547 results about "Wave detection" patented technology

The invention relates to the technical field of power distribution network fault positioning, in particular to a power distribution network fault positioning method based on traveling wave detection, and the method comprises the following steps: S1, distributed detection network construction and signal collection: collecting a three-phase current traveling wave signal in real time; s2, time-frequency joint analysis and feature extraction: extracting time-frequency energy distribution features; s3, intelligent screening of effective fault wave heads: screening out the effective fault wave heads; s4, initial arrival time parameter determination: determining the initial arrival time of each detection point; s5, according to topology self-adaptive error correction, transmission path wave velocity deviation is corrected through line parameter dynamic compensation, and clock errors of the asynchronous detection device are eliminated through adjacent node clock difference estimation; and S6, multi-source data fusion and fault positioning: resolving space coordinates of fault points. According to the invention, the method improves the recognition accuracy of fault features, and solves the problems of poor anti-interference capability and high misjudgment rate of a single feature in a conventional method.

The invention provides a distribution line fault location optimization method based on single-ended traveling wave location, and relates to the technical field of traveling wave detection. The method comprises the following steps: carrying out segmented modeling on a line, calculating the traveling wave propagation speed and wave impedance of each segment, and correcting the traveling wave propagation speed and wave impedance; collecting a voltage / current traveling wave signal of a detection point, and extracting a line mode component; performing wavelet transformation on the line mode component, detecting the wave head position of the initial traveling wave through a modulus maximum search algorithm, and recording the arrival time of the initial traveling wave, a corresponding first energy value and polarity; calculating a reflected wave time window based on segmented modeling parameters, screening reversed polarity waves with qualified energy attenuation, and calculating a fault distance; if no effective reflected wave is detected in the reflected wave time window, triggering a pseudo double-end mode; a result is verified through quadruple constraints; the problems of poor parameter adaptability, low signal processing precision, strong reflected wave dependence and incomplete verification in traveling wave distance measurement of a complex distribution line are solved, and the accuracy and reliability of fault distance measurement are improved.

The invention discloses a distribution network cable fault early warning method and system based on multi-terminal synchronous traveling wave detection, and relates to the technical field of cable fault detection. The method comprises the following steps: deploying at least three synchronous detection ends at each branch end point of a cable, and synchronously triggering a sampling rate; establishing a master-slave synchronous network, and performing frequency spectrum correction on the acquired data; constructing a real-time correction model based on the data of the distributed temperature sensor; extracting early fault features, and constructing a three-dimensional feature tensor; carrying out preliminary diagnosis on edge nodes and carrying out feature classification by adopting a transfer learning optimization network; and in combination with the cable load rate and the environment temperature and humidity, an early warning threshold self-adaptive model based on fuzzy PID (Proportion Integration Differentiation) is established. Through multi-terminal cooperative perception and intelligent analysis, the scheme converts a fault processing mode from passive first-aid repair to active defense, improves the manual inspection efficiency of the distribution network cable, and finds early fault hidden dangers in advance.

The invention discloses a multi-wave detection and imaging system for fish schools, which relates to the field of detection and imaging of fish schools and comprises a metamaterial acoustic emission module, a self-adaptive signal receiving module, a three-dimensional point cloud generation module, an image enhancement and generation module and an intelligent decision control module. According to the multi-wave detection and imaging system for the fish school, clustering parameters can be dynamically adjusted based on the real-time fish school density, the neighborhood radius is automatically expanded in a high-density area to avoid segmentation errors, and the core point judgment threshold is reduced in a low-density area to reduce missing detection; an acoustic attenuation model is constructed in combination with water body environment parameters, emission parameters are calibrated in real time, images can be enhanced, space and time dimension features are fused, the image resolution and definition are improved, accurate recognition of multiple fish species is achieved, the fish species can be better distinguished, meanwhile, a generative network is adopted to directly convert acoustic data into visual images, and the recognition accuracy is improved. Therefore, the generated image can reflect the actual distribution condition of the fish school more truly and accurately.

The invention discloses a fixed area unmanned aerial vehicle countering method and system. The method comprises the steps of obtaining a three-dimensional coordinate of a to-be-identified unmanned aerial vehicle; inputting the obtained spectrum data of the to-be-identified unmanned aerial vehicle into an invasive unmanned aerial vehicle detection model constructed by a deep learning algorithm to generate an invasive unmanned aerial vehicle signal; in response to the invading unmanned aerial vehicle signal, acquiring multispectral imaging data of the invading unmanned aerial vehicle based on the visual image acquisition base station; performing feature-level fusion on the radar wave detection data, the spectrum data and the multispectral imaging data based on a multi-modal fusion algorithm of space-time registration to obtain fusion feature data of the target area; performing feature extraction of different dimensions on the fused feature data, and establishing a threat situation three-dimensional thermodynamic diagram of the invading unmanned aerial vehicle based on the extracted features; and executing a security countering strategy generated based on the threat situation three-dimensional thermodynamic diagram. According to the method, the unmanned aerial vehicle countering requirement in a complex environment is met.

The invention relates to the technical field of power system fault positioning, in particular to a power distribution network fault accurate positioning method, device and system based on the traveling wave principle, and the method comprises the steps: deploying traveling wave detection equipment at two ends of a certain section of line of a power distribution network, and enabling the traveling wave detection equipment to be used for synchronously collecting potential fault traveling wave signals; analyzing based on the acquired potential fault traveling wave signal, and screening out a fault traveling wave; in the time union set of the local time windows at the two ends, marking the fault traveling wave with the maximum peak value in the single end as a direct wave, analyzing the waveform similarity between the direct wave and the fault traveling wave arriving after the direct wave, and screening out a reflected wave according to the peak value difference; and based on the arrival time of the direct waves and the reflected waves at the two ends, calculating the distance from the fault point to the two ends of the line through a positioning formula to position the fault point. The invention aims to improve the positioning precision of the fault point in the power distribution network by analyzing the distribution characteristics of the pseudo traveling wave signal.

The invention relates to the field of production quality detection, and provides a production defect identification method and system for a metal rubber composite board, and the method comprises the steps: carrying out the time domain semantic feature extraction of each ultrasonic echo detection signal in a set of ultrasonic echo detection signals through employing a signal processing and analysis technology based on deep learning; and automatically judging whether the first scanning position data is abnormal or not according to the ultrasonic echo detection signal semantic feature corresponding to the first scanning position data in the scanning position data set and the query comparison between each ultrasonic echo detection signal semantic feature. Therefore, tiny internal defects which cannot be touched by traditional optical or contact detection in the metal rubber composite plate can be detected, and the detection accuracy can be improved by performing defect detection through a deep learning technology.

The invention discloses a rapid maintenance method for a distribution line, and relates to the technical field of electrical variable measurement, and the method comprises the steps: transmitting a multi-frequency electromagnetic wave to the distribution line through a multi-frequency electromagnetic wave detection device, receiving an echo signal, and extracting the electromagnetic characteristic data of the line; performing hierarchical analysis on the electromagnetic characteristic data to generate a fault preliminary report; based on the characteristics of the fault preliminary report and the echo signal, constructing a three-dimensional fault positioning map, and outputting fault positioning data; based on the repair suggestion report, utilizing an electric field to guide liquid metal to flow to the fault point, and performing filling and curing repair on the fault area; after repairing is completed, secondary detection is carried out on the repairing area and the adjacent area through multi-frequency electromagnetic waves, and the operation characteristics of the repairing point and the adjacent area are verified; according to the method, the electromagnetic characteristic data is extracted by using the echo signals, the three-dimensional fault positioning map is generated, the spatial position, geometric characteristics and material distribution of the fault point are accurately identified, and accurate support is provided for repair.

The invention relates to the field of power distribution network ground fault accurate judgment, and discloses a power distribution network ground fault accurate positioning method based on traveling wave detection and artificial intelligence, which comprises the following steps: (a) collecting a power distribution network traveling wave voltage signal through a multi-node synchronous sensor array, and generating a preprocessing signal through baseline correction and wavelet threshold denoising; (b) constructing a Gaussian random field model of line resistance, inductance and capacitance, and simulating a traveling wave propagation process in combination with a nonlinear wave equation; and (c) defining Finsler measurement on the traveling wave signal manifold, and separating the aliasing mode by minimizing path integration. According to the invention, the technical scheme of multi-node synchronous signal acquisition and self-adaptive preprocessing is adopted, so that the effect of eliminating coupling of power frequency interference and high-frequency noise is achieved. Compared with a scheme depending on a single sensor and a fixed filtering threshold in the prior art, the method solves the defect that the wave head time difference extraction is not accurate due to asynchronous sampling.

The invention discloses an EEG signal spine wave detection method and system based on KAN feature fusion, and the method comprises the steps: carrying out the preprocessing of electroencephalogram data, and obtaining a training set and a test set; performing parallel feature extraction to obtain multi-KAN parallel features, and performing splicing and dimension reduction to obtain mixed features; performing gating dynamic selection on the obtained mixed features to obtain fusion features of comprehensive multi-scale analysis of the EEG signals; focusing the key period features based on a double attention mechanism to obtain weight fusion features; inputting the obtained weight fusion features into a classifier, and training a prediction model based on a cross entropy loss function; evaluating the performance of the model, and outputting a classification result. The epileptic seizure risk prediction is realized by extracting the electroencephalogram heterogeneous features, and inputting the electroencephalogram heterogeneous features into the classifier after gating dynamic fusion and double attention time sequence compression. The feature expression capability is obviously improved, the focusing capability in a key period is enhanced, and the classification performance is optimized.

The invention relates to the technical field of millimeter-wave radar detection, in particular to a high-precision biological sensing method and system based on a millimeter-wave radar. The method comprises the following steps: collecting a millimeter wave detection emission signal and a millimeter wave detection echo signal, and introducing a signal processing and analysis algorithm based on artificial intelligence and deep learning at a rear end to perform signal intrinsic mode decomposition and analysis on the millimeter wave detection emission signal and the millimeter wave detection echo signal; in this way, signal component frequency domain difference key semantic aggregation features between millimeter wave detection emission and echo signals are captured, so that perception result expression is carried out based on aggregation representation of the signal component frequency domain difference features, and the perception result is used as a biological perception recognition result. And thus, the accuracy of detecting biological perception by using the millimeter wave radar is improved.

The invention belongs to the technical field of power system power distribution network relay protection, and provides a distribution network T-connection line fault positioning method based on SVMD, which comprises the following steps: step 1, acquiring three-phase voltage by using a traveling wave detection device installed on a distribution network line, and performing phase-mode conversion to obtain a voltage traveling wave alpha-mode component; step 2, decomposing the voltage traveling wave alpha-mode component by using continuous variational mode decomposition (SVMD) to obtain intrinsic mode components of different frequencies; 3, calculating the kurtosis value of each modal component, and selecting the modal component corresponding to the maximum kurtosis value as a characteristic modal; compared with the traditional VMD, the SVMD method using the successive decomposition strategy is different in that the mode number does not need to be preset, and the mode component is extracted by optimizing the spectrum compactness, the residual signal energy minimization and the mode orthogonality principle, so that the method can eliminate the dependence on the prior knowledge, can better adapt to different signals, and can improve the robustness of the signal. The method can avoid the problem of mode aliasing, and has the characteristics of low calculation complexity and strong anti-noise capability.

The invention relates to the technical field of semiconductors, in particular to an ultrasonic transmitting circuit, a chip and an ultrasonic module. The ultrasonic transmitting circuit comprises a first control circuit and a signal generating circuit. The first control circuit is used for outputting a first control signal; the signal generation circuit is used for outputting a driving signal according to the first control signal; the driving signal is used for driving a first booster circuit to output an excitation signal, and the voltage of the excitation signal is higher than that of the driving signal; the excitation signal is used for exciting the ultrasonic transducer to emit ultrasonic waves. According to the embodiment of the invention, the energy of ultrasonic waves emitted by the ultrasonic transducer can be improved, so that the ultrasonic receiving circuit can receive echoes with higher energy, and the sensitivity and the signal-to-noise ratio of ultrasonic detection are improved.

The invention relates to the technical field of power transmission line safety monitoring, and particularly discloses a self-adaptive calibration method for an ultrasonic wind speed sensor, which comprises the following steps of: arranging an orthogonal ultrasonic probe array at the top of a tower, and compensating probe offset caused by swinging of a tower body in real time through GNSS / IMU (Global Navigation Satellite System / Inertial Measurement Unit) fusion positioning; integrating a temperature, humidity and pressure sensor and vibration spectrum analysis to construct an environmental parameter compensation model; double-frequency ultrasonic waves are adopted to detect the ice accumulation thickness of the protective cover and correct the sound path distance; deploying a lightweight LSTM model at an edge computing unit to realize minute-level dynamic parameter adjustment; electromagnetic interference is suppressed by combining linear frequency modulation spread spectrum and beam forming technologies; and establishing a three-height-layer wind speed gradient model to generate a galloping early warning. The problem of measurement distortion caused by tower deformation, icing and parameter solidification of a traditional sensor is solved, all-working-condition operation from-40 DEG C to 60 DEG C is supported, and the monitoring reliability in a strong electromagnetic environment is remarkably improved.

The invention provides a tunnel arch wall detection device and method, relates to the field of seismic survey, and aims to solve the problem that seismic survey elements are inconvenient to arrange in a tunnel at present, a loading assembly is carried by an unmanned aerial vehicle, a rod type structure is utilized to adapt to the tunnel space, and the arrangement problem of equipment in a narrow space is solved. Geophones of the wave detection assembly can be firmly pasted on the tunnel arch wall after being glued through a gluing structure, the arrangement accuracy and reliability are improved, the shock excitation assembly is directly installed on the loading assembly, the unmanned aerial vehicle is used for driving the shock excitation assembly to adjust the position to reach the needed shock excitation position, it is ensured that seismic waves can accurately act on the tunnel arch wall, and the shock excitation efficiency is improved. And the detection precision is improved.

The invention relates to the field of ocean internal solitary wave automatic detection, and discloses an internal solitary wave detection method based on sentinel No.3 satellite altimeter data, which comprises the following steps: acquiring sentinel No.3 satellite altimeter data of a sea area to be detected, and preprocessing the data; sequentially extracting a posterior scattering coefficient of each coordinate point of the Ku wave band and the C wave band, and response characteristic parameters SSHA and SWH; sequentially calculating the difference mean square slopes of the posterior scattering coefficients of the Ku wave band and the C wave band; the response characteristic parameters are smoothed twice, and variables # imgabs0 # and # imgabs1 # are obtained; and respectively calculating a differential mean square slope and a local standard deviation of variables # imgabs2 # and # imgabs3 #, and extracting data fluctuation characteristics by setting a threshold value so as to realize internal solitary wave detection. The method disclosed by the invention has higher accuracy and lower false detection rate, and still has a good detection effect under the condition that the propagation direction of the inner solitary wave is greatly different from the running direction of the satellite.

The invention relates to the technical field of road engineering detection, in particular to a road disease recognition method and system based on electromagnetic waves and photographic imaging, and the method comprises the steps: firstly obtaining electromagnetic wave detection data and photographic imaging data of a road region, then synchronously detecting the distribution position and thickness of a road thin water film, generating a rasterized water film feature map, and finally obtaining a road disease recognition result. Then, according to the water film characteristic pattern, dynamically correcting bimodal data interference, adjusting a reflected wave amplitude value and a time domain delay parameter for electromagnetic wave detection data, compensating a gray difference and an edge gradient value for photographic imaging data by applying a local reflection suppression algorithm, and finally, fusing the corrected data and associating the state of the water film characteristic pattern to eliminate coupling misjudgment. The system comprises a data acquisition unit, a water film detection unit, a double-domain correction unit and a fusion decision unit, the problem of insufficient identification precision caused by a thin water film is solved, the road disease identification precision is improved, and a reliable basis is provided for road maintenance.

The invention discloses a radar-wave-transparent light-emitting vehicle logo lamp, and relates to the field of vehicle lamps. The radar-wave-transparent light-emitting vehicle logo lamp comprises a base, an outer face cover connected to the top of the base, a circuit board fixed between the base and the outer face cover, a light guide plate fixed between the circuit board and the outer face cover and a heating film connected to the surface of the side, away from the base, of the outer face cover. The middle of the base protrudes upwards to form a radar wave-transparent area and a containing groove located in the bottom face of the radar wave-transparent area, and the containing groove is used for containing a millimeter wave radar sensor for emitting radar. The circuit board is provided with a first opening for a radar wave-transparent area to pass through and a plurality of LED lamps arranged at intervals in the circumferential direction. The top face and the bottom face of the light guide plate are attached to the outer face cover and the radar wave-transparent area respectively, and the surface of the side, away from the base, of the light guide plate is provided with a vehicle logo. According to the light-emitting vehicle logo lamp capable of transmitting the radar waves, the radar waves can pass through the light-emitting vehicle logo lamp for detection while the vehicle logo identification is lightened, the influence on radar wave detection is reduced to the maximum extent, and meanwhile the radar wave detection effect can be prevented from being influenced by ice and snow.

The invention discloses a signal countering intelligent monitoring system of an unmanned aerial vehicle, which relates to the technical field of signal countering and comprises a multi-source acquisition module, a dynamic verification module, a decision analysis module, an intelligent communication module, an integrated radar unit, a spectrum analysis unit, an infrared sensing unit and a sound wave detection unit. Multi-source data fusion is realized through the data association evaluation value, the signal countering effect of the unmanned aerial vehicle is improved through the threat evaluation value R, the hierarchical threat response and the precise matching countering strategy, and when the radar signal intensity normalization value S1 conflicts with the infrared feature matching degree S3, the verification value is calculated according to the voiceprint matching degree S4 and the spectrum matching degree S2, so that the reliability of the unmanned aerial vehicle is improved. Or a standby unmanned aerial vehicle signal recognition system is started for synchronous detection, multi-source data contradiction is solved, the misjudgment rate is reduced, satellite, 5G and Mesh network dynamic switching is achieved, the communication performance of the system is guaranteed through a priority strategy, and the data transmission delay rate is reduced.

The invention provides a logarithmic bus bearing roller defect detection method and system based on an ultrasonic technology, and the method comprises the steps: obtaining an ultrasonic detection signal of the surface of a logarithmic bus bearing roller, and carrying out the preprocessing of the ultrasonic detection signal, and obtaining a first echo signal sequence; obtaining an average value sequence, a skewness sequence, a kurtosis sequence and a first high-order cumulant sequence based on the first echo signal sequence; correcting the first high-order cumulant sequence based on the average value sequence to obtain a second high-order cumulant sequence; optimizing the first echo signal sequence based on the dictionary matrix and the signal sparse coefficient vector to obtain a second echo signal sequence; and fusing the sequences to obtain a multi-dimensional feature vector sequence, and comparing the multi-dimensional feature vector sequence with a preset vector threshold to detect whether the logarithmic bus bearing roller has defects or not. According to the method, the problem of beam distortion of traditional ultrasonic waves during detection of the roller defects of the logarithmic bus bearing is solved, and the detection precision is remarkably improved.

The invention discloses a wind wave and surge extraction method and system based on a GNSS and an anemometer. The method comprises the following steps: respectively obtaining water level height data and wind data detected by a buoy for multiple times according to a reference time sequence and a detection time sequence; the buoy water level height data and the wind data are subjected to data preprocessing, the water level height and the wind data are preprocessed, and the preprocessed water level height and the preprocessed wind data are obtained; calculating the water level height and the wind data in unit time by using the preprocessed water level height and the wind data according to a segmented interpolation function, and calculating wave height data under a corresponding detection time sequence according to the water level height and the wind data in the unit time; feature set classification is carried out according to wind speed grades, IFFT is carried out after screening, and final wind wave and surge data are obtained, the water surface wind wave and surge data extraction precision is effectively improved, and low-cost and long-time-efficiency water surface wind wave and surge detection can be achieved.

The invention relates to a long-gap discharge leader development speed measurement method based on radiated electromagnetic wave detection. The method comprises a set of high-frequency antennas and a high-speed oscilloscope and a long-gap discharge leader development speed calculating method. The method adopts a certain antenna arrangement form; long-gap discharge radiated electromagnetic wave pulse time is detected simultaneously through the plurality of antennas; leader head position is positioned according to time difference of a signal arriving at different antennas; and furthermore, the long-gap discharge leader development speed can be calculated. The method can effectively improve the measurement precision of the discharge leader development speed, and resolution reaches the level of 0.1 ns.

The invention relates to a power distribution network fault detection method and system based on distributed traveling wave detection, and relates to the technical field of power distribution network fault detection, and the method comprises the steps: employing a traveling wave detection device which is configured in a power distribution network in a distributed manner for detection, and obtaining a traveling wave parameter and a traveling wave distance; according to the traveling wave distance, a fault position interval is positioned, and a transmission line section is indexed; recovering the traveling wave parameters according to the line maintenance data of the transmission line section, obtaining recovered traveling wave parameters, and obtaining traveling wave consistency parameters through consistency analysis; and based on the traveling wave consistency parameter, positioning in the fault position interval to obtain a fusion fault position, and performing fusion processing to obtain a fusion traveling wave parameter as a fault detection result. According to the method, the problems of large single-point detection error, traveling wave parameter distortion caused by line loss and insufficient multi-source data fusion precision in traditional power distribution network fault detection are solved, the fault positioning precision and the detection reliability are improved, and an efficient solution is provided for power distribution network line fault detection and anomaly positioning.

The invention discloses a low-voltage transformer area fault positioning method and system, and relates to the technical field of transformer area fault analysis, and the method comprises the steps: constructing a network frame topology in a low-voltage transformer area; performing low-voltage fault simulation, and determining a preliminary fault area; performing electromagnetic, sound wave and critical fluctuation detection on the preliminary fault area to obtain an electromagnetic wave reflection signal, a secondary electromagnetic signal and critical effect fluctuation data; fusing the electromagnetic wave reflection signal, the secondary electromagnetic signal and the critical effect fluctuation data; and performing multi-modal analysis on the fusion signal to generate a fault report. By combining electromagnetism, sound waves and critical fluctuation detection, various signal characteristics generated when a fault occurs can be effectively captured, meanwhile, by means of multi-modal information fusion analysis, the fault positioning accuracy is remarkably improved, the operation and maintenance time is shortened, and more detailed fault level evaluation is provided for system operation and maintenance.

A passive human detection method and apparatus, a device, and a medium are provided. The passive human detection method includes: acquiring an infrared radiation signal in an environment, and acquiring a temperature of a target object; emitting an electromagnetic wave into the environment, and acquiring a reflected echo signal; determining a position of the target object in the environment based on the echo signal; matching a threshold range based on the position of the target object; and comparing the temperature of the target object with the threshold range to determine detection information of a human in the environment. The passive human detection method combines electromagnetic wave detection and thermal imaging to detect whether there is a human in the current environment. The passive human detection method can effectively filter out interference signals, greatly improving the accuracy and sensitivity of human detection.

The present invention provides a method for accurately locating distribution network faults based on distributed traveling wave detection, the method comprising the following steps: S1: configuring a traveling wave monitoring unit; S2: real-time monitoring of faults in the distribution network through the traveling wave monitoring unit; S3: filtering other types of interference traveling waves except for the fault traveling wave signal, identifying and selecting the fault traveling wave; S4: calibrating the time point of the traveling wave head; S5: calculating the minimum propagation time of the traveling wave from each node to each end point, the end points are the ends of the two ends of the main line of the distribution network and the ends of the branch line, and the nodes are all intermediate nodes except the end points; S6: presetting the fault point and calculating the Pearson coefficient r of the time array; S7: traversing the fault point, and judging the actual position of the fault point in turn based on the calculated Pearson coefficient r. The present invention aims at the problem that the distribution network architecture is complex, the faults occur frequently, and the current technology is difficult to accurately locate the faults, and realizes the accurate fault location of the distribution network based on the traveling wave positioning method.

The invention provides a multi-mode obstacle detection method and device, electronic equipment and a storage medium, relates to the technical field of computers, in particular to the fields of computer vision, data processing and the like, and can be applied to application scenes such as obstacle detection and automatic driving. According to the specific scheme, the method comprises the following steps: extracting a point feature and a laser point representation feature corresponding to any feature point in a laser point cloud according to the obtained laser point cloud; projecting the laser point cloud of the current frame to a visual image, and determining image point representation features corresponding to feature points in combination with image features extracted from the visual image; obtaining a multi-modal fusion feature according to the point feature, the laser point representation feature and the image point representation feature; generating a reserved feature point set and a millimeter wave detection frame based on the multi-modal fusion feature and the millimeter wave point cloud, and projecting the reserved feature point set and the millimeter wave detection frame to the grid map; and generating an obstacle detection frame according to the reserved feature point set and the millimeter wave detection frame. According to the scheme, the detection precision of heterogeneous dynamic obstacles can be improved.

The invention discloses an automatic grading device for thermal shock cracks of a coating and an application method. Comprising a terahertz wave detection module, a laser ultrasonic excitation and detection module, a high-precision three-dimensional motion control platform, a multi-mode signal synchronous acquisition and processing unit, a crack intelligent grading evaluation unit, an environment simulation and temperature control unit, a protective shell and a human-computer interaction interface. The near-surface dielectric property change of the coating is represented through the terahertz technology, the deep mechanical property evolution is detected through the laser ultrasonic technology, the thermal shock service condition is reproduced in combination with a high-temperature environment simulation unit, and a multi-modal feature fusion algorithm and a physical model and machine learning collaborative inversion model are adopted to obtain the thermal shock resistance of the coating. And automatic quantitative evaluation and severity grading of the depth, density, length and network connectivity of the thermal shock cracks of the thermal barrier coating are realized. The problems that an existing single detection method is insensitive to vertical cracks, poor in high-temperature environment adaptability and low in manual evaluation efficiency are solved.

The invention discloses a breathing and electrocardio information integrated recording and displaying method and system based on overall spatial-temporal characteristics. The method comprises the steps that a high-precision sensor synchronously collects electrocardiogram, phonocardiogram, arterial pulse waveform, venous pulse waveform and breathing wave signals and preprocesses the signals based on different signal characteristics; carrying out multi-cycle electrocardiogram merging by utilizing an R-wave detection algorithm and introducing a variational mode decomposition algorithm; carrying out feature extraction from the multi-mode signal, and carrying out respiratory wave and electrocardiowave conjoint analysis; carrying out feature fusion through a spatial-temporal feature fusion network of a multi-branch attention mechanism, and generating and displaying a combined electrocardiograph distribution diagram; carrying out anomaly detection through an anomaly detection model based on a Transform architecture; synchronously displaying in an integrated display interface; according to the system and the method, efficient acquisition, analysis and visualization of the multi-modal physiological signals are realized by integrating synchronous acquisition, intelligent interpretation and integrated display of various physiological signals, and the efficiency and the accuracy of clinical diagnosis are favorably improved.

The invention provides a receiving waveform position judging method of an ultrasonic flow metering device, and aims to improve the reliability of flight time measurement and reduce flow metering errors. According to the method, head wave and tail wave detection is achieved through the variable threshold comparator, the threshold is dynamically adjusted to adapt to different detection stages, and it is ensured that echo signals are accurately recognized. Through combination of zero crossing point flight time measurement and pulse width difference comparison, the method can determine the position of a tail wave, forwards presumes the position of an echo waveform, and judges the accuracy of a zero crossing point. In addition, the measurement accuracy is further improved by adjusting the phase of the transmitted ultrasonic signal to enhance the tail wave characteristic. The method is suitable for the field of flow measurement of water meters, heat meters, gas meters and the like, and metering errors caused by signal fluctuation or interference are effectively reduced.