525 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 starting control system and method for a main and auxiliary power hybrid ship, and relates to the field of ship power control, and the system comprises a multi-source data collection module which is used for obtaining a multi-source state data set of a ship power system; the fusion processing module is used for performing feature alignment processing on the multi-source state data set based on a multi-modal sensing data fusion algorithm to generate a hybrid power starting parameter set; the dynamic weight distribution module is used for constructing a dynamic weight distribution matrix according to the hybrid power starting parameter set; the cooperative control module is used for generating a main and auxiliary engine combined starting instruction set based on an improved cooperative control algorithm; and the execution and monitoring module is used for executing the main and auxiliary engine combined starting instruction set in a preset time window and monitoring the transient response characteristic of the propulsion system. According to the starting control system and method for the main and auxiliary power hybrid ship, the starting stability and the energy utilization efficiency of the hybrid ship are improved.

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 switching method and device of a primary and secondary fusion switch, a medium and a program product, and relates to the field of electric power intelligent dispatching, and the method comprises the steps: obtaining real-time electrical parameters and historical electrical parameters of an operation power supply; determining a system disturbance level based on the change rate of the real-time electrical parameters relative to the historical electrical parameters, and determining a system sampling period according to the system disturbance level; collecting a target electrical parameter of a power supply to be switched in a system sampling period, and calculating a switching evaluation index according to the real-time electrical parameter and the target electrical parameter; according to the switching evaluation index and the system disturbance level, determining a dynamic control parameter of the power supply to be switched in; when the adjustment rate of the dynamic control parameter is greater than a first preset threshold value and the switching evaluation index is smaller than a second preset threshold value, generating a switching enable signal; and controlling the primary and secondary fusion switch to execute switching operation according to the switching enable signal. According to the invention, the transient response performance in the power supply switching process can be improved.

The invention relates to the technical field of data processing, and discloses a large steel structure hoisting precision real-time monitoring method and system. The method comprises the following steps: scanning and collecting the edge of a steel structure through a distributed sensor network, and obtaining structure data to obtain an original monitoring data set; extracting and analyzing structure transient response features based on the original monitoring data set, and generating a state feature matrix; analyzing a coupling relationship between local deformation and overall torsion to obtain a dynamic response prediction matrix; performing space-time registration and data fusion on the monitoring data, and constructing a state evaluation data set; carrying out optimization calculation on the hoisting parameters to generate a hoisting control data packet; and performing intelligent reasoning analysis in combination with historical case data, and outputting a control instruction sequence. Real-time monitoring of the coupling relation between local deformation and overall torsional deformation in the hoisting process of the large steel structure is achieved, and the technical defect that in the prior art, the structural deformation development trend cannot be accurately recognized and predicted is overcome.

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 method and system for measuring inertia and damping parameters of a grid-forming type wind turbine generator, and relates to the technical field of grid-connected converter detection, and the method comprises the steps: connecting a test device to a to-be-tested grid-forming type wind turbine generator operating in an off-grid mode, enabling the test device to apply frequency disturbance and small-phase disturbance to the to-be-tested wind turbine generator at an initial moment, acquiring voltage and current of a unit port until the virtual synchronous generator reaches a stable working state, and respectively acquiring a steady-state value of the active power variation of the unit and a transient response curve of the active power variation in the whole response process; on the basis of the steady-state value of the active power variable quantity, the applied frequency disturbance is combined, and a damping coefficient is calculated according to a first relation; and on the basis of the transient response curve, an envelope line of the curve is calculated and obtained, then a product value of the natural oscillation angular frequency and the damping ratio is obtained, the reference angular frequency and the damping coefficient are combined, and the virtual inertia is calculated according to a second relation. According to the invention, accurate and efficient measurement of inertia and damping parameters can be realized.

The embodiment of the invention discloses a load transient enhancement circuit of a voltage mode DCDC converter, which is characterized in that a traditional 3-type compensation network is improved, a zero-point adjustable compensation network is generated by a compensation transconductance amplifier to carry out loop compensation, and then zero-point adjustment is carried out by detecting output voltage. When the output voltage exceeds the threshold due to the overshoot current, the transconductance can be increased through the change of the tail current, so that the zero point moves to the low frequency, the bandwidth can be increased, and the transient response capability is improved. According to the embodiment of the invention, a zero point adjustable compensation network is generated by adding a feedback path to compensate the loop, and the adjustable zero point is adjusted by detecting the output voltage. And when the load jumps, the bandwidth is increased to low frequency through zero point shift, so that the load response capability is enhanced, and the overshoot of the output voltage is reduced.

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 heat flow dynamic tracking AI prediction type server temperature control system and device. The system comprises an AI prediction model module, a dynamic temperature control execution module and a data acquisition and feedback module. The AI prediction model module is connected with the output end of the data acquisition and feedback module through a data bus, and the input end of the dynamic temperature control execution module is connected with the output end of the AI prediction model module; the input end of the data acquisition and feedback module is connected with a temperature sensor group deployed in a server, the input data of the AI prediction model module comprises a server real-time load, an environment temperature and a historical temperature time sequence, and the output data of the AI prediction model module is a temperature prediction value in the future 5 minutes. According to the method, the time advantage is established through AI prediction, space buffering is established through PCM cold storage, a three-dimensional regulation and control mechanism which is precisely executed is achieved through pump valve cooperation, optimization is achieved in the heat dissipation stability, the energy efficiency, the transient response, the self-adaptive capacity and the operation and maintenance mode, and intelligent temperature control is provided for the high-density computing power center.

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 relates to the field of PWM control, in particular to a high-precision PWM control method, device and equipment and a storage medium. The method comprises the following steps: collecting real-time charging monitoring parameters of a vehicle battery, carrying out dynamic load trend evolution, and constructing a time sequence load trend evolution curve; identifying a real-time PWM signal of the charging gun; carrying out nonlinear compensation adjustment based on the time sequence load trend evolution curve to obtain a nonlinear frequency compensation strategy; performing transient load fluctuation situation mining and transient load response matching adjustment according to the time sequence load trend evolution curve, and constructing a transient response frequency adjustment curve; and calculating the maximum safe charging power according to the real-time charging monitoring parameters, performing intelligent PWM waveform fine tuning on a transient response frequency adjustment curve, and constructing an optimal duty ratio fine tuning strategy. According to the invention, through dynamic and high-precision PWM control, the charging efficiency, safety and stability of the new energy vehicle are improved.

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 a simulation IC test method and system based on load transient response, and aims to improve the performance evaluation precision of a simulation IC under a complex load condition. The method comprises the following steps: firstly, acquiring transient response waveforms and response delay characteristics of an analog IC (Integrated Circuit) in various load jump modes, and constructing a mapping matrix between a load current step change rate and response delay time; in the real-time test, response delay time in a future preset time period is predicted according to the step change rate of the current load current. When the steady-state time window exists, a fixed sampling time sequence is set based on the window to generate a first test waveform; and if the steady-state window does not exist, correcting the transient waveform, and generating a compensated second test waveform. And then time sequence decomposition is carried out on the test waveform, and a load transient stability evaluation matrix is constructed, so that the dynamic performance of the simulation IC is accurately evaluated, and a detailed performance test report is output.

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 discloses a Ga2O3 SBD trap test method based on forward current transient response, and belongs to the field of semiconductor device reliability. Comprising the following steps: giving a filling voltage interval and a filling time interval of a Ga2O3 SBD trap filling stage; giving a bias condition and a test time interval of a trap test stage; giving a normalization processing method and range of the forward current transient response; a characterization range of a Ga2O3 SBD device level trap time constant is given. The transient response curve of the forward current of the Ga2O3SBD under the constant test voltage is collected, and the transient current variation caused by the trap is evaluated through a normalization processing method. And repeatedly testing under the condition of multiple groups of filling voltages, establishing a time constant spectrum of the trap by using a structure function method, and extracting a time constant parameter of the trap in the Ga2O3SBD to realize in-situ and online Ga2O3SBD trap test. The method is suitable for evaluating the trap effect of Ga2O3 SBD devices from different manufacturers, and can realize spectral valuation characterization of Ga2O3 SBD traps and in-situ and nondestructive testing of trap parameters.

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.

An optical metrology device includes a selectable pump arm that is used to produce a transient response in the target sample and a probe arm and may be configured to perform static Magneto-Optic Kerr Effect (MOKE) measurements or ellipsometry measurements when the pump arm is not selected, and time resolved (TR) MOKE measurements or ellipsometry measurements when the pump arm is selected. The optical metrology device may be further configured to perform opto-acoustic measurements when the pump arm is selected. A pulse shaper in the pump arm may be used to pulse duration, phase, or both, of the incident pump beam to enable measurement of the dependence of the magnetic dynamics on the pump pulse characteristics. The optical metrology device may be configured to operate at a single selectable wavelength or multiple wavelengths in a continuous or discontinuous spectrum.

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 provides a double-butterfly valve linkage anti-interference control method for a large air supply test device, which belongs to the technical field of aviation power devices and comprises the following steps of: simplifying inflow and outflow of an air supply adjusting system according to air mass flow into a one-way air inlet and three-way air exhaust structure; a slope pressure given signal and a pressure differential given signal are obtained based on a first discrete tracking differential module, and a filtering output signal and a differential extraction signal are obtained based on a second discrete tracking differential module; obtaining a total disturbance estimated value of the butterfly valve control loop by using the parameters based on a nonlinear discrete expansion state observation module; on the basis of the backstepping control module, the actual control quantity of the butterfly valve is obtained through the parameters; and adjusting the input gain estimation value of the butterfly valve control loop in real time based on a parameter self-adaptive adjustment module, and repeating the steps to realize the double-butterfly valve linkage anti-interference control of the large-scale gas supply test device. The method does not need manual intervention, and is fast in transient response, strong in anti-interference capability and high in robustness.

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.