805 results about "Discrete Fourier transform" patented technology

The invention relates to the technical field of geotechnical mechanics and engineering, and particularly discloses a soft soil foundation settlement monitoring method and system based on multi-field multi-source information, and the method comprises the steps: collecting multi-source data of a target soft soil area; performing constitutive parameter inversion, data standardization and discrete Fourier transform frequency domain conversion on the data to obtain a standardized frequency domain multi-field multi-source data set; based on the data set, a soft soil constitutive parameter library and a soil mechanics physical equation, constructing an FD-PINN frequency domain physical information neural network, embedding the physical equation as a prior constraint, and training the model by adopting an alternating optimization strategy; inputting the real-time frequency domain data flow into the model, and outputting the current settlement amount, the settlement rate and the multi-physical field frequency domain distribution; and in combination with a pre-established large scale model test result, through IDFT inverse transformation, a time-space domain settlement field is reconstructed, multi-stage early warning is triggered, a targeted reinforcement scheme is recommended, the soft soil foundation settlement monitoring precision and the engineering practicability are remarkably improved, and the method is suitable for construction, operation and maintenance of infrastructures such as high-speed rails and highways.

The present disclosure is generally related to wireless communications technologies, network topologies, and communication device implementations, and in particular, to multipletransmission reception point (multi-TRP) uplink (UL) transmission schemes. The multi-TRP UL transmission schemes include timing advance (TA) associations for multi-TRP operation, channel state information (CSI) enhancements, and codebook structural enhancements. To enhance the performance of Rel-16 codebook, a new codebook structure is constructed by preserving the Rel-16 codebook structure in the spatial dimension (SD) and frequency dimension (FD) while extending the Rel-16 codebook in the time dimension (TD) using a mutually orthogonal Discrete Fourier Transform (DFT) basis.

The invention discloses a time series data anti-noise anomaly detection method and system based on dynamic decomposition, and the method comprises the steps: obtaining a to-be-detected time series, inputting an anomaly detection model, and carrying out the dynamic decomposition of the time series; based on a channel-time mixed attention mechanism, obtaining channel feature representation of a trend component and time feature representation of a seasonal component; and obtaining a reconstruction sequence according to the channel feature representation and the time feature representation, calculating a difference value of each corresponding time step in the reconstruction sequence and the time sequence, and comparing and marking the difference value with an abnormal threshold value to obtain a marked abnormal detection sequence so as to complete sequence abnormal detection. According to the method, the time sequence needing to be detected is subjected to dynamic trend seasonal decomposition through learnable one-dimensional convolution and discrete Fourier transform of the anomaly detection model, the potential time pattern of the time sequence can be effectively extracted, the adaptability of the model to complex trends and periodic changes is improved, and the accuracy of abnormal data recognition is improved.

The invention relates to a reconfigurable battery energy storage system grid-connected and off-grid seamless switching control method and device. The method comprises the following steps of: sensing a real-time power grid state, pre-judging a system operation mode, and acquiring a millisecond-level panoramic information flow of the power grid state through a high-speed sampling voltage sensor, a high-speed sampling frequency sensor, a high-speed sampling phase sensor and a high-speed sampling communication interface. And pre-judging the stability and disturbance trend of the power grid through a deep learning algorithm, executing a power distribution and voltage support algorithm in grid-connected and off-grid modes, and calculating an optimal switching opportunity point and a system reconstruction target topology scheme. Meanwhile, the harmonic distortion rate and the voltage sag depth are calculated in real time through discrete Fourier transform, so that a mode pre-judgment mechanism is automatically triggered. After switching, key parameters such as voltage, frequency and power are automatically corrected through real-time sampling and observation, and zero-sensing switching is achieved. The method can effectively improve the response speed and stability of the battery energy storage system when the power grid fails, and guarantees the safe operation of the power grid.

The invention belongs to the field of edge computing acceleration, and relates to a fast Fourier transform and neural network reasoning collaborative computing acceleration method, which comprises the following steps of: deploying a computing method which is based on butterfly computing merging and a tensor mapping strategy and is mixed with DFT (Discrete Fourier Transform) and FFT (Fast Fourier Transform) in an operator deployment level; on the interface integration level, a bus interface of a register access path in a tensor accelerator control path is subjected to lightweight reconstruction, so that the bus interface is adaptive to an edge computing platform; in an operation level, a user-defined instruction is introduced into a tensor calculation unit, and an accelerator is enabled to independently complete a whole-process task of FFT signal processing and NN intelligent identification. According to the method, an operation instruction set is expanded on hardware, and delay overhead caused by returning a large amount of intermediate data to a processor is avoided, so that efficient execution of FFT and neural network tasks on unified hardware is guaranteed, and multiple requirements of high real-time performance, high precision and low power consumption are met.

The invention relates to the field of partial discharge source positioning, in particular to a large-scale substation multi-partial discharge source positioning method and system, and the method comprises the steps: forming a sensor array through a movable ultrasonic sensor and an ultrahigh frequency sensor, and collecting ultrasonic signals and ultrahigh frequency signals generated by partial discharge sources respectively; denoising the collected discharge signals, and calculating the distance between the partial discharge source and the sensor array; denoised ultrasonic array signals are divided into a plurality of narrowband signals by discrete Fourier transform, weighted covariance matrixes of all narrowband signals after focusing transformation are subjected to mean value calculation to obtain weighted covariance matrixes of focusing frequency points, and the weighted covariance matrixes of the focusing frequency points are subjected to unitary transformation to obtain a weighted covariance matrix of a plurality of narrowband signals. Estimating the number of partial discharge sources according to a signal source number estimation equation; and carrying out spectrum peak search in a specified space parameter range according to a preset step length to obtain a space parameter corresponding to each partial discharge source, and obtaining a partial discharge source coordinate in a three-dimensional space.

The invention discloses a photovoltaic grid-connected broadband oscillation suppression system based on network construction type energy storage, and belongs to the technical field of photovoltaic grid connection. The invention aims to solve the problem of broadband oscillation in photovoltaic grid connection. Comprising the following steps: acquiring voltage and current of a public grid-connected point and energy storage voltage and current by adopting a data acquisition module, and preprocessing; a data processing module is adopted to carry out autoregressive empirical wavelet transform on voltage and current of a common grid-connected point to obtain an oscillation signal, and then interpolation discrete Fourier transform of a Hanning window is adopted to obtain modal information; filtering a fundamental component of the energy storage voltage based on the modal information by adopting an adaptive impedance remodeling module, and then filtering to obtain a filtered energy storage voltage harmonic component; self-adaptive impedance remodeling is carried out to obtain an energy storage voltage harmonic component after amplitude and phase adjustment; and inputting the harmonic component of the energy storage voltage after amplitude and phase adjustment as a voltage feedback signal to a PWM control link of the inverter, thereby realizing broadband oscillation suppression at the public grid-connected point. The method is used for photovoltaic grid-connected oscillation suppression.

The invention relates to the technical field of image recognition, and discloses a laboratory animal identity recognition method and system based on image recognition, and the method comprises the steps: carrying out the preprocessing of an animal motion image sequence collected by a high-speed camera in a laboratory, and obtaining the image data of a target animal; performing two-dimensional discrete Fourier transform and inverse Fourier transform to obtain animal image high-frequency feature data and animal image low-frequency feature data; respectively inputting into a double-branch diffusion network for feature processing to obtain fused animal feature data; constructing a positive sample pair, constructing a negative sample pair through difficult sample mining, calculating a feature distance based on cosine similarity and performing momentum updating to obtain an animal identity feature template; and based on the double-branch diffusion network and the animal identity feature template, feature extraction and similarity calculation are performed on the real-time animal image sequence to obtain animal identity data, so that the distinguishing capability of identity features is improved, and the time sequence consistency of an identity recognition result is enhanced.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention discloses a noise suppression optimization method for a high-sensitivity camera module. The method comprises the following steps: S1, acquiring an original image and preprocessing the original image; s2, executing discrete Fourier transform, and converting the image into a frequency domain image; s3, inputting the frequency domain image into a Fourier neural operator model, and extracting frequency domain features; s4, inverse Fourier transform is executed, and a preliminary noise reduction image is generated to serve as a reference image; s5, adopting an improved jellyfish group optimization algorithm to perform global search and optimization on model structure parameters and hyper-parameters; s6, updating the Fourier neural operator model according to an optimization result, and performing closed-loop iterative optimization; and S7, performing image quality evaluation on the noise-reduced image and the reference image until performance indexes are met. According to the method, the Fourier neural operator and the improved jellyfish group optimization algorithm are fused, so that efficient noise suppression and image quality improvement of the high-sensitivity camera module in a low-light environment are realized.

The invention provides a power grid frequency detection method, system and device and a medium, and the method comprises the steps: carrying out the narrowband signal decomposition of an obtained input voltage signal of a power grid through a null-space tracking algorithm, and obtaining a fundamental main mode component of the input voltage signal; performing low-pass processing on the fundamental wave main mode component to obtain a fundamental wave signal of the fundamental wave main mode component; performing discrete Fourier transform on the fundamental wave signal to obtain phase information of the fundamental wave signal; according to the phase information of the fundamental wave signal, calculating phase deviation information of the fundamental wave signal in pre-selected adjacent cyclic waves to obtain frequency estimation information of the power grid; according to the invention, narrow-band decomposition is carried out on an input voltage signal through a null-space tracking algorithm, so that a fundamental main mode can be accurately extracted; and phase deviation information can be determined based on discrete Fourier transform, so that when the interference frequency of the power grid is close to the fundamental frequency, high separation precision can be kept, and the accuracy of power grid frequency estimation can be improved.

A method of beamforming for downlink (DL) transmission in a wireless communication system is provided. The method includes generating, by a base station of the wireless communication system, configuration information required to signal channel state information (CSI) to the BS, wherein the configuration information includes parameters of a code book, wherein the code book defines an overall set of spatial beams in which data streams can be transmitted from the BS, wherein each spatial beam is represented by a discrete Fourier transform (DFT) vector in the code book, and a parameter (NDFT) indicative of a maximum number of DFT vectors with one signal polarization in a precoding matrix, transmitting, by the BS, the configuration information from the base station to a user device (UE), based on CSI reference signals (CSI-RSs) received from the BS, choosing, by the UE, a number (L) of Multiple Input-Multiple Output (MIMO) layers.

The invention relates to a power load prediction system based on time sequence self-supervised representation learning, and the system comprises a data preprocessing module, an expansion time convolution network, a trend representation decoupler, a seasonal representation decoupler, a feature converter, and a predictor. The expansion time convolution network performs dynamic adaptive grouping on different variables in the same data processing unit, establishes an intra-group variable relationship by sharing a convolution kernel weight and stacking a plurality of expansion time convolution layers, and establishes an inter-group variable relationship through subsequent multi-group representation splicing operation and single-layer expansion time convolution; the trend representation decoupler is used for separating trend representation Z (T) from feature representation extracted from the expansion time convolution network based on a plurality of parallel one-dimensional causal convolution blocks with different scales; the seasonal representation decoupler adopts discrete Fourier transform to separate seasonal representation Z (S) from the feature representation extracted from the expansion time convolutional network; the feature converter maps a combined feature vector of trend representation and seasonal representation back to an original space from a potential space by stacking a plurality of deconvolution layers; and the predictor maps the feature representation of the original space into a power load prediction result by using a linear projection layer, and the adaptability, robustness and generalization ability of the model for modeling complex power load time series data can be improved.

Embodiments of the present disclosure relate to methods and devices for wake-up signals. A method implemented at a network device comprises forming an OOK subsequence with multiple OOK symbols that comprise at least one OOK ON-symbol and at least one OOK OFF-symbol. The multiple OOK symbols are associated with one OFDM symbol. The method further comprises mapping the OOK subsequence into multiple pre-Discrete Fourier Transform (pre-DFT) subsequences respectively to form a pre-DFT sequence, and performing transform precoding on the pre-DFT sequence. The method further comprises mapping output of the transform precoding into frequency resources allocated to a Wake-up Signal (WUS) and generating the WUS, and transmitting to a terminal device the WUS.

The invention discloses an electrochemical impedance spectroscopy detection method and system and related equipment. The electrochemical impedance spectroscopy detection system comprises: a battery pack comprising a plurality of cells; the battery cell management module is connected with the plurality of battery cells and is used for acquiring response voltage of the battery pack and performing discrete Fourier transform on the excitation current and the response voltage to obtain an electrochemical impedance spectrum of the battery pack under the target excitation frequency; the battery management module is connected with the excitation source module and used for sending the target excitation frequency to the excitation source module; and the excitation source module is used for generating excitation current according to the target excitation frequency sent by the battery management module.

The invention discloses a weight function phase shift method for deformation field measurement, belongs to the technical field of optical measurement, is suitable for phase extraction and deformation field measurement of periodic structure images such as gratings and stripes, and has the characteristics of high speed and high precision. Weight function windows (a rectangular window, a triangular window, a cosine window and a Gaussian window) are applied in image processing, discrete Fourier transform is carried out after pixel gray values in the windows are multiplied by corresponding weights, and image phases at the center pixel positions of the windows are obtained. After the weight function window traverses all image pixels, the phase distribution of the whole image is obtained. And solving an image deformation field by using the phase difference of the images before and after deformation. Compared with the existing phase-based deformation field method, the method does not need complex image processing operation, and can solve the high-precision phase in one step and obtain full-field deformation only by using the weight function and the global pixel gray scale.

According to the Mama network solenoid valve fault diagnosis method based on the frequency domain characteristics, the problem of solenoid valve system fault diagnosis can be solved. According to the method, a pneumatic solenoid valve fault data set is constructed by collecting operation signals such as voltage and current, and multi-scale frequency domain features of the signals are extracted by adopting Wavelet Packet Transform (WPT) and Discrete Fourier Transform (DFT). WPT-DFT preprocessing features are embedded into an improved channel-space joint attention mechanism module, and the improved channel-space joint attention mechanism module is combined with a Mamba network with extremely high sequence modeling capability to construct an end-to-end fault diagnosis model. Compared with a traditional convolutional neural network, the method can more effectively capture deep dynamic features in a time sequence and highlight a key frequency region, and experimental results show that the method has higher diagnosis precision and generalization ability and is suitable for intelligent detection of the electromagnetic valve under complex working conditions.

The invention relates to the technical field of geophysical exploration, in particular to a multi-scale three-dimensional gravitational-seismic joint frequency domain inversion method based on hybrid constraints. The method comprises the following steps: determining observation gravity anomaly on the ground by adopting discrete Fourier transform, and discretizing the observation gravity anomaly by adopting a method of weighted average of values at a plurality of Gaussian points; calculating spherical harmonics and spherical harmonic coefficients of two-dimensional density distribution of each layer in the inversion range, and calculating gravity anomalies on the observation surface based on the spherical harmonics and the spherical harmonic coefficients; converting the inversion range data into a three-dimensional gravitational field forward kernel matrix; constructing a three-dimensional gravitational field hybrid constraint inversion objective function; inputting pre-collected earthquake three-dimensional velocity model data, and converting the earthquake three-dimensional velocity model data into a reference density model; and calculating a gradient operator in the inversion objective function, simplifying the inversion objective function based on the gradient operator obtained through calculation, and then solving the simplified inversion objective function by using an alternating direction multiplier method.

The invention discloses a gas meter power supply abnormity monitoring system and method based on a signal acquisition technology, and relates to the technical field of gas meter power supplies, the system comprises a multi-dimensional sensing module, a dynamic adaptive module, a multi-dimensional fusion analysis module, an intelligent judgment module and a verification execution module; according to the invention, the multi-dimensional fusion analysis module accurately calculates key parameters such as a power supply ripple coefficient and load transient response time in a corresponding mode, the electromagnetic interference intensity is calculated through professional algorithms such as discrete Fourier transform in combination with an environment electric field and a magnetic field sequence, a threshold value is dynamically adjusted based on a working mode, and a comprehensive score is calculated through multi-parameter deviation weighted fusion. The power supply state is deeply and comprehensively evaluated; the intelligent judgment module scientifically divides anomaly levels according to comprehensive scores, quantifies and distinguishes real anomaly and environmental interference by means of an environmental interference contribution degree formula, solves the problem that traditional monitoring cannot accurately recognize power anomaly under complex working conditions, and improves anomaly judgment accuracy.

Methods, systems, and devices for wireless communications are described. A transmitter may modulate a set of bits into an on-off keying (OOK) sample sequence for wireless transmission to a receiver in a set of frequency resources. The transmitter may apply a transform (e.g., a discrete Fourier transform (DFT)) to the OOK sample sequence to generate a frequency domain representation of the OOK sample sequence. In some cases. the transmitter may, using an orthogonal frequency division multiplexing (OFDM) waveform generator, generate an OFDM waveform based on mapping the frequency domain representation of the OOK sample sequence to a set of resource elements of the set of frequency resources. In some cases. the transmitter may transmit the OFDM waveform to the receiver via the set of frequency resources.

The invention relates to a method (300) for detecting damage (201) to a part (200) made of conductive material, the damage (201) being a burn or a crack, the part (200) being heated (310) by induction by injecting an energy pulse. The method comprises receiving (330) a temporal sequence of thermal images of the part after heating the part (200), and the temporal signal of the intensity of each pixel of the thermal image is used to construct (370) a phase image of the part by means of a discrete Fourier transformation of the temporal signal.

The invention relates to the technical field of video recognition and analysis, and discloses a video recognition and analysis method based on physical characteristics. Video frame pixels are mapped to a two-dimensional coordinate system with the upper left corner as an original point, and mirror image expansion and median filtering are carried out on a gray level image; constructing a binary image based on a gray threshold value, and analyzing and extracting a target region by using a four-neighborhood connected domain; using neighborhood search and polar angle sorting to close the tracking contour, and generating equidistant re-sampling points based on Euclidean distance and an interpolation method; the curvature of the re-sampling points is estimated through a three-point difference algorithm, and zero denominator is avoided through numerical protection; performing discrete Fourier transform on the curvature sequence to extract a frequency spectrum, and normalizing an amplitude to form a standardized feature vector; and finally, inter-frame similarity is calculated based on the feature vector, and the most similar frame is automatically retrieved. By processing unified data standards in stages, edge noise is suppressed, sampling uniformity is ensured, and feature stability and cross-frame comparability are improved.

The invention discloses an intelligent power grid equipment fault monitoring system and method based on the Internet of Things, and relates to the technical field of the Internet of Things. Discrete Fourier transform and a Fisher information processing method are adopted to fuse multi-source data, meteorological interference is corrected, and accurate collection of electrical quantity and quantification of environmental influence are achieved; and setting a fault monitoring sliding window and a harmonic analysis time interval, and obtaining harmonic data of different time scales. Extracting harmonic features through slope calculation, and identifying voltage abnormal features; historical data is processed according to a time sequence prediction algorithm to construct a prediction set, voltage abnormity is judged based on a historical data threshold value, intelligent early warning of the voltage state is achieved, the abnormity capturing precision is improved, and when the system detects the voltage abnormity, information of strain section equipment sending out a fault early warning signal is obtained and transmitted to an OTA upgrading terminal. And enabling the OTA upgrading terminal to carry out state identification and data interaction on the upgrading process of the intelligent power grid equipment based on the equipment information.

The invention discloses an active bird shooting method and system based on voiceprint recognition and automatic localization, and belongs to the technical field of intelligent bird voiceprint collection and recognition, and the method comprises the steps: collecting bird sounds through a voiceprint collection device, and carrying out the discrete Fourier transform of the collected bird sounds; inputting the processed bird chirp data into a voiceprint recognition neural network model based on deep learning to obtain a high-dimensional feature vector; comparing the high-dimensional feature vector with each piece of bird voiceprint feature data in a bird voiceprint feature library to obtain bird species corresponding to the voiceprint feature data; according to the obtained closest bird species, based on a positioning sensor, determining the positions of the birds by using a multi-point cross positioning technology; according to the determined positions of the birds, the camera adjusts the shooting angle and the focal length to shoot the birds. According to the method, manual intervention is not needed, so that efficient and accurate bird monitoring is realized, and the monitoring efficiency and accuracy are improved.

The invention relates to the technical field of monitoring and correction, in particular to a ceilometer attitude correction method based on a buoy. The method comprises the following steps: step 1, collecting attitude data of a buoy for multiple times, and constructing a reference attitude quaternion according to the attitude data of the buoy collected each time; 2, extracting a wave dominant frequency component and a high-frequency residual component from a reference attitude quaternion sequence formed by all reference attitude quaternions by using discrete Fourier transform, and calculating a comprehensive attitude quaternion; 3, calculating a buoy wave disturbance variable by using wave spectrum analysis based on sea condition data obtained through real-time monitoring; and 4, performing attitude correction on the measured value of the ceilometer based on the buoy wave disturbance variable and the comprehensive attitude quaternion. According to the method, high-precision estimation of the attitude of the buoy under sea surface multi-band wave disturbance is realized, and real-time sea condition analysis and ceilometer optical axis correction are combined.

The invention provides a wind power prediction method based on a frequency domain space-time diagram convolutional network. The method comprises the following steps: firstly, respectively constructing a spatial proximity weight matrix and a similarity matrix based on a fan position and historical feature data, and adaptively fusing the spatial proximity weight matrix and the similarity matrix into an adjacent matrix; performing high-dimensional embedding on the normalized features, and converting a time domain signal to a graph frequency domain by using graph Fourier transform GFT; designing a prediction and reconstruction double-branch network, extracting key frequency features through discrete Fourier transform (DFT), causal convolution and channel attention, and performing inverse transformation reduction; and finally, constructing a joint loss function of prediction and reconstruction loss weighting, and realizing end-to-end training through back propagation and an Adam optimizer. According to the method, spatial topology and time-frequency dynamic characteristics are fused, prediction and reconstruction advantages are combined, and the precision and robustness of wind power prediction are improved.

The invention provides a project state summarizing method and device, and belongs to the technical field of DFT (Discrete Fourier Transform), and the project state summarizing method comprises the following steps: distributing a plurality of test tasks to a plurality of predefined categories according to the corresponding test steps according to the mapping relation between the test steps and the predefined categories; and based on the task state of the test task included in each predefined category, determining the category state of the corresponding predefined category. Through a predefined mapping relation, original and scattered test tasks and states thereof are aggregated into classified and macroscopic class states.

Provided are a communication method and a communication device, the method comprising: determining a transmission waveform of a signal, the transmission waveform being related to a threshold of a first parameter, the first parameter comprising: a modulation order and a roll-off factor, the transmission waveform is an orthogonal frequency division multiplexing QAM-DFTS-OFDM waveform of discrete Fourier transform spread spectrum of orthogonal amplitude modulation or an orthogonal frequency division multiplexing OQAM-DFTS-OFDM waveform of discrete Fourier transform spread spectrum of offset orthogonal amplitude modulation, and the roll-off factor is determined based on the transmission bandwidth and the number of symbols carried by the transmission waveform. And transmitting or acquiring the signal. In the method provided by the invention, the network equipment or the terminal equipment can determine the transmission waveform of the signal according to the set threshold value of the modulation order and the threshold value of the roll-off factor, so that the output power supporting higher power amplifiers can be obtained, and a larger signal coverage range can be obtained.

The present disclosure relates to a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE). The disclosure relates to a method for transmitting channel state information reference signal (CSI-RS). The technical result includes increased performance and flexibility of CSI-RS generation and transmission framework, unified CSI-RS structure, and reduced transmission overhead. A method for transmitting channel state information reference signal is provided. The method includes setting, at a base station, CSI-RS transmission configuration information, including one or more code-division multiplexing (CDM) groups with uniform frequency distribution of resource elements (RE) in each CDM group, transmitting the CSI-RS transmission configuration information from the base station (BS) to user equipment (UE), generating, for each antenna port of the base station, a CSI-RS modulated by orthogonal cover code (OCC) in frequency domain (FD) across resource elements according to discrete Fourier transform (DFT) vector and in time domain (TD) across one or more Orthogonal Frequency-Division Multiplexing (OFDM) symbols according to Walsh-Hadamard (WH) code, and transmitting the CSI-RS from the BS to the UE according to the CSI-RS transmission configuration.

The invention discloses a mode switching method, device and equipment of an energy storage converter and a storage medium, and relates to the technical field of power grid control, and the method comprises the steps: obtaining a multi-mode control grid-connected model of the energy storage converter, and the grid-connected model comprises a grid-following type control model and a grid-constructing type control model; based on the grid-connected model, injecting a high-frequency small signal into a power grid through an energy storage converter, and processing the high-frequency small signal by adopting a discrete Fourier transform algorithm to obtain grid-connected point voltage and current response; obtaining a power grid impedance parameter according to the grid-connected point voltage and current response; calculating an operation short-circuit ratio according to the power grid impedance parameter in combination with the actual output power of the energy storage converter and the power grid voltage amplitude; according to the operation short-circuit ratio and the short-circuit ratio threshold value, mode switching is carried out on the following network type control and the construction network type control, and the method can improve the accuracy of mode switching of the energy storage converter.