416 results about "Mems sensors" patented technology

The invention relates to the technical field of engineering mechanical structure health monitoring, and discloses a carry-scraper structural member crack real-time detection system and method based on vibration characteristics, and the carry-scraper structural member crack real-time detection system based on vibration characteristics comprises a sensing acquisition module which is used for acquiring structural member vibration response and obtaining an original signal; the signal processing module is used for carrying out wavelet denoising on the original signal and extracting effective vibration data; the feature extraction and dynamic calibration module is used for extracting multi-domain features and performing dynamic calibration in combination with working conditions; the crack recognition and intelligent diagnosis module is used for inputting an intelligent model to recognize a crack state; and the crack positioning and alarm feedback module is used for positioning cracks and giving an alarm in combination with the diagnosis result. According to the invention, the multi-modal sensor array is constructed, piezoelectric and MEMS sensors are cooperatively arranged, a magnetic snap-in type installation structure is combined, broadband response signals are stably collected under different working conditions, and then front-end signal optimization is completed in cooperation with a multi-scale wavelet denoising and standardization mechanism.

The invention relates to a high-precision data acquisition method and system for an MEMS sensor, and the method comprises the following steps: carrying out the multi-channel parallel sampling of an initial electric signal outputted by the MEMS sensor, and obtaining an original data flow matrix; performing adaptive denoising processing on the original data stream matrix to obtain a denoised data set; performing signal phase reconstruction on the noise reduction data set through a phase unwrapping technology to obtain a continuous phase feature sequence; performing dynamic drift compensation on the continuous phase feature sequence to obtain a calibration signal vector; performing transient characteristic extraction on the calibration signal vector to obtain a sensor response characteristic curve; and performing high-precision data resampling on the sensor response characteristic curve based on an adaptive quantization coding technology to obtain a high-precision data acquisition result, thereby solving the technical problem of how to effectively remove noise interference generated in a multi-channel parallel sampling process.

The invention provides a low-cost and high-integration meteorological detection system for low-altitude meteorological service, and belongs to the technical field of meteorological detection. The low-cost and high-integration-level meteorological detection system for the low-altitude meteorological service comprises a plurality of small meteorological stations and MEMS sensors integrated with temperature, humidity, air pressure, wind speed, wind direction and the like. A plurality of small laser wind finding radars are based on an optical fiber laser and a VCSEL (Vertical Cavity Surface Emitting Laser) technology and adopt a modular design. And the data fusion module is used for fusing multi-source data of a ground station, a laser radar, a satellite and the like by adopting a four-dimensional variation assimilation algorithm to generate a 100-meter-resolution grid meteorological product. The technical bottleneck of traditional low-altitude meteorological detection is broken through, and a meteorological service solution is provided for the fields of urban fine management, navigation safety, intelligent agriculture and the like. The system has the characteristics of high cost performance, high flexibility and high resolution, and promotes low-altitude meteorological observation to be upgraded from sparse monitoring to global sensing.

The invention discloses a multi-mode water-air-land cross-medium cooperative sensing system and method based on an MEMS (Micro Electro Mechanical System). The system comprises an underwater robot, a buoy station, an unmanned aerial vehicle and a ground control station, each node is provided with an MEMS sensor integrating acoustics, optics, magnetism, inertia and other modes, and data transmission and relay are achieved through acoustics, optical communication and radio links. And the ground control station carries out fusion processing, target identification and decision control on multi-source data through time synchronization and space registration, and issues a cooperative instruction to each node to realize a perception-decision-re-perception closed loop. The method comprises five steps of node deployment and calibration, multi-modal data acquisition, hierarchical transmission and sharing, adaptive weighted fusion and closed-loop cooperative control. According to the invention, global coverage, high-precision and multi-mode environment and target perception can be provided in a complex water-air-land environment, and the detection reliability and the communication efficiency are significantly improved.

The invention discloses an optical terminal intensity instrument and an earthquake monitoring service system and method, and the optical terminal intensity instrument comprises an optical fiber modem and an MEMS sensor module. The optical fiber modem comprises an MCU (Microprogrammed Control Unit) and an optical communication module electrically connected with the MCU; the optical communication module is used for realizing network access and data uploading; the MEMS sensor module is electrically connected with the MCU; the MEMS sensor module is used for collecting seismic intensity data in real time; and the optical fiber modem is used for providing a dual data transmission channel comprising broadband communication and the seismic intensity data. According to the technical scheme provided by the invention, the MEMS sensor is embedded in the optical fiber modem, and a more dense, more efficient and more economical solution is provided for earthquake early warning and intensity rapid reporting.

Microelectromechanical systems (MEMS) apparatuses and processes are described that can employ a spring pillar or flexible pillar coupled to a sensing membrane to enhance deformation of the sensing membrane while providing robust MEMS sensors or devices. Described MEMS sensors or devices can comprise an exemplary spring pillar or flexible pillar between the sensing membrane structure and the backplate structure. Exemplary spring pillar or flexible pillar can facilitate adjusting stiffness of the sensing membrane to provide MEMS sensors or devices having large sensing area and compact device size.

The invention relates to the technical field of energy storage cabinet safety monitoring, in particular to an energy storage cabinet multi-parameter intelligent monitoring device based on an MEMS sensor array, a sensor array unit in the energy storage cabinet multi-parameter intelligent monitoring device comprises distributed MEMS temperature and vibration sensors, the temperature sensors are installed in a non-vertical mode, detection deviation caused by direct blowing of airflow is avoided, and the energy storage cabinet multi-parameter intelligent monitoring device based on the MEMS sensor array is obtained. The airflow disturbance correction unit corrects temperature original data in real time and eliminates interference of airflow on module body temperature detection by means of a heat dissipation airflow vector field dynamic model and a compensation algorithm, and the multi-parameter coupling analysis unit processes vibration and temperature signals in a double-channel mode, extracts tab loosening high-frequency micro-vibration characteristics and correlates the temperature rising rate. Early fault logic is triggered through two-stage judgment, the fault decision and output unit fuses the correction temperature, the coupling strength coefficient and the speed-up ratio, graded early warning is generated through a fault tree model and transmitted to an external monitoring system, and accurate monitoring of early faults of the energy storage cabinet is achieved.

The invention discloses a deep learning correction system for an MEMS sensor, and relates to the technical field of sensor correction, and the system comprises a multi-source data collection module which collects various data in real time, verifies and caches the data, and transmits the data; the feature extraction and analysis module processes the data and extracts features, and transmits the features to the drift detection modeling and motion impact discrimination module; modeling, calculating, monitoring sudden change and synchronizing information; the event type output result is judged; the correction decision execution module executes correction accordingly, and is internally provided with self-checking and fine tuning functions to guarantee the stability of the system; according to the invention, technologies of multi-source data acquisition, multi-dimensional feature analysis, coupling dynamic regression, deep convolutional neural network and the like are fused, so that comprehensive sensing and accurate drift detection of the sensor are realized; and through collaborative operation of an attention fusion joint discrimination algorithm and the like, event types are accurately distinguished and targeted correction is performed, the system stability is maintained, and the measurement precision and the practical value are improved.

The invention provides an MEMS inertial three-dimensional displacement monitoring method and system, and relates to the technical field of MEMS sensors, and the method comprises the steps: obtaining acceleration and angular velocity signals of an inertial measurement unit, and generating a dynamic acceleration after attitude calculation, error compensation and gravity removal; updating a group of state variables containing position and speed information in real time through integral operation so as to obtain continuous first displacement information with drifting; under a preset condition, measuring the absolute position of the object by using an external acoustic positioning system so as to obtain accurate and drifting-free second displacement information; the state variable is corrected by a fusion process using a deviation between the first and second displacement information, and position information is extracted from the corrected state variable as a final output. The advantages of inertial measurement and external positioning are combined, integral drift is effectively inhibited, and the precision and reliability of long-term monitoring are remarkably improved.

The invention relates to an inertial navigation device based on multiple MEMS sensors. Comprising an MEMS sensor array and a control unit, wherein the MEMS sensor array comprises n MEMS sensors with a set layout, and n is greater than or equal to 2; each MEMS sensor is connected to the control unit through a control switch; the output end of each MEMS sensor is connected to the control unit; the control unit is configured to be used for collecting initial measurement data of each MEMS sensor, selecting a plurality of better target MEMS sensors from the MEMS sensor array, and correcting and fusing the initial measurement data of the target MEMS sensors to obtain inertial measurement data; and outputting a switch control signal to the control switch so as to open or close the MEMS sensor to output initial measurement data. According to the technical scheme, the initial measurement data of the better MEMS sensor is used for obtaining the inertial measurement data, so that the measurement precision is improved, and the use effect of inertial navigation is improved.

The invention belongs to the technical field of power equipment monitoring, and discloses a quantum sensing and MEMS sensing integrated partial discharge detection device and method. Partial discharge signals are collected through the quantum sensor and the electric field sensor; the data acquisition card transmits data to the microprocessor, the microprocessor judges the discharge type by using a wavelet transform method, extracts characteristic parameters, transmits the data to the computer, analyzes and determines calculation weights of different sensors according to the characteristic parameters to perform data fusion, and calculates and displays the partial discharge electric field intensity. According to the method, data fusion of the quantum sensor and the MEMS sensor is realized, more comprehensive and accurate information is provided, the source and characteristics of partial discharge can be better identified, the diagnosis accuracy and the reliability of a detection system are improved, false alarm and missing alarm are reduced, and reliable data are provided for the operation state, fault diagnosis and maintenance of power equipment.

An assembly is provided which includes a package and a printed circuit board. The package includes a housing bounding a region and an acoustic sensor within the region. The housing includes a base with a first hole. The sensor is configured to generate signals indicative of sound received by the sensor through the first hole. The printed circuit board is in mechanical communication with the base and includes a second hole aligned with the first hole such that sound received by the second hole propagates through the first hole to the sensor. The printed circuit board further includes an electrically conductive layer, at least a portion of which extends across the second hole and is configured to allow the sound to propagate through the second hole and to at least partially shield the region containing the sensor from electromagnetic interference.

The invention discloses an outdoor static measurement MEMS sensor sensitivity calibration method, and relates to the technical field of sensor calibration, and the method comprises the specific steps: firstly constructing dual references including a factory reference and a field relation; synchronously collecting multi-source data according to a set period, and preprocessing to obtain an effective sequence; then double check chain fitting key indexes are started in parallel; if a collaborative judgment result is conflicted, high-grade diagnosis is triggered; after diagnosis, the acquisition period of sensitivity drift is prolonged, and parameters are recalculated; and finally, if the conditions are met, solidifying calibration parameters, installing a fault output alarm, and uploading whole-process data records to the cloud. According to the method, the sensitivity calibration of the sensor is guaranteed by constructing double references, multi-source data are synchronously acquired, and the performance is evaluated through double check chains. The efficiency is improved through round-robin scheduling of time slices, effective data are reserved through data preprocessing, differential processing is carried out after fault diagnosis, whole-process data recording and uploading are carried out, calibration parameters are solidified, it is guaranteed that the result is effective, and the application range is widened.

The invention belongs to the technical field of MEMS sensor signal processing, and discloses an MEMS sensor signal processing method and system based on an RLS-VMD fusion algorithm, and the method comprises the steps: continuously collecting an original acceleration signal at a preset sampling frequency through an MEMS sensor; offset in the original acceleration signal is eliminated through iterative calculation based on an RLS algorithm, and an offset correction signal and a residual error are obtained; a dynamic energy threshold value is obtained based on residual calculation, and frequency characteristic secondary verification is carried out on each screened modal component; carrying out superposition reconstruction to obtain an acceleration signal; and taking the purified acceleration signal as input, performing two times of integral operation based on the Newton's law of motion to realize displacement calculation, and finally outputting a displacement signal. According to the method, a correlation mechanism of RLS offset correction and VMD screening parameters is established, deep collaboration is achieved, and finally the acceleration signal processing quality and the displacement calculation precision of the MEMS sensor are improved.

A manometric catheter probe includes a flexible printed circuit, one or more pressure sensor assemblies coupled to the flexible printed circuit along a length of the flexible printed circuit, and a first flexible sleeve. Each pressure sensor assembly includes a body. The body includes a central cavity configured to receive the flexible printed circuit and an annular recess of the body. Each pressure sensor assembly further includes a microelectromechanical systems (MEMS) sensor disposed in the annular recess, an electrical connector configured to electrically couple the MEMS sensor and the flexible printed circuit, and a first flexible sleeve disposed over the annular recess of the body. The first flexible sleeve includes a fluid configured to communicate pressure to the MEMS sensor, and a second sleeve is disposed over the pressure sensor assembly.

The invention relates to the technical field of gas parameter extraction, in particular to a gas physical property parameter extraction method based on micro thermal field dynamic characteristics, which comprises the following steps: applying a sweep frequency excitation electric signal to a micro heater of an MEMS sensor, collecting and processing an original dynamic response signal, and generating frequency domain response data; constructing a parameterized extraction model, extracting real-time physical property parameters from the frequency domain response data, substituting the real-time physical property parameters into a signal-to-noise ratio objective function, and calculating and generating optimized excitation parameters; generating an updated sweep frequency excitation electric signal based on the optimized excitation parameter; acquiring and generating reference frequency domain response data; actually measured physical property parameters are extracted from the reference frequency domain response data, a measurement deviation signal is generated through comparison, and online calibration and correction are conducted on internal coefficients of the parameterized extraction model. According to the method, the frequency domain response of the dynamic micro thermal field is subjected to decoupling analysis, and an online self-adaption and self-correction mechanism is combined, so that accurate and reliable online extraction of the physical property parameters of the complex mixed gas is realized.

The invention belongs to the field of deep foundation pit intelligent supporting systems, and particularly relates to a deep foundation pit intelligent supporting system based on oblique beam grid and digital twinning and a collaborative construction method.The deep foundation pit intelligent supporting system based on oblique beam grid and digital twinning comprises an oblique beam grid supporting structure composed of two-way crossed steel oblique beams, and the outer layer of each steel oblique beam is a Q355B steel pipe with the thickness not smaller than 12 mm; the inclined beam is filled with ultra-high performance concrete with the compressive strength not smaller than 150 MPa, the inclination angle of the inclined beam ranges from 25 degrees to 60 degrees, and an MEMS micro-electro-mechanical system sensor is pre-buried at a node; and the digital twin platform integrates the geological BIM model and the LSTM neural network prediction model. The bending rigidity is improved through the oblique beam grid supporting structure and the concrete filled steel tube composite beam, the inclination angle ranges from 25 degrees to 60 degrees, force flow transmission is optimized, and micro-strain-level stress monitoring is achieved through the MEMS sensors embedded in the nodes. A digital twin platform and an LSTM model are fused with 12-dimensional parameters, the 72-hour deformation prediction error is smaller than or equal to 1.5 mm, and compared with a traditional experience algorithm, the precision is improved.

The invention discloses an earphone space audio positioning method integrated with an MEMS sensor and a control system, and relates to the field of earphone space audio positioning. A temperature compensation MEMS sensor group and a microphone array are used for collecting motion data and sound wave signals, and signals are enhanced through orthogonal wavelet denoising and beam forming; the attitude is solved by adopting double-extended Kalman filtering and fusing data, and fuzzy logic is introduced to evaluate the credibility of the sensor; predicting a sound field by using a CNN model, and reconstructing the sound field in combination with an adaptive wave field synthesis technology; and based on auditory masking effect optimization balance, the influence of altitude on sound velocity is compensated. Through an ultra-precision sensor and an advanced algorithm, the positioning precision and reliability of earphone space audio are improved, and delay and power consumption are reduced; accurate sound source positioning, personalized sound field adaptation and cross-modal immersion experience in a complex scene are realized, and the audio experience of a user is improved.

The invention discloses a three-dimensional notation coding method and system based on spatio-temporal data fusion, and belongs to the technical field of data fusion, and the method comprises the steps: collecting geographic coordinates and environment voiceprints through Beidou, GPS positioning and COMSOL acoustic modeling, and constructing a sound-space correlation model; through OpenPose and MEMS sensors, limb motions, vocal organ motions and foot stamping intensity parameters are quantified, digital conversion of body memory is completed, and a three-dimensional music score visualization framework is constructed through AR and VR systems; the AR system uses the scanning mark as an interaction entrance, body movement guidance of the Shaanbei folk song and a stage movement design visual interface are matched through a space positioning technology, and dynamic association of a performer and a virtual scene is matched; the VR system generates personalized teaching feedback based on the motion capture data, and optimizes a learning path through real-time data analysis; and the virtual human model inputs action parameters of a three-dimensional notation method to realize automatic performance of non-peoples and folk songs.

A MEMS sensor may include multiple sense electrodes located relative to respective portions of one or more proof masses of a MEMS layer of the sensor. Individual sense electrodes are capable of individual calibration within the drive and / or sense path for the sense electrode. A distance between each individual sense electrode relative to a proof mass is determined for the at-rest state of the sensor. Calibration values are determined based on these distances, and individual drive and / or sense signals associated with each sense electrode are modified to adjust for changes in distance, such as are caused by shifting, tilting, or bending of the MEMS layer or substrate.

Apparatus and methods for equalizing microelectromechanical systems (MEMS) sensors are disclosed. In certain embodiments, measured sensor parameters of a MEMS sensor are stored in a non-volatile memory (NVM) of a sensor signal processor used to process a sensor output signal of the MEMS sensor. The measured sensor parameters are retrieved by a digital signal processor (DSP) and used for equalizing sensor data provided to the DSP by the sensor signal processor during operation. The measured sensor parameters can be determined at test, per individual part, by measurements of the MEMS sensor's characteristics, and thus equalize the MEMS sensor while accounting for manufacturing and / or processing variations.

The invention relates to the field of semiconductor MEMS (Micro Electro Mechanical System) devices, and discloses a medium-high-entropy alloy air suction target material which is composed of a main element A, an element C and an element B. The content of the main element A is 25-40 at%, the content of the element B is 30-45 at%, and the content of the element C is 30-45 at%; the formula equation is as follows: A < 25-40 > B < 30-45 > C < 30-45 >; the main element A is selected from one or two of V and Co; the element C is selected from one or more of Fe, Pr, Y, Gd, Nd, Er, Sm and Ce. And the element B is selected from one or two of Ti and Zr. According to the invention, the problem of serious contradiction between the air suction performance and the mechanical property of the current semiconductor MEMS sensor packaging material is solved by utilizing the advantages of rich or multi-principal-element alloy, high mixing entropy and the like in the medium-high-entropy alloy.

The invention provides a pressure and temperature cooperative measurement method for a piezoresistive MEMS pressure sensor, and the method comprises the following steps: S1, switching a resistor connection mode of an MEMS header of the piezoresistive MEMS pressure sensor through an MOS switch, and obtaining pressure and temperature signals in a time-sharing manner; s2, eliminating the influence of pressure on temperature measurement by using the equivalent resistance Req; and S3, based on the pre-stored temperature-pressure characteristic curve, compensating the temperature drift error in real time through a digital algorithm. According to the invention, cooperative measurement of pressure and temperature is realized by using a piezoresistive MEMS sensor gauge head piezoresistor, and calibration and compensation of temperature and pressure are realized through a digital algorithm; the design difficulty and process requirements of the piezoresistive temperature and pressure integrated sensor chip on the piezoresistive MEMS sensor header and the signal conditioning circuit are reduced, the measurement precision of temperature and pressure is improved, and the development and manufacturing cost of the piezoresistive temperature and pressure integrated sensor chip is reduced.

The utility model discloses a tunable laser light source for spectrum sensing, which comprises a tunable DFB laser array chip, a star coupler chip, an SOA array chip and an optical fiber array which are connected through waveguides, tunable lasers in each row are connected through waveguides, the lasers with adjacent wavelengths are distributed on different waveguides, and the optical fiber array is connected with the star coupler chip. The light output end of each waveguide is provided with an SOA. According to the utility model, the star-shaped coupler is adopted to uniformly distribute energy of light with different wavelengths of the tunable laser array to each light output waveguide, and the energy respectively enters different fiber grating sensors or fiber MEMS sensors and the like to realize temperature or strain sensing at the same time and realize multiplexing tree-shaped sensing and demodulation at the same time; the energy loss in the transmission process is reduced; the chips of different material systems are coupled by adopting a photon lead bonding or butt joint growth technology, the photon lead bonding technology does not need high-precision alignment, the processing efficiency is high, low-cost hybrid integration among the chips is realized, and the device is integrally compact.

The invention relates to the technical field of MEMS sensor calibration, and discloses a humidity drift correction method and system of an MEMS sensor. The method comprises the steps of tracking and identifying an external humidity source, extracting features of the external humidity source, and establishing a time-varying model to predict a humidity drift value; meanwhile, the internal running state of the sensor is monitored to obtain internal characteristic parameters. And fusing the predicted value with the internal parameter, constructing a drift influence quantity and generating an initial compensation quantity. And then, a sensor output feedback sequence is introduced to carry out dynamic iterative optimization on the initial compensation amount, an optimized drift compensation control sequence is generated, and finally the drift compensation control sequence acts on sensor output to complete correction. According to the method, through feature fusion modeling of the external environment and the internal state and a closed-loop dynamic optimization mechanism based on output feedback, the humidity drift correction precision and the adaptive capacity to the time-varying environment are improved.

Fully symmetric sensing structures for MEMS devices are disclosed herein. In certain embodiments, a MEMS sensor includes a proof mass that moves in a first direction. The proof mass includes moveable fingers that move with the proof mass. The MEMS sensor further includes fixed fingers that are fixed with respect to the moveable fingers, and the fixed fingers and moveable fingers serve to detect movement of the proof mass. For example, the moveable fingers and the fixed fingers can be interdigitated to form a comb finger set for sensing changes in capacitance arising from movement of the proof mass relative to a substrate. A layout of the fixed fingers is fully symmetric in at least the first direction.

The present application proposes a design method for a multi-stage adjustable MEMS sensor micro-signal amplification circuit, which belongs to the field of circuit design technology. The method includes: obtaining the design parameter range for the MEMS sensor signal amplification circuit design, and obtaining the micro-signal characteristic information to be amplified; randomly generating a first design parameter within the design parameter range, and obtaining multi-stage amplification parameters and multiple electronic noise parameters under the first design parameter; performing an amplification applicability analysis to obtain an amplification applicability parameter; performing an amplification quality analysis to obtain multiple amplification quality parameters, combining the amplification applicability parameters, calculating and obtaining a design score, and optimizing the design parameters to obtain the optimal design parameters as the design result. By comprehensively considering the micro-signal characteristic information and electronic noise parameters for optimization design, the amplification effect and applicability of the MEMS sensor micro-signal amplification circuit are improved.

Disclosed herein is a MEMS sensor package that includes a substrate, an annular-shaped first dry film pattern stuck to one surface of the substrate, and a MEMS sensor chip including a tubular support and a detection part which is supported on the support so as to overlap a cavity of the support. The MEMS sensor chip is fixed to the substrate by sticking an annular mounting surface of the support to the first dry film pattern.

The application discloses a kind of based on unstable roadway excavation blasting safety protection method, belong to the field of mine engineering and underground construction safety technology.The steps of the present application are:S1: multiple point strain gauge, MEMS sensor and fiber bragg grating sensor are arranged in the roadway, and strain value, stress value and bragg wavelength change data are collected in real time;S2: the data collected is cleaned, denoised and normalized;S3: use LSTM network to predict future strain value, stress value and bragg wavelength change;S4: use random forest model to classify LSTM prediction results and current monitoring data, and evaluate rock mass stability risk level;S5: according to the risk level output by the random forest model, the system automatically issues an early warning, and prompts to take corresponding physical protection measures.The present application significantly improves the safety and monitoring efficiency in the process of roadway excavation, reduces the hidden danger, and protects the safety of workers and equipment.