13 results about "Waterfall plot" patented technology

The embodiment of the invention provides a distributed optical fiber acoustic logging depth correction method and device, equipment and a medium, and the method comprises the steps: analyzing a distributed optical fiber acoustic logging waterfall plot, and determining an optical fiber length range corresponding to the position of a blowout preventer; performing fast Fourier transform on the distributed optical fiber acoustic logging data within the optical fiber length range, and determining the bottom position of the blowout preventer; and performing logging depth correction on the distributed optical fiber acoustic logging curve according to the bottom position of the blowout preventer to obtain the corrected logging depth. In the embodiment of the invention, the optical fiber length range roughly corresponding to the position of the blowout preventer is determined by analyzing the distributed optical fiber acoustic logging waterfall plot, the specific position of the bottom of the blowout preventer is determined by using the fast Fourier transform of the distributed optical fiber acoustic logging data, and then logging depth correction is performed on the distributed optical fiber acoustic logging curve. The corrected logging depth is obtained, and the accurate logging depth is provided for a distributed optical fiber acoustic logging curve.

This invention discloses a physically constrained, enhanced intelligent identification method and system for fiber optic monitoring intrusion events. The method includes: collecting and preprocessing multimodal data from the site to obtain a fiber optic vibration waterfall plot; performing target detection and category probability calculation on the fiber optic vibration waterfall plot, initially marking targets with a category probability consistently greater than a certain threshold as initial high-threat targets; calculating the vibration center of the initial high-threat targets and the maximum distance difference among all vibration centers; when the maximum distance difference reaches a certain threshold, the initial high-threat target is determined as a non-threatening signal and filtered out; and performing threat distance discrimination on the remaining initial high-threat targets to obtain the final high-threat targets and triggering an alarm. This invention performs multiple rounds of judgment and screening of high-threat targets, filtering out interference from non-threatening and long-distance low-threat signals, and performs multi-dimensional confidence assessment on the screened high-threat targets, thereby improving the accuracy of identifying and judging high-threat target intrusion events.

This invention discloses a method, system, and storage medium for atrial fibrillation detection based on a stacked waterfall plot of heartbeats on electrocardiograms, belonging to the field of biomedical signal processing and machine learning technology. The method extracts heartbeat waveform data using the R-wave position as an anchor point, performs individualized grayscale normalization using the voltage range of the PR segment as a reference, and stacks multiple normalized heartbeat waveforms vertically in chronological order to generate a two-dimensional grayscale waterfall plot image. Then, it achieves dense prediction per heartbeat through multiple independent sigmoid output nodes of a two-dimensional convolutional residual network. On a multi-center independent test set of approximately 5.9 million heartbeats, the method achieved an F1 score of 0.9944 and a recall rate of 99.88%. This invention preserves the original waveform morphology information, has good multi-device compatibility, and is suitable for electrocardiogram acquisition devices with various lead configurations.

This invention relates to the field of vibration detection technology, and more particularly to a method for determining the cause of a single abnormal vibration during actual vehicle operation. The method includes: acquiring several sets of experimental vibration data and converting them into experimental waterfall plots; obtaining the experimental vibration order and amplitude percentage from the waterfall plots; constructing a correspondence table and determining scoring rules; collecting real-time vibration data and converting it into a real-time waterfall plot, obtaining the real-time vibration order and amplitude percentage; determining possible causes of abnormal vibration based on the real-time vibration order and the correspondence table; calculating a vibration score; and determining the cause of the abnormal vibration based on the vibration score. This invention calculates vibration scores using scoring rules corresponding to the magnitude of vibration orders, and compares the vibration scores with corresponding score intervals for each abnormal vibration cause in the correspondence table, thereby obtaining the cause of the abnormal vibration and increasing the accuracy of the detection results.

The invention relates to an optical fiber sound wave sensing data feature extraction method and device and a computer storage medium. The method comprises the following steps of 1, obtaining video training data; step 2, obtaining a visible video block; step 3, obtaining a coding network input tensor; step 4, obtaining visible video data features; step 5, obtaining a reconstructed video; step 6, obtaining optimized network parameters of the auto-encoder; 7, waterfall plot training data are obtained; step 8, obtaining a visible space time frequency block; step 9, obtaining a cross-modal coding network input tensor; step 10, obtaining visible space time-frequency graph data features; step 11, obtaining a reconstructed space time-frequency diagram; 12, completing the overall training of the model; and step 13, obtaining new waterfall plot data features. According to the method, the high-quality optical fiber sensing vibration signal features can be effectively extracted from the waterfall plot, and the high-quality optical fiber sensing vibration signal features can be effectively extracted from the waterfall plot by effectively utilizing data of different modes.

This invention relates to a method, apparatus, and computer storage medium for feature extraction from fiber optic acoustic wave sensing data. The method includes the following steps: Step 1, obtaining video training data; Step 2, obtaining visible video blocks; Step 3, obtaining the input tensor of the coding network; Step 4, obtaining visible video data features; Step 5, obtaining the reconstructed video; Step 6, obtaining the optimized autoencoder network parameters; Step 7, obtaining waterfall plot training data; Step 8, obtaining visible spatial time-frequency plot blocks; Step 9, obtaining the input tensor of the cross-modal coding network; Step 10, obtaining visible spatial time-frequency plot data features; Step 11, obtaining the reconstructed spatial time-frequency plot; Step 12, completing the overall model training; Step 13, obtaining new waterfall plot data features. This invention can effectively extract high-quality fiber optic sensing vibration signal features from waterfall plots, and can also effectively utilize data from different modalities to extract high-quality fiber optic sensing vibration signal features from waterfall plots.

This invention discloses an anti-jamming zero-sum Markov game model and a maximum-minimum deep Q-learning method. The model considers a communication adversarial scenario involving a pair of transceivers, a fixed jammer, and an intelligent jammer. The fixed jammer releases frequency sweeping interference, the intelligent jammer optimizes the jamming channel selection, and the user optimizes channel and power selection to maximize transmission utility. The adversarial interaction process is modeled as an anti-jamming zero-sum Markov game. The method involves: initializing the anti-jamming network parameters and training hyperparameters; constructing a spectrum waterfall plot and inputting it into the anti-jamming network, outputting an adversarial Q-value matrix; calculating and executing anti-jamming actions, calculating the reward value obtained for the current action and saving the adversarial record; sampling the adversarial record, calculating the Q-value estimation error, and updating the anti-jamming network using a backpropagation gradient algorithm; repeating the above interaction process until network training is complete. This invention effectively solves the problem of non-stationary environmental changes caused by jamming strategy updates, obtaining a more robust anti-jamming strategy.

This application provides a method, apparatus, and processor for determining the completion rate of pipeline inspection. The method includes: preprocessing pipeline vibration signals acquired in real-time when a target user performs pipeline inspection work according to a preset inspection task; inputting the preprocessed pipeline vibration signals into a waterfall plot to generate a pipeline vibration image; inputting the pipeline vibration image into an image recognition model to output the vibration time period, vibration location, and signal type of the pipeline vibration signal; determining the actual number of taps corresponding to the vibration location and vibration time period when the signal type is a preset type; acquiring the corresponding target number of taps when the vibration location is an inspection location in a preset inspection task; determining the target user's attendance information at the vibration location based on the actual number of taps and the target number of taps; and determining the completion rate of the pipeline inspection work performed by the target user based on the attendance information, thereby accurately determining the inspection status and improving attendance accuracy.

The application discloses a kind of X-ray pulsar frequency estimation methods based on swarm optimization, belong to pulsar navigation technical field.The method includes: through waterfall chart folding to observation task, from the consistency of profile signal intensity and segmented phase two aspects to candidate frequency is jointly analyzed;According to the actual sample distribution characteristics reconstruction high-dimensional space sample distance model, nonlinear random neighborhood embedding dimension reduction is carried out to waterfall chart;Three-dimensional image information is converted into two-dimensional plane point group data, and the mean distance origin standard deviation of plane point group is used as the evaluation index of frequency estimation;Combined with the principle of local focusing, the search behavior of scout bees in the bee colony optimization algorithm is improved, and the optimal frequency is efficiently searched;Through simulation comparison with existing methods, the superiority of the method proposed in the application in the performance of pulsar frequency estimation is proved from the aspects of estimation accuracy and operation time.

This invention discloses a method and system for detecting atrial fibrillation (AF) on electrocardiograms based on the fusion of time-series and waterfall plot models, belonging to the fields of biomedical signal processing and deep learning technology. This method simultaneously constructs two complementary data representations of the same electrocardiogram signal: a fixed-duration one-dimensional time-series segment and a two-dimensional heartbeat stacked grayscale waterfall plot aligned with the R-wave. These are respectively input into independently trained one-dimensional and two-dimensional convolutional residual networks for classification. Finally, the outputs of the two models are aligned at the heartbeat time granularity, and the final AF detection result is obtained through a configurable fusion strategy (intersection, union, or weighted voting). The time-series model excels at capturing abnormal heart rate rhythms, while the waterfall plot model excels at identifying abnormal waveform morphology; the two are significantly complementary in boundary cases. In 2,433,594 heartbeat tests with balanced distribution of AF and NAF samples (AF accounting for 43.7%), the false alarm rate decreased from 4.30% to 1.30% and the missed alarm rate decreased from 1.07% to 0.59% after adopting the intersection fusion strategy. The prediction conflict rate between the two models was only 2.43%, which proves that the two-dimensional fusion has a significant complementary gain effect under neutral distribution conditions.

The invention provides an optical fiber sensing signal preprocessing effect evaluation method, device and system and a medium. The method comprises the following steps: acquiring an original space-time waterfall plot; obtaining at least one corresponding filtering space-time waterfall plot obtained by filtering the original optical fiber sensing signal based on at least one filtering algorithm; performing binarization processing on the original space-time waterfall plot to obtain an original binarized image, and identifying and labeling a region of interest on the original binarized image based on morphological operation; determining a first signal feature region in the original space-time waterfall plot and a second signal feature region in the filtered space-time waterfall plot based on the region of interest; determining a multi-dimensional evaluation index of the filtering space-time waterfall plot based on the first signal feature region and the second signal feature region; and determining a comprehensive score based on the multi-dimensional evaluation indexes. According to the invention, through multi-dimensional evaluation indexes, the problems of subjective evaluation blind areas and single dimension are solved, and the accuracy of comprehensive scoring is improved.

This invention discloses an electromagnetic situational awareness and visualization system based on geospatial positioning, comprising: a geospatial grid construction module, which acquires GIS engine data and divides the target area into a multi-scale non-uniform three-dimensional computational grid according to monitoring requirements; a geographic constraint inference and modeling module, which executes an adaptive electromagnetic propagation model and calculates physical trend values; a multi-source residual compensation module, which acquires the measured signal strength values ​​of monitoring nodes, calculates the residuals between the measured signal strength values ​​and the physical trend values, performs Kriging interpolation on the residuals using the residual Kriging algorithm, and synthesizes a global reconstructed situational map; and an interactive multi-dimensional display module, which renders the global reconstructed situational map as a dynamic heatmap superimposed on the geographic model, and synchronously displays the instantaneous spectrum map and waterfall map of the associated frequency points, outputting the electromagnetic situational field. This system can achieve high-precision situational awareness in complex geographic environments, providing a reliable basis for the analysis and management of the electromagnetic environment.

The invention relates to the field of wireless communication, in particular to a mountain complex environment sensing integrated intelligent anti-interference method, which comprises the following steps of: modeling a dynamic anti-interference problem into a Markov decision model; wherein the spectrum waterfall plot at the current moment is taken as an environment state, the decision of selecting the bandwidth and the frequency band at the current moment and whether to communicate is taken as actions, and a reward function is constructed according to the communication rate, the power consumption and the cost of switching the communication equipment. According to the method, the interference identification degree is enhanced through the OTFS waveform, the spectrum waterfall plot is constructed to realize high-dimensional state characterization, the deep recursion convolutional neural network is designed to optimize communication resource allocation, and finally the communication rate and the sensing stability are improved.