3990results about "Fluid speed measurement" patented technology

The invention discloses a device for measuring flowability of explosive modeling powder, which comprises a step-press-loading analogue unit, a timing and weighing unit and a computer control unit, wherein the step-press-loading analogue unit directly and indirectly drives a screw rod which extends into a blanking bin to rotate and perform reciprocating movements along an axial line and drives a pusher dog in the blanking bin to perform the reciprocating movements along an arc section through the combined action of a cylinder and a pneumatic reversal valve so as to ensure that the explosive modeling powder in the blanking bin falls into the timing and weighing unit, and the process more truly imitates an explosive conveying process of an explosive step-press process. The timing and weighing unit transmits a weighing value of the explosive modeling powder which falls in the timing and weighing unit to the computer control unit in real time, and the computer control unit calculates the instantaneous flow speed of the explosive modeling powder of a north side and generates a flow speed-time curve according to sampling data. The device can measure the flowability of the explosive modeling powder so as to provide an objective basis for choosing a proper explosive step-press process for the explosive modeling powder.

The invention discloses a high-temperature airflow two-dimensional temperature field and velocity field synchronous measurement system and method based on laser-induced phosphorescence, and the system comprises phosphorescence particles, a low-frequency double-pulse laser, an image collection device, a synchronous controller, and an image processing device. The image processing device analyzes the gray intensity ratio of different wave band images acquired by the two PIV cameras at the same time, and combines a temperature response function calibrated by an experiment to realize inversion of a temperature field; meanwhile, a double-frame time-resolved image acquired by any PIV camera is subjected to cross-correlation calculation, particle displacement is extracted, and then a velocity field is reconstructed. According to the invention, synchronous acquisition of temperature and speed based on the same data source is realized, and the system has the remarkable advantages of simple structure, high measurement precision and wide application environment.

The invention relates to the technical field of three-dimensional observation, in particular to a geological deformation three-dimensional observation system and method, and the method comprises the steps: multi-dimensional data collection: synchronously obtaining data, and collecting ground temperature gradient field data and underground water flow velocity field data of a monitoring region; data space-time calibration processing: performing space-time reference unification processing on the acquired data to generate a multi-source displacement data set after space-time calibration; cold door parameter extraction: generating an environment coupling parameter matrix based on ground temperature gradient field data and underground water flow velocity field data in multi-dimensional data acquisition; modeling a three-dimensional deformation field: generating an initial three-dimensional deformation field model; dynamic model correction: performing iterative correction on the initial three-dimensional deformation field model to generate an optimized three-dimensional deformation field model; and visual early warning output: generating an early warning signal with a risk level identifier. According to the method, a multi-source heterogeneous data fusion system is constructed, and a cold geological parameter dynamic compensation mechanism is combined, so that high-precision three-dimensional deformation field modeling and early warning are realized.

The invention provides a bridge structure monitoring method and device based on a microwave deformation radar, and relates to the technical field of bridge structure monitoring, and the method comprises the steps: obtaining the multi-point three-dimensional displacement data of a bridge structure through the microwave deformation radar, carrying out the thermal expansion pseudo displacement compensation and multi-point space smoothing through combining with temperature information, and obtaining a displacement field after environment correction; secondly, extracting a vertical component and separating the vertical component into a static deformation component and a dynamic vibration component by adopting variational mode decomposition; further analyzing and identifying a decoupling region through a time window coherence coefficient, performing recursive quantitative analysis, bispectrum analysis and energy distribution entropy calculation on a dynamic signal of the region, and constructing a high-order damage sensitive feature set; and finally, the dynamic characteristics and the static curvature change are fused to form a comprehensive degradation degree index, the deviation degree is judged according to working condition classification and the mahalanobis distance, and multi-dimensional and cross-working-condition degradation identification and risk early warning of the bridge structure are achieved.

The invention relates to the field of pressure sensors, in particular to a pressure sensor and a processing method, and a chip comprises a silicon substrate, a silicon dioxide dielectric layer, a device layer, a metal electrode and packaging glass. The device layer comprises a silicon carbide single crystal device layer and a silicon device layer; the silicon carbide single crystal device layer is arranged on the silicon device layer; the silicon device layer, the silicon dioxide dielectric layer and the silicon substrate are connected in sequence; the device layer is etched to form a sensitive resistor device layer, an annular sealing region and a micro-channel; the sensitive resistance device layer is isolated from the annular sealing area through the micro-channel; the device layer, the silicon dioxide dielectric layer and the silicon substrate are etched to form a cross beam etching area; the silicon substrate is etched to form a silicon pressure cavity and a beam film; the metal electrode is arranged on the top of the device layer; and the packaging glass anode is bonded at the top of the device layer or the bottom of the silicon substrate so as to solve the problems of low high temperature resistance and low sensitivity of the MEMS pressure sensor.

The invention discloses an indoor water tank sediment starting speed measurement system and method, and aims to solve the technical problem that data accuracy is affected due to the fact that a sensor disturbs a flow field in an existing measurement method. The core of the technical scheme is that the non-contact measurement characteristic of the PIV technology is applied to the whole process from flow field construction to data acquisition. The device is mainly composed of an imaging unit, a laser lighting unit, a movable sliding frame, a water flow adjusting unit and a buffer plate. Optical measurement is carried out through the imaging unit and the laser illumination unit, a physical sensor does not need to be arranged in a flow field, and interference of a measuring instrument on a water flow structure and a sediment starting process is fundamentally avoided. The movable sliding frame which is integrally designed integrates the functions of sediment laying, particle throwing and measuring, so that a test area can be quickly positioned; and the buffer plate and the water inlet and drainage adjusting unit are combined, so that a gradually-changed flow field from weak to strong can be accurately and stably simulated.

The invention belongs to the technical field of river surface flow velocity measurement, and discloses a method and system for predicting river surface flow velocity based on improved optical flow estimation. The RAFT optical flow estimation model established by the invention comprises a query frame residual error estimation module and an optical flow residual error estimation module. The model continuously corrects the deviation between a prediction result and a true value by introducing an IRE mechanism so as to improve the estimation accuracy of a query frame residual estimation module and an optical flow residual estimation module, the query frame residual estimation module is used for performing deep optimization on feature processing and correlation calculation, and the optical flow residual estimation module is used for performing optical flow estimation. The method comprises the following steps: generating query frame estimation through a differentiable internal optimization process, and performing feature convolution operation on the query frame estimation and a query frame image so as to explicitly consider similar areas possibly appearing in a scene. An optical flow residual error estimation module is introduced into a CBAM module, the weight of a feature map is adjusted in a self-adaptive mode, complex water surface information is better captured, and the accuracy of optical flow iteration is improved.

The invention relates to the field of wind speed and wind direction prediction, and discloses a wind speed and wind direction prediction method based on an improved TCN-LSTM, and the method comprises the following steps: collecting real-time monitoring multi-dimensional meteorological data of an offshore wind station, and carrying out the preprocessing; performing feature fusion and feature enhancement processing on the preprocessed data, wherein the feature enhancement processing comprises spatial feature extraction, tensor reconstruction and time sequence modeling; inputting the data after feature fusion and feature enhancement processing into a wind speed and wind direction prediction model based on an improved TCN-LSTM hybrid neural network, and predicting the wind speed and wind direction at a future moment; the wind speed and direction prediction model comprises a time-space separation TCN network, a multi-head self-attention mechanism, an adaptive Dropblock mechanism, an LSTM network and a Bayesian optimization module; according to the method disclosed by the invention, the accuracy, robustness and calculation efficiency of wind power prediction in a complex marine environment can be improved.

The invention discloses a river water surface two-dimensional flow field reconstruction method based on effective tracer detection and tracking, and the method comprises the steps: carrying out the distortion correction of an original video image sequence, and setting an ROI region according to the water level detection river water surface range; the method comprises the following steps: collecting a multi-scene floating object data set, screening floating objects with high relative brightness and clear outlines in different scenes, and training a YOLO model to detect the floating objects on the surface of a river; using an OCSORT matching tracking method to track adjacent frames of targets to obtain a trajectory, and filtering according to the length, angle and variance of the trajectory to obtain a stable trajectory; according to the method, object information obtained through target detection is utilized, according to the area, the area change rate and the length-width ratio of a floating object on an image, the following performance and stability of the floating object are evaluated, a track fusion weight is calculated, and the influence of unreliable floating objects is reduced; and gridding the river surface area after orthographic correction, and for a grid with a plurality of tracks, calculating the average flow velocity of the grid according to the confidence coefficient of each track to obtain a gridding two-dimensional flow velocity vector field. The method can accurately identify the floating object which meets the requirements of visibility, following performance and stability on the river surface as an effective water flow tracer, and is suitable for measuring the flow velocity of the river surface in complex scenes such as night, storm, flare, shadow and the like.

The invention discloses a low-altitude three-dimensional wind field inversion method and system based on a single wind measurement laser radar, relates to the technical field of radar detection, and solves the technical problems that a traditional wind measurement laser radar cannot directly obtain a velocity vector, data missing of a key area is caused by a blocking effect, and detection nodes are not uniform. The method specifically comprises the following steps: S1, executing a multi-elevation-angle body scanning detection mode and a wind profile detection mode by using a wind measurement laser radar, and obtaining radial wind speed data of a target area; s2, quality control is carried out on the radial wind speed, isolated abnormal points are eliminated, and isolated data missing points are filled; s3, carrying out blocking correction on a single scanning surface by using a smooth spline interpolation method; s4, after the radial wind speed is corrected based on the wind profile, an initial background three-dimensional wind field is obtained; and high-precision wind field inversion of a wind field in a three-dimensional space can be realized by using the radial wind speed of multi-layer body scanning of a single wind measurement laser radar.

The invention discloses a flow field measurement method based on an event camera, and the method comprises the steps: generating a PIV data set, each time sequence sample sequence comprising a plurality of frames of continuous particle images, a corresponding velocity vector field, and particle event data at all moments; establishing a flow field data acquisition device based on an event camera and a high-speed camera, acquiring real event data and real image data which are synchronous in time so as to adjust parameters of an event simulator, and verifying and updating particle event data in the PIV data set according to the adjusted event simulator so as to obtain a flow field data acquisition result; obtaining the updated PIV data set as a training data set; building an event camera optical flow method model, and training by adopting the training data set; and on the basis of the trained event camera optical flow method model, event sequences in two adjacent time periods are used as inputs to calculate a velocity vector field corresponding to a middle moment. According to the invention, the flow field velocity field at the required moment can be obtained based on the event data within a period of time.

The invention relates to the technical field of PIV (particle image velocimetry) flow field testing devices, in particular to an internal peeping type PIV testing device and method for an internal flow field of turbomachinery, and the device comprises the turbomachinery, a testing assembly and a connecting assembly. The testing assembly comprises a lighting mechanism, a camera shooting mechanism, a laser mounting pipe arranged on the air inlet pipe, a mounting base arranged on the laser mounting pipe and a camera shooting mounting mechanism arranged on the air inlet pipe, and the connecting assembly comprises an inserting mechanism arranged on the laser mounting pipe and a connecting mechanism arranged on the camera shooting mechanism. Through the arrangement of the mounting base and the camera mounting mechanism, a laser light source emitter and a camera are quickly mounted, the camera can adapt to various special-shaped shells such as volutes, the angle of a sheet light source emitted by the laser light source emitter is quickly adjusted through the arrangement of a rotating pipe, and the angle of the sheet light source is adjusted through the arrangement of a connecting mechanism and an inserting mechanism. And the sheet light source emitted by the laser light source emitter is quickly perpendicular to the shooting direction of the camera mechanism again.

The present disclosure provides a method for measuring flow velocity of multilayer groundwater using distributed optical fiber with point-source active heating, comprising: setting and optimizing field test parameters; conducting a background temperature monitoring test; performing a point-source active heating distributed temperature measurement test, including conducting multiple rounds of heating tests; denoising the obtained multi-point source thermal plume attenuation signal data of the groundwater; searching for the peak value of temperature-permeability curve based on an automatic multi-scale peak search algorithm; performing secondary processing on the peak data; estimating the groundwater flow velocity. The method of the present disclosure solves the problem that the existing groundwater flow velocity measurement technology cannot simultaneously measure multiple points and multiple layers, and it can avoid groundwater contamination during the measurement process.

The invention relates to a saline intrusion measurement method, system and equipment based on dynamic ship motion conditions and a medium, and aims to solve the technical problem that the salinity inversion precision is reduced due to spatial-temporal dislocation between particle image velocity measurement flow field data and plane laser-induced fluorescence concentration field data caused by ship six-degree-of-freedom motion. According to the method, future poses are predicted by collecting ship motion parameters and environment disturbance parameters, and space-time compensation parameters are generated in combination with a static calibration matrix; performing three-dimensional coordinate transformation and time synchronization on synchronously acquired original flow field and concentration field data by applying compensation parameters, and eliminating space-time deviation caused by ship movement; a three-dimensional salinity distribution field is calculated based on registration data fusion flow field diffusion characteristics and concentration field distribution; and meanwhile, the pose of the measuring equipment is dynamically adjusted through the closed-loop controller so as to optimize the data acquisition quality. According to the method, ship motion interference is effectively inhibited, and accurate characterization of saline invasion frontal surface spatial characteristics is guaranteed.

The invention provides a desert photovoltaic sand damage dynamic monitoring method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the flying dust data of a photovoltaic power station region, generating an initial data set, and enabling the flying dust data to be collected through a multi-mode sensor carried by the unmanned aerial vehicle; performing multi-modal data fusion processing on the initial data set to obtain a dust feature matrix, the dust feature matrix being used for representing dust reflectivity, terrain elevation and wind speed correlation features; calculating migration probability distribution of the flying dust particles based on the sand and dust characteristic matrix, wherein the migration probability distribution is used for representing spatio-temporal evolution laws of the particles with different particle sizes; a three-dimensional dynamic sand and dust concentration field model is generated according to the migration probability distribution, a sand damage dynamic monitoring result is output based on the three-dimensional dynamic sand and dust concentration field model, and the sand damage dynamic monitoring result comprises deposition density distribution and an erosion risk area. By adopting the method, the photovoltaic sand damage of the desert can be accurately and dynamically monitored with high timeliness.

The invention provides an urban road ponding monitoring and drainage system. The urban road ponding monitoring and drainage system comprises a monitoring module which collects ponding depth, water velocity, rainfall intensity and field image data in real time; the initialization configuration module sets parameters such as an accumulated water depth threshold value; the data transmission module encrypts and transmits data to the central control module; the model algorithm optimization module constructs a three-dimensional ponding diffusion model and outputs a predicted ponding depth and an optimized drainage strategy; the central control module generates a drainage pump rotating speed instruction and the like; the drainage execution module dynamically adjusts drainage pump power and a drainage path; the self-checking and fault-tolerant module calculates a health index of drainage equipment and switches redundant equipment; the early warning module sends risk level information; and the interaction module displays information such as a real-time accumulated water thermodynamic diagram. The urban road drainage efficiency can be improved, the water accumulation risk is reduced, and smooth urban traffic and public travel safety are guaranteed.

The invention discloses a wind speed forecasting system based on deep learning, and belongs to the technical field of weather forecasting. The system comprises a coupled mode framework which integrates a triple nested architecture of an atmospheric mode WRF, an ocean mode ROMS and a sea wave mode SWAN, and transmits variables through a model coupling tool MCT; the deep learning roughness parameterization module is integrated in an MYNN boundary layer scheme of the atmospheric mode WRF and calculates the sea surface roughness in real time; the full-wave spectrum integration module is embedded into the ocean mode ROMS, receives wave spectrum data provided by the ocean mode SWAN and calculates a Stokes drift profile; and the intelligent correction module is used for carrying out spatial interpolation on the wind speed data output by the mode, correcting errors by combining a LightGBM model, and outputting a final forecast product. According to the method, the deep neural network is embedded into the WRF boundary layer model kernel, the sea surface roughness parameter is updated in real time, the wave evolution process is dynamically responded, and the space-time adaptive capacity of wind field forecasting under the complex sea condition is remarkably improved.

The invention provides a manufacturing method of an MEMS device and the MEMS device, and the manufacturing method comprises the steps: providing a first wafer which is provided with a groove; a protective layer is arranged in the groove; providing a second wafer, and bonding the second wafer and the first wafer to seal the groove to obtain a first cavity; the second wafer is etched to form a comb tooth structure, and the protection layer is used for protecting the comb tooth structure. According to the embodiment of the invention, the protection layer is arranged in the first cavity of the first wafer as a buffer layer, so that when the second wafer is etched to form the comb tooth structure, the plasma backwash in the over-etching stage can be reduced, the comb tooth structure is further protected, the damage to the bottom of the comb tooth caused by the backwash of etching particles is reduced and solved, and the service life of the comb tooth structure is prolonged. Therefore, the mechanical strength and the anti-failure capability of the device are enhanced.

The invention discloses a radar detection blind area wind field fine reconstruction method based on anemograph collaborative networking, and the method comprises the specific steps: firstly, separating wind speed measurement deviation caused by vibration displacement in real time, eliminating vibration interference, carrying out the automatic diagnosis and restoration of data based on machine learning, and optimizing the integrity of wind speed data and the measurement continuity; secondly, an ultrasonic anemograph array optimization arrangement method oriented to global wind field sensing is established, time-space consistency calibration of multi-sensor data is achieved, and the precision and reliability of wind speed measurement are improved; and finally, carrying out data-physical fusion bridge flow field high-resolution intelligent reconstruction, and improving the data integrity of a complex flow field area. The reconstruction method effectively cooperates with the wind measurement laser radar and the anemograph, the advantages of high-frequency single-point measurement and wide-area space scanning of the wind measurement laser radar and the anemograph can be exerted, high-precision reconstruction of the global flow field of the bridge is expected to be achieved, and reliable support is provided for guaranteeing wind resistance safety of a large-span bridge.

The invention relates to the technical field of pipeline detection, and provides a non-contact pipeline fluid flow velocity measurement method based on a frequency wavenumber domain spatial spectrogram, and the method comprises the steps: collecting a turbulence signal in a pipeline through a piezoelectric film sensor; carrying out snapshot number segmentation on the turbulence signal to obtain a plurality of segmented turbulence time domain signals; fourier transform and narrowband signal processing are carried out on the segmented turbulence time domain signals, and a spatial spectrum corresponding to each narrowband signal component is obtained; constructing a three-dimensional frequency-wave number domain spectrogram according to the spatial spectrum function; and according to the three-dimensional frequency-wavenumber domain spectrogram, calculating the flow velocity of the pipeline fluid. According to the method, a three-dimensional spectrogram characteristic space is constructed through conjoint analysis of the frequency and the wave number, the angle, distance and frequency characteristics of the turbulence signals are decoupled through spatial spectrum estimation methods such as the MUSIC algorithm, multipath interference and noise are effectively restrained, the flow speed of fluid in a pipeline can be accurately estimated, and limitation in a traditional method is overcome.

The invention discloses a flow field measurement system and method fusing event data and a blurred image, and the method comprises the steps: turning on a sheet-shaped light source, and enabling the sheet-shaped light source to illuminate tracer particles in a gas flow field; starting a gas flow field to enable the gas flow field to carry the tracer particles to flow through the transparent box body; capturing the motion change of the tracer particles in real time through an event camera, and collecting event data; blurred images of the tracer particles are synchronously collected through an industrial camera; based on the event data and the blurred image, reconstructing a clear moving image at any moment by using an image deblurring technology EDI; and carrying out time synchronization and space alignment on the clear moving image and the high-resolution static image, and extracting flow field information in the gas flow field in combination with the movement track and brightness change of the particles. According to the method, the high-speed dynamic capture capability of the event camera and the high spatial resolution of the industrial camera are combined, the bottleneck of the spatial-temporal resolution in the traditional flow field measurement technology can be effectively overcome, and meanwhile, the method has a relatively good effect in a complex flow field and a high-speed dynamic scene.

The invention discloses a neural operator-based particle image velocimetry (PIVNO) method. Efficient mapping of a particle image pair to a flow field is realized through an end-to-end learning framework. According to the method, a neural operator normal form is adopted, cost volume construction with large calculation amount in a traditional method is avoided, and calculation efficiency and estimation precision are improved. The method comprises the steps of obtaining a synthetic data set and carrying out multi-scale down-sampling, constructing a PIVNO model to realize high-precision flow field prediction, supervising pre-training on the synthetic data set, self-supervising a fine-tuning optimization model on real data, finally predicting a real flow field, and supporting estimation of any resolution. According to the method, global feature aggregation, iterative optimization and high-resolution reconstruction technologies are combined, the flow field estimation precision is improved, and the adaptability of the model on real data is enhanced. The method is efficient in calculation, high in generalization ability and suitable for the fields of aerodynamics, turbulence research, biological fluid and the like, and provides technical support for high-precision flow field measurement.

The present disclosure relates to a MEMS inertial sensor device in a semiconductor chip package that includes an integrated circuit configured to process inertial sensor data. Preferred implementations utilize inertial sensors having different sensitivity ranges to adjust operation of dynamic system control such as motion and or attitude control of autonomous vehicles.

The invention discloses a speed measurement method based on single-frame multiple exposure, and belongs to the field of hypersonic velocity field measurement. The implementation method comprises the following steps: recording equal time interval change of molecular marking in a flow field along with movement of the flow field through single-frame multi-time exposure imaging of an image enhancement camera, and realizing continuous and rapid acquisition of speed information within a short time after single-time marking. The morphological position after the time interval is determined according to the molecular marker, and the velocity field with spatial resolution in the flow field is extracted by mapping the marker before and after displacement, so that the velocity measurement precision is improved. Compared with other two-dimensional velocity field acquisition methods based on femtosecond laser molecular marker tracing velocity measurement, the method provided by the invention does not need a complex focusing lens combination, can obtain spatial positions of molecular markers at different moments only through isochronous continuous triggering exposure of a camera, thereby obtaining velocity flow field displacement information. And corresponding acceleration information can be further deduced according to the obtained velocity field. The method is especially suitable for hypersonic velocity field measurement.

The invention belongs to the technical field of micro-nano processing, and particularly relates to a preparation method and application of a metal micro-nano tube array. Comprising the following steps: preparing a metal layer on the surface of a substrate; coining glue is sprayed on the surface of the metal layer, then coining is conducted through a template, and a micro glue column array is formed after curing; a plasma etching process is adopted, the micro glue column array is used as a mask, the metal layer exposed out of the gaps in the bottom of the micro glue column array is etched, and a structure with the micro glue columns wrapped by metal is formed; and removing the rubber columns in the structure of the metal-coated micro rubber columns to obtain the metal micro-nano tube array. According to the invention, a residual-layer-free imprinting technology is adopted, the problem of glue layer residue in traditional imprinting is avoided, and the etching uniformity is improved. A plasma dynamic balance process is adopted, the side wall redeposition effect of etching by-products is utilized, and the metal micro-nano tubes are formed in a self-assembly mode. The prepared metal micro-nano tube array has high aspect ratio compatibility, and the nano tube array with the aspect ratio as high as 10: 1 can be realized.

The invention provides a visual rock filling fracture seepage monitoring method. The visual rock filling fracture seepage monitoring method comprises the following steps: preparing a transparent rock filling fracture model based on real fracture morphology and filling treatment; a mixture which is used for simulating the underground water environment and contains tracer particles is injected into the filling fracture of the model, seepage is conducted, in the seepage process, an image I1 and an image I2 are continuously collected, the flow velocity field of the filling fracture is monitored for the image I1 through a PIV method, and the dissolution condition of filler and the corresponding filling fracture opening change condition are monitored for the image I2 through a DIC method; and identifying an eddy current area in the filling crack by the image I2, and calculating the overflowing capability of the filling crack according to the flow of the waste liquid discharged by the model. According to the method, disturbance to a natural structure in the fracture is avoided, dynamic monitoring of fluid in the fracture can be achieved, data support is provided for research on seepage field change in the dynamic fracture, and research on the relation between fracture geometric heterogeneity and seepage field characteristics is facilitated.

The invention discloses a river surface flow velocity measurement method and system based on physical constraint and multi-modal fusion, and relates to the technical field of multi-modal data processing, a target area is determined, and river surface image sequence data, depth information and environment data of the target area are acquired; preprocessing the collected data, embedding redundant codes in data representation, and synthesizing to form a basic data set; and based on the basic data set, performing preliminary optical flow estimation on the river surface image sequence data by using an optical flow method. According to the method, optical flow information, depth information and environment data are fused, comprehensive utilization of multi-source data is achieved, in the data fusion process, redundant codes in data representation are combined for error detection and correction, error points in the data are effectively recognized and corrected, the reliability and accuracy of the data are improved, and the data fusion efficiency is improved. And by utilizing a multi-modal data fusion and error correction mechanism, the robustness of the system is remarkably enhanced, so that the system can stably operate in a complex and changeable river environment.

The invention discloses a flow field velocity measurement and visualization device and method, and relates to the technical field of flow field display measurement, and the device comprises a two-color laser emission system, a surface dielectric barrier discharge system, a Cassegrain light path shaping system, a fluorescence signal detection system and a data processing system. Wherein the two-color laser emission system is used for generating femtosecond pulse laser and first laser, and the femtosecond pulse laser and the first laser are converged and collimated by the Cassegrain light path shaping system and then are focused to a detection area to cooperatively excite nitrogen molecules; the surface dielectric barrier discharge system forms a plasma environment in the detection area; the fluorescence signal detection system is used for collecting nitrogen molecule fluorescence signals in the detection area; and the data processing system is used for controlling a time sequence, realizing visual representation of a flow field structure in a detection area and realizing speed measurement of a flow field in the detection area, and the device and the method can realize synchronous acquisition of a speed vector and flow field structure parameters near a wall surface under a millimeter-level spatial resolution and a microsecond-level time scale.

The invention provides a pressure sensor based on silicon-silicon bonding and a preparation method thereof. The preparation method comprises the following steps: providing a low-resistance silicon wafer, forming a silicon deep hole in a first surface of the low-resistance silicon wafer, and depositing an isolation material in the silicon deep hole to form an isolation ring; etching the first surface to form a shallow groove and a conductive silicon column to obtain a low-resistance cap wafer; providing an SOI wafer, performing ion implantation on the first surface of the SOI wafer to form a resistance region, and obtaining a substrate wafer; bonding the low-resistance cap wafer with the substrate wafer; grinding the second surface of the low-resistance cap wafer until the isolating ring is exposed; depositing an insulating layer on the second surface of the low-resistance cap wafer, and forming a metal electrode on the insulating layer; and forming a back cavity structure on the second surface of the substrate wafer, exposing the top silicon of the SOI wafer, and forming a sensitive film layer. The conductive silicon column is made of low-resistance silicon, copper ion pollution is avoided, the preparation process is simple, and the period is controllable; the device obtained based on the silicon-silicon bonding process has high bonding strength and high air tightness.

The invention relates to the technical field of multi-peak spectrum demodulation, and particularly provides a fiber bragg grating multi-peak spectrum demodulation method and system. The method comprises the following steps: extracting local spectral features based on an experimental reference spectrum to form initial atoms, and performing translation offset and normalization processing to obtain an over-complete spectral atom dictionary; performing global offset preliminary estimation based on the dictionary, and obtaining preliminary estimation values of peak sites of the measurement spectrum and the reference spectrum through cross-correlation calculation; executing constraint orthogonal matching pursuit sparse recovery based on the estimated value, and recovering atomic displacement from the measurement spectrum by using block sparsity, translation consistency and non-negative constraint; and performing wind speed inversion and calibration based on the atomic displacement, and converting the wind speed into a wind speed estimated value through a nonlinear calibration model to obtain a final result. According to the method, a dictionary based on experimental data is constructed, dependence on large-scale labeled data is reduced, a physical mechanism and sparsity prior are fused, and the problems that a traditional method is insufficient in precision and weak in generalization ability in a complex environment are solved.