655 results about "Pulse sequence" patented technology

The invention discloses a motion recognition method and system based on a redox photoelectric memristor, a terminal and a storage medium, and the method comprises the steps: selecting classical motions based on a human body motion data set, extracting time sequence data, and coding the time sequence data into an optical pulse sequence; constructing a reservoir array composed of a plurality of photoelectric memristors, and expanding an optical pulse sequence signal into a high-dimensional state vector; constructing a supervised training model, solving a weight matrix, and constructing a memristive cross array; and outputting an action classification result through simulation domain operation based on the output current multi-path light current signals in combination with the memristor cross array. According to the method, the motion features can be directly fed into a rear-end classification network for action recognition without depending on a complex digital feature extraction algorithm, so that the transmission and processing overhead of redundant data is fundamentally eliminated; a high-efficiency, low-delay and high-robustness hardware solution is provided for real-time and anti-noise motion recognition in scenes such as intelligent monitoring and man-machine interaction.

The invention relates to the technical field of artificial intelligence, and discloses an artificial limb driving method based on a spiking neural network, which comprises the following steps: acquiring a multi-modal signal, and preprocessing the multi-modal signal; converting the preprocessed multi-mode signal into a pulse signal; inputting the pulse signal into a pulse neural network for sensing fusion processing, and outputting a control strategy; dynamically adjusting a control strategy based on pulse reinforcement learning according to the behavior feedback information through a reward function, and generating a learning result; and generating a bionic motion instruction according to the adjusted control strategy and the learning result, and driving an artificial limb execution mechanism to act according to the bionic motion instruction. According to the method, the electromyographic signals, the tactile pressure signals and the inertial measurement data are uniformly coded into the pulse sequence, the pulse neural network is utilized to realize multi-modal sensing fusion, the pulse reinforcement learning algorithm is introduced, the artificial limb control strategy is optimized in real time according to user behavior feedback, and the accurate control of the artificial limb joint is improved.

The invention discloses a high-power picosecond pulse train laser with programmable sub-pulse energy and a method, and relates to the related technical field of lasers, the high-power picosecond pulse train laser comprises a mode-locked fiber laser, an acousto-optic modulator and a solid amplification chain which are sequentially arranged along an optical transmission path; the mode-locked fiber laser generates a mode-locked picosecond pulse sequence; the acousto-optic modulator performs pulse selection and pulse string grouping on the mode-locked picosecond pulse sequence, and performs pre-programming shaping on sub-pulse energy in a pulse string according to set intensity distribution to generate a pre-shaped pulse string; the solid amplification chain comprises at least one stage of solid amplifier, and the solid amplifier comprises a dichroscope, a laser gain crystal, a coupling optical assembly and an LD pumping source; and the dichroscope is used for focusing the pre-shaped pulse string amplified at the previous stage and the LD pump light transmitted by the coupling optical assembly into the laser gain crystal. Independent programmable regulation and control of the energy of each sub-pulse in the pulse string are realized, and the energy distribution of each sub-pulse in the pulse string can be flexibly adjusted.

The invention discloses a step disturbance influence resistant micro-grid on-grid and off-grid state detection method and system, and the method comprises the steps: carrying out the filtering and normalization processing of a discrete time sequence, and obtaining a standard signal; identifying mutation points in the standard signal, and constructing a mutation point index set; screening out a point column meeting a time closeness feature from the mutation point index set, and constructing a continuous candidate section; performing stability analysis, monotonicity judgment and fallback trend identification on each continuity candidate section to judge whether the continuity candidate section is a step change section or not; whether a real continuous pulse sequence is formed or not is judged according to the time interval, the amplitude change rate and the amplitude peak-valley characteristic for the remaining sections excluding the step change section in the continuous candidate sections; and outputting the disturbance type of each continuity candidate section. According to the method, the accuracy of disturbance identification and the robustness of system control are improved, and the operation safety and the control decision reliability of the system under the complex disturbance background are guaranteed.

The invention discloses a multi-modal interaction evaluation system and method based on synchronous data acquisition, and relates to the technical field of biomedical engineering and man-machine interaction, and the method comprises the steps: generating a synchronous pulse sequence through a synchronous signal generator according to the same coding time base, and outputting the synchronous pulse sequence to an optical path and an electrical path at the same time; pulse events are recognized in the video and the electrophysiological data respectively, and observation pairs of counting numbers and timestamps are obtained; time drift is continuously estimated and compensated through robust regression in a sliding window, and a unified time axis is generated; furthermore, a dynamic time window of heart beat anchoring is constructed according to an R peak event of the electrocardiosignal, and consistent segmentation and feature extraction of cross-modal data are achieved.

The invention relates to the technical field of model training, and discloses an SNN training method for dynamic pulse coding, which comprises the following steps: acquiring input data, and extracting spatial dimension features and time sequence dimension features from the input data; calculating a real-time threshold value and distribution delay according to the spatial dimension features and the time sequence dimension features; performing dynamic pulse coding on the input data through a dual-mode coding mechanism according to the real-time threshold value and the distribution delay, and generating a spatial dimension pulse signal and a time sequence pulse sequence; and training the SNN according to the real-time threshold, the distribution delay, the spatial dimension pulse signal and the time sequence pulse sequence based on a hierarchical mixed training framework. According to the method, dynamic pulse coding is performed on the input data through a dual-mode coding mechanism according to the spatial dimension features and the time sequence dimension features, the spatial dimension pulse signal and the time sequence pulse sequence are generated, the SNN model is trained through the spatial dimension pulse signal and the time sequence pulse sequence on the basis of a hierarchical mixed training framework, and the model training efficiency is improved.

The invention relates to the technical field of IT support, and provides a time sequence processing method, device and equipment adopting a quantum pulse neural network, and the method comprises the steps: obtaining time sequence data which comprises network alarm data, network equipment performance index data and network operation and maintenance work order data; encoding the time sequence data into a first quantum state by using a quantum preprocessing layer, inputting the first quantum state into a pulse neural network layer, converting the first quantum state into a time sequence pulse sequence, and processing the time sequence pulse sequence to obtain an output result; by utilizing a quantum attention enhancement mechanism, calculating attention weight of an output result in a quantum state space, and weighting to obtain a second quantum state; and decoding the second quantum state by using the hybrid decoding layer to obtain a final prediction result. The final prediction result is used for realizing fault root cause positioning, abnormal work order identification or network service quality prediction. According to the method, the parallelism of quantum calculation and the superposition characteristic of the quantum state are utilized, and the calculation efficiency can be improved when large-scale time sequence data are processed.

The invention discloses an anti-electromagnetic interference coding method and system for a pulse sequence of an ultrasonic flowmeter, and relates to the technical field of industrial measurement. The method comprises the following steps that a pulse sequence of the ultrasonic flowmeter in the working process is obtained; the pulse sequence is processed, and the electromagnetic interference type is identified through machine learning; constructing a final feature matrix based on feature enhancement of an acoustic-electric coupling factor; a dynamic coding generation strategy: receiving an original pulse sequence output by the TDC chip, and selecting a coding scheme according to the current interference type; and error feedback calibration is carried out by adopting a bidirectional propagation time correction formula. Real-time interference classification is carried out through machine learning, and FPGA hardware completes level coding switching, so that a signal spectrum dynamically avoids an interference frequency band, and the anti-electromagnetic interference capability of the ultrasonic flowmeter is improved.

The invention provides a single-line communication method and device based on pulse counting, electronic equipment and a medium, and relates to the technical field of switches and servers. The method is executed by a main board in a system, the main board is connected with a sub-board through a single in-place signal line, and the method specifically comprises the following steps: within a first preset time period after the system is powered on, sending a first pulse sequence through the single in-place signal line, and verifying the connection state of a cable based on a received response signal; if the connection state is verified to be normal, sending a second pulse sequence through a single in-place signal line, and performing data communication with the daughter board; wherein the second pulse sequence forms communication bytes in a pulse number coding mode. According to the invention, an in-place signal line which is only used for static level detection traditionally is transformed into a dynamic multifunctional channel, so that the dual defects of low communication efficiency and high misplugging risk of a traditional scheme are synchronously overcome on the premise of not increasing any hardware cost and wiring complexity.

The invention discloses a high-resolution range profile feature retention enhancement method based on iterative weighted sparse decomposition, and the method comprises the steps: obtaining radar echo data collected by a radar receiver within a coherent processing time, and constructing a two-dimensional radar echo matrix composed of a range unit number and a pulse sequence number after range direction matching filtering; representing a two-dimensional radar echo matrix as a superposition relation of adding a low-rank clutter component matrix and a sparse target component matrix; based on the superposition relation, an optimization model is constructed, and the objective function of the optimization model is the sum of the weighted nuclear norm of the low-rank clutter component matrix and the L1 norm of the sparse target component matrix. According to the method provided by the invention, the adaptive weight is applied to the singular value, so that clutters are effectively suppressed under a strong clutter background, and key structure features such as the number, position distribution and relative amplitude relationship of scattering points in the target high-resolution range profile are kept.

The invention discloses an end-to-end radar working mode identification method based on hierarchical density clustering and an encoder, and the method comprises the steps: firstly obtaining the pulse repetition interval PRI, PW pulse width and carrier frequency RF data of a radar pulse, and constructing a radar pulse sequence; and meanwhile, recording an initial working mode label corresponding to each pulse. Secondly, inputting a radar pulse sequence for preprocessing to obtain a preprocessed radar pulse sequence; and finally, performing working mode identification on the preprocessed radar pulse sequence through an encoder structure neural network model, and outputting a working mode corresponding to the radar. According to the method, the input sequence quality is remarkably improved, data interference is reduced, and the recognition rate of a complex radar working mode is improved.

The invention relates to the field of motor rotor fault diagnosis, and particularly discloses a motor rotor fault diagnosis method based on high-resolution spectrum estimation and machine learning, and the method comprises the following steps: capturing a stator current signal, air gap flux fluctuation and an acoustic emission pulse sequence, and generating an initial spectrum cluster with a cross-scale disturbance characteristic; applying a variational high-resolution blind decomposition strategy to the initial spectrum cluster, and constructing an abnormal embedding vector set reflecting weak disturbance by using a spectrum energy migration coefficient; unwrapping the abnormal embedded vector set through a time-frequency hypergraph structure, and recognizing a potential fault channel in combination with a rotating speed disturbance phase chain and a tooth space modulation track; a virtual weak fault enhancement mechanism is introduced into sparse coupling representation, a simulated weak fault frequency spectrum is generated and subjected to difference fitting with an actual signal, and real features covered by noise are inverted and corrected; and introducing an energy adversarial migration network on the weak fault features to obtain multi-modal fusion features, and dynamically generating an early degradation risk curve of the motor rotor.

The invention discloses an air pressure sensor communication control method and system based on PFM pulse frequency modulation, in a multi-sensor networking scene, environmental parameters are converted into analog electric signals through signal conditioning and then mapped into a unique pulse change sequence, and coding optimization is carried out for the matching degree of the pulse sequence and the parameter variable quantity. High-fidelity and low-power-consumption data expression and transmission are realized; anti-interference filtering is implemented through a communication link in combination with a signal transmission protocol, and original parameter data is restored through inverse mapping after the integrity of a pulse sequence is ensured; then, the data of the multi-source sensor is integrated in the main control unit to form a networking parameter data set, abnormal deviation is judged through comparison logic, a control instruction is generated and fed back to a terminal to execute accurate adjustment, a complete link from sensing and optimized transmission to closed-loop regulation and control is formed, and finally high-reliability real-time monitoring and self-adaptive intelligent regulation and control of environmental parameters are achieved.

The invention relates to the technical field of spiking neural networks, in particular to a spiking neural network conversion method and system based on differentiable adaptive optimization, and the method comprises the steps: constructing and pre-training a target ANN model; in a pre-trained ANN model, performing simulation processing on the activation value of each layer by using a global differential value representation operator; on the basis of a loss function, an original weight parameter in the target ANN model and a trainable excitation threshold in a global differentiable value representation operator are placed in the same optimization framework for joint fine tuning, and an optimal weight and an optimal threshold learned by each layer are obtained; and an equivalent SNN model is constructed. According to the method, precise correspondence between continuous activation and pulse sequences can be optimized, the method is suitable for multiple pulse coding modes, quantization errors are reduced to the maximum extent, the deployment requirements of neuromorphic hardware and an edge intelligent computing platform can be deeply adapted, and an efficient and universal model conversion solution is provided for the neuromorphic computing industry.

The invention relates to the technical field of photoetching machine data transmission, and discloses an ultra-high-speed distributed data transmission synchronization system of a direct-writing photoetching machine. The system comprises a dynamic exposure pulse analysis module which constructs a distributed transmission delay calculation model based on historical operation data, collects data such as a laser trigger pulse sequence in real time and outputs a theoretical transmission synchronization value; the multi-channel time sequence offset detection module generates a node-level multi-channel time sequence offset matrix through multi-dimensional comparison of the theoretical value and the actual transmission time sequence; the spatial topology mapping module is combined with the physical position and the link impedance parameter to generate a time sequence abnormal propagation path probability distribution cloud picture so as to locate a high-delay area; and the dynamic bandwidth allocation module configures a transmission strategy according to the data, starts redundant channel parallel transmission for a high-delay area, and implements dynamic clock offset compensation for adjacent nodes, so that the synchronization stability and transmission efficiency of data transmission are effectively improved.

The invention relates to the technical field of epileptic seizure prediction, and provides an epileptic seizure prediction method based on a space-time attention dynamic pulse neural network. The method comprises the following steps: firstly, a preprocessing module is used for preprocessing collected electroencephalogram to obtain purified time window electroencephalogram; secondly, a pulse sequence generation module generates a sparse pulse sequence, a space-time pulse sequence and multi-scale space-time channel attention characteristics based on a sparse compression coding method, a space-time structure pulse mapping method and a multi-scale space-time channel attention method; thirdly, obtaining a space-time attention pulse sequence based on a pulse similar coupling smoothing method; and finally, constructing a residual pulse neural network, and training the network based on the space-time attention pulse sequence so as to realize epileptic seizure prediction. According to the method, the space-time attention dynamic pulse neural network is constructed based on the space-time dynamic complexity of the electroencephalogram signals, so that high-precision epileptic seizure prediction is expected to be realized.

Described are devices and methods for administering targeted ultrasound therapy. The device consists of a wearable housing designed to conform to a part of the user's body, with an array of ultrasound transducer units on the skin-facing side. These transducers can generate ultrasound therapy to a target therapy area of the user, with frequency, intensity, treatment area, and duration parameters optimized for a therapeutic application. Incorporated within the device is a communication system, an energy source, and a processor. The processor's role is to execute instructions, including assessing the user's health status based on received data, processing and analyzing ultrasound data to gauge transmission quality, determining if the current transmission quality is within thresholds, initiating ultrasound therapy when the conditions are right, and adjusting ultrasound therapy parameters real-time or near real-time if needed during treatment. The real-time or near real-time adjustment is made through various parameters like pulsing frequency, pulse train frequency, the number of treated spots, duration, and treatment zone area. This device provides a personalized, adaptive approach to ultrasound therapy, optimizing the treatment process for each individual user.

The invention discloses an arrhythmia detection method based on a pulse neural network. The method comprises the following steps: performing incremental modulation pulse coding and pooling operation on a target electrocardiosignal to obtain a corresponding pulse sequence; and inputting the pulse sequence into a trained multi-level pulse neural network classification model to obtain an arrhythmia detection result. Wherein the multi-level pulse neural network classification model comprises a weight sharing layer and a plurality of cascaded classifiers, the weight sharing layer is used for extracting electrocardiosignal feature information from the pulse sequence, and the plurality of classifiers are used for obtaining an arrhythmia detection result based on the electrocardiosignal feature information. According to the method, on the premise that the accuracy is equivalent, the classification result is more refined, and the requirements for hardware storage resources and computing resources are reduced.

The invention relates to the technical field of working face mine pressure multi-parameter intelligent monitoring and early warning systems, and particularly discloses a working face mine pressure multi-parameter intelligent monitoring and early warning system. The system comprises a multi-source sensing data acquisition device, a pulse sequence coding unit, a bionic pulse neural network early warning model, a dynamic attention regulation and control module and an early warning information output terminal, multi-source heterogeneous data such as hydraulic support resistance, micro-seismic energy and acoustic emission frequency are converted into a pulse event sequence similar to a biological neural signal, a rock stratum instability precursor is identified by using a bionic model simulating a neuron discharge mechanism and synaptic plasticity, key signals are self-adaptively strengthened in combination with a dynamic attention mechanism, and noise is suppressed. According to the technical scheme, multi-parameter coupling characteristics can be captured with high sensitivity, the early warning accuracy and advance are improved, the false alarm rate is reduced, and reliable guarantee is provided for deep coal mine safety.

The invention belongs to the technical field of optical thin film design and preparation, and particularly relates to a preparation method of a multilayer composite antireflection film on the surface of an optical lens. The preparation method comprises the step of sequentially performing atomic layer deposition on the surface of the optical lens to form the transition layer, the high-refractive-index layer, the low-refractive-index layer, the high-refractive-index layer and the packaging layer. According to the atomic layer deposition method, uniform coating can be achieved on the surfaces of aspherical lenses with different curvature radiuses, and five layers of oxide films are deposited through alternating pulse in an ALD pulse sequence with the structure of'substrate layer-transition layer-high refractive index layer-low refractive index layer-high refractive index layer-packaging layer '. And an excellent antireflection effect in a visible light wavelength range is realized. The atomic layer deposition is carried out at low temperature (25-150 DEG C), so that the atomic layer deposition can be directly coated on the high-molecular polymer lens, and the deformation of the base material due to high temperature is avoided; the method is especially suitable for different application scenes such as consumer electronics, personal protection and medical assistance.

The invention belongs to the technical field of laser precision machining and control, and particularly relates to a femtosecond laser single-point trigger control system and method based on subnanosecond synchronization, and the method comprises the following steps: S1, building a system synchronization clock locked with a femtosecond laser pulse signal phase; the specific mode of establishing the system synchronous clock is that phase locking is carried out on pulse synchronous signals or frequency division signals output by an internal high-frequency clock source and a femtosecond laser source through a digital phase-locked loop technology, and the low-jitter system synchronous clock which is strictly synchronous with a laser pulse sequence is generated. According to the invention, a system clock locked with a laser pulse phase can be generated through a digital phase-locked loop, and the influence of inherent frequency drift of a laser and circuit delay temperature drift is effectively overcome by combining real-time delay calculation based on a prediction algorithm and a high-resolution DDS delay line technology; the synchronization precision between the triggering moment of the single pulse and the external instruction is stabilized at the subnanosecond level, and the requirement of leading-edge application for the extreme time sequence precision is met.

The present application relates to the technical field of emotion recognition, in particular to an asynchronous multi-modal emotion recognition method, device, equipment and medium. The present application respectively carries out pulse coding on each modality biological data of the user, obtains the pulse sequence of each modality biological data, applies cross-modal synchronous neurons to each pulse sequence based on the firing time of pulse coding, obtains the emotion feature of the user, and finally applies an emotion classifier to the emotion feature to obtain the emotion classification result. Since the pulse sequence formed by the pulse coding of the present application can record the time when the biological data changes, and the cross-modal synchronous neurons are applied to the pulse sequence according to the firing time of the pulse coding to analyze the emotion feature, the combination of the pulse coding and the cross-modal synchronous neurons of the present application can identify the time when each multi-modal biological data changes, and identify the emotion feature of the user according to the time sequence before and after the change of the multi-modal biological data, so as to improve the accuracy of emotion recognition.

The invention provides a nutrition pump drop identification method, a nutrition pump and electronic equipment, and relates to the technical field of drop identification, and the nutrition pump drop identification method comprises the steps: obtaining liquid drop detection signals collected by multiple infrared receiving channels; performing signal conditioning processing on each path of liquid drop detection signal to obtain multiple paths of pulse signals; combining the multiple paths of pulse signals into a single-path effective pulse sequence by using redundancy decision logic; and calculating the real-time dripping speed according to the pulse frequency of the single-path effective pulse sequence. According to the scheme, the liquid drop detection signals of the multiple infrared receiving channels are collected in parallel, or operation merging is carried out on the multiple pulse signals through the redundancy judgment logic, effective recognition of liquid drop events can still be maintained when part of channel signals fail, and therefore the fault-tolerant capability and robustness of the detection process are improved.

The invention relates to the technical field of industrial safety control, in particular to a dual-system redundancy safety circuit and a self-diagnosis method, the dual-system redundancy safety circuit is composed of a first safety system, a second safety system, a test load and a switching unit, and the first safety system and the second safety system realize information interaction through an optical coupling isolation communication interface. According to the self-diagnosis method, an encrypted diagnosis trigger pulse sequence is generated through the second safety system, a test contactor of the first safety system is driven to be connected to a test load, and a main safety loop generates simulated real safety action current; the second safety system performs high-speed sampling, filtering, correlation analysis and response delay extraction on the current waveform to obtain a diagnosis result of the contact performance and the load capacity; and when an abnormity is detected, the second safety system controls the switching unit to execute safety switching through the solid-state switching output point, so that control right taking-over is realized, and the reliability and the online diagnosis capability of the safety loop are improved.

The invention discloses an electric energy meter operation state inspection and evaluation method and system, and relates to the technical field of electric power metering safety, and the method comprises the steps: collecting and preprocessing the electrical working data of an electric energy meter, obtaining the reference current and working current of the electric energy meter, extracting error features, and generating a current error signal; carrying out voiceprint collection on the electric energy meter, and capturing a voiceprint signal of the ammeter; converting the voiceprint signal into a discrete pulse sequence through a coding algorithm, and obtaining a standardized pulse sequence through dimension conversion and optimization compression; performing feature extraction on the standardized pulse sequence by applying a pulse neural network, and obtaining fault probability distribution through synaptic weight calculation and neuron state updating after obtaining pulse sequence features; the feedback control signal and the electric energy meter evaluation report are generated based on the fault probability distribution, and the problem of poor real-time performance of fault mode recognition is solved.

The invention relates to a photon pulse sequence atlas construction method, system and device based on a photon counting method, computer equipment and a readable storage medium. The method comprises the following steps: acquiring a plurality of photon counting data of a target insulation defect based on a photon counting method, constructing a phase resolution photon counting atlas based on the photon counting data, and splicing and sorting the phase resolution photon counting atlas to obtain a corresponding photon pulse sequence; the photon amplitude of each alternating current period can be obtained based on a photon pulse sequence built by a plurality of adjacent alternating currents, distribution characteristics of photon counting data can be obtained by combining a kernel density estimation formula, and a corresponding photon pulse sequence map can be built based on the distribution characteristics of the photon counting data. Therefore, a worker can directly distinguish the types of the insulation defects through the photon pulse sequence atlas.

The invention relates to the technical field of electric digital data processing, and discloses an Internet of Things water meter operation mode monitoring method, which comprises the following steps: acquiring an original pulse sequence acquired by a sensor and extracting a pulse time interval sequence; mapping based on the pulse time interval sequence and the system structure characteristic parameters to obtain an equivalent response numerical sequence; constructing a feature vector unit according to the evolution slope between the adjacent numerical points; extracting slope symmetry features of the feature vector units and matching the slope symmetry features with a physical causal criterion so as to separate continuous feature signals and asymmetric noise signals; according to the method, fluid kinetic energy time domain evolution characteristics are represented by using a nonlinear mapping mechanism, accurate decoupling of a real water load and a pipe network disturbance pseudo signal is realized through physical causal verification, the difficult problem of micro flow identification under a complex working condition is effectively solved, and the monitoring robustness of a system is enhanced.

The invention discloses a nuclear radiation energy spectrum double-domain collaborative optimization method and a related device, and belongs to the technical field of nuclear radiation measurement, and the method comprises the steps: obtaining an original distortion pulse sequence generated by nuclear radiation measurement; a double-domain collaborative energy spectrum optimization model is constructed, the double-domain collaborative energy spectrum optimization model comprises a pulse domain repair model and an energy spectrum domain reconstruction model which are arranged in parallel, and the pulse domain repair model is used for performing pulse-by-pulse repair on the amplitude and the arrival time of the original distorted pulse sequence and outputting a repaired pulse sequence; the energy spectrum domain reconstruction model is used for converting the repaired pulse sequence into an energy spectrum diagram; in the training process of the double-domain collaborative energy spectrum optimization model, the pulse domain repair model and the energy spectrum domain reconstruction model share the back propagation gradient of the joint loss function, so that bidirectional closed-loop collaborative optimization of the pulse repair model and the energy spectrum reconstruction model can be realized, the problem of step-by-step error amplification caused by a traditional serial processing mode is avoided, and the performance of the system is improved. And the authenticity and the accuracy of the output energy spectrum are obviously improved.

The invention discloses a linear kick rapid opening and closing control method for an isolating switch, and particularly relates to the technical field of opening and closing control of high-voltage isolating switches. Collecting a linear displacement signal between the conductive arm and the static contact and an electromagnetic driving current change curve, and constructing a kick behavior data matrix; establishing a multi-dimensional state energy threshold model based on the peak acceleration and the zero crossing point; combining a real-time current response curve to complete kick prediction trajectory fitting; fast phase torque compensation is carried out according to the current phase deviation angle, and an active suppression control pulse sequence is generated to realize kick phase locking; a damping recovery coefficient is further calculated based on aftershock feedback, an electromagnetic response factor at the moment of operation is fused, a final control instruction sequence is generated and output to a driving unit, and accurate and high-speed switching-on and switching-off operation is achieved; the method has the advantages of fast control response, strong anti-disturbance capability, high adaptability and the like, and is suitable for accurate control of the isolation switch in various complex power scenes.

The invention discloses a rail transit charging and discharging service life prolonging method based on an energy storage system health state attenuation model, and belongs to the technical field of rail transit energy management. The method comprises the following steps: acquiring data through a multi-dimensional sensor to construct a coupling tensor, extracting a main coupling singular value sequence, and establishing an advanced prediction model of an instantaneous attenuation rate and a system health state; dynamically setting a target threshold value based on a real-time monitoring result, and solving an optimal charging and discharging parameter boundary in the digital twin through double-engine simulation optimization; adopting the Pontryagin minimum principle to generate a reverse reference trajectory meeting the life constraint; and finally, quantizing the track into a pulse sequence, introducing power grid fluctuation and health attenuation rate as adjusting variables, and executing after dynamically adjusting pulse parameters through a fuzzy rule. Advanced sensing of the health state of the energy storage system and accurate optimization control of the charging and discharging process are achieved, and the service life of the system is remarkably prolonged.