112 results about "Fourier analysis" patented technology

Electroporation-based therapies (EBTs) employ high voltage pulsed electric fields (PEFs) to permeabilize tumor tissue, resulting in changes in passive electrical properties detectable using electrical impedance spectroscopy (EIS). Currently, commercial potentiostats for EIS are limited by impedance spectrum acquisition time (˜10 s); this timeframe is much larger than pulse periods used with EBTs (˜1 s). Fourier Analysis SpecTroscopy (FAST) is introduced as a methodology for monitoring tissue inter-burst impedance (diagnostic FAST) and intra-burst impedance (therapeutic FAST) during EBTs. FAST is a rapid-capture (<<1 s) technique which enables monitoring of inter-burst and intra-burst impedance during EBTs in real-time. FAST identified a frequency which delineates thermal effects from electroporation effects in measured impedance. Significance: FAST demonstrates the potential to perform EIS, in addition to intra-burst impedance spectroscopy, using existing pulse generator topologies.

The invention relates to the technical field of ground resistance detection, in particular to a single-core cable ground resistance detection method capable of resisting strong power frequency interference. High-frequency signals are injected in a non-contact mode through a coupling type capacitor clamp, background voltage and current signals are collected firstly, and injection frequency far away from power frequency and harmonic waves is selected through Fourier analysis; cancelling background noise by adopting a normalized least mean square adaptive filter to generate an enhanced signal; the signal is down-converted to a base band through digital quadrature demodulation, a Kalman filter estimates the amplitude and the phase in real time, and the state and noise parameters are periodically calibrated through a compressed sensing algorithm; and finally, loop impedance is calculated according to the voltage and current amplitudes and the phase difference to obtain grounding resistance. The method has the advantages of being non-contact, high in safety, strong in anti-interference capability, high in measurement precision and the like, and is suitable for monitoring the grounding state of the cable in a complex electromagnetic environment.

The invention discloses a six-winding high-voltage variable-frequency speed regulation control method and system, and belongs to the technical field of high-voltage variable-frequency speed regulation, and the method comprises the steps: building an electric-thermal-magnetic coupled six-dimensional nonlinear system model through collecting the structural parameters, thermosensitive data and an initial state of a motor; establishing a frequency domain regulation and control matrix among the six windings in combination with the operation condition and the load type; predicting a future disturbance power trend by using a weighted short-time Fourier analysis method, dynamically correcting the reference current of each winding, and generating six groups of PWM control signals; when the performance of the winding is degraded, fault-tolerant topology reconstruction and driving task redistribution are executed; periodically reconstructing a control matrix through a thermoelectric feedback mechanism, and executing parameter adaptive optimization according to a current balance coefficient and hot redundancy distribution; according to the method, high-precision current coordination control, frequency domain energy decoupling and winding thermal load balancing can be realized, and the speed regulation response speed, the stability and the fault-tolerant capability of the system are remarkably improved.

This application relates to the field of electrical technologies, and provides a full even harmonic digital demodulation method based on full-phase analysis for a magnetic modulator. According to the full even harmonic digital demodulation method based on full-phase analysis for a magnetic modulator, standard voltage timing data output by the magnetic modulator is obtained when a DC calibration current with a set amplitude is fed into the magnetic modulator during calibration of the magnetic modulator; an output voltage sequence from full-phase Fourier analysis is established based on the standard voltage sequence data, and the output voltage sequence is preprocessed; comprehensive analysis is performed on the preprocessed output voltage sequence from full-phase Fourier analysis to obtain all standard even harmonic signals and current conversion coefficients.

The present invention discloses a gear meshing noise evaluation method based on a gear transmission error curve, specifically comprising the following steps: Step 1: Acquisition of tooth surface waviness based on the gear measurement center; Step 2: Synthesis of the gear full tooth surface waviness curve; Step 3: Calculation of gear transmission error based on the envelope; Step 4: Fourier analysis of the gear transmission error curve based on sine fitting; Step 5: Calculation of the order spectrum; Step 6: Analysis of abnormal orders. The gear transmission error curve can be decomposed into harmonic components of different frequencies through sine fitting Fourier analysis, with each harmonic corresponding to a specific order in the gear error. Abnormal orders can excite vibrations of specific frequencies. Identifying and eliminating abnormal orders is a key step in analyzing gear noise problems.

The invention relates to the technical field of power grid safety evaluation, in particular to a power system short-circuit parameter prediction method, system and device based on time-space relevance and a storage medium. A multi-dimensional state vector including a node voltage vector, a branch current vector, a short-circuit impedance matrix, spatial position information and a time feature vector is constructed, and a space-time correlation feature vector including a time-dimensional feature, a spatial-dimensional feature, a space-time coupling feature and a state feature is extracted. Wavelet transform, Fourier analysis and graph theory analysis are adopted to identify a parameter change rule and a propagation effect. A multi-level prediction model of a statistical prediction layer, a deep learning layer and a fusion layer is constructed, and comprehensive description of dynamic characteristics of short-circuit parameters is realized through spatio-temporal feature fusion. A staged training strategy is adopted, and comprises the steps of establishing a basic model through offline training, updating parameters in real time through online learning, and quantifying uncertainty through a probability prediction framework. And a prediction correction mechanism and a reliability evaluation system are established, and a complete prediction-evaluation-correction closed loop is formed.

The invention relates to the technical field of ship navigation behavior prediction, and discloses a ship navigation behavior prediction method and system based on interpretable artificial intelligence. Comprising the following steps: acquiring ship AIS historical data and historical marine environment data in a target sea area; based on a time scale and a space scale, fusing the ship AIS historical data and the historical marine environment data, and constructing a spatio-temporal data set; performing feature decoupling on a static variable and a time-varying variable of the spatio-temporal data set, and determining historical input data, known future input data and static input data; an interpretable space-time fusion neural network model is constructed based on a dynamic gating residual neural network, a space-time characteristic variable selection network, a Fourier analysis gating neural unit and an interpretable multi-head attention mechanism so as to accurately predict ship navigation behaviors such as a ship trajectory, an over-the-ground navigational speed and an over-the-ground course. And a navigation behavior influence factor weight visualization result is output, and the method has interpretability.

The invention provides a data prediction method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining to-be-predicted data, and determining a first projection result based on the to-be-predicted data; performing Fourier feature extraction on the first projection result to obtain a first Fourier feature, and performing nonlinear activation feature calculation on the first projection result to obtain a first nonlinear activation feature; based on the first Fourier feature and the first nonlinear activation feature, determining a first Fourier analysis network output feature; and based on the first Fourier analysis network output feature, determining a target Fourier analysis network output feature of the to-be-predicted data, and calculating a target predicted value of the to-be-predicted data by using the target Fourier analysis network output feature. According to the embodiment of the invention, the accuracy of data prediction can be effectively improved.

The invention discloses a colon adenoma identification method and device based on Mueller matrix polarization measurement and regularization, a storage medium and electronic equipment. The method comprises the following steps: acquiring a polarization image corresponding to a fresh medical sample containing colon adenoma and normal tissue through a light polarization system, and preprocessing the polarization image; fourier analysis is carried out on a detection signal in the light polarization system to obtain a Mueller matrix, and a Lu-Chipman polarization decomposition algorithm is used for carrying out polarization decomposition on the Mueller matrix to obtain three polarization decomposition parameters; obtaining polarization data of a plurality of channels based on the Mueller matrix, and constructing a sample set based on the polarization data; constructing a target function based on the projection function and the sample set, solving the target function to obtain an analytic solution of the optimal projection direction, and calculating a characteristic value based on the analytic solution of the optimal projection direction; and classifying the polarization images corresponding to the fresh medical samples of the colon adenoma and the normal tissue based on the feature values to obtain a classification result. The lesion recognition rate can be improved.

The invention relates to the technical field of geological disaster assessment, in particular to a seafloor landslide mass stability assessment method based on multi-source geophysical data, which comprises the following steps: S1, acquiring multi-beam sounding, side-scan sonar, seafloor seismograph and sediment columnar sample data, completing space-time registration, and performing equivalent density conversion on acoustic wave impedance and shear wave velocity to obtain an acoustic wave impedance and shear wave velocity; constructing a three-dimensional grid model; s2, based on the three-dimensional grid model, calculating a curvature mutation index by adopting sliding window Fourier analysis, calculating a pressure gradient in combination with pore water pressure data, and generating a characteristic parameter matrix composed of density, curvature and pressure gradient; and S3, based on the characteristic parameter matrix, calculating shear strain energy and gravitational potential energy superposition field intensity, and generating a landslide mass stability map taking the instability probability as an index. According to the invention, a visual three-dimensional landslide mass stability map is constructed, and high-precision identification and three-dimensional early warning of submarine landslide risks are realized.

The application provides a method and device for graph data processing, and relates to the technical field of artificial intelligence, and the method comprises: a method for graph data processing, which comprises the following steps: acquiring graph data, extracting the basic structure features of the graph data through a static topology path, adaptively screening the key neighbor features in the graph data based on an attention mechanism and in combination with the basic structure features through a dynamic path, establishing long-distance semantic association between nodes through topology-aware coding based on the basic structure features and the key neighbor features through a linear graph transformer with O ( n ) of linear complexity, and establishing long-distance semantic association between nodes through topology-aware coding; performing frequency domain projection on the node features to generate frequency domain features, explicitly extracting and enhancing the periodic discriminant components in the frequency domain features through a Fourier analysis network to generate graph neural network data. Thus, the graph representation learning capability of the graph neural network is effectively improved.

The invention discloses an LC resonant converter control method, which relates to the technical field of LC resonant converters, and comprises the following steps: obtaining basic parameters of each part of the LC resonant converter, obtaining a state equation of each part according to the obtained basic parameters, and when the LC resonant converter is disturbed, determining the state equation of each part according to a disturbance signal received by the LC resonant converter. Obtaining each state variable in the resonant network module; all the state variables are substituted into the state equations, and steady-state solutions corresponding to all the state equations of the LC resonant converter in the steady-state state are obtained; according to the method, time domain signals are analyzed by means of a Fourier analysis method, approximate processing is carried out on harmonic waves of all orders, the method has high accuracy, an equivalent circuit model of the converter in a steady state can be established, in addition, simulation analysis does not need to be carried out on a circuit, and only steady-state signals under the action of non-modulation signals need to be known.

The present application relates to the technical field of image processing, and more particularly to a self-adaptive periodic background elimination method based on Fourier analysis, comprising the following steps: step one, image input; step two, horizontal and vertical frequency division; step three, respectively performing inverse color, enhancement and normalization preprocessing operations on the horizontal frequency graph and the vertical frequency graph; step four, performing Fourier transform on the image of step three, and performing Fourier transform on the horizontal frequency preprocessed graph and the vertical frequency preprocessed graph; step five, obtaining high-amplitude phase difference of the image of step four; step six, obtaining horizontal and vertical phases; step seven, high-response point elimination; step eight, inverse Fourier transform, performing inverse Fourier transform on the separated frequency response graph and phase response graph in step seven; and step nine, feature recognition; the algorithm of the present application can automatically identify the periodic grid background on the liquid crystal panel, automatically eliminate a number of frequency parts with the highest response in the horizontal and vertical directions, and the remaining non-periodic features after elimination are defects.

The present invention discloses a method and system for identifying a secondary silicon steel sheet dry-type transformer. The method comprises the following steps: performing no-load tests on a dry-type transformer to be identified and a brand-new dry-type transformer of the same model, and collecting two noise audio segments emitted by the two dry-type transformers at this time; performing short-time Fourier analysis on the noise audio to obtain the sound wave amplitude corresponding to each frequency, taking the 50 Hz frequency-multiplied sound wave amplitude therein to calculate the fundamental frequency energy proportion, spectrum complexity, and high-to-low frequency energy ratio of the dry-type transformer; calculating the deviation rate of the fundamental frequency energy proportion, spectrum complexity, and high-to-low frequency energy ratio between the brand-new dry-type transformer and the transformer to be identified, and judging whether the transformer to be identified is a secondary sheet transformer by comparing the deviation rate with a set threshold. The system and the method are based on the same concept. The present invention efficiently and accurately judges whether a transformer is a secondary silicon steel sheet transformer without disassembling the transformer, thereby improving the inspection efficiency of the transformer and reducing the labor and material costs required for random inspections.

The present invention relates to a method and system for extracting radar target motion characteristics. The method includes preprocessing radar one-dimensional range profile data to obtain the phase signal of the target; using empirical mode decomposition to separate the signal of the phase signal to obtain a plurality of intrinsic mode functions; and performing motion parameter estimation on the intrinsic mode functions respectively based on the spectrum analysis method of Fourier analysis to obtain the radar target motion characteristics. The present invention can adapt to the actual situations of more diverse, high-speed motion, and motion coupling of radar targets, and the extracted target motion characteristics have higher accuracy.

This invention discloses a specific harmonic cancellation pulse width modulation method suitable for digital power amplifiers. It involves fitting a periodic signal f(t) to obtain an approximate function g(t) and performing Fourier analysis to derive the Fourier coefficients b of the current. n Wherein, the Fourier coefficient b n With the switching angle sequence α q 'As a parameter; for the switching angle sequence α q 'Optimization is performed to obtain the optimized switching angle sequence α' q According to α q The corresponding circuit is controlled by pulse width modulation to output a voltage waveform with specific harmonics filtered out. This invention obtains a specific switching angle sequence by establishing and solving a system of nonlinear equations. The output voltage waveform with specific harmonics filtered out is then modulated based on this switching angle sequence. This method can generate the desired frequency domain component and eliminate unwanted harmonics. It is also applicable to the design of multiple H-bridge units. At the same switching frequency, this method can output higher-order harmonics than other modulation methods, thus reducing switching losses. Furthermore, its excellent harmonic suppression performance saves hardware costs.

[Problem] To provide a polishing end point detection device and method, and a CMP device capable of accurately detecting a polishing end point when polishing a workpiece having a thin film thickness. [Solution] A polishing end point detection device (70) is provided with: a measurement unit (71) that, during polishing of a workpiece (W), radiates measurement light onto the workpiece (W), disperses reflected light from the workpiece (W), and acquires a spectral waveform indicating the relationship between the wavelength and reflectance of the reflected light; the reflectivity is the ratio of the intensity of the reflected light to the reference intensity of each wavelength component obtained in advance; and a detection unit (72) that calculates the film thickness of the thermal oxide film by applying Fourier analysis to the spectral waveform. The reference intensity is calculated by bringing a sample workpiece having substantially the same reflectance characteristic as a substrate of a workpiece (W) into contact with a polishing pad (5) in a state where a CMP slurry is interposed between the polishing pad (5) and the sample workpiece, and irradiating the sample workpiece with measurement light through an observation window (80) of the polishing pad (5) by a measurement unit (71). And the reflected light from the sample workpiece is split.

The invention provides a chronic lumbago surface electromyogram discrimination method and system based on a Fourier analysis neural network and a storage medium, and the method comprises the steps: firstly obtaining a data set which comprises a plurality of surface electromyogram signals and corresponding real labels; preprocessing the surface electromyogram signal to obtain a preprocessed surface electromyogram signal; inputting the preprocessed surface electromyogram signals into a constructed neural network based on Fourier analysis to obtain a prediction label of each surface electromyogram signal; constructing a loss function according to the real label and the predicted label, and training the neural network based on Fourier analysis to obtain a trained neural network based on Fourier analysis; and inputting a surface electromyogram signal to be detected into the trained neural network based on Fourier analysis to obtain a corresponding prediction label. According to the method, the neural network based on Fourier analysis is constructed and trained, the potential periodic features with discrimination are extracted, automatic discrimination of the abnormal modes in the chronic lumbago surface electromyogram signals is achieved, and therefore discrimination with higher robustness, higher generalization and higher efficiency is achieved.

The invention relates to the technical field of control panel signal processing, and provides a method for improving the anti-interference capability of a control panel, and the method comprises the steps: collecting the frequency characteristics of each motor control signal in the control panel, carrying out the Fourier analysis of the frequency characteristics to obtain a spectrogram, and dividing the control signals into a high-frequency signal group and a low-frequency signal group according to the spectrogram; time slot distribution is carried out on the control signals in the high-frequency signal group, the transmission period of the high-frequency signal group is divided into N time slots, and each control signal exclusively occupies one time slot; based on the frequency characteristics of the low-frequency signal group, constructing an orthogonal modulation basis vector, and mapping the control signal of the low-frequency signal group to the orthogonal modulation basis vector; and according to the time slot distribution scheme and the quadrature modulation basis vector, reconstructing the signal transmission time sequence of the control panel, so that the control signals of the high-frequency signal group and the low-frequency signal group do not interfere with each other. The technical problem that when multiple motors operate at the same time, control precision of a control panel is reduced due to mutual interference of control signals is solved.

A method for monitoring the quality of an ultrasonic weld, wherein the vibration behavior during the joining process with respect to an actual value of the vibration frequency and / or the vibration amplitude of at least one joining partner (10, 20) involved in the joining process and / or of a tool of an ultrasonic welding device (30) used in the joining process is detected by means of measurement with at least one measuring device (40) designed to quantify mechanical vibrations, the detected vibration behavior is analyzed by means of a Fourier analysis and compared with a predetermined target value as a reference value, wherein a frequency shift between an excitation frequency of the ultrasonic welding device (30) and the frequency measured at a measuring point during the joining process is detected and this is compared with a stored target frequency shift,to determine the degree of purity of the joining partners (10, 20) by means of the degree of frequency shift, wherein an eddy current sensor is used to quantify the mechanical vibrations during the joining process.

The invention discloses a self-calibration method for an angle encoder, and belongs to the technical field of precision measurement. The method mainly comprises the following steps: optimizing the arrangement positions of a plurality of reading heads, and constructing three groups of reading head combinations; rotating an angle encoder and acquiring angle data of the three groups of reading heads to obtain three groups of measurement data; establishing error compensation functions corresponding to the three groups of reading heads through the three groups of measurement data; and finally, performing Fourier analysis on the three groups of error compensation functions to complete cross compensation of error harmonic components, and realizing self-calibration of the angle encoder. While fewer reading heads are used, the calibration order of the harmonic error can be effectively improved, and high-precision in-situ calibration of the angle encoder is realized with lower cost.

The invention provides a rolling bearing fault diagnosis method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining the real-time vibration signal data of a rolling bearing; inputting the real-time vibration signal data into a fault diagnosis network model to obtain a fault diagnosis result output by the fault diagnosis network model; wherein the fault diagnosis result is obtained by training according to historical vibration signal data of the rolling bearing and a corresponding fault type; the fault diagnosis network model comprises an auto-encoder and a long short-term memory network; the auto-encoder consists of a Fourier analysis network and is used for carrying out periodic feature extraction on the real-time vibration signal data to obtain periodic features; and the long short-term memory network is used for carrying out sequence modeling on the periodic characteristics to obtain a fault diagnosis result. According to the method, the self-encoder reconstruction loss and the cross entropy loss are combined, so that high-precision diagnosis of the rolling bearing fault is realized.

The present invention discloses a method for detecting phase loss in a variable frequency compressor. The method comprises the following steps: starting detection, initializing data, and setting the command electrical frequency to fref; sampling the three-phase current; performing principal component analysis on the three-phase current to determine if a phase loss has occurred; performing cross-analysis; and concluding whether a phase loss has occurred based on the cross-analysis results; and completing the detection. The method performs component analysis on the three-phase current using a Fourier analysis scheme. The analysis results are first subjected to principal component comparison analysis, then to pairwise cross-comparison analysis, and finally to DC quantity analysis, ultimately concluding whether a phase loss has occurred. The results are stable and applicable to both high and low current modes, eliminating the need for repeated threshold calibration, or even the need for threshold calibration at all.

The present invention discloses a multi-elastic modulus measurement system based on self-mixing interference. The optical unit includes two optical groups, and each optical group includes a semiconductor laser, a collimating lens, and a photoelectric receiver. The semiconductor laser is used to emit an initial optical signal to a sample, so that the initial optical signal is reflected or scattered by the sample excited by an external excitation signal to form a feedback optical signal. The feedback optical signal returns to the laser resonator of the photoelectric receiver and undergoes a self-mixing interference effect with the initial optical signal. The excitation unit is used to excite the sample with an external excitation signal to cause the sample to generate bending vibration and shear vibration. The calculation module is used to perform a fast Fourier analysis on the electrical signal to obtain the bending resonance frequency and shear resonance frequency of the sample, so as to calculate the Poisson's ratio and bulk modulus of the sample. The present invention realizes the simultaneous and high-precision measurement of the bending resonance frequency and shear resonance frequency of the specimen in one measurement, and further achieves the goal of obtaining multiple elastic moduli.

The invention discloses a method and a system for online monitoring harmonic signal abnormity of a high-voltage cable. The method comprises the following steps: respectively arranging harmonic sensors in grounding wire preset areas at two ends of a tested high-voltage cable; a harmonic signal output by the harmonic sensor is collected through a signal collection module based on a sampling interval, and the harmonic signal is transmitted to a data analysis processing module through a data transmission module; performing windowed Fourier analysis on the harmonic signal through the data analysis processing module to obtain harmonic current data in the harmonic signal; calculating a key quantitative index of the harmonic signal based on the harmonic current data; and analyzing the harmonic abnormal degree of the tested high-voltage cable based on the key quantitative index, and evaluating the deterioration degree of the tested high-voltage cable based on an analysis result. According to the invention, through on-line measurement, the harmonic signal detection result is analyzed in real time according to the on-site actual situation.

This invention discloses a system and method for psychotherapy that integrates virtual reality scenes, belonging to the interdisciplinary field of psychotherapy and virtual reality. It addresses the problems of fixed scenes, delayed adjustment, weak interactivity, and lack of cultural adaptability in traditional psychotherapy. The method includes using non-contact biosensors to capture facial blood oxygen fluctuations, combining this with a hemodynamic model to remove artifacts, and then quantifying these fluctuations into emotional energy values ​​based on an individual emotional sample set and an incremental learning algorithm to generate scene seeds containing parameters such as color temperature. A virtual physics engine is then driven to reconstruct elements such as light and shadow, and fluids, forming a physically-level dynamic scene. Inertial sensors capture user movements and convert them into virtual pressure gradients, constructing a coupled feedback loop between movement and scene. Psychological trends are predicted through hierarchical Fourier analysis of the seed sequence. This invention achieves real-time adaptation and proactive intervention between psychological states and virtual scenes, enhancing user immersion and cultural and emotional resonance, and improving the accuracy and effectiveness of psychotherapy.

The invention belongs to the technical field of electric quantity prediction, and provides a hierarchical decoupling-based modularized long-time sequence user electricity consumption prediction method, which comprises the following steps of S1, acquiring user electricity consumption data; s2, a modular time series prediction framework based on a'trend-local change-multi-scale period 'decoupling concept; s3, designing three efficient sub-modules; according to the invention, a modularized time series prediction framework of a'trend-local change-multi-scale period 'decoupling concept is constructed; designing a trend module fusing reversible normalization and a trend feature processor, a local change module introducing a local importance pooling mechanism, and a period module combining a Fourier analysis network and a frequency mapping network; the problems that a traditional time sequence prediction model is high in calculation complexity and insufficient in information extraction capacity in long sequence prediction are effectively solved, and in addition, due to introduction of the lightweight fusion network, the feature integration efficiency and the accuracy of a prediction result are further improved.

The invention discloses an MMC impedance prediction method and system of a flexible interconnection system, equipment, a medium and a product, and the method comprises the steps: injecting positive and negative sequence voltage disturbances with different voltage frequencies into a pre-constructed MMC simulation model of a network construction type flexible interconnection system under a plurality of different working condition variables; the method comprises the steps of obtaining output voltage and current time-domain signals of an MMC under disturbance of multiple different voltage frequencies, obtaining output voltage and current frequency-domain data through fast Fourier analysis, determining equivalent impedance of the MMC of the network-forming flexible interconnection system at each frequency according to the output voltage and current frequency-domain data, and determining the equivalent impedance of the MMC of the network-forming flexible interconnection system according to the equivalent impedance. And taking the equivalent impedance and the working condition variable corresponding to each frequency as mapping samples, constructing a training data set, and training the initial fuzzy neural network, thereby enabling the trained MMC equivalent impedance prediction model to output an equivalent impedance prediction value of the MMC at the current calculation moment. Therefore, the impedance characteristics of the network-forming type MMC can be accurately represented in a wide working range.

A quantum computing system includes a classical computer coupled in combination with a quantum computer, wherein the quantum computing system is configurable to execute program instructions to process input data to generate corresponding output data. The program instructions include one or more arithmetic functions to be executed using the quantum computer. The quantum computing system is configured to apply a transformation to transform the one or more arithmetic functions into a series of Fourier components that are executable using the quantum computer by using one or more quantum circuits utilizing rotation gates acting on qubits representing the Fourier components, and to process outputs from the one or more quantum circuits to generate results of the one or more arithmetic functions, wherein the results are used to generate the corresponding output data.

The invention discloses a frequency coupling characteristic calculation method and device, equipment, a storage medium and a product, and relates to the technical field of frequency coupling characteristic analysis, and the method comprises the steps: respectively injecting a positive sequence disturbance signal and a negative sequence disturbance signal into tested power generation equipment, and obtaining a positive sequence disturbance response signal and a negative sequence disturbance response signal of a port of the tested power generation equipment; wherein the positive sequence disturbance signal and the negative sequence disturbance signal are two groups of linearly independent DIBS signals; respectively performing Fourier analysis on the positive sequence disturbance response signal and the negative sequence disturbance response signal to obtain a corresponding voltage signal component and a current signal component; performing phase sequence separation on the voltage signal component and the current signal component to obtain a voltage signal phase sequence component and a current signal phase sequence component; and calculating a frequency coupling admittance matrix based on the voltage signal phase sequence component and the current signal phase sequence component to obtain a frequency coupling characteristic calculation result. According to the invention, the efficiency and accuracy of frequency coupling characteristic calculation can be improved.