120results about How to "Eliminate noise interference" patented technology

The invention relates to the technical field of battery health state analysis, and discloses a battery health state comprehensive evaluation method, system and device and a storage medium, and the method comprises the steps: carrying out the preprocessing of data collected by a battery management system, and obtaining a standardized data sequence; calculating a first battery characteristic parameter according to the standardized data sequence; performing covariance matrix adaptive Kalman filtering processing on the first battery characteristic parameter to obtain a second battery characteristic parameter; constructing a capacity-internal resistance change rate index and a temperature-efficiency correlation index based on the second battery characteristic parameters to obtain a dual-threshold evaluation matrix; inputting the dual-threshold evaluation matrix into a deep reinforcement learning model, and performing forward propagation calculation and iterative training through an Actor network and a Critic network to obtain a feature fusion weight coefficient; and carrying out weighted calculation on the dual-threshold evaluation matrix to generate a battery health state value and a residual life prediction value, and realizing multi-dimensional evaluation of the battery health state.

The invention provides a multi-target collaborative optimization control method and system for an igniter robot production line, and relates to the technical field of automatic control, and the method comprises the steps: collecting real-time operation parameters of the production line, carrying out the wavelet denoising, feature extraction and fusion, inputting a hierarchical asynchronous dominant actor commentator model, and in a strategy network layer, carrying out the wavelet denoising, feature extraction and fusion; a robot trajectory planning scheme is optimized and calculated through an improved near-end strategy, a production line speed control scheme is obtained through a layered dual-state action value function in combination with an adaptive Coubeck-Leibler divergence constraint, an energy consumption distribution scheme is obtained based on pseudo-parallel Monte Carlo tree search and an improved soft actor commentator algorithm, and an energy consumption control scheme is obtained. The value network layer evaluates an energy consumption distribution scheme and generates a combined control strategy, after the strategy is executed, a cycle converter model is improved to evaluate production indexes, model parameters are dynamically optimized, and multi-target collaborative optimization is achieved.

The invention relates to the technical field of data processing, and discloses a BIM-assisted steel structure construction safety management early warning method, device and equipment. The method comprises the following steps: carrying out multi-source heterogeneous data acquisition and space-time alignment processing on a steel structure construction site to obtain an engineering data stream; carrying out dynamic noise reduction and feature extraction on the engineering data flow to obtain a standardized multi-modal feature vector, and carrying out time sequence correlation analysis and mode mining to obtain a potential risk index set; performing multi-level threshold calculation and context sensitivity evaluation on the potential risk index set to obtain an adaptive early warning trigger matrix; performing multi-dimensional cross validation and causal chain analysis on the self-adaptive early warning trigger matrix to obtain a graded and classified risk assessment report; and carrying out scene analysis and response scheme generation on the graded and classified risk assessment report to obtain an executable multi-step emergency plan. According to the invention, the efficiency and accuracy of BIM-assisted steel structure construction safety management early warning are improved.

The invention relates to the technical field of image processing, and discloses an industrial article appearance detection method and system based on machine vision. The method comprises the steps that a workpiece image is collected, and a standardized image is obtained through Gaussian filtering, histogram equalization and Otsu algorithm preprocessing; feature vectors are extracted through edge detection, Hough transform and a gray-level co-occurrence matrix; training a defect identification model by adopting a CNN of an attention mechanism; inputting an image for real-time detection; fusing the detection data and the environmental parameters by using a D-S theory; and establishing a database based on a distributed architecture to generate a quality report. According to the method, the detection accuracy is improved through multi-source information fusion, and efficient management and analysis of mass detection data are realized by adopting a distributed architecture.

The invention discloses an electrical automatic control method and system. The method comprises the following steps: acquiring temperature data and load current data in an electrical cabinet; denoising the temperature data by adopting a wavelet transform algorithm; filtering the load current data by adopting a Kalman filtering algorithm; establishing a temperature rise compensation model based on a long short-term memory (LSTM) neural network, wherein the output of the temperature rise compensation model is a temperature rise predicted value in the electrical cabinet; setting a multi-objective function of temperature rise control in the electrical cabinet; solving by adopting a multi-target ant colony algorithm to obtain an optimal control strategy of the cooling fan; and starting, stopping and speed adjustment of the cooling fan are controlled according to the optimal control strategy of the cooling fan. Aiming at frequent starting and stopping of a cooling fan of an electrical cabinet in the prior art, a temperature rise compensation model is established based on a long short-term memory (LSTM) neural network, a multi-objective optimization function including temperature rise deviation and starting and stopping times of the cooling fan is set, an improved multi-objective ant colony algorithm is adopted for solving, and starting and stopping of the cooling fan are reduced.

The invention relates to the technical field of infrared image recognition, and discloses a hot target extraction method and system based on infrared image recognition, and the system comprises a multi-sensor collection module, a data dimension reduction module, a data verification module, a segmentation optimization module, and a dynamic feedback module. Multi-modal data dimension reduction processing is achieved by collecting infrared images, visible light images and environment dynamic parameters and combining Laplacian feature mapping, and a target area is extracted through geometric verification and topological analysis. An energy optimization model and a distribution alignment method are adopted to complete accurate segmentation of a target area, and meanwhile, segmentation model parameters are adjusted in real time based on environment dynamic parameters, so that the system stability is ensured. According to the method, high-precision and robust extraction of the thermal target is realized in a complex dynamic environment, the problems of fuzzy target boundary, noise interference and insufficient dynamic environment adaptability in the prior art are effectively solved, and the method is suitable for the fields of military reconnaissance, fireproof monitoring, electric power inspection and the like.

The invention provides a multi-data fusion fire-fighting early warning method and platform, and relates to the technical field of fire fighting, and the method comprises the steps: collecting a multi-dimensional environment data set of a target region; performing noise filtering on the multi-dimensional environment data set to obtain a multi-dimensional environment de-noised data set; comprehensively analyzing the plurality of data sources based on the multi-dimensional environment de-noised data set to generate fire-fighting comprehensive data, and fusing the multi-dimensional environment de-noised data set to obtain a multi-dimensional environment fused data set; building a fire risk model according to the fire-fighting comprehensive data, synchronizing the multi-dimensional environment fusion data set to the fire risk model for evaluation according to a plurality of data sources, and obtaining a fire risk level of the target area; and generating a fire-fighting early warning instruction based on the fire risk level, and sending the instruction to the remote control terminal. According to the method, the technical problem that a traditional fire early warning method is single in monitoring means and is easily interfered by environmental factors to cause false alarm or missing alarm is solved, and the accuracy and response speed of fire early warning are remarkably improved.

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

The invention relates to the technical field of yarn motion state monitoring, in particular to a yarn motion state monitoring method and system.The yarn motion state monitoring method comprises the steps that firstly, deformation during yarn motion is monitored and converted into tension data, and the tension data are uploaded to a yarn motion state database in real time; a yarn motion video is captured through an industrial camera, the offset and angle between the yarn motion video and a preset track are calculated, whether an offset coefficient exceeds a threshold value or not is judged, and offset abnormal points and related tension data are recorded and uploaded in time; combining acoustic and vibration signals, analyzing and monitoring the breakage condition of the yarn through the composite signal, and recording the breakage tension and the composite signal at the abnormal moment. Besides, the data in the database is analyzed through the Gaussian mixture model, the motion state of the current yarn is accurately obtained, and an efficient and reliable monitoring means is provided for monitoring the motion state of the yarn in the spinning process.

The invention provides a grounding grid topological structure detection method and device based on adaptive differential. The method comprises the following steps: obtaining a transverse magnetic field component and a longitudinal magnetic field component of an area to be detected; performing peak extraction on the transverse magnetic field component and the longitudinal magnetic field component, and performing peak-dividing fitting to obtain parameters of each peak; and constructing a differential threshold, carrying out adaptive differential based on the parameter of each wave crest to obtain a differential result, and carrying out superposition imaging on all the differential results to obtain a grounding grid topological structure of the to-be-detected area. According to the scheme, different types of magnetic field wave crests of each magnetic field observation line are identified through the KNN algorithm, and adaptive differentiation is carried out on different wave crests through a threshold value judgment mode, so that the grounding topology magnetic field characteristics of a weak peak area are remarkably enhanced.

The invention relates to the technical field of medical information extraction, in particular to an image feature selection method for a schizophrenia medical record Spark model. The method comprises the following steps: acquiring brain magnetic resonance image data and resting state functional magnetic resonance imaging data of a plurality of subjects; performing multi-scale layered segmentation on the brain image of each subject by using a preset layered brain region template for the brain magnetic resonance image data to obtain brain region positioning data including a local level, a regional level and a global level; and performing adaptive hierarchical noise reduction processing on the resting state functional magnetic resonance imaging data in a Spark distributed environment to obtain brain function time sequence data after noise reduction. According to the method, the brain magnetic resonance image data is accurately divided into a local level, a regional level and a global level by using the preset layered brain region template, so that local structure details and global network characteristics of the brain region are considered, and effective integration of multi-scale information is realized.

The invention discloses an urban emergency refuge congestion risk assessment method and system based on image enhancement. The method comprises image acquisition, image enhancement and risk assessment. The invention belongs to the field of image processing, and particularly relates to an urban emergency refuge congestion risk assessment method and system based on image enhancement. According to the scheme, global-local dynamic weights are introduced, and texture information of a dense area and the overall site layout are considered; the fusion of real-time and historical data features is optimized through feature consistency constraint; constructing loss based on the dynamic region weight, and optimizing the urban emergency refuge image; a dynamic area sensing weight is introduced, a crowd dense distribution center is focused, and invalid features of an open area are inhibited; the density perception weight is fused with the multi-scale expansion convolution features, and fine dense region boundary segmentation is achieved; carrying out risk assessment on the real-time urban emergency refuge image based on the dynamic region weight and the historical correlation; and the congestion risk assessment effect is improved.

The invention relates to the technical field of optical fiber communication, in particular to an optical fiber optical frequency transmission field optical fiber link noise simulation method and system, and the method comprises the steps: dividing laser generated by a laser into reference light and simulation light; modulating the analog light, performing field optical fiber transmission, and performing optical frequency processing on the analog light and the reference light to obtain a radio frequency signal; performing phase processing on the radio frequency signal to obtain continuous phase information and frequency conversion data; the method comprises the following steps: constructing an optical fiber optical frequency transmission field optical fiber noise simulation model, converting phase information and frequency conversion data into voltage data by using the noise simulation model, setting a modulation depth parameter according to the voltage data, performing modulation according to the modulation depth parameter, and adjusting the length of a laboratory winding optical fiber according to a modulation result to realize noise simulation. According to the method, different types and different magnitudes of field optical fiber link noise can be simulated.

The invention relates to a network abnormal behavior detection method based on dynamic data balance and generation, and the method comprises the steps: obtaining original network flow data, inputting the original network flow data into a dynamic abnormal data balance model, and obtaining an enhanced data set; the method comprises the steps of marking original network flow data based on an abnormal data balance model, performing selective undersampling on marked data, adjusting a ratio of normal data to abnormal data, obtaining a balance data set, generating new abnormal data according to the balance data set, and merging the new abnormal data with the balance data set to obtain an enhanced data set; and inputting the enhanced data set into a TimeDiT noise reduction model for denoising to obtain a target data set after noise reduction, inputting the target data set into an LSTM-Transform weighted training model for network abnormal behavior detection, and weighting abnormal data in the target data set in combination with a weighted loss function to obtain a detection result. According to the invention, the network traffic abnormal behavior detection efficiency and accuracy can be improved.

The invention provides a two-stage hybrid propulsion amphibious unmanned platform active disturbance rejection control method and system, and belongs to the field of amphibious unmanned platforms. The objective of the invention is to solve the problems that the expected speed or expected depth is difficult to track and the data processing speed is slow when an existing active-disturbance-rejection control method is applied to control of a multi-habitat unmanned platform. Compared with a traditional control method, the method has the advantages that the requirement for the precision of a controlled object is not high, the anti-interference capacity is high, meanwhile, control parameters of the active-disturbance-rejection controller can be adjusted, nonlinear error feedback control is changed into a linear form, and the control precision is high. The linear expansion state observer is adopted to well estimate external disturbance of the amphibious unmanned platform and internal disturbance of the system, compensation is given to the control quantity, the parameter setting difficulty is reduced, adaptability and robustness are higher, and operation is easy and convenient; a sliding window filtering algorithm is adopted to filter sensor data, noise interference of signals is filtered out, data fluctuation is smoother, and the data processing speed is increased.

The invention relates to the technical field of drilling tool attitude dynamic measurement, and discloses a dynamic measurement method and system for attitude parameters of a rotary steering tool, and the method comprises the steps: obtaining a triaxial angular rate, a triaxial earth gravitational field component and a triaxial earth magnetic field component of the rotary steering tool in real time; determining an attitude angle priori estimated value according to the triaxial angular rate; determining an attitude angle posterior observation value according to the three-axis earth gravitational field component and the three-axis earth magnetic field component; fusing the attitude angle priori estimation value and the attitude angle posteriori observation value through iterative extended Kalman filtering to obtain a posteriori optimal attitude angle estimation result; and calculating attitude parameters according to a posterior optimal attitude angle estimation result. According to the method, adaptive fusion is carried out on the dynamic observed quantities measured by the three-axis gyroscope, the three-axis accelerometer and the three-axis magnetometer through the iterative extended Kalman filter, so that noise interference of the external environment, such as high rotating speed, strong vibration and magnetic anomaly, in the dynamic measurement process is eliminated, and the control and measurement precision of the rotary steering tool is improved.

The invention relates to a self-oscillation fluxgate sensor and a measuring system, and relates to the technical field of electrical variable measurement. The self-oscillation fluxgate sensor comprises a detection assembly used for being connected with a detected wire and used for sensing a detected electric signal on the detected wire; the measurement and control circuit is used for providing a control signal; the feedback filter circuit is connected with the measurement and control circuit and is used for filtering the control signal, outputting a reference signal and adjusting the output reference signal according to the reference signal and the filtered control signal; the driving excitation circuit is respectively connected with the detection assembly, the feedback filter circuit and the measurement and control circuit, and is used for carrying out self-oscillation according to the reference signal and the detected electric signal and generating excitation current; and the measurement and control circuit is also used for determining a measurement value of the measured electric signal according to the excitation current. The method can improve the measurement precision.

The invention discloses a salient target detection method based on boundary guidance of depth map generation and RGB image fusion. The salient target detection method is specifically implemented according to the following steps: step 1, constructing a data set and an encoder; 2, constructing a depth map generation and RGB fusion module based on an attention mechanism; step 3, constructing a boundary feature guidance module based on a self-supervision mode; and step 4, constructing a double-branch feature fusion module based on progressive fusion. According to the method, a multi-modal fusion strategy and self-supervised edge feature guidance are adopted to perform feature enhancement, in order to effectively generate a depth image and remove noise, a depth image generation technology based on Depth Anything is adopted, a self-supervised edge feature module is designed to extract edge feature information, and the edge feature information is extracted by using the self-supervised edge feature module. Noise and artifacts in the generated depth image are effectively eliminated through the process.

The invention discloses a pulse wave monitoring method and system based on telemetry photoplethysmography, and the method comprises the steps: collecting a video file of a carotid artery region based on telemetry photoplethysmography, obtaining a plurality of frames of images in the video file, and determining a gray level image corresponding to each frame of image; acquiring two regions of interest in the grayscale image, and extracting two original pulse wave signals from the two regions of interest; performing zero phase shift filtering and interpolation processing on the two original pulse wave signals to obtain two processed pulse wave signals; performing normalization processing and cross-correlation calculation on the two processed pulse wave signals to obtain a time difference between the two pulse wave signals; according to the time difference of the two pulse wave signals and the distance between the two interested areas, the pulse wave speed is calculated, and the arteriosclerosis degree is evaluated based on the pulse wave speed. The carotid artery pulse wave is monitored in a non-invasive mode, arteriosclerosis is evaluated, and cardiovascular health is guaranteed.

According to the laser radar complex working condition recognition-based load reduction control method for the wind turbine generator, a high-precision laser radar system is installed on the wind turbine generator, three-dimensional wind speed distribution in a wind field is monitored in real time, data are transmitted to a control system, and the real-time performance and accuracy of the data are ensured. Collected original data are preprocessed, a time sequence analysis method is adopted, the rules of wind speed and wind direction changing along with time are mined, and key features of sudden wind speed change and rapid wind direction change are captured. According to historical data, a fuzzy logic rule is defined, and various complex working conditions are accurately described. And designing an adaptive fuzzy logic controller (AFLC), and when the AFLC recognizes a complex working condition, immediately starting load reduction control logic to adjust operation parameters of the wind turbine generator. Meanwhile, the AFLC dynamically adjusts the load reduction threshold value according to the wind field condition, and it is ensured that complex working condition changes are responded in time. In the load reduction control process, the control effect is evaluated, and a fuzzy rule and a membership function in the AFLC are optimized according to the control effect.

The invention relates to the technical field of image processing, in particular to a numerical simulation method and system based on erosive wear of a titanium alloy pump valve, and the method comprises the steps: eroding the titanium alloy pump valve based on the titanium alloy pump valve, the flow velocity range, the pressure range, the particle concentration and the particle size distribution to obtain a worn workpiece and a three-dimensional scanning wear morphology image, constructing an erosion simulation model, calculating particle impact force, wear depth and particle kinetic energy, constructing a simulated wear depth histogram, calculating a height gradient and a gradient dynamic threshold value, segmenting a gradient segmentation image, calculating a depth dynamic threshold value and depth information, segmenting an actual wear distribution diagram, marking the actual wear distribution diagram to obtain a marked area, calculating the wear degree of the marked area, and calculating the wear degree of the marked area. And an actual abrasion depth histogram is constructed, the erosion simulation model is adjusted, an adjusted erosion simulation model is obtained, and numerical simulation is completed. According to the method, the accuracy of numerical simulation of the erosive wear of the titanium alloy pump valve can be improved.

The invention provides a method for repairing an underwater degraded image. The method comprises the following steps: S1, inputting the underwater degraded image; s2, constructing an unsupervised deep learning image reconstruction network based on a physical model; s3, constructing a reconstruction network consistency verification module according to an underwater image imaging physical model and the network output reconstruction degraded image; s4, according to an image equivariant theory, constructing a fidelity image network equivariant loss and an underwater imaging equivariant loss; s5, according to the image isomorphism, constructing a fidelity image to restore an image isomorphism verification module; s6, according to the noise distribution of the underwater degraded image, constructing noise loss by using an unbiased estimation method, and correcting the deviation of reconstruction loss; s7, according to a gray world hypothesis, compensating the information of the blue channel and the green channel with relatively small color weakness to the red channel, and constructing a color fidelity verification module; and S8, carrying out constraint training on the network according to the steps S3-S7 until loss convergence, and obtaining a fidelity image estimation module for repairing the degraded image. The invention further provides a storage medium and electronic equipment.

According to the gait event detection method based on the built-in sensor of the smart phone and deep learning, diversified gait features can be accurately understood and recognized, high efficiency and convenience are achieved, interference on real gait information is effectively reduced, a more reliable gait event detection result is ensured, and the gait event detection efficiency is improved. And a widely used tool is provided for gait analysis in a non-laboratory environment. The method comprises the following steps: collecting real-time acceleration, angular velocity and equipment rotation angle data generated during walking through a built-in sensor of the smart phone; noise in the original data collected by the sensor is removed; excluding sensor data with small fluctuation amplitude; constructing an input and output data pair by using a sliding window mode; modernTCN is used as a basic model architecture, and a bidirectional gating cycle unit BiGRU and a cross attention mechanism are introduced; training a model by using the constructed data set; and inputting real-time data collected by a built-in sensor of the smart phone into the trained model, and outputting a corresponding gait event detection result by the model.

The invention discloses a threshold setting method and system under different working conditions based on fusion of MVMD noise reduction, KNN classification and an adaptive threshold. Acquiring n groups of vibration signal data of the equipment at different moments; performing MVMD decomposition to obtain a plurality of basic mode components IMF; calculating a correlation coefficient of each IMF and an original signal; the adding module is used for adding the basic mode components IMF of which the correlation coefficient is greater than a correlation coefficient threshold value to obtain a reconstruction signal; calculating a speed effective value and kurtosis of each group of reconstructed signals; setting KNN classification parameters, and selecting a parameter k with the maximum accuracy rate; threshold values under different working conditions are calculated; and determining a final threshold according to the standard value. A KNN classification method is adopted to separate characteristic values of different operation conditions of equipment, and then an adaptive threshold method is adopted to calculate thresholds of different conditions. The defect that a traditional method cannot set threshold values according to working conditions to cause pre-alarm is overcome.

The invention discloses an AI vision-based transparent bottle body curved surface printing graph and character detection system, and relates to the technical field of machine vision detection, and the main scheme is as follows: collected images are preprocessed, including de-noising and enhancement, and a position deviation mean value P can be calculated through position accuracy data according to the characteristics of a transparent bottle body curved surface; whether the position accuracy meets the standard or not is evaluated according to the position deviation mean value P, images are collected with high unit pixel density and high frame rate, and it is ensured that each subtle part of the graphic characters can be clearly recorded; from image acquisition to result output, the whole system achieves automatic operation, manual intervention is not needed, the detection speed is greatly increased, a large number of transparent bottles can be rapidly processed, the requirement for large-scale production is met, all modules of the system have high adaptability and flexibility, and the system is suitable for popularization and application. And different types of transparent bottles, patterns, characters and printing processes can be processed.

The invention discloses an oxide skin detection method and device, electronic equipment, a storage medium and a product, and relates to the technical field of nondestructive detection.The oxide skin detection method comprises the steps that a terahertz echo signal sparse decomposition model for oxide skin on the surface of hot-rolled strip steel is constructed; based on the terahertz echo signal sparse decomposition model, solving the pulse response sequence optimal solution for the hot-rolled strip steel surface oxide skin to obtain a sparse reconstructed pulse response function; and generating a binary scanning image of the hot-rolled strip steel surface oxide skin based on the pulse response function, wherein the binary scanning image is used for representing the distribution condition of the hot-rolled strip steel surface oxide skin. The method can guarantee the nondestructive and comprehensive measurement of the oxide skin on the surface of the hot-rolled strip steel.

The invention relates to a monitoring ball signal extension system and method based on a multi-mode networking module, and belongs to the technical field of power transmission and transformation construction safety monitoring. The system comprises a signal detection module, a networking mode selection module, a signal processing module, a signal transmission module, a monitoring system integration module and an external signal enhancement device. The signal detection module monitors signal intensity and interference conditions; the networking mode selection module automatically selects a networking mode in sequence; the signal processing module optimizes the video signal; the transmission module transmits signals; the monitoring system integration module interacts with the monitoring platform; the external signal enhancement device comprises an antenna gainer and a signal repeater. The system can realize automatic networking, adapts to a complex environment, ensures stable transmission of signals, improves imaging quality, enhances monitoring flexibility, optimizes data processing, is suitable for safety monitoring of a power transmission and transformation construction site, and ensures smooth engineering.

The invention relates to a fluorescent microscopic image enhancement method and device based on multi-saliency guide filtering fusion and a storage medium. The method comprises the following steps: inputting a single-layer fuzzy pathological image I; performing correction algorithm on the single-layer fuzzy pathological imaging to obtain a pseudo exposure image sequence; respectively calculating saturation S in three channels for each pixel through a spatial linear saturation adjustment algorithm, carrying out normalization processing on saturation increment to obtain a saturation-adjusted pseudo-exposure image, and carrying out two-scale decomposition through mean filtering to obtain a base layer sequence and a detail layer sequence; the base layer sequence obtains a base layer fusion result through an energy significance guide filtering algorithm, and the detail layer sequence obtains a detail layer fusion result through a Gaussian significance guide filtering algorithm; and performing two-scale reconstruction on the base layer fusion result and the detail layer fusion result to obtain a single-layer deblurring pathological image. According to the method, the color fidelity of the sample can be improved, and the cell texture structure is clearer while noise interference is eliminated.

The invention relates to the technical field of optical fiber sensing, and particularly discloses a monitoring system of a vital sign monitoring optical fiber sensor. Comprising a database module, an optical fiber sensor signal acquisition module, an optical fiber sensor signal processing module, a data analysis module, a comparison verification calibration module, a data comprehensive analysis module, an optical fiber sensor monitoring evaluation module and a user interface module. The database module is used for collecting vital sign data, including the heart rate, the body temperature, the respiratory rate and the oxyhemoglobin saturation, of healthy people collected by the optical fiber sensor, analyzing, screening, classifying and statistically analyzing the data to form normal numerical ranges under the conditions of different age groups, genders, weights and heights, and storing the normal numerical ranges in a database; the vital signs of the patient can be accurately monitored in real time, and monitoring result analysis suggestions and health warning information are provided by comparing and analyzing the vital signs with normal values of the vital signs in the database.

The invention belongs to the technical field of hydroelectric generating set vibration prediction, and particularly relates to a hydroelectric generating set mechanical vibration trend prediction method and device, equipment and a medium, and the method comprises the steps: processing a vibration signal of a hydroelectric generating set through employing a variational mode decomposition algorithm; training the deep belief network by using the training set to obtain a trained deep belief network model; using a sparrow search algorithm to optimize parameters of the deep belief network model to obtain a vibration trend prediction model; and collecting a vibration signal of the hydroelectric generating set, processing the vibration signal by using a variational mode decomposition algorithm, inputting the processed vibration signal into the vibration trend prediction model to obtain a vibration prediction value, and taking the obtained vibration prediction value as an input of next prediction to realize multi-step prediction of the vibration trend of the hydroelectric generating set. By means of multi-step prediction, operation and maintenance personnel can know the vibration trend of the hydroelectric generating set in a period of time in the future in advance, potential fault hidden dangers can be found in time, and the operation safety and stability of the hydroelectric generating set are improved.