3971results about "Force measurement" patented technology

The invention relates to the technical field of intelligent detection in the electronic manufacturing industry, in particular to a PCB warping degree intelligent evaluation system integrating visual positioning and multi-modal sensing, which comprises a multi-modal sensing unit, a visual positioning unit, an intelligent evaluation engine and a closed-loop control unit, the multi-modal sensing unit integrates laser displacement, infrared thermal imaging and strain sensors to acquire three-dimensional deformation, temperature and stress data; the visual positioning unit realizes sub-pixel-level positioning by using a high-resolution industrial camera and a feature point matching algorithm, and compensates vibration errors; the intelligent evaluation engine fuses data based on a time-space synchronization protocol, predicts a thermal deformation trend through an improved multi-modal convolutional neural network, and dynamically adjusts a qualified threshold value; and the closed-loop control unit executes sorting and rechecking according to an evaluation result, and optimizes warping and leveling parameters. According to the system, multi-dimensional accurate detection and intelligent control are realized, the PCB warping degree detection accuracy is effectively improved, the process can be dynamically optimized according to the production working condition, and the equipment fault risk is reduced.

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention provides an intelligent pressure sensor temperature compensation system, and relates to the technical field of pressure sensors, and the system comprises a hardware sensing module which comprises an elastic sensitive element and is used for reducing the temperature drift magnitude of a pressure sensor; the environment data acquisition module comprises a flexible thermoelectric sensor array and is used for acquiring temperature and humidity data; the humidity correction module is used for integrating a dynamic humidity correction algorithm and establishing a cross-sensitive matrix model to correct the original signal to generate a first pressure correction signal; the temperature compensation module is used for carrying an incremental neural compensation network and constructing a compensation model to perform temperature compensation on the first pressure correction signal to generate a second pressure compensation signal; and the fault compensation module is used for triggering a digital twinborn compensation model to perform fault compensation on the second pressure compensation signal to generate a target pressure output signal when a fault of the single sensor is monitored, so that the pressure measurement error in the whole temperature range can be accurately controlled, and the environmental adaptability and the intelligent level of the system are improved.

The invention discloses a bolt pre-tightening force intelligent monitoring method and system based on acoustic characteristics, and relates to the technical field of structure monitoring, and the method comprises the steps: synchronously obtaining three types of acoustic characteristic data of resonance characteristics, sound velocity ratio and nonlinear characteristics, carrying out the interactive calibration, and generating a credibility evaluation result; according to a credibility evaluation result, dynamically adjusting weight coefficients of the three types of acoustic characteristic data in a pre-tightening force calculation weight distribution strategy; inputting the weighted three types of acoustic characteristic data into a staged fusion model by adopting a staged fusion strategy to carry out pretightening force joint calculation; and establishing a long-term monitoring database, and according to deviation analysis of historical monitoring data and a real-time resolving result, updating acquired acoustic characteristic data parameters and a weight distribution strategy to form a closed-loop calibration mechanism. The technical problems of poor dynamic adaptability of multi-source acoustic characteristic data and calculation error accumulation in bolt pre-tightening force monitoring under complex working conditions are solved.

The invention provides an on-line monitoring method and system for crack propagation of silicon-based new material equipment in a high-temperature environment, and relates to the technical field of crack detection, and the method comprises the steps: collecting stress distribution data through arranging a stress sensor, and generating a stress field distribution diagram by using an attention mechanism deep neural network model; identifying a stress concentration region based on a region growing algorithm, when a stress value exceeds a preset threshold value, acquiring temperature distribution data, extracting temperature distribution characteristics through a deep mixed probability model, performing multi-modal data fusion in combination with a stress field distribution diagram, and establishing a crack propagation prediction model by using a graph structure neural network. The crack propagation rate and direction are solved through a swarm intelligence optimization algorithm, when the crack propagation rate exceeds a preset threshold value, a control system adjusts equipment operation parameters, the sampling frequency of a sensor is adjusted according to the crack propagation direction, and model parameters are updated through an incremental learning method to achieve real-time monitoring.

The invention discloses an automatic soft soil foundation settlement monitoring system based on multi-source data fusion, and relates to the technical field of soft soil foundation monitoring, the system comprises an information acquisition module, a fusion processing module, a settlement prediction module and an intelligent monitoring module; the information acquisition module is used for acquiring foundation settlement sensing data and inputting the acquired data into the fusion processing module; the fusion processing module is used for preprocessing and integrating the collected data; the settlement prediction module is used for soft soil foundation settlement prediction; the intelligent monitoring module comprises a self-adaptive processing module and an interaction alarm module, the self-adaptive processing module is used for generating an optimization strategy, and the interaction alarm module is used for carrying out user interaction and multi-mode abnormal alarm reminding. An early warning response window is provided for engineering personnel, and the occurrence rate of sudden settlement accidents is reduced.

The invention relates to the technical field of power equipment state monitoring, in particular to a lightning arrester state monitoring method, system and equipment based on multi-physics field coupling and a storage medium. Acquiring current-voltage characteristic parameters, temperature distribution data and mechanical stress data of the lightning arrester, performing electro-thermal-mechanical coupling analysis based on multi-physical field monitoring data, identifying overlapping positions of an electric field distortion area, a temperature abnormal area and a stress concentration area, and determining the overlapping positions as a degradation key area; establishing a correlation response relationship between the leakage current and the temperature and a transfer response relationship between the temperature and the mechanical stress for the deteriorated key area; on the basis of the leakage current change trend of the degradation key area and the coupling influence of the superimposed temperature field and stress field, a degradation feature fusion factor is constructed, the evolution law of the degradation feature fusion factor in the time sequence is analyzed, and the degradation threshold value under the multi-field synergistic effect is determined in combination with the electric-thermal coupling acceleration effect and the thermal-mechanical coupling weakening effect.

The invention provides a composite board detection method and system, and relates to the technical field of board detection, and the method comprises the steps: carrying out the ultrasonic scanning of a composite board, dividing the composite board into a plurality of acoustic characteristic layers, building a sound wave propagation model based on a recursive transfer matrix method, and calculating the reflection coefficient and transmission coefficient of each layer interface. A deep learning algorithm is combined with physical constraints to dynamically correct acoustic parameters, the corrected parameters are substituted into a model reconstruction signal, and defects are accurately positioned. And performing grid division on the defect position to analyze stress field distribution, and determining a stress concentration position. And fine scanning is carried out on a stress concentration area by adjusting probe parameters, and high-resolution data is obtained to update defect distribution information. And finally, outputting detection data including defect positions, stress concentration distribution and hazard levels, and realizing accurate detection and evaluation of the defects of the composite board.

The invention discloses a curved surface stress detection method, which comprises the following steps of: 1, fixing a detected piece on an object placing table by using a self-adaptive clamp; step 2, scanning the measured piece to generate three-dimensional shape data of the measured piece; 3, calibrating the initial position of the measured piece by a displacement sensor, and establishing a coordinate system; step 4, adjusting the position of the measured piece by using a self-adaptive clamp, emitting X-rays to the measured piece by using an X-ray tube, and collecting diffraction signals by using a second detector; and step 5, the calculation module calculates the stress distribution state of the measured piece. According to the curved surface stress detection method, the problems of low irregular surface positioning precision and poor adaptability of traditional equipment are solved, and the detection efficiency and reliability are remarkably improved.

The invention relates to the technical field of material mechanical property testing, in particular to a method for measuring residual stress of a metal component. According to the technical scheme, the method for measuring the residual stress of the metal component comprises a working process for measuring the residual stress of the metal component; according to the method, a technical path of multi-modal data assimilation is adopted, and a mechanical release mechanism of a drilling method and an ultrasonic stress measurement technology are deeply fused to calibrate and correct mass sound velocity data obtained by carrying out rapid ultrasonic scanning on the whole component on site, so that the precise measurement of the residual stress state of the metal component is realized, and the measurement precision is improved. According to the method, data authority and high credibility of a drilling method in a key area are reserved, the advantages of high efficiency and wide coverage of full-field measurement of an ultrasonic method are brought into full play, and efficient and accurate stress information is provided on the premise that components are not excessively damaged.

The invention provides a method and a device for evaluating and measuring microscopic mechanical properties of TC4 alloy, relates to the technical field of microscopic mechanical properties, and realizes accurate evaluation of material properties through combination of multi-scale characterization and numerical simulation. Firstly, a standard sample is prepared through selective laser melting, and residual stress field distribution is obtained through X-ray diffraction; dividing a stress field into sub-regions, and establishing a quantitative relationship between microscopic structure parameters and residual stress by combining SEM and EBSD analysis; the method comprises the following steps: acquiring local mechanical properties through a nanoindentation test, and constructing a stress-performance correlation model; and finally, establishing a finite element model, transmitting stress and tissue parameters by adopting trilinear interpolation and an inverse distance weighting method, verifying data reliability through a stress difference criterion, and calculating mechanical properties of each unit. According to the method, multi-scale correlation from microstructure characteristics to macroscopic mechanical properties is innovatively realized, so that the mechanical property distribution of the material is accurately evaluated.

The invention discloses a preparation and measurement method of a polymer-based multi-point force flexible sensor for knee joint pose measurement, and relates to the technical field of polymer-based flexible sensors and soft measurement. The structure of the multi-point force polymer-based flexible sensor shaped like a Chinese character'ao 'is designed for knee joint stress and posture state measurement, and the flexible sensor is prepared and packaged through a silk-screen printing method. A flexible sensor strain input-output knee joint stress data mapping set and a flexible sensor strain input-pose angle output data mapping set required by modeling are completed through a flexible sensor, a thin film force sensor corresponding to multi-point force and an inertial sensor corresponding to pose angle measurement of installed multi-point force polymers. And establishing a neural network soft measurement model based on an MLP algorithm, and carrying out training and verification. And the knee joint pose angle with reliable precision and the maximum stress state of the knee joint are obtained. The sensor has the advantages of being flexible, thin, skin-friendly, high in sensitivity and stable in signal output.

The invention relates to the technical field of flexible pressure sensors, in particular to a preparation method and application of a capacitance flexible pressure sensor with an adjustable gradient random micro-cone structure, and the sensor comprises an upper electrode and a lower electrode which are transparent indium tin oxide conductive films (PET / ITO); the packaging layer is a polyimide (PI) adhesive tape and covers the outer side of the electrode; the flexible isolation layer is polydimethylsiloxane (PDMS) with a hollow structure, and is positioned between the upper electrode and the lower electrode; the dielectric layer of a gradient random micro-cone structure is located in the flexible isolation layer and is formed by compounding polyvinyl alcohol (PVA) and phosphoric acid (H3PO4) according to the mass ratio of 1: 1; the gradient micro-cone structure and the ionization effect are combined, the electric double layer effect grading phenomenon occurs, and the gradient micro-cone sensor has the advantages of being high in sensitivity, wide in detection range, capable of keeping linearity under large pressure, short in response time and good in cycling stability.

The invention relates to a temperature drift compensation method for a pressure instrument, and belongs to the technical field of pressure instruments, and the method comprises the following steps: obtaining output signal values of a pressure sensor at different temperature points, and building a temperature-output signal data set; constructing a polynomial fitting model as a temperature drift compensation model, fitting a temperature coefficient through a least square method, and storing the coefficient into an instrument memory; original values of temperature and pressure are collected in real time and substituted into the model to calculate a compensated pressure value. The method also supports various strategies such as temperature sensor calibration, nonlinear error compensation, Kalman filtering noise reduction, dynamic temperature change rate compensation and neural network compensation, and has self-calibration and remote updating functions. According to the invention, the measurement precision and stability of the pressure instrument in a variable-temperature environment are significantly improved, and the method is suitable for high-precision industrial measurement and intelligent monitoring scenes of the Internet of Things.

The invention discloses a preparation method of a cellulose acetate / thermoplastic polyurethane / polypyrrole composite conductive nanofiber aerogel composite pressure sensing material. The preparation method comprises the following steps: (1) preparing a cellulose acetate and thermoplastic polyurethane spinning solution; (2) preparing a cellulose acetate / thermoplastic polyurethane composite nanofiber membrane; (3) preparing cellulose acetate / thermoplastic polyurethane short nano dispersion liquid; (4) preparing a cellulose acetate / thermoplastic polyurethane / polypyrrole polymerization solution; and (5) preparing the cellulose acetate / thermoplastic polyurethane / polypyrrole composite conductive nanofiber aerogel. According to the preparation method, a high-temperature thermal annealing process is not needed, and the prepared nanofiber composite aerogel has high conductivity, ultralow volume density, high compression deformation performance and high pressure resistance sensing sensitivity.

The invention relates to the technical field of intelligent monitoring of petroleum equipment, and particularly provides an industrial equipment state diagnosis method based on multi-modal fusion deep learning. The method comprises the following steps: synchronously acquiring load, vibration and current data through an explosion-proof RTU (Remote Terminal Unit), and transmitting the data to an edge node through an industrial LoRaWAN (Load RaWide Area Network); aligning a multi-source signal time sequence by adopting Kalman filtering, and fusing to generate data streams with consistent time domains; features are extracted by using wavelet packet energy entropy, and a lightweight ResNet18 model is input to realize high-precision diagnosis; based on the diagnosis result, a dynamic optimization strategy is generated through an LSTM-Transform parallel model; and finally, issuing a control instruction to the equipment for execution, and collecting feedback data to update the cloud model. Through the multi-rate signal time sequence alignment and feature-decision two-stage fusion technology, the problem of diagnosis errors caused by data asynchronization in a traditional method is solved, the fault recognition precision and the equipment energy efficiency are remarkably improved, and meanwhile the requirements for explosion prevention and real-time performance of the industrial environment are met.

The utility model provides a flexible temperature-pressure dual-mode sensor for battery expansion force detection. The flexible temperature-pressure dual-mode sensor comprises an upper-layer substrate, a pressure sensitive layer, an upper temperature sensitive layer, an electrode layer, a middle-layer substrate, a lower temperature sensitive layer and a lower-layer substrate which are sequentially arranged from top to bottom, the electrode layer comprises a pressure sensing electrode layer and a temperature sensing electrode layer; the pressure spacing layer is connected with the upper-layer substrate and the middle-layer substrate so as to form a space between the upper-layer substrate and the middle-layer substrate, the pressure sensing electrode layer is arranged on the middle-layer substrate, and the pressure sensitive layer and the pressure sensing electrode layer are separated from each other; the upper temperature sensitive layer and the lower temperature sensitive layer are connected in series through the temperature sensing electrode layer. By applying the technical scheme, the flexible temperature-pressure dual-mode sensor for detecting the expansion force of the battery can be provided, the temperature and pressure changes during expansion of the battery can be accurately measured, the process complexity is reduced, and the practical application range is widened.

The invention relates to the technical field of sensors, in particular to a high-sensitivity pressure-temperature dual-mode sensor and a preparation method thereof.The preparation method comprises the steps that a carbon nano tube is subjected to acid treatment and then reacts with a silane coupling agent KH550, and an aminated carbon nano tube is prepared; the method comprises the following steps: carrying out hydrothermal reaction on a phosphorus source and a graphene oxide aqueous solution to prepare phosphorus-doped reduced graphene oxide; the preparation method comprises the following steps: synthesizing Au-Ag heterojunction nano dispersion liquid through an Au nano seed method; mixing the three components and carrying out ultrasonic treatment to obtain mixed dispersion liquid; the preparation method comprises the following steps: modifying a PDMS substrate with KH560 and polyethylene glycol diamine to prepare a modified PDMS substrate; and coating the mixed dispersion liquid on a modified substrate to form a sensitive layer, and preparing an electrode to obtain the sensor. The aminated carbon nanotubes and the phosphorus-doped reduced graphene oxide construct a three-dimensional conductive network, so that high-sensitivity pressure detection is realized; the phosphorus-doped reduced graphene oxide and the Au-Ag heterojunction form a Schottky barrier, so that stable temperature sensing is realized; and the modified PDMS and the sensitive layer are strongly combined to improve the mechanical durability.

A system may include an array of sensor elements, the array of sensor elements each comprising a first type of passive reactive element, a second type of passive reactive element electrically coupled to the array of sensor elements, a driver configured to drive the array of sensor elements and the second type of passive reactive element, and control circuitry configured to control enabling and disabling of individual sensor elements of the array of sensor elements to ensure no more than one of the array of sensor elements is enabled at a time such that when one of the array of sensor elements is enabled, the one of the array of sensor elements and the second type of passive reactive element together operate as a resonant sensor.

The invention relates to the technical field of quality detection, and provides an aluminum product quality multi-scale detection method and system, and the method achieves the comprehensive detection and analysis of the multi-scale features of an aluminum product from the surface to the interior through the construction of a closed-loop detection system from data acquisition, feature association to feedback adjustment. According to the method, the defect mode of the aluminum product can be accurately identified, the defect reason can be accurately positioned, and the process parameters are effectively adjusted based on the defect mode, so that the quality of the aluminum product is remarkably improved, the production efficiency is improved, the production cost is reduced, the market competitiveness of the product is enhanced, and an innovative and efficient solution is provided for quality control in the aluminum product production process.

The invention discloses a miniature six-dimensional force sensor for a humanoid robot. The miniature six-dimensional force sensor comprises an elastic body which is integrally disc-shaped, and an upper cover plate and a lower cover plate which are arranged at the upper end and the lower end of the elastic body; the elastic body comprises an elastic body hub, a stress table and four strain beams, and the cross section of each strain beam is rectangular; the stress table is hollow and is arranged at the central position of the elastomer hub, the four strain beams are uniformly distributed between the stress table and the elastomer hub, one end of each strain beam is connected with the elastomer hub, and the other end of each strain beam is connected with the stress table; the elastomer hub is provided with a cable outlet. A plurality of strain gauges are adhered to the four strain beams to form six Wheatstone bridges for respectively measuring forces and moments in six directions of Fx, Fy, Fz, Tx, Ty and Tz. The miniature six-dimensional force sensor has the advantages that the thickness of the sensor is very small, the thickness is only 9.2 mm, the diameter is 45 mm, and the miniature six-dimensional force sensor is suitable for the humanoid robot industry.

The invention discloses a multi-stage micro-nano structure flexible wearable pressure sensor inspired by skin, and a preparation method and application of the multi-stage micro-nano structure flexible wearable pressure sensor. The sensor is composed of a piezoresistive layer, an electrode layer and a packaging layer. The piezoresistive layer takes polydimethylsiloxane (PDMS) as a flexible substrate, a submillimeter-level cylindrical array main structure and a micron-level skin-like spinous secondary microstructure randomly distributed at the top end of the submillimeter-level cylindrical array main structure are constructed through a composite mold, and a nano-level conductive network is formed by adopting an MXene / MWCNT hybrid material through a dispensing process, so that multi-scale stress synergistic response is realized; the electrode layer takes a degradable lignocellulose biological plastic film prepared from bagasse as a base material, and a silver paste interdigital electrode is loaded through a screen printing process; the packaging layer is fully coated with a medical-grade polyurethane (PU) film so as to ensure signal stability. The sensor shows ultrahigh sensitivity, wide detection range and low detection limit through the synergistic effect of the submillimeter-micron-nano multilevel structure, and can be applied to the fields of human motion monitoring, intelligent electronic skin and the like.

The invention provides an electromagnetic ultrasonic bolt stress detection system based on a space-time complementation algorithm, and the system comprises the steps: carrying out the multi-channel ultrasonic signal collection of a to-be-detected bolt, and obtaining a multi-channel echo signal group; performing time-varying attenuation compensation and adaptive entropy weight denoising on the multi-channel echo signal group to obtain a denoised echo signal group; extracting echo time-domain features from the de-noised echo signal group, performing segmented time-frequency feature extraction on the de-noised echo signal group based on a short-time sliding window to obtain echo time-frequency features, and fusing the echo time-domain features and the echo time-frequency features into ultrasonic time-frequency features; performing wave field reconstruction on the de-noised echo signal group to obtain a spatial ultrasonic energy diagram, and extracting ultrasonic spatial features of the spatial ultrasonic energy diagram based on a self-attention map convolutional network; performing correlation detection and space-time complementary modeling on the ultrasonic time-frequency features and the ultrasonic space features to obtain ultrasonic space-time joint features, and performing stress analysis on the ultrasonic space-time joint features to obtain bolt stress data.

The invention discloses an electrochemical pressure sensor based on polyelectrolyte gel and a preparation method thereof, and belongs to the technical field of pressure-sensitive elements and pressure sensors. The electrochemical pressure sensor comprises a polyelectrolyte gel pressure sensitive layer, a positive electrode and a negative electrode, wherein the surface of the polyelectrolyte gel pressure sensitive layer is provided with a bowl-shaped groove array structure; the positive electrode is positioned on the upper surface of the pressure sensitive layer; the negative electrode is positioned on the lower surface of the pressure sensitive layer; the polyelectrolyte gel pressure sensitive layer comprises PVA, LiCl and PSS. The surface bowl-shaped groove array structure prepared by using the nucleation effect has excellent pressure-sensitive performance: on one hand, the sensitivity of contact between polyelectrolyte gel and an electrode to pressure is improved, so that the sensor realizes excellent pressure response characteristic; and on the other hand, the surface microstructure gel electrolyte preparation method with less damage to the mold and wider applicability is realized. The electrochemical pressure sensor provided by the invention has the characteristics of high response rate and high output power, and has important value for development of high-performance self-energized pressure sensors.

The invention provides a multi-axial fatigue damage evaluation method and device for a steel bridge welding seam considering welding residual stress, and relates to the technical field of fatigue strength analysis and service life prediction of a steel bridge structure. The method comprises the following steps: measuring three-dimensional residual stress in a geometric transition area of a steel bridge welding seam by adopting X rays, acquiring heat distribution and surface topography data in combination with infrared thermal imaging and structured light scanning, and realizing multi-source data space alignment based on a unified coordinate system; secondly, random disturbance is applied to the residual stress, stress fluctuation in service is simulated, and a robustness weight risk factor is constructed; the method comprises the following steps: constructing a bimodal deep neural network, adaptively fusing information of an infrared thermal imaging image and a welding seam three-dimensional topography image through an attention mechanism, training a fatigue risk regression model by taking a robustness weight risk factor as a supervision signal, completing prediction of risk factors at all positions, dividing low, medium and high risk levels based on a preset threshold value, and obtaining a fatigue risk prediction result. And accurate grading evaluation of the fatigue damage of the welding seam is realized.

The invention relates to the technical field of sensitive elements, in particular to a sensitive element of a multi-gradient interlocking structure, a preparation method and a flexible pressure sensor. The sensitive element comprises a packaging layer and a functional layer, and the packaging layer is used for maintaining the form of the functional layer. The functional layer comprises an upper electrode layer, an ionic medium layer and a lower electrode layer which are sequentially arranged from top to bottom. A plurality of first protrusions are arranged on the side, facing the ionic dielectric layer, of the upper electrode layer, and a plurality of second protrusions are arranged on the side, facing the upper electrode layer, of the ionic dielectric layer. The heights of the first protrusions are gradually increased from left to right, and the heights of the second protrusions are gradually reduced from right to left. The first protrusions and the second protrusions are arranged in a staggered mode so as to form a capacitance change area of a multi-gradient interlocking structure. The flexible pressure sensor made of the sensitive element has high sensitivity on the basis of a wide detection range, and is suitable for application scenes such as human health information monitoring and wearable equipment.

The utility model relates to an armored temperature and pressure integrated sensor, and the sensor comprises an armored housing which comprises a housing body and a sleeve connected with the housing body, and the sleeve is internally provided with a closed cavity; the circuit board is arranged in the armored shell; the temperature sensing element is mounted in the closed cavity; the support is arranged in the armored shell and comprises a support body, a metal terminal is arranged in the support body, one end of the metal terminal extends into the armored shell to be connected with the temperature sensing element, and the other end of the metal terminal extends out of the support body to be connected with the circuit board; and the pressure sensing element is mounted in the bracket and connected with the circuit board, and the pressure sensing element is used for detecting the pressure of an external pressure source. While temperature and pressure are measured synchronously, the excellent performance of the armored shell effectively protects internal elements from external physical impact, electromagnetic interference, severe temperature fluctuation and the like, it is ensured that the sensor can work stably and reliably in a complex environment and a complex medium, and the measurement precision is ensured.

A force sensor is provided for use with an instrument. Accordingly, the force sensor includes a beam and a set of half-bridges that are arranged on the surface of the beam. Two of the half-bridges include tension resistors, while the other two half-bridges compression resistors. The half-bridges are arranged on the beam surface such that redundant measurements of orthogonal components of a force imparted to the beam can be made using four different combinations of three of the half-bridges.

The invention provides an accurate calculation method and system for the axial pretightening force of an ultrasonic bolt, and the method combines a high-order statistic theory with a nonlinear filtering technology, and solves the problem that the measurement precision of a conventional method is insufficient under the conditions of strong noise and nonlinearity. Particle filtering and Kalman filtering are combined, so that the problem that a traditional Kalman filtering method easily causes sensitivity of an initial value of filtering divergence is solved; a double-Gaussian attenuation model is adopted to fit an echo envelope, the problem that a single Gaussian or single index model cannot adapt to envelope distortion caused by stress change is solved, and a nonlinear state space model is established to adapt to nonlinear echo signal characteristics under dynamic stress; gaussian noise is effectively suppressed by using a third-order cumulant cross-correlation algorithm, the time difference resolution in a low signal-to-noise ratio environment is remarkably improved, and the Gaussian noise and asymmetric interference are effectively suppressed.

The invention belongs to the technical field of sensors, and particularly discloses a capacitive sensor based on multistage compression deformation and a preparation method thereof, the capacitive sensor comprises an upper electrode layer and a lower electrode layer; a deformable corrugated structure is arranged between the upper electrode layer and the lower electrode layer, and the deformable corrugated structure comprises three deformation areas with the same structure; the deformation area is internally provided with three cavities, and the three cavities are filled with a first dielectric layer, a second dielectric layer and a third dielectric layer respectively; magnetic adsorption units are arranged on the outer sides of the adjacent deformation areas. According to the capacitive sensor based on multi-stage compression deformation and the preparation method thereof, the sensor realizes wide-range and multi-resolution pressure measurement capability; the sensor can present different sensitivity characteristics in different pressure ranges, can flexibly switch working modes, meets various measurement requirements from low pressure to ultrahigh pressure, and solves the technical problem that a traditional capacitive sensor is difficult to consider wide range and high resolution at the same time.