94 results about "Wave packet" patented technology

The invention relates to the technical field of data processing and analysis, and discloses a parallel sensor data analysis method and system for cable fault detection, and the method comprises the steps: synchronously collecting cable fault transient traveling wave signals through distributed monitoring terminals, and forming a multi-channel signal data set; and carrying out wavelet packet decomposition and reconstruction, and denoising to obtain a denoised data set. The method comprises the following steps: firstly, generating node time sequence data with a timestamp, constructing an undirected weighted graph model, calculating a plurality of candidate fault positions through a double-end positioning formula on the basis of corrected time data, and finally, allocating dynamic weights to effective candidate positions according to signal quality and confidence, and generating a fusion positioning result by adopting a weighted fusion algorithm. And physical correction is carried out based on cable laying constraints, and accurate fault position coordinates are output. According to the method, through multi-level data processing and fusion, the fault positioning precision and the system robustness under complex working conditions are improved.

The invention relates to the technical field of intelligent detection, and discloses a building quality evaluation method and system based on concrete nondestructive testing and a storage medium. The method comprises the steps that a piezoelectric ceramic sensor array is arranged to collect micro-vibration response signals, and an original vibration data set is obtained; extracting an energy distribution coefficient of each frequency band by using a wavelet packet decomposition algorithm, and constructing a damage feature vector matrix; establishing a physical constraint neural network model, and outputting a damage variable time sequence; fusing the damage variable with ultrasonic and rebound data, and calculating comprehensive strength and damage degree indexes; and calculating the remaining service life by using a time sequence prediction algorithm, and generating an evaluation report. According to the method, the technical problem that the existing concrete nondestructive testing technology cannot realize microstructure damage evolution dynamic monitoring and residual life prediction is solved, and the accuracy of building quality evaluation and the scientificity of predictive maintenance decision are improved.

The invention relates to the technical field of power quality monitoring, in particular to a method and system for constructing a power quality disturbance recognition model based on multidimensional data, and the method comprises the steps: obtaining voltage and current waveform data and environmental parameter data of a key node of a power transmission line, carrying out the hardware defect self-inspection and phase compensation of the voltage and current waveform data, and obtaining a power quality disturbance recognition model; clean transmission electric energy data is obtained; and performing multi-scale noise suppression and time sequence correlation analysis on the clean transmission electric energy data, and constructing a high-fidelity disturbance sequence. According to the method, through the hardware defect self-inspection and phase compensation steps, denoising preprocessing is carried out by utilizing wavelet packet transformation, the frequency response deviation of equipment is identified through Fourier transformation, a frequency domain interpolation method is adopted to reconstruct a frequency-closed defect mark segment, the phase deviation error can be accurately compensated, and the detection accuracy is improved. And self-systematic errors of hardware are eliminated from a data acquisition source, high fidelity of clean transmission electric energy data used for subsequent analysis is ensured, and a foundation is laid for high-precision identification.

The invention provides a sensor fusion-based pig feed crushing particle size monitoring method, which comprises the following steps of: synchronously acquiring vibration, acoustic emission and optical signals, carrying out band-pass filtering, normalization, feature extraction and wavelet packet decomposition, and dynamically adjusting the confidence coefficient of each channel in combination with working condition perception and a sensor performance knowledge base so as to monitor the crushing particle size of the pig feed. Weighted fusion and conflict evidence modulation of multi-source signals are achieved, the improved Dempster-Shafer evidence theory is introduced to improve the abnormal granularity recognition accuracy, key parameters of crushing equipment are controlled in a linkage mode through a closed-loop feedback mechanism, high-robustness detection and self-adaptive adjustment of the granularity state are achieved, and the method has the advantages of being high in robustness and high in robustness. And the stability and the automation level of the feed crushing process are improved.

The invention relates to the technical field of fluid mechanics modeling and aerodynamic acoustic analysis, in particular to a spectral orthogonal decomposition-based separated flow wall surface pressure pulsation dominant structure modeling method, which comprises the following steps of: acquiring a whole flow field and / or wall surface unsteady pressure data of a separated flow under a preset working condition; performing spectral orthogonal decomposition on the unsteady pressure data, performing frequency domain decoupling on multi-scale features, and extracting feature values and spatial dominant feature modes under each feature frequency; constructing a complex wave packet physical parameterized model, fitting the spatial dominant feature modals, and compressing the spatial dominant feature modals into sparse physical parameter vectors after nonlinear regression solution; and reconstructing a wall surface pressure pulsation dominant component based on the sparse physical parameter vector and the characteristic value, and carrying out at least one of flow mechanism analysis and pneumatic order reduction modeling according to the dominant component. Therefore, the problems of low reconstruction precision and the like caused by the fact that asymmetric evolution and variable acceleration convection of a large-scale structure in separation flow wall surface pressure pulsation cannot be accurately represented in a frequency domain in related technologies are solved.

The invention discloses a system and method for generating, regulating and controlling a partially coherent space-time light field. The system comprises a pulse shaping assembly composed of a plurality of optical elements and an incident light source used for providing a completely coherent ultrafast pulse light field. An optical element in the pulse shaping assembly comprises a first blazed grating, a first cylindrical lens, a two-dimensional dynamic holographic modulator, a second cylindrical lens and a second blazed grating which are sequentially arranged in the light field propagation direction. The focal lengths of the first cylindrical lens and the second cylindrical lens are the same, and the distance between the adjacent optical elements is equal to the focal lengths of the first cylindrical lens and the second cylindrical lens; the two-dimensional dynamic holographic modulator is used for loading a specific complex amplitude modulation function to reconstruct the spatial-spectral characteristics of the light field. According to the invention, the two-dimensional space-spectrum function of the pulse is dynamically modulated, the generation of the partially coherent space-time wave packet is realized, and the amplitude and structure distribution of the space-time coherence can be designed and regulated as required.

The invention discloses a phase modifier bearing fault diagnosis method, which belongs to the technical field of power equipment fault diagnosis, and comprises the following steps of: analyzing independent components, performing blind source separation on a received phase modifier bearing vibration signal, and extracting three types of independent source signals of impact, abrasion and noise; the gradient driving window length is self-adaptive, and the window length of short-time Fourier transform is dynamically adjusted based on the instantaneous frequency gradient of the independent source signal; wavelet packet frequency band energy screening: performing wavelet packet decomposition on the signal after window length adaptive processing, screening a fault characteristic frequency band based on an energy contribution rate, and reconstructing the signal; and enhancing stochastic resonance, and inputting the reconstructed signal into a stochastic resonance system. According to the method, independent component analysis, gradient driving window length self-adaption, wavelet packet frequency band energy screening and stochastic resonance enhanced fourth-order diagnosis chain are constructed, so that multi-stage cooperative processing of phase modifier bearing faults is realized, and the technical problems of time-frequency resolution contradiction, insufficient feature decoupling and weak generalization ability are effectively solved.

The invention discloses an ultrasonic guided wave damage ellipse imaging positioning method and equipment for a layer decreasing composite material laminated plate. The method comprises the following steps: S1, acquiring thickness change direction information of the laminated plate and position coordinates of an excitation transducer and a receiving transducer; s2, according to the material characteristic parameters, simulating and calculating the propagation time of ultrasonic guided waves transmitted from an excitation transducer to any point to generate a first theoretical propagation time map, and simulating and calculating the propagation time of the ultrasonic guided waves transmitted from any point to a receiving transducer to generate a second theoretical propagation time map to generate a second theoretical propagation time map; coupling influence of thickness change and material anisotropy on guided wave propagation is considered in the simulation process; s3, transmitting an ultrasonic guided wave signal, and extracting the actual flight time of a damaged wave packet; s4, calculating theoretical total propagation time, and determining a candidate area where damage exists; and S5, on the basis of repeated operation of the multiple groups of transducer pairs, taking a plurality of candidate region intersections to determine a final damage position. The objective of the invention is to solve the positioning error problem caused by the structural characteristics of a laminated plate in a traditional algorithm, and to realize more accurate damage detection and positioning.

The invention provides a radar wave intensity measurement method and system based on multistage filtering amplification, and the method comprises the steps: carrying out the preprocessing of a radar echo signal, and obtaining a preprocessing signal through time domain wavelet packet decomposition and spatial domain wave beam forming collaborative optimization; performing intelligent dynamic gain reconstruction on the preprocessed signal to obtain a gain adjustment signal; performing nonlinear harmonic tracking compensation on the gain adjustment signal to obtain a compensation calibration signal; and performing dynamic threshold intensity extraction on the compensation calibration signal to obtain a final measurement result. According to the method, an environment factor-attenuation compensation mapping relation is established through a support vector regression model, and a dynamic environment compensation coefficient is generated; in combination with wavelet packet multi-scale energy integration and signal-to-noise ratio dynamic weighting, space attenuation is corrected in nonlinear harmonic tracking compensation, and an adaptive threshold and energy estimation are optimized in dynamic threshold intensity extraction; and finally, through Kalman filtering post-processing and spectrum purity monitoring closed-loop verification, the system can perform high-precision measurement in a dynamic environment.

The application relates to the field of alloy defect detection, and specifically discloses a praseodymium-neodymium alloy nondestructive detection method and system based on acoustic feature analysis, which comprehensively captures defect information contained in an original probe signal from two complementary physical perspectives of instantaneous dynamic characteristics and frequency band energy distribution by simultaneously adopting Hilbert-Huang transform and wavelet packet transform. Further, the scheme discards simple feature splicing, and instead utilizes canonical correlation analysis as an information decoupling tool to online decompose two groups of original feature vectors into a shared part describing defect commonality and unique information parts respectively representing the unique resolution capabilities of HHT and wavelet packet. Finally, the three decoupled components are structurally recombined to form a fusion feature vector which can effectively eliminate redundancy, amplify differences and has higher information density, thereby providing a clear structure and highly refined input for a subsequent classification model.

The application relates to the technical field of power system monitoring and fault diagnosis, and discloses a corrosion degree detection method based on a mine double-loop power supply line. The method comprises the following steps: injecting a dynamic frequency modulated micro-amplitude excitation signal into the double loop; acquiring an interference characteristic spectrum and generating an anti-aliasing filter parameter through an environmental noise sensing module; separating high-frequency transient and low-frequency steady-state components by using wavelet packet decomposition and empirical mode decomposition dual-channel preprocessing; combining a deep time sequence encoder embedded with an electrochemical model prior to extract corrosion features, and reconstructing a pure corrosion signal through an interference suppression decoder driven by an attention mechanism; and finally outputting a quantitative index by a corrosion rate inversion model and triggering a graded early warning. The system comprises a signal injection unit, a noise sensing module, a dual-channel preprocessing unit, a deep encoding-decoding network and an early warning module. The application significantly improves the accuracy and robustness of corrosion detection through multi-scale feature decoupling, physical constraint modeling and noise adaptive suppression.

The present application relates to the technical field of composite material structure monitoring, in particular to a composite material structure damage identification method based on acoustic emission wavelet packet energy characteristics, which comprises: S1, obtaining acoustic emission signal data of the composite material and performing pretreatment; S2, using a wavelet base function and a discrete wavelet inverse transform to perform wavelet packet decomposition on the denoised acoustic emission signal data of the composite material; S3, obtaining the processed acoustic emission signal data of the composite material, and extracting the characteristic energy of each acoustic emission signal data; S4, using a clustering method to perform clustering analysis and completing the composite material structure damage identification. The present application uses acoustic emission signals as the basis for analysis, uses wavelet transform and k-means clustering to accurately realize the structure damage identification and judgment of the composite material, can realize dynamic and continuous monitoring, is not affected by the complexity of the structure, has wide applicability, and is suitable for various composite materials.

The invention discloses a process root cause positioning method and system based on causal resonance attenuation, and the method comprises the steps: firstly collecting key node data of a process event chain, and constructing a state vector of the process event chain; then based on the state vector of the process event chain, constructing an event transfer operator; based on an event transfer operator, carrying out chain type evolution on a causal wave packet in a whole process event chain, and calculating causal wave packet energy; and finally, based on causal wave packet energy, calculating an energy attenuation rate, and positioning an energy loss root cause event in the process event chain. According to the method, a modeling mechanism of'causal wave packet 'and'event transfer operator' is embedded in a process event chain, and from the angle of causal energy transfer, the attenuation degree of each event on the overall chain causal effect is quantified in a complex business process, so that accurate positioning of a real disease cause node in the complex process is realized, and the accuracy of the process flow is improved. And an explainable and verifiable evidence chain is provided for project management and cross-department collaboration.

The application discloses a kind of multi-variable DC fault arc adaptive detection method based on similarity measure and transfer learning, to the system output current of different time period applies wavelet packet decomposition and coefficient reconstruction processing after extraction feature, and input to state identification model to carry out multi-period fault arc judgment, to current working condition through machine learning evaluation feature similarity measure value D and model output probability distribution P joint distribution mode, according to this, the extracted feature is distinguished into unidentifiable working condition type data and identifiable working condition type data, using field adaptive strategy to the simplest feature group of unidentifiable working condition type data carries out transfer learning, to update state identification model, to realize real-time accurate detection of DC fault arc under complex and variable working condition in this way.

The invention provides a Rayleigh wave adaptive filtering method and system for wind wave noise resistance, and the method comprises the steps: extracting the multi-dimensional features of wind wave noise through multi-resolution wavelet packet analysis and empirical mode decomposition, and constructing a feature library; establishing an active and passive signal spectrum correlation model, and quantifying the spectrum overlap ratio; a dual-channel attention filtering network is utilized, dual-channel parallel processing of noise identification and signal reconstruction is carried out, and improved adversarial training is combined, so that accurate separation of signals and noise and effective reservation of overlapped spectrum regions are realized. The system comprises a corresponding multi-channel signal acquisition module, a noise feature extraction module, a deep learning filtering module and a quality evaluation module. According to the method, the three-stage processing flow of feature extraction, correlation modeling and deep learning filtering is adopted, and the data quality and reliability of underwater Rayleigh wave exploration are remarkably improved.

This application relates to the field of centrifugal pump fault diagnosis technology, specifically to a method and apparatus for centrifugal pump fault diagnosis based on acoustic radiation signal processing. The method involves acquiring the acoustic radiation signal of a centrifugal pump to obtain a test acoustic radiation signal; performing wavelet packet decomposition on the test acoustic radiation signal; processing the sub-frequency band information in different ways to obtain a reconstructed signal; extracting various features from the reconstructed signal; combining the features to obtain a dataset; inputting the dataset into a pre-trained centrifugal pump fault diagnosis model; and outputting the centrifugal pump acoustic radiation signal fault diagnosis result. This application utilizes a combination of wavelet packet decomposition and singular value decomposition for acoustic radiation signal feature extraction, effectively improving the accuracy and efficiency of centrifugal pump acoustic radiation signal fault identification. It can accurately identify centrifugal pump fault types even under strong background noise interference, which is significant for risk warning of centrifugal pump faults.

The present application belongs to the technical field of compressor abnormal sound detection, and particularly relates to a compressor abnormal sound detection method and system based on acoustic spectrum pattern recognition, which comprises the following steps: extracting a characteristic signal from a sound signal through wavelet packet transform and kurtosis criterion, locating the occurrence time of an impact event contained in the characteristic signal and calculating the time interval of adjacent impact events, calculating a rhythm stability index based on all time intervals in an analysis time window, intercepting a time domain signal window containing each impact event and calculating its energy envelope, calculating the intrinsic damping factor of each energy envelope according to the logarithmic difference between the energy envelope peak value and the energy value after a set time length, calculating the fault authenticity score of each impact event according to the energy value of the impact event in the characteristic signal, the rhythm stability index corresponding to the analysis time window and the intrinsic damping factor, and performing early warning determination, so as to realize high-reliability and accurate early warning of compressor early faults.

The invention belongs to the technical field of air compressor efficiency evaluation, and provides an air compressor efficiency evaluation method and system based on decline trend prejudgment, and the method comprises the steps: comprehensively judging whether the high-frequency oscillation of an efficiency signal is caused by a rotating stall cluster or not through the time domain analysis and judgment of high-frequency periodic oscillation and the frequency domain judgment of a characteristic peak; if yes, performing dual-band division of a stall mass oscillation characteristic band and an efficiency decline low-frequency characteristic band; the method comprises the following steps: acquiring a compressor efficiency oscillation and decline composite signal, performing wavelet packet decomposition according to divided dual bands, removing decomposed oscillation characteristic frequency band signals, only retaining efficiency decline characteristic frequency band signals, and reconstructing an oscillation-interference-free pure efficiency decline trend curve through spline interpolation and moving average; and on the basis of the pure efficiency decline trend curve, fitting calculation analysis of the linear / nonlinear decline rate is conducted, and judgment of the efficiency decline trend of the gas compressor is achieved in combination with pure efficiency decline trend curve morphological analysis.

The invention provides a top tension riser damage identification and positioning method based on wavelet packet transformation, and belongs to the technical field of ocean structure intelligent monitoring. Firstly, a vibration acceleration response signal of a preset node of the riser is collected and preprocessed; using a 6-order multi-Bezi wavelet as a mother wavelet, setting a four-layer wavelet packet decomposition layer number, and iteratively decomposing the preprocessed signal through a Mallat algorithm to obtain 16 frequency band component signals; secondly, calculating wavelet packet component energy of each node under 16 frequency bands by utilizing the sensitivity of the method to structural rigidity damage based on an arch bridge damage identification method based on the energy of the Singxiang wavelet packet; solving the wavelet packet energy curvature of each node through a variable quadratic difference value; and finally, the difference between the curvature and the curvature of the same node of the healthy riser is obtained to obtain an IWPECD index, the distribution rule of the index along the length of the riser is analyzed through five-point moving average smoothing, the node corresponding to the peak value is the damage position of the riser, and effective damage positioning of the jacking tension riser is achieved.

The invention provides an epilepsy spine wave automatic identification method and system based on wavelet packet transformation, and relates to the technical field of biomedicines.The method comprises the steps that collected original electroencephalogram signals are preprocessed, and clean electroencephalogram signals are obtained; constructing a candidate set comprising a plurality of wavelet packet primary functions according to the clean electroencephalogram signal, and calculating an energy concentration index corresponding to each primary function in the candidate set; selecting a final wavelet packet basis function from the candidate basis function set according to an energy concentration ratio quantitative index; and performing wavelet packet multi-scale decomposition on the clean electroencephalogram signal through the final wavelet packet basis function to obtain a group of sub-signals with different frequency bands. According to the method, efficient, accurate and automatic recognition of epilepsy spine waves in long-time-interval electroencephalogram signals is achieved.

The invention relates to the technical field of electric power detection, in particular to an electricity consumption abnormity real-time detection method and system based on an intelligent electric energy meter, and the method comprises the following steps: S1, collecting the electricity consumption parameter data of the intelligent electric energy meter in real time; s2, calculating a benchmark reference value of load fluctuation based on the current data in the preset statistical period; and S3, carrying out time-frequency analysis on the current data, dynamically reconstructing frequency band energy distribution of the current data, and generating a spectral entropy feature representing the chaos degree of the energy distribution. The method comprises the following steps: dynamically adjusting the number of frequency bands (compressing high-frequency noise when a load is stable and expanding low-frequency resolution when the load is abnormal) of wavelet packet decomposition according to a fundamental wave energy ratio, then introducing a cross-frequency-band energy transfer matrix to quantify an energy coupling relation between the frequency bands, and correcting the energy weight of each frequency band according to the energy coupling relation; and finally, calculating a self-adaptive and anti-aliasing reconstructed spectrum entropy. The problem of characteristic fuzziness caused by characteristic similarity (frequency band aliasing) of normal load fluctuation and electricity larceny behaviors in frequency spectrums is effectively solved.

This invention discloses an improved method for extracting gear fault diagnosis features by combining Complete Empirical Mode Decomposition (CEEMDAN) with wavelet packet segmentation threshold denoising and energy entropy. The reconstructed signal after CEEMDAN threshold denoising is re-CEEMDAN decomposed to obtain the energy values ​​of each modal component, which are then used as fault features of the gear. The method includes the following steps: 1) acquiring vibration signals in the X, Y, and Z mutually perpendicular directions at the gear of a rotating device using a vibration sensor; 2) performing CEEMDAN decomposition on the data to obtain multiple intrinsic modal components; 3) using the Pearson correlation coefficient method to divide the modal components into noise-dominant components and signal-dominant components; 4) applying different wavelet denoising methods to different dominant components; 5) reconstructing the signal and re-CEEMDAN decomposing it to extract the energy values ​​of each mode, which are then used as features under different fault states.

The application discloses a method and device for implementing a single quantum bit gate sequence. The method comprises the following steps: in response to a gate operation in the single quantum bit gate sequence, controlling the starting time and running time of the gate operation to satisfy the following conditions: when the gate operation is performed, t1 < t < t2, wherein t2-t1 is an integer; when the gate operation is performed, t3 < t < t4; using a time-dependent driving wave packet to apply a periodic driving to a quantum bit, the wave packet being non-zero within time t1-t2 and zero at other times; calibrating the wave packet so that the quantum bit is transformed from a state to a state after two gate operations are continuously performed; adjusting the integer to change the running time, determining different corresponding gate fidelities, and selecting the maximum value of the fidelity as the optimal running time. The application improves the fidelity of the single quantum bit gate.

This application relates to the field of DC transmission protection technology, and particularly to a fault identification method for hybrid three-terminal high-voltage DC transmission lines based on the DC boundary voltage energy ratio and its application. By analyzing the boundary frequency characteristics of the DC line, rectifier side, and inverter side of the system, frequency characteristic curves of each boundary are obtained. Based on the frequency response characteristics of the overall system boundary frequency characteristic curve, highly distinguishable characteristic frequency bands are determined. When a fault occurs in the system, DC voltage signals at both ends of the rectifier / inverter side are acquired, and wavelet packet decomposition is performed on the acquired signals to extract the energy within the characteristic frequency bands. Based on the energy and characteristics of the two characteristic frequency bands, identification criteria for fault location and fault polarity are constructed. This enables rapid fault identification of hybrid three-terminal DC systems without the need for communication, thereby improving the safety and reliability of hybrid three-terminal high-voltage DC transmission lines. The aim is to solve the problem of fault type identification in hybrid three-terminal DC systems.

This invention discloses a method, storage medium, and method for early warning of perimeter collapse of buried pipelines based on a water hammer wave feature parameter set. By analyzing the vibration characteristics of water hammer during valve closure, a large amount of data was collected. Sensitive features in the time and frequency domains of the vibration signal were extracted and analyzed. Various machine learning algorithms were used for preliminary training and testing. The radial basis function neural network machine learning method with the best recognition performance was selected, and the model parameters were optimized using an ant colony algorithm. Furthermore, wavelet packet technology was used to extract and reconstruct the original signal from the original dataset. The extracted sensitive frequency band feature information parameters were combined with the original parameter set, significantly improving the model accuracy to nearly 98%. This method system can not only be used for safety monitoring of pipeline systems but can also be integrated with climbing robots and other equipment to achieve real-time monitoring and maintenance of key equipment such as solenoid valves. Therefore, it provides effective protection for pipeline transportation safety.

The invention relates to the field of water environment monitoring and prediction, and discloses a river channel station total nitrogen concentration prediction method, system and device and a storage medium, and the method comprises the steps: carrying out the adaptive dynamic wavelet packet decomposition of a total nitrogen concentration time sequence of each river channel station, and obtaining the signal components of a plurality of frequency bands corresponding to each river channel station; constructing a weighted directed hydraulic time delay diagram; spatial convolution is carried out on the weighted directed hydraulic time delay graph by using a graph convolution network, and spatial enhancement features are extracted; a cross-band self-attention mechanism is used to output the fused spatial-temporal characteristics; and performing depth time sequence modeling on the fused spatial-temporal characteristics, and outputting a total nitrogen concentration predicted value in a future time period. The method has good generalization ability and application prospects, is suitable for multi-site and multi-scale total nitrogen concentration high-precision prediction, and particularly shows better prediction performance and stability than a traditional method under abnormal events such as rainstorm.

This invention discloses a training method for an equipment fault diagnosis model and a method for equipment fault diagnosis, relating to the fields of fault diagnosis and deep learning technologies. The method includes: acquiring at least one sound sample data and corresponding label data; processing the sound sample data through a background noise suppression channel and an impulse interference suppression channel to obtain a denoised sound sample vector; performing dual-tree complex wavelet packet decomposition on the denoised sound sample vector to obtain a sample time-frequency feature matrix; processing the denoised sound sample vector to obtain a sample operating condition auxiliary feature vector; the sample operating condition auxiliary feature vector is used to characterize the equipment operating condition state corresponding to the sound sample data; and training a deep learning model based on the sample time-frequency feature matrix, the sample operating condition auxiliary feature vector, and the label data to obtain an equipment fault diagnosis model. The above technical solution can improve the accuracy of equipment fault diagnosis.

This invention relates to the field of traffic safety technology, specifically to an efficient simulation method for quantum systems based on parallel order reduction. The method includes the following steps: receiving the physical parameters of the quantum system and simulation requirements, whereby the physical parameters include electron mass, potential well size, and initial wave packet parameters; and the simulation requirements include simulation physical time and accuracy requirements. Based on the physical parameters, a time-dependent Schrödinger equation describing the dynamic behavior of the quantum system is established, and the Schrödinger equation is rearranged into matrix form using the Kronecker product. This invention utilizes Arnoldi to reduce the order of the matrix-form Schrödinger equation. By reducing the order, the large matrix in the original space is projected into a relatively small subspace, improving the efficiency of the simulation solution. The reduced-order Schrödinger equation is solved using a time-parallel algorithm. This approach overcomes the time step limitation imposed by the CFL condition, ensuring that a stable solution can be obtained with fewer time steps.

The invention discloses an ammeter relay resonance fault detection method and system based on harmonic analysis, and relates to the field of electrical variable measurement, and the method comprises the steps: obtaining a current signal and a voltage signal, and carrying out the preprocessing, and obtaining a current analysis signal and a power grid harmonic spectrum of a current period; quantifying the resonance risk of the power grid based on the power grid harmonic spectrum to obtain a resonance sensitivity index of the power grid in the current period; presetting a candidate wavelet basis set, and obtaining a time-frequency focusing capability evaluation index and a task suitability index of each candidate wavelet basis in the set; based on the three indexes, constructing a dynamic comprehensive cost function and selecting an optimal wavelet basis; and performing wavelet packet decomposition on the current analysis signal based on the optimal wavelet basis, extracting fault features, and identifying and judging the current working state of the relay. According to the method, adaptive selection of the wavelet basis can be realized, and the problems of inaccurate fault feature extraction, high false alarm rate and unreasonable computing resource utilization of an existing detection method are solved.

This application relates to a method, apparatus, equipment, medium, and program product for assessing railway track bed compaction. The method includes: acquiring simulated ground-penetrating radar (GPR) data corresponding to multiple simulated track bed models with different compaction degrees; performing wavelet packet decomposition on the simulated GPR data to obtain wavelet packet decomposition results for each of the multiple simulated track bed models; wherein the wavelet packet decomposition results include multiple dimensions; these dimensions include at least two of the following: energy entropy dimension, subband energy kurtosis dimension, and low-to-high frequency energy ratio mean dimension; based on the compaction degree of each of the multiple simulated track bed models and their corresponding wavelet packet decomposition results, determining a target dimension sensitive to the compaction degree from the multiple dimensions; and determining the compaction assessment result of the track bed under test based on the wavelet packet decomposition data of the target GPR data in the target dimension. This method enables the quantitative assessment of railway track bed compaction.