1875 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.

The invention belongs to the technical field of intelligent health monitoring, and particularly relates to an FMCW radar-based night respiration and heartbeat monitoring signal processing method, which comprises the following steps of S1, acquiring a chest vibration displacement signal of a human body by using an FMCW radar, and completing the acquisition of a radar signal; according to the recovery separation method based on the two-parameter LMS filter, monitoring and target positioning of multiple life signals can be achieved by the FMCW radar under the condition of low signal-to-noise ratio, and compared with a band-pass filtering (BPF) method and a wavelet transform (WT) method, more accurate breathing and heartbeat signals can be obtained by the method based on the two-parameter LMS filter, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Particularly, when the breathing signal has multiple harmonic waves and the frequency changes, the method is more accurate compared with other methods, breathing and heartbeat signals can be rapidly converged by setting a proper step length, and small steady-state deviation is kept when the signals are received next time.

The invention relates to the technical field of information transmission, in particular to a multi-band processing system for electronic communication, which comprises a signal separation module, a frequency deviation compensation module, an amplitude correction module, a phase correction module and a dynamic feedback adjustment module. According to the invention, a plurality of frequency bands are extracted to execute band-pass filtering to separate signal components, short-time power spectral density is calculated to describe energy distribution, normalization coefficient operation is carried out to enhance frequency domain balance, high-energy component screening is carried out to reduce noise interference, signal effectiveness is improved, and frequency offset compensation calculates a change rate based on adjacent phase data and eliminates abnormal fluctuation. A time domain coefficient is adjusted to correct a frequency offset error, amplitude correction depends on short-time Fourier transform to screen the maximum amplitude to optimize signal consistency, phase adjustment fits a group delay error curve to optimize compensation parameters, dynamic feedback optimizes a strategy according to a signal-to-noise ratio to enhance compensation precision, and signal transmission quality and stability are improved.

The invention relates to the technical field of inverter control, and discloses an inverter output power control method and device, equipment and a storage medium. The method comprises the following steps: performing power frequency fundamental wave extraction and multi-band band-pass filtering on acquired voltage and current sampling data; performing phase compensation, first coordinate transformation and positive and negative sequence decoupling operation on the filtered fundamental wave principal component data; on the basis of a synchronous phase angle and a power grid fundamental wave frequency obtained by positive and negative sequence decoupling operation, carrying out second coordinate transformation, moving average and harmonic power delay difference operation on three-phase current and voltage data of an alpha-beta coordinate system; performing multi-level power calculation, power capacity distribution, error characteristic evaluation and decoupling operation on the inversion fundamental component and the inversion harmonic component subjected to the difference operation; and performing output signal modulation on the inversion power control quantity of the decoupling operation based on the fundamental wave synchronization phase angle, and generating a power control result. According to the invention, the control performance of the inverter on the output power in actual operation is improved.

The invention discloses an emotion recognition method based on electroencephalogram (EEG) and expression data, which combines fusion analysis of electroencephalogram (EEG) signals and facial expression data, and provides an innovative solution for the complexity and accuracy of emotion recognition. The method comprises the following specific implementation steps: 1, finding an optimal frame in an expression video by using multi-instance learning, extracting global visual features by adopting Swin Transform, and realizing dimension reduction and balance representation of cross-modal information through a cross attention mechanism; 2, preprocessing the electroencephalogram signal by using band-pass filtering and a fast ICA algorithm, and extracting effective features by using PSD; and 3, designing a fusion module based on Transform, combining the characteristics of the two modes, and giving full play to the complementary advantage of the modes. Experimental results show that 94.85% of classification accuracy is achieved in four types of emotion classification tasks, and compared with an existing method, the accuracy is remarkably improved.

The invention relates to the technical field of data transmission, and discloses an ultrahigh frequency RFID system data transmission method, device and equipment and a storage medium, and the method comprises the steps: carrying out the band-pass filtering and orthogonal demodulation of a multi-tag backscattering signal received by an RFID reader, and obtaining a tag feature data sequence; performing multi-dimensional feature analysis and time sequence correlation calculation to obtain an independent label signal frame; boundary identification is carried out, and a label data frame starting point is determined; carrying out continuous sampling analysis and label signal conflict identification, and outputting a conflict position sequence; performing matrix interleaving transformation and position rearrangement on the data marked by the conflict position sequence to generate an anti-conflict data packet; the label access scheduling queue is constructed based on the transmission time sequence and the priority attribute of the anti-collision data packet, the multi-label concurrent transmission time sequence is output, and the data transmission performance of the ultrahigh-frequency RFID system is improved through multi-stage signal processing and an intelligent scheduling mechanism.

The invention discloses a rotating component fault early warning method and device based on multi-modal data fusion. The method and device are used for solving the technical problem that an existing rotating component fault early warning method is poor in early warning effect. The method comprises the following steps: acquiring multi-modal signal data, and performing preprocessing according to the multi-modal signal data by adopting a band-pass filtering algorithm to generate a multi-modal feature vector set; performing feature fusion on the multi-modal feature vector set by adopting a recurrent neural network based on an attention mechanism, and outputting a target health index; based on a preset optimization search algorithm, adopting an initial hidden Markov model to generate a target hidden state sequence according to the target health index; and adopting a preset nonlinear Wiener degradation model to generate a rotating assembly fault early warning result according to the target hidden state sequence and the multi-mode signal data.

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 relates to a double-end traveling wave distance measurement method and device suitable for a power distribution network and a storage medium, and the method comprises the steps: firstly, carrying out the preprocessing of a fault phase current traveling wave through the normalization and rough characteristic frequency band zero-phase band-pass filtering of a double-end current traveling wave; using spline wavelet transform to calibrate a proper modulus maximum point as initial estimation of a wave head starting point; further estimating a wave head on the double-end time domain original traveling wave waveform by using a threshold method, and obtaining an accurate wave head starting point through double-end amplitude interpolation correction; and finally, calculating the position of a fault point according to a double-end traveling wave distance measurement principle. According to the method, a fault phase current waveform is preprocessed into a current traveling wave in a rough characteristic frequency band through a zero-phase filtering means, interference outside the characteristic frequency band is eliminated, a wave head break point serves as a wave head starting point, the break point is estimated step by step through a wavelet modulus maximum value and a signal amplitude threshold value method, and the fault phase current waveform is obtained. And more accurate approximation of the break point is obtained through double-end amplitude interpolation, so that the range finding precision exceeding the sampling rate limit is achieved.

A method and apparatus for mitigation of GNSS-signal interference using an adaptive notch filter (ANF) operates based on signals received from one or more satellites of a Global Navigation Satellite System (GNSS) such as GPS, GLONASS, etc. In one embodiment, an apparatus comprises a notch filter having a tunable zero frequency of a transfer function receives an input signal and generates an output signal. A bandpass filter coupled to the output of the notch filter receives the output signal. An adaptive block is coupled to the bandpass filter and adjusts the notch filter parameters in order to achieve the minimization of a specific cost function.

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 relates to the technical field of electrical variable measurement, in particular to a rolling mill speed reducer monitoring and diagnosing system based on multi-source information fusion, which comprises an electrical parameter signal acquisition module for acquiring continuous readings of three-phase voltage and three-phase current of a rolling mill driving motor and synchronizing the readings. According to the method, the three-phase voltage and current data of the rolling mill driving motor are synchronously acquired, and the amplitude and phase are calibrated, so that a more accurate calibration electrical measurement set is obtained, and error propagation in a signal acquisition stage is effectively avoided; using the calibrated electrical data to directly calculate instantaneous input electric power, deducting motor loss to obtain an accurate motor shaft power sequence, and then using a Fourier transform method to extract key harmonic characteristics in a shaft power spectrum; and meanwhile, band-pass filtering and Hilbert transform analysis are carried out on the motor stator current, so that a clearer and more recognizable spectrum kurtosis value is obtained, and the judgment capability of current impact and periodic characteristics is enhanced.

The invention discloses an electromagnetic field observation data-based sea wave characteristic analysis method, which comprises the following steps of: processing electromagnetic field data by adopting a time-frequency spectrum analysis method, identifying distribution characteristics of electromagnetic field signals caused by sea waves at different time and frequencies through time-frequency decomposition, and extracting periods and time-varying characteristics of the sea waves; preprocessing the electromagnetic field data by using dominant frequency analysis and band-pass filtering technologies to obtain sea wave induced magnetic field time domain data; time domain and frequency domain induction electromagnetic field polarization direction analysis technologies are combined, and the movement direction of sea waves and the change characteristics of the sea waves along with time are inferred by measuring the change of the electromagnetic field polarization direction; the quantitative relation between the sea wave induction electromagnetic field intensity and the sea wave height is established, and the sea wave height change of the place is effectively estimated through the sea wave induction electromagnetic field intensity. By comprehensively analyzing the electromagnetic field data, the sea wave period, the time-varying characteristics, the motion direction and the sea wave height are evaluated, and a new way is provided for marine environment monitoring.

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 discloses a motor imagery electroencephalogram recognition method based on a domain adaptation network, and the method comprises the steps: collecting multi-channel motor imagery electroencephalogram signals EEG of a subject, carrying out the band-pass filtering, obtaining a plurality of EEG signals of different frequency bands, taking the EEG signals of the target subject as target domain data, and taking the rest as source domain data. Secondly, on the basis of the source domain data and the target domain data, source domain features and target domain features are obtained through a multi-view convolution Transform feature extractor composed of a parallel space-time convolution module and a Transform module in sequence; and finally, performing adversarial domain adaptation by using the source domain features and the target domain features to obtain a model capable of adapting to target domain data, and identifying a motor imagery category corresponding to the electroencephalogram signal. According to the method, source domain data distribution can be guided to be transferred to a target domain, and the classification accuracy of cross-subject motor imagery electroencephalogram signals is improved.

The invention belongs to the technical field of electrical safety, and particularly discloses an insulation resistance intelligent detection system and method for an energy storage converter, and the system is based on a bridge balance structure, integrates a temperature and humidity sensor and a voltage sensor, and collects environment parameters and bridge unbalance signals in real time. A digital potentiometer and a resistor array are driven through a PID algorithm to dynamically adjust bridge balance, and environmental interference is eliminated in combination with a temperature and humidity compensation model; and carrying out intelligent analysis on the preprocessed multi-dimensional data by utilizing a band-pass filtering and detection model, and outputting a high-precision insulation resistance value. The method achieves full-automatic detection, is high in precision, is quick in response, is suitable for complex working conditions, and remarkably improves the reliability of the insulation performance evaluation of the energy storage converter.

The invention discloses a vibration displacement measurement method and system based on multi-source data fusion, and belongs to the technical field of vibration measurement. The vibration displacement measurement method based on multi-source data fusion comprises the following steps: acquiring motion sequence images of an elastic tube and a rigid tube through a high-speed camera, and acquiring a vibration acceleration signal of the elastic tube through an acceleration sensor; displacement tracks, obtained by the high-speed camera, of the elastic tube and the rigid tube are extracted, and a flow-induced vibration displacement time travel curve of the elastic tube is separated; performing frequency domain analysis on the displacement track data, and determining frequency bands of the rigid tube and the elastic tube; carrying out band-pass filtering on signals of the acceleration sensor based on a frequency band of flow-induced vibration, and compensating an error of an integral trend term of the acceleration sensor by utilizing time domain displacement data of the rigid tube; and carrying out secondary integration on the filtered acceleration sensor signal, and outputting corrected displacement information. By adopting the vibration displacement measurement method and measurement system based on multi-source data fusion, the problem that effective unification of measurement precision and engineering applicability cannot be realized by an existing vibration measurement method can be solved.

The invention discloses a method for estimating the transmittance of a time-varying channel based on a frequency division multiplexing pilot frequency, and aims to solve the problem that the existing method for estimating the transmittance by adopting average transmittance does not have the capabilities of timely feedback and dynamic adjustment. The method comprises the following steps of: measuring a regular position component and a regular momentum component of a received signal by receiving a quantum signal and a pilot signal of frequency division multiplexing in a local local oscillator continuous variable quantum key distribution system generated by a transmitting end in real time; compensating phase noise caused by frequency offset and channel jitter due to different center frequencies of two independent lasers at a receiving end and a transmitting end by using a frequency offset compensation and phase recovery algorithm, and performing band-pass filtering to obtain a recovered pilot signal; and calculating the power ratio of the obtained pilot signal to the pre-calibrated pilot signal at each moment to obtain the time-varying free space transmittance. The performance change of the system in different time periods can be accurately reflected, the communication performance of the CVQKD system can be optimized, the success rate of key transmission can be improved, and the adaptability and robustness of the system can be improved.

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.

One or more systems, devices, methods of use and / or methods of fabrication provided herein relate to a device that can facilitate qubit measurement with isolation imposed between a quantum processor and a respective qubit measurement circuit and / or which respective qubit measurement circuit can have a small footprint, such as within a respective cryogenic chamber of a quantum system. According to one embodiment, a device comprises an isolator circuit having a bandpass filter configuration coupled between a pair of ports and the bandpass filter configuration comprising two or more poles. Two or more shunt resonators can be realized as the two or more poles, wherein the two or more shunt resonators can comprise DC SQUIDs and can be coupled together with one or more admittance inverters. A non-reciprocal signal transmission can be generated between the two ports by RF pumping the DC SQUIDs.

The invention discloses a communication anti-interference method and device of a navigation system and electronic equipment. The method relates to the communication field of the navigation system, and comprises the following steps: acquiring a first signal received by a satellite communication antenna of the navigation system and second signals received by a plurality of sampling antenna units in a preset antenna array; performing band-pass filtering on the first signal and the plurality of second signals to obtain a filtered first signal and a plurality of filtered second signals; performing nonlinear transformation processing on the plurality of filtered second signals to obtain interference cancellation signals; the filtered first signal and the interference cancellation signal are subjected to signal synthesis, a cancellation output signal is obtained, and the average power of the cancellation output signal is smaller than or equal to preset power. According to the invention, the technical problem of poor communication anti-interference capability of a navigation system in the prior art is solved.

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 embodiment of the invention provides a method for suppressing vibration disturbance of a photoelectric pod infrared assembly refrigerating machine, and relates to the technical field of automatic control, and the method comprises the steps: S1, estimating the current vibration frequency of the refrigerating machine; s2, selecting a band-pass filter according to the current vibration frequency of the refrigerator; s3, calculating a disturbance torque estimation value of the refrigerator on the platform according to the gyro angular velocity component after band-pass filtering; and S4, calculating a feedforward control quantity according to the obtained disturbance torque estimated value, and superposing the calculated feedforward control quantity on a feedback control quantity of the photoelectric pod stabilized platform. According to the method, the disturbance torque of the refrigerator to the platform can be estimated, and the influence of the vibration disturbance torque of the infrared refrigerator assembly is counteracted before the disturbance torque does not influence the stability of the platform. The method does not need to increase the cost and complexity of the system, and can be directly integrated into a stability control algorithm of the photoelectric pod, thereby greatly reducing the influence on a stable platform when the infrared refrigerator works, and obviously improving the stability precision of the photoelectric pod.

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.