12 results about "Wavelet transform analysis" patented technology

The invention provides an underground water environment automatic monitoring super station state supervision method and system. The method comprises the following steps: collecting a multi-source dynamic time sequence data set and carrying out time-space alignment; generating a dynamic characteristic index set by using a nonlinear dynamic characteristic extraction method; based on the index set, constructing a self-adaptive cooperative measurement network to perform anomaly monitoring, and generating an anomaly detection result and a cooperative control instruction; generating real-time monitoring and early warning information by using a dynamic threshold adjustment algorithm; and generating an adaptive control instruction and a dynamic resource allocation scheme by using a neural network adaptive control strategy and a resource scheduling optimization algorithm. According to the method, a dynamic characteristic index set is generated, a C-C method is adopted to reconstruct a high-dimensional phase space, a Wolf algorithm and a G-P algorithm are combined to calculate related indexes and dimensions, and a multi-scale fractal mode is analyzed through an R / S analysis method and wavelet transform. The methods capture complex dynamic behaviors of the underground water system, and solve the problem that the traditional method is insufficient in non-linear feature extraction capability.

The application relates to the technical field of market share analysis, in particular to a drug market share analysis method and system based on multi-source heterogeneous data. First, drug sales data is collected, a unified data set is constructed through time alignment and unit standardization processing; the regional occurrence time and propagation order of the sudden event are identified based on the daily average sales sudden increase rate index, the regional drug sales is taken as the overall demand signal, the wavelet transform technology is used to realize demand signal decomposition, and the regional panic demand component and the actual medical demand baseline are obtained; the regional dynamic market share is calculated through weighted synthesis, and the final prediction result is obtained by correcting the historical data; through multi-source data fusion and wavelet transform analysis, the accuracy of market share analysis is improved.

The application relates to the technical field of mechanical processing control, and discloses an alloy saw blade production and processing automatic control method and system.The method comprises the following steps: determining abnormal frequency distribution by acquiring an original vibration signal and converting the original vibration signal into a frequency domain spectrum; extracting a feature vector and determining an abnormal separation vector when an energy proportion exceeds a limit; calculating the deviation of the feature vector from a normal mode by using a support vector machine, combining a fault mechanism topology graph to determine a coupling influence index; adjusting the rotating speed and the feed amount according to a leading abnormal source and a machine tool stiffness characteristic to determine an adjustment scheme and update equipment control instructions; and adopting wavelet transform to analyze signal multi-scale components, and realizing minimum coupling influence control of the whole intermittent cutting process through an iterative optimization cycle condition. The method can solve the problem of low early abnormal identification precision in the prior art.

The invention discloses a continuous production control method and system based on gravure multi-station linkage, and relates to the technical field of production line automatic control, and the method comprises the steps: collecting the tension, speed, position and other state quantity information of adjacent control nodes, carrying out the compensation processing of the upstream node state quantity according to the material transmission characteristics, and obtaining the state coupling deviation value between the nodes; performing frequency domain conversion and wavelet transform on the deviation value, and analyzing an energy gathering area to identify a dynamic fluctuation type; constructing a state sequence by using the state coupling deviation value, and obtaining inter-node transfer characteristics through online recursion calculation; and selecting reference parameters from the self-learning control strategy according to the fluctuation type, performing self-adaptive optimization in combination with the transfer characteristics, generating a control instruction, and sending the control instruction to an execution mechanism for adjustment. The state coupling relation between the stations can be accurately recognized, self-adaptive coordination control is achieved, and the control precision and the response speed are improved.

The invention relates to the technical field of new energy power grid connection, and discloses a new energy grid-connected line fault positioning system, which comprises a distributed architecture module which is formed by cascading a management chassis, an expansion chassis, a distributed terminal and a fault positioning master station through an optical fiber or wireless mode and supports multi-node collaborative data processing and nanosecond time synchronization; the software and hardware platform module comprises a hardware module and a software function module and is used for acquiring and processing fault signals and outputting alarms; and the synchronous time service module is integrated with a GPS / BD dual-mode receiver and a crystal oscillator compensation algorithm, and outputs an IRIG-B code time synchronization signal. According to traveling wave propagation characteristics and wavelet transform characteristics, the system utilizes a novel double-end traveling wave fault positioning method in which traveling wave arrival time is determined by traveling wave feature point positions extracted by appropriate wavelet transform and traveling wave propagation speed is determined by structural parameters of a power transmission line and analysis scales of corresponding wavelet transform. And the requirement of accurate fault positioning of the power transmission line can be met.

This invention relates to a method for monitoring and evaluating the utilization rate of urban public service facilities, comprising: acquiring nighttime light satellite data, GIS data, POI data, and EVI data of the study area; extracting the spatial range of urban public service facilities based on the GIS data and POI data, and delineating an external buffer zone for the spatial range of urban public service facilities; calculating the time-series data of the difference in nighttime light intensity between the spatial range of urban public service facilities and the corresponding external buffer zone; analyzing the time-frequency characteristics of the difference time-series data using a three-level discrete wavelet transform based on the Haar function to obtain the residual sequence and high-frequency detail sequence of the nighttime light intensity difference time-series data; identifying outliers in the residual sequence and high-frequency detail sequence located outside a preset confidence interval, thereby achieving monitoring of abnormal use of urban public service facilities. This invention significantly reduces monitoring costs while improving monitoring accuracy and real-time performance, achieving efficient and automated monitoring of the utilization rate of urban public service facilities.

The invention discloses a wave velocity field inversion method for a deep mining environment, and the method comprises the steps: obtaining seismic data of a target region, and carrying out the preprocessing of the obtained seismic data; constructing an initial model; according to the observation data and the synthetic data, introducing cross wavelets to construct a joint objective function; solving the wave equation to obtain synthetic data; performing wavelet transform analysis on the observation data and the forward modeling data, and calculating a coherent difference between the observation data and the forward modeling data in a time-frequency domain; using an adjoint state method to jointly solve the traditional waveform and the wavelet error gradient and update model parameters; and carrying out visual display on the obtained velocity model, and completing wave velocity field inversion of the deep mining environment of the target area. The invention also discloses a system for realizing the wave velocity field inversion method for the deep mining environment. According to the method, the wave velocity field inversion for the deep mining environment is realized, the reliability is higher, and the accuracy is better.

The invention relates to the technical field of smart power grids, and particularly discloses a power grid marketing side system platform data processing and analyzing method based on edge computing, which collects power consumption and power factor data of a user in real time through a smart electric meter, and performs efficient processing by using an edge computing node close to a data source. Analyzing the power consumption change rate by adopting Haar wavelet transform, analyzing the power factor fluctuation degree by adopting fast Fourier transform, constructing a power consumption change rate characteristic value and a power factor characteristic value so as to evaluate the power utilization stability of a power grid and the stability of a power system, and integrating the characteristic values into a comprehensive characteristic vector so as to evaluate the power utilization stability of the power grid and the stability of the power system. And as the input of the support vector machine model, the power grid marketing response speed is evaluated, and a power grid operator can dynamically adjust a marketing strategy according to an evaluation result.

The invention discloses a method and system for rapidly calibrating performance parameters of a mobile phone mainboard, and relates to the technical field of electronic manufacturing automation, and the method comprises the steps: carrying out the rapid feature scanning of a to-be-calibrated mainboard, and obtaining an initial feature vector; inputting the initial feature vector into a deep Bayesian network to obtain a prediction result set of multiple times of sampling, and performing uncertainty quantization processing and calibration processing to generate a prediction value of each parameter to be calibrated and a corresponding prediction uncertainty measure; based on the predicted value of each to-be-calibrated parameter and the corresponding prediction uncertainty measure, constructing multi-dimensional risk field distribution, and executing multi-scale wavelet transform analysis to generate a calibration quality risk comprehensive index; and obtaining production line state information in real time, fusing the production line state information with the calibration quality risk comprehensive index, and generating a calibration strategy threshold through a meta reinforcement learning decision maker. According to the method, the problems of excessive calibration and insufficient calibration are effectively avoided, so that resource use is optimized, and the consistency of product quality is ensured.

The application discloses a kind of direct current charging pile detection systems and methods applied to field, it is related to the online monitoring technical field of electric power equipment, including, three-dimensional data set is input Toeplitz cyclic measurement matrix and is compressed sampling, and high-resolution signal is reconstructed using improved orthogonal matching pursuit algorithm, local variation coefficient is analyzed and calculated by sliding window, and abnormal position is marked, and is integrated into abnormal position set;Abnormal position set is input pre-trained long short-term memory prediction model, assesses the remaining life of insulation and fault risk level, and real-time calculation direct current charging pile maximum allowable output current;Through memristor array dynamic adjustment detection load impedance, real-time acquisition direct current charging pile output current, and using synchronous compression wavelet transform analysis harmonic ridge line and extract harmonic component;Significantly improve the accuracy of fault early warning, and realize the rapid dynamic adjustment of output current, effectively solve the technical problem of fast charging pile multi-physical field coupling monitoring.

The invention discloses a life prediction method, device, equipment, medium and product of a rotating mechanical component, and relates to the technical field of state monitoring and fault prediction of mechanical equipment, and the method comprises the steps: obtaining an operation monitoring signal of a target rotating mechanical component in a current time period, and extracting a preset key feature; analyzing the characteristic stability through multi-scale wavelet transform to obtain a current steady-state index, and determining the health state of the rotary mechanical part according to the current steady-state index; and inputting the characteristics into the corresponding steady-state or non-steady-state working condition life prediction model according to the state, and outputting a residual life prediction value. According to the method, calculation redundancy is reduced by screening key features, working condition self-adaptive division is realized by using multi-scale wavelet transform, and differentiated prediction models are constructed for different health states, so that the precision and robustness of service life prediction of the rotary mechanical part are remarkably improved under complex working conditions, and the service life prediction efficiency of the rotary mechanical part is improved. And light-weight and high-reliability intelligent maintenance decision support is realized.

The present application belongs to the field of medical and health informatization technology, and discloses an infectious disease prevention and control big data AI resource scheduling system and method; by constructing a load temperature field model of a computing power node cluster, the node load intensity is mapped to a temperature distribution, and the temperature gradient between adjacent nodes is calculated to identify hot and cold node pairs. Based on the data dependency topology, the energy consumption cost and time delay penalty of task migration are analyzed, and a multi-path heat diffusion network is constructed combined with the temperature gradient to realize the optimized shunting of tasks from hot spots to multiple cold nodes. The system analyzes the periodic fluctuations and sudden peak characteristics of resource occupation through wavelet transform, dynamically adjusts the task decomposition granularity and load distribution weight, and adapts to changes in resource usage patterns. The present application realizes global heat balance and efficient utilization of computing power resources in epidemic analysis, improves computing efficiency, and enhances the system's ability to respond to sudden computing demands.