338 results about "Median filter" patented technology

The invention discloses a carbon dioxide booster pump control system and method, particularly relates to the technical field of carbon dioxide booster pump control, and aims at transient interference pulses caused by starting and stopping of a pump body in the batch production process, millisecond-level timestamp mapping is adopted to achieve interference and steady-state pulse separation. Performing sliding median filtering on the steady-state pulse, extracting a window center peak value, and performing incremental accumulation in sequence to obtain operation accumulated flow in real time; based on the last peak time label, synchronously collecting temperature and pressure measurement values, generating a correction amount through a predefined temperature correction function and a user-defined pressure correction function, adding the correction amount according to a fixed weight, and converting the correction amount into a compensation pulse count through pulse-volume conversion to correct an accumulated error; high-precision flow metering and compensation are achieved through the synergistic effect of start-stop interference elimination, filtering peak value extraction and a correction function, and the reliability of a carbon dioxide booster pump control system under the complex working condition is improved.

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 metallographic detection method and system based on image recognition, and belongs to the technical field of metallographic detection. The detection method comprises the following steps: image acquisition and preprocessing: acquiring a metal metallographic image by an optical microscope, performing median filtering and denoising, and extracting a grain boundary by adaptive binarization; and field-of-view and scale calibration: selecting a corresponding scale according to the shooting magnification. The multi-parameter automatic measurement comprises grain size analysis, grain size rating, second phase analysis and intelligent report generation, a report is automatically generated based on a preset template and comprises an original image, a binary image, measurement data and a histogram, and report fonts and layout can be adjusted in real time through a visual control. According to the detection method disclosed by the invention, the efficiency, the precision and the reliability of metallographic structure analysis of the metal material are remarkably improved through the full-process design of image acquisition standardization, preprocessing intellectualization, data analysis automation and report generation templating.

The invention provides a tight gas micro-crack detection method and system based on seismic data, and relates to the technical field of tight gas micro-crack detection, and the method comprises the steps: obtaining seismic wave data, and obtaining the preprocessed seismic wave data through wavelet transform denoising, compressed sensing reconstruction and median filtering smoothing processing. Using a preset multi-dimensional feature extraction model to extract micro-crack related features from the preprocessed data, including crack edge, curvature, frequency and similarity features, and constructing a multi-dimensional feature matrix; and based on the feature matrix, modeling and training are carried out by adopting a convolutional neural network model of a spatial attention mechanism added with a channel, and then the crack probability of each to-be-detected position is predicted. And carrying out smoothing, denoising and connectivity analysis on the crack probability image through image conversion and optimization processing to obtain a final crack detection result. According to the invention, the precision of micro-crack detection can be effectively improved.

The invention discloses a deep learning-based sonar image target detection method, electronic equipment and a storage medium, and belongs to the technical field of underwater target recognition. In order to improve the recognition accuracy of an underwater target sonar image, the method comprises the steps of collecting a side-scan sonar image; constructing a self-adaptive mixed median filtering method, and carrying out image denoising processing on the side-scan sonar image; performing image enhancement processing by adopting a Retinex method to obtain a side-scan sonar image after image enhancement processing; according to the method, improvement is carried out based on a YOLOv5 model, a CBAM attention mechanism module is added on the basis of the YOLOv5 model, a bidirectional feature pyramid network is used to replace an FPN + PAN structure, and a deep learning-based sonar image target detection model is obtained; and inputting the side-scan sonar image after image enhancement processing into a deep learning-based sonar image target detection model to carry out deep learning-based sonar image target detection. According to the invention, the recognition accuracy of the underwater target sonar image is improved.

The invention relates to an environment monitoring method based on multi-source data fusion and a self-adaptive dynamic model, and the method comprises the steps: deploying a plurality of types of sensors in a monitoring region, and synchronously collecting atmosphere, water quality and soil environment parameter data. Noise of the collected data is removed through a median filtering algorithm, and then fusion is carried out by using an improved D-S evidence theory; and then, establishing an adaptive dynamic environment prediction model based on an LSTM neural network, and learning and predicting fused data by taking a mean square error as a loss function. And finally, comparing a prediction result with a preset threshold value, and automatically sending an environment abnormity alarm to a related department through the Internet of Things terminal once a prediction value exceeds the threshold value. The invention aims to solve the problems of single data, simple processing mode, insufficient prediction accuracy, untimely early warning, high efficiency and the like in the existing environment monitoring technology. Through multi-source data fusion and a self-adaptive dynamic model, accurate acquisition, effective processing and accurate prediction of environment parameter data are realized, and environment abnormity early warning is triggered in time.

The invention relates to an adaptive noise floor energy detection method and system based on FFT frequency spectrum accumulation, and the method comprises the steps: carrying out the FFT frequency spectrum accumulation of an electromagnetic signal sampled by an ADC in an FPGA, and carrying out the channel division of a broadband frequency spectrum into sub-bands; counting and estimating the top and the bottom of the noise by sub-bands one by one; carrying out median filtering processing, subtraction and modular operation according to the obtained data to obtain optimal values of the energy bottom and the noise energy fluctuation range of each channel, carrying out summation to obtain a noise floor of each sub-band, and carrying out median filtering once to obtain a final noise floor value of each sub-band; and a noise factor is introduced to obtain a final adaptive constant false alarm threshold, and adaptive detection of noise floor energy is realized through the threshold. According to the invention, a parallel operation architecture based on the FPGA is adopted, so that the method is simple in calculation and high in sensitivity, and has the capability of detecting each sub-band signal at the same time.

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 relates to the technical field of pupil boundary detection, in particular to a multi-algorithm fusion pupil boundary detection method and system, and the method comprises the following steps: obtaining an eye image through an infrared camera, converting the eye image into a grey-scale map, optimizing the grey-scale map, carrying out the median filtering noise reduction, maintaining the edge information, and determining the pupil boundary through a Sobel operator and Hough transform. And the pupil center displacement is analyzed, the motion consistency is evaluated, the pupil position is corrected, and the pupil positioning accuracy is optimized. According to the method, the image preprocessing process is optimized by applying infrared camera shooting and image processing algorithms, accurate detection, median filtering and local contrast enhancement of pupil boundaries are achieved through multi-algorithm fusion, and particularly noise and edge information in the images are optimized; according to the method, the edge detection precision and the image definition are effectively improved, the pupil boundary is accurately mapped through combined use of the Sobel operator and Hough transform, and the misjudgment rate is greatly reduced.

The invention discloses a reinforcement corrosion degree detection method based on a quantum image processing algorithm, and the method comprises the steps: collecting a reinforcement corrosion image, and carrying out the graying and median filtering noise reduction processing of the image; constructing a quantum image model, and performing morphological processing on the image; carrying out Otsu threshold segmentation on the image based on a PES algorithm; integrating a gray histogram and three-level wavelet decomposition energy for the image, constructing a multi-dimensional feature set, carrying out standardized preprocessing and focusing three-level decomposition, abandoning redundant status data, and carrying out feature extraction on the quantum image based on a positive index relationship between image information entropy and thickness loss; and repeating the above steps to obtain a reinforcement corrosion degree prediction data set, and training the convolutional neural network through the reinforcement corrosion degree prediction data set to obtain the convolutional neural network for diagnosing the reinforcement corrosion type. The image processing effect is improved, the feature extraction efficiency and precision are improved, and the classification efficiency is improved.

The invention discloses a disturbance positioning method and system integrating coarse positioning and fine correction, and the method comprises the steps: obtaining phase original data, preprocessing the phase original data into a two-dimensional matrix A, sequentially carrying out the spatial difference operation, phase unwrapping in the time direction, and DC removal operation in the time domain on the A, and obtaining a preliminary phase reduction matrix A3; solving the variance of column data corresponding to each spatial point in the A3 to obtain a variance curve in space; performing median filtering on the variance curve to obtain a vibration point preliminary judgment position L1; for each L1, extracting phase original data of a certain length T from front to back in space to obtain a data matrix B near each vibration point; and carrying out phase unwrapping operation in a space direction and direct current removal operation in a time domain and a space domain on the phase original data of the B, positioning a disturbance relative position L2, and correcting the L1 according to the L2 to obtain a final actual position L3. The method has the advantages of high positioning efficiency, accurate and rapid positioning and the like.

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 aims to solve the problems of low ground target detection precision mAP and poor target positioning accuracy of an unmanned aerial vehicle, and discloses an unmanned aerial vehicle target identification and positioning method considering adaptive learnable parameters, and the method specifically comprises the steps: in a detection stage, collecting RGB-D image information through employing a depth camera, employing an adaptive anchor frame generation strategy, and carrying out the recognition and positioning of the RGB-D image information; a self-adaptive anchor frame suitable for the view angle of the unmanned aerial vehicle is generated, model parameters are optimized by adopting depth separable convolution, an L-ECA module containing learnable parameters is proposed to enhance the extraction capability of the model on image features, and image detection is performed through an image detection model; in the target position calculation stage, the depth value of the center point of the bounding box is obtained by using a median filtering algorithm, and the position information of the target is obtained through coordinate transformation in combination with the internal reference of the camera and the depth value, so that target positioning is realized. The problems of low detection precision mAP and poor target positioning accuracy in the prior art are solved, and the method is suitable for scenes such as search and rescue and inspection of the unmanned aerial vehicle.

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 an aluminum bar surface quality detection method in the field of aluminum bar production, and the method comprises the following steps: S101, image collection: employing a high-definition line-scan digital camera to move at a constant speed along the axial direction of an aluminum bar for shooting, and cooperating with a multi-angle annular light source to enhance the surface defect contrast, and obtaining a continuous image of the surface of the aluminum bar; step S102, image preprocessing: sequentially carrying out graying processing, median filtering denoising and histogram equalization enhancement on the acquired image; through high-definition image acquisition, an advanced image processing algorithm and a machine learning classification model, tiny defects on the surface of the aluminum bar can be accurately detected, the defect type and severity can be accurately judged, the detection accuracy and reliability are improved, an automatic image acquisition and processing system is adopted, and the detection efficiency is improved. The surface of the aluminum bar can be rapidly detected, compared with manual visual detection, the detection efficiency is greatly improved, and the real-time detection requirement on a large-scale production line can be met.

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 provides a water level data anomaly detection processing method based on multi-method fusion. The water level data anomaly detection processing method comprises the following steps of data acquisition and environment configuration; library importing and environment setting: importing a pandas library, a numpy library and a matplotlib library for data processing and visualization, realizing an isolated forest algorithm by sklearn.ensemble. Isolation Forest, and drawing a box graph by means of seaborn; setting Chinese font display: ensuring normal Chinese display of the chart; and collecting and classifying multi-source data. According to the water level data anomaly detection processing method based on multi-method fusion provided by the invention, the provided water level data anomaly detection processing method based on the fusion of the box graph model, the isolated forest model and the median filtering + 3sigma model is utilized to detect and clean the water level data of the selected gate station in one month, and the RMSE and the MAE are calculated to obtain the abnormal water level data of the selected gate station. A detection method suitable for three conditions of high-precision requirement, high-risk scene comprehensive screening and rapid analysis is obtained; drawing a curve graph of the original data and the processed data, marking abnormal points, and visually comparing the cleaning effects of the three methods.

The invention provides an improved RPSEMD-based millimeter wave radar vital sign detection method, which comprises the following steps of: capturing an echo signal of a target by using a frequency modulation continuous wave radar module to carry out a signal preprocessing step, carrying out distance dimension fast Fourier transform on the captured signal, and positioning a distance unit where a target object is located by using a distance gate locking technology; phase extraction is carried out on the signal, and phase unwrapping processing is carried out; signal stability is enhanced through differential operation, and signal quality is improved through a median filtering technology; vital sign signals are separated and extracted by improving an RPSEMD algorithm model, and millimeter wave radar vital sign detection is achieved. The quadratic sum of the cross correlation coefficients is used as a target function, Bayesian optimization is utilized to optimize parameters of a traditional RPSEMD algorithm, and a group of optimal parameters are found out in a given parameter range. The algorithm verification result of the actually measured signal shows that the modal aliasing problem of the traditional algorithm in the signal decomposition process is reduced.

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 provides a safe driving assistance method and system based on the Internet of Vehicles, and relates to the technical field of traffic, and the method comprises the steps: obtaining positioning information in a vehicle driving process; the reliability of the positioning information is judged, and if the reliability of the positioning information is lower than a preset threshold value, a multi-source information fusion mode is adopted to obtain a high-precision positioning result of the vehicle; according to the fused high-precision positioning result, whether the vehicle is located in a sensitive area is judged, and if yes, the vehicle is located in the sensitive area. According to the method, vehicle-mounted sensor data are fused, abnormal values are removed through median filtering, a comprehensive positioning result is obtained through a weighted fusion algorithm based on Kalman filtering, and meanwhile, a differential privacy protection mechanism is introduced, so that the positioning information security of a sensitive area is ensured. Besides, fault detection and automatic repair are carried out by adopting a distributed computing architecture and a deep learning model, resource allocation can be dynamically adjusted according to a system state, and real-time processing of positioning information is guaranteed.

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 provides a radar water level gauge high-stability data processing and measuring method, which comprises the following steps that 1, a radar transmits an electromagnetic wave signal to the water surface, and the electromagnetic wave signal generates an echo when encountering the water surface and is received by a radar receiver; an effective signal and an invalid signal are distinguished by detecting the intensity of the echo signal, when the invalid signal is detected, a communication signal abnormity indication is sent out, and the reflected electromagnetic wave signal is set as original measurement data; step 2, processing echoes generated in the step 1 through an echo signal processing module in the radar receiver, obtaining median filtering processing data through median filtering, synchronously removing random noise through median filtering and reserving effective signals, and improving the stability of a measurement result; a median filtering module is arranged in the echo signal processing module; and step 3, processing the median filtering processing data in the step 2 through a median filtering processing module.

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.