectric MEMS and optoelectronic devices.

The invention relates to a piezoelectric-piezoresistive collaborative mechanism-based sensor array and a preparation method and application thereof. The sensor array comprises a flexible packaging substrate layer, a piezoelectric layer, a piezoelectric layer and a piezoelectric layer which are sequentially integrated from bottom to top, the bottom electrode layer is composed of a stretchable silver electrode array subjected to silk-screen printing and serves as a lower electrode of the piezoelectric sensor; the piezoelectric functional layer is composed of a lead zirconate titanate-glass fiber composite film, and the piezoelectric functional layer is subjected to corona polarization treatment; the piezoresistive sensing layer is formed by spraying a polyvinylpyrrolidone modified carbon material dispersion liquid, the piezoresistive sensing layer is used as an upper electrode of the piezoelectric sensor, the piezoresistive sensing layer is directly deposited on the surface of the piezoelectric functional layer, and the piezoresistive sensing layer and the piezoelectric functional layer pass through a common electrode; and packaging a protection layer on the top layer. Compared with the prior art, the method has the advantages of high sensitivity, wide linear range and multi-parameter synchronous detection capability, high-resolution real-time deformation identification can be realized, and key technical support is provided for high-precision sensing application such as profile inversion.

The invention belongs to the technical field of intelligent traffic, and particularly relates to a dynamic weighing analysis method, system and equipment for a high-speed driving vehicle, and the method comprises the following steps: constructing a multi-mode sensing layer: integrating a fiber grating sensor, a quartz piezoelectric sensor, a millimeter wave radar and a laser radar to form a ternary fusion sensing matrix, wherein the laser radar accurately captures the tire grounding form through three-dimensional reconstruction of the tire grounding form, extraction of a three-dimensional contour multi-modal sensing matrix of a tire contact area and combination of laser radar three-dimensional contour extraction; a space-time dynamic correction algorithm and scene exclusive correction are adopted, and the error control effect under all working conditions is greatly improved.

The invention discloses a self-adaptive vibration suppression vortex shedding flowmeter and a method. The self-adaptive vibration suppression vortex shedding flowmeter comprises a flowmeter shell, a pipe connecting flange, a vortex shedding generator and a piezoelectric sensor, the piezoelectric sensor is embedded into the damping device, the damping device restrains measurement vibration of the piezoelectric sensor, the free end of the piezoelectric sensor is connected with a detection probe, and the detection probe prevents a medium in a flow field from making contact with the root of the detection probe through a joint ring; an inner cavity is formed in the vortex street generating body, and a temperature sensor is integrated in the inner cavity to sense the temperature of fluid flowing through the vortex street generating body in real time; according to the invention, the temperature sensor is integrated in the vortex street generator, so that accurate sensing and real-time compensation of the temperature of a core flow field are realized; multi-stage vibration isolation is constructed through the damping device, the vibration isolation pad, the damping device protection shell and the top base at the same time, and vibration interference is reduced. Due to the optimal design of the detection probe, the durability is improved, and the adverse effect of vibration on signals can be reduced; and the applicability in a complex environment and the reliability of long-term operation are obviously enhanced.

The invention discloses a deicing device and a deicing method for deicing an iced line. Deicing of the iced line is completed under the synergistic effect of an opposite clamping mechanism, an electromagnetic oscillation system, a direction controller and a clamping driving mechanism. The deicing efficiency and safety are improved through cooperation of electromagnetic multidirectional oscillation and intelligent control, multidirectional impact is achieved under driving of switching current of a direction controller by means of oscillation cavities and ferromagnetic small balls arranged in a left clamping plate and a right clamping plate, ice layer stress weak areas are monitored in real time in combination with a piezoelectric sensor, accurate directional deicing can be achieved, and the deicing efficiency and safety are improved. The problem of insufficient dead angle coverage is avoided; a hydraulic push rod and connecting rod structure ensures stable opening and closing of clamping and adapts to different line diameters, a multi-direction covering deicing process is simplified through a switch integrated with a handheld rod, the frequency is optimized through dynamic regulation and control to adapt to ice thickness changes, finally, deicing energy consumption is remarkably reduced, deicing time is remarkably shortened, and meanwhile the risk that an ice layer is broken and splashed is reduced.

The invention discloses a wide-temperature-range solid insulation space charge measurement method, and belongs to the technical field of electrical insulation test and dielectric physical measurement. A solid insulation sample is mounted between double-layer composite electrode modules in a closed high-low temperature test cavity, and a flexible compensation gasket is adopted to absorb thermal expansion difference; a PID closed-loop control system is utilized to realize accurate and stable control of the temperature of the sample in a wide temperature range; applying a direct-current bias voltage and pulse voltage superposition signal, and collecting an acoustic signal generated by space charge displacement through a wide-temperature-range PVDF piezoelectric sensor module; and based on a sound velocity and sensitivity temperature calibration curve established by a pre-experiment, carrying out deconvolution processing on a sound wave signal to correct a temperature dependence error, and finally obtaining accurate space charge density distribution at different temperatures. According to the invention, the precision and reliability of space charge measurement of the solid insulating material in a wide temperature range are effectively improved.

The invention relates to the technical field of grain loss monitoring, in particular to a wheat combine harvester cleaning loss monitoring device which comprises a collecting hopper, a two-way array type piezoelectric sensor, a vibration signal compensation device, a collision signal processing system and a man-machine interaction system. A plurality of outlets are formed in the bottom of the system, a plurality of groups of base boxes are arranged below the system, the bidirectional array type piezoelectric sensors and the vibration signal compensation devices are integrated in the base boxes, the collision signal processing system is composed of a signal amplification module and a TMS320F28335 core processor module, the signal amplification module amplifies electric signals output by the piezoelectric sensors and the vibration signal compensation devices, and the TMS320F28335 core processor module outputs the amplified electric signals. The core processor module carries out vibration signal compensation and collision grain recognition counting to obtain cleaning loss data, the man-machine interaction system achieves data transmission and display through the CAN communication module and the serial port display screen, and a driver can conveniently adjust operation parameters in real time.

The invention belongs to the technical field of battery fault recognition, and provides a battery early fault recognition method and system based on acoustic fingerprints, and the method comprises the steps: collecting an output signal of a piezoelectric sensor on the surface of a battery at a high-frequency sampling rate, removing background noise in a mode of combining a time domain spectral subtraction method with a statistical threshold, and dynamically updating noise spectrum estimation; performing parallel calculation on short-time Fourier transform, continuous wavelet transform and Mel-frequency cepstral coefficients to form a feature tensor; learning a low-dimensional fingerprint vector through a sparse auto-encoder, wherein a loss function comprises a reconstruction error term and a sparse constraint term; and a mahalanobis distance or a single-class support vector machine is used to measure a deviation degree between the fingerprint vector and the health fingerprint database, a fault confidence coefficient is output, and when the confidence coefficient exceeds a preset threshold value, early fault early warning is triggered. According to the method, characterization processing can be carried out on the acoustic data of the battery within milliseconds, the fault confidence coefficient is given, and high-sensitivity detection, easy migration, early warning and real-time performance are achieved.

The invention discloses a cellulose-based polyvinylidene fluoride porous composite piezoelectric material and a preparation method and application thereof, and belongs to the technical field of piezoelectric material preparation. The preparation method of the cellulose-based polyvinylidene fluoride porous composite piezoelectric material provided by the invention comprises the following steps: dissolving cellulose in a pre-cooling solvent system, and carrying out gel treatment to obtain cellulose hydrogel; performing solvent exchange on the cellulose hydrogel, and performing primary freeze drying to obtain cellulose aerogel; and soaking the cellulose aerogel in a PVDF solution, inducing PVDF regeneration treatment by using a non-solvent, and carrying out secondary freeze drying to obtain the cellulose-based polyvinylidene fluoride porous composite piezoelectric material. The composite piezoelectric material prepared by the invention can induce generation of a polyvinylidene fluoride beta crystal phase, so that the piezoelectric property of the composite material is greatly improved, meanwhile, the flexibility is greatly improved, and the composite piezoelectric material has a wide application prospect in flexible piezoelectric sensors.

The invention provides a mining motor explosion-proof housing airtightness and internal pressure fault monitoring method, and relates to the technical field of mechanical equipment monitoring, and the method comprises the steps: arranging a piezoelectric sensing array at a sealing key point of an explosion-proof housing, obtaining a vibration signal generated in a motor operation process, and converting the vibration signal into a stress distribution matrix through a piezoelectric effect; and analyzing the stress distribution matrix by using singular value decomposition, and identifying the spatial distribution of the stress abnormal region. Acoustic tracer gas is injected into the shell, acoustic response signals of the piezoelectric sensor in different frequency bands are collected, an acoustic characteristic coefficient group is extracted through wavelet transform, and the mapping relation between the characteristic coefficient group and a leakage path is established. And generating a fault probability density function by using a recurrent neural network in combination with the stress anomaly region and the time sequence change rule of the feature coefficient group, and determining the space coordinates of the leakage point. According to the invention, the real-time evaluation of the airtight state of the explosion-proof housing and the accurate positioning of the leakage point can be realized, and the reliability and accuracy of the positioning result are effectively improved.

The invention relates to the technical field of equipment perception, and discloses an equipment surface interaction and state perception system based on distributed piezoelectric sensors. The system integrates a plurality of piezoelectric sensing units, a signal processing unit, an evaluation analysis unit and a state output unit. The piezoelectric sensing units are distributed on the surface of the device and collect piezoelectric signals generated by surface interaction. The signal processing unit preprocesses the piezoelectric signals, extracts signal features and generates a signal response curve; the evaluation and analysis unit calculates the state sensitivity, the initial state correlation degree, the real state correlation degree and the environment interference degree of each piezoelectric sensing unit based on the signal response curve and the equipment state parameters, and screens key sensing positions; and the state output unit constructs a state sensing model based on the signal data of the key sensing position, and senses the surface interaction and state of the equipment in real time. The system can realize comprehensive perception of the equipment surface, improves the perception precision and efficiency, and meets the perception requirements of multi-scene equipment.

Apparatus and associated methods relate to autonomous devices configured to inspect surface topologies of airfoils of a gas turbine engine. The autonomous device moves across the airfoil while remaining coupled thereto while sensing the surface topology of the airfoil using a plurality of topological sensors. Each of the plurality of topological sensors includes a piezoelectric sensor that generates an electrical signal indicative of a loading to the piezoelectric sensor, as well as an airfoil contacting member. The airfoil contacting member extends between the piezoelectric sensor and the airfoil when the body is coupled thereto. The airfoil contacting member provides changes to the loading to the piezoelectric sensor in response to changes in the surface topology of the airfoil as the body moves thereacross.

Braking device for a vehicle, with a piezoelectric sensor (2) comprising: a piezoelectric material, two electrodes (6, 7), wherein the piezoelectric material comprises a first flat face (4) and a second flat face (5), the faces extending in parallel planes identified by two orthogonal y and z directions, wherein an electrical signal is collected by the electrodes when the piezoelectric material is simultaneously subjected to a normal force in an x-direction and to a shear force in the z-direction, wherein the first electrode is positioned on the first face and the second electrode is positioned on the second face and has extensions (7a, 7b) on the first face separated by the first electrode, wherein each of said extensions extends on a corresponding side of said first face, and wherein each of said extensions is symmetrically configured with respect to a central axis of the first face.

The invention provides a sleep physiological parameter real-time monitoring method based on flexible piezoelectric sensing, and relates to the technical field of biomedical signal processing.The method comprises the steps that a human body sleep biological mechanical signal is collected through a flexible piezoelectric sensor array, and after the signal is converted through a charge amplifier, phase-space reconstruction denoising and self-adaptive gain control are carried out; wherein the gain is dynamically adjusted based on the weight of the user, the rigidity coefficient of the mattress and the real-time signal amplitude; extracting heartbeat wave crest intervals, respiratory cycles and body movement frequency domain energy characteristics from the preprocessed signals; inputting the features into a cascaded convolutional neural network and a long-short-term memory network model, and synchronously outputting the heart rate, the respiration rate and the heart rate variability; in combination with the historical health data of the user and the cardiovascular health mapping model, sleep quality indexes and disease risk scores are generated through graph convolutional network analysis, and the monitoring frequency is dynamically adjusted and an apnea alarm is triggered.

A histotripsy therapy system configured for the treatment of brain tissue is provided, which may include any number of features. In one embodiment, the system includes an ultrasound therapy transducer, a drainage catheter, and a plurality of piezoelectric sensors disposed in the drainage catheter. The ultrasound therapy is configured to transmit ultrasound pulses into the brain to generate cavitation that liquefies a target tissue in the brain. The drainage catheter is configured to detect the ultrasound pulses. An aberration correction algorithm can be executed by the system based on the ultrasound pulses measured by the drainage catheter to automatically correct for an aberration effect caused by the ultrasound pulses passing through a skullcap of the patient.