436 results about "Transient response" patented technology

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

The invention relates to the technical field of power systems, in particular to a multi-dimensional network performance index evaluation system and method based on measured data of a new energy station. Comprising a data acquisition preprocessing unit; a multi-dimensional index construction unit; the weight adjustment unit dynamically adjusts the index weight according to the operation condition based on a fusion algorithm of an improved analytic hierarchy process and an entropy weight method; the performance evaluation model unit adopts a comprehensive evaluation model fused with a random forest algorithm to generate multi-dimensional performance scores and risk early warning; and the visual interaction terminal unit is used for displaying the evaluation result in real time and generating a trend analysis report. According to the invention, the three-dimensional evaluation system covering the static electrical parameters, the dynamic stability indexes and the harmonic pollution indexes is constructed, so that the limitation of single-dimensional evaluation in the prior art is broken through, and the extension of the network-related performance of the new energy station from local detection to full-dimensional coverage is realized; and the multi-scene evaluation requirements of the power grid on the steady-state operation, the transient response and the electric energy quality of the station are accurately matched.

The invention discloses an energy storage system state evolution trend prediction method based on multi-source data fusion. The method comprises the steps of terminal voltage, current and temperature time sequence data acquisition, time sequence segmentation normalization, multi-physics field coupling feature construction, trend prediction model construction and training and energy storage system state evolution trend prediction. According to the method, the distinguishing capacity of the model for charging and discharging physical characteristics is improved, meanwhile, the voltage change rate, the multi-dimensional feature vector of the differential internal resistance and the thermal-electric coupling effect and the explicit encoding electric-thermal-resistance coupling relation are constructed, the transient response and the temperature hysteresis effect can be effectively captured, and then the model can be used for analyzing the charging and discharging physical characteristics. A degradation-aware cross-cycle feature extraction and gating mechanism is adopted, short-term fluctuation and long-term trend are adaptively balanced in multi-scale prediction, the prediction conflict problem is relieved, finally, physical constraints based on the electrochemical law and the internal resistance temperature characteristic are embedded in a loss function, it is ensured that the prediction result is accurate in numerical value and conforms to the physical law, and the prediction accuracy is improved. And generation of physically impossible solutions is avoided.

The invention discloses a method and system for testing the electrical performance of a semiconductor chip, and the method comprises the steps: building a mapping relation between dielectric loss and loss frequency through obtaining the dielectric loss data, internal material characteristics and dynamic load power factors of the chip under each frequency band, recognizing a loss abnormal region, and analyzing the influence of the loss abnormal region on the performance of the chip; correcting test data by adopting a minimum mean square error algorithm, and optimizing the impedance of the power supply network and the dynamic switching characteristic of a functional module; calculating the breakdown voltage distribution of each layer in the chip, identifying a high-voltage risk region, optimizing the voltage distribution by using a gradient descent algorithm, and dynamically adjusting the working voltage and current; power factor fluctuation is analyzed through a sliding window algorithm, an abnormal area is identified, a power factor is optimized through a PID algorithm, and the working state and transient response of a chip in a dynamic load are improved. According to the method, the problems of nonlinearity of high-frequency dielectric loss, non-uniform breakdown voltage distribution and unstable power factor response under a dynamic load are solved, and comprehensive and accurate evaluation and optimization of chip performance are realized.

The invention relates to the technical field of petroleum drilling engineering, and particularly discloses an analysis and prediction system based on petroleum drilling and production data, which realizes virtual reconstruction of a physical drilling system by constructing a digital mapping body, and adopts a multi-source sensor network to collect drill string vortex frequency and wellbore temperature field gradient data. Forming a time sequence feature set through time sequence alignment and fusion processing; performing differential transformation and modal decomposition on the feature set to extract transient response feature vectors and generate a risk distribution diagram; establishing a parameter-risk correlation model, and decoupling mechanical vibration and thermal stress interference by solving a physical equation to obtain a dynamic risk index; the risk indexes and the process parameters are mapped to a three-dimensional grid to construct a multi-dimensional feature space, a feature fusion grid is adopted to achieve information aggregation and reconstruction, and comprehensive risk assessment indexes are generated; a dynamic early warning threshold value is established according to the evaluation indexes, and drilling parameters are optimized in real time through closed-loop control.

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

The invention discloses a nonlinear correction method, device and equipment for excitation characteristics of a mutual inductor, and particularly relates to the technical field of mutual inductors. The method comprises the following steps: acquiring original data corresponding to a mutual inductor, wherein the original data comprises an original current signal, historical magnetic flux and distributed temperature data; performing noise reduction processing on the original current signal to obtain a purified current signal, and determining a magnetic conductivity correction coefficient and a temperature gradient according to the distributed temperature data; inputting the purification current signal and the magnetic conductivity correction coefficient into a hysteresis model to obtain a real-time magnetic flux, and inputting the purification current signal, the distributed temperature data, the temperature gradient and the historical magnetic flux into a magnetic flux correction network to obtain a magnetic flux correction value; and compensating the real-time magnetic flux according to the magnetic flux correction value to obtain a corrected magnetic flux result of the mutual inductor. According to the invention, multi-physical field fusion input can be realized, transient response can be improved, harmonic distortion compensation can be carried out, and high precision of data can be maintained.

The invention provides a line loss reason analysis and positioning method and system based on artificial intelligence and a medium, and relates to the technical field of power system management.The method comprises the steps that when the bus loss rate of a target station area exceeds a preset line loss threshold value, an excitation sequence is issued to a general meter, a circuit breaker and a user electricity meter of the target station area, respectively obtaining corresponding transient response data, and further constructing a dynamic topological graph of the target transformer area; and a topological structure is optimized by combining a graph neural network and an attention mechanism, and abnormal connection points are detected. And determining downstream associated user electric meters, and calculating the power utilization abnormity score based on the load data. And injecting a steep front traveling wave signal into the electric meter with the highest score to obtain a reflection characteristic parameter. And performing iterative analysis by using a Double DQN reinforcement learning algorithm, determining a fault point, a line loss reason type and confidence, and realizing efficient and accurate line loss reason positioning and analysis. According to the invention, efficient and accurate troubleshooting of line loss reasons is realized.

The invention discloses an equipment health state operation and maintenance method based on multi-channel perception, and relates to the technical field of equipment health monitoring, and the method comprises the steps: collecting the perception data of equipment in a steady-state operation stage and a dynamic working condition transition stage; according to the method, time domain segmentation marking is carried out on sensing data of a dynamic working condition transition section, transient response parameters and parameter change rate characteristics of all segments are extracted, a dynamic working condition characteristic library is established, early degradation signs are found by identifying the consistent trend of parameter slow change characteristics and interval shrinkage, and self-adaptive operation and maintenance of the equipment health state are achieved.

The invention discloses an unattended power distribution room intensive power distribution system and method based on machine learning, and the method comprises the steps: collecting the operation data of an unattended power distribution room, executing the preprocessing, and generating a standardized input data set; constructing a topology, working condition and equipment feature association relationship to form a three-dimensional coupling feature structure; inputting a hierarchical graph time sequence decision network, and fusing topology and time sequence characteristics to output candidate control actions; constructing a dynamic feasible region according to the joint features, and screening a safety action set meeting constraints; inputting into a topological reversible neural differential control module, predicting transient response and optimizing an execution action; and issuing a control instruction, collecting feedback, and triggering model increment update to realize closed-loop optimization. According to the invention, by introducing a hierarchical graph time sequence decision network and a topological reversible neural differential control mechanism, intelligent intensive scheduling and self-learning closed-loop optimization control of the unattended power distribution room are realized.

The invention discloses a physical constraint multi-scale deep learning battery SOC estimation method and device and a medium. The method comprises the following steps: performing multi-scale time sequence feature extraction by using multi-branch separable convolution, and modeling fast transient response and long-term trend dynamics; a cross-scale feature enhancement strategy is configured, the problems of feature redundancy and network degradation in the multi-branch fusion process are solved, and cross-scale integration is achieved in combination with a dimension self-adaptive residual path and a channel attention mechanism; a physical information constraint module is embedded, soft charging and discharging constraint, zero-current constraint and hard SOC boundary constraint are fused, dynamic consistency and physical reasonability are ensured, multi-scale feature representation and physical information constraint are combined, the model generalization ability and field consistency are remarkably enhanced, SOC estimation precision is improved, and the method is suitable for large-scale SOC estimation. And a reliable solution is provided for a battery management system.

The invention provides a peak prediction method and system for fault current, and the method comprises the steps: obtaining a target current change rate of a power grid when the power grid breaks down, constructing a DC component and an AC component of the fault current based on the target current change rate and a preset trapezoidal integral algorithm, and carrying out the prediction of the peak value of the fault current; when the target current change rate meets the triggering condition of the all-phase composite triggering module, controlling the analog-to-digital converter to be switched from the first sampling frequency to a second sampling frequency, and collecting at least two pieces of sampling data based on the second sampling frequency; and performing parameter fitting processing on the at least two pieces of sampling data to obtain transient model parameters of the fault current, and performing peak prediction on the fault current by using the direct-current component, the alternating-current component and the transient model parameters to obtain a peak value of the fault current. Dynamic switching of the sampling frequency is realized through the all-phase composite trigger module, and a trapezoidal integral algorithm and a parameter fitting technology are combined, so that the transient response speed is effectively improved, a trigger blind area is eliminated, and the prediction precision is improved.

The invention discloses an unmanned aerial vehicle steering effect analysis method based on wind disturbance, and belongs to the technical field of unmanned aerial vehicle overall design and flight control. Comprising the following steps: 1, constructing a coupling simulation model; step 1.1, constructing a six-degree-of-freedom rigid body dynamic model; step 1.2, integrating control laws; step 2, dynamically injecting a wind disturbance scene; step 3, dynamically evaluating the steering effect and extracting parameters; step 4, carrying out multidisciplinary collaborative optimization; and 5, carrying out closed-loop iteration verification. Dynamic closed-loop test: breaking through traditional static analysis, realizing real-time interactive simulation of wind disturbance-control-steering effect, and accurately capturing nonlinear transient response; multi-source disturbance fusion: discrete gust and continuous turbulence are compositely injected, so that challenges of complex meteorological conditions on steerage are reflected more truly; and cross-domain parameter linkage: through dynamic iteration of pneumatic, control and structural parameters, system performance loss caused by excessive design of a single specialty is avoided.

The invention discloses an electric power system emergency control method and system fusing DRL and STGNN. The method comprises the steps that an international standard example IEEE-39 model is built, electric power data are collected, and a data set is built; and performing feature extraction on the data in the data set, inputting the obtained features into the improved DDPG model fused with the DRL, and outputting a generator tripping action vector. And optimizing the generator tripping action vector, and updating network parameters in the model. According to the power system emergency control method and system fusing the DRL and the STGNN provided by the invention, the perception capability of the transient response time-space change trend is effectively improved through the time-space diagram neural network, and the real-time performance and the accuracy of the model are improved. On the basis, through the DRL model, learning of a complex control strategy is achieved, effective actions can be rapidly obtained, and the effectiveness of an emergency control scheme is improved.

The invention discloses an intelligent sewage pump maintenance method based on fault prediction, and relates to the technical field of sewage pump management, and the method comprises the following steps: 1, collecting a state jump signal appearing in the operation process of a sewage pump control system; marking each type of state jump signals as state jump events, and establishing a state jump event sequence according to an actual occurrence time sequence; 2, for each state jump event identified in the step 1, setting a response window with a fixed time length, collecting a transient response process of a plurality of operation parameters in the window, extracting behavior characteristic indexes reflecting the stability and the recovery capability of the sewage pump system, and forming a structured response vector set; according to the method, the response atlas model with the state jump event as the core is constructed, behavior characteristic indexes in various running states are bound to the atlas nodes in a structured mode, and the system stability performance and evolution path characteristics are fused while the running event time sequence relation is reserved.

The invention relates to the technical field of exploration geophysics. The time-frequency domain three-dimensional forward modeling and feature analysis method based on the controllable source electromagnetic method comprises the steps that electrical parameter measurement processing is conducted on a target area rock ore specimen, and an electrical parameter set is obtained; based on the electrical parameter set, performing electrical category division processing through clustering analysis to generate an electrical classification knowledge base; acquiring an electromagnetic emission signal; performing time domain forward modeling calculation processing to obtain transient response; based on the electromagnetic emission signal and the electrical property classification knowledge base, performing frequency domain forward modeling calculation processing to obtain a specified frequency band response; performing parameter iterative optimization processing to generate a three-dimensional electrical parameter model; performing abnormal region extraction processing based on the three-dimensional electrical parameter model to obtain a target abnormal region; and carrying out boundary delineation processing on the target body to obtain a spatial position of the target body so as to achieve the technical effects of improving the characterization precision of the electrical parameters, reducing the reconstruction distortion of the time-frequency response and enhancing the geological interpretability of an inversion result.

The invention relates to the technical field of power supply management, in particular to a transient response optimization method based on a linear voltage regulator, and the method comprises the steps: obtaining transient response data of the linear voltage regulator when a load suddenly changes, and judging whether the linear voltage regulator enters a transient early warning state or not. And if entering the early warning state, carrying out high-frequency noise threshold segmentation on an output voltage waveform, extracting voltage change rate characteristics, constructing a compensation efficiency model based on dynamic response time and effective compensation bandwidth, calculating an optimal open-loop compensation coefficient, generating a compensation control instruction, and driving a compensation network to carry out dynamic compensation. And if the pre-warning state is not entered, ripple uniformity data of the output voltage is acquired, a deviation matrix of an actual ripple distribution curved surface and an ideal ripple distribution plane is determined, and whether a local ripple abnormal region exists is determined. According to the method, through dynamic monitoring and intelligent compensation, the transient response speed and stability of the linear voltage regulator are improved, and high-frequency noise processing and ripple uniformity are optimized.

The invention discloses an intelligent switching method and an intelligent switching system for double incoming lines of a supply guarantee type micro-grid based on a dynamic threshold value and multi-source cooperation, and relates to the field of intelligent control of micro-grids. In order to solve the defects of unstable switching, large energy storage investment, lagging operation state identification and low synchronous grid-connected efficiency of the existing supply guarantee type double-incoming-line micro-grid, electric energy quality abnormity of an original incoming line branch of the micro-grid is identified, instantaneous disturbance and continuous faults are distinguished, and judgment of an off-grid state is realized. Stable operation regulation of the island is carried out by adopting a multi-source coordination control and load grading optimization management mode; performing wide-frequency-domain electric energy quality analysis and dynamic impedance detection on the standby incoming line, and evaluating grid-connected compatibility and transient response characteristics of the standby incoming line; and in the island steady state, the micro-grid adaptively adjusts the voltage and the phase of the micro-grid side to meet the quasi-synchronization condition, and incoming line switching is realized. The method is mainly used for intelligent switching of the double incoming lines of the supply guarantee type micro-grid.

The invention relates to the technical field of converter analysis, in particular to a converter transient stability analysis method and system considering HPFR structure influence. The method comprises the steps that an HPFR structure is considered, and a transient response large disturbance dynamic model of a virtual synchronous generator under large disturbance is constructed; based on the constructed transient response large-disturbance dynamic model, analyzing the transient stability of the system by adopting an extended equal-area method to obtain a VSG dynamic response characteristic jointly influenced by a gain coefficient and a cut-off frequency in a speed regulation link; based on VSG dynamic response characteristics, a phase diagram analysis method is adopted to evaluate a gain coefficient and a cut-off frequency in a speed regulation link, and a value interval of the gain coefficient and the cut-off frequency in the speed regulation link is obtained; and optimizing a gain coefficient and a cut-off frequency in a speed regulation link of the virtual synchronous generator, so that a power angle value of the system is smaller than a power angle value of the system tending to be unstable under a fault condition. The method has the characteristics of high analysis precision, difficulty in conservative judgment result and the like.

The invention discloses a power distribution network small current grounding fault diagnosis positioning method and system based on edge calculation, and belongs to the field of power system automation, and the method comprises the steps: extracting and recognizing transient features, if a suspected grounding fault is detected, carrying out the multi-source feature fusion analysis through a regional main node, and constructing a transient response time sequence map; a weighted fuzzy clustering algorithm is combined with a multi-channel criterion to determine a fault channel, and a specific fault branch is further positioned through a topology tracking algorithm; introducing a multi-scale wavelet packet energy analysis method into the regional main node, performing energy inversion calculation on the transient current signal under multiple frequency bands, and identifying an energy concentration region near the grounding point through an energy distribution gradient; and constructing a robustness data redundancy model based on the historical data of the edge nodes and the topological relation, and identifying and eliminating abnormal feature data containing noise or distortion. According to the method, transient characteristics are rapidly extracted and preliminary judgment is made after a fault occurs, so that the real-time performance of fault response is greatly improved.

The embodiment of the invention discloses a power grid optimization method and device based on an improved fast marching algorithm. The method comprises the steps of obtaining topological structure data and operation data of a power grid; predicting the dynamic load change and operation state of the power grid by using a pre-trained artificial intelligence model, and generating an optimization initial condition set and a dynamic adaptive speed function; initializing an improved fast marching algorithm based on the optimization initial condition set, constructing a non-uniform propagation model by loading the dynamic adaptive speed function, and calculating an energy transmission optimal path meeting multi-target constraints, wherein the multi-target constraint comprises the steps of minimizing the active loss of the system, maximizing the static stability margin and optimizing the transient response time; and generating an energy distribution scheme including a generator output instruction and a topology switching strategy according to the optimal path, and optimizing the power grid according to the energy distribution scheme. According to the invention, efficient real-time optimization control of the power grid is realized.

The present application relates to power management technical field, especially to a kind of transient response optimization method based on linear voltage regulator, method includes: obtaining the transient response data of linear voltage regulator when load mutation, judge whether into transient early warning state.If into early warning state, high-frequency noise threshold segmentation is carried out to output voltage waveform, extract voltage rate of change characteristic, construct compensation efficiency model based on dynamic response time and effective compensation bandwidth, calculate optimal open-loop compensation coefficient, and generate compensation control instruction, drive compensation network to carry out dynamic compensation.If not into early warning state, the ripple uniformity data of output voltage is obtained, determine the deviation matrix of actual ripple distribution surface and ideal ripple distribution plane, judge whether there is local ripple abnormal area.The method improves the transient response speed and stability of linear voltage regulator by dynamic monitoring and intelligent compensation, optimizes high-frequency noise processing and ripple uniformity.

The invention discloses a self-adaptive control method and system for a two-stage swing type rotor aircraft hoisting system, and the method comprises the steps: building a two-stage swing type rotor aircraft hoisting system dynamics model, taking the rotor aircraft hoisting system to simultaneously drive four rotors to reach an expected position and inhibit the swing of a lifting hook and a load as a control target, and carrying out the control of an outer ring, generalized lifting hook and load position signals are introduced, an energy storage function is constructed, and the energy storage function is dissipated through the generalized lifting hook and load position signals; the outer loop tracking error is converted into a generalized load position error, and a shaped total storage function is obtained; and designing an adaptive law to estimate the load mass online, obtaining a reconstruction energy function of outer ring control, further obtaining an outer ring controller, and realizing control of system displacement and a load swing angle. By adopting a generalized displacement method of the lifting hook and the load and reconstructing an energy storage function, the dynamic coupling characteristic among the four rotors, the lifting hook and the load is remarkably enhanced, and therefore the transient response characteristic of the system is effectively improved.

The invention discloses a load storage charging and discharging integrated power grid stability simulation analysis method and system, and belongs to the technical field of simulation analysis, and the method comprises the steps: obtaining power grid operation data and resource state data; constructing a load storage charging and discharging resource unified equivalent model containing self-adaptive virtual inertia and self-adaptive transient damping; power grid transient stability simulation is executed, and transient response data are generated; processing the response data, and calculating to obtain a multi-dimensional transient stability comprehensive index fusing the oscillation damping ratio and the voltage deviation integral; and iteratively adjusting the virtual inertia and the transient damping parameter according to the comprehensive index until an optimization condition is met, and generating an optimization control strategy. According to the method, the load storage charging and discharging heterogeneous resources are aggregated into the unified equivalent model, parameter optimization is guided through the transient stability comprehensive index, the grid-connected stability of the high-permeability power electronic equipment can be accurately evaluated and optimized, and the transient reliability and operation safety of a power grid when the power grid is subjected to fault disturbance are improved.

The invention relates to the technical field of battery management, in particular to an electric vehicle battery detection system and method, and the system comprises a rhythm recognition module, an offset analysis module, an excitation feature extraction module, an evolution trend judgment module and a classification output module. According to the method, early recognition of potential anomalies is achieved by capturing the fine difference of the temperature response time sequence between the adjacent battery cells, transient interference can be effectively filtered out by tracking the difference in a cross-cycle mode and continuously comparing the difference with the response track of the surrounding normal battery cells, the abnormal battery cells with consistency deviation can be accurately locked, and on the basis, the abnormal battery cells with consistency deviation can be accurately recognized. A comprehensive decline quantitative index is constructed by evaluating changes of multi-dimensional transient response parameters such as temperature, voltage and impedance under a specific discharge working condition relative to a historical reference, and then stability and continuity of a current decline trend can be judged by utilizing long-term memorability analysis of a temperature evolution sequence.

The invention discloses a load transient detection circuit of a COT Buck, a corresponding COT Buck and a switching power supply. The load transient detection circuit comprises an input end, three resistors connected in series, two comparators and a switched capacitor sampling hold circuit, wherein the input end is grounded after passing through the three resistors in sequence. A node between the input end and the first resistor and a node between the second resistor and the third resistor are connected to the input ends of the first comparator and the second comparator through a switched capacitor sampling hold circuit structure. Valley voltage information and peak voltage information of inductive current are obtained through the switched capacitor sampling hold circuit, whether a load is transiently changed or not is judged by comparing inductive current ripples with a valley value and a peak value, and it is guaranteed that the switching power supply has appropriate transient detection trigger threshold voltage under any load. According to an output signal of the load transient detection circuit, the COT Buck can increase the turn-on or turn-off time so as to enhance the transient response speed.

In a general aspect, a radar method includes transmitting an outgoing series of RF pulses toward a target region. The radar method also includes receiving an incoming series of RF pulses that is based on the outgoing series of RF pulses after passing through the target region. The radar method additionally includes producing, by operation of a vapor cell system, output signals that are based on incoming series of RF pulses and the laser signals interacting with a vapor of the vapor cell system. The radar method also includes operations of a signal processing system, such as generating response data, determining a Doppler shift, and determining a velocity of an object in the target region. The response data includes transient response data that represents transient responses of a vapor in the vapor cell system to respective phase changes in the incoming series of RF pulses.

The invention discloses an automatic testing and screening method for electronic components, which relates to the technical field of electronic engineering and microelectronics and comprises the following steps: generating a multi-dimensional stress field through a temperature control module, a humidity control module and an electromagnetic interference simulation module; calling a preset extreme working condition combination sequence based on the target application scene of the component; under a dynamic coupling environment, dominant parameters and recessive parameters including a dielectric relaxation spectrum, a carrier mobility transient response and a thermoacoustic emission signal are synchronously collected; inputting the hidden parameter time sequence data into physical failure models including an electrochemical corrosion model and a thermal mechanical fatigue model; dynamically adjusting the passing threshold of the test item according to the model output; the step of applying accelerated degradation excitation to the screened component comprises the following steps of: circulating a set temperature for a set number of times; applying a bias voltage-temperature stress; and repeating the step S2 to monitor the implicit parameter drift amount, and eliminating the device with out-of-tolerance drift. The device guarantee is provided for the fields of new energy automobiles, aerospace electronics and the like.

The invention discloses a Bayesian optimization estimation method for kinetic parameters of a rotating machine, and the method comprises the steps: synchronously collecting a key phase signal and a transient vibration signal when the rotating speed of the rotating machine changes, and reconstructing a rotating speed pulse time sequence and a time domain signal; fourier transform is carried out on the vibration signal, and transient response data of a specified order is extracted; deleting the displacement offset in the transient response data; function fitting is carried out on the transient response data after offset elimination, and a transient response function with the rotating speed or the frequency as an independent variable and the amplitude as a dependent variable is generated; defining an optimization parameter space and a target function; and transmitting the target function and the optimization parameter space to a Bayesian optimization model to obtain an optimal parameter combination, and identifying the dynamic parameters of the rotating machinery based on the optimal parameter combination. The method can reduce the cost of computing resources, has wide parameter adaptability, and can estimate various kinetic parameters such as support stiffness and structural damping.