728 results about "Mems sensors" patented technology

The invention relates to the technical field of medical equipment, in particular to an intelligent dynamic monitoring and evaluation system for altitude sickness and wearable equipment thereof, and the system comprises a physiological parameter monitoring module, an environmental parameter monitoring module, a data processing and analysis module and a user interaction and feedback module; high-precision physiological parameter monitoring is carried out by combining multi-lead ECG and PPG technologies, intelligent data processing and real-time health early warning are realized by utilizing a multi-layer deep learning neural network and a fuzzy logic system, precise environmental parameter monitoring is carried out by integrating an MEMS sensor technology, and personalized health management suggestions are provided for users.

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 relates to the technical field of non-destructive testing, and particularly discloses an aero-engine blade non-destructive flaw detection method for a miniature unmanned aerial vehicle, which comprises the following steps: acquiring stress data of a blade in a use process in real time by using an MEMS sensor, analyzing the data, and identifying a stress concentration area; carrying out real-time monitoring on crack data of the stress concentration area, extracting crack propagation fluctuation characteristics by utilizing a wavelet transform algorithm, and calculating a crack propagation fluctuation index; fatigue strength data of the material is monitored, fatigue failure characteristics are extracted, and a fatigue failure index is generated through a support vector machine algorithm; comprehensively analyzing the crack propagation fluctuation index and the fatigue failure index, and judging whether the blade is in a healthy state or not; and finally, based on the blade health state, determining whether to carry out further detection or maintenance.

The invention belongs to the field of MEMS sensors, and particularly relates to a low-temperature-drift MEMS resonant pressure sensor sensitive chip and a preparation method thereof. Comprising a cap layer, a resonance layer, a pressure sensing layer and an isolation base which are sequentially bonded and connected from top to bottom, the cap layer is provided with an electrode through hole and a vacuum cavity groove, and a getter is sputtered in the vacuum cavity groove; two Si-SiO2 composite resonators and a plurality of lead wire discs are arranged on the resonance layer, and a metal electrode is arranged on each lead wire disc; the number of the metal electrodes is equal to that of the cap layer electrode through holes, and the metal electrodes are located under the electrode through holes; two groups of silicon islands are arranged on the front surface of the pressure sensing layer, and three rectangular sensitive films with the same size are arranged on the back surface; the isolation base is a glass table; according to the invention, the output sensitivity of the sensor is greatly improved, the chip processing complexity is reduced, and the introduction of additional stress and materials is reduced.

A MEMS sensor and a preparation method therefor. The method comprises: forming a first oxide layer on a substrate wafer, performing patterned etching on the first oxide layer, and performing deposition and doping on the first oxide layer that has undergone the patterned etching, so as to form a first polycrystalline silicon layer; performing patterned etching on the first polycrystalline silicon layer to form a wiring, and depositing a second oxide layer on the first polycrystalline silicon layer; upon performing patterned etching on the second oxide layer, performing deposition and doping to form a second polycrystalline silicon layer; performing patterned etching on the second polycrystalline silicon layer to form bonding anchor points and a capacitive plate having a lower height than the bonding anchor points; bonding a device wafer with the bonding anchor points, and performing patterned etching on the device wafer to generate a movable structure, so as to obtain a device layer; and bonding the side of the device wafer distant from the substrate wafer with a cover layer.

According to the mobile phone terminal multi-source fusion 3D visual high-precision indoor navigation method, firstly, an improved robust PDR scheme based on a built-in MEMS sensor of an intelligent terminal is constructed, a dynamic optimal filtering measurement model is constructed by taking INS as core optimization and utilizing multilayer constraint data as observation data, sensor errors are estimated and compensated in real time, and robustness and positioning precision are improved; secondly, establishing a shortest path constraint improved magnetic field matching algorithm, adding the magnetic declination as a magnetic field fingerprint of one dimension into the magnetic field matching algorithm, respectively solving a shortest path constraint result by utilizing the magnetic field fingerprints of multiple dimensions, solving an overall matching result, and reducing a mismatching probability; and finally, by utilizing complementary characteristics of different positioning data sources, an algorithm fusing PDR, magnetic field and Bluetooth multi-source data is established, a quality control mechanism is set, the precision and the robustness of the positioning system are improved, the visual indoor positioning system based on the intelligent terminal is realized, and the integrated navigation positioning precision and the robustness are good.

The invention relates to a MEMS sensor (100) comprising: a substrate (40) having a main plane of extension; a vibration mass (60) which is movably suspended in the z-direction (1000) relative to the substrate (40); and a sensor device (90) for detecting a measurement signal dependent on the position of the vibration mass (60). The at least two resonators (10, 11, 12) are suspended in an elastically movable manner relative to the substrate in the z-direction (1000). The actuatable electrode arrangement (30) is designed to resonate each of the at least two resonators (10, 11, 12) in order to generate a mechanical resonant vibration and to capacitively detect an interference variable dependent on at least one resonant frequency of the resonant vibration. The invention also relates to a method for compensating for systematic measurement deviations of a MEMS sensor (100).

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.

A complementary metal oxide semiconductor (CMOS) device embedded with micro-electro-mechanical system (MEMS) components in a MEMS region is disclosed. The MEMS components, for example, are infrared (IR) thermosensors. The MEMS sensors are integrated on the CMOS device monolithically after CMOS processing. For example, the MEMS sensors are formed over a BEOL dielectric of a CMOS device. The device is encapsulated with a CMOS compatible IR transparent cap to hermetically seal the MEMS sensors in the MEMS region.

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 resonance type flow field MEMS sensor based on PT symmetry and a preparation method thereof, and the method comprises the steps: two double-end fixed tuning fork resonators are coupled, are in mirror symmetry in structure, and are equal in mass; the two double-end fixed tuning fork resonators are positioned on the same straight line; the comb tooth capacitors are symmetrically distributed on two sides of each double-end fixed tuning fork resonator; the two adjustable damping circuits are connected with the electrode layer through leads, so that the two adjustable damping circuits are correspondingly connected with the two double-end fixed tuning fork resonators; the equivalent damping of the two adjustable damping circuits is opposite in sign and equal in size; the cilium supporting structure is suspended at one end of a double-end fixed tuning fork resonator; the micro lever is connected with the cilium supporting structure and the double-end fixed tuning fork resonator; the cilia is arranged on the cilia supporting structure; glass serves as a substrate of the sensor. According to the invention, the resonance type flow field MEMS sensor can obtain perturbation response performance with higher sensitivity, and sensitive measurement of weak flow velocity is realized.

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 discloses an MEMS sensor intelligent design method and system based on a generative network, which are used for efficiently generating a plurality of feasible structures under given sensor performance requirements, and the method comprises the following steps: obtaining MEMS sensor experimental data based on finite element simulation; constructing and training a verification network to realize forward design from a sensor structure to performance parameters; constructing and training a generative network CVAE-GAN to realize reverse design from performance to structure parameters; and constructing a multi-solution generator G-MS model for efficiently generating multiple groups of structure parameters meeting performance requirements. Compared with an MEMS sensor design method based on finite element simulation, time-consuming multi-physical field simulation and manual parameter adjustment are not needed, and the method has the advantages of being high in speed, high in precision and intelligent; compared with other MEMS sensor intelligent design methods, the method has the advantages that the efficiency and quality of generating a plurality of different design schemes under given performance requirements are remarkably improved, the design period is shortened, and rapid iterative design is achieved.

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 discloses a steel bar strength prediction system based on digital twinning, and the system comprises a high-precision data collection module which employs an optical fiber sensor and an MEMS sensor, and monitors the stress, strain and temperature parameters of a steel bar in real time; according to the digital twinborn model, the high-precision digital twinborn model is established by adopting a multi-source data fusion technology and combining physical characteristics and environmental factors of reinforcing steel bars, and the digital twinborn model can adapt to reinforcing steel bar structures of different types and scales and support expansion to adapt to new technologies and data sources. According to the method, efficient data processing is adopted, real-time data streams can be better processed and analyzed, the accuracy and the real-time performance of data are ensured, the accuracy of the digital twin model and the adaptability to different steel bar main structures are enhanced by introducing machine learning and deep learning technologies, and the accuracy and the real-time performance of the digital twin model are improved. The safety risk of the steel bar main body structure can be evaluated in real time, and related personnel can be notified in time through the early warning system, so that potential safety problems can be prevented.

The invention discloses a comprehensive safety monitoring system for a building structure. The comprehensive safety monitoring system comprises an equipment sensing unit, a data processing unit, a system management unit, a terminal monitoring platform and a communication unit. The sensing module is integrated in the equipment main body of the equipment sensing unit, the equipment sensing unit is convenient to install and use, the MEMS sensor technology based on microelectronic elements and micromechanical elements is adopted, the acceleration, angle, temperature and humidity, wall internal stress and settlement change of a building structure are accurately monitored, and the construction quality is improved. According to the method, a plurality of algorithm models in an algorithm processing module of a data processing unit are matched, the health state of a monitoring structure is comprehensively judged, abnormal data are classified and managed according to disaster types, corresponding emergency rescue measures are intelligently recommended, and according to the autocorrelation and seasonality of sensing data and based on historical data, the monitoring structure is monitored. The method is used for predicting the trend of future sensing data, and predicting potential risks possibly occurring in the future in cooperation with a danger data threshold value, set by the system, of a building structure and big data analysis.

The invention discloses a handheld security check imaging method based on MEMS assistance, and aims to combine an MEMS sensor with an MIMO radar plate to form a handheld convenient security check imaging method with high precision and low cost according to the rapid and fine security check requirement of people in a security check place with dense people. Firstly, the radar board transmits a linear frequency modulation signal and receives an echo signal reflected by a target, and during the period, the MEMS sensor feeds back the motion track and position information of the handheld security check system in real time, and provides accurate attitude and position information. And secondly, position calibration is carried out on the track information of the MIMO radar antenna through the data of the MEMS sensor, and the zero drift of the accelerometer is compensated based on the wavelet packet change, so that the error caused by the system motion and the position change is reduced. And finally, residual phase errors are estimated and eliminated based on a particle swarm algorithm, non-contact three-dimensional imaging of the target is realized in combination with a wavenumber domain imaging algorithm on this basis, and hazardous article inspection is realized in combination with technologies such as deep learning and image recognition.

The invention provides an orthodontic pressure real-time detection device integrated with MEMS (micro-electromechanical systems) sensors, which comprises a plurality of internal terminal systems packaged on a flexible printed circuit board and an external host system connected through wireless network signals, and the internal terminal systems are integrally mounted in an invisible orthodontic appliance diaphragm or an invisible orthodontic appliance resin accessory. The MEMS flexible diaphragm can also be manufactured into an independent MEMS flexible diaphragm which can be used for orthodontic force measurement; wherein the internal terminal system comprises an ASIC (Application Specific Integrated Circuit) chip assembly and an antenna, and the ASIC chip assembly comprises an ASIC chip and an MEMS (Micro Electro Mechanical System) sensor array connected with the ASIC chip; compared with the prior art, the invention has the following beneficial effects: the MEMS sensor array which is small in size and easy to integrate is utilized to realize multi-point stress measurement on each tooth at the same time, pressure information of the teeth of a user is collected through the MEMS sensor array and then is sent to the ASIC chip, the ASIC chip sends the information to the computer, and the computer is connected with the MEMS sensor array. Therefore, the stress condition of the teeth can be monitored in real time in the correction process.

The invention belongs to the technical field of sensors, and particularly relates to a TSV-based MEMS capacitive accelerometer wafer-level packaging structure and packaging method. A gold-silicon bonding and TSV (Through Silicon Via) combined technology is adopted, and a gold layer is used as a bonding medium and a sensor capacitor polar plate, so that the problem of integration of a polar plate structure and signal lead-out in an MEMS sensor is solved, the design that an independent electrode layer or an additional polar plate is needed in traditional packaging is avoided, and the packaging complexity is reduced. Meanwhile, the gold layer has low resistance and good conductivity, compared with a traditional electrode material, signals can be better transmitted, and the electrical performance of the MEMS sensor is improved. After the TSV technology is combined, short-path signal extraction can be realized in the wafer, signal attenuation and interference are reduced, and signal integrity and precision are 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 provides a gas detection system and method, electronic equipment and a medium. The gas detection system comprises a sensor module and a data processing module. The sensor module comprises a sensor array and is used for detecting different gases in the environment and generating different detection signals; the data processing module is used for converting different detection signals into different digital signals; performing feature extraction and fusion on different digital signals by adopting a neural network model to obtain data features corresponding to each digital signal; identifying types and concentrations of different gases in the environment according to the data features; wherein the sensor array comprises MEMS sensors, an optical sensor, a temperature and humidity sensor and an air pressure sensor, and the number of the MEMS sensors is m * n. The system is used for providing a sensor array integrating various sensors, and feature extraction and intelligent processing are carried out on signals recognized by the sensor array through an artificial intelligence algorithm so as to improve the accuracy of measurement results.

The embodiment of the invention provides a Beidou water vapor inversion method and system based on a buoy platform. The method is applied to the technical field of data processing, and comprises the following steps: receiving a Beidou dual-frequency signal through a direct antenna to obtain original observation data; the multi-path effect and the platform shake are inhibited by combining buoy motion characteristics obtained by the MEMS sensor to obtain pre-processed data; calculating a troposphere delay estimated value; establishing a delay-water vapor conversion model; suppressing short message interference to obtain effective observation data; and inputting the data into a model to calculate the amount of precipitable water and the relative humidity. According to the method, the key technical problems of sea surface multi-path effect suppression, platform shake compensation, marine atmosphere modeling, short message interference elimination and the like are solved, so that reliable water vapor parameter inversion on the buoy platform is realized.

The invention discloses a PT symmetry-based three-degree-of-freedom weak-coupling high-sensitivity MEMS sensor and a preparation method thereof, and the sensor comprises double-end fixed tuning fork resonators which are in mirror symmetry pairwise and are equal in mass and size; the three double-end fixed tuning fork resonators are positioned on the same straight line; the adjustable mechanical weak coupling structure is used for coupling and connecting the adjacent double-end fixed tuning fork resonators; the movable comb tooth capacitor structures are located on the two sides of each double-end fixed tuning fork resonator. The detection comb teeth and the driving comb teeth are located on the outer sides of the movable comb tooth capacitor structures corresponding to the two double-end fixed tuning fork resonators. The two adjustable damping circuits are correspondingly connected with the electrode layer and the two double-end fixed tuning fork resonators through leads; glass serves as a substrate of the sensor. According to the invention, a new principle and a new idea are provided for the design of a micro-electro-mechanical system, and the ultra-sensitive measurement of the resonance type MEMS sensor on weak signals is facilitated.

The invention belongs to the field of MEMS sensors, and discloses a high-temperature-resistant MEMS pressure sensor chip and a packaging structure thereof. The chip is provided with an SOI substrate, an AlN layer and a SiO2 layer from bottom to top, a plurality of P-type piezoresistors are arranged on the uppermost layer of the SOI substrate and are symmetrically distributed at the stress concentration position of the edge of the elastic diaphragm; a channel which continuously penetrates through the AlN layer and the SiO2 layer is arranged above the P-type piezoresistor; a metal Pad is arranged in the channel; the upper end of the metal Pad is located on the uppermost layer of the high-temperature-resistant MEMS pressure sensor chip, and the lower end of the metal Pad is in contact with the P-type piezoresistor. According to the invention, the relatively thick buried oxide layer can resist a high-temperature severe environment, and an AlN heat dissipation material with a high heat conductivity coefficient is deposited, so that rapid heat dissipation at a high temperature is ensured; and meanwhile, the stability and the reliability of the sensor are improved by adopting leadless packaging, and the packaging density is further optimized.

The utility model discloses an MEMS sensor and an electronic atomization device. The MEMS sensor comprises a substrate, a base plate, a first polar plate, a second polar plate and a conductive contact. The first surface of the substrate is provided with a groove, the second surface of the substrate is provided with a Wheatstone bridge circuit, and a bridge arm of the Wheatstone bridge circuit comprises a piezoresistor; the substrate covers the groove to form a sealed cavity; the first polar plate is arranged on the bottom wall of the groove; the second polar plate is arranged on the substrate and is spaced from the first polar plate; the conductive contact is arranged on the substrate and / or the base plate and is electrically connected with at least one of the first polar plate and the second polar plate; the first pole plate and the second pole plate are configured to be capable of repelling or attracting each other under the power-on condition. Through the mode, the deflection of the strain film is always small, so that the problem that the deformation of the substrate does not have ideal linear change with the pressure is well improved, and the detection precision of the MEMS sensor 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.