111 results about "Time domain correlation" patented technology

The invention relates to an AI-based automatic adaptation method for a work approval process, and the method comprises the steps: collecting multi-source approval data, and carrying out the preprocessing of the multi-source approval data, and forming standardized approval data; analyzing the system rule text by using the pre-trained AI large model, and extracting a structured approval rule comprising a trigger condition, an approval role and a process node sequence; matching a basic examination and approval template according to the form field and the user permission information, and dynamically generating an adaptive process comprising multiple stages of examination and approval nodes, aging parameters and an additional examination and approval link; and through resource scheduling optimization and time domain correlation analysis, establishing an optimized mapping relation based on flow execution characteristics such as approval timeliness deviation and node skipping frequency, and outputting a visual flow chart and execution parameters. A business logic writing mode is replaced by automatic analysis of an AI large model on an unstructured rule; the dynamic template matching and continuous iterative optimization mechanism can quickly respond to business changes, and resource scheduling optimization and automatic process generation shorten the implementation period and reduce the operation and maintenance cost.

The invention relates to the technical field of industrial laser precision measurement, in particular to a laser centering device for a coupler and a using method of the laser centering device for the coupler. Light spot displacement signals and environment vibration signals are synchronously collected through a laser transmitter and an optical sensor array, and a frequency domain feature decoupling module is adopted to separate a shafting displacement component and an interference component; the time domain correlation verification module is used for calculating signal time sequence correlation and generating a deviation vector, and finally the adaptive compensation decision module is used for analyzing the deviation vector and outputting an adjustment instruction. According to the invention, effective separation of real misalignment and transient noise in a dynamic environment is realized, the technical problem of measurement misalignment of an existing laser centering device in a complex industrial environment is solved, and the coupling installation precision and the equipment operation stability are improved.

The invention discloses a high-precision Beidou satellite navigation and positioning data acquisition method and system, particularly relates to the technical field of satellite navigation and positioning processing, and is used for solving the problem of positioning error increase caused by receiver hardware delay drift in the prior art. Inverting the first dynamic reference value by using carrier phase integral displacement, and extracting a third-order intermodulation component through coherent demodulation to generate a second dynamic reference value; adopting mutual information entropy analysis to select an effective reference value to calculate a residual error; fitting a theoretical value according to the carrier phase change rate to calculate deviation; marking a hardware drift error when the time domain correlation continuously exceeds a threshold value; finally, the ionosphere residual error is dynamically compensated to generate a corrected observation value, effective separation of the hardware drift error and the ionosphere residual error is realized, and the precision and reliability of long-time observation in a dynamic environment are improved.

The invention discloses a dynamic three-dimensional scene representation method based on Gaussian splashing, and belongs to the field of machine vision. A first time feature constructed through a first neural network based on the time index is fused with the Gaussian feature of the original Gaussian point cloud, and then decoding is performed to obtain various parameters of the original Gaussian point cloud; and fusing a second time feature obtained through second neural network coding based on the time index with the feature obtained by the micro renderer, so that time domain information is introduced in the image reconstruction process to assist in constructing the incidence relation of adjacent video frames, and the time domain expression capability of the model is further enhanced. According to the method, the convolutional neural network is combined with Gaussian splash, the modeling capability of a model for a dynamic scene can be effectively enhanced by fusing time domain features, and efficient expression of a dynamic three-dimensional scene is realized; time domain redundancy is eliminated by modeling a time domain incidence relation, and efficient expression of a dynamic scene is achieved.

The invention discloses a transformer monitoring method and system based on a multi-dimensional signal, and relates to the technical field of transformer monitoring, and the method comprises the steps: obtaining the multi-dimensional signal and working condition parameters of a transformer, carrying out the time domain feature extraction of the multi-dimensional signal, obtaining a corresponding time domain correlation feature, carrying out the frequency domain feature extraction of the multi-dimensional signal, obtaining a corresponding frequency domain correlation feature, and carrying out the time domain feature extraction of the multi-dimensional signal; fault analysis is carried out according to the working condition parameters and the frequency domain correlation characteristics, and a corresponding initial fault detection result and an electromagnetic coupling residual value are obtained; and if so, carrying out fuzzy evaluation on the time domain correlation feature, the frequency domain correlation feature, the electromagnetic coupling residual value and the multi-dimensional signal based on a pre-trained fault detection model to obtain a corresponding fault monitoring result. The technical problems that a traditional transformer monitoring method mainly depends on electrical signal analysis, but key parameters such as oil temperature are not included in a monitoring system, so that potential fault hidden dangers cannot be found in time, and the operation reliability of the transformer is reduced are solved.

The invention discloses a wind power blade structure health monitoring method based on multi-field decoupling optical fiber sensing, particularly relates to the technical field of wind power equipment state monitoring, and is used for solving the problem of health state misdiagnosis caused by coherent interference of environmental noise and damage signals. The method comprises the following steps: collecting vibration, strain and temperature signals and generating a background noise vector; calculating a frequency domain coherence function of the vibration signal and the noise vector to identify an interference danger frequency band; analyzing the polarization angle distribution of the vibration signal in the frequency band, and inverting the physical path topology of the noise transmitted into the blade; when the path passes through a predefined weak area, improving a damage weight coefficient of a strain signal of a corresponding frequency band and carrying out Hilbert phase shift reconstruction to generate an anti-interference strain feature vector; calculating the energy dissipation rate of the vector in a dangerous frequency band, and judging an energy leakage event caused by damage by combining a viscoelastic constitutive threshold value of the composite material; the damage type and the spatial position are judged according to the time domain correlation between the energy leakage event and the temperature signal, and the hidden damage detection reliability is improved.

The invention belongs to the technical field of human body electroencephalogram signal processing and brain regulation and control, and provides a multi-modal coupling type closed-loop electrical stimulation sleep synergy signal regulation and control device and system.The technical scheme includes that multi-modal physiological signals including EEG slow wave power, cerebral blood flow and cerebrospinal fluid flow signals are obtained; screening and preprocessing the obtained multi-modal physiological signal to obtain a preprocessed multi-modal signal; calculating the time domain correlation between the EEG slow wave power and the cerebral blood flow and the frequency domain coherence between the cerebral blood flow and the cerebrospinal fluid flow signal; introducing regulatory factors of time domain correlation of EEG slow wave power and cerebral blood flow and frequency domain coherence of cerebral blood flow and cerebrospinal fluid flow signals to obtain a signal stimulation intensity calculation formula; and optimizing the signal stimulation intensity by combining a signal stimulation intensity calculation formula and the constructed double-layer optimization architecture to obtain the optimized signal stimulation intensity. And an accurate sleep regulation and control signal is generated.

The invention discloses a distributed photovoltaic power prediction method and device based on dynamic spatial correlation, and relates to the field of distributed photovoltaic technology, and the method can reflect the real influence relation between photovoltaic power stations at different moments in real time through constructing dynamic graph structure data which can reflect the spatial relation between the photovoltaic power stations more comprehensively and accurately. And the prediction result of the distributed photovoltaic power is improved. The method comprises the following steps: acquiring an influence factor feature set of distributed photovoltaic power prediction; by taking the distributed photovoltaic power station as a node, constructing dynamic graph structure data of the photovoltaic power station according to the influence factor feature set and the spatial weight matrix fused with the meteorological condition information; performing spatial correlation extraction on the dynamic graph structure data by using a pre-trained first network to obtain spatial correlation characteristics of the distributed photovoltaic power station; and performing time domain correlation extraction on the spatial correlation characteristics by using a pre-trained second network to obtain a point prediction result of the distributed photovoltaic power.

The invention provides a hydraulic turbine set valve fault diagnosis method, system, equipment and product, and is applied to the field of hydroelectric equipment fault diagnosis. On one side of the edge end, an initial voiceprint signal, an initial vibration signal and temperature data of a hydraulic turbine set valve are collected; taking the initial voiceprint signal as a reference signal, and performing noise reduction processing on the initial vibration signal based on the time domain correlation between the fluid noise in the initial voiceprint signal and the first noise in the initial vibration signal; by taking the vibration noise reduction signal as a reference signal, performing noise reduction processing on the initial voiceprint signal based on the frequency domain correlation between the mechanical resonance noise in the vibration noise reduction signal and the second noise in the initial voiceprint signal; and based on the vibration noise reduction signal, the voiceprint noise reduction signal and the temperature data, performing fault diagnosis on the hydraulic turbine set valve to obtain a fault diagnosis result. Therefore, fault diagnosis is carried out through cooperative noise reduction of the voiceprint signal and the vibration signal in combination with multi-dimensional data, and the fault diagnosis accuracy of the hydraulic turbine set valve is improved.

A communication method and related devices, in which a first communication device may transmit the second channel information, enabling a second communication device to communicate based on the second channel information. Wherein the second channel information is determined according to the first channel information, the time information of the first channel information and the characteristic information of the historical channel information, namely, the second channel information can reflect the time domain correlation corresponding to the time information of the first channel information and can reflect the time domain correlation corresponding to the time information of the historical channel information. And one or more of time domain correlation, space domain correlation and frequency domain correlation corresponding to the characteristic information of the historical channel information can be reflected. Therefore, the accuracy of the second channel information can be improved by utilizing various correlations, so that the performance of data transmission based on the second channel information is improved. In addition, the scheme can be applied to a channel information compression feedback scene, and the overhead of channel information feedback can be reduced.

The embodiment of the invention discloses a power grid line state monitoring method and device based on vibration acoustics. The method comprises the following steps: acquiring a power grid power transmission line vibration signal and a sound wave signal; according to the topological structure of the power transmission line, the spatial distribution of adjacent lines and the measuring point positions of different sensors in the same line, constructing a signal propagation model, and calculating a multi-dimensional correlation index between the vibration signal and the sound wave signal, the correlation index including a time domain correlation coefficient, a frequency domain power spectral density and a phase difference feature; simulating signal propagation characteristics in a normal operation mode by using the signal propagation model, generating a reference correlation index, and identifying whether the power transmission line has an abnormal state; and carrying out accurate positioning on the abnormal state by adopting a spatial positioning algorithm, and carrying out multi-level evaluation according to the abnormal confidence coefficient and the influence range to generate a line abnormal report. According to the invention, real-time monitoring and accurate positioning of the abnormal state of the power transmission line are realized.

The present disclosure relates to the technical field of wireless communications. In particular, an information transmission method and apparatus, and an electronic device and a computer program product are provided. The method comprises: on the basis of a first system message, determining at least one first occasion, or on the basis of the first system message and a second system message, determining at least one first occasion, wherein the first system message is related to a beam; or on the basis of at least one of a third system message, a time-domain-related parameter of the third system message, and a time-domain-related parameter of a synchronization signal block (SSB), determining at least one first occasion; and performing information transmission at the at least one first occasion. In the present disclosure, a terminal device determines an information transmission occasion, and thus energy consumption waste caused by transmission failures and repeated transmissions of the terminal device is reduced.

The invention discloses a CAN bus abnormal flow detection method based on multi-scale and frequency domain characteristics, and relates to the technical field of Internet of Vehicles security, and the method comprises the following steps: carrying out the preprocessing of the original message data of a CAN bus, dividing a continuous message sequence according to a preset time window, and constructing a time sequence sample; inputting the time sequence sample into a feature extraction and fusion model, and sequentially executing multi-scale time sequence feature extraction, time sequence correlation modeling and frequency domain feature enhancement processing to obtain feature representation fusing time domain correlation information and frequency domain structure information; and constructing a classification model based on the feature representation, and carrying out anomaly detection and classification on the CAN bus flow. According to the CAN bus abnormal flow detection method based on the multi-scale and frequency domain characteristics, effective fusion of time domain correlation enhancement and frequency domain noise suppression is realized, the detection robustness and accuracy in a complex vehicle-mounted noise environment are improved, and the false alarm rate in the complex vehicle-mounted noise environment is reduced.

A channel estimator circuit receives in one slot a multiplexed representation of one or more time- domain code division multiplexed, TD-CDM, spread received signals. An initial estimator circuit processes a pre-estimation signal that is formed therefrom, uses a base pilot signal as a reference signal, and outputs an initial estimate signal. A frequency-domain processing circuit receives and processes a first set of one or more TD-CDM spread channel estimate signals that is representative of the initial estimate signal, and outputs a second set of signals. An interpolation buffer buffers the second set of signals. A TD-CDM de-spreading circuit de-spreads the buffered second set of signals and outputs TD- CDM de-spread channel estimate signals. In this manner, the frequency-domain-related signal processing and the time-domain-related signal processing are separated, and a significant reduction in the amount of hardware, energy and time resources required may be achieved, improving latency and reducing cost.

The present invention relates to the technical field of point cloud data compression, and in particular to a method, device, computer equipment, and medium for compressing dynamic point cloud data. The method divides a target space into subspaces, counts the number of data containing point cloud data in the subspaces, sorts them using the number of data as a spatial identifier, obtains a primary identifier sequence, determines a subspace that meets preset conditions as a target subspace, determines the sorting position of the target subspace, obtains a secondary identifier sequence of the target subspace using the above steps, forms target description information based on the sorting positions and secondary identifier sequences of all target subspaces, obtains a target probability distribution based on the target description information and reference description information, encodes the target probability distribution as a compression result, obtains a refined spatial domain description through spatial averaging, and extracts the time domain correlation of dynamic point cloud data, thereby improving the compression efficiency and compression rate of dynamic point cloud data, thereby reducing the storage pressure of point cloud modeling in medical scenarios.

The application provides a fracture patient muscle mass detection method based on electromyographic signal analysis, and relates to the technical field of medical treatment. The method comprises the following steps: acquiring electromyographic signals of a target muscle and its adjacent muscles of a patient; when the correlation coefficients of the target muscle and the adjacent muscle signals and their envelope lines meet preset conditions, judging whether the target muscle is interfered by the adjacent muscles based on the signal difference degree; when the adjacent muscles interfere, extracting independent frequency component sequences of the target muscle and the adjacent muscle signals, removing interference components based on the time domain correlation coefficients and the frequency overlap degree of each independent frequency component group, and obtaining a real target muscle signal; determining the muscle recovery state of the patient based on the amplitude variation trend of the real target muscle signal sequence of the patient in a target period, and the amplitude difference between the contraction state sequence segment and the relaxation state sequence segment in the signal envelope line of the target sub-sequence of the sequence, and formulating a rehabilitation training scheme accordingly. The application can more accurately determine the muscle recovery state of the patient.

Provided in the present disclosure are a channel estimation method, a device, and a storage medium. The method is executed by a first device, on which a first model is deployed. The method comprises: when it is determined that a first time unit meets a preset condition, on the basis of first information corresponding to the first time unit, using a first model to determine a channel estimation result corresponding to a second time unit; or when it is determined that the first time unit does not meet the preset condition, receiving second information sent by a second device, wherein the second information is used for indicating whether a first device can use the first model to determine the channel estimation result corresponding to the second time unit. Therefore, channel estimation can be performed on a second time unit on the basis of a first model and first information corresponding to a first time unit, and channel estimation is performed by using the time-domain correlation of a channel, such that the accuracy and efficiency of channel estimation can be improved.

The invention relates to the technical field of distribution network cable evaluation, and particularly discloses a distribution network cable state evaluation method and system and a terminal, which adopt semantic segmentation to suppress background information in a current oscillogram, and then extract time domain correlation information among local current waveforms as an input basis of a cable state evaluator. And the state of the cable is intelligently identified from the dynamic change information of the current waveform. In this way, an intelligent scheme is adopted to replace common manual recognition, and a large number of manpower resources are saved.

The application belongs to the field of helicopter strength design, and relates to a rotor shaft cross section moment time domain correlation graph analysis method of a helicopter. The method comprises the following steps: extracting measured data of rotor shaft moment measuring strain gauges on all cross sections under a specific working condition, constructing a moment data matrix M of each cross section according to the geometric arrangement of the rotor shaft moment measuring strain gauges m ; wherein each cross section has two-channel orthogonal measured data; forming data of the same time in two groups of measured data of the same direction channels of different cross sections into a measuring point horizontal coordinate and a vertical coordinate of a Cartesian coordinate system, or forming data of the same time in two groups of measured data of different direction channels of different cross sections into a measuring point horizontal coordinate and a vertical coordinate of a Cartesian coordinate system; drawing positions of all measuring points in the time domain to obtain a time domain correlation graph; obtaining a rotor shaft moment correct measurement data time domain correlation graph paradigm of a helicopter with the same working condition and the same number of rotor arms; and comparing the time domain correlation graph with the time domain correlation graph paradigm.

The invention belongs to the technical field of human body electroencephalogram signal processing and brain regulation and control, and provides a noradrenaline oscillation time-frequency-phase three-dimensional coupling sleep coordinated regulation and control device which comprises the following steps: calculating a three-dimensional coupling coefficient by combining a time domain correlation coefficient, a frequency domain correlation coefficient and an instantaneous phase difference between multi-modal physiological signals; determining the coupling state of the signal based on the three-dimensional coupling coefficient and a preset threshold value; predicting to obtain an optimal regulation factor and a modal weight based on a coupling coefficient matrix constructed based on a time domain correlation coefficient and a frequency domain correlation coefficient and a constructed three-dimensional coupling double-layer optimization architecture in combination with the multi-modal physiological signal; calculating the optimized stimulation intensity in combination with the modal weight, the optimal regulation factor and the multi-modal signal characteristic parameters; and adaptively selecting a stimulation mode and outputting a stimulation signal according to the coupling state of the current multi-mode signal and the optimized stimulation intensity. Accurate matching of stimulation parameters and nerve-blood vessel-cerebrospinal fluid system collaborative rhythm is realized.

The invention provides an SSB time-frequency synchronization method and device, and relates to the technical field of mobile communication. The method comprises the following steps: receiving a synchronization signal block SSB signal sent by a base station; processing the SSB signal to obtain a target time domain sequence generated after processing, and performing sliding correlation on the target time domain sequence to obtain a time domain correlation value sequence; acquiring a peak value of the time domain correlation value sequence, and performing time synchronization of the SSB signal based on time offset corresponding to the peak value; after the time synchronization of the SSB signal is completed, obtaining a cyclic prefix (CP) removal sequence corresponding to the time domain sequence of the SSB signal, and obtaining a pre-frequency offset PSS time domain sequence generated after a primary synchronization signal PSS time domain sequence is subjected to multiple times of frequency offset adjustment; performing correlation calculation based on the CP-removed sequence and the pre-frequency offset PSS time domain sequence to determine a target estimated frequency offset; and completing frequency synchronization of the SSB signal based on the target estimation frequency offset. According to the invention, the PSS can be locked quickly and accurately to realize time-frequency synchronization.

The invention relates to the technical field of radar signal processing, and discloses a false target processing method based on radar secondary reflection, and the method comprises the steps: distinguishing a newly-built target from a non-newly-built target, extracting the historical features of the non-newly-built target through exponential decay weighting, recognizing a strong reflection source, and calculating a dynamic region correction factor and the azimuth width in combination with a relative geometric attitude angle; therefore, a protection area which comprises a primary reflection area and a multi-reflection area and changes adaptively along with the attitude is constructed. Judging whether a new target falls into a protection area or not, and sequentially executing kinematics consistency, RCS strength physical upper limit constraint and amplitude fluctuation time domain correlation cascade verification on the falling target; and finally, integrating verification results to remove false targets. Through multi-dimensional feature coupling analysis, the problem that a traditional method is difficult to adapt to target dynamic maneuvering and signal fluctuation is solved, false targets are effectively restrained, and meanwhile the false deletion rate of real targets is reduced.

Techniques are provided for utilizing a hybrid of ultra-wideband (UWB) and narrowband (NB) signaling to provide more efficient operating range and operating efficiency. In one example, a first subsystem of a first device may determine parameter data for a ranging round (e.g., UWB and / or NB-assisted (NBA) UWB multi-millisecond (MMS) ranging), and transmit the parameter data to a second subsystem of the first device. The parameter data may indicate a first time associated with a first time domain for performing ranging. The second subsystem may determine a second time based on the first time, whereby the second time is associated with a second time domain for performing NB signaling (e.g., via a Bluetooth Low Energy (BLE) protocol). The first device may transmit via an NB signal an advertisement packet that indicates the second time. The first device may then initiate the ranging round at the first time.

The present invention discloses a radar altitude estimation method based on forward-looking squint SAR echo signals, comprising the following steps: acquiring forward-looking squint SAR echo signals and performing range-direction pulse compression; performing frequency-domain range-direction segmentation on the pulse compression signals; transforming the range-direction sub-band signals back into the time domain; estimating the first baseband Doppler frequency of range unit data in the range-azimuth data in the time domain using a time-domain correlation method; resolving fuzzy numbers based on the first baseband Doppler frequency; performing time-domain range-direction segmentation on the pulse compression signals; estimating the second baseband Doppler frequency of the range-direction sub-block signals using a time-domain correlation method; and inferring the Doppler center frequency based on the second baseband Doppler frequency and the fuzzy numbers; calculating the radar altitude based on the Doppler center frequency and slant range by fitting a straight line; and determining a final estimated radar altitude based on the radar altitudes resolved for each range-direction sub-block signal. The present invention achieves accurate radar altitude estimation for high-speed platforms.

A method includes capturing a first sample data signal associated with a first time domain, and storing a first value associated with the first sample data signal in a first element position of a first memory buffer. The method also includes, in response to completion of a sampling window and in response to a request from a data consumer, generating a snapshot of the values stored in the first memory buffer, and storing the snapshot of the values in a data consumer memory. The method also includes extracting, by the data consumer, at least one value from the snapshot of the values in a second time domain, and calculating, by the data consumer, at least one of a motor position of a motor and a motor speed of the motor using the at least one value from the snapshot of the values.

The invention discloses a video change detection method and system based on double models, and relates to the field of intelligent traffic, and the method comprises the steps: combining a time model and a space model, achieving change detection through the judgment of a minimum normalized residual threshold, achieving the complementation of the time and space models, and achieving the inhibition of natural dynamic noise through the time model. The spatial model suppresses sudden jitter noise. Meanwhile, the time domain correlation of historical frames is captured through low-dimensional subspace projection, background estimation and standard deviation are dynamically updated, subspace base vectors can be updated in real time with low complexity, and background gradient is tracked. Pixel intensity change caused by jitter is predicted based on single-frame gradient distribution, a spatial mean value and variance are calculated through a probability grid, and adaptive adjustment is performed in combination with an inter-frame scaling factor. Historical jitter data is not needed, the jitter influence is directly predicted based on the single-frame gradient, and the detection accuracy is improved.

The invention relates to the technical field of LED backlight source testing, in particular to an LED backlight source brightness detection device and a detection method thereof. The method comprises the following steps: firstly, acquiring a static frame sequence of a brightness image of an LED backlight source to be detected and a synchronous driving current time sequence signal; constructing a brightness disorder coefficient based on the static frame sequence, and generating a detection distortion value by combining edge curvature mutation analysis of the superpixel region; generating a brightness fluctuation index based on the static frame sequence and the driving current time sequence signal, and constructing a defect comprehensive factor; analyzing the time domain correlation between the brightness fluctuation index and the defect comprehensive factor, and determining a brightness anomaly dynamic index; fusing the detection distortion value and the brightness abnormity dynamic index to generate a key space-time dynamic index; and inputting the key time-space dynamic index and the brightness image into a neural network model, and outputting a brightness detection index of the LED backlight source to be detected. According to the invention, the recognition capability of highlight area details and dark area gradient during brightness detection of the LED backlight source is improved.

The invention relates to an audio encoding method, an audio decoding method, a song audio encoding and decoding method, equipment, a medium and a program product, relates to the technical field of audio encoding and decoding, and can improve the audio encoding and decoding quality. The method comprises the following steps: acquiring an original audio signal to be coded; obtaining a first high-frequency sub-band signal and a first low-frequency sub-band signal corresponding to the original audio signal based on a sub-band segmentation result of the original audio signal in a time domain; respectively inputting the first high-frequency sub-band signal and the first low-frequency sub-band signal into respective corresponding sub-band encoders to obtain sub-band encoding results output by the sub-band encoders; and determining time domain association information of the plurality of sub-band coding results, and obtaining an audio coding result of the original audio signal according to the time domain association information and the plurality of sub-band coding results.

The present invention discloses a multi-slot channel estimation method and device based on a neural network. Through the steps of multi-slot channel feature extraction, deep channel feature extraction and channel reconstruction, the time domain correlation characteristics of the channel are fully utilized to improve the accuracy of channel estimation. A series of parameters are learned by the neural network to adaptively assign weights to different channel features, so that it can cope with variable channel conditions. In addition, the present invention is based on a conditional generative adversarial network that can learn the probability distribution characteristics of the channel, rather than just numerical fitting, which is more suitable for the reality of variable channels and can improve the robustness of the algorithm to different channels.

This document is directed to methods, systems, and devices related to wireless communication, and more specifically, to provide data transmission service for multiple users with different moving speeds. A method of wireless communication, comprising receiving, by a wireless communication device, a reference signal (RS); receiving, by the wireless communication device, a channel state information (CSI) reporting configuration signaling; determining, by the wireless communication device, according to the RS and the CSI reporting configuration signaling; and transmitting, by the wireless communication device, to a network device, a CSI according to the CSI reporting configuration signaling.