360 results about "Frequency analysis" patented technology

The invention discloses a method for identifying a multi-model fusion signal modulation mode based on a time-frequency diagram, and belongs to the field of signal processing. Preprocessing the original signal to obtain a signal sample; constructing a data set by using the normalized signal samples; constructing a fusion model; training a modulation identification module in the fusion model; performing modulation mode identification on an input unknown signal by using the fusion model to obtain a preliminary identification result; and performing comprehensive judgment on the preliminary recognition result of the fusion model by using a comprehensive judgment device to obtain a final recognition result. According to the invention, by combining a plurality of time-frequency analysis methods, the time-frequency domain feature information of the signal is fully extracted; the reliability and robustness of a final decision are improved by adopting a multi-model fusion framework, and the modulation recognition performance of the system under the condition of a low signal-to-noise ratio is improved.

The invention discloses a wellhead data acquisition modeling system. The wellhead data acquisition modeling system comprises a data acquisition module, a hydraulic characteristic processing module, a well layer coupling modeling module and an interaction control module, the invention belongs to the technical field of well water monitoring management, based on an adaptive time-frequency analysis method, a dynamic window function enhanced by local frequency spectrum entropy is introduced, feature extraction and abnormal early warning are performed on transient hydraulic fluctuation signals, and the time-frequency resolution and change recognition capability of the hydraulic signals are remarkably improved; a dynamic coupling model between a water pumping system and an aquifer is constructed by combining a multi-scale parameter coordination mechanism, and accuracy and dynamic adaptability of well layer response modeling are realized by introducing a gap compression lag function and a joint parameter calibration method; and the interaction control module implements model prediction control according to the hydraulic feature vector, the hydraulic anomaly early warning signal and the well layer coupling prediction parameter combination, and outputs wellhead data acquisition decision support data.

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention provides a large-scale equipment early fault detection system and method based on energy flow time-frequency analysis. The large-scale equipment early fault detection system based on energy flow time-frequency analysis comprises a multi-sensor data acquisition and preprocessing module, an energy flow time-frequency feature extraction module, a deep transfer learning fault identification module, a data enhancement and dynamic balance module and a multi-scale energy flow modeling module. According to the energy flow time-frequency analysis-based large-scale equipment early-stage fault detection system and method provided by the invention, in order to solve the problems of poor time-frequency feature extraction adaptability, insufficient noise robustness and weak cross-domain migration capability in a variable working condition scene in the prior art, through collaborative optimization of multi-modal data fusion, adaptive time-frequency analysis and dynamic migration learning, the time-frequency feature extraction adaptability is improved; and the fault detection precision and generalization capability under the complex working condition are obviously improved.

The invention belongs to the field of artificial intelligence, particularly relates to a method for predicting the residual life of a key part of a coal mill, and aims to solve the problems of low prediction precision and poor extrapolation caused by working condition disturbance interference and inaccurate degradation characterization in a traditional method. The method comprises the following steps: collecting vibration, temperature, current and acoustic emission multi-source synchronous data; constructing a dynamic working condition decoupling model of fusion of the variational auto-encoder and the attention mechanism, and separating degradation sensitive components; and a high-fidelity degradation index sequence is generated through fusion of time-frequency analysis and a gating circulation unit. According to the method, multi-modal sensing and physical priori knowledge are fused, the monotonicity of a degradation index is remarkably improved to 98% or above, the extrapolation error of an extreme working condition is reduced by 40%, edge-cloud collaborative deployment and digital twinborn visualization are supported, multi-component collaborative early warning is realized, and the early warning amount of non-planned shutdown is improved from 7 days to 21 days or above; and a high-precision, strong-robustness and landing life prediction solution is provided for intelligent operation and maintenance.

The invention discloses a multi-factor coupling dynamic error compensation method, system and device and a storage medium, and the method comprises the steps: obtaining an original signal sequence, and carrying out the time-frequency analysis of the original signal sequence, and obtaining a time-frequency matrix; performing feature extraction on the time-frequency matrix to obtain feature information; according to the feature information, calculating a Lagrange interpolation reference node of a corresponding time point, and carrying out signal reconstruction to obtain a synchronous sampling sequence; performing harmonic analysis on the synchronous sampling sequence to obtain harmonic parameters; the dynamic compensation amount is calculated in combination with the characteristic information and the harmonic parameters, the electric energy metering value is corrected, and the real-time performance and accuracy of electric energy measurement are effectively improved.

The invention discloses a time-frequency analysis method for impact signal positioning based on transient scale extraction transformation, and belongs to the technical field of mechanical vibration signal processing. The method comprises the following steps: collecting a rotating machine fault vibration signal, and reconstructing a signal model through Hilbert transform and a Dirac function; a transition matrix is generated by using a Gaussian window function traversal model, and matching and Fourier transform are carried out in combination with a discretized scale basis function; solving a frequency partial derivative of a transformation result to generate a time redistribution operator, and redefining by a Dirac function to obtain a transient scale extraction operator; based on the sub-time-frequency representation of scale-based rotation discretization, screening optimal matching results of each time center through a maximum kurtosis value, and integrating the optimal matching results into a complete time-frequency representation; and finally, redistributing a time-frequency coefficient by using a transient extraction operator to realize accurate positioning of the impact component. According to the method, the problems of serious impact energy diffusion and insufficient positioning precision in existing time-frequency analysis are solved, and the time-frequency representation readability and the impact positioning reliability are remarkably improved.

The invention discloses a window-based decoupling short-time fractional Fourier transform time-frequency analysis method. The method comprises the following steps: S1, acquiring non-stationary signal data generated under power quality disturbance in a power grid; s2, segmenting the non-stationary signal into a plurality of parts based on signal waveform characteristics, and setting an initialized virtual window function for discrete short-time fractional Fourier transform, wherein the window function only provides an interface for calling of a specific window function; s3, calling different window functions for each segmented sub-segment signal to obtain windowed sub-segment signals under a specific window function; and S4, discrete fractional Fourier transform is applied to the sub-segment signals obtained in the step S3 to obtain time-frequency results of the sub-segment signals under different windows, and finally, frequency spectrums of the sub-segment signals are spliced according to a time sequence and output by an oscilloscope. Based on the characteristics of non-stationary signals generated under power quality disturbance in a power grid, different window function analysis signals can be called in one task by setting a virtual window function interface, and the window-based decoupling short-time fractional order Fourier transform time-frequency analysis method is formed. According to the method, the coupling between fractional Fourier transform and a specific window is reduced, the expandability of a program is improved, and more accurate signal local spectrum features can be obtained.

The invention relates to the technical field of data processing, in particular to a multi-series short-duration rainstorm data frequency analysis method. The method comprises the following steps: obtaining rainfall original data of multiple series of short duration, and carrying out missing data interpolation and sequence extension processing, duration consistency and rainfall intensity rule check to obtain a rainstorm data set; respectively performing reliability, representativeness and consistency review and sample validity screening on the rainstorm data set; a Pearson III type probability density function model is constructed, probability distribution fitting is carried out, and a frequency distribution curve is generated; and carrying out extra heavy rain value analysis and empirical frequency adaptive line analysis adjustment, generating a corrected frequency parameter, further calculating a design heavy rain value of each design return period, and carrying out duration consistency and spatial rationality check to obtain a final design result output data set. According to the invention, through multi-dimensional data complementation, checking and optimization fitting, the system improves the accuracy, reliability and engineering applicability of short-duration rainstorm frequency analysis.

The invention relates to the field of charging pile use, and discloses a charging pile use frequency analysis method based on charging frequency data, which comprises the following steps: acquiring charging frequency original data of a plurality of charging piles at different time periods and different dates, performing structured processing in combination with a time label, and constructing a charging behavior data set containing a time dimension label; introducing a sliding window algorithm and a periodic spectrum analysis method, and performing frequency feature extraction on the charging behavior data set; using a density peak clustering algorithm to classify the use frequency matrixes of different charging piles under the same time scale, and identifying use preferences and active features of the charging piles under various typical time scenes; based on a lightweight time sequence neural network structure, a frequency fluctuation sensing mechanism is introduced; and predicting the use condition of the charging pile in a future time period based on the trained frequency model in combination with regression prediction and an anomaly detection algorithm. The method has the advantage of improving operation scheduling.

The invention relates to the technical field of harmonic intelligent positioning, and discloses a coupling harmonic intelligent positioning method and system based on multi-modal fusion, and the method comprises the steps: obtaining the voltage amplitude time sequence data of each observation point, constructing a frequency distribution characteristic curve, and extracting a frequency center and a broadening parameter; parameter updating is realized in combination with priori information of power grid impedance, and an adaptive frequency analysis window is constructed on this basis to extract energy distribution characteristic quantity; the reliability weight of an observation point and the time sequence weight of harmonic source equipment are further introduced, and the dynamic characteristics of the space credibility and the operation state are comprehensively reflected; and finally, taking the two types of weights as weighting factors, carrying out weighted calculation on the harmonic propagation model deviation of the candidate nodes to obtain a comprehensive residual value, and positioning the node with the minimum comprehensive residual value as a coupling harmonic source node, thereby remarkably improving the accuracy of harmonic source positioning in a complex power grid environment.

According to the anti-frequency analysis searchable encryption method and system, a certificateless encryption system is introduced, for a scene with limited computing resources, the scheme is ensured to be safe and suitable for the scene with the limited computing resources, meanwhile, a trap door algorithm is designed based on a probability trap door generation algorithm, and the security of the scheme is improved. The method improves the resistance of a system to frequency analysis attacks, solves the problem of secret key trusteeship due to the fact that identity information and secret key distribution are concentrated in a private key generation center in a traditional encryption scheme, and improves the encryption efficiency. The technical problem that malicious users are easy to obtain the privacy information of the target keyword by performing frequency analysis on trap doors and initiating keyword guessing attacks is solved.

The invention discloses a time-frequency analysis method and system for non-stationary signals of periodic pulses, and the method comprises the following steps: S1, carrying out the preprocessing of a vibration signal through a rapid CMSP method, and obtaining a periodic pulse component in the vibration signal; s2, carrying out iterative estimation on group delay by utilizing fixed point iteration; s3, utilizing iteration group delay estimation, and redistributing time-frequency coefficients of the STFT through time rearrangement multiple times of synchronous extrusion transformation; s4, performing integration on a time rearrangement multiple-time synchronous extrusion transformation result along a time direction so as to reconstruct the signal; according to the method, the problem that STFT time-frequency representation is fuzzy is solved through a fixed point iterative algorithm, meanwhile, signal reconstruction can be achieved, synchronous extrusion operation is carried out through iterative group delay estimation, an estimated value is closer to real group delay, divergent time-frequency coefficients in STFT after FC processing are redistributed, and the estimation accuracy is improved. Therefore, the energy concentration degree of time-frequency representation is improved.

The invention relates to the technical field of biological radar signal processing, in particular to an FMCW radar-based heart beat interval estimation method, which comprises the following steps of: S1, converting a chest vibration echo phase time sequence obtained by irradiating a chest area of a monitored object by an FMCW radar into an acceleration time sequence by using a second-order time derivative; s2, dynamically mapping the center frequency and the wavelet order in a preset frequency analysis interval, constructing a self-adaptive wavelet dictionary, then executing multi-order wavelet time domain convolution operation on the acceleration time sequence by using the dictionary, and aggregating time-frequency energy distribution to generate a time-frequency energy diagram; and S3, performing a deconvolution operation reconstruction strategy based on an energy-guided multi-stage time-frequency feature screening technology and wavelet function conjugation, generating an approximate time domain signal of heart beat vibration, and completing heart beat information inversion. According to the method, the accuracy and robustness of IBI extraction can be effectively improved under the condition of low signal-to-noise ratio, so that stable monitoring of light and moderate HRV (heart rate variability) is supported.

According to the hardware mold machining control method and system, energy of flutter characteristic frequency components is obtained by collecting cutting torque signals and conducting frequency analysis, the flutter risk is evaluated by dynamically setting an energy threshold value based on the overhanging length of a tool, the feeding rate and the rotating speed of a main shaft are adjusted when the response risk exists, and the machining accuracy of the hardware mold is improved. Therefore, fluttering in the machining process can be effectively recognized and inhibited, and the method has the advantages that fluttering in the machining process can be effectively recognized and inhibited, the machining quality is improved, and the service life of a tool is prolonged.

The invention discloses a line fault detection method and device based on morphological wavelets, relates to the technical field of line detection, and solves the problem that in the prior art, when a single-phase grounding fault occurs in a small-current grounding system, the fault current is small, the characteristics are not obvious, and when line fault detection is carried out by using time domain, frequency domain and time frequency analysis methods, the fault current is not obvious. And the technical problems of low fault line selection accuracy and poor reliability exist. The method comprises the following steps: acquiring zero-sequence current signals in a period before and after a line fault occurs; pre-processing the zero sequence current signal; decomposing the preprocessed zero sequence current signal by using a morphological Haar wavelet; calculating modulus maxima of the plurality of lines according to the decomposition result; correcting the modulus maxima of the plurality of lines based on the line length; screening fault lines according to the corrected modulus maxima; the accuracy and reliability of single-phase earth fault line selection can be improved.

The invention provides an interest point recommendation method based on a dynamic hierarchical attention fusion space-time network, and the method comprises the following steps: 1, obtaining user historical sign-in and interest point feature data, and carrying out the preprocessing of the user data; 2, learning robust spatio-temporal information representation of the interest points through an adaptive spatio-temporal double-graph convolutional network module; 3, constructing a'interest point-functional unit-regional unit 'multi-level semantic hierarchical structure, and learning hierarchical semantic information representation of interest points by using a bidirectional hierarchical graph attention network module; 4, after fusing the two interest point representations, coding a user track and inputting the user track into an attention-frequency fusion Transform module, so as to obtain a multi-level semantic hierarchical structure; the module separates long and short term dynamic signals in a user behavior sequence through frequency analysis and dynamically fuses the long and short term dynamic signals with a self-attention mechanism to predict a next point of interest; and 5, training by adopting a combined loss function. According to the method, the complex characteristics of the user and the POI are effectively modeled, and the accuracy and the capability of capturing the dynamic preference of the user are improved.

The invention discloses a ship signal adaptive time-frequency analysis method based on instantaneous frequency direction self-adjustment. The ship signal adaptive time-frequency analysis method comprises the steps of collecting ship signals to be subjected to time-frequency analysis and performing short-time Fourier transform; estimating the instantaneous frequency of the ship signal based on short-time Fourier transform time-frequency distribution in combination with time-frequency narrow-band features; calculating the instantaneous frequency and the optimal window length after smoothing; segmenting the smoothed instantaneous frequency in time, calculating time-frequency domain direction pre-guidance of each time period, and constructing a Chirp atomic direction over-complete dictionary; segmenting the ship signal in time, and calculating the matching direction of the ship signal in each time period so as to calculate and improve Chirplet transformation time-frequency distribution; and calculating the optimal short-time Fourier transform in combination with the optimal window length, and correcting and improving Chirplet transform time-frequency distribution. According to the method, the robustness of short-time Fourier transform and the anisotropy of improved Chirplet transform are utilized at the same time, and time-frequency distribution with high robustness and high time-frequency resolution is obtained.

The invention belongs to the field of non-intrusive power load monitoring, and discloses a non-intrusive power load identification method, which comprises the following steps of: constructing a load identification framework utilizing frequency domain characteristics, and aims to solve the problem that transient characteristics cannot be effectively applied in non-linear load identification in non-intrusive load monitoring. A current feature extraction technology based on fast Fourier transform and Hilbert-Huang transform and a classification method based on an extreme learning machine as a main body are used for training and testing a public data set to verify that the method is used for extracting transient load features and identifying nonlinear loads.

The invention provides a rotating speed extraction method, system and equipment based on time-frequency analysis and a medium. The method comprises the following steps: acquiring a signal x (t) to be analyzed; performing spectral analysis transformation on the to-be-analyzed signal x (t) to obtain a spectral analysis result; acquiring a three-dimensional time-frequency distribution diagram A (t) (f) of the to-be-analyzed signal x (t) according to the spectral analysis result; searching a peak frequency in the three-dimensional time-frequency distribution diagram A (t) (f) according to a preset extraction rotating speed frequency Nfn and a preset search range so as to obtain the rotating speed fn (t); according to the method, the rotating speed of the equipment can be accurately extracted according to the monitoring data under the condition of not depending on an external rotating speed sensor; the system has the same beneficial effects.

The invention discloses an ant colony optimization energy focusing cable insulation damage positioning method and device, and relates to the technical field of cable detection engineering, and the method comprises the steps: generating a Gaussian envelope linear frequency modulation chirp detection signal, injecting the detection signal into a to-be-detected cable, and collecting an incident signal and an echo signal containing a reflection component; constructing a parameterized time-frequency analysis kernel function family under a Cohen type time-frequency distribution framework, and searching and optimizing kernel parameters by using an ant colony optimization algorithm; and calculating the time frequency distribution and energy-time curve of the signal based on the energy focusing time frequency analysis kernel function obtained through optimization, extracting the arrival time corresponding to an energy peak value, and realizing cable insulation damage positioning according to the reflection time delay and the propagation wave velocity. According to the method, energy peak focusing and peak position stability are enhanced while cross terms are restrained, defect positioning precision and robustness are remarkably improved, and the method is suitable for engineering online detection and automatic implementation of cable insulation damage.

The invention belongs to the technical field of nuclear equipment monitoring, and aims to solve the problems of spatial resolution reduction caused by a multi-source acoustic signal aliasing effect in a complex industrial environment and feature extraction difficulty caused by industrial noise and fault sound source spectrum overlapping. According to the method, spatial decoupling is carried out, sparse Bayesian learning and minimum variance undistorted response beam forming joint optimization is carried out, a sound source orientation is preliminarily estimated, synchronous extrusion wavelet transform and NMF joint time-frequency analysis is carried out, and a spatial anomaly acoustic diagnosis result is obtained. Sound source transient characteristics and energy evolution laws are extracted through time-frequency conjoint analysis, a multi-dimensional characteristic space is constructed, the number of fault types is determined for fault type classification, and three-dimensional coordinate mapping and error feedback are displayed; the system comprises an airspace decoupling module, a time-frequency decomposition module, a feature fusion module and a dynamic imaging module. According to the invention, the sound source positioning precision can be realized, and the resolution limitation of traditional beam forming is broken through.

The invention discloses a damage quantitative evaluation method based on an intelligent agent model and knocking scanning, and aims to realize accurate recognition and quantitative evaluation of beam structure damage through the agent model. The evaluation method comprises the following steps: firstly, according to a theoretical relational expression of a beam structure, selecting a cross-sectional inertia moment, a structure length, a damage position and a detection vehicle equivalent acceleration as input random variables, and taking a bending stiffness ratio of a damage position as a damage quantitative index, constructing and optimizing an agent model; establishing a nonlinear mapping relation between input and output; secondly, applying excitation by utilizing an intelligent knocking scanning detection vehicle, acquiring an acceleration signal, processing the signal by adopting a time-frequency analysis technology, and extracting feature information related to structural damage; and finally, predicting a damage quantitative index according to the agent model in combination with the identified damage position and feature data, and verifying and outputting a result.

The invention discloses a time-frequency analysis method based on local entropy optimization synchronous demodulation reassignment transformation, relates to the technical field of signal processing, and solves the problem that a traditional time-frequency analysis method is not easy to carry out when complex non-stable vibration signals generated by rotating machinery and the like are processed. The problems of difficulty in balancing time and frequency resolution, easiness in energy diffusion, difficulty in multi-component signal separation and the like exist; according to the method, zero padding is carried out on the two ends of a target signal, a discrete Gaussian window is constructed to generate a sliding windowing signal matrix, a projection angle sequence is generated in an angle space, a time axis and a frequency axis are discretized, a demodulation function containing undetermined parameters is constructed to generate a complex number time-frequency representation matrix, the Renyi entropy value of time-frequency sub-blocks is calculated to determine an optimal demodulation parameter, and the optimal demodulation parameter is obtained. And setting a frequency difference threshold to construct a frequency redistribution operator to generate an optimized time-frequency matrix. The method is mainly used for time-frequency analysis of complex non-stationary vibration signals in the fields of rotating machinery and the like, and high-precision frequency modulation feature extraction and enhancement processing are achieved.

Example systems, methods, and apparatus are disclosed herein for a high-resolution time-frequency analysis for nonstationary signals. Time-frequency distributions (TFDs) are essential tools for analyzing nonstationary signals in various applications. The proposed technology, Zoom, a novel time-frequency distribution (TFD) designed to enhance the resolution and reduce cross-term interference. The performance of the proposed TFD is rigorously evaluated by studying its behavior across different smoothing values, allowing for a detailed analysis of its time-frequency localization capabilities. To quantify its effectiveness, the proposed technology computes several performance metrics, including time-frequency concentration and cross-term reduction.

The invention relates to the technical field of remote monitoring, in particular to a boiler pressure data remote monitoring system which comprises a data feature extraction module, and the data feature extraction module analyzes time sequence data layer by layer based on boiler pressure data flow, extracts pressure fluctuation information and obtains short-term and long-term change parameters through step-by-step convolution operation. According to the invention, through analyzing time sequence data, extracting short-term and long-term change characteristics of boiler pressure and capturing details of pressure fluctuation, understanding of changes of different time dimensions is enhanced, and in combination with low-dimension processing and multi-term parameter optimization of hidden variables, expression precision of pressure characteristics is improved. According to the dynamic clustering method, through parameter distance judgment and sample information updating, self-adaptive tracking of real-time data changes is achieved, abnormal modes are effectively recognized, feature division is carried out, the time length and frequency analysis of the abnormal states are combined, alarm signals are more targeted, faults are helped to be prevented in time, and the safety and operation stability of the boiler are integrally enhanced.

To predict whether there is no sound in a predetermined space based upon whether onomatopoeia indicative of silence that a human perceives in a quiet environment is generated.SOLUTION: A determination device comprises: an input part which inputs predetermined sound data generated by converting a predetermined sound into numeric data through frequency analysis to a determination model outputting whether a specific frequency of 1,800 Hz or more to 2,200 Hz or less indicative of induced otoacoustic emissions is included in addition to the frequency of sound data once the sound data is input; and a control part which displays, on a display unit, the generation of onomatopoeia indicative of silence that a human perceives in a quiet environment when the determination model receiving the predetermined sound data generates an output showing that the specified frequency is detected, but displays, on the display unit, no generation of onomatopoeia when the determination model generates an output showing no detection of the specific frequency.SELECTED DRAWING: Figure 1

The invention relates to the technical field of jitter analysis, in particular to a high-speed oscilloscope eye diagram real-time jitter detection method and system, and the method comprises the following steps: carrying out high-speed collection waveform zero-cross detection to obtain a timestamp, calculating offset grouping to obtain a cross-period edge time sequence, merging a jump cluster according to a sequence hierarchy cluster, calculating offset, obtaining a drift trend vector, and calculating the jitter of the drift trend vector; and based on the trend vector distribution compensation weight, weighting and correcting the time coordinate, calling a corrected coordinate detection period boundary to calculate a difference value exceeding a threshold value, triggering correction to obtain a period correction rear edge coordinate, and extracting a jitter frequency amplitude according to corrected coordinate Fourier analysis to generate an eye pattern jitter detection result. Phase correlation is established based on a cross-period sequence, an analyzable offset mode is formed through a clustering identification distribution structure, accumulated deviation is suppressed through a drift trend and weight correction, an abnormal period is calibrated through period dynamic discrimination, and the relation between a jitter structure and amplitude is separated through frequency analysis, so that the overall stability and integrity are enhanced.

The invention relates to the technical field of image processing, in particular to a real-time video analysis system and method based on edge computing, and the system comprises a central management and control service module, an edge computing module, an algorithm adaptation module, a node management optimization module and a network scheduling module. According to the invention, through analysis of the video stream frame rate and the processing delay change amplitude, a high-load time period can be identified in real time, task allocation can be dynamically optimized, a frame processing strategy is ensured to be matched with a current load state, system delay is reduced to the greatest extent, and meanwhile, the accuracy of video analysis is ensured; according to the method, the network influence time period can be accurately identified, task scheduling can be optimized according to network fluctuation, inaccurate analysis results or task execution delay caused by network problems are avoided, the real-time performance and stability of a frequency analysis system are remarkably improved by comprehensively considering load changes and environmental factors, and the method is suitable for large-scale popularization and application. And the performance bottleneck caused by fixed frame interval or excessive dependence on the central server is avoided.

The application discloses a flood scene division method and system based on flood characteristics and belongs to the technical field of flood control. The existing scheme needs to set a flood peak threshold and other limit conditions artificially. Due to the subjectivity and non-uniformity of the standard, the flood scene extraction process is unreasonable. The flood scene division method based on flood characteristics constructs a flood characteristic simulation model, an event screening model, an event merging model and a scene division model, performs frequency analysis on flow data, obtains a flood peak threshold and is used for representing flood characteristics. According to the flood peak threshold, a plurality of flow data points higher than the flood peak threshold are screened out, and then the flow data points are marked as events with potential flood risks, so that the flood scene can be accurately screened, the flood peak threshold and other limit conditions need not be set artificially, the subjectivity and non-uniformity of the threshold set artificially are effectively avoided, and therefore the identification efficiency of flood events can be effectively improved.