2163 results about "Accelerometer" patented technology

Locator systems are disclosed for locating buried utilities such as conduits, cables, pipes. or wires. The locator system may include image-capturing and position and orientation measuring devices such as magnetic compass, accelerometers, gyros, GPS, and DGPS. The system may associate images captured during the locate process with buried utility position data and the other sensor data. These data and images may be further associated with terrain images from satellites or aerial photography to provide a highly precision map and database of buried utility locations and visual images of the burial site. Information so obtained may be transferred to a hand-held personal communication device, such as a smart phone to show the location of buried utilities in combination with photo-images and / or terrain maps.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention relates to the technical field of geological landslide monitoring, in particular to a tower footing geological landslide monitoring and early warning system and method. The method comprises the following steps: arranging an inclination angle sensor, a Beidou-GNSS module, a soil moisture content probe and a micro-seismic accelerometer on a tower footing and the periphery of the tower footing, and collecting the inclination amount, three-dimensional displacement, soil moisture content and vibration response information of the tower footing to form a monitoring original data set; timestamp unification, WGS-84 projection coordinate system conversion and multi-frequency vibration elimination are carried out on the monitored original data set, and an air-ground integrated displacement profile is constructed; the whole process realizes high integration and automation in the aspects of data acquisition, processing, fusion and transmission, so that the timeliness, accuracy and data integrity of landslide risk monitoring are ensured, and scientific decision support is provided for disaster prevention and reduction.

The invention relates to a grid steel structure node failure diffusion prediction method, which comprises the following steps of: forming a failure inducement and evolution path model according to a connection form of a bolt-sphere node and a welding node, a stress transfer path, node local yield and fatigue accumulation factors; a diffusion logic of node failure-force flow redistribution-adjacent node stress concentration-chain reaction is constructed; nodes are regarded as middle points of the graph, rod pieces are regarded as edges, and propagation weights between the nodes are defined; the weight parameters are dynamically assigned based on material attributes, node connection types, spatial geometric positions and historical stress states; multi-source monitoring sensors including strain gauges, displacement meters, thermometers and accelerometers are arranged at key nodes of the grid structure, so that node states are sensed; in combination with real-time monitoring data, extracting strain rate change, vibration characteristics, frequency drift and residual deformation key characteristics of each node; a node health state feature vector set is constructed by using time sequence analysis, and the gradual degradation trend of nodes is reflected.

The invention relates to the technical field of data processing, in particular to an accelerometer vibration rectification error analysis method, which comprises the steps of receiving multiple paths of accelerometer original current signals, processing acquired data and outputting a feature point coordinate sequence with confidence; according to the confidence and the optimization algorithm combination in the frequency band distribution characteristic dynamic scheduling knowledge base, calculating a local statistical data set; inputting the data set into a mechanical dynamics model to execute trajectory simulation, and inputting a control model reconstruction signal to compare a dual-path positioning drift distance difference to generate an error correction coefficient matrix; outputting a knowledge base updating instruction and a parameter resetting instruction by adopting a matrix reconstruction vibration rectification error quantized value; a quantized value is imported to calculate theoretical angle deviation and verify convergence, and the neural network is triggered to be retrained when the deviation exceeds a threshold value. Through a non-smooth feature extraction algorithm and a closed-loop feedback mechanism, the problem of signal distortion caused by smooth operation in the prior art is solved.

The invention discloses a full-automatic cable vibration high-precision measurement method based on a machine vision technology, and relates to the technical field of structural vibration measurement. The method comprises the following steps: installing and calibrating equipment, and ensuring that a cable plane coincides with an imaging plane; high-frame-rate video acquisition is carried out, and comparison verification is carried out in combination with an accelerometer; screening effective pixels based on the pixel intensity time sequence variance, and constructing a pixel intensity space-time matrix by using the time sequence of the pixels; sVD decomposition is adopted to extract an accumulated energy ratio gt; 90% of the main modes are reconstructed, and real signals are revealed; a plurality of inhaul cables are automatically positioned and coded through gradient features and Hough transform; sub-pixel-level vertical displacement is calculated by applying an optical flow method, and median filtering is combined to resist noise; fourier transform is carried out to obtain a spectrogram, and a fundamental frequency is identified; and calculating cable force based on a string pulling theorem. According to the method, remote, full-field and multi-target synchronous monitoring is realized, the displacement measurement precision reaches a sub-pixel level, and a high-precision and full-automatic solution is provided for health monitoring of structures such as bridges.

The invention relates to the technical field of information processing, in particular to a mine microseism positioning control system and method.The mine microseism positioning control method comprises the steps that microseism signals are synchronously collected through a geophone, an accelerometer, an acoustic emission sensor and an optical fiber sensor, and data timestamps are aligned based on a GPS / Beidou clock; blind source separation is carried out on the signals after wavelet filtering, independent seismic source components are extracted, multi-sensor features are fused through a graph neural network, and a space-time joint feature matrix is constructed; classifying micro-seismic event types by using a ResNet-LSTM hybrid model, generating a seismic source coordinate initial solution based on deep reinforcement learning, and iteratively converging to an optimal solution through a particle swarm optimization algorithm; and mapping a positioning result to a three-dimensional geological grid model, simulating an energy diffusion path, triggering a three-level alarm according to the event energy density, and automatically pushing an emergency instruction to a mine safety management system. Therefore, the problem of low precision of the existing mine microseism positioning control system is solved.

The invention belongs to the technical field of underground engineering, and discloses an omnibearing intelligent monitoring system, method and device for an underground anchoring pull-out test. According to the method, electromagnetic signals generated in the stress process of the anchor rod are captured in real time through an electromagnetic sensor, and internal stress changes and energy distribution are reflected; the deformation and stress states of the anchor rod in the dynamic environment are evaluated through a strain gauge and accelerometer combined data combination method, the deformation condition of the anchor rod is monitored through the strain gauge, and the stress-strain relation is recorded; an accelerometer is used for detecting the dynamic response of surrounding rock mass so as to evaluate the overall stability of the rock mass; a coupling medium is injected, the computer terminal stores the collected signals locally or in a cloud end and analyzes the signals, stress and deformation parameters in an anchor rod pull-out test are extracted, and an anchor rod stress cloud picture is generated in real time. According to the method, the safety and reliability of underground engineering are remarkably improved, and a new technical support is provided for evaluation of anchoring quality.

The invention provides an MEMS acceleration sensor null drift determination method and device and a terminal, and relates to the technical field of MEMS acceleration sensors. According to the invention, the first accelerometer and the second accelerometer which are relatively fixed in position and different in measuring range are arranged and synchronously move in the use process, and the real-time zero-offset drift distance of the wide-range accelerometer is determined based on the difference between the monitoring values of the small-range accelerometer and the wide-range accelerometer by utilizing the accuracy of the small-range accelerometer. Therefore, the wide-range accelerometer can be corrected in real time by using the zero-offset drift amount, and the accuracy of the measured value of the acceleration sensor is improved in a relatively wide range. In addition, the two accelerometers move synchronously in the using process, the two accelerometers are the same in environment and inclination angle, the zero offset drift distance of the large-range accelerometer is determined based on the monitoring value of the small-range accelerometer, the influence of environment vibration can be avoided, the influence of gravity components of the inclination angles of the accelerometers can also be avoided, and the accuracy of determining the zero offset drift distance is improved.

The invention discloses a GNSS (Global Navigation Satellite System) / inertial navigation tightly integrated navigation method and system based on robust auto-velocity constraint factor graph optimization, and the method specifically comprises the following steps: extracting pseudo-range and Doppler observation data from original observation information of a GNSS satellite, and obtaining accelerometer and gyroscope data from an INS (Inertial Navigation System); constructing a pseudo-range residual block, a Doppler residual block, an inertial navigation pre-integration residual block and a speed constraint factor, and integrating into a sliding window estimator based on factor graph optimization; according to the size of a set sliding window, adding all residual blocks in the window to form an optimized objective function; performing a first round of factor graph optimization to obtain a preliminary state estimation result, and endowing the observed quantity containing gross error with a relatively low weight; and updating the prior weight matrix, and carrying out second round of optimization to obtain a joint optimal estimation result of a plurality of moment states in the current time window. According to the method, the influence of gross error-containing observation information on weight estimation and state estimation is suppressed, the positioning precision is high, the robustness is strong, and the reliability is high.

A wearable device includes an accelerometer, at least three electrodes to detect electromyography signal, a communication circuitry, and a processor circuitry. A user wears a primary and a secondary wearable device on two limbs. When the user performs a two-hand gesture, the wearable devices detect a primary and a secondary movement pattern. The primary device recognizes the two-hand gesture from the primary and secondary movement pattern.

The invention discloses a seismic geological disaster monitoring and early warning device, and the device comprises an intelligent sensing system which carries out the real-time collection of geological parameters, facility states and environmental factors through a multi-mode sensor array, and comprises the steps: capturing the strain of a pipeline through a distributed optical fiber sensor, monitoring the vibration through an MEMS accelerometer, and providing deformation data through an InSAR satellite; the analysis system constructs a complex network modeling engine, fuses the historical transition probability matrix and the real-time deformation rate parameter by using a directed weighted network model, and combines a two-channel causal inference engine which comprises a physical causal channel and a data causal channel; the physical causal channel is embedded into a coulomb fracture criterion to calculate a fault stress accumulation rate, the Darcy law simulates pore pressure propagation, and the data causal channel generates an anti-fact sample through a CaualGAN and extracts a real causal chain in combination with a time causal convolutional network; and the execution and feedback system is used for triggering graded early warning based on an analysis result of the analysis system.

The invention discloses a fusion filtering attitude estimation method based on EKF and Mahony, and the method comprises the steps: firstly obtaining the original data information of an IMU sensor at the current moment, carrying out the normalization of the attitude information of an accelerometer and a magnetometer, carrying out the error calculation, and compensating the error to the angular velocity of a gyroscope through a PI controller, the method comprises the following steps: performing Mahony filtering correction on an angular velocity and a corrected attitude quaternion to obtain an angular velocity subjected to Mahony filtering correction and a corrected attitude quaternion, constructing a state equation of an EKF by using the corrected attitude quaternion and a gyroscope zero offset value, and constructing an observation equation by using data of an accelerometer and a magnetometer so as to obtain an optimally estimated attitude quaternion and a gyroscope zero offset value; and finally, compensating the optimally estimated zero offset value of the gyroscope to the angular velocity correction process in the Mahony filtering process.

Provided herein are methods for Inertial Measurement Unit (IMU) sensor compensation utilizing deep neural network (DNN) technology. In some embodiments, the methods involve receiving sensor values from gyroscopes, accelerometers, and non-motion sensors, then loading these values into a deep learning algorithm alongside true sensor values. Unlike traditional calibration techniques that rely on complex parametric models, this approach may utilize a neural network to directly process raw sensor data and enhance output signal accuracy. In some embodiments, the training process employs ground truth data for approximately 70% of inputs, with the remaining 30% dedicated to sensor compensation. The technique may be implemented in standalone IMUs, augmented IMUs, and navigation systems, offering improved accuracy and reduced computational overhead across various technological applications.

The invention relates to a CNN-GRU-MHSA navigation method and a CNN-GRU-MHSA navigation system for improving pseudo measurement noise covariance under GNSS interruption. Comprising the following steps of: combining three-dimensional output of a gyroscope and three-dimensional output of an accelerometer in an IMU (Inertial Measurement Unit) in a GNSS (Global Navigation Satellite System) observation period as well as a three-dimensional speed and a three-dimensional course angle calculated by an INS (Inertial Navigation System) into an input vector, inputting the input vector into a trained CNN-GRU-MHSA (Global Navigation Satellite System) hybrid neural network, and outputting to obtain a pseudo GNSS position increment in the GNSS observation period. And synthesizing the pseudo GNSS position increment and the GNSS position information of the previous epoch to obtain pseudo GNSS position information of the current epoch. And then, adding an accumulated error compensation factor related to time into Sage-husa adaptive filtering to estimate a pseudo measurement noise covariance matrix. And finally, transmitting the pseudo GNSS position information and the pseudo measurement noise covariance matrix into an integrated navigation Kalman filter for resolving so as to correct an INS navigation resolving result.

The invention relates to the field of welding pressure control, in particular to a friction stir welding pressure control method, system and equipment and a storage medium. A three-axis acceleration sensor is used for monitoring the vibration acceleration of a main shaft to start a pressure compensation algorithm, a thermal infrared imager is used for collecting temperature field distribution of a welding area to dynamically correct welding pressure, and after welding is completed, control parameters are reversely optimized through ultrasonic flaw detection to form a self-learning control loop. The invention further comprises a corresponding control system, electronic equipment and a computer readable storage medium. The technical effects that the friction stir welding pressure is accurately controlled, the pressure can be adjusted in real time according to factors such as material characteristics, vibration and temperature, the welding process is optimized, the welding seam quality is improved, and the control parameters are continuously improved through self-learning are achieved.

An automated urinary output (“UO”)-measuring system includes a fluid container including a console coupled to one or more ultrasonic sensors and one or more accelerometers. The console includes a processor configured to receive accelerometer values from the one or more accelerometers, to determine an acceleration state of the fluid container, to activate the one or more ultrasonic sensors when the acceleration state of the fluid container is below a threshold, and to measure a volume of fluid in the fluid container when the acceleration state of the fluid container is zero. The system can further include a removable valve coupled to the fluid container and an attachment device including a pocket configured to accommodate the fluid container.

The invention discloses a method and system for calibrating an inertial sensor by a Doppler log, and relates to the technical field of underwater integrated navigation, and the method comprises the following steps: obtaining an IMU pre-integration factor and a DVL speed residual factor according to IMU observation data and DVL observation data; the method comprises the following steps: constructing a factor graph structure model by taking prior information of initial states of a gyroscope and an accelerometer as prior factors, taking the prior factors, an IMU pre-integration factor and a DVL speed residual factor as factor nodes, taking state variables of the gyroscope and the accelerometer as variable nodes and taking a relationship between the factor nodes and the variable nodes as edges; and correcting the state variables by using the factor graph structure model to obtain theoretical outputs of the gyroscope and the accelerometer after inertial navigation error compensation. Observed DVL data are used for carrying out online calibration on IMU errors, an underwater integrated navigation problem is modeled as nonlinear least square optimization, global optimal estimation of all-time state quantities is achieved, and the divergence speed of inertial navigation position errors is restrained.

“THREE-DIMENSIONAL MONITORING SYSTEM FOR KINGPIN ON FIFTH WHEEL” describes an invention patent, for a monitoring system of Kingpin / Fifth Wheel articulation presented on trucks, with a great applicability for autonomous steering systems and vehicle dynamics control. This system allows the precise monitoring of translational, rotational movements and the relative accelerations of the king pin (5) in relation to the fifth wheel (3) on the X, Y and Z axes, totaling measurements in six degrees of freedom. The present patent presents two ways to make this monitoring of the three-dimensional position of the kingpin (5): The first through a set of sensors (6) accelerometers and gyroscope integrated to an electromagnet (4) that when electrified, is coupled to the king pin (5) by magnetic attraction. The second mode through a reference device (20) that is magnetic, identifiable, independent and easy to apply to the kingpin (4).

The invention discloses a fault detection method and a fault detection device for an accelerometer of a downhole tool. Taking an accelerometer and a gyroscope as state quantities, establishing a generalized nonlinear system for the downhole tool sensor model, and performing linearization processing to obtain a generalized linear variable parameter system; based on a T-N-L observer architecture, constructing a system state estimation error equation and a residual error generation equation, establishing a fault sensitivity and interference robustness evaluation system on the basis of the system state estimation error equation and the residual error generation equation, and determining observer parameters; and the sensor obtains a system state value, an estimated value is obtained through the observer, a residual value of a current system state quantity is calculated to judge an occurrence event, and a threshold value of sensor fault occurrence under the current event is calculated to judge a fault occurrence condition. A nonlinear model is adopted, the system dynamic state is described more accurately, the model is linearized, it is guaranteed that the model and the actual working condition have the high matching degree, and meanwhile a corresponding linear variable parameter system is obtained to reduce the calculated amount.

System and method for monitoring vehicular traffic on a bridge, including receiving digital data representing the response of the bridge to a traffic event, where the digital data has been collected during the traffic event, from displacement sensors and accelerometers mounted on the superstructure of the bridge, wherein the digital data from the displacement sensors embody bending responses of the bridge, the digital data from the accelerometers embody acceleration responses of the bridge, and the digital data from the sensor are synchronised in the same time space, providing a parametric model which uses modal parameters to simulate generalized boundary conditions and two-dimensional behaviour of the bridge; using the parametric model to process the digital data to solve for deformation of the bridge and characteristics of the vehicle traffic, and generating an output that describes the deformation of the bridge and characteristics of the vehicle traffic.

The invention provides a train attitude precision keeping method and system based on inertial quaternion modeling, and belongs to the technical field of train operation positioning. Through a quaternion modeling train attitude feedback compensation model, auxiliary calibration of a gyroscope is realized by using an accelerometer, and calculation of an inertial attitude is realized; converting the acquired wheel speed sensor information into three-dimensional speed under a navigation coordinate system through attitude information resolved by a quaternion modeling train attitude feedback compensation model, and further integrating to obtain a position; and fusing the speed and position information of the wheel speed sensor after attitude conversion with the speed and position information obtained by inertial navigation dead reckoning to realize error correction of the inertial navigation system. According to the method, the problem that the train state sensing precision is degraded due to the fact that a satellite navigation / inertial navigation system positioning model is difficult to overcome inertial device error accumulation when a satellite fails is solved, the train attitude precision is kept, and then error divergence of a combined positioning system during satellite signal denial is effectively restrained.

The invention discloses a high-precision intelligent distance measurement positioning device for a heading machine, and belongs to the technical field of heading positioning. An elastic base station network comprises a plurality of groups of miniature base stations arranged on the side wall and the top plate of a roadway; the multi-source sensing module fuses multi-source data through the data acquisition and processing device to realize continuous dynamic positioning in the working process of the heading machine, the target card provides an absolute distance reference, the inertial navigation unit calculates the pose of the heading machine in real time through a built-in MEMS gyroscope and an accelerometer, the geological radar scans the stratum structure in the heading direction in real time, and the real-time positioning of the heading machine is realized. And the laser radar scans a roadway contour by emitting a laser beam to perform point cloud matching with a preset three-dimensional digital map. The probability of tunneling deviation caused by settlement or displacement is reduced by arranging the vertical crossing positioning network and dynamically correcting the positioning reference, the working environment and self state information of the tunneling machine can be comprehensively sensed from different dimensions, and the actual distance and position relation between the tunneling machine and objects around a roadway can be judged in an auxiliary mode.

Disclosed are automated urinary output (“UO”)-measuring systems and methods. An automated UO-measuring system can include a container configured to collect a fluid such as urine. The container can include a console, one or more ultrasonic sensors coupled to the console for determining a fluid level within the container, one or more accelerometers coupled to the console for determining a near-zero acceleration state of the container for the determining of the fluid level within the container, and a valve configured to pass fluid therethrough by way of a fluid line coupled to the valve. The automated UO-measuring system can also include a container holder. The container holder can have a pocket for holding the container and a sleeve for securing the container to a user. A method of the automated UO-measuring system can include a method of using the automated UO-measuring system to collect and measure urine output of the user.

The present invention relates to the technical field of dynamic measurement of drill tool attitude, and discloses a dynamic measurement method and system for attitude parameters of a rotary steering tool. The method includes: obtaining in real time the three-axis angular rate, the three-axis earth gravity field components, and the three-axis geomagnetic field components of the rotary steering tool; determining a prior estimate value of the attitude angle according to the three-axis angular rate; determining a posteriori observation values of the attitude angle according to the three-axis earth gravity field components and the three-axis geomagnetic field components; obtaining a posteriori optimal attitude angle estimation result by fusing the prior estimate value of the attitude angle and the posteriori observation values of the attitude angle through iterative extended Kalman filtering; and calculating the attitude parameters according to the posteriori optimal attitude angle estimation result. In this application, an iterative extended Kalman filter is used to adaptively fuse the dynamic observables measured by a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer, so as to eliminate the noise interference of external environments such as high rotational speed, strong vibration, and magnetic anomaly faced during the dynamic measurement process, thereby improving the control and measurement accuracy of the rotary steering tool.

The invention relates to the technical field of inertial navigation and sensor fusion, in particular to a multi-sensor fusion and adaptive filtering attitude estimation method, which comprises the following steps: S1, acquiring sensor data of a gyroscope, a magnetometer and an accelerometer, and respectively acquiring angular velocity, magnetic field direction and gravitational acceleration information; s2, angle information is obtained, and magnetometer and accelerometer sensor data are converted into attitude information; s3, carrying out weighting processing on the angle information and the attitude information through a fading memory weighting method and a limited memory weighting method; S4, carrying out filtering fusion on the weighted angle information and attitude information through an RAEKF filtering fusion algorithm; s5, an adaptive factor and a robust factor are introduced into the RAEKF filtering fusion algorithm; s6, outputting attitude angle estimation; according to the method, the theoretical contradiction that long-term drifting and stability cannot be achieved at the same time is fundamentally solved, stable precision is kept under abnormal data interference, and the robust performance is remarkably improved.

The invention discloses a catastrophe early warning system for a tunnel passing through an active fault zone, and relates to the technical field of tunnel early warning, the system comprises a multi-mode sensing layer used for obtaining micro-seismic wave, vibration, inclination and strain data along the tunnel fault zone, the multi-mode sensing layer comprises an optical fiber distributed sound wave sensing unit DAS, a data processing unit DAS and a data processing unit DAS, the optical fibers are arranged along the inner side of the tunnel lining at intervals of 10m and are used for monitoring tiny sound wave signals of a fracture surface in real time; the MEMS accelerometer arrays are arranged at four corners of each section and are used for detecting high-frequency vibration; gNSS and INS combined inclination strain sensors which are installed on the top plate and the side wall of the tunnel and are used for obtaining millimeter-level inclination and strain changes; and the acoustic cavity sensor is arranged in a near area of the sliding surface of the fault zone and is used for capturing an air sound wave precursor signal. According to the invention, through multi-mode fusion sensing, the function of capturing fracture micro-shock, vibration and deformation in all directions is realized, the problem of missed alarm and false alarm of single sensing is solved, the sensitivity order of magnitude is improved, and the false alarm rate is reduced.

An unidentified aerial phenomena field disturbance detector includes an accelerometer configured to measure a gravitational field surrounding the unidentified aerial phenomena field disturbance detector; a magnetometer configured to measure a magnetic field surrounding the unidentified aerial phenomena field disturbance detector; a microwave frequency detector configured to measure a specific band of microwave frequencies surrounding the unidentified aerial phenomena field disturbance detector; a controller; and an optical indicator configured to optically communicate information to a user of the unidentified aerial phenomena field disturbance detector. The controller determines a change in the gravitational field surrounding the unidentified aerial phenomena field disturbance detector when generates gravitational field values are outside a predetermined range of non-event field values. The controller controls the optical indicator to optically communicate the determined change in the gravitational field surrounding the unidentified aerial phenomena field disturbance detector.

The invention provides a cattle behavior recognition method based on a three-axis attitude angle, and belongs to the technical field of intelligent animal husbandry management. According to the method, three-axis attitude angle signals such as the yaw angle (Yaw), the pitch angle (Pitch) and the roll angle (Roll) of the head of a cattle are collected in real time through a gyroscope sensor arranged in wearable equipment such as a cattle ear tag. The method does not depend on images, accelerometers, microphones or other modalities, only based on attitude angle signals, multiple algorithms such as a time sequence sliding window, frequency domain analysis, weighted scoring, sudden change detection and state transition modeling are utilized, and multiple typical behaviors such as ingestion, rumination, water drinking, bed lying, limping, licking, fright jumping and low activity are recognized. The method is suitable for multiple cattle breeds such as dairy cattle, beef cattle and buffalo, has the advantages of low power consumption, lightweight calculation, low deployment cost, accurate identification and the like, and is convenient for realizing behavior monitoring and health management of large-scale cattle individuals. The algorithm path can be flexibly selected, but a core input signal is only limited to attitude angle data, so that the technical boundary of the patent protection of the method is formed.