873results about How to "Cancel noise" patented technology

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention relates to the technical field of data processing, and discloses a double-end fault positioning method based on signal enhancement, which is used for improving the efficiency of double-end fault positioning based on signal enhancement. The method comprises the following steps: acquiring and preprocessing electrical signals at two ends of a power transmission line to obtain preprocessed synchronous electrical signals; performing adaptive enhancement processing on the preprocessed synchronous electrical signal to obtain an enhanced fault feature signal; performing time-frequency analysis and feature extraction on the enhanced fault feature signal to obtain a fault feature vector and a key parameter; performing machine learning analysis on the fault feature vectors and the key parameters to obtain a fault type identification result and initial fault position estimation; performing multi-algorithm fusion analysis on the preliminary fault position estimation to obtain an accurate fault position coordinate; and performing geographic information mapping and field verification on the accurate fault position coordinates to obtain a fault positioning result and system optimization data.

The invention discloses an unmanned aerial vehicle path planning method and system based on digital twinning. The method comprises the following steps: constructing a three-dimensional environment model based on first target environment data in first target multi-source environment perception data; constructing a digital twin based on the geometric model, the physical model, the control model, the target physical data hybrid dynamic model and the three-dimensional environment model; inputting the second target multi-source environment sensing data into the digital twin, and dynamically simulating the flight behavior of the physical unmanned aerial vehicle based on the second target multi-source environment sensing data to obtain simulated flight environment state data; and based on the simulated flight environment state data, performing path planning on the physical unmanned aerial vehicle in combination with a prediction control algorithm and a path planning algorithm to obtain a target flight path, transmitting the target flight path to the physical unmanned aerial vehicle, and controlling the flight of the physical unmanned aerial vehicle. The method can dynamically simulate the real-time change of the physical unmanned aerial vehicle, plan the optimal flight path and effectively avoid obstacles, and can be widely applied to the technical field of unmanned aerial vehicle path control.

The invention relates to a real-time river water level monitoring and early warning method and system based on deep learning. The method comprises the following steps: acquiring a target watershed remote sensing image and real-time hydrological data of multiple monitoring points; performing water flow direction topology analysis on the remote sensing image of the target watershed, and constructing a water flow topology network; performing basin structure dynamic change feature analysis on the target basin remote sensing image to generate basin structure dynamic change data; carrying out topology situation evolution fitting on the water flow topology network based on the basin structure dynamic change data to obtain a dynamic topology evolution network; performing multi-scale time sequence trend evolution analysis on the multi-monitoring-point real-time hydrological data to generate water flow time sequence evolution characteristics; and evolution characteristic space position mapping is performed on the dynamic topology evolution network based on water flow time sequence evolution characteristics, and a three-dimensional hydrological evolution model is constructed. According to the invention, timely and accurate river water level monitoring and early warning are realized.

The invention relates to the technical field of artificial intelligence research, in particular to an intelligent desulfurization prediction system based on an FT-LSTM-XGBoost algorithm, which comprises a data acquisition module, a data preprocessing module, a feature engineering module, a model training module, a model fusion module and a control execution module, the data acquisition module monitors and acquires multi-dimensional parameters in a desulfurization process in real time; the data preprocessing module is used for preprocessing the acquired multi-dimensional parameters; the feature engineering module forms a final feature set related to desulfurization; the model training module adopts Fourier transform, a long-short-term memory network and a limit gradient boosting tree to train a desulfurization prediction model; the model fusion module obtains a fused prediction result; the control execution module is used for adjusting the overall operation parameters of the desulfurization system in real time; the method has high-precision prediction capability, so that the accurate control of the desulfurization effect is ensured.

The invention relates to the technical field of side slope monitoring and early warning, in particular to a mine site side slope remote online real-time monitoring and early warning system which comprises a multi-source data monitoring unit, a data processing and analyzing unit, a side slope instability judging unit, an instability grading early warning unit and an early warning verification pushing unit. The method comprises the following steps: collecting monitoring data of a plurality of data sources to obtain time sequence monitoring data of each data source, carrying out noise reduction on the time sequence monitoring data, then decomposing the time sequence monitoring data, calculating a time-frequency feature vector through an energy density function, and optimizing the time-frequency feature vector through feature screening; calculating a slope stability evaluation index by optimizing the time-frequency feature vector, constructing an instability risk index to judge a slope instability risk level, constructing an early warning index, and combining the early warning index with the slope instability risk level to establish a graded early warning mechanism to obtain a slope instability early warning level; and verifying the slope instability early warning grade, and performing early warning according to the slope instability early warning grade passing the verification. According to the invention, the false alarm rate of slope monitoring and early warning can be reduced.

The invention relates to the field of image enhancement, in particular to an image enhancement method and system of a vehicle-mounted image sensor. The method comprises the following steps: acquiring driving record images of a vehicle at multiple angles; dynamic scene self-adaptive exposure adjustment is carried out on the driving record images of the vehicle at the multiple angles, so that multiple self-adaptive exposure driving images are obtained; carrying out deep network noise identification on the plurality of adaptive exposure driving images, and carrying out dynamic filtering and noise reduction on sub-images one by one so as to generate a plurality of filtering and noise reduction optimized images; carrying out image texture analysis on the plurality of exposure sub-images, and carrying out texture distortion reconstruction to construct a plurality of texture reconstruction images; and road object visual identification is carried out on the plurality of texture reconstruction images, and adaptive contrast enhancement processing is carried out, so that a plurality of detail contrast enhancement images are constructed. According to the invention, a high-definition and stable panoramic vehicle-mounted image is realized.

The invention discloses a distribution network multi-parameter data fusion fault positioning method and system, and the method and system achieve the omnibearing monitoring of a power distribution network fault through the collection of multiple types of data, such as electrical signals, video images, meteorological information and environmental parameters. Compared with a traditional fault positioning method of a single information source, the system can reflect the fault condition more truly, and the positioning accuracy and reliability are remarkably improved. Machine learning or a deep learning model is utilized to analyze fusion data, fault types and positions are automatically identified, intelligent diagnosis is realized, manual intervention is reduced, the operation and maintenance cost is reduced, and the processing efficiency and accuracy are improved. The real-time data acquisition and transmission technology guarantees the system to monitor in real time and rapidly alarm potential faults. According to the method, historical data and an expert knowledge base are combined, fault causes are deeply analyzed, accurate information and maintenance suggestions are provided for maintenance personnel, operation and maintenance decisions are optimized, and the maintenance efficiency and quality are enhanced. The system provides an efficient and intelligent solution for distribution network fault positioning.

The invention discloses a physiological signal fragment analysis method based on an adaptive bidirectional selection state space, and the method specifically comprises the following steps: 1, carrying out the preprocessing of an original ECG signal X in a data set, and dividing the preprocessed data set into a training set Itrain and a test set Itest; step 2, constructing a physiological signal fragment analysis model based on an adaptive bidirectional selection state space, wherein the model comprises a multi-scale feature extraction module, a context encoder module and a classification module; 3, training the model constructed in the step 2 by using the training set Itrain processed in the step 1; and step 4, sending the test set Itest processed in the step 1 into the model trained in the step 3, and finally outputting a classification detection result. By adopting the method provided by the invention, the accuracy of analyzing whether the sleep apnea event occurs in the physiological signal fragment can be improved.

The invention provides a traditional Chinese medicine abnormal state recognition method, system and device and a storage medium, and relates to the field of abnormal recognition. The method comprises the following steps: acquiring product information of a to-be-detected traditional Chinese medicine, and determining a data acquisition standard of the to-be-detected traditional Chinese medicine based on the product information of the to-be-detected traditional Chinese medicine and a preset detection rule; based on a data acquisition standard of the traditional Chinese medicine to be detected, inputting the image data into a preset image anomaly detection model to obtain a preliminary image anomaly region; performing feature fusion on the visual feature data and the spectral feature data to obtain an abnormal feature vector; and inputting the abnormal feature vector into a preset abnormal state judgment model, outputting abnormal state data, and performing abnormal state judgment based on the abnormal state data and a preset abnormal state judgment threshold to obtain an abnormal state detection result of the to-be-detected traditional Chinese medicine. Through the scheme, the abnormal state of the traditional Chinese medicine can be identified timely and accurately.

The invention relates to the technical field of spine deformation recognition, in particular to a spine deformation recognition method and system based on image recognition, and the method comprises the following steps: S1, data collection and preprocessing: obtaining multi-view and multi-time-point spine image data of the spine of a patient; s2, soft tissue shielding correction: automatically identifying and correcting interference of the soft tissue on the spine image data; s3, dynamic change prediction of the spine: performing dynamic change prediction on the spine image data subjected to soft tissue shielding correction; and S4, deformation judgment and result output: judging the deformation condition, the deformation type and the severity of the spine, and generating a visual report containing the spine deformation condition and future change prediction. According to the method, the noise in the image is effectively removed, the definition of the spine structure is enhanced, and meanwhile, the interference problem of soft tissue on the spine image is solved.

The invention discloses a mine high and steep slope landslide machine learning prediction method based on an unmanned aerial vehicle technology and multi-source data fusion. The method comprises the steps that a high-resolution digital surface model is shot by an unmanned aerial vehicle; removing non-topographic features from the digital surface model data in ArcGIS software, then converting the digital surface model data into digital elevation model data, obtaining a surface refining model of the surface mine in the digital elevation model, taking the surface refining model as a constraint to trim the surface of the three-dimensional geologic model, and fusing factors of ArcGIS software and three-dimensional geologic model analysis by using Python to obtain a three-dimensional geologic model surface refining model; complete slope data are constructed; inputting the fused complete side slope data into a machine learning algorithm training model, and generating a side slope landslide susceptibility prediction model; the optimized model is used for carrying out landslide susceptibility prediction on new slope data. According to the method, various factors influencing the slope stability can be comprehensively analyzed, high efficiency can be kept during large-scale data processing, and the slope stability in mine engineering can be evaluated more accurately.

The invention relates to the technical field of charging load prediction, in particular to an electric vehicle charging load prediction method and system combining sequence decomposition and feature extraction and a storage medium. Comprising the steps of performing variational mode decomposition on time sequence load data to obtain a plurality of intrinsic mode functions; performing zero padding expansion on each intrinsic mode function, and then converting the intrinsic mode function to a frequency domain; enhancing the frequency domain data through a multi-layer perceptron based on complex domain calculation; constructing a prediction model based on a complex field attention module and a residual module, inputting the enhanced spectrum data into the prediction model, extracting a data characteristic frequency combination through the complex field attention module and the residual module, and carrying out weighted summation on all intrinsic mode functions as the output of the prediction model; and calculating a difference value between real data and prediction data according to a mean square error of a time sequence and an average absolute value error of a frequency spectrum to obtain time domain and frequency domain mixed loss, and updating prediction model parameters. According to the invention, the problem of large prediction error under the influence of random load fluctuation is solved.

The invention discloses an electric power project risk prediction method and system based on data mining, and relates to the technical field of electric power project risk prediction.The method comprises the steps that electric power project data are collected, the electric power project data are preprocessed, key features influencing project risks are extracted, and feature vectors are established; constructing an electric power project risk prediction model, and performing risk prediction analysis on the electric power project based on the feature vector; and taking a part of areas with risks as nodes to form a topological graph, identifying key paths and bottlenecks, and formulating corresponding resource allocation strategies. According to the method, the electric power project risk prediction model is constructed based on the feature vectors, risk prediction analysis is carried out on the electric power project in combination with the time change trend, the accuracy and foresight of risk prediction are improved, the topological graph is formed by taking part of areas with risks as nodes, key paths and bottlenecks are identified, and the risk prediction accuracy is improved. And a corresponding resource allocation strategy is formulated, so that the security and the stability of the project are improved.

The invention discloses a transcutaneous electrical stimulation device based on a large language model, and relates to the technical field of wearable devices. The transcutaneous electrical stimulation equipment based on the large language model comprises a data acquisition and preprocessing module, a feature extraction and fusion module, a model training and prediction module and a real-time information processing and response module, real-time data processing and intervention capabilities are integrated, human body data can be analyzed in real time, electrical stimulation parameters can be automatically adjusted, an instant treatment or adjustment scheme is provided for the problems of arrhythmia, chronic pain and the like, and the timeliness and effectiveness of treatment are greatly improved. And secondly, by fusing the physiological signal features and the symptom text features, the equipment can more comprehensively understand the health condition of the user, so that a more accurate treatment scheme is provided. Besides, the equipment also has the functions of anomaly detection and trend analysis, can predict potential health risks, and triggers an alarm in time when a serious anomaly is detected, thereby effectively guaranteeing the life safety of the user.

The invention relates to the field of rail transit, and discloses a railway ballast detection method and a railway ballast detection system.The method comprises the steps that navigation data in vehicle movement are obtained through an inertial navigation unit, and the navigation data are combined with GPS positioning data to conduct fusion calculation to obtain POS data; the method comprises the following steps: scanning a ballast bed, a railway ballast and a sleeper through a laser radar to obtain original three-dimensional point cloud data, and fitting POS data with the original three-dimensional point cloud data to obtain standard point cloud data; preprocessing the obtained standard point cloud data to obtain orbit data, and performing rotation correction on the orbit data through a data correction module; and calibration is performed based on the height difference between the sleeper and the track, an included angle between the laser radar and the ground is obtained, target detection is performed based on the included angle between the laser radar and the ground, and structured measurement data is generated and output.

The invention relates to the technical field of automobile exhaust pollution, in particular to a sensor-based automobile exhaust networking analysis system, which comprises a data acquisition module, a data processing and fusion module, an analysis and prediction module, an intelligent decision module, a data storage module and a user interaction module, the data processing and fusion module is used for processing the received pollutant component data, the data storage module is used for storing the pollutant component data, the analysis and prediction module is used for analyzing the pollutant component data in real time and predicting the tail gas emission trend, and the intelligent decision module is used for generating optimized vehicle operation parameters and providing maintenance suggestions. And the user interaction module provides a visual interaction interface and displays the tail gas emission data, the analysis result and the decision suggestion of the vehicle in real time. According to the automobile exhaust monitoring system, the technical problems existing in an existing automobile exhaust monitoring system are effectively solved, and the accuracy and reliability of data are remarkably improved.

The invention relates to the technical field of power distribution cabinet control, in particular to an electrical power distribution cabinet and a control method thereof. The method comprises the following steps: acquiring multi-dimensional data of the power distribution cabinet; performing data preprocessing on the multi-dimensional data of the power distribution cabinet to generate a power distribution cabinet preprocessing data set; performing contribution degree sorting on the preprocessed data set of the power distribution cabinet to generate contribution degree sorting data of the power distribution cabinet; constructing an electric cabinet model according to the contribution degree sorting data of the power distribution cabinet, and generating an electric cabinet power consumption prediction model; acquiring real-time power distribution cabinet data; performing power distribution cabinet power utilization prediction on the real-time power distribution cabinet data based on the power utilization cabinet power utilization prediction model to generate a power distribution cabinet prediction result; therefore, by comprehensively integrating the multi-dimensional data of the power distribution cabinet, intelligent prediction and optimal scheduling are performed, the problems of low prediction precision, non-uniform resource distribution, response lag and the like in traditional power distribution cabinet management are solved, and the operation efficiency and the resource utilization rate of the electrical power distribution cabinet are improved.

The invention relates to the technical field of industrial intelligent manufacturing, in particular to a flexible production line appearance defect optical detection method. Comprising the steps of performing image acquisition on semi-finished products and finished products on a production line; carrying out preprocessing and spatial transformation on the collected image; performing feature extraction and clustering analysis on the positive sample image of the to-be-detected product by using unsupervised learning to form a feature library; comparing the feature vector of the to-be-detected image with a feature library, and outputting the defect position and type of the sample; an improved RTDETR model with supervised learning is adopted, a sample judged as a defect is used for training, and the improved RTDETR model adopts a ResNet and Visual Transform network structure; and performing real-time detection and result feedback on the deployment model. According to the technical scheme, the appearance defect detection efficiency and the product quality of products manufactured on the flexible production line can be improved.

The invention discloses a photovoltaic equipment networking control method and system, and relates to the technical field of photovoltaic power generation, when the system runs, a data acquisition module ensures high-precision current signal acquisition, and a digital signal processing module removes noise and enhances signal quality through low-pass filtering and convolution operation; the signal conversion module comprehensively analyzes frequency spectrum information by using FFT (Fast Fourier Transform), and the data analysis module extracts harmonic components, calculates a total harmonic distortion rate THD and provides an accurate filter adjustment scheme; the regulation and control module calculates a power factor Pf and a flash variable Fb, comprehensive power quality evaluation is carried out in combination with THD, it is ensured that the system operates in the optimal state, and the system effectively solves the problems that in traditional photovoltaic equipment networking, current signal collection precision is low, signal processing is unstable, harmonic distortion is difficult to monitor, and power quality is difficult to comprehensively evaluate. And the power output quality of the photovoltaic equipment and the reliability and economic benefits of the whole power system are remarkably improved.

The invention provides an analog-to-digital converter, a conversion method and an electronic product. The analog-to-digital converter comprises a differential analog-to-digital conversion module and a compensation module, the differential analog-to-digital conversion module is used for eliminating sampling thermal noise based on a double-sampling mode and realizing analog-to-digital conversion of an input signal; the compensation module receives the compensation signal, the output end of the compensation module is connected to the input end of a pre-amplifier in the differential analog-to-digital conversion module, and the compensation module compensates the change of the input signal coupled to the input end of the pre-amplifier based on a differential mode; wherein the input signal and the compensation signal which are connected to the same input end of the pre-amplifier are differential signals. According to the analog-to-digital converter, the conversion method and the electronic product, the sampling thermal noise elimination technology is adopted, the signal bandwidth limited by the saturation problem of the amplifier is increased by adopting the first-order approximate compensation module based on the differentiating circuit, and the analog-to-digital converter has the advantages of being small in sampling capacitance, low in noise, large in signal bandwidth and the like.

The invention provides an entropy-based muscle fatigue assessment method and application thereof, and particularly relates to the technical field of signal processing, and the method comprises the steps: firstly collecting a surface electromyogram (sEMG) signal, and carrying out the preprocessing; then, fractional calculus is combined with a fluctuation dispersion entropy (FDE) algorithm, and a multi-scale processing process is introduced, so that an RCMFFDE algorithm is innovatively proposed. Performing multi-scale analysis on the preprocessed sEMG signal by using the algorithm to generate a plurality of entropy curves; the entropy curve meeting the preset condition is selected as the final entropy curve, and the final entropy curve can accurately reflect the muscle fatigue state. According to the method, the muscle fatigue degree can be effectively and reliably evaluated, and the method has important kinematics and medical significance for preventing human body muscle diseases and sports injuries.

The invention discloses a remote sensing image segmentation method based on local consistency and pseudo label filtering. The method comprises the following steps: acquiring an optical remote sensing data set; constructing a remote sensing image segmentation model, wherein the remote sensing image segmentation model comprises a local consistency loss calculation module and a pseudo label filtering module; and inputting the optical remote sensing data set into the remote sensing image segmentation model to obtain a remote sensing image segmentation result graph. A local consistency loss and pseudo label filtering strategy is provided, local consistency features are effectively learned, pseudo label noise is removed, a good remote sensing image segmentation effect is obtained, more label-free remote sensing images are utilized to improve remote sensing image segmentation precision, remote sensing images acquired by satellites can be segmented, and remote sensing image segmentation efficiency is improved. The problems that in a remote sensing image, objects are dense, the background difference is large, the size change range is large, the color and texture difference is large, local consistency features are difficult to obtain, and a large amount of noise inevitably exists in a pseudo tag are solved.

The invention relates to the technical field of spectrum detection, and particularly provides a passive laser heterodyne double-balance spectrum detection method and device, and the method comprises the steps: carrying out the frequency mixing and differential processing of an input narrow-linewidth laser local oscillator signal and a wide-spectrum incoherent target optical signal through employing an optical mixer and a photodiode, and obtaining a laser heterodyne double-balance spectrum. Two paths of random time beat frequency photocurrent signals with the phase difference of 90 degrees are generated, the random time beat frequency photocurrent signals comprise information such as the spectrum of signal light, and the spectrum information of the signal light can be recovered by processing the output photocurrent signals through methods such as filtering and integration. The narrow linewidth and wavelength tunable characteristics of the narrow linewidth laser local oscillation signal can be utilized to perform high spectral resolution information acquisition on the wide spectrum incoherent optical signal, the utilization efficiency of the local oscillation light is effectively improved, common mode noise and part of system noise are eliminated, and the signal-to-noise ratio of the output signal is enhanced.

The invention relates to a cloth setting machine energy-saving optimization method based on artificial intelligence. The method comprises the following steps: S1, collecting process operation data in a cloth setting process; s2, preprocessing the collected data, and eliminating noise and abnormity; s3, dividing the setting machine control system into a heating control subsystem, a moisture removal control subsystem and a circulating air control subsystem according to different functions; s4, according to the predicted energy consumption demand, a multi-objective optimization algorithm is used, two objectives of shaping quality and energy consumption are optimized at the same time, and an optimal temperature, humidity and airflow velocity combined curve scheme is output; and S5, introducing a deep reinforcement learning training intelligent controller, directly feeding back the working condition of the setting machine through real-time data, and dynamically adjusting the operation of each subsystem based on the optimal temperature, humidity and airflow velocity combined curve scheme. The cloth shaping quality and the energy utilization efficiency are remarkably improved.

The embodiment of the invention discloses an ultrasonic harmonic image generation method and device, electronic equipment and a medium. A specific embodiment of the method comprises the following steps: controlling an ultrasonic transmitting probe to transmit a low-frequency ultra-harmonic signal; controlling an ultrasonic receiving probe to receive the echo signal set to be detected; carrying out compressed sampling processing on the echo signal set to be detected and then carrying out signal reconstruction to obtain a reconstructed echo signal set; carrying out delay superposition processing on the reconstructed echo signal set to obtain a superposed echo signal; performing signal filtering decomposition processing on the superposed echo signals to obtain a super-harmonic signal set; performing envelope detection on the super-harmonic signal set to obtain an echo amplitude information set; and generating a target area ultrasonic harmonic image set, and sending the target area ultrasonic harmonic image set to a target terminal for display. According to the embodiment, by transmitting the low-frequency signal and receiving the ultra-harmonic signal, the attenuation of the received signal can be reduced, the signal penetration depth can be improved, the purity of the received signal can be improved, and the quality and the resolution of the generated ultrasonic harmonic image can be improved.

The invention belongs to the field of artificial intelligence and digital medical treatment, and relates to a drug recommendation method, which comprises the following steps: preprocessing medical record data and gene data according to a preset data standard specification to obtain standard medical record data and standard gene data, and fusing the standard medical record data and the standard gene data through a health analysis model for prediction to obtain a drug recommendation result. And analyzing the health analysis result, the standard medical record data and the standard gene data through a drug recommendation model to obtain a drug recommendation scheme. The invention further provides a drug recommendation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the medical record data and the gene data can be stored in a block chain. According to the method, the efficiency and accuracy of drug recommendation can be improved, the personalized level of drug recommendation is remarkably improved, drug treatment is more targeted and effective, and the drug use safety of patients is improved.

The invention provides an anesthetic gas concentration detection and early warning method, and relates to the technical field of medical treatment. The anesthetic gas concentration detection and early warning method specifically comprises the following steps: S1, setting biofeedback equipment; s2, biological feedback signal processing and analysis; s3, performing gas concentration adjustment and biological feedback closed-loop control; s4, implementing a double-layer early warning mechanism; s5, performing predictive early warning and deep nerve regulation and control; s6, anesthetic gas management and non-invasive feedback enhancement; and S7, context awareness and personalized adjustment are carried out. Through signal processing, intelligent closed-loop control, comprehensive multi-dimensional monitoring and personalized dynamic adjustment, the anesthetic gas concentration detection and early warning method has the remarkable advantages in the aspects of ensuring the safety of a patient, improving the anesthetic precision, reducing side effects and achieving efficient management, the anesthetic management process can be optimized, and the anesthetic gas concentration detection and early warning method has the advantages that the anesthetic gas concentration detection and early warning effects are improved. And a solid guarantee is provided for safe operation of the operating room.

The invention provides a secret-related carrier trajectory tracking and anomaly detection method and system based on deep learning, and relates to the technical field of trajectory tracking, and the method comprises the steps: obtaining multi-source heterogeneous data during the task execution period of a target secret-related carrier; distributing a corresponding data weight for the multi-source heterogeneous data in combination with an adaptive weighting strategy, and fusing the data weight and the multi-source heterogeneous data to obtain carrier trajectory data; on the basis of a hybrid filtering algorithm combining unscented Kalman filtering and particle filtering, optimized carrier trajectory information is obtained in combination with a hybrid weight distribution strategy; calculating a local reachable density and a local abnormal factor of each trajectory data point in the optimized carrier trajectory information under different neighbor values; integrating the local anomaly factor values under multiple scales to obtain a multi-scale anomaly score of the trajectory data point; and according to a preset anomaly judgment threshold value, determining the trajectory data points with the multi-scale anomaly scores exceeding the anomaly judgment threshold value as abnormal points.

The invention relates to the technical field of image processing, and discloses an image denoising method and device, computer equipment and a storage medium, and the method comprises the steps: carrying out the preliminary denoising of a noise-containing image based on a diffusion coefficient function model, and obtaining a preliminary denoised image; and performing second denoising processing on the preliminarily denoised image by using a denoising convolutional neural network, and outputting a denoised image. And according to the feature information of the image, adjusting the threshold parameter and the number of iterations of the diffusion coefficient function so as to adapt to the noise characteristics of different images, and finally outputting an optimized target de-noised image. According to the image denoising method, noise in the image can be effectively removed, and detail and edge information of the image can be accurately reserved. The optimal balance between noise removal and detail protection is realized by adaptively adjusting the threshold parameter and the number of iterations, and the denoising effect is remarkably improved. And the finally generated image is higher in quality, richer in details and clearer in edge.