48 results about "Sample Mode" patented technology

The invention discloses an automatic test system and method for a high-frequency switch matrix, and relates to the technical field of signal processing, and the method comprises the steps: calculating a confidence coefficient spectrum based on a frequency response compensation curve, adjusting a compression sampling rate, and constructing a random sampling mode; according to a random sampling mode, full-band response characteristic verification is carried out on the space-time-environment baseline data, and abnormal frequency points are identified; carrying out point-by-point comparative analysis on abnormal frequency points and historical reference data, calculating a mean square error and a maximum deviation, generating an error frequency response curve and a test report through a piecewise polynomial fitting method, and constructing a random sampling mode through adjusting a compression sampling rate to realize an adjustment mechanism aiming at different frequency band characteristic changes; and equally dividing and calculating a confidence coefficient weight factor based on the confidence coefficient spectrum so as to generate a sub-band sampling scheme with a weight mark, so that high-efficiency and high-quality automatic batch testing is realized.

Described herein are methods and systems for adjusting a camera system to account for high-lux flicker condition while maintaining camera high sensitivity capability. Automatic switching is performed from quad-Bayer binning (QBB) mode which provides for high sensitivity capability and sub-sampling mode that addresses high-lux condition. An engine, such as an auto exposure engine, performs the switching determining scene data captured by an image sensor and adjusting exposure time and gain, and instructing the image sensor to operate at QBB mode or sub-sampling mode.

The application relates to a method and device for extracting features of a target object in a video frame by using a convolutional neural network, wherein the method comprises the following steps: calculating a proportion of a target object in a current video frame; determining a sampling mode of the current video frame according to the proportion of the target object, so as to generate video frames with different sizes; inputting the video frames with different sizes into corresponding receptive fields respectively, so as to generate a plurality of sampling feature sets; and generating a video frame feature set according to the plurality of sampling feature sets.

The invention discloses a control method of an intelligent lock induction handle, the intelligent lock induction handle and an intelligent lock, and the method comprises the steps: after the intelligent lock is powered on, a capacitive induction array module is in a low-power-consumption awakening mode by default, and only when a certain group of capacitive induction PADs detects that the capacitance variation exceeds a preset threshold value, the capacitive induction array module is in a low-power-consumption awakening mode; switching to a high-frequency sampling mode for data acquisition; the signal acquisition and preprocessing module is used for filtering the multiple groups of PAD data sampled at high frequency and transmitting the filtered multiple groups of PAD capacitance change data to the micro-control unit; the micro-control unit operates a machine learning model, analyzes the multiple groups of PAD capacitance change data in real time, and judges whether a door opening intention exists or not; if the door opening intention exists, the micro-control unit sends an unlocking trigger signal to the intelligent lock master control through the communication module, and the intelligent lock master control controls unlocking according to the unlocking trigger signal. Whether the user has the door opening intention or not can be accurately judged, and the recognition accuracy of the door opening intention is remarkably improved.

This application provides a method, apparatus, and related products for constructing a training set for fine-tuning intent recognition of a large model, relating to the field of artificial intelligence technology. The method constructs and generates a training set to fine-tune the intent recognition of a third-large model. The model parameters of this third-large model are smaller than those of the first-large model, allowing the fine-tuned third-large model to replace the online analysis and inference work of the first-large model, saving computational resources. Furthermore, each training data sample in this application's training set uses intent category-level labels, effectively increasing the distinguishability between different categories during training, thereby improving the accuracy of intent recognition. In addition, each training data sample in this application's training set uses multiple intent discrimination reasons corresponding to intent category-level labels, employing a synonym random sampling mode, which not only overcomes the problem of homogeneity in the description of the same intent discrimination reason but also enhances the model's expressive power.

This invention provides a gas meter metering method and system. The gas meter metering method includes: recording the full-cycle sampling time in the conventional sampling mode of a diaphragm gas meter; adjusting the sampling mode according to the relationship between the full-cycle sampling time and a preset sampling cycle threshold; and calculating the real-time gas flow rate using the adjusted sampling mode. By setting a preset sampling cycle threshold, the sampling mode is rationally switched according to the relationship between the current full-cycle sampling time and the preset sampling cycle threshold, making the adjusted sampling mode more suitable for the current application scenario, meeting the real-time data reading requirements, improving data reading efficiency, and thus achieving real-time monitoring of gas flow rate.

The application relates to the technical field of urban underground pipe network safety monitoring, and discloses a wave corrugation compensator leakage positioning method and system based on an Internet of Things, which is applied to an Internet of Things system comprising a cloud server and a plurality of sensing nodes, and the sensing nodes are installed in the interlayer space of the wave corrugation compensator. The method comprises the following steps: collecting pressure and temperature data of the interlayer space by the nodes at a low frequency, triggering an adjacent node to enter a high-frequency acoustic sampling mode when the pressure change rate is greater than a dynamic threshold value; collecting an acoustic emission signal, filtering mechanical noise through a noise reduction model, and extracting fluid jet noise characteristics; performing cross-correlation analysis on the acoustic signals between the nodes to calculate the actual sound wave conduction distance, and verifying whether real leakage occurs by comparing with a physical topological distance; and finally evaluating the leakage grade according to the steady-state peak value of the pressure data and the acoustic frequency energy data, and outputting device coordinate information. The application has both ultra-low power consumption and strong anti-interference capability, and realizes accurate element-level leakage positioning.

The embodiment of the present application relates to a kind of single-ended conversion differential capacitor sampling compensation device and sampling compensation method, the device includes: compensation module, at least including one compensation capacitor;The compensation module is connected with the capacitor to be measured, for compensating the reference value of the capacitor to be measured;Sampling amplification module, at least including one charge amplifier and feedback capacitor;The sampling amplification module is connected with the capacitor to be measured and compensation module, for converting the capacitance value of the capacitor to be measured into differential voltage value output, to obtain the capacitance value of the capacitor to be measured.The technical scheme of the embodiment of the present application realizes the operation of single-ended conversion differential with one single-ended circuit.Due to the fact that a pseudo branch can be saved, this structure reduces the area of chip when applied in the scenario of large measured capacitor;And due to the innovation of sampling mode between different working phases, it is realized that the standard value of large measured capacitor can be offset with smaller compensation capacitor.

The embodiment of the utility model discloses an ambient air benzene series on-line monitoring system, which has a sampling mode, a purging mode and an analysis mode, and comprises a sample gas inlet and outlet device for collecting and discharging sample gas; the enrichment module is used for adsorbing and analyzing benzene series in ambient air; the separation and detection device is connected with the enrichment module and is used for separating and analyzing the benzene series; the auxiliary gas path device is connected with the enrichment module and the separation detection device and provides auxiliary gas for a sampling mode, a purging mode and an analysis mode; the gas path switching device is connected with the sample gas inlet and outlet device, the enrichment module, the separation detection device and the auxiliary gas path device, and the gas path switching device is used for switching gas paths among the sample gas inlet and outlet device, the enrichment module, the separation detection device and the auxiliary gas path device so as to switch among a sampling mode, a purging mode and an analysis mode. According to the technical scheme, the real-time monitoring precision of the benzene series can be improved.

The present application relates to the field of gas detection, in particular to a wireless intelligent gas monitor; comprising: a portable detection terminal, a remote safety management platform and a gateway device; the portable detection terminal comprises a device host and a separate pumping accessory, and the device host is integrated with a diffusion air inlet for collecting gas; through the micro air pump on the separate pumping accessory and the diffusion air inlet on the device host, two sampling modes of diffusion sampling mode and pumping sampling mode are realized respectively. The present application realizes the tool-free quick switching of diffusion and pumping dual sampling modes by the combination mode of the separate detachable pumping accessory and the device host, and adapts to different sampling scene requirements; the present application collects parameters such as gas density, wind speed, temperature and humidity at multiple heights through the environment collection unit, and the collection and judgment unit quantitatively analyzes the gas interference value and the environmental interference value through density gradient calculation and environmental parameter time opening comparison, so as to realize the automatic switching of the sampling mode.

The invention relates to a control method and device of a flexible direct current power transmission system for new energy access. The method comprises the following steps: acquiring a real-time load rate of a voltage source converter; obtaining a power updating adaptive sliding step length; acquiring real-time environment parameters of the new energy access area; obtaining a power extraction adaptive time window; acquiring real-time active power of a new energy power generation end; obtaining an output fluctuation intensity value of the new energy power generation end; comparing the output fluctuation intensity value of the new energy power generation end with a preset output fluctuation intensity threshold value to obtain an output fluctuation intensity value comparison result; according to the output fluctuation intensity value comparison result, adjusting the sampling mode of the new energy power generation end to obtain an adjusted sampling mode; and according to the adjusted sampling mode, the operation state of the voltage source converter is controlled, and the operation stability of the flexible DC power transmission system is improved.

The invention discloses a power plant real-time data intelligent analysis and early warning method, and the method comprises the steps: collecting the operation parameters of three main engines: a boiler, a steam turbine and a power generator, and carrying out the cleaning and normalization processing, and constructing a data cube with consistent space-time dimensions; establishing an adaptive dynamic baseline by using historical data based on an improved isolated forest algorithm; generating an equipment causal inference map with a weight through a map convolutional network of transfer learning; the deviation degree of parameters and a baseline is calculated in real time, multi-stage early warning is triggered in an over-limit mode, a fault evolution model is called to deduce a fault propagation path, and core abnormity is identified; the acquisition frequency is dynamically adjusted according to the risk level, and the high-speed sampling mode is switched when the risk is high. Unified management of multi-source data is realized, the early warning false and missing report rate is reduced, the troubleshooting time is shortened, the operation and maintenance efficiency is improved, and the safe, stable and intelligent operation and maintenance requirements of a power plant are met.

This invention relates to the field of program control technology, specifically to a method for switching sampling modes of an oscilloscope applied to instrument data acquisition. The method includes the following steps: acquiring an electrical signal conversion sequence and generating marked data by combining it with scale division; calculating interval differences to construct a set and executing judgment comparisons to generate a crossing command; calculating frequency band distribution to construct an energy sequence and filtering intervals to generate mode commands; detecting interval differences and executing boundary judgments to generate signal change commands; and combining multiple commands to execute synchronous matching and adjust timing to generate a switching result. In this invention, by mining the actual characteristics of the underlying electrical signal and dynamically adjusting parameters, the inherent limitations of the preset lookup table mechanism are completely broken, eliminating the hysteresis caused by fixed processes. It accurately captures the core details of frequency band changes, effectively avoiding the risk of losing key data and comprehensively deepening the breadth of waveform energy and dynamic timing analysis. This effectively ensures the authenticity of waveform acquisition in complex environments and significantly enhances the comprehensive applicability of equipment operation.

(a) In the heating mode, a step of driving the resonant circuit of an induction heater at a predetermined resonant frequency of the resonant circuit, wherein the induction heater comprises a switching circuit and a resonant circuit, and the induction heater is for heating a susceptor; (b) A step of measuring the current flowing through the induction heater during the heating mode, (c) A step of comparing a first measurement of current with a second measurement of current, (d) A method comprising the step of controlling the trigger of a sampling mode based at least in part on the comparison in step (c), wherein a predetermined resonant frequency is updated during the sampling mode.

The invention discloses a solid and liquid sample integrated full-automatic titration detection analysis system, which comprises a mechanical arm, and an annular conveying device, a sampling specific gravity device, a titration device, a cleaning and drying device, a sample dissolving device and a control part which are arranged around the mechanical arm, and the detection analysis comprises the following steps: a, sample identification; b, solid and liquid sample mode selection; c, a solid sample detection mode; and d, a liquid sample detection mode. Through the whole titrimetric analysis, liquid sample analysis such as chemical analysis and solid sample titrimetric analysis of ore or metal can be realized, manual intervention is completely not needed from sample uploading to result output, and the mode that traditional titrimetric analysis depends on cooperation of multiple devices and manpower is thoroughly changed; the method is especially suitable for processing batch and continuous samples in an industrial scene.

The application discloses a food inspection sampling device, which comprises a sampling vehicle, an automatic control mechanism is integrated in the sampling vehicle, a fixing box is installed on the sampling vehicle, a mechanical hand is installed on the fixing box, a first storage device and a second storage device are installed in the fixing box, wherein the second storage device comprises a plurality of independent second storage cavities, a movable receiving box is arranged in each second storage cavity, and a second electromagnet for vertically adjusting the position of the receiving box is arranged on the side of the second storage cavity. The application has the beneficial effect that the sampling route can be autonomously planned according to preset merchant information and site positioning information, and the sampling can be carried out at fixed points, thereby replacing the traditional manual sampling mode of visiting each household, effectively reducing the labor intensity, greatly improving the sampling coverage efficiency of multi-merchant and multi-category bulk food in large agricultural wholesale markets, and being suitable for high-frequency and large-batch normal sampling operation requirements.

The invention discloses a continuous sampling system for new pollutants in industrial exhaust, and relates to the technical field of pollution sampling, the continuous sampling system for the new pollutants comprises a sampling setting module, a dilution correction module, a mode judgment module and a dynamic adjustment module, a default sampling period is formulated before sampling of an adsorption tube, and flue gas is obtained; the method comprises the following steps: detecting volume flow, setting an initial dilution multiple, collecting temperature and humidity, calculating a thermal decomposition coefficient and correcting the dilution multiple, correcting and diluting flue gas, detecting diluted flue gas by using a built-in optical ion sensor of an adsorption tube, obtaining instantaneous current, analyzing a reaction fluctuation trend, and judging whether a rapid sampling mode is triggered or not. After triggering, the number of transient peak values is counted according to the fluctuation trend, a default sampling period is adjusted accordingly to obtain a rapid sampling period, sampling is conducted according to the period, the historical sampling amount of the adsorption tube is obtained, whether replacement is conducted or not is judged, efficient capture of the transient peak values is achieved, the utilization rate of the adsorption tube is improved while data integrity is guaranteed, and operation and maintenance cost is reduced.

The invention discloses a self-supervision real image denoising method based on a dual random double sampling mode. Comprising the following steps: providing a random double-sampling method of dual denoising branches to solve a noise fitting problem and generate more sub-images for training; introducing complementary attributes of dual branches into the design of a loss function, and proposing a dual double-sampling cross loss function; putting forward a fixed'subgraph-sampling 'strategy according to a mode collapse phenomenon in a test process; in the pre-training and testing process, designing an artifact remover to further remove chessboard artifacts; and training the de-noising network of the dual branch structure through the training steps to obtain a de-noising model. And then, inputting an image with noise into the trained network model, and performing corresponding test steps to obtain a high-quality de-noised image. According to the method, clean images are not needed for training, the performance of the method is better than that of an existing self-supervision denoising method, and the method is an effective real image denoising method.

The invention discloses a goodness vector calculation method for measuring modal filtering performance of a vertical linear array. The goodness vector calculation method comprises the following steps: (1) initializing marine environment parameters, line spectrum signal parameters emitted by a sound source and vertical receiving array parameters; (2) calculating each order normal wave mode function excited by a line spectrum signal emitted by a sound source and a receiving sound pressure value at each hydrophone depth of a vertical array by using a Kraken model, and establishing an array receiving sound pressure vector; (3) constructing a sampling mode function matrix, multiplying a pseudo-inverse matrix of the sampling mode function matrix by the sampling mode function matrix to obtain a mode filtering matrix, and calculating a relative attenuation coefficient of each order mode; (4) calculating to obtain a contribution degree matrix by using the modal filtering matrix and the relative attenuation coefficient of each order of modal, and taking out main diagonal element values of the contribution degree matrix to form a goodness vector; according to the method, the modal filtering performance can be quantitatively represented, and a quantitative analysis means is provided for measuring the modal filtering performance under the vertical linear array.

The invention relates to the field of gas detection, in particular to a wireless intelligent gas monitor. Comprising a portable detection terminal, a remote security management platform and gateway equipment, the portable detection terminal comprises an equipment host and an independent pump adsorption part, and a diffusion type gas inlet for collecting gas is integrated on the equipment host; two sampling modes, namely a diffusion type sampling mode and a pumping sampling mode, are respectively realized through a micro air pump on an independent pump adsorption part and a diffusion type air inlet in an equipment host. According to the invention, through the combination mode of the independent and detachable pump adsorption part and the equipment host, the tool-free quick switching of diffusion type and pump suction type double sampling modes is realized, and the requirements of different sampling scenes are met; parameters such as gas density, wind speed and temperature and humidity at multiple heights are acquired through the environment acquisition unit, and the acquisition judgment unit quantitatively analyzes a gas interference value and an environment interference value through density gradient calculation and environment parameter time mine mouth comparison, so that automatic switching of sampling modes is realized.

The invention discloses a multi-band adaptive data processing method and device based on an FPGA, and a storage medium, the number of sub-bands is dynamically determined through a sampling rate identifier, a differential data packaging rule is formulated in combination with the number of sub-bands, a bit width identifier and a mode identifier, a multi-band adaptive mapping strategy is realized, it is ensured that a 128-bit PCIe data packet is always free of redundant bits, and the data processing efficiency is improved. The PCIe interface bandwidth is utilized to the maximum extent; meanwhile, data packaging rules can be dynamically adjusted by modifying configuration parameters, the combination requirements of multiple sampling rates, multiple bit widths and multiple sampling modes can be met without modifying FPGA hardware logic, the technical problem that a traditional scheme is poor in flexibility is solved, and diversified data transmission scenes in multiple fields of wireless communication, radar signal processing and the like are adapted.

The invention provides a generative model driven sampling for adaptive sparse multi-modal sensing of user environments and intent. The disclosed computer-implemented method comprises: (1) predicting a user state, where the user state is measurable via a plurality of different sensor sampling patterns; (2) determining a level of uncertainty associated with the predicted user state; and (3) selecting a sampling pattern from the plurality of different sensor sampling patterns to measure the user state. Selecting the sampling pattern includes: selecting a first sampling pattern in response to determining that the uncertainty level is above a threshold; alternatively, a second sampling pattern is selected in response to determining that the uncertainty level is below the threshold. Other various methods, systems, and computer readable media are also disclosed.

The invention discloses an optical fiber landslide monitoring sampling frequency control method based on signal-to-noise ratio feedback regulation. The method comprises the steps of S1, system initialization, S2, signal acquisition, S3, signal data processing, S4, real-time signal-to-noise ratio SNR calculation, S5, system adoption frequency regulation and S6, sampling and feedback closed loop forming. The method solves the technical problem that an existing optical fiber landslide monitoring system lacks real-time identification and dynamic sampling control on the quality of collected data, can overcome the defects that a fixed sampling mode is low in efficiency, high in data redundancy and poor in environmental adaptability, and provides a self-adaptive sampling frequency adjusting method based on signal-to-noise ratio feedback. And more efficient data acquisition and landslide precursor capture are realized.

The application discloses a multi-frequency-band adaptive data processing method and device based on FPGA, a storage medium, and the like. The number of subbands is dynamically determined through a sampling rate identifier, and a differentiated data packaging rule is formulated in combination with the number of subbands, a bit width identifier, and a mode identifier, so that a multi-frequency-band adaptive mapping strategy is realized, 128-bit PCIe data packets are ensured to have no redundant bits at all times, and PCIe interface bandwidth is maximally utilized. Meanwhile, the data packaging rule can be dynamically adjusted by modifying configuration parameters, and the combination requirements of multiple sampling rates, multiple bit widths, and multiple sampling modes can be compatible without modifying FPGA hardware logic, so that the technical pain points of poor flexibility of a traditional scheme are solved, and diversified data transmission scenes in multiple fields such as wireless communication and radar signal processing are adapted.

The present disclosure relates to a flexible tower resonance avoidance control method, an electronic device and a system. The method comprises: determining a first operating parameter of a current period in a first sampling mode; if the first tower vibration peak value and the first tower vibration effective value of the current period meet the preset condition, controlling the wind turbine to adopt a first rotating speed jump control strategy when the rotating speed of the wind wheel is in a first rotating speed limit interval; if the first wind wheel rotating frequency or the first tower vibration frequency is within a first frequency range, and the number of times that the first tower vibration peak value and the first tower vibration effective value meet the preset condition is greater than a first number threshold, controlling the wind turbine to adopt a second rotating speed jump control strategy when the rotating speed of the wind wheel is in a second rotating speed limit interval, and the first rotating speed limit interval is smaller than the second rotating speed limit interval. In this way, the control strategy adopted by the wind turbine can be adjusted to ensure the safe operation of the wind turbine.

The application discloses a frozen soil moisture-ice crystal distributed monitoring system based on neutron scattering and TDR combination. A synchronous trigger response module, optical fiber transmission and hierarchical sampling mode are adopted to realize accurate alignment in time dimension, and trigger delay is controlled within plus or minus 10 mu s. Through low-temperature modification, constant temperature control and lightweight shielding design, the adaptability of the system to special working conditions of frozen soil is improved, and long-term monitoring stability is ensured. The phase state of the application is clear, and the data dimension is complete. Total water is measured by neutron scattering, and unfrozen water is measured by TDR. The ice content is obtained by the difference between the two. Combined with the double calibration framework and error compensation algorithm, the quantitative measurement of the three elements of total water, unfrozen water and ice is realized. The application accurately solves the technical combination difficulty, realizes deep cooperation, solves the volume matching problem of two kinds of technical measurement through rigid positioning components, space calibration mechanism and deformation compensation design, and reduces the space correspondence error to less than 5%.

The application discloses a gas meter leakage alarm device and method, relates to the technical field of gas meter leakage alarm, and comprises a base mechanism, the inside of the base mechanism is internally provided with a substrate mechanism for preventing poisoning of the leakage alarm and performance decline caused by poisoning, and the bottom of the substrate mechanism is provided with a rotating blade mechanism for extracting surrounding air for gas detection. The base mechanism is installed on the top of the area where gas may leak, surrounding air is actively extracted, stable airflow is formed to enter the inside of the equipment, the airflow is guided into the area of the net flocculation assembly through the inner rotating blade assembly, the assembly not only realizes effective filtration of large-particle impurities and protection of internal elements, but also realizes dynamic change of the air inlet direction and periodic scanning of the airflow path through linkage design of the inner rotating blade assembly and the rotating assembly, a multi-point, wide-area coverage sampling mode is formed to expand the detection range, and the risk of missed detection caused by local airflow dead angle is avoided.

The invention discloses a voiceprint advanced recognition and early warning method for runaway critical faults of a mixed-flow unit of a hydropower station, and aims to solve the problems of lag, low sensitivity, difficulty in positioning and poor universal voiceprint adaptation of the existing monitoring technology. According to the method, a high-frequency acoustic emission AE and broadband sound pressure composite sensing array is arranged, a key phase sensor rotating speed pulse signal is embedded, and an equal-angle increment sampling mode is switched in a self-adaptive mode according to the rotating speed change rate; a sound signal is converted from a time domain to an angular domain, and a fault signal is effectively enhanced in combination with an order domain blind source separation technology; sensitive features such as rotating speed normalized sound energy and transient impact repetition frequency entropy TIRFE are extracted; simulation data are generated by means of a digital twinning technology to pre-train a one-dimensional convolutional neural network, a model is finely adjusted and optimized through measured data, grading early warning is achieved by combining a dynamic threshold value, faults such as the blade early cracks are accurately positioned and early recognized through a TDOA technology, positioning is accurate, the anti-interference capacity is high, the small sample problem is effectively solved, and the method is suitable for large-scale popularization and application. Safe and stable operation of the unit is ensured.

The invention relates to a navigation type greenhouse gas monitoring system based on autonomous exploration. The navigation type greenhouse gas monitoring system comprises an unmanned ship body, an autonomous sensing and dynamic planning module integrated on the unmanned ship body and an integrated greenhouse gas collecting and monitoring module. The integrated greenhouse gas collecting and monitoring module is used for working in a sailing type continuous pre-sampling mode to obtain real-time pre-sampling data when the unmanned ship body carries out autonomous cruise scanning in a water area to be detected, and is also used for obtaining real-time pre-sampling data when the unmanned ship body arrives at any optimized sampling point according to the optimal sailing path. Automatically switching to a fixed-point closed accurate measurement mode for measurement; the autonomous sensing and dynamic planning module is used for generating dynamic space distribution information of greenhouse gas based on the real-time pre-sampling data, acquiring environment obstacle information in real time, and generating an optimized sampling point position sequence and an optimal navigation path for the unmanned ship body to arrive at each optimized sampling point based on the environment obstacle information and the dynamic space distribution information; according to the invention, the monitoring efficiency and the monitoring accuracy are improved.