234 results about "Median filter" patented technology

The invention discloses a production line layout scheme evaluation system based on a dynamic simulation technology, and the system comprises the steps: converting equipment coordinates, detecting equipment spacing and AGV channel interference, and generating a geometric data set containing position constraint; performing median filtering on the equipment state data to generate a state data set; drag-and-drop layout is carried out through the template, safety spacing and channel width are verified, and an initial layout parameter combination is generated; generating an orthogonal table through a Taguchi orthogonal experiment method, and constructing an optimization space less than or equal to 8 dimensions; performing parameter search by adopting a particle swarm optimization algorithm, and outputting optimized layout parameters; deploying a discrete event simulation engine, and generating a layout evaluation report; and generating a verification data set and performing t verification, and triggering parameter correction and updating the scheme label according to a verification result. The design can effectively improve the feasibility, efficiency and landing reliability of the layout scheme, and significantly reduce the trial and error cost and operation risk of enterprise production line planning.

The invention discloses a water quality monitoring method and system based on big data analysis, and the method comprises the steps: obtaining water quality parameter data collected by multiple types of sensors, carrying out the format unification processing of the water quality parameter data through a standardization algorithm, and carrying out the elimination through a median filtering algorithm if abnormal data points are detected, thereby obtaining a standardized data set; aiming at the standardized data set, performing feature extraction on the chemical parameters, the spectral features and the biological indexes by adopting a principal component analysis algorithm to obtain feature vectors, and if the variance contribution rate of the feature vectors exceeds a preset threshold value, retaining the feature vectors and generating a dimension reduction feature data set; according to the dimension reduction feature data set, a random forest algorithm is adopted to construct a pollution evaluation model, a pollution index is calculated, if the pollution index exceeds a preset threshold value, a high pollution state mark is generated, and a pollution evaluation result is output. According to the invention, real-time monitoring, evaluation, early warning and traceability of water quality pollution are realized, and comprehensive technical support is provided for water environment protection.

The invention relates to the field of character recognition systems, and discloses a Chinese ancient book character recognition method based on an image recognition technology, which comprises the following steps: reading an ancient book image according to an open source code computer vision library to obtain an original image; performing gray processing on the original image to obtain an image after gray equalization; according to a median filtering algorithm, carrying out de-noising processing on the image after gray scale equalization to obtain a pre-processed image; according to the method, multi-level feature extraction is carried out on the image through the real-time multi-scale detection model and the convolutional neural network, and character features of different scales and angles in the image can be captured; and therefore, the character recognition accuracy is improved, especially for the common font, typesetting, inclination or blurring conditions in ancient book images.

The invention discloses a construction method of a multi-underwater robot cooperative control system, which relates to the field of underwater robots, and establishes a multi-parameter coupled data acquisition and preprocessing mechanism by using a Doppler current profiler ADCP, an inertial measurement unit IMU and controller software. A combined algorithm of sliding median filtering and Kalman filtering is adopted, and data disturbance caused by environment noise and attitude drift is effectively removed; and through abnormal point triple standard deviation determination and double adjacent point interpolation completion, stable correction of sampling data is realized. A standardized data set obtained after normalization processing unifies parameter scales of different physical magnitudes, so that the local average flow velocity, the flow velocity fluctuation root mean square and the average acceleration can be directly compared in a feature space. Therefore, according to the method, the high-consistency expression of the water flow disturbance characteristics in time and space is realized, so that the synchronous measurement precision of multiple underwater robots in a non-uniform flow field is remarkably improved.

The invention relates to the technical field of three-dimensional scene reconstruction methods, in particular to a multi-modal data three-dimensional scene reconstruction method and device, and the method specifically comprises the following steps: 1, carrying out the data collection of a target scene through a plurality of sensors, comprising a color image sequence acquired by an optical camera, point cloud data acquired by a laser radar and a depth image sequence acquired by a depth camera; 2, denoising processing is carried out on the collected color image sequence, and a median filtering algorithm is adopted; 3, rough alignment is carried out on preprocessed multi-modal data by adopting a method based on feature points; according to the method, the alignment precision is remarkably improved, accurate matching of different modal data in space is ensured, the geometric structure of a reconstruction model is accurate, and the relative position of an object fits an actual scene.

The invention belongs to the technical field of image processing, and particularly relates to an image denoising processing system based on multistage filtering collaboration, which comprises a noise feature analysis module, an image semantic analysis module, a collaboration strategy generation module, a multi-path parallel filtering module, a self-adaptive weighted fusion module and a perception quality refining module. According to the invention, blind noise evaluation is carried out on the lightweight convolutional neural network, the input image is preprocessed, the image is divided into different semantic regions, and then noise information and the semantic regions are matched through a filter library and a plurality of filters optimized for different features. And dynamically selecting the most suitable filter for each semantic partition, calling an adaptive median filter to process a smooth region containing impulse noise, and ensuring that each filter strictly performs denoising operation according to a region and parameters specified by a strategy set, so that the filtering strategy is changed from blindness to intelligence, and the filtering accuracy is improved. And the visual bolster degree and the intelligent level of the denoising effect are improved.

The invention discloses a detection method for fertilizer production, and relates to the technical field of fertilizer detection.The detection method comprises the following steps that a near-infrared multispectral visual detection system is installed at the working section before packaging of a fertilizer production line, the positions of a camera and an illumination source are adjusted, and multispectral images of fertilizer particles are collected; sequentially carrying out dark current correction and flat field correction on the acquired image, eliminating noise and uneven illumination, and then carrying out median filtering to remove random noise; selecting a waveband image with the best contrast ratio, separating particles from the background through threshold segmentation, and positioning each independent particle after optimizing the boundary through morphological processing; and based on the multi-band reflectivity difference, analyzing the surface material of the particles pixel by pixel, and counting the proportion of the coating area of each particle to obtain the coating coverage rate of the single particle. By introducing the near-infrared multispectral visual detection technology and the coating uniformity quantitative analysis model, non-destructive and high-precision online detection of the coating coverage rate of the fertilizer can be realized.

The invention discloses a coal mine production anomaly detection system and method based on time series decomposition and prediction, and provides an end-to-end decomposition-prediction anomaly detection framework, the framework firstly constructs a deep STL iterative decomposition network, and uses a differentiable median filtering and iterative orthogonalization mechanism to obtain a deep STL iterative decomposition network; an original power load sequence is decoupled into three orthogonal components, namely a trend component, a season component and a residual error in a robust manner, and modal aliasing is effectively inhibited; then, a parallel multi-branch prediction architecture is adopted, aiming at the physical characteristics of each component, an implicit neural predictor based on a polynomial basis / Fourier basis and a hierarchical frequency domain Transform-CNN are respectively used for special modeling and prediction, and anomaly judgment is realized based on a prediction error; according to the method, objective and tampering-resistant power data can be fully utilized, the anomaly detection performance of the non-stationary sequence is remarkably improved, and a reliable non-intrusive intelligent monitoring solution is provided for coal mine production safety.

The invention discloses a thyroid ultrasound image nodule segmentation method based on spatio-temporal characteristics and frequency enhancement, and relates to the technical field of medical image processing, and the method comprises the following specific steps: multi-scale characteristic extraction: carrying out the gray normalization and median filtering preprocessing of a thyroid ultrasound image, and carrying out the segmentation of the thyroid ultrasound image nodule; extracting multi-scale features under different resolutions by using a pre-trained backbone network, obtaining the multi-scale features under different resolutions by the backbone network through multilayer convolution and pooling operation, and generating basic and advanced features through convolution operation; by designing an intra-frame feature extraction architecture based on frequency feature fusion and an efficient spatial-temporal feature aggregation mechanism, intra-frame and inter-frame collaborative optimization is realized, intra-frame and inter-frame high and low frequency features are solved by means of octave convolution, the perceptual ability of a model to ultrasonic image details and semantic information is improved, inter-frame and inter-frame feature fusion is realized, and the perceptual ability of the model to ultrasonic image details and semantic information is improved. A local circulation neighborhood propagation mechanism is adopted, and interference of thyroid movement on time sequence feature extraction is effectively restrained.

The invention discloses a no-load state monitoring method and system for an aircraft power supply system, and relates to the technical field of electrical parameter measurement and aircraft power supply system monitoring. Initializing a monitoring system, calibrating a voltage sensor, a current sensor, a frequency sensor and an impedance sensor, setting a no-load state judgment threshold system, and importing historical no-load data as a comparison reference; no-load voltage, current, frequency and impedance signals are synchronously acquired through sampling frequency of the sensor group, and the signals are transmitted to the data processing unit through the shielding cable; median filtering and low-pass filtering are adopted to remove noise interference, normalization processing is carried out on the signals, and basic electrical parameters are calculated; calculating core characteristic values of the voltage stability, the frequency fluctuation coefficient, the impedance stability and the harmonic distortion rate; comparing the characteristic value with a preset threshold value, judging whether the state is normal or not by combining historical data, and identifying an abnormal type; and outputting a judgment result in real time, and starting acousto-optic early warning in case of abnormality. Abnormal traceability and trend prediction are realized, and safe and stable operation of an aircraft power supply system is guaranteed.

The invention relates to the technical field of infrared imaging, in particular to an infrared image self-adaptive denoising algorithm and system based on an FPGA (Field Programmable Gate Array), and is characterized in that an adaptive blind pixel searching algorithm module captures data of a certain frame number at an FPGA end, performs multi-directional gradient detection on each pixel point, sets conditions to judge to obtain a blind pixel position, and marks the blind pixel position; the convolution kernel self-adaptive blind pixel replacement module performs self-adaptive blind pixel replacement multi-time module multiplexing at the PL end of the FPGA through a convolution kernel sliding block and gradient comparison to ensure complete coverage of a large blind pixel group, and the self-adaptive median filtering module intelligently selects and replaces pixels meeting requirements through automatic sorting of center pixels and surrounding pixels; according to the method, surface fuzzy filtering is carried out through a surface-blue algorithm module, so that a smooth area in an image is effectively smoothed, details of an edge area are reserved, blind pixels in a video stream can be effectively detected and compensated, and meanwhile traditional noise is suppressed and image details are reserved through multi-stage filtering.

The invention discloses a fry counting method based on image processing, and relates to the technical field of image processing. Comprising the steps of image acquisition, fry image target segmentation, image region division, fry target identification, preliminary fry counting, overlapped fry identification and final fry counting. Median filtering denoising is adopted, and the image quality is guaranteed; a threshold value is determined in combination with global and local maximum between-cluster variance methods, expansion and corrosion operations are assisted, fry and a background are accurately distinguished, and the processing efficiency and segmentation precision are improved through a sub-region division strategy; through connected region marking, multi-feature fusion screening and adjacent region splicing de-weighting, single fry is effectively identified, repeated counting is avoided, distance transformation and watershed algorithm accurate segmentation are adopted for overlapped fry, then a correction coefficient optimization result is introduced, and finally high efficiency and high precision of fry counting are achieved. And powerful support is provided for intelligent counting management of fishery breeding.

The invention discloses a microstructure mapping modeling and mechanical simulation method suitable for a two-phase structure alloy, and the method comprises the steps: carrying out the self-adaptive median filtering denoising and Retinex enhancement of a metallographic diagram, so as to improve the gray scale comparison of a matrix and a second phase; training a U-Net + + segmentation model by using the enhanced image to complete pixel-level phase region extraction; a two-phase geometric model is established according to the segmentation result, a finite element feature data set is obtained through multi-physics field coupling calculation, lossless fusion and topological consistency verification are conducted on the finite element feature data set and a prior model, and a fusion model is obtained; simulation parameters are set based on actual working conditions, mechanical simulation is executed, and mechanical property parameters and response curves are output. The method can accurately reflect the mechanical behaviors of the two-phase structure alloy under different working conditions, provides a scientific basis for material design and performance optimization, has high universality, and can be widely applied to performance analysis and evaluation of various types of alloys.

The invention discloses a seismic observation data automatic calibration method based on median filtering, and the method comprises the steps: firstly analyzing an original signal, determining a time window of a seismic signal, extracting a signal segment in the time window of the seismic signal, carrying out the band-pass filtering of the signal segment according to the type of the original signal, and obtaining an initial band-pass filtering signal; processing the initial band-pass filtering signal by using median filtering windows with different lengths, calculating indexes such as a signal-to-noise ratio and an effective signal retention rate, and screening an optimal initial window through second-order difference and sliding standard deviation; and processing the initial band-pass filtering signal by using an optimal signal-to-noise ratio window to obtain an initial median filtering signal, calculating a point-by-point correlation coefficient of the initial band-pass filtering signal and a residual error, carrying out regional dynamic window adjustment according to the correlation coefficient, carrying out median filtering processing again, and finally carrying out band-pass filtering processing again to obtain a final calibration signal. According to the design, the selected length of the window is not fixed, signals of different sampling rates and observation frequency bands can be adapted, the universality is high, and the noise reduction effect and effective signal retention are considered at the same time.

The invention discloses a resistivity model inversion method, device and equipment for grounding detection and a medium, and the method comprises the steps: obtaining three-component time domain data corresponding to a grounding grid, and carrying out the preprocessing of the three-component time domain data to obtain normalized observation data; constructing a first objective function containing a Tikhonov smooth item and a cross gradient item, and executing preliminary inversion through an L-BFGS algorithm according to the first objective function and the normalized observation data to obtain an initial three-dimensional resistivity model; carrying out logarithmic transformation on the model, selecting a k-means initial mass center based on a nearest neighbor density peak value algorithm, and carrying out clustering, abnormity elimination and median filtering on the model to obtain a smooth three-dimensional resistivity model; and generating a partition soft constraint based on a clustering partition result, combining the partition soft constraint with the first objective function to obtain a second objective function, and inverting the smooth three-dimensional resistivity model again to obtain a target three-dimensional resistivity model. The geometric structure, the electrical parameters and the health state of the power transmission tower grounding grid are efficiently and accurately detected, and the detection efficiency and accuracy are remarkably improved.

The invention provides a GRU-attention mechanism-based metering terminal meter fault rate prediction method, relates to the technical field of smart power grid and power equipment health management, and aims to solve the problems of insufficient feature capture and low prediction precision of complex time sequence data in the prior art. The metering terminal meter fault rate prediction method based on the GRU-attention mechanism comprises the following steps: constructing a multi-dimensional fault prediction index system based on equipment operation parameters, age limit parameters and environmental parameters, and preprocessing original data by adopting a median filtering method and Min-Max normalization; an attention mechanism is embedded into a GRU layer to form an end-to-end GRU-attention mixed prediction model, and the steps of model training and optimization, failure rate prediction, result analysis and the like are carried out. According to the method, through fusing the time sequence modeling capability of the GRU and the feature focusing capability of the attention mechanism, the accuracy of metering terminal meter fault rate prediction is remarkably improved, the early warning of the metering equipment fault rate is effectively realized, and an auxiliary decision basis is provided for operation and maintenance work.

The invention relates to the technical field of energy spectrum data processing, in particular to a germanium spectrometer energy spectrum data adaptive optimization method and device, equipment and a storage medium. Performing feature extraction on the historical energy spectrum data set based on a wavelet transform method and a least square method; constructing a baseline initial fitting curve according to the corrected low-frequency coefficient features and the reconstructed high-frequency coefficient features; optimizing the baseline initial fitting curve based on a difference operation method and a preset Kalman filtering algorithm; carrying out median filtering processing on the real-time energy spectrum data set based on a median filtering method and a preset sliding window length; performing iterative optimization on the median filtering data set based on a preset particle swarm optimization algorithm and the baseline optimization fitting curve; according to the scheme, multiple algorithms are fused, baseline features are deeply mined, high and low frequency signals are separated, drift is corrected, spectral peak features are reserved, accuracy is improved, and the method is suitable for complex scenes and assists high-precision application of the germanium spectrometer.

The invention discloses a risk comprehensive assessment system and method based on image acquisition and identification analysis, and relates to the field of power transmission line safety monitoring. Comprising an image acquisition module, a data preprocessing module, a risk identification module, a multi-dimensional risk assessment module and an early warning linkage module. The image acquisition module acquires a multi-view real-time image; the preprocessing module carries out median filtering denoising and Laplacian sharpening; the risk identification module uses a risk identification model to detect water and soil loss, equipment abnormity and environmental risks; the multi-dimensional evaluation module fuses an image recognition result, machine account data, meteorological factors and historical maintenance records, and dynamically adjusts weights through an angular momentum model and trigonometric series to calculate a comprehensive risk score; and the early warning linkage module performs hierarchical response according to the score. According to the scheme, multi-source data fusion, accurate identification and dynamic early warning are realized, and the accuracy and timeliness of risk prevention and control of the power transmission line are improved.

The invention relates to the technical field of data processing alignment, in particular to a long-distance photoelectric sensor focusing calibration method and system, and the method comprises the following steps: starting a laser transmitter with an end to monitor a photoelectric array, obtaining the light intensity and three-dimensional coordinates of multiple sampling points, and carrying out the filtering; and extracting edge and radial light intensity, calculating a gradient vector through derivative difference and generating an offset direction vector, extracting symmetrical sampling points based on the vector to construct a balance ratio sequence, generating an offset angle through a mean square error model, and mapping the angle and the vector to generate a calibration instruction. According to the method, multi-sampling-point light intensity is associated with three-dimensional coordinates, median filtering is executed to remove noise, gradient is extracted through derivative difference to achieve spatial offset direction mapping, an offset angle value is finely solved in combination with a balance ratio sequence and a mean square error model, and parameter stability is ensured; the offset vector and the angle are correlated to execute coordinate mapping operation to compensate the angle deviation, real-time automatic correction of the pose of the transmitting end is achieved, and the long-distance alignment efficiency and the signal intensity of the sensor are improved.

The invention relates to an infrared pot cover shape non-uniformity correction system based on scene gradient processing and a correction algorithm thereof. The system comprises an image input stream module, an initialization module, a down-sampling module, a median filtering module, a horizontal and vertical gradient processing module, an image reconstruction module, a bilinear interpolation module, an up-sampling module, an algorithm processing updating matrix module and an image output stream module which are connected in sequence. The median filtering module, the horizontal and vertical gradient processing module and the image reconstruction module are respectively connected with the RAM through a cache; the bilinear interpolation module and the up-sampling module are respectively connected with the FIFO through a cache, and the algorithm processing updating matrix module is connected with the DDR through a cache. The method has the advantages that the pot cover effect of an infrared image can be directly improved, system-level optimization of the infrared imaging quality is achieved, the method is adaptive to various types of infrared detectors after an input data protocol is configured, codes do not need to be changed one by one, and the method is low in hardware scheme resource consumption, high in convergence speed and high in transportability and universality.

The application discloses an electronic certificate forgery detection method, which comprises the following steps: performing median filtering and sharpening operation processing on obtained electronic certificate data, constructing a residual set containing sharpening residual error, sharpening-median filtering residual error and median filtering residual error by using the processed electronic certificate; performing discrete cosine conversion on the residual set to obtain the features of frequency domain variation before and after capturing the original image; using an autoregressive model and a Markov model to extract multiple residual features in the spatial domain to evaluate the detail difference between the electronic certificates before and after median filtering; finally, integrating the features from the spatial domain and the frequency domain to construct a feature set; inputting the extracted feature set into a support vector machine for training to obtain a median filtering detector to perform a detection task. The application solves the deficiencies of traditional electronic certificate forgery detection in low-resolution and high-compression image processing, has a high detection rate, and can be well applied in certificate monitoring.

The embodiment of the invention provides a field vehicle braking distance detection method and related equipment, and the method comprises the steps: controlling a corresponding device to collect vehicle position data of a target field vehicle in a braking distance detection test after receiving a triggering signal transmitted by the corresponding device, and forming distance time sequence data; carrying out filtering processing on the distance time sequence data by adopting a median filtering algorithm, and carrying out distance change rate calculation based on the filtered distance time sequence data to obtain speed time sequence data; determining an initial system state variable at each moment based on the filtered distance time sequence data and speed time sequence data; updating the initial system state variable at each moment based on a Kalman filtering algorithm to obtain system state data; and performing calculation based on the system state data and the trigger signal, and outputting a braking distance test result of the target field vehicle. According to the embodiment of the invention, the efficiency and precision of field vehicle braking distance detection can be improved. The method can be widely applied to the technical field of field vehicle measurement.

The invention relates to the technical field of molecular diagnosis and graphic processing, in particular to an electronic microarray chip dot matrix negative and positive judgment method based on an ISFET sensor. A chip hole site corresponds to an ISFET sensor, and after a hole bottom specific nucleic acid probe and a target sequence are hybridized and extended, the voltage of the sensor is changed. The method specifically comprises the following steps: data preprocessing: filtering median values of multi-frame electric signals of a hole site and taking a mean value; the dot matrix identification comprises the steps of threshold demarcation, Canny edge detection, circular Hough transform, optimal circle screening by Kdtree, array numbering and abnormal deletion processing to position a dot matrix; and calculating a result, extracting a signal difference value of the D1 stage and the D4 stage, evaluating numerical values of the positive quality control array and the to-be-detected sample array through a numerical value of the negative quality control array, and judging a final calculation result. According to the method, response changes are rapidly analyzed through image recognition, false detection and missing detection caused by numerical value differences in different arrays are reduced through multiple times of detection, and the detection accuracy is improved by selecting the most possible area through tickets.

The invention discloses a printer motor control method and system based on a current detection front-end conditioning circuit, and relates to the technical field of current detection, and the current detection front-end conditioning circuit comprises a precise sampling resistor and a multi-stage RC filter network; the method comprises the following steps: collecting a motor phase current signal through a current detection front-end conditioning circuit, and converting the motor phase current signal into a digital current signal after differential amplification and filtering; obtaining a direct-axis current component and a quadrature-axis current component through coordinate transformation; setting a reference quadrature-axis current, and setting a direct-axis current reference value as 0; calculating a direct-axis voltage and a quadrature-axis voltage, and generating a switching signal for driving an inverter; in each control period, performing median filtering on the current sampling signal to adjust the integral gain of the PI controller; and identifying the abnormal state of the motor based on the time domain characteristics of the digital current signal so as to adjust the operation strategy of the 3D printer. High-precision current sensing and abnormal state recognition are both considered, and the printing quality of the 3D printer is improved.

The application relates to the technical field of quantifiable test indexes of new energy vehicles, and particularly discloses a method for constructing a typical road spectrum working condition based on a genetic algorithm and new energy platform data, data acquisition, invalid data section elimination rules including static data section elimination and abnormal continuous duration elimination; data abnormal value identification rules: speed abnormal values are defined and a correction method is set; data smoothing and denoising rules: a median filter is used to preliminarily denoise a speed sequence; characteristic parameter calculation: including kinematic characteristic parameters and new energy light truck characteristic parameters; construction of an initial section library; multi-objective genetic algorithm optimization; road spectrum synthesis and post-processing: optimal sections filtered by the algorithm are spliced, and smoothing processing is carried out to ensure the continuity of a speed curve; multi-objective genetic algorithm selection: selecting an NSGA-III algorithm as a multi-genetic objective algorithm condition; the application realizes rapid construction and iteration of the road spectrum, and is high in efficiency.

This application relates to a pipeline inspection method, apparatus, computer equipment, storage medium, and computer program product. The method includes: acquiring a multi-channel raw signal sequence collected within a target pipeline; performing median filtering on the raw signal sequence based on a preset sliding window to obtain an intermediate observation sequence; performing dynamic prediction filtering on the intermediate observation sequence to obtain a reference filtered signal; wherein the dynamic prediction filtering is used to predict candidate sequences based on the intermediate observation sequence; and fusing the intermediate observation sequence and the candidate sequences; identifying abnormal sequences in the raw signal sequence based on the reference filtered signal; correcting the abnormal sequences to obtain an initial corrected sequence; and performing secondary filtering on the initial corrected sequence to obtain a target filtered signal. This method can effectively improve the signal-to-noise ratio and anomaly identification accuracy of pipeline inspection data.

The application relates to a Beidou RTK measurement point cloud self-adaptive filtering processing method, belonging to the field of high-precision three-dimensional positioning technology based on the Beidou system. The method firstly determines whether it is suitable to directly take the median filtering result as the filtering output according to the data amplitude span state of any measurement point in the Beidou RTK measurement point cloud and the effective satellite quantity when the point cloud is acquired, and quantifies the reliability of the median filtering result through the data fluctuation and direction change of the local neighborhood of the measurement point when it is not suitable, so that the fusion filtering result determination of the median filtering result and the original measurement value is completed in the case that the direct median filtering result is not suitable for filtering output. The application can avoid the reverse filtering optimization effect of the median filtering in the Beidou RTK measurement point cloud, and improve the RTK measurement point cloud data filtering accuracy of the median filtering.

This invention belongs to the field of power monitoring technology and discloses a brightness balancing and regional smoothing fusion algorithm for power monitoring images. The method includes: image denoising, using mean filtering and median filtering to smooth the image; image registration, performing optimal matching on two or more images obtained from different sensors, at different times, or from different angles; and image region fusion, smoothly fusing images with different angles and brightness contrasts. This method is applicable to panoramic video monitoring systems. Designed specifically for the complex lighting and high electromagnetic interference characteristics of power operation sites, this invention effectively eliminates on-site noise through adaptive preprocessing, and achieves a balance between global brightness uniformity and local detail preservation through layered brightness balancing, solving the problems of incomplete brightness balancing and detail loss in power scenarios encountered by general algorithms.

This application provides a TDLAS gas sensor and its anti-interference filtering and noise reduction method, relating to the field of gas concentration detection technology. The method includes: acquiring the original analog-to-digital conversion data of the gas to be measured; using an adaptive median filtering algorithm to filter, reduce noise, and correct the baseline of the original data to obtain a time-domain signal; and calculating the gas concentration based on Beer-Lambert's law. The adaptive median filtering algorithm uses multi-resolution analysis to decompose the signal into low-frequency and high-frequency components, identifies noise regions and absorption peak regions by calculating local gradient changes, and performs multi-scale reconstruction after adaptive threshold classification. This invention addresses the problem of TDLAS gas sensors being susceptible to interference from environmental temperature and pressure fluctuations. By protecting the absorption peak region while filtering out the noise region, it significantly improves the signal-to-noise ratio and detection stability, and has the advantages of strong anti-interference capability, fast response speed, and significant noise reduction effect.