23 results about "Pulse flow" patented technology

The invention relates to the technical field of electromagnetic interference suppression, and discloses an intelligent control method for electromagnetic interference suppression in a photovoltaic energy storage system, and the method comprises the steps: obtaining an electromagnetic transient pulse flow signal of an electric field sensor in the photovoltaic energy storage system; carrying out electromagnetic transient pulse interference signal suppression by adopting a self-adaptive time-varying filter and recording suppression time; performing electric field change detection on the distribution transformer by adopting an electric field sensor to judge whether the electric field change is detected or not; if yes, performing steady-state electromagnetic interference suppression on the distribution transformer; and if not, performing cyclic detection according to the time length from the current time to the suppression time. According to the method, a transient pulse interference signal automatic suppression mode based on the adaptive time-varying filter is adopted, the measurement precision of the electric field around the distribution transformer is improved, steady-state electromagnetic interference suppression is performed on the distribution transformer, the influence of electromagnetic interference on the electric field sensor and the distribution transformer can be effectively suppressed, and the measurement accuracy is improved. And the operation stability of the photovoltaic energy storage system is improved.

The embodiment of the present application discloses a method for inhibiting integrated circuit from static damage, comprising: providing a first chip, and arranging an integrated circuit on the first chip; providing a second chip, and arranging an electrostatic protection circuit on the second chip, the electrostatic protection circuit comprising: a first output port, a second output port and a first resistor; wherein, when an electrostatic pulse flows through the first resistor, the resistance of the first resistor increases; connecting the first output port with an output port of the integrated circuit, so that the electrostatic protection circuit can inhibit the integrated circuit from static damage. Moreover, the electrostatic protection circuit is arranged on the second chip, and the integrated circuit is arranged on the first chip, so that the integrated circuit and the electrostatic protection circuit are arranged on different chips, avoiding the problem that the integrated circuit cannot be designed for electrostatic protection due to the limitation of the base material of the chip on which the integrated circuit is arranged, and realizing the electrostatic protection of the integrated circuit of new material.

The invention relates to the field of cooling, and discloses a liquid cooling system and electronic equipment, and the system comprises a cold plate which can accommodate a heat dissipation medium; the liquid supply assembly is used for providing a heat dissipation medium for the cold plate; and the pulse flow adjusting assembly is arranged between the liquid supply assembly and the cold plate and used for enabling the heat dissipation medium to form pulse flow, and the pulse flow enters the cold plate. A cooled surface is directly impacted by a pulse flow, a heat boundary layer on the surface can be instantly damaged and washed away, the heat exchange efficiency can be seriously hindered by the almost static fluid layer with very high temperature, the limitation can be broken by periodic impact brought by the pulse flow, new cold fluid is in direct contact with the heat exchange surface, and the heat exchange efficiency is improved. Therefore, a high instantaneous convective heat transfer coefficient is generated, and the problem that the heat transfer coefficient is difficult to increase due to stable thickening of the heat transfer boundary layer is effectively solved.

The application discloses a mouse operation precision improving method based on AI posture reconstruction and relates to the technical fields of hand capturing and mouse. An asynchronous space-time pulse flow sequence carrying ultrahigh time resolution is generated; a continuous-state hand high-dimensional dynamics hidden space representation is formed; a Laplace domain continuous motion trajectory function is solved; a full-link system dynamic forward-looking compensation time is calculated; zero-delay hand motion state prior parameters eliminating hardware physical delay are deduced; sub-millisecond continuous mouse control signals are generated; and stepless smooth state refreshing and zero-delay interactive driving of a mouse pointer are completed. The application significantly improves the accuracy of hand super-speed motion trajectory extraction and the space-time calculation efficiency of the system, and the trajectory fidelity and anti-interference robustness in an ultrahigh-frequency interaction scene are qualitatively improved compared with traditional interpolation algorithms.

The invention relates to the technical field of educational informatization, and discloses a smart classroom interactive behavior real-time supervision method fused with attention recognition, and the method comprises the steps: collecting classroom video streams, carrying out the time domain differential coding through an integral issuing neuron model, and generating teacher and student end pulse streams; constructing a self-adaptive confidence mask based on the visual pulse stream of the student side, performing gating repair and interpolation on the original video signal, and reconstructing a clean physiological blood volume pulse wave signal; extracting a student physiological feature vector, and calculating a dynamic lag order parameter according to the teacher end pulse stream; and performing time-varying Granger causality test on the teacher end pulse stream and the student physiological feature vector by using the parameters, and outputting an interactive behavior judgment result. By quantifying the causal association between the teaching stimulation and the physiological response of the student, the problems that the false attention state is difficult to distinguish and the non-contact physiological signal is susceptible to action interference in the prior art are solved, and the accuracy and robustness of classroom supervision are improved.

The application discloses a kind of light weight multi-focus pulse fusion reconstruction methods based on compressed sensing, constructs light weight multi-focus pulse fusion reconstruction network, contains multi-focus pulse input module, feature fusion module, sparse coding optimization module, image reconstruction module;Multi-focus pulse input module handles two multi-focus pulse flow data, generates single first fusion feature map and single second fusion feature map, feature fusion module extracts and fuses two fusion feature maps, obtains dictionary matrix and long exposure image, based on dictionary matrix and long exposure image, by iteratively sparse coding optimization module solution, obtain optimal sparse coding, image reconstruction module utilizes dictionary transpose matrix and optimal sparse coding, reconstructs output clear image richer in detail, overall loss function improves the approximation degree of reconstructed image and real image.The application effectively solves the problem of pulse camera imaging depth of field blur.

The application discloses a turbine shaft torsional vibration in-situ detection method under an aero-engine whole machine state, relates to the field of aerospace engines, and comprises the following steps: converting the rotating speed fluctuation generated when the turbine shaft of the aero-engine occurs torsional vibration into the change on the triggered pulse time sequence when the rotor blade reaches the optical sensor, and collecting the blade pulse flow signal through the optical sensor; wherein the optical sensor is fixedly connected with the bore probe structure assembly of the aero-engine; the blade pulse flow signal is high-frequency sampled by using high sampling rate dynamic data sampling, and the rotating speed time domain signal is obtained; the rotating speed time domain signal is subjected to preliminary analysis of turbine shaft torsional vibration, and the torsional angle result of the turbine shaft is determined; and the torsional angle result of the turbine shaft is converted into the torsional vibration result of the turbine shaft based on the strain relationship function between the shaft torsional angle and the torsional vibration. The application can realize efficient and accurate detection of the turbine shaft torsional vibration under the aero-engine whole machine state.

The invention relates to the technical field of machine control, and discloses a distributed cognitive decision-making method based on an SNN model, and the method comprises the steps: constructing the SNN model in advance, and configuring the initial parameters of the SNN model; acquiring environmental data, and encoding the environmental data to generate a pulse stream; the pulse flow is input into an SNN model, the SNN model carries out calculation based on an event-driven mechanism, and signal processing features are output; determining a local cognitive decision according to the signal processing characteristics; transmitting the local cognitive decision to an edge node for feature matching and verification through a cooperative communication module, and feeding back a verification signal; and generating a final global decision through a global fusion module according to the verification signal fed back by each edge node. According to the invention, the changed environmental data is processed through the pre-constructed SNN model, the pulse stream is generated, the data matching and verification are carried out according to the pulse stream, the final global decision is generated according to the feedback verification signal, the redundant computing power is reduced, and the system power consumption is reduced.

The application discloses a target segmentation method and system based on a pulse neural network, and belongs to the field of target segmentation. The application converts continuous event pulse streams into input representations suitable for the pulse neural network through an event pulse conversion relationship; improves the feature extraction capability of a target segmentation model for continuous event pulse inputs and reduces energy consumption by extracting information and memorizing past features through pulse neurons; improves the utilization rate of the target segmentation model for features by fusing multi-time step features through a space-time feature aggregation module; improves the fusion capability of the target segmentation model for coarse-grained and fine-grained semantic information through a multi-scale feature fusion module, and uses multi-time step loss to constrain the convergence of the target segmentation model, reduce the dependence of the target segmentation model on time steps, and improve the robustness of the target segmentation model. The application can not only process continuous space-time event pulse streams with low power consumption, but also obtain accurate segmentation results in low-light and high-speed scenes.

The invention discloses an intelligent hydrostatic level communicating pipe device for deformation monitoring of a super high-rise structure, and relates to the technical field of deformation monitoring of the super high-rise structure. A three-dimensional position, an equivalent diameter, morphological characteristics and a moving track of a bubble are obtained through a bubble multi-dimensional characteristic monitoring module; the bubble risk grade evaluation and dredging decision module realizes bubble risk grade division and dredging strategy matching based on a digital twinborn model and an interference weight evaluation model; the differential bubble dredging execution module is used for regulating and controlling small-size free bubbles by adopting a low-pressure pulse flow field, stripping large-size adhesion bubbles through high-frequency micro-vibration and then pushing the large-size adhesion bubbles to an exhaust cavity; the deformation monitoring-bubble dredging cooperative regulation and control module ensures that dredging operation does not influence the structural deformation monitoring stability through time sequence data comparison; the dredging effect verification and strategy iteration optimization module achieves strategy dynamic optimization through secondary scanning and quantitative evaluation, and the problem of liquid level measurement distortion caused by bubble retention in the super high-rise long-distance communicating pipe is effectively solved.

The invention discloses a random information freedom degree ultrasonic data compression sampling method, and relates to the field of data compression acquisition during ultrasonic detection of complex components. According to the method, information of component interface echoes and defect echoes can be reserved, the degree of freedom of detection echo signal information with randomness is obtained according to an effective peak value in a detection signal, and an effective peak value Gaussian pulse flow is obtained by utilizing a designed pulse flow extraction method; inputting the pulse flow into a designed adaptive bandwidth sampling core to modulate the pulse flow, performing sampling time compensation on the signal in combination with a time domain support interval of the sampling core, and obtaining sparse sampling data at a high innovation rate; a singular value decomposition method is adopted to intercept singular values conforming to the number of information freedom degrees and corresponding sparse data Fourier coefficients, feature parameters of the random information freedom degree ultrasonic detection signals are obtained through calculation, and signal recovery can be achieved.

The invention relates to the field of signal sorting, and provides a reliable extraction and utilization method of complex pulse stream sequence features, and the whole process is divided into three stages of feature sequence preprocessing, feature extraction and feature utilization. For feature sequence preprocessing, PA sequence coarse segmentation, outlier removal, smooth noise reduction, sequence characterization and approximate equal interval processing are carried out; for extraction of complex pulse flow sequence features, from the perspective of a time sequence, possible complex pulse flow sequence features in a radar sample database are mined through similarity measurement and complete sequence search of the sequences; for the utilization of complex pulse stream sequence features, the features have certain randomness, are rough and are difficult to be finely applied to signal sorting, simple fuzzy matching is adopted to extract target pulse data, and signal sorting is completed. According to the method, reliable extraction and utilization of complex pulse sequence features are realized, and the signal sorting accuracy can be further improved.

The invention belongs to the technical field of electronic information, and particularly relates to a signal sorting method based on an associated pulse pair and a transfer matrix, which can estimate a skeleton period of a staggered PRI sequence after a first-level sequence difference histogram statistical result by utilizing the relevance between pulse pairs, and avoids making a multi-level time difference of arrival histogram statistical result; the limitation that a low-pulse flow density region needs to be determined in advance in a transfer matrix method is avoided by utilizing an improved sequence difference histogram statistical method; compared with an existing method, the method has the advantage that the staggered PRI and the jitter PRI can be better estimated.

The application discloses an excitation system working condition evaluation method and device based on simulation modeling, and a medium, relates to the twin simulation technical field, and comprises the following steps: collecting real-time operation data of the excitation system, unifying time benchmarks, and marking state sources to obtain a real-time state frame; grouping the real-time state frame, performing frequency measurement, correcting a sampling area, and switching data sources while retaining sampling credibility and state source marks to obtain standardized working condition state quantities; dividing nodes of regulating calculation states, limit auxiliary ring states, pulse readback states, and cabinet flow response states in the standardized working condition state quantities, configuring state transmission relationships and execution feedback relationships, and constructing a working condition causal twin simulation model. The application constructs pulse flow normal fingerprints corresponding to working condition boundary labels, realizes stable correlation expression of the pulse readback states and the cabinet flow response states under specific operation working conditions, and enables the twin simulation to obtain a standardized reference benchmark for deviation identification.

Embodiments of the present application provide an eye movement tracking method and device based on pulse data, equipment and a storage medium. The method comprises determining a tracking feature at a starting time according to a labeled pupil position of the target pupil at the starting time of the target pulse stream and a target visual feature corresponding to the starting time; for each time, determining a tracking feature at the time and a predicted pupil position at the time according to a target visual feature corresponding to the time, a tracking feature at a last adjacent time, a predicted pupil position at the last adjacent time, and a labeled pupil position; and obtaining an eye movement tracking result based on the predicted pupil position at each time. With the help of an end-to-end network architecture and a timing iterative updating mechanism, the tracking drift problem caused by high-speed eye movement is effectively solved. At the same time, the slow changes in the appearance of the pupil during the tracking process are adapted, and the efficiency, stability and accuracy of the tracking are significantly improved by relying on the historical state to smooth the predicted trajectory.

The invention relates to the technical field of intelligent water meter metering, and discloses a water meter detection method and system with a multi-pulse flowmeter, and the method comprises the following steps: collecting flow data through three pulse flowmeters with different measuring ranges, and preprocessing the flow data; dynamically generating quantum noise matched with the frequency spectrum of the high-quality multi-source signal for the high-quality multi-source signal; performing feature extraction on the signal after the quantum noise is injected; mapping the four-dimensional feature tensor into an Isin model; training by using a generative adversarial network of an adversarial learning framework, and detecting and identifying unknown abnormal signals; a detection result is physically verified by using a hydroelectric coupling equation, and a communication protocol of flow management is adjusted in real time according to the detection result. According to the technical scheme, the hydroelectric coupling equation is combined with real-time data monitoring, efficient verification of flow abnormal signals is achieved, and the problem of detection delay caused by equipment faults is solved.

The present application proposes a behavior recognition method and device, which includes: obtaining a pulse stream of an object to be identified; for any time scale among a plurality of predefined time scales, dividing the pulse stream into a plurality of sub-pulse streams according to the time scale; performing feature fusion on all sub-pulse streams corresponding to the plurality of time scales to obtain a pulse fusion feature; the pulse fusion feature includes the light intensity change information of each pixel under the plurality of time scales; restoring the pulse fusion feature to obtain a plurality of video frames; for any video frame, calculating the deterministic weight of the video frame according to the probability values ​​of the plurality of behavior categories in the video frame and the total number of the plurality of behavior categories; predicting the target behavior of the object to be identified according to the probability values ​​of the plurality of behavior categories in the plurality of video frames and the deterministic weight of each video frame. The embodiment of the present application enhances the key features in the blurred image, improves the target behavior recognition capability, and enables the drone to have a stronger target perception capability in a complex environment.

The application discloses a neuromorphic camera denoising method for general lighting conditions, and belongs to the field of video denoising. According to the relationship between the intensity of the pulse stream noise and the scene lighting, the continuous space-time pulse stream is converted into the picture feature representation under a specific window, and the input of the traditional neural network is adapted; the dark current noise and the response inconsistency noise are removed through the calibrated dark current matrix and the response inconsistency matrix; the image features are converted into the frequency domain features through the two-dimensional discrete wavelet transform, the low-cost noise removal is realized by using the frequency domain noise separation characteristics; the shot noise and the clipping noise are removed by fusing the frequency domain features of adjacent windows; the residual noise on the frequency domain is thoroughly removed through the deep self-attention network; finally, in order to inhibit the excessive smoothing caused by the denoising, the fusion features on the frequency domain are used to refine the denoising result. The application is suitable for the field of video denoising, is used for reconstructing a clean image, reducing the noise in the image and reducing the influence of the noise on the downstream task.

The present application relates to a kind of solvent-resistant label stretch forming device in the field of label processing device, heating is carried out when the first heating roller is stretched in label base film, so that label base film is stretched more uniformly, the surface temperature of the first heating roller is monitored using contact temperature sensor, the temperature of heat medium injected into multi-channel rotary joint is controlled, and the temperature of heat medium injected into feeding inlet and flowed out of discharging outlet is detected respectively, to judge the heat exchange efficiency of heat medium in the first heating roller, further judge whether scale exists in the first heating roller, and in the early stage of scale formation, use pulse flow to break, finally use collection unit to capture and concentrate scale fragments, reduce the risk of multi-channel rotary joint blockage, discard the complex scale cleaning device set in the heating roller in prior art, increase its reliability, reduce the maintenance cost of heating roller.

The application discloses a brushless pump intelligent control method and system based on a single-chip microcomputer. The method comprises the following steps: in the same single-chip microcomputer, collecting a pressure signal output by an analog pressure sensor through an ADC module at a sampling rate of no less than 500 Hz, collecting a temperature signal through the ADC module, and collecting a flow signal output by a pulse flow sensor through a timer capture unit; performing cascade digital filtering on the pressure signal, wherein the cascade digital filtering comprises median filtering and sliding average filtering; performing PID calculation on the filtered pressure signal as a main feedback quantity to output a speed correction quantity; switching between multiple operation modes according to the temperature signal and the flow signal; driving the brushless pump according to the speed correction quantity through FOC vector control; and performing a communication task with an external device. The application realizes the coordinated operation of high real-time pressure closed-loop control and the communication task through the single-chip microcomputer, and helps to eliminate the false triggering caused by the sensor signal fluctuation.

This invention discloses a spiking neural network (SNN) mapping method and system for congestion awareness on neuromorphic hardware, belonging to the field of neuromorphic computing technology. The method includes: mapping neurons in a spiking neural network model to physical cores of neuromorphic hardware to form an initial mapping scheme; iterating through congestion hotspots and updating the mapping scheme in each iteration to alleviate network communication congestion on the hardware; calculating the distribution of congestion hotspot regions composed of physical cores under the current mapping scheme based on pulse flow evaluation between physical core pairs; for each congestion hotspot region, selecting multiple physical cores with high pulse flow contribution as hub cores to be swapped; determining the optimal swap candidate core for each hub core based on a heuristic swapping decision mechanism; and swapping hub cores with the optimal swap candidate cores to update the mapping scheme. This promotes the efficient deployment of SNNs on neuromorphic hardware and provides key technical support for high-energy-efficiency, low-latency neuromorphic computing.

The invention discloses a flow determination method and device, a medium and equipment, and the method comprises the steps that the first flow pulse number of a flow meter in the current first unit time is obtained, and the flow meter is used for monitoring the flow of protective gas of an annealing furnace; performing frequency division processing on the first flow pulse number according to a preset frequency division number to obtain a second flow pulse number; according to the second flow pulse number, the preset frequency division number and a preset instrument coefficient of the flowmeter, the flow of the protective gas is obtained, and the preset instrument coefficient represents the pulse number of unit flow. Frequency division processing is adopted to replace a pulse flow converter, cost is lower, and work is more stable.

The invention discloses a radar signal pre-sorting method based on simultaneous multiple beams, which comprises the following steps of: reading pulse description words in a time slice, identifying N beams of which the frequencies, the pulse widths and the time differences of arrival are smaller than a set threshold value as a group of simultaneous multiple beams, simultaneously completing pulse width division and storing until all current pulses are traversed; performing angle measurement on each group of obtained simultaneous multi-beams by adopting a three-point power centroid method, compensating a beam pointing error caused by frequency deviation of a radar and a radiation source to obtain a unique accurate direction of the beams in the group, taking a beam direction with the maximum amplitude as an output beam, and replacing the direction with the accurate direction to obtain an accurate direction of the beams in the group; keeping amplitude information of other beams in multiple beams at the same time to an output beam, and eliminating other beams to complete false beam suppression; and on the basis of the output pulse flow, interval division based on system or precision requirements is carried out in a time domain, a frequency domain and a space domain in sequence, and pre-sorting is completed. According to the invention, high-precision angle measurement, false wave beam and distorted wave beam removal and pre-sorting are synchronously completed.