90 results about "Sample Mode" patented technology

The invention discloses a geological disaster real-time monitoring method and system based on a ground sensing network, and belongs to the technical field of geophysical exploration, and the method comprises the steps: obtaining an acceleration parameter and a moisture content parameter of each sensor node; according to the change rate of the acceleration parameter and the moisture content parameter, identifying an environment sudden change event, generating an environment trigger signal to activate an emergency sampling mode of a target node and an adjacent node, and constructing a dynamic monitoring cluster; requesting a plurality of nodes in the dynamic monitoring cluster to synchronously measure similar parameters, and comparing measurement results to generate a compressed alarm packet; adjusting weight factors of different monitoring parameters by using the compressed alarm packet and pre-acquired real-time environment parameters, and generating an environment calibration risk index; and when the environment calibration risk index exceeds a preset risk threshold value, outputting a geological disaster early warning instruction. According to the method, multi-parameter cooperative triggering, dynamic cluster response, weight adaptive evaluation and compression transmission technologies are adopted, and the monitoring precision, timeliness and environmental adaptability can be comprehensively improved.

The invention relates to a dynamic speculation sampling method and system for large model reasoning. The method comprises the following steps: processing requests for multiple groups of input test data of similar scenes based on a predetermined application scene reasoned by the large model, obtaining the maximum throughput data volume of the throughput data volume of the target model in the conventional sampling mode and the maximum throughput data volume of the throughput data volume of the target model in the speculation sampling mode, wherein the maximum throughput data volume is larger than or equal to the maximum throughput data volume of the throughput data volume of the corresponding conventional sampling mode. Configuring a request number span sequence which is used for dynamic speculation sampling and is arranged from large to small and a draft token number sequence which is arranged from small to large and has corresponding length; aiming at a specific data processing request, determining a span value interval formed by adjacent request values of the current survival request number in the request number span sequence according to the current survival request number, and setting a draft token number for each request in the current survival request number based on a draft token value in a draft token number sequence corresponding to a lower request value of the span value interval.

The invention provides a data sampling method, electronic equipment and a medium, the data sampling method is applied to the electronic equipment, and comprises the following steps: receiving N paths of data acquired by a plurality of sensors, N being an integer greater than 1; the sampling mode of each path of data in the N paths of data is obtained, the sampling mode comprises a resampling mode and a direct connection mode, the resampling mode indicates that the sampling rate is changed, the direct connection mode indicates that the sampling rate is unchanged, the sampling mode of at most M paths of data in the N paths of data is the resampling mode, and M is smaller than N; and sampling the N paths of data based on the sampling mode.

The disclosed computer-implemented method may include (1) predicting a user state, wherein the user state is measurable via a plurality of different sensor sampling modes, (2) determining a level of uncertainty associated with the predicted user state, and (3) selecting, from the plurality of different sensor sampling modes, a sampling mode to measure the user state. Selecting the sampling mode may include selecting a first sampling mode in response to determining that the level of uncertainty is above a threshold or selecting a second sampling mode in response to determining that the level of uncertainty is below the threshold. Various other methods, systems, and computer-readable media are also disclosed.

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.

The invention relates to the technical field of three-dimensional modeling, in particular to a few-sample target detection method and system based on a neural radiation field, and the method comprises the steps: collecting a to-be-modeled object, and carrying out the processing of the to-be-modeled object, and constructing a target data set; setting multi-resolution Hash mapping through the three-dimensional coordinates, establishing a Hash table, and obtaining mixed features based on the Hash table; performing neural network reasoning by using MLP to obtain density and RGB color, generating light and delimiting a light range, and determining a sampling mode to construct a probability density function; rendering to obtain a multi-view texture image, and fusing and outputting a three-dimensional model by using Poisson reconstruction; constructing two-dimensional images of different angles, generating a training data set, training the three-dimensional model by adopting the training data set, obtaining a to-be-detected sample, and inputting the to-be-detected sample into the trained three-dimensional model to obtain a detection result; through a cooperation mechanism of explicit three-dimensional reconstruction and implicit feature coding, a three-dimensional model is reconstructed to carry out few-sample target detection, and the view angle constraint of traditional two-dimensional detection is broken through.

The invention discloses a gas meter leakage alarm device and method, and relates to the technical field of gas meter leakage alarm, the gas meter leakage alarm device comprises a base mechanism, the base mechanism is internally provided with a substrate mechanism used for preventing performance reduction caused by poisoning of a leakage alarm; the bottom of the substrate mechanism is provided with a rotary vane mechanism used for extracting surrounding air for gas detection. The base mechanism is installed at the top of a possible gas leakage area, surrounding air is actively extracted, stable airflow is formed and enters the equipment, the airflow is guided by the inner rotary vane assembly to enter the area of the net wadding assembly, large-particle impurities are effectively filtered through the assembly, internal elements are protected, and the safety of the equipment is improved. And through the linkage design of the inner rotary vane assembly and the rotary assembly, the dynamic change of the air inlet direction and the periodic scanning of the detection airflow path are realized, a multi-point and wide-area coverage sampling mode is formed, the detection range is expanded, and the risk of missing detection caused by local airflow dead angles is avoided.

The present application relates to the technical field of coal sampling machinery, and particularly relates to a novel automobile sampling machine, which comprises a sampling execution device, a sample processing device and a control device. The sampling execution device is provided with a screw drill type sampling head, a driving mechanism, a lifting mechanism and a cleaning mechanism; the sample processing device comprises a sample collector and a sample preparation unit. The control device is connected with each device through an industrial communication bus and is configured to perform a sampling point random determination operation to generate coordinate information, control the sampling head to move to a target position, then drive the outer cylinder sawtooth structure to rotate and cut and break the lump coal in a breaking mode, and then switch to a sampling mode to drive the internal drill rod to drill and sample; the sampling amount is monitored through a sensor to generate a stop signal, the sampling head is controlled to be lifted to the sample collector to release the coal sample, and finally the cleaning mechanism is started to clean the residues. The present application realizes the automatic sampling of coal of all particle sizes and effectively solves the sample distortion problem caused by the fact that lump coal cannot be sampled.

The utility model discloses an explosion-proof high and low temperature test chamber, which comprises a fresh air fan, a stirring fan and a strong exhaust fan, and further comprises a temperature control module used for controlling the temperature in the chamber of the test chamber; the humidity control module is used for controlling the humidity in the cabin; and the sampling module is used for sampling and detecting gas in the cabin. The oil temperature heat exchange mode is adopted, and a heat source is arranged outside the anti-explosion area, so that the temperature can be accurately controlled, the anti-explosion effect can be ensured, and the application of an anti-explosion scene is realized; by adopting a sampling mode of condensing and then analyzing, not only is the real-time monitoring of the state of the gas in the cabin in a high-temperature state realized, but also the safety is greatly improved.

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.

A first artificial intelligence (Al) engine receives a plurality of incomplete magnetic resonance (MR) K-space data matrices of an object scanned by an MR device. Each of the incomplete MR K-space data matrices comprises complex values and is the result of a corresponding san of the object by the MR device using a sparse-sampled MR scan acquisition sequence. Each sparse- sample MR scan acquisition sequence employs a unique sampling pattern. The first Al engine reconstructs a complete MR K-space data matrix of the scanned object, corresponding to a complete MR K-space acquisition. The reconstruction is based on the data in the plurality of incomplete MR K-space data matrices.

The present invention belongs to the field of radio signal processing technology, and specifically relates to an infinite amplitude line spectrum estimation method based on robust compressed sensing and analog sampling ADC analog sampling when the amplitude of the signal arriving at the front end of the receiver exceeds the dynamic range of the ordinary ADC input. The analog sampling ADC analog sampling mode of the present invention and the proposed line spectrum estimation method utilize the robust compressed sensing theory to realize infinite line spectrum estimation when the amplitude of the signal arriving at the front end of the receiver is higher than the dynamic range of the ordinary ADC input, accurately estimate the amplitude of the signal and its carrier frequency information, and can overcome the problem of weak signal loss caused by direct sampling and quantization using an ordinary ADC. Experiments show that the present invention successfully estimates the amplitude and carrier frequency of multiple source signals when the input signal amplitude is higher than the ADC input dynamic range, thereby achieving reliable weak signal estimation performance at a low quantization level.

The invention discloses a multi-source heterogeneous satellite remote sensing image matching method based on sparse sampling description, and relates to the technical field of remote sensing image processing, and the method comprises the steps: generating an image pyramid of an input image, extracting a feature point set based on a convolution image of each layer of image, and carrying out the fusion to obtain a convolution image corresponding to a convolution direction; applying the constructed sparse descriptor at the feature points, determining the main direction of the feature points according to the statistical result of the convolution value of each pixel point in the sparse descriptor range, sampling at each sampling point of the sparse descriptor to obtain description vectors, and splicing the description vectors to obtain feature description vectors; matching is carried out based on the feature description vectors of the multiple input images to obtain a matching point pair set; the number of sampling points of the sparse descriptor is lower than that of the descriptor in a dense sampling mode in the related technology, the matching calculation amount can be further reduced, feature point matching between the images can be completed only through fewer sampling points, and the calculation efficiency can be improved.

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.

The hematology analyzer comprises a main machine and a puncture device, a front face cover is fixedly arranged at the front end of the main machine, a sampling groove of a concave structure is formed in the edge of the lower end portion of the front face cover, and the puncture device is fixedly arranged in the upper end portion of the front face cover. A sampling needle of the puncture device can vertically and downwards extend out of the front mask into the sampling groove; the control end of the puncture device is electrically connected with the host; a sampling key or a puncture test tube adapter is arranged on the inner wall of the sampling groove, and the sampling key or the puncture test tube adapter is electrically connected with the host. Compared with the prior art, the utility model realizes the switching between an automatic sampling mode and a manual sampling mode, and has the advantages of flexible conversion, stronger independence, convenience in installation and adjustment, efficient and convenient sampling and low cost; and operation is convenient, size is small, parts are few, and maintenance is convenient.

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 invention belongs to the technical field of power marketing data acquisition, and particularly relates to a power marketing dynamic data acquisition method based on load adaptation. Load self-adaption is achieved by counting the number of data collection tasks in real time and evaluating the system resource utilization rate. In a high-load state, the system is automatically switched to a sampling mode of zero crossing point feature windowing, and a dual-channel verification mechanism is started. And different data transmission mechanisms are selected according to the load state. And after receiving the data, the platform performs digital simulation comparison, and triggers phase calibration and resampling when the deviation exceeds the standard. When a plurality of nodes are continuously in a high-load state, the system starts a zero crossing point characteristic window shunting technology and dynamically adjusts a transmission priority according to an equivalent loss value. Intelligent and efficient power marketing data acquisition is realized, and remarkable economic benefits and technical advancement are achieved.

The invention provides a large-model and multi-mode recommendation method and device, electronic equipment and a medium. The recommendation method comprises the steps that online feature extraction is carried out, and extracted features are combined to obtain fusion features; creating a cue word project of the sorting task according to the fusion features; the cue word project of the sorting task is input into a large language model to obtain a sorting result, and the sorting result comprises the interpretability of the sorting result. According to the technical scheme, the working efficiency is greatly improved, the sorting task can be completed in a zero-sample and / or few-sample mode, the interpretability of the sorting result is provided by the sorting method provided by the invention, so that the credibility of the user on the sorting result is enhanced, and the user experience is improved. And research and development personnel can conveniently explain the internal mechanism of the model in the business direction when putting into use in the future.

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 application relates to a multi-brand water quality sampling instrument adaptive access and remote unified management and control method, a general object model abstract layer including a unified object model specification and an object model mapping configuration file is constructed, a mapping relationship between private fields of each brand and model sampling instrument and a unified object model is stored by taking brand identification and model identification as indexes; corresponding mapping configuration files and protocol adaptation plug-ins are loaded through protocol adaptation middleware, bidirectional conversion is carried out between sampling instrument private protocol messages and standard object model attribute values to complete adaptive access; then sampling mode of the sampling instrument is remotely configured, sampling control instructions with unique identifiers are issued, device states and execution feedback are collected, abnormalities are detected according to preset abnormality detection rules, and operation and maintenance work orders are automatically generated. The application enables newly added brand and model sampling instruments to be accessed without modifying platform core codes, and realizes unified remote management and control of heterogeneous multi-brand sampling instruments.

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.

According to the high-fidelity pattern printing method based on the non-uniform sampling holographic femtosecond laser, the shape precision of a holographic light field is improved by conducting non-uniform dense sampling on the pattern contour of the holographic light field, and the data storage pressure and computing power consumption are reduced through a low-sampling-density uniform sampling mode in a background light field area; therefore, the design precision of the diffraction light field and the diffraction calculation efficiency are both considered; meanwhile, based on the complex amplitude iteration regulation and control strategy of the non-uniform sampling holographic light field, the calculation accuracy of the non-uniform sampling holographic light field is achieved, and finally high-precision and high-light-field-uniformity femtosecond laser high-fidelity patterning printing is achieved.