1034 results about "Pulse sequence" patented technology

The invention relates to an intelligent power distribution harmonic monitoring and dynamic compensation system which comprises a monitoring unit, a correction unit and a compensation unit. The monitoring unit continuously collects high-frequency harmonic voltage and current data in a distribution line at a high sampling frequency, extracts transient harmonic components through wavelet packet transformation and empirical mode decomposition, and generates low-dimensional feature vectors based on sparse representation. And the correction unit decodes the low-dimensional feature vector, recovers harmonic time-frequency features, calculates a phase drift rate, predicts a harmonic propagation path and an accumulation node by combining real-time power distribution network topology construction and adopting a nonlinear dynamic prediction model, and generates a correction instruction when abnormality is detected. And the compensation unit adopts pulse sequence density modulation to dynamically adjust a compensation current phase according to the correction instruction, and meanwhile, an inductive coupling device is utilized to transfer harmonic energy to a low-risk node, so that harmonic voltage distortion of a target node is quickly recovered to a stable level in a fundamental wave period after early warning.

The invention relates to the technical field of bushing detection, and discloses a bushing insulation state detection method based on multi-parameter real-time monitoring. The method comprises the following steps: acquiring multi-dimensional monitoring data such as a partial discharge pulse sequence, dielectric loss angle tangent value fluctuation data and surface leakage current density distribution in a bushing operation environment in real time; carrying out insulation defect feature extraction on the data set, and generating an insulation defect distribution map containing a creeping discharge track, an internal air gap position mark and a carbonization channel density index; high-risk and potential defect areas are divided by adopting a dynamic threshold segmentation algorithm, boundary coordinates are determined, and an insulation state evaluation vector containing parameters such as a surface discharge intensity index and the like is constructed according to the boundary coordinates; and inputting the vector into a pre-trained insulation aging prediction model, and outputting a bushing residual insulation life estimated value and a defect development priority sequence. According to the method, all-around accurate monitoring and pre-judgment of the insulation state of the sleeve are achieved, and scientific support is provided for operation and maintenance decision making of the sleeve.

The invention provides an intelligent brain-like decision-making method, system and device with a body, and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a task instruction; analyzing the received task instruction based on a pre-trained large language model, outputting a task description semantic vector, and then carrying out multi-modal data acquisition on the environment to obtain multi-modal perception data; task description semantic vectors are supplemented based on the multi-modal perception data, and task description parameters are obtained; converting the task description parameter into a control signal based on a pre-constructed pulse neural network model, wherein the control signal comprises a plurality of pulse frequencies arranged according to the sequence of the action execution time; and executing corresponding actions according to the sequence of the plurality of pulse frequencies. In this way, the multi-modal perception data and the semantic vectors are combined in the large language model to generate the task description parameters to improve the perception precision of the environment, and the task description parameters of the large language model and the pulse sequence of the pulse neural network are combined to improve the decision response efficiency.

The invention discloses a capacity adaptive coordination control system and method for distributed energy storage charging equipment, and relates to the technical field of charging safety control. The method is used for solving the problems of capacity sudden drop, vehicle charging delay and power grid interference caused by lithium precipitation. The method comprises the following steps: firstly, applying micro-current excitation in a charging pulse turn-off period by using a lithium precipitation characteristic monitoring module, extracting a phase deviation angle in a voltage relaxation curve, judging a lithium precipitation risk and generating a risk tag; then, the risk decision-making module calculates a charge migration proportionality coefficient and a floating coefficient in combination with the battery type and infrared thermal imaging information; then, the demand coupling module fuses the departure moment, the electric quantity gap and the power grid frequency fluctuation to generate a urgency degree weight and a pulse compensation parameter; and finally, the power reconstruction module completes charge migration and pulse sequence reconstruction according to the weight and the compensation parameter, triggers relaxation retest, and constructs a whole-flow dynamic coordination mechanism of the energy storage system.

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 discloses an abnormity monitoring method and system for a digital pressure transmitter, and relates to the technical field of transmitter monitoring, and the method comprises the following steps: obtaining an original pressure response sequence in a high-frequency pressure pulsation environment, and synchronously recording a sampling clock pulse sequence in an acquisition process, establishing double-track mapping based on the original pressure response sequence and the sampling clock pulse sequence, and forming an input condition with a unified time baseline; and performing instantaneous phase difference extraction on the double-track mapping on the unified time base line. Through double-track mapping, phase difference extraction and nonlinear cross spectrum reconstruction, the stroboscopic aliasing risk is quantified and recognition in advance is realized, and the sensitivity and accuracy of abnormal monitoring of the digital pressure transmitter are improved; meanwhile, the multi-scale risk amplification and reversible time-frequency rearrangement technology is combined, covered high-frequency components are separated and recovered, a real pressure pulsation curve is output after dynamic correction, continuous and stable monitoring is guaranteed, and pressure measurement reliability and operation safety are improved.

The invention relates to the technical field of laser deposition control, in particular to an unmanned aerial vehicle metal part ultrasonic field auxiliary laser deposition system which comprises the following modules: a data acquisition module for acquiring a molten pool thermal field signal, a sound field signal and a laser scattering signal, and performing sampling, filtering and synchronous processing to obtain molten pool original data; the micro-flow digital twinning module is used for establishing a molten pool micro-flow digital twinning model according to the molten pool original data and simulating molten pool micro-flow behavior to obtain micro-flow prediction data; and the microflow ultrasonic regulation and control module is used for generating an ultrasonic pulse sequence according to the microflow prediction data and applying the ultrasonic pulse sequence to the molten pool to regulate and control the flow field. According to the invention, through the molten pool microflow digital twinning module and the microflow ultrasonic regulation and control module, real-time modeling and regulation and control of the molten pool microflow structure in the complex geometric part deposition process are realized, the problem that local vortexes, shear layer differences and flow field anomalies cannot be identified in real time in the traditional technology is solved, and the molten pool microflow evolution can be actively reconstructed.

The invention relates to the technical field of laser micromachining, in particular to a TGV drilling machining system and method based on a space-time modulation Bessel beam, an initial laser beam is output through a laser device, the initial laser beam is modulated into a dynamic pulse sequence through a pulse chirp regulation and control module, axial energy attenuation is compensated, a machining subsystem generates the Bessel beam, and the TGV drilling machining system and method based on the space-time modulation Bessel beam are obtained. The angle is finely adjusted in real time through piezoelectric driving, and a dynamic spatial light modulator loads an exponential decay type phase mask, so that light beam airspace shaping, non-diffraction light beam generation, pulse time sequence and phase parameter synchronous control and high-peak power pulse array are realized, and finally, integrated micro-perforation and channel modification of the glass substrate are realized; and the machining efficiency and precision are obviously improved.

The invention relates to the technical field of power system fault monitoring, and discloses a measurement switch fault identification and early warning system, which comprises a signal acquisition unit, a feature analysis unit, a fault judgment unit and an early warning trigger unit. The signal acquisition unit acquires a potential fluctuation sequence and an exciting current pulse sequence through a contact voltage sensor and a coil current transformer; the feature analysis unit extracts time-frequency domain and statistical domain feature groups by means of wavelet transform and moment function analysis; the fault judgment unit generates a fault judgment vector by adopting kernel principal component analysis fusion features; and the early warning triggering unit triggers four-level early warning by using dynamically updated grading threshold values (including historical reference values and online dynamic values). According to the system, through multi-modal feature fusion, dynamic threshold adjustment and a grading early warning strategy, accurate identification and grading early warning of measurement switch faults are achieved, the fault identification accuracy and early warning timeliness are improved, and the system is suitable for the field of electric power and industrial automation.

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 crack defect detection method and system based on a pulse neural network. The method comprises the following steps: collecting an eddy current detection signal of a turbine blade and converting the eddy current detection signal into a pulse sequence; performing feature extraction of different scales on the pulse sequence in sequence, and fusing the extracted features of different scales to obtain a feature extraction pulse sequence; carrying out attention enhancement processing on the feature extraction pulse sequence in a time dimension to obtain a time enhanced pulse sequence; converting the spatial features of the time enhanced pulse sequence into frequency domain features, and weighting the frequency domain features to obtain weighted frequency domain features; converting the weighted frequency domain features into spatial features to obtain pulse signals; and performing crack defect detection according to the pulse signal. According to the method, the feature representation capability is enhanced through multi-scale feature extraction and fusion, the feature representation efficiency is improved through enhancement of time, space and frequency domains, and the training complexity is reduced while the detection precision is ensured.

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 provides a digital twin unmanned aerial vehicle circuit state monitoring method and system, and the method comprises the steps: firstly collecting a partial discharge pulse sequence of an unmanned aerial vehicle switching power supply circuit, separating environment electromagnetic noise through polarization filtering operation, and extracting real discharge pulses to generate partial discharge fingerprint data; inputting the fingerprint data into a pre-established fault search tree model, judging the type of a capacitor or inductor component through a first-stage branch node, and matching a degradation mode such as capacitance attenuation or magnetic saturation through a second-stage branch node to determine the fault type; then, corresponding component parameters of the equivalent circuit model are dynamically updated in the digital twin body, and the circuit operation state change trend is simulated; and finally, according to the state change trend, predicting the residual effective working time of the fault component and outputting a monitoring result. According to the invention, the accuracy of service life evaluation of key components in a complex electromagnetic environment is improved.

The invention discloses a circuit board test method and a related device, and the method comprises the steps: sequentially applying a power pulse sequence to a plurality of power supply rails under a control condition, and collecting the reference data of a differential link before triggering; collecting frequency change data, bit error rate data and phase change data at the same time in the pulse action period to obtain a multi-physical-quantity response sequence; performing time synchronization processing based on the pulse triggering time and extracting a multi-scale feature vector; combining the feature vectors into a feature tensor with time, frequency and response type dimensions, and identifying a time interval with coupling energy exceeding a threshold value as an abnormal window; and comparing the abnormal window characteristics with reference data, generating a standardized defect index, and outputting test judgment in combination with a dynamic judgment threshold. According to the technical scheme, the multi-physical-quantity coupling response of the differential link can be accurately captured under the multi-interference condition, and stable detection and judgment of fine defects are achieved.

The invention relates to a human body health state assessment system and assessment method based on multi-point acquisition, and the system comprises an event sensing module which is used for collecting an image data signal, a physiological signal and an environment data signal of a human body, and outputting an asynchronous pulse event flow when the signal change is detected; the pulse coding module is used for generating a space-time pulse sequence through a preset adaptive threshold coding technology; the neural network processing module is used for performing event driving processing by utilizing leakage integral distribution neurons in a preset pulse neural network according to the space-time pulse sequence to obtain a current health characteristic pulse mode; and the health state decoding module is used for obtaining a health state score and a risk classification result of the current human body through a preset pulse distribution rate analysis algorithm and a preset time sequence decoding technology according to the current health characteristic pulse mode. Therefore, the problems of high power consumption, high delay, low data transmission efficiency, low early pathological feature recognition accuracy and the like of a human health state evaluation system are solved.

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 data processing, and discloses a digital feedback dynamic adjustment method and system for a neutron generator control signal, and the method comprises the steps: calling a reconfigurable waveform parameter set corresponding to a target neutron from a mapping relation according to the yield and beam spot characteristic demands of the target neutron; generating a basic high-frequency pulse sequence of the neutron tube, and performing pre-distortion filtering on the basic high-frequency pulse sequence to obtain a high-frequency pulse sequence of the neutron tube; collecting an anode current and a target voltage signal of the neutron tube applied with the high-frequency pulse sequence during operation, and converting the anode current and the target voltage signal into a digital feedback current; comparing the digital feedback flow with a reference waveform in the reconfigurable waveform parameter set in real time to obtain an error correction amount of the high-frequency pulse sequence; dynamically adjusting the high-frequency pulse sequence according to the error correction; applying the dynamically adjusted high-frequency pulse sequence to a corresponding electrode in a neutron tube through power; the neutron tube control signal processing efficiency can be improved.

The invention discloses a resistance voltage divider calibration system, method and device based on a self-calibration frequency counter and a medium, and relates to the technical field of metering calibration. The system comprises a stabilized voltage source, a resistance voltage divider, a high-speed electronic switch, a voltage-frequency converter, a self-calibration frequency counter, a satellite signal receiver, an electric energy metering chip and a CPU. According to the invention, the output signal of the voltage divider is connected to the voltage-frequency conversion channel through a specific time sequence, the pulse sequence in proportion to the voltage is generated by using the inherent linear characteristic of the output signal, then the pulse sequence is subjected to high-precision counting and comparative analysis through the self-calibration frequency counter, and finally the dynamic correction of the voltage division ratio parameter is realized. According to the technical path, an external reference source and high-precision voltage measurement equipment do not need to be introduced, in-situ calibration of the characteristic parameters of the voltage divider can be completed in a remote mode, and the long-term stability of the high-voltage electric energy metering device in a complex electromagnetic environment is remarkably improved.

The present invention relates in one aspect to an absorption spectroscopy apparatus for measuring one or more gases in an atmosphere, wherein the apparatus comprises a wavelength-tunable laser source, a control circuit configured to provide an electrical drive current to the wavelength-tunable laser source; wherein the control circuit is configured to create the electrical drive current as a modulated drive current, wherein the modulated drive current alternates between at least a first and a second drive current, the first drive current configured to cause the wavelength-tunable laser source to emit laser radiation at a first wavelength and the second drive current configured to cause the wavelength-tunable laser source to emit laser radiation at a second wavelength, different from the first wavelength, and wherein the modulated drive current defines a sequence of pulses, each pulse having a non-rectangular pulse shape.

The invention belongs to the technical field of nuclear magnetic resonance detection, and particularly relates to a method for detecting fluorine ions in an ion exchange membrane and application of the method. The method comprises the following steps: (1) soaking an ion exchange membrane to prepare a sample, winding the sample on a bar, and loading the sample into the bottom of a nuclear magnetic tube in a screw-in manner; the shape of the sample is a polygon, the polygon has a symmetrical structure, and the number of edges is not less than 4; (2) putting the nuclear magnetic tube prepared in the step (1) into a nuclear magnetic resonance spectrometer, selecting a pulse sequence, tuning, shimming, adjusting nuclear magnetic resonance test parameters, and collecting signals; the nuclear magnetic resonance test parameters comprise: 90-degree pulse width is 9-15 [mu] s; and the power is 20-55W. The pulse width of a nuclear magnetic sample obtained by adopting a specific sample preparation method at 90 degrees is 9-15 microseconds; under the power of 20-55W, < 19 > F in a bulk phase of the ion exchange membrane can be detected, and a powerful support is provided for detecting and analyzing structural information in the bulk phase of the ion exchange membrane.

The invention relates to the technical field of sports biomechanics, in particular to an automatic track and field action recognition and capture method based on image data, which comprises the following steps: synchronizing a visible infrared image, a polarization image and an event stream according to a mutual information criterion and generating a polarization phase diagram; inputting a polarized neural radiation field network and a skeleton graph neural network based on Lie group message passing, and alternately optimizing to obtain a continuous voxel light field and a joint rotation sequence; performing Vietoris-Rips persistent homology and third-order path signature on the sequence to extract topological path features, encoding the topological path features into a pulse sequence to drive a liquid state machine, and generating a semantic confidence flow by combining bone potential tensor and action text embedding; and constructing a saliency curve by using the voxel light field time derivative, the neuromorphic difference, the semantic complementary value and the skeleton difference, carrying out strategy gradient on-line adjustment on the weight, taking curve minimum mapping as a key frame index, and outputting an image frame. The track and field action key frames can still be accurately captured in real time under complex illumination and shielding conditions.

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 power distribution cabinet protection, and discloses a power distribution cabinet safety protection and alarm system. The system comprises an operation parameter acquisition module, a dynamic constraint optimization module, a waveform reconstruction module, a pulse adjustment module and a phase synchronization module. The operation parameter acquisition module acquires electrical operation parameters and environment state parameters in real time; the dynamic constraint optimization module divides a fault risk grade interval, defines an action parameter range of a protection actuator and generates a protection action instruction; the waveform reconstruction module combines the opening and closing time sequence requirements of the circuit breaker and real-time load fluctuation to generate an optimized driving pulse sequence; the pulse adjusting module corrects the pulse width and the duty ratio based on the protection action instruction; and the phase synchronization module corrects the trigger phase offset, so that the opening and closing actions of the circuit breaker are synchronized with the fault risk level. The system achieves the comprehensive monitoring and precise protection of the power distribution cabinet, and improves the operation safety and stability.

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 a ranging communication fusion miniaturized system and method based on single photon detection. The system adopts a time division multiplexing common-path structure, and a ranging pulse sequence and a communication pulse sequence are sequentially transmitted in the same detection period to form a PPM modulation laser pulse chain. A single-photon detector is adopted to receive signals, background light interference is suppressed by combining a narrow-band filter and a time gating technology, and the type and boundary of a pulse sequence are identified through a signal demodulation and discrimination module. When the distance measurement pulse sequence is identified, the distance measurement path realizes distance measurement based on the round-trip time of the distance measurement pulse in the period; when a communication pulse sequence is identified, the communication path demodulates the PPM communication data. The method effectively decouples the contradiction between the distance measurement fuzzy and the communication rate, supports communication pulse high repetition frequency modulation while realizing non-fuzzy distance measurement, improves the distance measurement precision and the communication rate, and is suitable for free space communication, mobile robot platforms and other application scenes.

The invention discloses a real-time reserve pool calculation method and system based on pulse amplitude information coding, and belongs to the technical field of reserve pool calculation of space-time sequence tasks. The method comprises the steps that modal information in a real environment is received and preprocessed, encoding is carried out according to time steps, and the codes are pulse sequences with different pulse amplitudes; feeding the pulse sequence into an SL end of a volatile memristor array, writing the pulse sequence into the volatile memristor array, and receiving nonlinear response current of dynamic volatile memristor array space projection at a BL end in real time; receiving the response current by the FPGA, sampling at equal intervals to construct a virtual node, converting the response current into a voltage signal through a TIA, and feeding the voltage signal into a pre-trained nonvolatile memristor array for reasoning; and receiving the response current, outputting a maximum value corresponding neuron index through the WTA winner eating circuit, representing a classification or prediction result, and transmitting the result back to an upper computer or performing action decision at a local end.