5144 results about "Signal acquisition" patented technology

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

The invention discloses a mechanical transmission system fault trend prediction system based on dynamic feature recognition, and relates to the technical field of mechanical state monitoring. Comprising the following steps: synchronously acquiring a load torque signal and a lubrication state parameter signal of a transmission system and vibration acceleration signals of a plurality of measuring points through a signal acquisition module; the working condition decoupling characteristic generation module carries out time-frequency analysis on the vibration signal, calls a pre-stored load disturbance spectrum template according to a load torque signal to carry out adaptive differential processing so as to eliminate load fluctuation interference, and calls a correction rule set according to a lubrication state parameter signal to carry out form recombination on the signal so as to compensate the lubrication state influence; and finally outputting a working condition decoupling feature representing the health state of the mechanical part. And the trend prediction module calculates and obtains fault development trend and residual life estimation data through a pre-trained fault prediction model. According to the method, the dynamic characteristics representing the essential degradation of the part are effectively extracted, and the accuracy and reliability of fault trend prediction of the mechanical transmission system are improved.

The invention relates to the technical field of fault detection, and discloses a diesel generating set fault detection method and system based on deep learning, and the method comprises the steps: obtaining first vibration signal data, and carrying out the time-frequency decomposition, and obtaining a dynamic change feature; de-noising processing is carried out on the dynamic change features to obtain a time-frequency feature sequence; extracting a peak energy distribution data set, and calculating each frequency band entropy value to obtain a frequency band entropy value sequence; classifying the frequency band entropy sequence, determining a random fluctuation reference mode, and separating to obtain an abnormal frequency component; calculating a spectral line spacing and amplitude ratio, obtaining a spectral line feature data set, classifying the spectral line feature data set, and determining a fault classification result; obtaining current second vibration signal data, performing similarity calculation on the current second vibration signal data and a pre-established normal mode library, and outputting a fault feature vector; and verifying the fault feature vector to obtain a final fault detection result. According to the method, closed-loop diagnosis from signal acquisition to fault classification can be realized, and the fault detection precision of the diesel generating set is improved.

The embodiment of the invention provides a multi-configuration relay-oriented aircraft electrical box parameter measurement system and method, and belongs to the field of aircraft electrical box test and diagnosis. According to the hardware level, flexible access of a multi-configuration relay electrical box is achieved based on a universal adapter interface, core parameter measurement is completed in combination with an integrated measurement and control circuit, and synchronous acquisition and processing are conducted on multiple paths of signals through a signal acquisition and processing unit; according to the software level, the pull-in / release time, the rebound time and the voltage parameters are accurately calculated through a parameter calculation module on the basis of waveform feature point recognition and dynamic excitation-response theories, and the functions of fault diagnosis, safety protection and data storage are integrated. According to the method, the crossing from on-off judgment to parameterized precise evaluation is realized, the detection efficiency, the measurement precision and the equipment reliability evaluation capability are remarkably improved, and an integrated solution is provided for external field maintenance and state prediction of the aircraft relay.

The invention discloses a tank equipment sealing micro-leakage intelligent early warning and positioning method, which comprises the following steps of: deploying acoustic sensors, vibration sensors, gas concentration sensors, temperature and humidity sensors and the like aiming at different positions of a tank to realize multi-mode original signal acquisition; various signals are standardized and corrected through denoising, normalization and structural difference mapping, and the influence of the environment and the material structure on the signals is compensated; extracting multi-dimensional feature parameters, and carrying out space-time weighted fusion by adopting an attention mechanism to generate feature vectors marked by confidence; according to the method, a self-learning anomaly recognition algorithm is combined, a judgment model is continuously optimized through transfer learning and online sample accumulation, micro-leakage anomaly judgment is achieved, accurate positioning of a tank leakage source point is executed based on a physical propagation model, and the sensitivity and the self-adaptive capacity of micro-leakage detection are improved; and leakage identification and positioning requirements in a complex environment and a dynamic working condition can be effectively met.

The invention belongs to the technical field of grouting compactness detection, and particularly discloses and provides an intelligent detection system and method for the grouting compactness of a bridge prestressed pipeline, and the system comprises a signal acquisition and analysis module, a defect boundary calibration module, a defect recognition and analysis module and a compactness judgment and processing module. The method specifically comprises the steps that acoustic emission and fluctuation response signals are collected in real time through a sensor array arranged on the outer wall of a pipeline, and three-dimensional slurry density distribution is recognized; constructing an inner wall three-dimensional model in combination with a pipeline design drawing, dynamically setting a slurry density threshold value according to a grouting material formula and an environment temperature, and further calibrating a slurry defect boundary; extracting multi-dimensional features such as spatial positions, effective volumes, geometric regularity and types of the defects on the basis of boundaries, and calculating grouting compactness; and finally, according to whether the compactness reaches the standard or not, a grout supplementing early warning instruction or a detection report is automatically generated. According to the method, the reliability of defect identification and compactness calculation under complex working conditions is remarkably improved.

A concrete mortar mixing and stirring system belongs to the technical field of concrete production equipment. Aiming at the problems that an existing stirring system is difficult to accurately judge the mixing uniformity of concrete mortar and the quality is easily influenced due to excessive stirring or insufficient stirring, the system comprises a stirring tank, a vibration signal acquisition unit, a torque monitoring unit and a control unit, stirring blades in a stirring tank are driven by a motor, a vibration signal acquisition unit captures a tank body vibration signal, a torque monitoring unit detects the torque change of the motor, and a control unit decomposes the tank body vibration signal into a frequency domain signal and extracts a preset aggregate collision vibration frequency band energy value to generate a vibration energy sequence; and analyzing the torque change fluctuation intensity to generate a torque variation coefficient sequence, calculating a correlation coefficient sequence of the two, generating an adaptive shutdown threshold by a sliding time window, and cutting off a motor power supply when the correlation coefficient sequence is continuously lower than the threshold for a preset number of times. The device is mainly used for automatic stirring control of concrete mortar, and precise starting and stopping of the stirring process are achieved.

The invention belongs to the technical field of power cable fault positioning. The invention discloses a TDR power cable global fault positioning method based on multi-frequency cooperative excitation and environment adaptive correction. The method comprises the following steps: 1) multi-frequency pulse cooperative injection and signal acquisition; 2) environment parameter dynamic sensing and speed correction: embedding a miniature temperature and humidity sensor and a cable insulation state monitoring unit along the cable at an interval of 500m, and collecting laying environment temperature, humidity and insulation material dielectric constant change data in real time; constructing a dynamic speed correction formula based on a preset environment-dielectric constant correlation model; according to the method, the complex fault positioning precision is improved, the environment and scene adaptability is enhanced, and the troubleshooting efficiency is optimized.

The invention discloses an electrocardiosignal preprocessing system and method based on filtering and deep learning, and relates to the field of electrocardiosignal data processing. The multi-stage adaptive filtering module comprises a baseline drift elimination unit, a power frequency interference suppression unit and a myoelectricity noise removal unit, and all the units are connected in sequence to form pipelined parallel processing; the deep learning fusion module is used for performing deep feature extraction and noise classification on the output signal and feeding back a classification result to the multi-stage adaptive filtering module; the signal quality evaluation module carries out quality evaluation on the electrocardiosignals subjected to multi-stage adaptive filtering and deep learning fusion and judges whether the signal quality is qualified or not; a feature enhancement and standardization module; the technical effects of improving the self-adaptability and robustness, improving the calculation efficiency of heart disease classification, the signal fidelity and the diagnosis reliability, and enhancing the signal quality evaluability and the self-adaptive ability are achieved.

The invention belongs to the technical field of underwater acoustic engineering and digital signal processing, and particularly relates to a fractional delay FIR (Finite Impulse Response) phase calibration method and system of a multichannel sonar system. Comprising the steps of signal acquisition and reference channel selection; high-precision relative delay estimation; calculating a cross-correlation function of other channel signals and the reference channel signal, performing coarse estimation by searching a peak point of the amplitude of the cross-correlation function, and performing fine estimation on the peak position by using a parabola interpolation method to obtain the delay time of each channel relative to the reference channel; designing an FIR fractional delay filter; digital filtering and phase calibration; verifying a calibration effect; and calculating the phase of each channel signal at the target frequency point after calibration, and counting the standard deviation of each channel signal so as to evaluate the improvement degree of phase consistency. The method aims at overcoming the defect that traditional integral multiple sampling delay correction precision is insufficient, high-precision phase compensation of a sub-sampling point level is achieved, and therefore the performance of algorithms such as beam forming of a multi-channel sonar system is remarkably improved.

The invention discloses a cable fault accurate positioning system based on an intelligent sensor system, and relates to the technical field of power fault positioning, and the cable fault accurate positioning system comprises a cloud monitoring center which is in communication connection with the following modules: a signal acquisition and processing module which is used for deploying the intelligent sensor system along a cable, operation environment data and traveling wave signals in the cable operation process are obtained, and the signals are preprocessed and stored in a cloud data warehouse. According to the invention, the front band-pass filter and the self-adaptive trap filter are combined to filter out high-frequency harmonic waves and specific-frequency interference signals, and the differential sampling technology is adopted to counteract common-mode interference, so that the signal-to-noise ratio of the signals is remarkably improved, a system can accurately capture and process weak traveling wave signals caused by a high-resistance grounding fault in a complex environment, and the fault detection accuracy is improved. Therefore, the fault positioning precision is greatly improved, the positioning error caused by signal interference is reduced, and the determination of the fault position is more reliable and accurate.

The various implementations described herein include techniques and apparatuses for multi-channel biopotential signal acquisition, biopotential signal pre-processing, and adaptive signal conditioning. In one aspect, an analog frontend circuit includes an instrumentation amplifier (INA) and an analog-to-digital circuit (ADC) in a forward signal path. A digital circuit receives input from the ADC. An adaptive baseline tracking and compensation circuit tracks and compensates moving motion artifact driven changes. A power line interference (PLI) detection and compensation circuit tracks a desired number of PLI harmonics, and magnitude, phase and frequency for the PLI harmonics in real time. A digital-to-analog converter (DAC) circuit combines output of the adaptive baseline tracking and compensation circuit and the PLI detection and compensation circuit to output an analog output. A passive filter receives the analog output and drives an analog compensation signal at an input of the INA.

The invention discloses a hydropower station water level detection method and system. The method comprises the steps that an original water flow characteristic parameter set is obtained through multiple sets of Doppler effect radar flow measuring devices; performing segmentation extraction to obtain a water flow feature sub-data set; inputting a Transform network coding layer to generate a water flow feature coding vector; outputting a preliminary water level predicted value sequence through a decoding layer; a radar flow measurement model is called for correction to obtain a correction sequence; time sequence integration is carried out to obtain a water level change curve. The system comprises a multi-source Doppler radar signal acquisition unit, a spatial-temporal feature segmentation extraction unit, a Transform coding processing unit, a water level preliminary prediction unit, a radar model correction unit and a time sequence integration output unit. According to the method and system, multi-source parameters are deeply fused, the environmental adaptability is improved, the continuity and reliability of a detection result are enhanced, the water level of the hydropower station can be accurately monitored, and support is provided for scheduling decision and safety prevention and control.

The invention discloses a duct grouting fullness detection method and device, and relates to the technical field of duct grouting fullness detection.The duct grouting fullness detection method comprises the following steps that an ultrasonic probe is arranged at an inlet of a duct and transmits an ultrasonic signal, and a receiver is arranged at an outlet of the duct and used for receiving reflection and transmission signals and collecting ultrasonic propagation data in the duct; and performing frequency decomposition on the acquired signals, and analyzing energy attenuation conditions of the signals in different frequency bands by using penetration characteristics of ultrasonic waves with different frequencies. According to the method, multi-band ultrasonic detection, self-adaptive signal compensation and machine learning technologies are combined, so that the accuracy and robustness of duct grouting fullness detection are remarkably improved. Multi-band detection covers different depths, and an adaptive compensation algorithm maintains signal stability and reduces misjudgment. The machine learning optimizes data fusion, ensures that the system adapts to different construction conditions and outputs stable results, and the generated grouting map provides clear visualization to help engineers to quickly identify and maintain defect areas.

The invention discloses a sudden death risk real-time assessment system and method based on multi-modal physiological signal fusion, relates to the field of human physiological state monitoring and early warning, and solves the problems of low accuracy and poor real-time performance of sudden death risk assessment by single-modal signals. The system comprises a signal acquisition module, a preprocessing module, a high-dimensional feature extraction module, a multi-modal feature fusion module, a sudden death risk quantification module, a model updating engine module and an early warning feedback module, and each module integrates a self-adaptive filtering unit, a time sequence convolutional network unit, an improved multi-head self-attention mechanism unit and the like. According to the scheme, multi-mode signals such as electrocardio are synchronously collected through multiple channels, a dynamic risk index is calculated through self-adaptive noise reduction, parallel extraction of time-frequency domain nonlinear features and mutual information weighted fusion in combination with kernel density estimation, and online incremental updating and multi-stage early warning of a model are achieved; the sudden death risk can be accurately evaluated in real time, the anomaly detection sensitivity and the early warning timeliness are improved, and the method is suitable for daily health monitoring and high-risk group risk management and control.

The invention relates to the technical field of physiological signal processing and sleep monitoring, in particular to a sleep staging method and a sleep staging system for collecting multiple physiological signals, and the sleep staging method and the sleep staging system for collecting the multiple physiological signals synchronously collect auditory meatus photoelectric volume pulse waves, temperature and head micro-motion signals through an in-ear sensor array. According to the method, the signal quality index is calculated, time domain, frequency domain and nonlinear features are extracted, a dynamic weighted fusion mechanism is adopted, feature weights are adjusted according to the signal quality index, a hierarchical depth time sequence learning model is input for sleep staging, and the sleep staging accuracy is improved to 89% or above and is improved by 15-20% compared with a single brain wave method. A sleep state evaluation report and a personalized feedback intervention strategy generated by the system are beneficial for improving sleep quality, a dynamic weighted fusion mechanism enhances system robustness, adapts to different signal qualities and ensures stable performance, and the invention provides an efficient and accurate new method for the field of sleep monitoring.

The invention discloses a multi-turn memory optomagnetic encoder hybrid system, belongs to the technical field of position and angle measurement encoders, and aims at solving the problems that an existing optomagnetic hybrid encoder is complex in signal processing, long in axial length, insufficient in precision stability and prone to losing the number of turns and the position. The system comprises an optical encoder, a gallium arsenide magnetic signal acquisition sensor, a multi-antipode unequal magnetic ring, a processor, a hardware circuit, an ultra-low power consumption circuit, a signal acquisition module, a signal processing module and the like, integrates self-developed magnetic angle analysis, turn number memory correction and other algorithms, and adopts dual-redundancy design and intelligent signal mode switching. The method simplifies the signal processing flow, shortens the axial length, improves the measurement precision and stability, enhances the environmental adaptability and fault-tolerant capability, prolongs the endurance, and is suitable for the position angle measurement in a high-precision and severe environment.

The invention discloses a memory signal integrity enhancing system based on multi-dimensional parameter self-adaption, and belongs to the technical field of signal transmission. The system comprises a multi-dimensional signal acquisition module, a multi-dimensional parameter analysis module, a self-adaptive adjustment module, a memory signal optimization module and a feedback monitoring module, the multi-dimensional signal acquisition module is combined with multi-dimensional parameter analysis, signals of a data bus, an address bus and a control bus are synchronously acquired, a dynamic adjustment strategy is generated according to a model, and the self-adaptive adjustment module and the memory signal optimization module are optimized according to the dynamic adjustment strategy. Different optimization means are adopted for different parameter types, a customized optimization system with one parameter and one scheme is formed, it is ensured that the optimized target signal is optimal in amplitude stability, time sequence accuracy, anti-noise capacity and impedance matching degree, the feedback monitoring module is combined with the multi-dimensional parameter analysis module, and the multi-dimensional parameter analysis module is combined with the feedback monitoring module. And through full-dimension index monitoring and dynamic calibration, the continuous adaptability of the system to the running state of the memory and the long-term stability of signal integrity are guaranteed.

The invention relates to the technical field of signal processing, and provides an intelligent sensing device electromagnetic interference suppression method and system based on AI identification, and the method comprises the steps: obtaining a multi-source electromagnetic signal set of a target intelligent sensing device in a preset monitoring period, and carrying out the electromagnetic interference feature extraction processing of the multi-source electromagnetic signal set, performing interference mode matching processing on the electromagnetic interference characteristic set based on a preset interference suppression model to generate an electromagnetic interference suppression strategy of the intelligent sensing equipment; the electromagnetic interference suppression strategy comprises a frequency band adjustment direction corresponding to an interference frequency band distribution characteristic, a signal gain compensation direction corresponding to an interference intensity fluctuation characteristic and an anti-interference filtering direction corresponding to an interference source correlation characteristic; and performing interference suppression operation on a signal acquisition module of the intelligent sensing equipment according to the electromagnetic interference suppression strategy so as to reduce the electromagnetic signal distortion rate of the intelligent sensing equipment.

The invention relates to the related technical field of dust detection, and provides a mining induced charge and light scattering fusion dust detection device and method.The dust detection device comprises a workbench, a sampling assembly and a detection assembly, and the sampling assembly comprises a sampling air pump, a conical airflow homogenizer and a closed sampling cavity; the detection assembly comprises an annular sliding rail, a light scattering detection module and an electrostatic induction detection module; the dust detection method comprises the following steps: S1, device initialization and zero calibration; s2, sampling preparation of dust-containing airflow; s3, dual-module synchronous signal acquisition; s4, data processing and temperature and humidity compensation; s5, carrying out anti-interference protection assembly; and S6, data fusion and result output. S7, device cleaning and standby; through cooperative work of the double detection modules, dust detection signals in different concentration intervals are accurately captured, wide-range coverage from low concentration to high concentration is achieved, and synchronous monitoring of dust concentration and particle size characteristics is achieved.

The invention discloses a high-precision charging pile direct-current electric energy metering method based on ripple compensation, and relates to the technical field of charging pile control. Dual-path signal acquisition and processing are executed to obtain a direct-current component metering result and ripple characteristic parameters; averaging the sampling points to obtain a high-frequency direct current component; subtracting the high-frequency direct-current component from the original sampling signal to obtain a pure ripple signal, performing FFT conversion and time domain analysis on the pure ripple signal, and extracting ripple characteristic parameters; according to the charging pile direct-current electric energy high-precision metering method based on ripple compensation, a main metering path and ripple analysis path dual-path architecture is adopted, the main path guarantees the direct-current component metering precision through a low-sampling-rate and high-precision ADC, the ripple path only extracts features rather than reconstructs waveforms, high hardware cost is not needed, and the charging pile direct-current electric energy metering precision is improved. Metering errors caused by ripples can be reduced to meet and be superior to national standard requirements. The transient scene is defined through the ripple mutation coefficient Ktrans and the ripple decay time tau, and the problem that the transient precision of a traditional method is out of control is solved.

The invention discloses a motion precision compensation system and method of a numerical control precision automatic lathe, and aims to solve the problems that an existing precision compensation method depends on external calibration, production is interrupted, dynamic errors cannot be responded in real time, and the self-learning closed-loop capability is lacked. The method comprises the steps that servo current, spindle vibration and tool turret position signals are collected in real time; extracting signal features and inputting the signal features into a pre-training model to evaluate a rigidity degradation degree and predict a position deviation; querying a compensation parameter library according to an evaluation result; and injecting the compensation offset into a subsequent numerical control instruction in real time. The system comprises a multi-source signal acquisition module, an edge AI inference engine, a precision state evaluation model, a self-adaptive compensation parameter library and a CNC instruction injection interface. By the adoption of the technical scheme, online real-time precision sensing and self-adaptive compensation without external high-precision equipment and production interruption can be achieved, and machining size consistency and long-term stability are remarkably improved.

The invention discloses a blade bolt state monitoring system and method based on an acoustic emission technology in the technical field of state monitoring of wind power generation equipment. The method comprises the following steps: acquiring acoustic emission signal data of a blade bolt connection area acquired by an acoustic emission sensor array; sequentially carrying out preprocessing and feature extraction processing on the basis of the acoustic emission signal data, and then calculating a wavelet packet energy entropy value; judging whether a triggering condition of multi-sensor data fusion weighting judgment is met or not based on the wavelet packet energy entropy value; when any triggering condition is met, it is judged that the state of the bolt is abnormal, and after comprehensive state recognition is conducted through multi-sensor data fusion weighted judgment and modal acoustic emission wave velocity correction positioning, early warning information including the damage type, the severity degree and position information is output; when the triggering condition is not met, it is judged that the bolt state is normal, and circulating monitoring continues. According to the invention, accurate evaluation and early warning of the blade bolt state are realized through signal acquisition, processing, analysis and early warning.

The invention relates to the technical field of chemical automation control, and discloses an intelligent closed-loop control system for producing p-dichlorobenzene by a biomass method, which comprises a signal acquisition module, a residence time reconstruction module, a thermal coupling analysis module, a parameter self-adaptive scheduling module and an execution control module. The rheological characteristics of raw materials are monitored by collecting the current of a stirring motor, the liquid level is dynamically increased through a retention time reconstruction module to prolong the retention time of the materials, and a liquid level periodic migration strategy is introduced to prevent scaling of a gas-liquid interface; the thermal coupling analysis module and the parameter self-adaptive scheduling module are used for calculating thermal retardation post factors, temperature control parameters are corrected in real time, the upper limit of cooling output is limited, inner wall supercooling crystallization is prevented, and the problems that the conversion rate is reduced, the heat transfer efficiency is reduced and equipment is hung on the wall due to viscosity fluctuation of biomass raw materials are effectively solved. And the self-adaptive stable control of the reaction process is realized.

The invention relates to the technical field of debugging systems, in particular to an adaptive debugging system and method for touch screen FW parameters, and the system comprises a signal acquisition module, a trend triggering module, a temperature compensation calculation module, a window generation module and a parameter output module. According to the invention, the edge of the driving voltage is carefully collected, the sliding difference algorithm is introduced to filter out high-frequency noise, meanwhile, real-time sampling of environment temperature and baseline capacitance adjustment are combined, the influence of external variables on signals can be dynamically corrected, and accurate judgment of capacitance fluctuation is improved by utilizing periodic delay deviation and regression trend analysis. By means of main frequency feature extraction and zero crossing point sudden change detection, threshold setting can be refined, self-adaptive window expansion can be achieved, touch signal response is kept sensitive and consistent all the time, adjustment and optimization can still be automatically completed even under the condition that the temperature changes frequently or signal interference is complex, parameter drift and debugging failure are effectively prevented, and user experience is improved. And the parameter automatic matching efficiency and the finished product consistency in batch production are improved.

The invention relates to the technical field of power cable fault detection and positioning, in particular to a middle and high voltage cable insulation defect live detection and positioning method, which comprises the following steps: step 1, injecting a composite detection signal into a cable conductor through a signal injection device; 2, arranging signal acquisition devices at a plurality of preset point positions of the cable; step 3, carrying out adaptive processing on the acquired response signal; step 4, extracting insulation defect features from the response signals after adaptive processing; step 5, based on the extracted insulation defect features, classifying defect types by using a pattern recognition algorithm, and calculating defect positions based on a signal propagation time difference principle; and 6, outputting a defect identification and positioning result to a monitoring system, verifying, and if the deviation between the positioning result and historical data exceeds a threshold value, re-executing the steps 1 to 5. By combining a multi-point synchronous measurement technology and a precise space-time analysis method, the position where the defect occurs can be positioned more accurately.

The invention belongs to the field of power transmission line parameter measurement, and discloses a live-line testing device and method for mutual inductance line parameters and zero-sequence impedance parameters. The device comprises a double-circuit line simulation module, a pilot frequency excitation module, a power frequency simulation module, a synchronous acquisition module and a signal processing module. The isolation transformer simulates a double-circuit line, the pilot-frequency power supply provides pilot-frequency excitation signals, the measurement unit realizes synchronous acquisition, and the signal processing module performs anti-interference and amplification processing. The method comprises the steps of line simulation wiring, synchronous time synchronization, pilot frequency signal acquisition, parameter calculation and error verification, and pilot frequency parameters are converted into power frequency parameters by using a frequency conversion formula. According to the invention, power frequency interference can be effectively suppressed in a charged environment, high-precision measurement of zero-sequence impedance and mutual inductance parameters of a mutual inductance line is realized by using pilot frequency excitation and Beidou synchronization technologies, the measurement error is remarkably reduced, the safety and reliability are remarkably improved, and powerful technical support is provided for safe and stable operation of a power system.

The invention relates to the technical field of bio-electricity signal acquisition, and particularly discloses a multi-channel bio-electricity signal acquisition system and a multi-channel bio-electricity signal acquisition method.The multi-channel bio-electricity signal acquisition system comprises a bio-electricity signal acquisition unit and a data processing unit; the data processing unit is used for compensating the original electrophysiological signals according to the real-time impedance value transmitted by the analog-to-digital conversion unit, performing primary screening on the original bio-electricity signals through the lowest quality threshold value, and performing primary compensation on the original bio-electricity signals according to the real-time impedance value, the standard impedance value and the breathing phase; according to the method, more flexible quality requirement setting is realized, the quality threshold setting difficulty is reduced, secondary screening is performed on the corrected bio-electricity signals through the highest quality threshold, and secondary compensation is performed on the corrected bio-electricity signals by adopting the LSTM network, so that the influence of bio-impedance fluctuation on the bio-electricity signals is reduced, and the quality of the bio-electricity signals is improved. And the acquisition quality and stability of the bio-electricity signals are improved.

The invention discloses a gas meter power supply abnormity monitoring system and method based on a signal acquisition technology, and relates to the technical field of gas meter power supplies, the system comprises a multi-dimensional sensing module, a dynamic adaptive module, a multi-dimensional fusion analysis module, an intelligent judgment module and a verification execution module; according to the invention, the multi-dimensional fusion analysis module accurately calculates key parameters such as a power supply ripple coefficient and load transient response time in a corresponding mode, the electromagnetic interference intensity is calculated through professional algorithms such as discrete Fourier transform in combination with an environment electric field and a magnetic field sequence, a threshold value is dynamically adjusted based on a working mode, and a comprehensive score is calculated through multi-parameter deviation weighted fusion. The power supply state is deeply and comprehensively evaluated; the intelligent judgment module scientifically divides anomaly levels according to comprehensive scores, quantifies and distinguishes real anomaly and environmental interference by means of an environmental interference contribution degree formula, solves the problem that traditional monitoring cannot accurately recognize power anomaly under complex working conditions, and improves anomaly judgment accuracy.