532 results about "Correlation function" patented technology

The invention relates to the technical field of anomaly detection, and discloses a context-aware-driven multi-dimensional anomaly detection early warning method. The method comprises the following steps: collecting real-time context data in a target monitoring scene, and generating an initial feature set containing an environment parameter sequence and a behavior pattern map; a first detection model and a second detection model matched with the scene type are constructed according to the scene types, the first model comprises a dynamic correlation function of environment indexes and abnormal probabilities, and the second model comprises a nonlinear mapping rule of behavior characteristics and risk levels; and based on the real-time context deviation degree and the characteristic fluctuation coefficient, a target model is triggered to generate a dynamic early warning instruction, and the dynamic early warning instruction is pushed to an execution module to adjust a trigger threshold of an abnormal response strategy or a priority of a risk disposal process. According to the method, multi-dimensional data is combined, the adaptability and accuracy of anomaly detection are improved through dynamic model triggering and response strategy adjustment, and the method is suitable for various monitoring scenes.

The invention relates to the technical field of data processing, in particular to a standardized detection result calibration method based on multi-modal fusion, which comprises the following steps of: performing high-precision space-time synchronization and standardization on different modal data, performing dynamic compensation by utilizing timestamp alignment, a cross-correlation function and an IMU (Inertial Measurement Unit), and performing dynamic parameter adjustment by adopting a local abnormal factor algorithm. Carrying out cross validation by utilizing inherent relevance of multi-modal data, constructing and continuously optimizing a high-confidence system state representation model, and realizing real-time identification and self-adaptive calibration of model parameters by adopting a Bayesian online learning framework; the method further comprises the steps that closed-loop recalibration is conducted through multi-sensor cross validation and residual analysis, an optimal action sequence is generated through a reinforcement learning agent, accurate prediction of key indexes of a monitored object is achieved, instant calibration and situational prediction can be conducted according to a specific event, and the intelligent level and maintenance efficiency of system monitoring are comprehensively improved.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention discloses a power grid cross-correlation harmonic suppression method, device and equipment and a storage medium, and the method comprises the steps: carrying out the wavelet packet decomposition of a subnet electric signal, and obtaining the harmonic component of a subnet at each frequency band; calculating the harmonic phase difference between the subnets according to the cross-correlation function between the harmonic components of the subnets and the topological impedance matrix, and calculating the total harmonic distortion rate according to the harmonic components of the subnets in the frequency bands; constructing a harmonic transfer equation according to the harmonic phase difference between the subnets and the equivalent output impedance of the inverter so as to calculate the harmonic responsibility proportion of the subnets; solving the double-layer game model based on the total harmonic distortion rate, the reference value of the total harmonic distortion rate and the harmonic responsibility proportion of the subnet, and obtaining the output of the governance equipment; and inputting the output of the treatment equipment into the fuzzy controller to obtain the impedance adjusting quantity of the subnet, and adjusting the virtual impedance according to the impedance adjusting quantity to update the harmonic backflow path. According to the invention, harmonic suppression among multiple subgrids during high-permeability distributed grid connection is realized, and the operation stability of the power grid is improved.

The invention relates to the technical field of audio optimization, and discloses an audio data offset detection and compensation method and system, a storage medium and equipment, and the method comprises the steps: building a timestamp synchronization mechanism with nanosecond precision, and distributing a unique time identifier for each floating-point audio sampling point; detecting time sequence offsets of input and output signals through a self-adaptive sliding window, and positioning and determining an offset value based on a cross-correlation function extreme value; a compensation strategy is dynamically selected according to different intervals where the offset absolute value is located, and meanwhile a dynamic cache management mechanism is established to support real-time offset compensation operation; constructing a time sequence relation model through inter-channel signal correlation analysis in the multi-channel system, and implementing coordinated offset compensation based on a correlation analysis result and the compensation strategy; a performance monitoring index system is established to evaluate the offset compensation effect in real time, compensation parameters are adaptively adjusted according to audio content characteristics and system performance feedback, and accurate time sequence control over audio data is achieved through the method.

The invention relates to the technical field of intelligent detection, and further relates to a non-excavation underground water supply and drainage pipe network leakage detection method based on multi-data fusion. The method comprises the steps that 1, acoustic sensors are arranged along a pipe network at equal intervals, each acoustic sensor collects an acoustic time domain signal generated by pipeline leakage, and then the frequency spectrum intensity of the acoustic time domain signal at the pipeline characteristic frequency is calculated; step 2, calculating a cross-correlation function between two different acoustic sensors according to the frequency spectrum intensity, and determining acoustic delay of the acoustic time domain signal between the different acoustic sensors by searching a peak value position of the cross-correlation function; step 3, according to the acoustic delay, calculating the position of a leakage point by using a sound wave propagation principle; according to the position of the leakage point, the water leakage amount is evaluated in combination with pipeline use data. According to the method, through multi-data fusion and intelligent analysis, effective combination of trenchless, high-sensitivity, accurate positioning and scientific quantitative evaluation is realized.

The invention belongs to the technical field of underwater acoustic engineering and digital signal processing, and particularly relates to a fractional delay FIR (Finite Impulse Response) phase calibration method and system of a multichannel sonar system. Comprising the steps of signal acquisition and reference channel selection; high-precision relative delay estimation; calculating a cross-correlation function of other channel signals and the reference channel signal, performing coarse estimation by searching a peak point of the amplitude of the cross-correlation function, and performing fine estimation on the peak position by using a parabola interpolation method to obtain the delay time of each channel relative to the reference channel; designing an FIR fractional delay filter; digital filtering and phase calibration; verifying a calibration effect; and calculating the phase of each channel signal at the target frequency point after calibration, and counting the standard deviation of each channel signal so as to evaluate the improvement degree of phase consistency. The method aims at overcoming the defect that traditional integral multiple sampling delay correction precision is insufficient, high-precision phase compensation of a sub-sampling point level is achieved, and therefore the performance of algorithms such as beam forming of a multi-channel sonar system is remarkably improved.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention relates to the technical field of GNSS (Global Navigation Satellite System) deception detection, and provides a multi-modal GNSS deception detection method based on dynamic weighting, which comprises the following steps: acquiring a one-dimensional SQM time sequence and a two-dimensional correlation function and carrying out preprocessing; performing feature extraction on the standardized two-dimensional correlation function through a Transform channel to obtain a global feature; performing feature extraction on the standardized two-dimensional correlation function through a CNN channel to obtain local features; obtaining a Transform channel dynamic weight matrix and a CNN (Convolutional Neural Network) channel dynamic weight matrix through the one-dimensional SQM time sequence; multiplying and adding the global features and the local features by corresponding weights to obtain fusion features; global average pooling is carried out on the fusion features, and the fusion features are mapped into deception probabilities through a full-connection network; and detecting spoofing according to the spoofing probability and the probability threshold. According to the method, the detection accuracy in a complex environment is remarkably improved, and the safety and reliability of the system are enhanced.

The invention provides a GNSS deception jamming joint detection system and method, and relates to the technical field of GNSS. GNSS navigation signals are received and processed through a baseband signal processing module, and advanced, instant and lagged related function values of an I path and a Q path are generated; a signal layer index unit calculates a moving variance of a code phase difference based on the correlation function value to generate a signal layer index; the information layer index unit calculates a code carrier difference value based on the pseudo-range, the carrier frequency and the integral carrier phase to generate an information layer index; and the spoofing detection module performs data fusion on the signal layer index and the information layer index to generate a joint detection index, and judges a spoofing state through a threshold value. The distortion of an IQ code correlation value integral signal is captured through a signal layer, and on the other hand, the inconsistency of a deception signal carrier, a pseudo-range relative measurement value and a real signal is captured through an information layer, so that the technical effect of rapid complementary detection of the signal layer and the information layer on deception interference is realized.

The invention relates to the technical field of process control, and discloses a multiple cyclic utilization process control method of regenerated polyethylene for tailing seepage prevention, which comprises the following steps: synchronously acquiring melt pressure, screw driving motor current and a control input sequence, calculating a cross-correlation function of the current and the pressure, and identifying physical transmission lag time; monitoring an uncommanded residual component of the current in real time, deducing an impending pressure fluctuation amplitude based on a cross-correlation function, and generating an inertia feedforward compensation amount; according to the method, a dual-time scale model is utilized to update parameters online and construct a disturbance observer to generate a feedback compensation control quantity, feedforward and feedback compensation are superposed to a basic output quantity to generate a final driving instruction, a heterogeneous signal time sequence correlation mechanism is utilized, reverse adjustment is carried out before pressure fluctuation is transmitted to a machine head through a screw, and the pressure fluctuation is reduced. And a control blind area caused by physical transmission lag is avoided.

The invention relates to the technical field of bridge engineering structure health monitoring, in particular to a service life prediction method and system for a bridge connection part, and the method comprises the steps: collecting the strain of a grouting sleeve, a reinforcement-grouting interface ultrasonic reflection signal and environment temperature and humidity data through a distributed sensor array; a three-dimensional dynamic void rate model is generated through fusion of a spatial topological mapping algorithm, a correlation function of load circulation and void expansion is established, the dynamic void rate is input into a bond strength coupling degradation model, after strength attenuation is quantized in combination with steel bar corrosion data and bond failure early warning is triggered, stress redistribution is calculated through multi-physics coupling simulation, and the dynamic void rate is obtained. Early warning is taken as a starting node, the actual load spectrum and temperature and humidity data are combined, Monte Carlo sampling is adopted to simulate rigidity degradation and output the residual life probability, the system comprises a multi-source collaborative sensing unit, a micro-void evolution analysis unit and a life prediction decision unit, and the safety control precision of bridge connection parts is improved.

The invention discloses a distributed OFDR sensing signal demodulation method and system based on local ridge line tracking, relates to the technical field of optical fiber sensing, and is used for solving the problem that a traditional cross-correlation algorithm is difficult to consider robustness and real-time performance at the same time. The method comprises the following steps: respectively processing a reference beat frequency signal of a sensing optical fiber to be measured in a strain-free state and a measurement beat frequency signal of the sensing optical fiber to be measured in a strain state to obtain a reference complex Rayleigh scattering signal and a measurement complex Rayleigh scattering signal containing a plurality of sensing positions along the optical fiber; calculating a cross-correlation function based on the two signals; stacking the cross-correlation functions corresponding to all sensing positions along a distance domain, and constructing a two-dimensional cross-correlation thermodynamic diagram matrix; performing filtering processing and weighting processing on the two-dimensional cross-correlation thermodynamic diagram matrix to obtain an optimal frequency drift distance; and demodulating based on the optimal frequency drift amount to obtain strain distributed along the to-be-measured sensing optical fiber. According to the invention, high-robustness and rapid demodulation of the signal in an application scene with extremely high real-time requirement can be realized.

The invention discloses an intelligent identification method for an electricity larceny prevention behavior of an electric energy metering box, which relates to the technical field of electricity larceny behavior identification and comprises the following steps: continuously acquiring data of the metering box, calculating a difference value between metering energy and estimated energy, extracting a symbol direction to generate a metering deviation accumulation index and determining an abnormal starting moment; taking the moment as an anchor point to extract a current ripple phase and a zero-sequence current direction, calculating a correlation function of a differential symbol sequence, and determining a characteristic response time delay and a coupling weight search range through combined evaluation of time delay peak sharpness and time delay peak stability to construct a coupling projection signal; traversing a search range in a verification window to calculate forward and reverse cumulants, and solving a self-consistent coupling weight and an abnormal effective moment; a coupling projection is constructed by using the self-consistent coupling weight and the time delay, and a verification accumulation index is generated; when the overlap ratio of the verification accumulation index and the metering deviation accumulation index falls into a preset credible interval, determining that an electricity stealing behavior exists; according to the invention, the accuracy and reliability of electricity stealing behavior identification are improved.

The invention relates to the technical field of power systems, and discloses a cable defect positioning method, system and device, a medium and a product, and the method comprises the steps: taking a Gaussian envelope linear chirp signal as an initial incident signal for frequency domain reflection, optimizing the signal characteristic parameter of the initial incident signal, obtaining an incident signal, and carrying out the positioning of a cable defect. The method comprises the following steps: exciting a target cable through an incident signal, obtaining a reflection signal on a preset measuring point of the target cable, respectively carrying out time-frequency conversion on the incident signal and the reflection signal to obtain an incident time-frequency domain signal and a reflection time-frequency domain signal, and obtaining a time-frequency domain signal and a reflection time-frequency domain signal on the basis of a cross-correlation function. The peak position of a cross-correlation function is determined through the incident time-frequency domain signal and the reflected time-frequency domain signal, the signal propagation defect time delay of the target cable is determined according to the peak position of the cross-correlation function, and the defect position of the target cable is determined according to the signal propagation defect time delay and the propagation speed of the incident signal. Therefore, surrounding signal interference can be filtered out, and the accuracy of cable defect positioning is improved.

The invention relates to the technical field of fault diagnosis, in particular to a circuit breaker mechanical fault pre-diagnosis method based on time sequence analysis, which comprises the following steps of: firstly, acquiring mechanical operation data of a circuit breaker in real time by installing a sensor, and preprocessing the data; then, on the basis of standard operation data, a time sequence segmentation strategy is adopted, the data is segmented into independent time sequence sequences, time domain features and frequency domain features are extracted for each independent time sequence sequence, and a time sequence dependence mode is extracted in combination with an autocorrelation function and a cross-correlation function; fusing the time domain feature, the frequency domain feature and the time sequence dependency mode through a time frequency-time sequence collaborative modeling framework; and finally, on the basis of the time sequence features, a federated Bayesian unit is constructed for fault diagnosis, local units provide fault type and confidence prediction, and a global unit integrates models of a plurality of local units through a federated learning framework. According to the invention, circuit breaker mechanical fault pre-diagnosis is realized through time sequence analysis.

A gear fault diagnosis method based on the joint weighted envelope anti-noise correlation of sub-signals. The method comprises: first, converting an original vibration signal sequence into envelope signals by means of a squaring-low pass filtering-square root calculation process of a signal sequence element by element; then, reconstructing the envelope signals on the basis of different time intervals, so as to obtain a series of sub-signals, and calculating a fault information representation metric of each sub-signal on the basis of L-moment theory indexes; next, assigning a weight to each sub-signal by combining the fault information representation metric and Sigmoid function transformation; then, on the basis of the envelope signals, and reconstructed sub-signals and corresponding weights thereof, calculating a joint weighted envelope anti-noise correlation function of an envelope signal sequence and the reconstructed sub-signals; and finally, determining a characteristic frequency on the basis of the reciprocal of a time interval value corresponding to a characteristic peak in a drawn graph showing the change of the joint weighted envelope anti-noise correlation function along with the time intervals, and finally recognizing a gear fault. By means of the method, a gear fault can be reliably recognized simply on the basis of time domain analysis when a signal length is limited and there is complex noise interference.

The invention discloses a temperature measurement calibration method and system for an infrared temperature measurement thermal imager, a medium and a product, and the method comprises the steps: uniformly selecting a plurality of temperature points in a temperature measurement range, and building a first mapping relation between a response value and a temperature measurement value of the infrared temperature measurement thermal imager through a least square method; based on the acquired environment temperature, establishing a first correlation function of the shutter response value and the environment temperature; based on the environment temperature, establishing a second correlation function of the blackbody response value of the middle reference temperature and the environment temperature; on the basis of a preset three-item calibration model, the first correlation function and the second correlation function are combined, and the response value of the temperature measurement thermal imager is calibrated; based on the calibrated response value of the temperature measurement thermal imager and the first mapping relation, temperature data after pixel difference calibration, temperature drift calibration and additional radiation calibration are calculated; according to the invention, the temperature measurement precision of the infrared temperature measurement thermal imager is improved, the calibration process is simplified, the implementation difficulty is reduced, and the calibration cost is reduced.

The invention relates to the field of metal powder processing, in particular to a sintering process intelligent control method and system based on a powder metallurgy metal material, and the method comprises the steps: obtaining a temperature curve of a powder metallurgy sintering process, determining the offset of each curve through a cross-correlation function, and carrying out the alignment; the method comprises the following steps: determining a segmentation length according to segmentation adaptability of a local area, analyzing an abnormal reference degree based on correlation between a mahalanobis distance of each segment and sintering quality, obtaining an overall abnormal degree of each curve through normalization processing and weighted calculation, and judging whether an abnormal temperature curve exists according to the overall abnormal degree and a preset abnormal degree threshold value. And if the abnormal degree exceeds the preset abnormal degree threshold value, early warning that the sintering process is abnormal, and prompting adjustment control. According to the invention, local abnormity is accurately detected, temperature control is optimized, the production cost is reduced, and the product quality and the production efficiency are improved.

The invention discloses a structural damping ratio identification method and device considering frequency spectrum leakage error compensation. The method comprises the following steps: firstly, obtaining a cut-off free attenuation signal under impact vibration, and carrying out Fourier transform on an autocorrelation function and a cross-correlation function of the signal to obtain a response power spectral density matrix influenced by a spectrum leakage error; singular value decomposition is carried out on the response power spectrum density function through a frequency domain decomposition method to obtain a damping ratio and an inherent frequency, a spectrum leakage error is calculated through the calculated inherent frequency and the damping ratio, and the error obtained through calculation is substituted into an output power spectrum. And the inherent frequency and the damping ratio are identified by using the power spectrum after the frequency spectrum leakage error is compensated, and the above process is iterated for multiple times, so that the frequency spectrum leakage error in damping ratio identification can be compensated. When the free attenuation data is used for identifying the damping ratio, spectrum leakage errors caused by data truncation can be made up, the signal-to-noise ratio of the data can be improved, the influence of noise can be reduced, and the identification precision of the damping ratio is improved.

The invention discloses an underwater sound environment sensing method based on multi-array-element sparse channel estimation. An underwater sound receiving end receives signals transmitted through an underwater sound multipath channel through a multi-array-element array; performing Hilbert transform on the receiving signal of each array element to obtain an analysis signal, and calculating a cross-correlation function of the analysis signal and the transmitting signal; based on the cross-correlation function, sparse channel parameters, including path amplitude and time delay, of each array element are estimated by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold value; the method comprises the following steps: constructing an array response vector by using sparse channel parameters of a multi-array element array, and searching and estimating angles of arrival, including a direction angle and a pitch angle, of a multipath signal through a spatial spectrum peak value; and based on the arrival angles, amplitudes and time delays of the direct path and the reflection path, inverting an underwater environment structure through a ray acoustic theory, including a reflection point distance and a reflection surface normal vector, so as to realize three-dimensional perception of the underwater reflector. According to the invention, multipath resolution can be improved, and false alarm and missing detection can be effectively reduced.

The invention discloses a storage medium, and an optimization method, device and equipment for a multi-effect rectification process flow. The method comprises the following steps: constructing a mechanism model comprising each rectification tower in a multi-effect rectification system; respectively constructing a first correlation function and a second correlation function; generating a data set of a preset group number of process parameters; the data set serves as input, and the reboiler thermal load of the highest-pressure rectifying tower is obtained through mechanism model calculation and serves as theoretical output; taking the data set of the process parameters and theoretical output as modeling data, and generating a prediction model capable of predicting the thermal load of the reboiler of the highest-pressure rectifying tower according to the process parameters through deep learning; and obtaining predicted parameter values of the controllable process parameters when the maximum pressure rectifying tower reboiler has the minimum energy consumption through the prediction model. According to the method, the number of process parameters participating in calculation and the solving difficulty in subsequent optimization calculation can be greatly reduced, and the calculation efficiency of obtaining the optimal solution can be effectively improved.

The present invention discloses a method for modeling ultra-large-scale MIMO geometric random and beam-domain wireless channels, and the specific steps include: 1) setting simulation parameters, network layout, etc., generating initial large-scale parameters, and generating initial clusters based on model initialization parameters; 2) updating large-scale and small-scale parameters; 3) generating geometric random channel model channel coefficients, steering matrices, and beam-domain channel matrices; 4) analyzing channel characteristics, and verifying the accuracy of the model by comparing with measured data. The wireless channel modeling method established by the present invention supports spherical waves and spatial non-stationary characteristics, and can realize the conversion from geometric random to beam-domain channel models under near-field conditions. The model accuracy is verified by the consistency of simulation and measurement results of space-time-frequency correlation functions and channel capacity. The present invention has guiding significance for the design and performance evaluation of ultra-large-scale MIMO communication systems.

The present application discloses a method, apparatus, device and storage medium for cross-correlated harmonic control in power grids. The method includes: performing wavelet packet decomposition on subnet electrical signals to obtain the harmonic components of the subnet in each frequency band; calculating the harmonic phase difference between subnets based on the cross-correlation function and topological impedance matrix between the harmonic components of each subnet, and calculating the total harmonic distortion rate based on the harmonic components of the subnet in each frequency band; constructing a harmonic transfer equation based on the harmonic phase difference between subnets and the equivalent output impedance of the inverter to calculate the harmonic responsibility ratio of the subnet; solving a two-layer game model based on the total harmonic distortion rate and its reference value and the harmonic responsibility ratio of the subnet to obtain the output of the control equipment; inputting the output of the control equipment into the fuzzy controller to obtain the impedance adjustment amount of the subnet, and adjusting the virtual impedance according to the impedance adjustment amount to update the harmonic return path. The present application realizes the harmonic control between multiple subnets during high-penetration distributed grid connection, thereby improving the stability of power grid operation.

The present invention provides an energy consumption management method and a system, which are characterized in that: after energy consumption index related information is parsed, structured management is carried out; after corresponding energy consumption analysis, an indication of an energy consumption anomaly can be provided to aid in energy consumption management; and further, a rule parser is invoked in parsing, which can automatically run a specific algorithm and extract variable information of a parsing rule correlation function so as to implement automated parsing configuration of the rule parser.

The invention discloses a feature point identification method of a river hydrodynamic condition. The method comprises the following steps: S1, hydrological data collection; s2, dividing flow levels and simulating a flow field of each flow level; s3, arranging alternative control sections; s4, determining a target control section; and S5, constructing a correlation function to determine a target feature point. According to the method, historical hydrological and geographic data of a to-be-monitored river reach are utilized, hydraulic engineering simulation software is combined, a target control section is selected and determined by calculating section similarity indexes, relevancy values of all nodes in the target control section are calculated and selected through a constructed relevancy function, and the flow velocity of the nodes is combined; determining alternative feature points, and finally rejecting points influencing navigation, namely target feature points; the identified feature points are optimal monitoring points for arranging the river flow velocity monitoring device, a scientific basis is provided for reasonably arranging flow velocity monitoring points, only the water flow velocity of a limited number of feature points can be monitored, and the hydrodynamic change condition of the whole river can be mastered.

The invention belongs to the technical field of working modal analysis, and discloses a current-carrying pipeline complex modal parameter identification method and system. The method comprises the following steps: acquiring vibration responses of a plurality of measuring points on a current-carrying pipeline under load excitation; calculating a vibration response correlation function of each measuring point and the reference point; decomposing the vibration response correlation function into a series of independent intrinsic mode functions by using variational mode decomposition; dividing the intrinsic mode functions into two types: intrinsic mode functions with variable amplitude and constant amplitude; eliminating an intrinsic mode function with a constant amplitude, and establishing an intrinsic mode function matrix; performing Hilbert transformation on the intrinsic mode function matrix, and combining the intrinsic mode function matrix with a Hilbert transformation matrix thereof to obtain a signal analysis matrix; and identifying the amplitude and the phase of the complex modal shape of the current-carrying pipeline and the inherent frequency and the damping ratio of the current-carrying pipeline based on the signal analysis matrix. According to the invention, the complex mode parameters of the current-carrying pipeline in the operation state can be identified.

The invention discloses a general multi-bias noise model parameter extraction method for a microwave compound transistor, and relates to the technical field of semiconductor device modeling, the transistor is tested, and static direct current IV, S parameters and noise parameter actual measurement data under different biases are obtained; establishing a large signal model, and performing nonlinear model parameter extraction in combination with multi-bias static direct current IV and S parameters to obtain nonlinear model parameters; carrying out noise characteristic modeling under different biases, and extracting noise model parameters under different biases in combination with actually measured data and nonlinear model parameters; fitting a noise model parameter through a piecewise function to obtain a bias correlation function of the noise model parameter; and establishing a multi-bias noise model according to the nonlinear model parameters of the device, the noise model parameters and the bias correlation function, and verifying by combining actual measurement data of static direct current IV, S parameters and noise parameters. According to the invention, multi-bias noise modeling can be carried out based on a general noise model parameter bias correlation function equation.

The invention discloses an intelligent monitoring method and device for operation energy efficiency of a transformer. The method comprises the following steps: acquiring multi-dimensional time sequence data such as iron loss and copper loss of the transformer; generating a cross-domain influence factor or a cross-domain influence matrix for quantifying the cross-domain correlation strength of the iron loss and the copper loss by calculating a cross-correlation function between related time sequences of the iron loss and the copper loss; and the correlated quantitative features are fused with the standardized loss trajectory and the working condition parameters, and a machine learning prediction model is constructed, so that a hidden fault mode of'iron loss-copper loss cross-domain correlated degradation 'in which the copper loss is driven to abnormally increase by iron loss deterioration is identified and early warned. According to the method, the defect of isolated analysis of loss in the traditional technology is overcome, early warning and traceability of a specific composite fault can be realized, and the safety and reliability of operation of the transformer are remarkably improved. The invention further provides a device for executing the method.

The invention discloses a universal 3D codebook design method and device for super-large-scale MIMO (Multiple Input Multiple Output). The 3D codebook design method comprises angle design and distance design. According to the design method, a design scheme of an angle and a distance is obtained based on a target of minimizing a correlation function. Although the angle and distance design scheme is initially designed for the near-field area, the design principle and the implementation effect of the angle and distance design scheme are also suitable for the far-field area, and the angle and distance design scheme has good universality. Compared with the existing work, the 3D codebook design theory and method can realize higher discrimination of the wave beam in each sampling angle and sampling distance, and reveals the performance gain brought by the excavation of the degree of freedom of the vertical dimension under the ideal condition.