142 results about "Signal modeling" patented technology

The invention discloses a self-adaptive switching system and method for power supply and distribution, and the system comprises a data collection module, an analysis module, a modeling module, a decision module, an execution module and an early warning module, and the data collection module generates a real-time monitoring signal; the analysis module performs dynamic weighted evaluation according to the real-time monitoring signal and generates an evaluation signal; the modeling module constructs a virtual model of the power supply system and generates an analog signal; the decision signal generates a control signal containing a path selection instruction; the execution module controls the semiconductor device to switch the physical connection state of the power supply according to the control signal and generates a switch feedback signal; and the early warning module forms an equipment health assessment result according to the switch feedback signal and an abnormal arc signal when the execution module switches the connection state and transmits the result to the decision module. The self-adaptive switching system for power supply and distribution can solve the problem that a traditional power supply and distribution switching system depends on a single threshold to judge the state of a power supply and cannot comprehensively evaluate the quality of electric energy.

The invention discloses a power coupling-considered capacity ratio estimation method for a network-constructing / network-following multi-machine hybrid system, and the method comprises the steps: building a GFL-GFM hybrid system model, combining VSG power coupling small signal modeling and a dynamic relative gain matrix (DRGA), and quantifying the coupling strength of active / reactive power to output voltage; the frequency domain impedance characteristic of the GFM converter is simplified, and an ideal voltage source series equivalent reactance equivalent model is constructed; deriving an analytical relationship between a generalized short-circuit ratio (gSCR) and a capacity ratio based on the model, and generating a ratio formula in combination with engineering boundary sub-scenes to form a ratio range suitable for multiple scenes; for coupling time-varying characteristics, a closed-loop mechanism of'mixed mode-coupling sensing-correction calculation-execution feedback 'is constructed, and the ratio is dynamically adjusted. According to the method, coupling quantization and multi-machine coordination control are deeply fused, the problem that the external characteristic model precision and the multi-working-condition adaptability are difficult to consider under complex environment coupling is effectively solved, and the engineering applicability of the hybrid fan system is enhanced.

The invention discloses an impedance remodeling method for suppressing harmonic oscillation based on a superconducting magnetic energy storage system, and belongs to the technical field of power generation, power transformation or power distribution. The method comprises the following steps: analyzing theoretical characteristics of the superconducting magnetic energy storage system, analyzing mathematical models and working characteristics of an SMES and a storage battery, and carrying out theoretical modeling on the hybrid energy storage system; the method comprises the following steps: performing small signal modeling on a grid-connected converter control system under voltage control type virtual synchronous control, and establishing a grid-connected converter small signal sequence impedance model; a sequence impedance remodeling model is introduced, the influence of the sequence impedance remodeling model on the low-frequency impedance of the virtual synchronous control system is revealed, in order to obtain the optimal sequence impedance of the grid-connected converter, real-time and accurate power grid impedance information is obtained by injecting a multi-harmonic disturbance signal into the grid-connected inverter, and a traditional impedance remodeling strategy is dynamically adjusted according to the information. It is ensured that the system still has good stability margin when the power grid impedance fluctuates in a wide range, and the adaptability of the grid-connected inverter under different power grid working conditions is improved.

The invention provides a multi-inverter grid-connected system broadband oscillation suppression method based on impedance collaborative reconstruction, and the method comprises the steps: firstly carrying out the frequency domain small signal modeling, and constructing a station-level system frequency domain impedance model on the basis of a single machine; therefore, the design of an additional control branch is expanded, a power signal is superposed to the control branch through an additional damping control branch so as to realize the improvement of the impedance amplitude of the inverter, and the phase improvement of a capacitive frequency band is realized by adopting an active damping strategy based on a filter; secondly, constructing an impedance collaborative reconstruction method under a multi-inverter system, establishing a two-stage optimization structure, comprehensively solving the position and parameters of a control branch, and achieving the suppression of system oscillation. According to the invention, through collaborative optimization, conflicts of different control strategies are reduced, and the influence of a coupling relationship is minimized; furthermore, through cooperation of the multiple inverters, dual optimization of damping in the space and the frequency domain is achieved, and efficient suppression of broadband oscillation of the multi-inverter grid-connected system is achieved.

The invention discloses a high-frequency shallow water bottom reverberation signal modeling method, and belongs to the technical field of underwater acoustic engineering and signal processing. The method comprises the following steps: setting a modeling hypothesis; a cylindrical array geometric structure is constructed, scattering units are divided, and channel parameters and scattering functions are obtained in combination with a Bellhop tool and a GABIM model; generating a reverberation signal time domain expression, deducing an array receiving signal spatial domain expression and calculating a spatial spectrum; the model can also adjust underwater environment parameters to realize simulation under different conditions, and performs time domain and space domain theoretical analysis on reverberation signals based on a cylindrical array. Multi-dimensional underwater environment parameters are fused, and the modeling precision is improved; a receiving and transmitting combined cylindrical array is adapted, and spatial gain is embodied; experiments prove that the average error is within 3dB, the practicability is outstanding, support can be provided for design and optimization of the sonar system, and the method is suitable for underwater reverberation signal simulation and characteristic analysis of the sonar system in the high-frequency shallow water environment.

The invention discloses an electromagnetic metasurface sparse signal modeling method and device. The method comprises the steps that S1, sparse electromagnetic signals of an electromagnetic metasurface are acquired; s2, solving through signal sparse prior knowledge under the constraint of an electromagnetic constitutive equation to obtain a sparse signal distribution function; s3, according to the sparse signal distribution function, obtaining the coordinate system representation of the sparse signal in the Riemannian manifold space; and S4, according to the coordinate system representation of the sparse signal in the Riemannian manifold space, establishing a signal electromagnetic field manifold embedding model of the sparse signal mapped in the Riemannian space based on the electromagnetic metasurface. By the adoption of the technical scheme, the problems of an existing electromagnetic metasurface sparse signal modeling method in the aspects of algorithm efficiency, model adaptability and hardware collaboration are solved.

The invention relates to the technical field of satellite navigation signal processing, and discloses a signal noise suppression method in a Beidou navigation system, which comprises the following steps of: performing three-dimensional modeling on navigation signals, and extracting time-frequency characteristics to generate a signal model; constructing an interference heat prediction map, and dynamically updating and identifying a high-interference area; analyzing a signal phase change, calculating a phase rate, and identifying abrupt change interference; adopting adaptive filtering and L1 norm sparsification to remove multi-source interference; correcting deviation in signal modeling by using a redundant navigation module; and adjusting a signal processing strategy based on a real-time control and scheduling mechanism. According to the method, a signal modeling guide mechanism based on an interference heat prediction map is introduced, and the dynamic change of electromagnetic environment interference is combined with a signal modeling process, so that an interference sensitive area is recognized in advance, and structural sparse constraint is applied; and a blind area lacking sensing of interference at the initial modeling stage in the traditional technology is eliminated.

The invention discloses an induction detection system for production and manufacturing based on artificial intelligence and a regulation and control method, and belongs to the field of artificial intelligence. Comprising an edge data acquisition module, a depth image recognition module, a dynamic sensor integrated array, an artificial intelligence analysis module, an intelligent abnormity feedback module and a linkage execution control module. The edge data acquisition module is deployed at key nodes of a production line, models pressure, current, temperature, vibration and other signals, and performs cross sampling with the sensor array. The depth image recognition module adopts a structured light laser array and a multi-phase shift interference technology to extract a morphology gradient map in real time. And the artificial intelligence analysis module fuses the multi-scale convolutional network and the time sequence reasoning unit, and performs high-dimensional alignment and modeling on the double-source data to form a multi-resolution sensing field. And the intelligent abnormity feedback module generates a response grade score, and realizes millisecond-level control mapping with the linkage control module based on a state path. The method has the beneficial effect that the accuracy and response speed of abnormal recognition in the manufacturing process are improved.

The invention discloses a control method and system of an intelligent massage robot, and relates to the field of intelligent robots. The method comprises the following steps: performing multi-modal dynamic perception fusion, and processing sensor data by using an attention mechanism deep learning model to obtain a body state; modeling and judging a muscle state based on the bio-electricity signal; adjusting a massage reference in combination with environmental parameters; a personalized massage strategy is generated through reinforcement learning; and real-time feedback correction ensures the effect and safety. The system comprises a multi-mode sensing module and the like. The method solves the problems that in the prior art, accuracy is insufficient, and the method is difficult to adapt to individual and environment differences, and has creativity and practicability.

The invention relates to the technical field of signal processing, in particular to a periodic signal modeling method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a coordinate time sequence of a target navigation satellite system; preprocessing the coordinate time sequence to obtain a preprocessed coordinate time sequence, and correcting the preprocessed coordinate time sequence to obtain a corrected coordinate time sequence; estimating the corrected coordinate time sequence to obtain a sine coefficient of periodic amplitude, a cosine coefficient of periodic amplitude and periodic frequency deviation; the method comprises the following steps: establishing a coordinate time sequence model, decomposing, establishing an initial harmonic model, estimating to-be-estimated parameters of the initial harmonic model by using a preset estimation strategy to obtain an estimation result, and establishing a final harmonic model according to the estimation result and the initial harmonic model, thereby solving the problem of systematic deviation caused by empirical period length assumption in coordinate time sequence modeling. And the seasonal deformation inversion precision and the earth crust movement rate estimation reliability are improved.

The invention discloses a virtual synchronous machine key parameter optimization method giving consideration to transient power angle stability and short-circuit current suppression. The method comprises the following steps: establishing a VSG dynamic model; analyzing and calculating VSG short-circuit current and transient power angle stability respectively; analyzing key parameters influencing VSG short-circuit current and transient power angle stability by using small signal modeling to obtain a key parameter configuration range; according to the key parameter configuration range and the requirements for short-circuit current suppression and transient power angle stability, a VSG parameter optimization model is designed, and an improved genetic algorithm IGA is used for solving. And it is ensured that short-circuit current is suppressed during the optimized VSG fault period, and the transient power angle is kept stable. The invention aims to realize the effects of transient stability improvement and short-circuit current suppression only by optimizing parameters without adding extra strategies.

A segmented modeling method based on symmetrical segmentation of a travelling-wave electro-optic modulator, comprising steps: performing modeling simulation on electrical properties of an electro-optic modulation material of a silicon photonic modulator, and extracting electrical parameter data; performing optical simulation on a laser waveguide formed by the preprocessed electro-optic modulation material, and extracting optical waveguide parameter data; performing modeling simulation on an unloaded traveling-wave electrode, and extracting transmission line parameter data; performing small signal modeling analysis and calculation, and performing step-by-step segmentation processing on a medium-loaded traveling-wave electrode in a symmetrical segmentation manner, and performing data analysis and extraction on a simulation result to obtain a parameter result of system design; and the like. The problems of large amount of calculation, low speed and low efficiency can be solved.

The invention discloses a robust adaptive beam forming method based on a depth migration reconstruction network, which mainly solves the problems of signal self-cancellation and insufficient robustness of the existing beam former under non-ideal conditions, and comprises the following implementation steps of: modeling an array receiving signal; constructing real value signal covariance matrix input of the reconstruction network; designing an interference and noise covariance matrix reconstruction network based on a Unet framework; designing a guide vector estimation network based on a dual migration mechanism; real-value label construction and network generalization training are carried out; and calculating a beam forming weight vector. Under typical non-ideal conditions of random signal observation errors, coherent source local scattering errors and the like, the interference and noise covariance matrix and the expected signal guide vector can be accurately reconstructed, the signal self-cancellation phenomenon is effectively avoided, the robustness of a beam former is enhanced, and the method can be used for low-altitude target tracking, medical ultrasonic imaging and other scenes.

The invention discloses a digital self-interference cancellation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed signal, carrying out the modeling of a transmitting signal and a receiving signal containing a self-interference signal, and obtaining a mathematical model; and according to the mathematical model, performing dechirp processing, filtering processing and reconstruction processing in different time periods to obtain a signal completing self-interference cancellation. According to the method, modeling is carried out on a receiving and transmitting signal containing a self-interference signal and a jammer transmitting and receiving signal; establishing mathematical models for transmitting and receiving signals in different time periods; according to a mathematical model of a transmitting signal and a receiving signal containing a self-interference signal, dechirp processing, filtering and reconstruction are carried out in each time period, the implementation process is simple and convenient, digital self-interference cancellation can be efficiently realized, and the method can be widely applied to the technical field of signal processing.

The invention discloses an RIS-assisted millimeter wave satellite communication system channel estimation method based on PARAFAC. The method comprises the following steps: constructing an RIS-assisted millimeter wave satellite communication system cascade channel model; the method comprises the following steps: modeling received signals of an RIS-assisted satellite communication system into a third-order parallel factor (PARAFAC) model through data reconstruction based on a multi-dimensional structure of an RIS-assisted millimeter wave satellite communication system channel; and performing parallel factorization on received signals of the RIS-assisted satellite communication system by using an alternating optimization method to obtain an estimated value of a model factor matrix, and finally reconstructing an RIS-assisted satellite communication channel by using the estimated value of the factor matrix to obtain a channel estimated value. The RIS-assisted millimeter wave satellite communication system channel estimation method can accurately achieve RIS-assisted millimeter wave satellite communication system channel estimation, and the performance of the RIS-assisted millimeter wave satellite communication system channel estimation method is superior to that of a traditional least square method (LS)-based channel estimation method.

The invention discloses a multi-scale full-parameter large-signal modeling system for an ultra-wideband high-power transistor, which relates to the field of multi-scale full-parameter large-signal modeling and comprises a data module, an extraction module, a model module, an adjustment module and a generation module. Parasitic stripping and parameter extraction are carried out to obtain a frequency domain feature matrix, feature tensor construction and double-branch network training are carried out based on the frequency domain feature matrix and physical size parameters and electrical parameters of a transistor, mapping to an ASM model is carried out to obtain a multi-dimensional fine calibration scalable ASM model MASM, and electrothermal coupling and polarization scattering effect dynamic correction are carried out to obtain a polarization scattering effect. According to the method, the all-working-condition electrode drain physical base ASM model M * ASM-T is obtained, polarization scattering modulation analysis is carried out based on the all-working-condition electrode drain physical base ASM model M * ASM-T, the finally optimized all-working-condition electrode scattering drain physical base ASM model M * ASM-T-S is obtained, and the reliability of system modeling is enhanced.

The invention discloses a GNSS deception jamming suppression method based on inertial navigation auxiliary tracking, and the method specifically comprises the steps: carrying out the signal modeling through coherent integration after a radio frequency front end of a GNSS receiver receives a signal, and obtaining a coherent integration value of an in-phase branch and an orthogonal branch; constructing an observation equation and a state equation through coherent integration values of the orthogonal branch and the in-phase branch, and constructing a code loop phase discriminator error and a carrier loop frequency discriminator error by using coherent integration to obtain the observation equation and the state equation; obtaining deception jamming of different traction speeds by using combined detection quantity of constructed SQM and satellite and inertial information; on the basis of an NEYMAN-PEARSON detection theory framework, the optimal detection performance is sought under the constraint condition that the maximum false alarm probability PFAMAX is met, and robust Kalman filtering suitable for deception jamming suppression is designed; and inputting the state equation and the observation equation into robust Kalman filtering to obtain an interference coefficient and an anti-deception adaptive factor for deception interference suppression. According to the method, deception jamming of slow traction is effectively suppressed.

The invention provides a multi-input and multi-output frequency coupling admittance modeling method for a follow-up network fan, and belongs to the technical field of large-scale follow-up network type and follow-up network type fan frequency coupling admittance modeling. The technical problem that a network following type GFL and network constructing type GFM doubly-fed fan series-parallel system in a wind power plant lacks an accurate small-signal modeling and analysis method is solved. According to the technical scheme, the method comprises the following steps: S1, establishing an open-loop doubly-fed motor self admittance considering stator and rotor flux linkage dynamics; s2, obtaining frequency domain multiple-input multiple-output admittances of the following network type doubly-fed fan and the constructed network type doubly-fed fan; s3, obtaining a unified multiple-input multiple-output admittance frequency domain model containing a closed loop interconnection system formed by the following network type doubly-fed fan admittance YGFL and the construction network type doubly-fed fan admittance YGFM; and S4, quantitatively evaluating a phase-locked loop parameter and a virtual synchronous control parameter. According to the invention, accurate determination of frequency coupling oscillation is realized.

The invention discloses an enhanced power decoupling control method for a virtual synchronous generator, relates to the technical field of virtual synchronous generators, and solves the problems that a systematic analysis method for a resistance-inductance line power coupling mechanism is lacked and a collaborative design framework of a dynamic power decoupling condition and a steady-state power transmission requirement cannot be established in the prior art. According to the method, firstly, a dynamic coupling coefficient is accurately quantified through small signal modeling, then a voltage amplitude compensation mechanism based on virtual impedance is established, steady-state power transmission constraints are embedded into dynamic decoupling conditions, and virtual impedance parameters have the dual functions of eliminating dynamic coupling and guaranteeing steady-state tracking. The method does not depend on ideal working condition assumption, can calculate the optimal virtual resistance and virtual reactance on line according to real-time line parameters and operation working conditions, improves the adaptability of the VSG to a weak power grid resistance-inductance line, effectively inhibits dynamic cross coupling between power, and enhances the frequency support and voltage support capability of the VSG under a complex power grid condition.

The invention discloses a rolling bearing vibration response modeling and fault diagnosis method, equipment and a medium. The method comprises the following steps: establishing an unequal-interval and amplitude-modulated exciting force sequence signal model; and modeling a vibration impact response signal, simulating an envelope demodulation numerical value, and diagnosing faults. According to the invention, the rolling bearing is taken as a research object, a vibration impact response signal model is established, the model is finer, and the vibration mechanism of the bearing can be simulated more accurately; the characteristics of impact excitation, response signals and envelope signals can be analyzed in detail in sequence, unequal interval and amplitude modulation characteristics of excitation force are decoupled from the vibration principle, fault characteristics of impact response and envelope are revealed, and rolling bearing fault diagnosis considering unequal interval and amplitude modulation excitation force can be realized.

The invention provides a wind power integration system small disturbance modeling and stability analysis method considering control delay, and the method comprises the steps: introducing a sampling delay link, a zero-order holding link and a switch delay link in a small disturbance linearization modeling process, carrying out the linearization processing through employing a first-order inertia link, and building a unified state space model; respectively constructing control delay mathematical models of the direct-driven fan and the double-fed fan, and analyzing and identifying the influence of the control delay on the oscillation mode and stability of the system through characteristic values and impedance; and finally, performing time domain simulation in the simulation platform, and comparing a calculation result with a simulation result to verify the accuracy of the model. The method can accurately reflect the small disturbance characteristics of the wind power grid-connected system in the sub-synchronous frequency band and the super-synchronous frequency band, provides a theoretical basis for optimization of control parameters of a wind turbine generator and evaluation of grid-connected stability, and solves the technical problem that the influence of control delay is generally ignored in modeling of an existing wind power grid-connected system.

This invention proposes an integrated beamforming method and system for communication and sensing based on reconfigurable smart surfaces, belonging to the field of wireless communication technology. The aim is to design suitable beamforming to maximize communication and data rate while ensuring adequate radar sensing performance. The method includes: first, constructing an ISAC system containing J RIS elements, a base station (BS), and M single-antenna mobile users (MUs), and completing the modeling of baseband signals and user received signals; second, constructing user communication and data rate models and radar target detection signal-to-noise ratio models, establishing unit mode constraints on RIS reflection coefficients and transmit power constraints; third, employing an improved algorithm combining alternating optimization and fractional programming to decompose the non-convex optimization problem into sub-problems for iterative solution, ultimately maximizing communication and data rate and radar sensing performance, while also meeting the dual functional requirements of communication and sensing. This invention is applicable to providing target detection and communication functions for vehicles.

The application discloses a non-uniform noise background-based sparse array gridless parameter estimation method, and the implementation steps are as follows: a sparse array structure is arranged at a receiving end; a sparse array receiving signal in a non-uniform noise background is modeled; a covariance matrix of the array receiving signal is calculated; receiving data of a virtual differential common array is calculated through vectorization and de-redundancy; a distribution to which a fitting error is subjected is derived, and a parameter estimation optimization model based on atomic norm minimization and avoiding a complex regularization parameter determination process is constructed; a semi-positive definite programming form of a primal problem and a dual problem is derived; and a wave direction estimation result is obtained through a MUSIC algorithm or polynomial root seeking. In addition, on the basis of fully utilizing a virtual differential common array of the sparse array to provide extended degrees of freedom and all information, the application restrains the influence of non-uniform noise by constraining the sparsity of data atoms to be recovered and selecting a regularization parameter from a chi-square distribution table according to a distribution to which a fitting error between the data to be recovered and actual receiving data not affected by noise is subjected, overcomes the grid mismatch problem caused by parameter domain discretization, avoids a complex process of determining the regularization parameter, and can obtain a higher-precision source direction estimation result.

The invention discloses a metal workshop process intelligent management system and method, and relates to the technical field of intelligent workshop production management. Comprising a workshop working condition module, a production line distribution module, a workshop section progress module, a machine abnormity analysis module and an intelligent management module. Order information and a disassembling rule are obtained to generate a processing task set, matching is carried out in combination with the real-time state of a production line resource database, an optimal production line is output, and a tracking closed loop is realized; when the progress of the workshop section is abnormal, modeling is carried out on process parameters and processing signals, predicted processing duration and a quality coefficient are calculated, quality judgment and personnel abnormity judgment are triggered in combination with actual data, and the intelligent management module carries out linkage early warning and feeds back to a workshop supervisor. According to the invention, order decomposition, production line distribution, progress monitoring, quality analysis and personnel management full-process intelligentization are realized, the problems of insufficient resource matching, untimely abnormal positioning and single quality monitoring are solved, and the productivity utilization rate and the production management accuracy are improved.

The invention provides a distributed radar-oriented low-interception probing integrated signal design method and system, and relates to the field of wireless communication and radars, and the method comprises the steps: obtaining first information and second information; performing distributed node emission signal modeling according to the first information and the second information to obtain a signal expression of each emission node; illegal reconnaissance interception receiving signal modeling is carried out according to the signal expression, and a total signal model received by an interception machine is obtained; performing low interception performance optimization target design according to the total signal model to obtain an optimization target function; constructing a distributed low-interception probing integrated signal design model according to the optimization objective function to obtain a joint optimization problem model; and performing optimization solution according to the joint optimization problem model to obtain a low-interception probing integrated signal. According to the invention, the anti-interception and anti-eavesdropping capabilities of the distributed co-detection system are improved, and balanced optimization of detection, communication and safety performance is realized.

This invention belongs to the field of linear periodic time-varying system modeling and analysis technology, and discloses a small-signal modeling method and application for LCC-HVDC systems. The method includes: constructing mathematical models of the segmented and continuous parts of the LCC-HVDC system based on its circuit topology within each operating interval, and integrating these models as the mathematical model of the LCC-HVDC system; linearizing the trajectory of this mathematical model to obtain its small-signal model within each operating interval; designing a small-signal model of the LCC-HVDC system during switching between adjacent operating intervals using the Filippov method; and combining the small-signal models of the LCC-HVDC system within each operating interval and the small-signal models at the switching time to constitute the small-signal model of the LCC-HVDC system, thus completing the small-signal modeling. This invention can accurately characterize the switching dynamics of the LCC-HVDC system, achieving accurate and low-complexity small-signal modeling of the LCC-HVDC system.

The invention relates to a movable array-based communication and induction integrated positioning method and system. The method comprises the steps of (1) time division ISAC mode configuration; (2) establishing a mobile array continuous moment signal transceiving model in the sensing time slot; (3) establishing a discrete signal transceiving model of the movable array in the sensing time slot; (4) performing joint processing on the signals in the plurality of discrete sensing time slots, enabling Doppler-induced time phase change to be equivalent to space phase deviation, and constructing a space-time steering vector model; (5) converting the space-time steering vector model into an equivalent virtual array; and (6) modeling the received signal into a sparse representation model, and performing angle positioning on the target through a compressed sensing algorithm. Compared with the prior art, the method provided by the invention can realize angle positioning with higher precision under the same physical antenna array element.

The present invention discloses a method for predicting the internal leakage level of a valve based on an acoustic emission signal, which solves the problems in the prior art of being unable to automatically extract valve internal leakage features, as well as the problems of low modeling efficiency and low prediction accuracy. The method comprises: first, collecting the acoustic emission signals of the valve under different internal leakage rates, and performing bandpass filtering preprocessing on the original signals. Then, wavelet scattering transform is used to extract features of the valve internal leakage acoustic emission signal, the second-order scattering coefficient is converted into a two-dimensional feature matrix by averaging in the time dimension, and the ReliefF algorithm is used to extract the optimal scattering coefficient feature. The optimal feature subset and pressure are used as inputs of the classification model, and the AdaBoost.M1 method is used for classification modeling. Finally, the trained AdaBoost.M1 model is used to predict the internal leakage level of the unknown valve internal leakage acoustic emission signal. The present invention realizes the automatic extraction of valve internal leakage features and improves the efficiency and prediction accuracy of valve internal leakage acoustic emission signal modeling.

The invention belongs to the technical field of power electronic control and system stability analysis in a new energy power generation system, and discloses a small signal modeling method for a module-level power optimizer. According to the method, a unified small signal model among a photovoltaic module, a DC / DC power converter, an inductor, a capacitor, a load and a controller is established; determining a transfer function relationship among output voltage disturbance, duty ratio disturbance and a control error signal; packaging a single-module model into a standard negative feedback control structure, and supporting Bode and Nyquist stability analysis; the method can be popularized to a multi-module series or parallel system, and system-level small signal stability evaluation is realized.

The invention discloses a breath sound signal-based multi-feature fusion enhanced feature extraction method and system, and relates to the field of machine learning. The problems that according to a breathing sound signal modeling method in the prior art, due to the fact that average processing and dimension reduction splicing are conducted on different features in the time dimension, time sequence information loss is likely to be caused, the complementary advantages of the features cannot be effectively exerted due to the fact that the multiple features are fused in a simple splicing mode, and the recognition capacity of a model for breathing sound dynamic changes is limited are solved. The method comprises the following steps: acquiring original breath sound signal data, and preprocessing; extracting time-frequency characteristics of the preprocessed original breath sound signal data; a dual-channel synchronous random masking mechanism is designed, and random masking of time and frequency dimensions is carried out on the static features and the dynamic features at the same time; and constructing an adaptive feature fusion and enhancement module, and inputting the fused and enhanced features into a backbone network of the pre-trained AST to complete feature extraction of multi-feature fusion and enhancement based on the breath sound signals.