355 results about "Adaptive filtering algorithm" patented technology

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

The invention provides a road construction project carbon emission ratio toughness analysis method, and belongs to the technical field of carbon emission, machine learning and big data analysis and mining, and the method comprises the steps: obtaining a historical change trend of a carbon emission ratio from historical data of a road construction project, and constructing a dynamic change model of the carbon emission ratio in combination with real-time monitoring data; determining the influence degree and the action mechanism of the interference factors on the carbon emission ratio dynamic change model by analyzing the interaction relationship between the carbon emission ratio and the interference factors; key fluctuation characteristics are extracted from the corrected change trend of the carbon emission ratio, and whether fluctuation of the carbon emission ratio exceeds an acceptable range or not is judged in combination with a preset threshold value; if the fluctuation of the carbon emission ratio exceeds a preset threshold value, performing noise reduction processing on the data by adopting an adaptive filtering algorithm to generate noise-reduced carbon emission ratio data; and training the segmented analysis result by adopting a random forest model in a machine learning algorithm to generate a prediction model of the carbon emission ratio.

The invention relates to a building module data interface intelligent monitoring system based on the Internet of Things and a deployment method, and belongs to the technical field of building information monitoring. The system is composed of a distributed sensing unit, an edge computing gateway, a cloud platform and a visual terminal, a self-adaptive filtering algorithm and an abnormal mode recognition model are built in the edge computing gateway, the cloud platform adopts a time sequence database to construct a multi-dimensional data warehouse, and structural health degree evaluation is carried out by fusing an LSTM neural network and a random forest algorithm. The deployment method comprises the steps of optimizing a sensor distribution strategy based on a BIM model, establishing a wireless Mesh ad hoc network communication architecture, and configuring a grading early warning mechanism and a fault tracing function. The innovation point is that a dynamic threshold adjustment algorithm and an interface performance degradation prediction model are provided, and real-time monitoring of the connection state of the building module and life prediction are realized. The system has the advantages of flexible deployment, high detection precision and low maintenance cost, and the intelligent level of building structure safety monitoring is effectively improved.

The invention provides a power supply equipment temperature characteristic detection method and system. The method comprises the following steps: acquiring temperature data sets of power supply equipment under different working conditions; performing partial derivative calculation on the mathematical model, quantifying the contribution degree of each working condition to the temperature characteristic, and determining a main influence factor and a secondary influence factor; a time sequence analysis method is introduced, the temperature data set is analyzed, the dynamic change trend of the working conditions is captured, and nonlinear relation characteristics between the working conditions and temperature characteristics are identified; a self-adaptive filtering algorithm is adopted, changes of working conditions are tracked in real time, and parameters and structures in the mathematical model are dynamically adjusted, so that the mathematical model can adapt to the dynamic changes of the working conditions; and training and optimizing the adjusted mathematical model by using a support vector machine algorithm, thereby improving the prediction precision and generalization ability of the mathematical model.

The invention discloses a combined navigation method and system of an unmanned surface vehicle. The method comprises the following steps: forming a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation System) integrated navigation system by using a GNSS (Global Navigation Satellite System) and a strapdown inertial navigation system (INS), and establishing a mathematical model of a GNSS / INS integrated navigation system filter; the mathematical model comprises a state equation and a measurement equation; establishing a mathematical model of the influence of the GNSS auxiliary information on the GNSS positioning precision so as to preliminarily adjust the measurement noise in the measurement equation; through an improved Sage-Husa adaptive filtering algorithm, adaptive estimation is carried out on the preliminarily adjusted measurement noise, and a measurement noise estimation final value is obtained; and according to the motion characteristics of the unmanned surface vehicle, adding motion constraint conditions to the measurement equation. The GNSS auxiliary information mathematical model based on multivariate function fitting is established, the influence of the external environment on the positioning precision is objectively reflected, the measurement noise in the filter is dynamically adjusted, and the positioning precision of the integrated navigation system under the interference condition is remarkably improved.

The invention provides an intelligent conference memo generation method based on a robot, and the method comprises the steps: collecting a multi-channel voice signal through a microphone array, and enhancing the voice of a target speaker through a beam forming technology; background noise is separated by adopting a self-adaptive filtering algorithm, and the voice signal quality is improved; speech features are extracted, language model parameters are adjusted, and a transliteration text is generated; analyzing the text structure, and extracting conference themes, participants and decision contents to form structured information; calculating semantic similarity among the knowledge graph nodes, and if the semantic similarity is higher than a preset threshold value, associating historical records to generate extension information; constructing a structured memorandum based on the extended information, organizing a conference theme, participants, decision contents and associated historical records, and generating an initial memorandum; and monitoring a memorandum editing operation, updating a knowledge graph node relationship, and generating a final memorandum document. According to the method, the accuracy, integrity and availability of conference records are effectively improved, and the conference efficiency is remarkably improved.

The invention relates to the technical field of adaptive filters, and discloses a wide linear adaptive filter, which comprises the following steps of: performing tensor integral solution on a system weight vector at the moment of the wide linear adaptive filter to obtain two adaptive weight vectors; constructing an input matrix based on the sampling value of the input signal, and calculating an estimation signal of the moment based on the product of the input matrix of the moment and the two adaptive weight vectors; and calculating the difference value between the expected signal and the estimation signal at the moment, obtaining an estimation error signal, and updating the gain vector at the obtaining moment and the estimation error signal by using a minimum error mode criterion, updating the two adaptive weight vectors at the obtaining moment, and reconstructing the system weight vector at the moment of the wide linear adaptive filter. According to the invention, the recursive adaptive filtering algorithm resistant to impulse noise interference is realized with low calculation complexity by using weight decomposition and selection of a robust cost function.

The invention discloses a state monitoring system for a railway electric switch machine. The state monitoring system comprises a multi-mode sensor module, a data preprocessing module, an intelligent analysis and decision module and a visualization and interaction module. The multi-mode sensor module is distributed at a key part of the switch machine and is used for acquiring multi-dimensional data such as the rotating speed of a power transmission shaft, displacement and vibration of an actuating rod, an expression notch image, temperature and humidity, air pressure and the like; the data preprocessing module performs noise reduction through a Kalman adaptive filtering algorithm and fuses asynchronous data by using a space-time alignment technology; the intelligent analysis and decision-making module constructs a hierarchical index system, determines a weight in combination with an analytic hierarchy process and an entropy weight method, dynamically evaluates a health level through a fuzzy grey clustering algorithm, predicts and represents a gap size trend by adopting an ARIMA model, performs monthly early warning on jamming faults by applying a random forest model, and optimizes and maintains resource configuration according to historical data; the visualization and interaction module develops a Web mobile terminal interface and supports multi-level health state display, real-time alarm and maintenance work order generation.

The invention relates to the technical field of optical communication, and discloses an optical fiber loss evaluation method and system. The method comprises the steps that an optical fiber sensor collects real-time optical signal data and generates a dispersion velocity data sequence; extracting a fluctuation feature vector by adopting an adaptive filtering algorithm, and determining a dispersion velocity change trend; inputting the change trend and the transmission distance into a neural network model, predicting a loss fluctuation amplitude and generating a prediction loss sequence; calculating a coupling effect correlation coefficient, judging abnormal coupling and outputting an evaluation result; adjusting signal compensation parameters according to an evaluation result, optimizing nonlinear influence and determining a compensated dispersion velocity value; calculating a signal attenuation factor according to the compensated dispersion velocity value and the transmission distance, and generating an optimized transmission scheme; and updating optical communication network parameters according to the scheme, outputting stable signal quality and generating an optical fiber loss evaluation result. According to the invention, the problem of inaccurate evaluation caused by nonlinear fluctuation and signal coupling is solved, and the stability of optical fiber transmission and the signal quality are improved.

The invention discloses a BIM (Building Information Modeling)-based large-span post-tensioned bonded prestressed beam construction method. The BIM-based large-span post-tensioned bonded prestressed beam construction method comprises the following steps: collecting a vibration signal of a working rib and a matt rib reference signal of a matt rib; inputting the dummy rib reference signal into an adaptive filtering algorithm to which phase preserving constraint is applied, and performing noise cancellation processing on the working rib vibration signal to generate a purified pressure wave signal; performing multi-scale decomposition on the purified signal, extracting propagation time delay of the direct wave based on coherence analysis among components, and generating a phase-time delay characteristic matrix; and based on pre-configured geometric parameters of the BIM three-dimensional model, a real-time stress distribution field is calculated through inversion according to the phase-time delay characteristic matrix, and the real-time stress distribution field is synchronously updated to the BIM three-dimensional model. According to the method, the phase distortion problem under noise interference is solved through a physical reference channel and phase preserving filtering, the accuracy of time delay extraction is improved through a multi-path separation technology, and high-precision and high-robustness real-time monitoring of the prestress is achieved.

The invention provides a power factor correction method and system for AC-DC conversion of a server power supply, and relates to the technical field of server power supplies, and the method comprises the steps: collecting an input AC signal and a DC bus output signal, calculating a real-time power factor value, constructing a compensation function based on the power factor value, and calculating a deviation value; and generating a compensation parameter matrix through recursive calculation, processing a direct current output signal by adopting an adaptive filtering algorithm, fusing the direct current output signal with the compensation parameter matrix, calculating to obtain a current adjusting signal and a duty ratio adjusting signal, and accurately adjusting a power factor.

The invention discloses a patient improvement effect analysis method for controlling spinal cord electrical stimulation through an implantable brain-computer interface, and relates to the technical field of medical rehabilitation, and the method comprises the steps: multi-dimensional collaborative data collection: implanting electrodes in a target brain region and below a spinal cord injury segment, installing a detection element at an exoskeleton key part, and carrying out multi-dimensional collaborative data collection; a sensor is attached to a lower limb preset muscle group, electroencephalogram signals, SCS stimulation parameters, EXS motion data and neuromuscular response data are synchronously collected, and time correlation marks are embedded; according to the method, the reliability of motion intention decoding is remarkably improved by adopting a mode of combining multi-source signal preprocessing and a multi-mode intention recognition model, and in the signal preprocessing stage, the self-adaptive filtering algorithm combining Kalman filtering and wavelet threshold denoising is applied, so that the motion intention decoding efficiency is improved. SCS electrical stimulation interference, EXS motor noise and physiological noise in the electroencephalogram signals are effectively removed.

The invention relates to a detection-interference integrated unmanned aerial vehicle electromagnetic interference device, which belongs to the technical field of electronic countermeasure, and comprises an integrated design of an antenna module, a radio frequency front end module, a digital signal processing module and a main control module, the radio frequency front-end module comprises a detection receiving channel and an interference transmitting channel to process signal conversion, the digital signal processing module executes signal detection and analysis, intelligent interference decision and anti-self-interference signal processing based on a self-adaptive filtering algorithm, and the main control module is responsible for overall control and power supply. According to the invention, the technical problems of large response delay, complex system cooperation and incapability of continuous reconnaissance in the interference process of traditional anti-unmanned aerial vehicle equipment are solved. Real-time signal cancellation is achieved through the normalized least mean square algorithm, the reconnaissance capability is kept while interference is applied, the method has the advantages of being rapid in response, accurate in interference, low in power consumption and the like, and the countering efficiency is remarkably improved.

An AI-driven emotion recognition system for learning adaptation in educational institutions, comprehensive: • an emotion recognition unit that tracks students' facial expressions in real time and categorizes the resulting expressions into a specific emotional class from a given set using a Convolutional Neural Network (CNN) trained on a large emotion dataset; • a real-time processing unit that extracts facial features and dynamic emotional characteristics of students through adaptive filter algorithms; • an LMS integration unit, adapted to combine discovered emotional knowledge with online and offline learning environments for customizable learning adjustments; • a personalization engine programmed to intelligently adapt training content, response approaches, and training materials according to the identified emotional states, so that each learner receives individually tailored and more intensive learning experiences; • a feedback loop that sends educators real-time alerts and recommended videos to address emotional feedback or lesson gaps, allowing teachers to tackle negative signals in the moment (e.g., frustrated face, etc.); • AI data storage and adaptive learning model unit that stores anonymized data for emotion recognition, updates the AI ​​model and improves it through learning over time, and ensures that the use of the information is in compliance with data protection regulations; • A user interface module specifically designed for educators, providing a visual overview of students' emotional patterns and real-time analysis of their emotions and engagement as a basis for important decisions that lead to improvements in teaching processes.

The invention provides a permanent magnet frequency conversion double-sine modulation swimming pool pump energy-saving control method, and relates to the technical field of motor driving and control, and the method comprises the steps: building a double-sine carrier modulation model of a permanent magnet synchronous motor, and calculating a dynamic harmonic suppression factor and a double-sine modulation depth in real time; feedforward compensation is carried out on the inverter current based on the DHSIF, and a q-axis current reference value is generated by adopting double-closed-loop vector control; dynamically adjusting a carrier frequency range according to the DSMD, generating double sine carriers with a phase difference of pi / 3, and superposing the double sine carriers into a composite modulated wave; the modulation wave amplitude is corrected through a DHSIF, and fifth and seventh harmonics are eliminated in combination with an adaptive filtering algorithm; a carrier wave ratio is dynamically optimized to generate a PWM signal, and a transient compensation mechanism is triggered to correct a voltage instruction when a load suddenly changes; and an energy efficiency optimization database is constructed to call historical optimal parameters, and nonlinear mapping of DHSIF and motor characteristics is established through parameter self-tuning. According to the invention, cooperative control is realized through dynamic harmonic suppression, modulation depth optimization and intelligent thermal protection.

The invention discloses a tear total IGE detection method and system based on multi-modal data fusion. The method comprises the following steps: acquiring ocular surface images, physiological parameters and environmental allergen data, and acquiring historical medical data from a hospital information system; preprocessing and feature extraction are performed, patient symptom description is analyzed in combination with a natural language processing technology, and related image block features are recognized; adjusting a space-time attention weight based on frequency domain analysis and transfer learning, inputting a lightweight Transform model, and predicting a tear total IgE concentration and an allergy risk score; whether a trace tear sampling program is started or not is judged, and the actually measured concentration of total tear IgE is detected through a disposable micro-fluidic chip; and integrating all information by using a Bayesian adaptive filtering algorithm to determine the total tear IgE level. By implementing the method provided by the invention, the sampling amount is extremely small, the detection time is short, the environmental interference can be dynamically corrected, and the absolute concentration value is output, so that the dual requirements of clinical precise diagnosis and treatment and home monitoring are met.

The invention relates to the technical field of sensing data processing, and discloses a sensing data chip method based on multi-source information fusion and dynamic parameter adjustment. The method comprises the following steps: collecting original data streams of a plurality of heterogeneous sensors, and generating synchronized sensing data through timestamp alignment and format standardization processing; extracting an environment noise component, and dynamically suppressing noise by using an adaptive filtering algorithm to obtain de-noised sensing data; performing multi-dimensional feature decomposition on the de-noised data, calculating time domain, frequency domain and space domain feature vectors, and generating a multi-dimensional feature set; dynamically adjusting a feature weight distribution strategy in combination with a current system load state and a multi-dimensional feature set historical distribution rule, and generating an optimized fusion weight matrix; and carrying out weighted aggregation on the multi-dimensional feature set and the optimized fusion weight matrix, and outputting a multi-source fusion feature vector. According to the method, the problems of data synchronization, noise suppression and feature fusion suitability in multi-source heterogeneous sensing data processing can be solved, and the processing requirements of different scenes are met.

The invention discloses an unmanned aerial vehicle airborne geophysical prospecting method and system based on big data, and the method comprises the steps: obtaining the positioning data of a global navigation satellite system and the measurement data of an inertial measurement unit, carrying out the correction and compensation through the combination of a differential positioning technology and a terrain height model, and obtaining an accurate three-dimensional coordinate data set; acquiring a magnetic field signal and a spectral signal according to the three-dimensional coordinate data set, and performing de-noising processing on the magnetic field signal and the spectral signal by adopting an adaptive filtering algorithm to obtain preprocessed signal data; judging whether the signal data volume of the preprocessed signal data exceeds a preset threshold value or not, if the signal data volume exceeds the preset threshold value, performing fragmentation processing on the preprocessed signal data through a distributed computing framework, extracting feature vectors in each piece of fragmented data by adopting a convolutional neural network, and generating a feature vector set; according to the invention, full-link optimization from data acquisition to ore body prediction is realized, and the precision and efficiency of geological exploration are improved.

The invention provides a method for monitoring cracks in a high-pressure vibration state of a wind tunnel in real time, and belongs to the technical field of wind tunnels. A piezoelectric ceramic sensor array is arranged on the wall surface of the wind tunnel, a vibration fatigue matrix and a structure resonance matrix are established, and a self-adaptive filtering algorithm is adopted to preprocess sensor signals; establishing a vibration coupling equation set to extract crack characteristic parameters, constructing a dynamic threshold discrimination matrix and a risk assessment matrix, utilizing a multi-sensor data fusion algorithm to accurately position crack positions and sizes, calculating risk levels according to a crack risk assessment index equation, and performing three-level safety discrimination. A complete crack real-time monitoring and risk assessment system in the high-pressure vibration environment is formed, and the technical problems that in the prior art, crack monitoring precision is low in the high-pressure vibration environment, and real-time risk assessment cannot be achieved are solved.

The invention relates to a safety risk prevention and control method, system and device for an external envelope structure of a building and a medium. The method comprises the steps of obtaining three-dimensional scanning data of the building and generating a three-dimensional structure model of the building; structural features of the building are extracted from the three-dimensional structural model, a corresponding environment monitoring scheme is generated in combination with a clustering analysis algorithm, and the collection position and the data type of real-time environment data are determined; collecting real-time environment data of the external envelope structure, and dynamically filtering the real-time environment data based on an adaptive filtering algorithm to obtain standard environment data; analyzing the standard environment data by using a support vector machine algorithm, and triggering a safety early warning message under the condition that a sudden structure safety risk is identified; and obtaining structure multi-source data, predicting the structure multi-source data by using a preset safety prediction model to obtain a safety risk development trend of the building structure, and estimating a potential risk based on the safety risk development trend. The method has the effect of improving the safety of the external maintenance structure of the building.

The invention relates to the technical field of intelligent power regulation, discloses a charger control method and system based on intelligent power regulation, and dynamically adjusts a power distribution strategy by monitoring equipment power consumption and a battery state in real time. The method comprises the following steps: firstly, acquiring an equipment power consumption curve and a battery health index, judging the priority of a power consumption demand and a charging demand according to a preset threshold value, determining a preliminary power distribution scheme, optimizing power distribution by adopting a pulse width modulation technology and a digital signal processor according to the actual conditions of equipment and a battery, and generating a frequency-coordinated power control signal; and finally, calculating progressive power transfer parameters among multiple energy paths through a proportional-integral-differential control algorithm to realize smooth power transfer, and processing feedback data by using an adaptive filtering algorithm to ensure stable power output. Intelligent power balance between equipment operation and battery charging can be achieved, the energy utilization efficiency is effectively improved, the service life of the battery is prolonged, and stable operation of equipment is guaranteed.

The invention relates to the technical field of environmental monitoring and pollution control, in particular to a dust and noise online control system, which comprises a monitoring module, which is used for acquiring data of dust concentration C, particle size D, dust raising position P, noise level N and frequency F through video monitoring equipment and various sensors; the data communication module is responsible for transmitting the data to the central control system; the central control system constructs a fuzzy control rule base according to the dust-related data, adjusts parameters of dust removal equipment in combination with a PID algorithm, and controls noise reduction equipment by adopting an adaptive filtering algorithm according to noise data; the risk assessment and early warning optimization module uses a risk assessment model to calculate a risk grade R and performs graded early warning; the governance execution module comprises a dust hood, a sprayer and noise reduction equipment and carries out dust and noise governance according to an alarm, and the air volume regulation module regulates the air volume of a dust suction port through a neural network algorithm by means of feedback of a sensor on a regulation valve; the invention aims to solve the problems of effective monitoring, precise treatment and scientific early warning of dust and noise.

The invention discloses a wide fundamental frequency electric energy metering method and device suitable for rapid change of a tidal current direction. The method is based on a Fir zero-step angular difference compensation algorithm, a self-convolution multi-order cascade adaptive filter algorithm and a single-cycle fast pulse forming algorithm. The metering device comprises a signal conditioning unit, an algorithm data processing unit and a function management unit. According to the invention, point-by-point compensation of a sampling signal angle difference is realized, residual high-frequency components in an integral interval under a wide fundamental frequency signal are filtered, the problem that forward active electric energy and reverse active electric energy counteract each other when a power flow changes frequently is solved through a single-cycle fast pulse forming algorithm, and the metering accuracy is improved.

The invention provides a gradient coil model correction method for ultra-low field magnetic resonance, and relates to the technical field of gradient coil control in medical imaging equipment, and the method comprises the steps: collecting a real voltage signal d (n) and a real current signal x (n) of a gradient coil in real time, and n represents a sampling moment; a gradient coil model is constructed, the gradient coil model comprises a linear model constructed based on an adaptive filtering algorithm and a nonlinear model constructed based on a neural network algorithm, and a first prediction voltage signal # imgabs0 # output by the linear model and a second prediction voltage signal # imgabs1 # output by the nonlinear model are determined according to the real current signal x (n); determining a mixed prediction voltage signal # imgabs4 # of the gradient coil model according to the first prediction voltage signal # imgabs2 # and the second prediction voltage signal # imgabs3 #; and updating the weight of the linear model and the weight of the nonlinear model according to the real voltage signal d (n) and the mixed predicted voltage signal # imgabs5 # so as to adapt to gradient coil model correction under complex working conditions.

The invention relates to a focused ultrasonic wave-based target positioning method, which comprises the following steps of: acquiring original data of an ultrasonic signal through a sensor array, recording propagation time and intensity distribution, separating a main signal from an interference signal by adopting a time domain analysis method, and determining a preliminary energy distribution characteristic; according to the energy distribution characteristics, correcting a signal propagation path by using a sound velocity distribution model, and determining a corrected energy concentration degree value; signal boundaries are enhanced through an adaptive filtering algorithm, half-power point features are extracted through wavelet transform, and a beam width quantization result is determined; according to a beam width quantification result, calculating peak distribution of a main lobe and a side lobe by using an array response function, and determining an initial evaluation value of a side lobe suppression ratio; adjusting weight distribution through a multi-dimensional parameter joint optimization algorithm, and determining an environment adaptive weight distribution scheme; and combining the energy distribution predicted value and the beam width quantification result through a weighted fusion algorithm, and determining a focusing precision evaluation value by adopting Kalman filtering smoothing processing.

The invention provides a voice dialogue interaction method and system for industrial equipment, and relates to the technical field of man-machine interaction, and the method comprises the following steps: obtaining a voice signal of a user, and carrying out the voice enhancement processing of the voice signal of the user through an incremental adaptive filtering algorithm, and obtaining an enhanced voice; according to the enhanced voice, using an information gain transfer learning method to identify an interaction intention of the user, and generating a to-be-interacted voice based on the interaction intention of the user; based on the enhanced voice, utilizing a time delay estimation method to identify an interaction position of the user, and performing position prediction on the position of the user; according to the position prediction result, an industrial equipment horn output strategy is constructed; and outputting the to-be-interacted voice based on the industrial equipment loudspeaker output strategy so as to realize voice dialogue interaction between the user and the industrial equipment. According to the invention, convenience, high efficiency and accuracy of interaction between the user and the equipment in an industrial scene are greatly improved, and intelligent development of industrial production is facilitated.

The invention discloses a weak discharge signal identification method and related equipment. Intelligent identification of weak discharge signals in a complex environment is realized through cooperation of multiple technologies. The broadband sensor is installed at a key node of a distribution network line, the problems that a traditional sensor is prone to being affected with damp and narrow in signal receiving range are solved, and weak signals can be captured. A three-stage cascade low-noise amplification circuit and a robust adaptive filtering algorithm are adopted, gain parameters are adjusted in real time based on an H-infinity control feedback mechanism, various noise interferences are effectively inhibited, and the defects of a traditional fixed threshold filtering algorithm are overcome. And inputting the adjusted signal into a detection model, forming a local and global feature combined capture capability by means of a feature pyramid network, an SE module, a bidirectional long-short term memory network and an attention mechanism, and then realizing fine-grained extraction and accurate classification of the signal by using a Softmax classifier in combination with a dynamic threshold strategy, thereby meeting the on-line monitoring requirements of the insulation state of the electrical equipment.

The invention discloses narrowband communication equipment detection equipment and a detection method. The method comprises the steps that a pre-established wireless channel model data set is acquired, and the data set comprises attenuation values and interference intensity distribution data in different environments; constructing a multipath propagation simulation model according to the attenuation value and the interference intensity distribution data, obtaining a time delay extension range value by adopting time domain analysis, and obtaining an amplitude distortion range value by combining frequency domain analysis; and inputting actual measurement data of the environmental noise, the time delay extension range value and the amplitude distortion range value into an adaptive filtering algorithm to generate signal quality evaluation data after filtering processing, and the like. According to the method, through analysis and simulation of channel model data in different environments, various test means and algorithms are combined, and the performance of the equipment under various conditions, including signal quality, bit error rate, stability, risk and the like, is comprehensively evaluated, so that the reliability of the equipment in practical application is ensured.