125 results about "Triaxial accelerometer" patented technology

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention relates to the technical field of machining, and discloses a lathe tool wear self-inspection alarm system which comprises a multi-source sensing module, an intelligent analysis module, a dynamic response module, an interactive display module and a self-adaptive energy supply module. The multi-source sensing module synchronously acquires the surface abrasion loss, the vibration spectrum and the acoustic emission signal of the cutter through an optical sensor, a capacitive sensor and an MEMS triaxial accelerometer, and outputs the surface abrasion loss, the vibration spectrum and the acoustic emission signal to the intelligent analysis module; and the intelligent analysis module performs fusion analysis on the multi-source data by adopting a mixed feature extraction and double-model decision mechanism. High-precision monitoring and dynamic evaluation of lathe tool wear are realized through optical, vibration and acoustic emission sensors of the multi-source sensing module in combination with mixed feature extraction and a double-model decision mechanism of the intelligent analysis module. By means of the mode of combining multi-dimensional monitoring and intelligent analysis, machining defects and equipment failure risks caused by tool abrasion can be reduced.

The invention relates to the field of computer medical engineering, and discloses a blood pressure continuous measurement abnormity early warning method and system based on AI data analysis, and the method comprises the steps: collecting a pulse wave signal, triaxial accelerometer data, an ambient light intensity signal and a skin conductance signal through a sensor on a preset wearable device; generating multi-scale physiological features; systolic pressure and diastolic pressure are calculated, and blood vessel stiffness is calculated; calculating the main arteriosclerosis risk level; when the main arteriosclerosis risk level is higher than a preset risk level, a user is prompted through the wearable device. According to the invention, the pulse wave, the triaxial accelerometer, the ambient light intensity and the skin conductance signal are synchronously acquired and analyzed to obtain the systolic pressure, the diastolic pressure dynamic trend and the blood vessel stiffness, and the main arteriosclerosis risk level can be accurately quantified. In addition, a'physiological parameter-angiosclerosis-risk decision 'closed-loop management mechanism is established, and automatic and active health management of the user is realized.

The invention provides a weak current pipeline settlement monitoring system based on multi-source data fusion. The system comprises a ground sensor array which comprises a plurality of fiber bragg grating static level gauges, a strain sensing optical cable and a plurality of MEMS triaxial accelerometers and is used for collecting horizontal displacement, deformation stress and vertical settlement in sequence; the satellite remote sensing module comprises a synthetic aperture radar module and a GNSS module and is sequentially used for acquiring earth surface deformation data and elevation data of a monitoring area; and the data analysis platform is used for carrying out feature extraction on the data acquired by the ground sensor array, carrying out multi-source data fusion on the extracted features and the satellite remote sensing data acquired by the satellite remote sensing module by adopting a multi-source data fusion algorithm, and carrying out settlement monitoring and early warning based on a fusion result. According to the scheme, the problems that traditional single monitoring precision is insufficient and response lags are solved, the pipeline settlement early warning reliability can be remarkably improved, and the pipeline breakage risk is effectively prevented.

The invention discloses a ship ladder braking and lift car leveling safety state monitoring system based on digital twinning, which relates to the technical field of mechanical and electrical engineering, and comprises a system integrated leveling monitoring module, which adopts a salt mist resistant fiber grating displacement sensor and a magnetic railing ruler-Hall array closed loop verification mechanism and is combined with an anti-corrosion stay wire encoder; high-precision leveling detection is realized; the vibration signal separation module effectively recognizes braking abnormity through a triaxial accelerometer and a self-adaptive signal processing technology; a dynamic twin adaptive engine is fused with the ship ladder dynamic model and a transfer learning algorithm, and control parameters are adjusted in real time; and the cross check logic module automatically reconstructs data when the sensor is abnormal, so that the reliability and the fault-tolerant capability of the system are ensured. According to the invention, the berthing position error of the lift car is controlled within a safety range under different loads, speeds and sea conditions of the ship ladder, so that the riding comfort and safety are improved.

The invention relates to the technical field of strip mine side slope slippage trend early warning, and particularly discloses a strip mine side slope slippage trend early warning system based on active micro-vibration excitation, which comprises the following components: an excitation unit which is composed of a frequency-adjustable micro-vibration exciter which periodically applies low-amplitude controllable mechanical disturbance signals to a side slope rock mass, the rock-soil medium structure response is excited, and the early warning sensitivity is improved; the sensing unit comprises a three-axis acceleration sensor, a displacement meter, a GNSS (Global Navigation Satellite System) sensor and a ground acoustic wave sensor, and is used for recording response change in a vibration disturbance signal propagation process and obtaining data; the analysis unit comprises an edge intelligent terminal, a slippage trend identification model built in the edge intelligent terminal and a lightweight neural network, and the slippage trend identification model performs sensing processing on the data and outputs a slippage trend label; and the communication control unit is used for ensuring that the data streams of the units are stable and cooperatively run with the modules.

The invention discloses a spatial sound effect test method and system for a sound system. The method comprises the following steps: S1, synchronously acquiring multi-modal sensing data of a test environment in a preset stage; s2, generating a three-dimensional sound pressure distribution thermodynamic diagram, and constructing an initial sound field model based on the three-dimensional sound pressure distribution thermodynamic diagram; s3, generating a corrected sound field model, and synchronously recording vibration data acquired by the triaxial accelerometer; s4, generating a sound image positioning trajectory diagram, and calculating a deviation angle between an actual sound image position and a theoretical position; and S5, determining the sound effect quality score of each test stage. According to the method, a closed-loop test framework of physical environment perception, sound field real-time modeling, man-machine interaction analysis and fault linkage traceability is constructed, a full-process test solution covering a hardware state, acoustic characteristics and human factor perception is provided for an immersive audio system, and the spatial sound effect reliability of digital playing equipment is remarkably improved.

The invention provides an intelligent vehicle paint thickness detection system and method based on gyroscope positioning, and relates to the technical field of vehicle detection. The system comprises a sensor module for acquiring three-dimensional point cloud and temperature data of a vehicle; the data preprocessing module extracts surface geometry and temperature information; the three-dimensional construction module establishes a three-dimensional model with a coordinate system; the thickness measurement module calculates the paint thickness by combining geometric characteristics and temperature compensation; the data integration module generates a spatialized thickness data set; and the data management module stores the data and outputs a thermodynamic diagram. Accurate positioning of the scanner is achieved through the MEMS gyroscope and the triaxial accelerometer, the measurement accuracy is improved through temperature compensation and curvature correction algorithms, the problems that traditional detection is low in efficiency, poor in accuracy and non-uniform in standard are solved, and the method is suitable for second-hand car evaluation, production line quality inspection and other scenes.

The invention relates to the technical field of security and protection equipment, and discloses a portable multi-mode intelligent alarm terminal based on Beidou positioning, which comprises a Beidou third-generation positioning module, a multi-mode communication module, a dual-mode power supply system, a physical key assembly, a central processing unit and a sensor array, the Beidou third-generation positioning module supports BDS / GPS / GLONASS multimode fusion positioning, the positioning precision is less than or equal to 1m (in an outdoor enhancement mode), and the multimode communication module integrates 4G / 5G data communication, Beidou short message transmission and Bluetooth LE near field communication functions. According to the invention, through Beidou third-generation / GPS / GLONASS multimode fusion positioning, the Kalman filtering algorithm and the signal-to-noise ratio dynamic weighting technology are combined, and the outdoor positioning precision of less than or equal to 1m is realized. When a satellite signal is shielded, an inertial navigation mode is automatically switched to, a trajectory is corrected by utilizing data of a triaxial accelerometer, and an accumulative error is controlled within 5m / min. Through actual measurement, the positioning success rate in the urban canyon environment reaches 98% and is improved by 40% compared with that of a traditional single-mode GPS terminal, and it is ensured that rescue force quickly and accurately arrives at the site.

The invention relates to the technical field of navigation, and particularly discloses a GNSS / INS integrated navigation dynamic error correction method and system, and the method comprises the steps: S1, receiving GNSS original data, and calculating multipath interference intensity; s2, three-axis accelerometer data and yaw velocity information are received, and corrected displacement is calculated; s3, a fuzzy C-means clustering algorithm is adopted, and the motion state is obtained according to the corrected displacement; s4, generating a signal quality threshold value and a motion state threshold value according to the motion state; s5, constructing an error decoupling matrix according to the multipath interference intensity, the corrected displacement, the signal quality threshold value and the motion state threshold value; and S6, performing navigation calculation after error compensation by using the error decoupling matrix. According to the method, accurate compensation of dynamic errors is realized through accurate decoupling analysis of the motion characteristics of the carrier, so that the adaptability and reliability of the GNSS / INS integrated navigation system in a complex environment are improved.

The invention discloses an intelligent livestock and poultry ear tag device based on edge calculation and lightweight reasoning, and the device comprises a dynamic sampling module which collects the motion behavior data of livestock and poultry through a three-axis acceleration sensor based on a low-power-consumption sampling mechanism. The local processing module carries out local real-time processing on the collected data by adopting feature screening and a light-weight behavior recognition algorithm based on a TinyML model, and the low-power-consumption transmission module processes the collected data based on a bitmap compression coding form to carry out internal communication transmission, so that wireless data transmission is carried out by a low-power-consumption transmission mechanism based on AI edge calculation; according to the method, a low-power-consumption sampling mechanism and a lightweight algorithm based on a TinyML model are matched with feature screening, so that the power consumption and the calculation amount can be greatly reduced; an AI edge computing architecture is utilized, only key events and features are uploaded, the wireless transmission frequency is reduced, the communication power consumption is reduced, and local intelligent operation in a network disconnection scene is supported.

The invention provides a substation settlement monitoring method and system, and relates to the technical field of settlement monitoring, and the method comprises the steps: deploying a bionic root system mesh array sensor network which comprises a plurality of monitoring nodes; collecting current phase data of a transformer substation bus in real time to calculate magnetic field interference, and performing inverse compensation on an original displacement signal of the magnetostrictive displacement sensor; carrying out vibration monitoring through a triaxial accelerometer, and carrying out data weight adjustment on a plurality of monitoring nodes; and performing settlement prediction on the transformer substation according to the displacement compensation data in combination with the weight parameters of the plurality of nodes, performing backtracking calibration on the bionic root system mesh array sensor network, and performing closed-loop monitoring on the settlement of the transformer substation according to a calibration result. The technical problem of inaccurate settlement monitoring caused by interference in a high-intensity magnetic field environment of the transformer substation in the prior art is solved, and accurate prediction and closed-loop monitoring of settlement of the transformer substation are realized by deploying a plurality of intelligent sensors and performing magnetic field interference compensation.

The invention provides an automobile fault identification method and a detection system. The method comprises the following steps: acquiring running state data of the automobile seat through a multi-mode sensor network; wherein the multi-mode sensor network comprises a current sensor, a temperature sensor, a triaxial accelerometer and a displacement sensor, and the operation state data at least comprises a current signal, a vibration signal, a temperature signal and a displacement signal; generating a seat state mapping matrix based on the multi-modal data; inputting the seat state mapping matrix into a preset hybrid diagnosis model to execute fault recognition; and according to a fault identification result output by the hybrid diagnosis model, carrying out fault early warning and generating a fault risk quantification report. Through the method provided by the invention, the fault of the automobile seat can be effectively identified.

The invention relates to the technical field of biological recognition, in particular to a multi-scene-oriented dynamic voiceprint recognition updating method, which comprises the following steps: synchronously acquiring voice signals through a 16-channel MEMS microphone array and a throat three-axis acceleration sensor, and constructing a three-dimensional data set containing acoustic scene fingerprints, semantic contexts and emotional characteristics; extracting features by adopting a scene adaptive dual-mode processing architecture, and extracting semantic features and emotional state coding vectors to form a multi-dimensional feature set; based on the multi-dimensional feature set, calculating a matching degree by using a Gaussian mixture model, monitoring a scene mutation index, and triggering an online clustering reconstruction mechanism to optimize a feature weight; deploying a three-level pipeline architecture for real-time processing; and generating a scene adaptation factor according to the MFCC-PLP mixed feature matrix and the environmental parameters to perform multi-feature joint matching. According to the invention, the adaptability of voiceprint recognition in a complex scene can be improved.

The invention relates to a dual-mass-block three-axis acceleration sensor and a processing method thereof. The sensor comprises an X-axis and a Y-axis which are arranged on a first mass block, and a Z-axis which is independently arranged on a second mass block. The X-axis movable comb tooth structure and the Y-axis movable comb tooth structure are arranged outside the first mass block; the movable comb tooth structure of the Z axis is arranged in a central area between the first mass block and the second mass block, and an included angle of 135 degrees or 45 degrees is formed between the extension direction of the movable comb tooth structure of the Z axis and the extension directions of the movable comb tooth structures of the X axis and the Y axis; the first elastic supporting piece is connected to the first mass block and the substrate and used for sensing the X-axis acceleration and the Y-axis acceleration. The second elastic supporting piece is connected to the second mass block and the substrate and used for sensing the Z-axis acceleration. The fixed comb tooth structures of the X axis, the Y axis and the Z axis are fixed to the substrate through anchor points and arranged in a staggered mode with the movable comb tooth structures. The X-axis, the Y-axis and the Z-axis are arranged on different mass blocks, the comb tooth structure forms an included angle, the cross-axis sensitivity is reduced, and the size is reduced.

The invention provides a backpack multi-sensor data fusion-based motion attitude anomaly real-time detection method, which comprises the following steps of: acquiring multi-channel original data through a backpack-type integrated triaxial accelerometer, a gyroscope, a magnetometer and a barometer, implementing synchronous sampling and time-space alignment, and constructing a standardized time sequence data stream through wavelet denoising and multi-dimensional normalization preprocessing; high-dimensional features representing motion differences are extracted, nonlinear dimensionality reduction is achieved through principal component and Laplacian feature mapping, and a motion mode prototype library is generated; according to the method, a new motion situation can be continuously and adaptively learned, and the motion recognition accuracy, the system robustness and the attitude anomaly detection capability in a complex scene are effectively improved.

The invention discloses a uterine contraction detection method based on multi-modal signal fusion reconstruction, which comprises the following steps of: 1, synchronously acquiring an abdominal surface uterine electromyographic signal in real time through a high-density electrode array, acquiring a uterine contraction pressure signal in cooperation with a fundus pressure sensor, synchronizing a triaxial accelerometer signal to acquire a maternal motion signal, and preprocessing; 2, performing time sequence alignment and resampling on the data, and constructing a quality mask to form a multi-mode uterine contraction signal data set; 3, constructing a reconstruction network based on multi-modal feature fusion; 4, constructing a total loss function of the multi-modal feature fusion reconstruction network; and 5, training a reconstruction network of multi-modal feature fusion, wherein the reconstruction network is used for fusion reconstruction of the multi-modal uterine contraction data. According to the invention, through multi-modal signal fusion and deep feature correlation analysis, accurate monitoring and prediction of uterine contraction and premature delivery risks in the pregnant and birth period can be realized.

The invention discloses an urban and rural pavement maintenance monitoring system based on intelligent well lid data linkage, and the system comprises a data collection module which detects the acceleration data, angular motion data and inclination angle change data of a well lid in real time through a three-axis acceleration sensor, a gyroscope and an inclination angle sensor which are disposed on the well lid; the edge calculation module is used for calculating a vibration value of the well lid according to the acceleration data, the angular motion data and the inclination angle change data of the well lid and correcting the vibration value; the cloud data center obtains a risk index of a road surface where the well lid is located through a pre-constructed association model according to the corrected vibration value and the position of the well lid corresponding to the vibration value; and the maintenance decision terminal module performs maintenance decision on the road surface where the well lid is located according to the risk index of the road surface where the well lid is located. According to the invention, the well lid on the road surface is taken as the basis of data acquisition, and the maintenance condition of urban and rural road surfaces is detected in real time by acquiring the data on the well lid, so that the maintenance condition can be accurately obtained in real time.

An anti-kinetosis device for a motor vehicle includes a triaxial accelerometer, a display means, and a control unit. The triaxial accelerometer detects accelerations of the vehicle along three axes emits a corresponding acceleration signal. The display means includes light markers forming a first horizontal plane. The control unit includes an acceleration signal input, a processor, and a memory. The control unit receives the acceleration signals emitted by the accelerometer at the acceleration signal input. The memory stores instructions configured to cause the processor to align the first horizontal plane perpendicular to a gravitation vector by driving the means for displaying light markers and configured to cause the processor to align the first horizontal plane with a physical parameter of a first person seated in the motor vehicle. The first horizontal plane is positioned just beside the first internal surface and just in front of the second internal surface.

The invention provides an angle calculation method and system based on a triaxial accelerometer, and relates to the technical field of inertial navigation, and the method comprises the steps: obtaining an acceleration signal and preprocessing the acceleration signal to obtain an effective acceleration component, constructing a nonlinear error feature vector, carrying out the real-time parameter identification through a recursive least square method, and building a nonlinear error compensation model. And performing dynamic correction by adopting a self-adaptive sliding time window, and finally calculating a pitch angle and a roll angle. The non-linear error of the accelerometer can be effectively inhibited, the angle calculation precision is improved, and the method has the characteristics of good real-time performance and strong adaptability.

The present application provides a method and device for evaluating the stability of cover plate pressing, which relates to the technical field of cover plate pressing. The method includes: loading a calibrated test workpiece onto a target fixture and fixing it with a cover plate; controlling the machine tool to start and perform machining; reading monitoring data in real time and normalizing the contact two-dimensional standard force field into a time-series grayscale image; establishing a feature recognition result for the time-series grayscale image; synchronously obtaining the acquisition information of a triaxial accelerometer arranged on the fixture and using the micro-vibration signal as the first verification signal; monitoring the key points of the workpiece and using the time-series displacement of the workpiece as the second verification signal; performing stability verification on the feature recognition result and outputting a stability verification evaluation result. Through the present application, the technical objectives of multi-dimensional fusion modeling and stability intelligent evaluation based on the workpiece structure characteristics and the machining dynamic process can be achieved, and the technical effects of improving the recognition accuracy of pressing stability, reducing the misjudgment rate, and enhancing the adaptability of the system to complex machining environments can be achieved.

The invention relates to intelligent wearable bioelectronic equipment for monitoring and treating temporal muscle and masseter dysfunction and a control method, and belongs to the technical field of medical equipment. The device comprises a circuit board, a core processing unit MCU is arranged on the circuit board, the core processing unit MCU is respectively connected with an ACC triaxial accelerometer, a digital isolator and an NFC wireless communication and charging patch, the ACC triaxial accelerometer is connected with a surface myoelectricity electrode patch through an analog front end, the digital isolator is connected with a stimulation electrode patch through an electrical stimulation driving module, and the NFC wireless communication and charging patch is connected with an NFC wireless communication and charging module. And the NFC wireless communication and charging patch is connected with a reference electrode patch. According to the invention, efficient, safe and comfortable treatment of functional TMD is realized.

The invention relates to the technical field of brain-computer interfaces, and discloses a multi-channel EEG-based personalized model training and common feature extraction system, which comprises a three-channel EEG sensor electrode, a triaxial accelerometer, an electro-oculogram auxiliary electrode, a signal conditioning and acquisition circuit module, a local processing unit, a wireless communication module and a power management module. The invention also provides a tag generation method of the multi-channel EEG-based personalized model training and common feature extraction system, and the method comprises the following steps: S1, multi-modal signal synchronous collection: through cooperation of all parts, bio-electricity signals and behavior signals of a user in different states can be monitored and captured in real time, and the multi-modal signal synchronous collection is realized; in addition, the local model can actively align cross-user feature distribution while optimizing task performance, inter-domain differences are effectively reduced, and the generalization ability of the global model is improved.

This invention discloses a near-bit engineering parameter measuring device and method. The measuring device includes a novel drill bit and an engineering parameter measuring mechanism connected to each other. The novel drill bit includes a drill bit body, a drill pressure strain gauge, a torque strain gauge, and a data transmission system, accurately measuring operating parameters such as torque and thrust at the drill bit during drilling. The engineering parameter measuring mechanism includes an outer cylinder, an inner cylinder, a triaxial accelerometer, a signal processing module, a ground signal receiving device, and a battery module. The engineering parameter measuring mechanism is a double-layer, double-cylinder structure that uses the principle of magnetoelectricity to generate current and power the measuring system. The magnitude of the current generated when the inner cylinder rotates relative to the outer cylinder provides feedback on the relative rotational speed of the drill bit. Simultaneously, a triaxial accelerometer is arranged on the outer cylinder to acquire the angular acceleration information of the outer cylinder, calculate the rotational speed of the outer cylinder, and sum the velocities to obtain the actual rotational speed at the drill bit, thus achieving accurate measurement of the drill bit rotational speed.

A laryngeal pacemaker is configured for external placement on skin of a patient to produce respiration stimulation signals. An implantable stimulation electrode delivers the respiration stimulation signals to adjacent target neural tissue for vocal fold abduction during respiration of the recipient patient. A triaxial accelerometer produces a body motion signal reflecting energy expenditure of the recipient patient. A respiration sensor includes a flexible skin-transferrable printed tattoo electrode having a tetrapolar configuration for impedance pneumography measurement to produce a sensed respiration signal for the laryngeal pacemaker. The respiration sensor is configured for transfer and release by guided placement from a sensor applicator to the skin at the angulus sterni of the recipient patient. And the laryngeal pacemaker is configured to interpret the body motion signal and the sensed respiration signal to make a real time determination of respiratory phase and frequency for adaptively adjusting the respiration stimulation signals accordingly.

The invention belongs to the field of embedded device security design, and particularly relates to a security starting system and method based on dynamic environment binding and heterogeneous inspection. A safe starting framework is constructed through a triaxial accelerometer, a main control chip and a verification module, wherein the main control chip integrates an annular oscillator array, a temperature sensor and a hardware hash engine, and the verification module comprises an AI coprocessor and a block chain communication module. The system firstly collects three-dimensional acceleration and environment temperature data, combines dynamic physical signals generated by an annular oscillator array, and generates a dynamic decryption key through fusion of a hardware hash engine; and then heterogeneous dual-channel verification is formed through the AI coprocessor and the block chain communication module, so that the key validity is ensured. According to the method, a static key is replaced by a dynamic trust root, the bottleneck of a single chain is broken through heterogeneous verification, protection is enhanced through environmental data binding, security and light weight are considered, a real-time industrial control scene with limited adaptive resources is adapted, and attacks such as physical detection and environmental simulation are effectively resisted.

This invention discloses a real-time detection method and system for steel slag flow based on a triaxial accelerometer, belonging to the field of steel metallurgical process detection technology. It acquires triaxial vibration data to fully capture the impact energy of steel slag in any direction in three-dimensional space, overcoming the signal loss problem caused by the mismatch between the installation direction and the impact direction of traditional single-axis sensors. Based on this, a dynamic threshold pre-screening mechanism is adopted, requiring only the calculation of a few core features to quickly determine the slag-free state. For suspected slag-containing periods, a CNN-LSTM hybrid model is further used for fine identification. The model automatically extracts the nonlinear combinations and temporal dependencies between feature dimensions, achieving accurate classification of four states: initial flow, stable slag flow, and impact of large slag pieces. This invention integrates comprehensive perception, efficient prediction, and intelligent identification, providing a safe, reliable, and low-cost online detection and classification control solution for steel slag flow in the steelmaking process.

The invention provides a medical flatcar with spine maintaining and anti-seismic functions, and belongs to the technical field of medical instruments, the flatcar absorbs vertical oscillation and horizontal jolt in the transfer process through a multi-stage damping structure (buffer springs, air cushions and buffer pads); pavement pre-judgment and self-adaptive damping adjustment are realized through an intelligent sensing system (a laser ranging sensor / laser obstacle avoidance radar, a triaxial accelerometer and a central controller); an ergonomic protection structure (including a spine fixing system, a protection plate and a back cushion) is matched to guarantee the body position safety of the patient; and the equipment management efficiency is improved through a bottom drawing type storage structure. Transfer jolt is remarkably reduced, the device is particularly suitable for patients with spinal injury, cerebral hemorrhage, organ rupture and the like, head displacement is reduced according to clinical actual measurement, and the discomfort rate of the patients is decreased.

A testing method and system for lithium battery protection boards is disclosed. The method includes: sampling the resonant frequency of a connector structure using an acoustic resonant sensing unit to obtain an acoustic baseline signal indicating the mechanical connection state of the connector; real-time acquisition of environmental vibration intensity and impact event time using a triaxial accelerometer and an impact sensor, and time alignment with the acoustic baseline signal; applying excitation pulses of a predetermined frequency to the connector at a preset cycle, and acquiring the real-time resonant response curve of the connector to the excitation pulses using the acoustic resonant sensing unit to calculate the baseline profile offset; comparing the baseline profile offset with a preset safety threshold to convert the acoustic offset into a loosening risk indicator based on the comparison result; and performing a load switching test of a predetermined amplitude in response to the loosening risk indicator to determine whether the connector is abnormal. This invention can promptly detect poor contact caused by changes in connector gaps due to vibration, ensuring the accuracy of the alarm.

The invention provides a dynamic tool surface measurement method and system based on multi-sensor fusion, reliable tool surface measurement is realized through three-sensor data fusion and dynamic weight adaptive adjustment, and the method comprises the following steps: measuring the angular velocity of a drilling tool based on a single-axis gyroscope, and calculating GTF and delta GTF; measuring a gravity component based on a triaxial accelerometer, and calculating HTF; measuring a magnetic field component based on a three-axis magnetoresistive sensor, and calculating MTF and delta MTF; working condition modes are divided according to the rotating speed of the drilling tool, the weight of each sensor is dynamically adjusted, a fusion tool face TF = a * HTF + b * MTF + c * GTF is obtained through calculation, when the drilling tool is in a space static state, the drilling tool enters a static mode, and GTF and HTF data are fused; when the rotating speed of the drilling tool is smaller than a first threshold value, entering a low-rotating-speed mode, and fusing data which takes HTF and MTF as main data and GTF as auxiliary data; and when the rotating speed of the drilling tool is greater than or equal to a first threshold value, entering a high-rotating-speed mode, and fusing the fusion data of the low-rotating-speed mode and the incremental data delta GTF and delta MTF.