1256 results about "Bandpass filtering" patented technology

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

The invention relates to the technical field of radar sensing, and discloses a sensing method and system based on millimeter radar waves. The method comprises the steps that millimeter wave radar receiving signals are subjected to low-noise amplification, frequency bands are screened through a band-pass filter and then mixed with local oscillation signals to obtain distance and speed information, after analog-to-digital conversion digitization, features are extracted to distinguish a human body target in a closed / open space, and finally multi-scene adaptability analysis is executed to generate human body position state information. According to the invention, through construction of a signal processing link, from signal amplification, filtering, frequency mixing and digital processing to target identification and multi-scene adaptability analysis, comprehensive perception of a human body target is realized, a static human body and a non-human body interference source can be effectively distinguished, a processing strategy is automatically adjusted according to different space environment characteristics, and the processing efficiency is improved. And the accuracy and reliability of human body perception are greatly improved.

According to the cooling tower early fault early warning method based on vibration state monitoring, vibration signals and working condition labels of key parts of the cooling tower are synchronously collected through multiple channels, and data quality is improved through preprocessing operation such as band-pass filtering and normalization; time-frequency features are extracted in a multi-scale mode through self-adaptive variational mode decomposition and wavelet packet transformation, signal complexity is quantized through energy entropy, and weak fault detection capacity is enhanced; the obtained features are input into a deep belief network after being subjected to principal component analysis dimensionality reduction, and automatic classification and recognition of the equipment operation state are achieved; dynamic early warning grade adaptation is carried out according to an identification result in combination with a working condition label, the environmental adaptability and stability of early warning are effectively improved, the method further has the functions of early warning sample recording and periodic model iterative optimization, and the fault identification precision and robustness in a complex noise environment are remarkably improved.

The invention discloses a defect identification system of an ultrasonic flaw detector, and relates to the technical field of nondestructive testing, the system comprises a signal acquisition and preprocessing module, a defect feature identification module, a physical modeling analysis module, a life prediction and evaluation module and an intelligent decision visualization module; the signal acquisition and preprocessing module adopts a multi-frequency-point phased array transducer array, obtains an original signal through low-noise amplification, band-pass filtering and analog-to-digital conversion, and outputs a time domain signal matrix through adaptive noise reduction and gain compensation processing; the multi-frequency-point phased array transducer array and the advanced signal processing technology are integrated, the defect recognition precision and efficiency are remarkably improved, the system obtains high-quality original signals through low-noise amplification, band-pass filtering and analog-to-digital conversion technologies at first, then the high-quality original signals are subjected to self-adaptive noise reduction and gain compensation processing, and the defect recognition accuracy is improved. The background noise interference is effectively eliminated, and the purity of the signal is ensured.

The invention provides a large-model cross-modal collaborative understanding method and device. The fusion efficiency and the understanding capability of multi-modal information can be improved. The large-model cross-modal collaborative understanding method comprises the following steps: preprocessing visual data, language data and sound data to obtain preprocessed visual data, language data and sound data; wherein the preprocessing comprises the steps of performing adaptive size adjustment and normalization on visual data, performing word segmentation and dynamic truncation on language data, and performing band-pass filtering and spectral noise reduction on sound data; extracting visual features, language features and sound features based on the preprocessed visual data, language data and sound data; on the basis of an adaptive mapping network and a mixed granularity cross-modal attention mechanism, performing feature alignment on the visual features, the language features and the sound features to obtain aligned feature vectors; and based on a dynamic routing architecture, fusing the aligned feature vectors to generate a unified multi-modal representation.

The invention discloses a body feeling evaluation method based on electroencephalogram-electromyographic signal fusion and dynamic interaction modeling. The body feeling evaluation method comprises the steps that EEG signals and EMG signals in the lower limb movement process of a subject are synchronously collected; carrying out band-pass filtering, artifact removal and wavelet transform processing on the acquired signals, extracting multi-channel time-frequency features, and forming a preprocessing feature matrix; fusing the time-frequency features of the EEG signal and the EMG signal, constructing a multi-modal feature set, and compressing feature dimensions by adopting a sparse coding method; inputting the compressed feature sequence into a neural network model combining a long short-term memory network and an attention mechanism, and carrying out dynamic interaction modeling; and an interaction index sequence is generated based on model output, and an interaction matrix is constructed through a sliding window and Gaussian kernel smoothing processing, so that visualization of brain-muscle interaction strength and dynamic quantification of a proprioceptive function are realized. The invention further provides a system for implementing the method. The method is high in objectivity, high in feature extraction precision and excellent in dynamic modeling capability.

The invention relates to the technical field of quantum sensing and ocean detection, and discloses an ocean magnetic field sensor based on an NV color center and an ocean magnetic field measuring method.The sensor comprises a cylindrical pressure-resistant cabin, an optical platform arranged in the pressure-resistant cabin and a main control panel; a laser, a beam shaping unit, a dichroscope, a beam shaping lens group, an objective lens, a diamond NV color center chip, a plano-convex lens I, a band-pass filter and a photoelectric detector are mounted on the optical platform; two microstrip loop antennas are mounted on the outer side of the diamond NV color center chip; an FPGA + ARM dual-core processor, a digital lock-in amplifier and two microwave signal generators are installed on the main control board, and the microwave signal generators are connected with the microstrip loop antenna and achieve electromagnetic coupling with the diamond NV color center chip. The invention has the advantages of high sensitivity, good long-term stability and strong anti-interference capability, and provides a new generation of quantum sensing solution for applications such as ocean geomagnetic monitoring and underwater target identification.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention belongs to the technical field of aquaculture and food engineering, and relates to a fish anesthesia recovery activity prediction method based on acoustic signal time-frequency domain feature fusion. The method comprises the following steps of: acquiring acoustic respiratory vibration signals in real time in a fish anesthesia recovery process, performing 0.1-50Hz band-pass filtering, denoising and other pretreatment on the acoustic respiratory vibration signals, and extracting ZCR and Gammatone characteristic spectrums; meanwhile, fish swimming data are collected by adopting a YOLOv11 visual tracking algorithm, and activity grading standards are established by calculating activity indexes; zCR and Gammatone maps are used as double-branch input data of a deep learning model, corresponding activity grading data are used as labels, and a multi-category classification prediction model is constructed. According to the method, the anesthesia recovery state of the fish can be accurately predicted, and excessive stress and death loss in the transportation process are avoided; a non-invasive and intelligent detection means is provided for activity monitoring in the aquatic product transportation process, and the market application prospect is wide.

Disclosed in the present invention is a photometric stereo-based device for rapidly detecting surface features of an object, capable of rapidly acquiring a plurality of images meeting the requirements of a photometric stereo algorithm. The device comprises a light emitting assembly, an acquisition assembly located above the light emitting assembly, and a data processing module; the acquisition assembly comprises a light path system and a camera group; the light path system comprises: a beam splitting prism group used for splitting reflected light from the surface of an object into a plurality of beams of light and respectively outputting the plurality of beams of light in different preset directions to form images to be processed, and a plurality of bandpass filters correspondingly arranged on the output sides of the plurality of beams of light used for forming said images, wherein the central wavelengths of the bandpass filters are respectively configured to be different wavebands of the reflected light from the surface of the object; the camera group comprises a plurality of cameras which are arranged behind the bandpass filters in a one-to-one correspondence mode and acquire filtered light beams; and the data processing module receives image information sent by the cameras, and performs compensation and photometric stereo reconstruction on the image information, so as to obtain surface features of the object. Further disclosed is a method implemented on the basis of the device.

The invention relates to the technical field of electromagnetic measurement, and discloses a line partial discharge positioning method and system based on pulse signal analysis, and the method comprises the steps: carrying out the band-pass filtering of pulse signal data, and obtaining a preprocessing signal; constructing a waveform template library based on the historical partial discharge pulse waveform data; screening out a partial discharge pulse and an interference pulse in the preprocessed signal to obtain a pulse matching result of the preprocessed signal; determining time difference data of the preprocessed signal according to time differences of pulses reaching different sensors in the pulse matching result; constructing an initial propagation velocity model according to the parameters and the environment data, performing iterative optimization on the initial propagation velocity model according to the time difference data, and determining an initial position of a partial discharge source in the line; correcting the initial propagation velocity model according to the initial position and the actual propagation path to obtain a target propagation model, and determining position coordinates of a partial discharge source in the target line according to the target propagation model; according to the invention, the accuracy of line partial discharge positioning can be improved.

The invention discloses a cable operation environment optimization method based on cable trench collection information, and relates to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: collecting an original parameter data set in a cable trench; a pulse current signal is collected at a cable grounding wire connection point, and a standard pulse discharge capacity sequence is generated through band-pass filtering and amplitude integral conversion; based on the original relative humidity measurement value and the environment temperature value, calculating a compensated relative humidity value through a multi-scale heat conduction-diffusion coupling equation; calculating a cable insulation risk index through a quantum annealing algorithm by using the compensated relative humidity value and the standard pulse discharge capacity sequence; the dynamic measurement deviation of the temperature and humidity sensor is subjected to double compensation of thermal radiation and molecular adsorption by constructing a multi-scale heat conduction-diffusion coupling equation, so that the reliability of environmental state sensing is greatly improved, and the operation safety, stability and intelligent operation and maintenance level of a power cable system are effectively enhanced.

The invention provides an exoskeleton mountaineering intention recognition method and system and a storage medium, and belongs to the technical field of exoskeleton robot control, and the method comprises the steps of signal preprocessing and compensation, hierarchical feature extraction, multi-modal fusion recognition, control output, safety protection and the like. The method comprises the following steps: performing 50Hz power frequency filtering and 20-450Hz band-pass filtering on an electromyographic signal, and compensating signal distortion caused by muscle deformation by combining IMU (Inertial Measurement Unit) acceleration data; a dynamic four-dimensional channel is adopted to preferably extract myoelectricity features, and an improved attention mechanism model is utilized to extract multi-scale features; fusing multi-modal information such as myoelectricity and terrains based on a Bayesian fusion framework to realize intention recognition; meanwhile, triple safety protection strategies of joint angle hard limiting, falling detection and protection and signal loss fault-tolerant processing are set; the accuracy and control speed of motion intention recognition are improved, good safety performance is achieved, and the auxiliary effect and reliability of the exoskeleton in the mountaineering motion are effectively improved.

The invention provides a real-time electroencephalogram phase locking closed-loop stimulation method and system, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: firstly, acquiring a cortex electrical activity signal and establishing a baseline statistic, then carrying out causal band-pass filtering on the signal, and carrying out sampling and holding substitution in a stimulation artifact window; setting an amplitude and a duration threshold based on the baseline, and performing phase judgment after conditions are met; estimating an average period through zero crossing and calculating an instantaneous phase; outputting a trigger instruction when the three-point crossing judgment is established; and outputting stimulation under the constraint of charge balance and a safety limit value, setting a minimum prohibition period and link abnormity suppression, and recording a hit rate and a response rate for quality evaluation. According to the method, through causal phase estimation, artifact isolation and a schedulable triggering mechanism, the high hit rate of non-stationary signal phase locking stimulation is achieved, and the safety and effectiveness of a stimulation system are ensured.

The invention relates to the technical field of deep learning, in particular to a dialysis hypotension risk prediction system and method based on deep learning, and the system comprises a multi-point signal collection module, a signal processing module, a multi-scale causal expansion convolutional network prediction module, an osmotic pressure correction module and a dialysis plan automatic adjustment module. The method comprises the following steps: synchronously collecting physiological signals of a patient through a finger tip photoelectric volume sensor and a radial artery piezoelectric sensor array, carrying out band-pass filtering and morphological refinement processing on the signals, extracting features, and calculating pulse wave conduction time and a change rate thereof; inputting the characteristic parameters into a multi-scale causal expansion convolutional network to predict the hypotension risk in dialysis; dynamically correcting the prediction result according to electrolyte monitoring data; based on the corrected risk, a hierarchical response strategy is adopted to automatically adjust dialysis parameters, a multi-scale causal expansion convolutional network is adopted to capture physiological parameter changes on different time scales at the same time, and high-precision prediction of hypotension is achieved.

The invention discloses a background noise body wave extraction method and device based on distributed optical fiber acoustic sensing, and the method comprises the steps: obtaining an original phase difference signal along an optical fiber channel through a distributed optical fiber acoustic sensing system, and converting the original phase difference signal into strain rate data according to system demodulation parameters; performing preprocessing such as noise suppression and frequency energy balance on the strain rate data; calculating an autocorrelation function for the preprocessed signal, and superposing autocorrelation results of a plurality of time segments; performing band-pass filtering on the superposition autocorrelation function in a target frequency band, and extracting a body wave reflection response in background noise; and in order to eliminate the influence of the zero-time pulse in the shallow layer or weak reflection signal, carrying out mean value removal processing on the extraction result along the space direction to obtain a zero-offset body wave reflection response. The method can effectively describe the discontinuity from a superficial sedimentary layer and a fracture to a deep mourhua surface and even a lithosphere, and makes up for the defects of a conventional seismic station method.

The invention discloses an encrypted communication system and method based on cognitive multi-carrier spread spectrum, and belongs to the field of covert communication. The encryption communication system comprises a transmitting device and a receiving device. The transmitting device comprises a data encryption module, a constellation encryption modulation module, a multi-carrier spread spectrum module, a frequency hopping module and a cognitive power distribution module. The receiving device comprises a band-pass filtering module, a de-hopping frequency module, a de-spreading module, a constellation decryption demodulation module, a data decryption module and a coherent combination module. The data encryption module is divided into an interleaving coding unit and a double-rule reversible cellular automaton encryption unit. The cognitive power distribution module continuously scans a wireless electromagnetic environment and carries out real-time spectrum analysis to give power spectrum density results sensed under all frequency hopping frequency sets. According to the invention, continuous detection and dynamic response to the spatial frequency spectrum state can be realized, and the communication concealment, the anti-interference performance and the resource utilization efficiency in a complex electromagnetic environment are remarkably improved by utilizing the frequency spectrum hole.

The invention relates to a mine monitoring method based on millimeter-wave radar multi-feature fusion, and the method comprises the steps: transmitting an FMCW signal through a millimeter-wave radar, receiving a target echo, sequentially carrying out the signal preprocessing, 1DFFT distance measurement, 2DFFT speed measurement, velocity ambiguity resolution and DOA estimation angle measurement, and extracting the distance, velocity, angle and energy variation features of a target point cloud; then millimeter-level deformation is obtained through phase analysis; obtaining a vibration frequency through band-pass filtering and spectrum analysis; fusing the six types of features of the target point cloud, adopting a confidence counter weighting decision mechanism, and triggering early warning when a fusion score exceeds a preset threshold value; according to the invention, through the advantages of high reliability, strong anti-interference performance and high non-contact safety of multi-feature fusion decision, the false alarm rate is significantly reduced, and the real-time accurate early warning capability of the mine dangerous state is improved.

The invention discloses a power industry electroencephalogram cap-oriented key channel screening method and system, and the method comprises the following steps: firstly, collecting electroencephalogram signals of a power worker in typical states of waking, fatigue and the like by adopting a 32 or 64 channel electroencephalogram cap, and carrying out the preprocessing operations of band-pass filtering, ICA (Independent Component Analysis), signal-to-noise ratio evaluation and the like; artifacts and inferior channels are removed, and then multi-dimensional features such as the frequency domain, the time domain, the entropy value and statistics of each channel are extracted to comprehensively score the importance of the channels. Constructing a plurality of channel subsets, verifying the recognition performance of the channel subsets through a classification model, and screening out a group of key channels with the highest information expression capability in cognitive state discrimination; according to the method, the number of electroencephalogram channels is reduced, the complexity and calculation overhead of acquisition equipment are reduced, the feasibility of real-time deployment of the model in an electric power field is improved, and the method has relatively high industry adaptability and engineering application value and is suitable for various key scenes with relatively high requirements on personnel state perception.

The invention provides a sensor fusion-based pig feed crushing particle size monitoring method, which comprises the following steps of: synchronously acquiring vibration, acoustic emission and optical signals, carrying out band-pass filtering, normalization, feature extraction and wavelet packet decomposition, and dynamically adjusting the confidence coefficient of each channel in combination with working condition perception and a sensor performance knowledge base so as to monitor the crushing particle size of the pig feed. Weighted fusion and conflict evidence modulation of multi-source signals are achieved, the improved Dempster-Shafer evidence theory is introduced to improve the abnormal granularity recognition accuracy, key parameters of crushing equipment are controlled in a linkage mode through a closed-loop feedback mechanism, high-robustness detection and self-adaptive adjustment of the granularity state are achieved, and the method has the advantages of being high in robustness and high in robustness. And the stability and the automation level of the feed crushing process are improved.

The invention relates to the technical field of image processing and biological signal detection, discloses a facial physiological detection method and system based on signal quality driven ROI selection, and aims to solve the problem of signal degradation of a traditional fixed geometric ROI in a complex scene. The method comprises the following steps: collecting a user face video stream through a camera and preprocessing the user face video stream; detecting a face bounding box and dividing the face bounding box into a plurality of sub-regions; calculating the signal-to-noise ratio, the periodic intensity and the motion artifact interference degree of each sub-region; screening an optimal sub-region according to a weighted fusion formula to generate a dynamic ROI mask; extracting a pure rPPG signal from the dynamic ROI mask coverage area; detrending and band-pass filtering are carried out on the rPPG signals, and physiological parameters such as the heart rate and the blood oxygen saturation degree are extracted. The system comprises a face video acquisition module, a face region positioning and segmentation module, a signal quality evaluation module, a dynamic ROI selection module, an rPPG signal extraction module and a physiological parameter estimation module. According to the technical scheme, signal degradation caused by local shielding, illumination abrupt change or attitude offset can be effectively avoided, the signal-to-noise ratio and the stability of the rPPG signal are remarkably improved, and the universality and the robustness of the method are enhanced.

A device for implementing a non-reciprocal bandpass filter is provided. The device includes a plurality of capacitively-coupled shunt resonators. Each capacitively-coupled shunt resonators includes a plurality of superconducting quantum interference devices (SQUIDs) connected to each other, a flux line inductively coupled to the plurality of SQUIDs, and a capacitor connected in parallel to the plurality of SQUIDs.

The invention relates to the technical field of bag type dust collector filter bag abrasion detection, in particular to a filter bag damage non-contact detection method and system based on acoustic array positioning. The method comprises the following steps: arranging an acoustic sensor array in an air purification chamber of the dust remover, and collecting acoustic signals in real time; after band-pass filtering is carried out on the signals, feature vectors of the signals are extracted and matched with a pre-established acoustic fingerprint database, so that a filter bag damage event is recognized; after identification, based on the time difference of arrival of the signal to at least three sensors in the array, combining the coordinates of the sensors and the sound velocity, and utilizing a TDOA positioning algorithm to calculate the space coordinates of the damaged sound source; and finally, matching the coordinates with a pre-stored filter bag coordinate mapping table, determining the number of a specific damaged filter bag, and performing visual highlight marking on a pattern board plane layout of an application layer terminal. The online, real-time, automatic and accurate positioning of the damaged filter bag is realized, the problem that the prior art can only give an alarm but cannot position is solved, and the operation and maintenance efficiency and safety are improved.

The invention provides a ripple signal detection and identification system based on an IED event and time-frequency characteristics, and the system comprises a signal obtaining module which obtains electroencephalogram data of a target object, a preprocessing module which carries out the preprocessing of each channel signal of the electroencephalogram data, the preprocessing comprises power frequency notch and band-pass filtering, the signal is a first frequency band signal at a 25-80Hz frequency band, and the signal is a second frequency band signal at a 70-180Hz frequency band; the IED detection module performs IED signal detection on the first frequency band signal to determine an IED event node; a ripple detection module carries out sliding window analysis on the second frequency band signal, carries out ripple signal detection and determines a ripple event node; and the decision identification module determines the ripple signal category based on the IED event node and the ripple event node. By detecting an IED event and a ripple event, the IED event is introduced into ripple event classification, and automatic detection and identification of physiological ripples and pathological signals are realized in combination with frequency domain features.

The present invention belongs to the technical field of mechanical fault diagnosis and signal processing, and specifically relates to a bearing fault diagnosis method, system, equipment and medium based on a multi-channel analog filter feature network. The method constructs a multi-channel analog filter feature extraction network, uses multiple parallel bandpass filter channels to extract bearing vibration signal characteristics, and combines an analog neural network classifier to perform fault classification. Signal preprocessing includes removing DC components, amplitude normalization and segmented processing; the filter passband parameters and classifier parameters are jointly optimized through a particle swarm algorithm, with classification accuracy and decision confidence as fitness functions. The system includes signal acquisition, preprocessing, multi-channel filtering, feature extraction, analog neural network classification and result display modules, achieving low-power, high-real-time bearing fault diagnosis. The present invention solves the problems of high power consumption and large delay of traditional digital processing methods, and is suitable for long-term monitoring and large-scale deployment in industrial sites.

The invention discloses an emotion analysis method and system based on multi-mode electroencephalogram eye movement fusion. An emotional induction stimulation sequence is alternately or synchronously displayed through a stimulation presentation module according to preset time sequences such as 5-second vision and 3-second auditory sense, a 64 conductive electrode cap (10-20 system layout, the sampling rate is larger than or equal to 1000 Hz) is triggered to collect electroencephalogram data of a prefrontal lobe, a temporal lobe and the like, and meanwhile eye movement data are obtained through an infrared pupil tracking technology (the sampling frequency is larger than or equal to 120 Hz). 0.5-70 Hz band-pass filtering and ICA artifact removal processing are carried out on the electroencephalogram data, smooth interpolation and Kalman filtering optimization are carried out on the eye movement data, and then electroencephalogram alpha / beta / gamma wave power spectrums, eye movement pupil change rates and other characteristics are extracted respectively. A Transform model based on an attention mechanism is adopted, deep fusion of electroencephalogram eye movement features is realized through feature embedding and multi-head self-attention calculation, and emotion recognition accuracy greater than or equal to 85% is achieved through confusion matrix optimization and five-fold cross validation in combination with an SVM or DNN classifier.

According to the electroencephalogram signal collecting device and method, the common-mode rejection ratio is increased through the right leg driving module, high-frequency noise and low-frequency noise are filtered out through the band-pass filtering module, power frequency interference is eliminated through the notch module, and filtering processing is conducted on the power frequency interference and the power frequency multiple in a software mode through the power frequency filtering model; therefore, the radio frequency interference is improved, the anti-interference capability of electroencephalogram signal acquisition is greatly improved, and the accuracy and stability of electroencephalogram signal acquisition are improved.

The invention belongs to the field of near-inertia internal wave modeling, and particularly relates to a near-inertia internal wave modeling method based on a reverse echo observation array, which comprises the following steps: arranging a reverse echo measurement device array in an observation area, and acquiring echo signals from the seabed to the sea surface; performing data cleaning and band-pass filtering on the signals, and extracting near-inertia internal wave signals; calculating the depth and the relative vorticity of a mixed layer by combining historical temperature-salinity data; calculating a wind input energy flux by using the wind stress data; calculating a net level energy flux by adopting a boundary integration method; judging whether the boundary energy flux influence is smaller than a threshold value or not, and if not, adjusting array layout; and finally establishing a multivariable linear model containing wind energy input, mixed layer depth, relative vorticity and energy flux. According to the method, the temporal-spatial resolution and prediction precision of near-inertia internal wave modeling are effectively improved, the problems that in a traditional method, the boundary effect is remarkable, and multi-factor comprehensive modeling is insufficient are solved, and the method is suitable for long-term efficient monitoring of a large-range marine environment.

The invention provides a target positioning method and system based on a distributed networking radar system, and belongs to the technical field of radars, and the method comprises the steps: controlling a plurality of radar nodes to transmit stepping linear frequency modulation continuous wave pulse signals, and collecting target echoes; carrying out band-pass filtering, pulse compression and fast time synchronization processing on the echoes, and generating interference-free data through a moving target extraction algorithm; realizing cross-node clock synchronization based on a moving target behavior cognition model and a template matching algorithm; constructing a multi-node distance estimation equation set, generating an accurate distance value through calibration broadband synthesis and FFT, and determining a target three-dimensional coordinate; and generating a motion track and speed prediction through Kalman filtering based on the target position sequence. The system comprises a signal emission and acquisition module, a signal processing module, a clock synchronization module, a distance estimation and coordinate fusion module and a trajectory prediction module. The method supports multi-target tracking, improves positioning precision and time synchronization precision, has few errors, and is compatible with any node deployment form.

The invention discloses a GIS equipment defect three-dimensional visual positioning method based on acoustic imaging, and relates to the related technical field of GIS equipment, and the method comprises the steps: constructing a sound intensity detection dual system; sound source signals of the GIS equipment are synchronously acquired, and near-field acoustic signals and far-field acoustic signals are output; performing frequency band separation through a self-adaptive band-pass filter, and identifying near acoustic abnormal features and far acoustic abnormal features of the separated frequency band; carrying out spatial positioning fusion and outputting a positioning point; and importing a GIS equipment three-dimensional model according to the sound source fusion positioning point, and outputting a GIS equipment visual positioning result. The technical problems that in the prior art, the GIS equipment defect detection range is limited, the positioning precision is insufficient, the defect space position is difficult to visually present, the equipment defect positioning is not rapid and accurate enough, and the equipment operation and maintenance efficiency and reliability are affected are solved. The technical effects of quickly and accurately positioning the equipment defects and effectively improving the operation and maintenance efficiency and reliability of the equipment are achieved.