109 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 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 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 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 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 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 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 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.

The invention discloses a microwave transistor small signal modeling method and system, and belongs to the technical field of microwave circuit and device modeling, and the method is based on a Kolmogorov-Arnold network (Kolmogorov-Arnold Network, KAN), and comprises the following steps: firstly, constructing the Kolmogorov-Arnold network and an adjoint network of the Kolmogorov-Arnold network; secondly, building a model training architecture based on small signal S parameter data under direct current and multi-bias conditions; then, training the training architecture by using small signal S parameter data under direct current and multiple biases; and finally, exporting the trained microwave transistor small signal model. According to the method, the novel Kolmogov-Arnod network is introduced to realize small-signal modeling of the microwave transistor, so that the modeling precision and generalization capability are remarkably improved, and an efficient and reliable technical solution is provided for a transistor modeling task in microwave circuit design.

The invention discloses an oil field pipeline intrusion behavior generation type early warning method and system based on radar signal time sequence prediction and a Diffusion model, and relates to the field of oil field security and protection monitoring. The problems that in the field of oil field security and protection monitoring, single-model detection robustness is insufficient, normal signal modeling is not accurate, sample dependence and data scarcity contradictions exist, an anomaly judgment mechanism is rigid, and full-process collaboration is missing are solved, and the method comprises the steps that synthetic data and real data are generated through a GUI; the generated synthetic data and the real data are preprocessed; constructing an LSTM (Long Short Term Memory) time sequence prediction model and a Diffusion model, and carrying out standardization processing; a weighted summation strategy is adopted to calculate a comprehensive abnormal score, a self-adaptive abnormal threshold value is set, and when the comprehensive abnormal score exceeds the threshold value, it is judged that an intrusion behavior exists, and early warning is triggered; and naming and storing an intrusion behavior detection result according to a timestamp, so as to support historical data query and statistical analysis and complete oilfield pipeline intrusion behavior generation type early warning.

The present application relates to a kind of myelin repair auxiliary method based on neural signal modeling.The method obtains the multi-modal neural signal data such as electroencephalogram signal of individual to be rehabilitated and is preprocessed, forms preprocessed data set;Subsequently, individualized time series modeling process is executed, and the time series characteristic representation including nerve conduction velocity recovery rate, phase consistency and neural pathway integrity is generated, and individualized calibration is carried out by dynamic baseline updating mechanism.The calibrated feature representation is input to the pre-trained deep learning inference engine.The inference engine outputs myelin repair index and imbalance risk score, which is used to represent the degree of repair and predict future abnormal risk.Based on the above index, a set of intervention parameters is generated, including electrical stimulation intervention parameters, virtual rehabilitation training task difficulty and rehabilitation training rhythm, and further forming control instructions to configure virtual rehabilitation training environment and electrical stimulation execution interface, so as to realize the individualized assistance and dynamic optimization of myelin regeneration repair.

The present application relates to the field of biological signal processing, in particular to a frequency estimation method based on rootMUSIC and signal dimension conversion, a storage medium and equipment. The method comprises the following steps: chest displacement signals extracted from radar echoes are modeled as the sum of sine waves with integer multiple frequencies of basic respiration and heart rate, and corresponding single-channel analytic signals are generated through Hilbert transform; the single-channel analytic signals are converted into multi-channel data through a signal rearrangement method, the chest wall signal model is equivalent to a uniform linear array multi-shot receiving signal model, so that the vital sign frequency and the signal source direction of arrival establish a one-to-one correspondence; the received data of the multi-shot receiving signal model is processed through an alternating direction multiplier method framework, and a denoised signal matrix is solved; the rootMUSIC algorithm is used for the denoised signal matrix to solve the frequency estimation. The present application converts the signal model, makes the frequency estimation range equivalent to the angle estimation range, and improves the frequency estimation accuracy.

The invention, which relates to the technical field of the thermal power plant intelligent inspection robot, discloses a thermal power plant intelligent inspection robot system based on a 5G private network, comprising an inspection robot module, a signal modeling module, a path planning module, a communication monitoring module, an AI regulation and control module, a slice management module and an edge decision module. The inspection robot module is used for executing an inspection task in a thermal power plant, collecting equipment state data and uploading the equipment state data through a 5G private network; the signal modeling module is used for constructing a 5G signal intensity spatial distribution model in the factory based on the reference signal receiving power reported by the inspection robot module and the factory space structure information; by constructing a 5G signal intensity space distribution model and introducing the model into path planning, collaborative optimization of physical reachability and communication reliability of a routing inspection path is realized, the problem of communication interruption caused by metal shielding in traditional routing inspection is effectively avoided, and a communication effectiveness index output in real time by a communication monitoring module is combined.

The application provides a single-loop PI parameter design method and system based on a two-line DC power flow controller, comprising the following steps: step 1: performing working condition analysis on a two-line common inductance type DC power flow controller, including transient operation analysis on the common inductance and the capacitor; step 2: small signal modeling of the two-line common inductance type DC power flow controller; step 3: building a simplified transfer function block diagram through a state space equation, and then calculating a PI parameter expression through the gain of an open-loop transfer function, and obtaining the designed PI parameter through the selection of a control bandwidth. The application comprehensively considers the control bandwidth of a control loop and the phase margin actual demand of the two-line DC power flow controller, designs the single-loop PI parameter of the two-line common inductance type DC power flow controller, and realizes the optimal phase margin and stability.

The invention discloses a method and a system for suppressing low-frequency oscillation of a network construction type energy storage converter, and belongs to the technical field of energy storage, and the method comprises the following steps: constructing a small-signal impedance model of the network construction type energy storage converter based on virtual admittance control by using a small-signal modeling method; order reduction simplification is carried out on the impedance model, key characteristics of a 0.1-10Hz synchronous frequency band are reserved, and oscillation core inducements in a current steady-state value matrix are identified; and adding a q-axis voltage feedforward control link, introducing a regulation coefficient, and modifying the regulation coefficient to enable the modified regulation coefficient to be negative to counteract oscillation inducements. The method can effectively suppress the interactive oscillation of the grid-forming type energy storage converter and the power grid at the low frequency band, improves the system stability, is suitable for various operation conditions, and has important engineering application value.

The present application relates to LLC resonant converter technical field, specifically to a kind of LLC resonant converter small signal modeling method based on time domain correction;Mainly include the following steps: establishing the equivalent model of LLC resonant converter each element and third-order average value equivalent circuit, LLC resonant converter small signal modeling method is reduced to second-order equivalent circuit by third-order average value equivalent circuit, add disturbance in second-order equivalent circuit, and separate large signal component and second-order small signal disturbance to obtain the analytical form of second-order average value small signal model;The LLC resonant converter small signal modeling method proposed in the scheme considers high precision and ease of use, improves the applicable frequency range, can guide LLC resonant converter controller design and stability analysis, the average value small signal model established by time domain analysis has higher precision, and analytical form transfer function is given, to facilitate practical engineering application.

The application relates to a DNN-based large-signal model modeling method, system, device and storage medium, and relates to the technical field of integrated circuits. The method is based on a GaN microwave device including a GaN transistor. The method comprises the following steps: performing large-signal testing on the GaN transistor to obtain a traveling wave characteristic; processing input layer characteristics and output layer characteristics in the traveling wave characteristic to obtain input layer data and output layer data; extracting real part and imaginary part data and training a high-power area deep neural network model by using a DNN; extracting amplitude and phase data and training a low-power area deep neural network model by using a DNN; training the traveling wave characteristic according to the high-power area deep neural network model and the low-power area deep neural network model to obtain a trained traveling wave characteristic, and converting the trained traveling wave characteristic into a voltage-current characteristic; and constructing a large-signal behavior model according to the voltage-current characteristic. The application has the technical effect of improving the accuracy of a large-signal modeling model of a GaN microwave device.