11results about How to "Increase sampling density" patented technology

The invention discloses a neural radiation field trusted twin modeling method based on evidence fusion and a storage medium. The method comprises the following steps: acquiring multi-view image data; constructing a neural radiation field model based on evidence deep learning, and modeling the volume density, color and uncertainty of spatial points through normal inverse gamma distribution; training the model by adopting a weighted loss function with data uncertainty; in the training process, the sampling density of a high-uncertainty region is increased through a self-adaptive resampling strategy, and the rendering quality is improved; applying the trained model to a defect sample test data set; and generating a three-dimensional reconstruction image and an uncertainty quantization graph through the model, performing credibility evaluation on the defect area, and generating a final detection result in combination with a rendered image and the uncertainty quantization graph. According to the method, a fusion method of evidence deep learning and a neural radiation field is introduced, so that high-precision and high-reliability sample supplement work of product surface defect detection is realized, and the reliability and robustness can be improved.

The invention discloses an outdoor environment monitoring method and system, a storage medium and a program product, and the method comprises the steps: obtaining a plurality of initial detection sub-regions; performing preliminary sampling at the central point of the initial detection sub-region to obtain initial environment data; constructing an environment parameter space distribution model; calculating a variation coefficient, and determining a high-variation sub-region; judging whether the area of the high-variation sub-region is greater than a preset minimum area or not; if yes, the minimum sub-region is divided, and the center point serves as a sampling point; if not, increasing a preset number of sampling points; controlling the detection unmanned aerial vehicle to perform secondary sampling at the sampling points to obtain initial environment data, and updating the environment parameter spatial distribution model; calculating an updated overall prediction error, and judging whether the updated overall prediction error is greater than a preset error; and if not, determining the initial environment data with the maximum similarity, and determining the region corresponding to the sampling point as a candidate experiment region. According to the invention, the accuracy of the environmental parameters detected in different outdoor environments is improved.

This invention discloses a downscaling optimization method for mountain wind fields based on high-precision simulation, belonging to the field of meteorological numerical simulation technology. The method includes acquiring topographic elevation data and meteorological monitoring data of the target mountain area; identifying abrupt slope changes and constructing a spiral sampling path; calculating aspect variation values ​​to determine the sampling point spacing and generate target sampling data; performing orthogonal decomposition on the sampling data to obtain frequency components; calculating the surface wave coefficient to construct boundary perturbation equations to obtain surface stress distribution; calculating airflow motion state based on surface stress distribution; solving the vorticity equation to obtain vorticity characteristics of the leeward region and calculating airflow motion correction parameters; and performing downscaling iterative calculations with the meteorological monitoring data to generate target area wind field data with a spatial resolution of hundreds of meters. This invention improves the simulation accuracy of local wind fields under complex terrain conditions.

The application relates to the technical field of radar signal processing, and discloses a vehicle-mounted radar anti-interference method and system based on time-frequency-space three-dimensional adaptive sampling. In view of the problems of low detection efficiency and unreasonable sampling resource allocation in a multi-interference source scene, a time-frequency-space three-dimensional joint interference feature tensor is constructed, and a three-dimensional cross-coupling effect is used to accurately identify interference; based on interference intensity and gradient information, the local sampling density is dynamically adjusted, the sampling is adaptively increased in interference dense and boundary areas, and a compression sensing algorithm is used to perform high-precision reconstruction on the non-uniformly sampled signals. The application effectively improves the interference identification accuracy in a complex electromagnetic environment, significantly reduces the system data throughput and calculation cost while guaranteeing target detection performance, and is suitable for high-performance vehicle-mounted radar systems.

The application discloses a kind of multi-parameter integrated casing damage detection method and system, it is related to casing detection technical field.The method is by downhole detection system synchronous acquisition vision, deformation, residual magnetism eddy current and positioning data, after pre-processing and multiplex transmission to ground;Ground system completes data restoration, spatial position alignment and feature level fusion, intelligently identifies defect and generates multidimensional defect distribution atlas;Again according to defect comprehensive density and severity grade self-adaptive control downhole instrument speed, finally output integrated detection report.Downhole detection system uses integrated downhole instrument string, integrates multiple detection modules and FPGA+ARM preprocessing unit, cooperates with ground closed-loop control system.The application realizes multi-parameter synchronous detection, multi-source data accurate fusion, defect intelligent identification and adaptive speed regulation, greatly improves detection precision and efficiency, reduces operation cost, and is suitable for integrated efficient detection of oil and gas well casing damage.

This invention relates to a universal contour detection device and assembly method for assisted robotic optical processing, belonging to the field of advanced optical manufacturing technology, and solves the technical problem of the lack of an in-situ high-precision detection device in the optical processing of large-aperture mirrors by robotic arms in the prior art. The contour detection device includes: a workpiece turntable and a swing-arm air-floating turntable; a workpiece table is provided on the upper surface of the workpiece turntable; the workpiece to be measured is placed on the workpiece table; the upper end of the swing-arm air-floating turntable is connected to the end effector of the robotic arm via a quick-change detection fixture; the lower end of the swing-arm air-floating turntable is connected to the arm via an arm fixture; a probe is provided at one end of the arm, which is located near the upper surface of the workpiece to be measured. In the contour detection device of this invention, the end effector of the robotic arm remains stationary during the measurement of the optical element's surface shape; compared to the measurement mode of a moving probe at the end effector, the accuracy and stability of the robotic arm in the stationary state are better.

The application provides a positioning method and device applied to a ship environment. The method provided by the application comprises the following steps: collecting a group of positioning related data when a current sampling period arrives; determining a joint fluctuation index of an IQ sample according to a magnitude variation and a phase variation of the IQ sample, and determining an updated sampling period of next sampling according to the joint fluctuation index, a preset minimum sampling period and a preset standard sampling period; performing next round of data collection when the updated sampling period arrives, and repeating the above steps; screening and preprocessing a plurality of groups of continuously collected positioning related data to obtain a training sample set; training an initial positioning model based on the training sample set to obtain a trained positioning model; inputting a group of to-be-tested data collected in real time into the trained positioning model, so that the trained positioning model outputs a predicted position of a transmitter corresponding to real-time IQ samples in the group of to-be-tested data based on the group of to-be-tested data.

The present invention discloses a holographic imaging and device applied to ultrafast light sources, belonging to the field of microscopic imaging technology. The method comprises: when using a broadband light source, respectively collecting background spot diffraction data and sample diffraction data, dividing the sample diffraction data by the background spot diffraction data to obtain a normalized hologram; applying amplitude limitation and phase limitation to the sample function in the near-field plane to obtain a reconstructed image with no twin images at all. The present invention applies holographic imaging technology to ultrafast imaging, and only needs to input a pair of diffraction patterns to realize large-field super-resolution holographic imaging of broadband light sources. Compared with the stacking algorithm, the time spent on reconstruction is greatly reduced, and the time spent on collecting data is also much less than that of the stacking algorithm; the sampling density of the spectrum can be improved, and the loss of resolution of the reconstructed image can be reduced.

The invention relates to the technical field of computer networks, and discloses a consumption finance mobile terminal rendering scheduling method based on bandwidth prediction. The method comprises the following steps: continuously acquiring downlink bandwidth at a mobile terminal side, obtaining a local change rate based on difference and moving average, predicting a next-period target bandwidth in combination with a window average bandwidth, and performing lower limit correction; a terminal page is divided into a plurality of regions, the total thread amount and the minimum and maximum configuration of each region are preset, and fine allocation according to priority is realized through mapping of a target bandwidth and a GPU thread in combination with resource compression, expansion and integer correction; meanwhile, rendering starting and ending moments of all areas are periodically collected, a response time sequence is constructed, abnormity is cut off, time delay and performance alarm are calculated, the bandwidth sampling period and the number of sampling points are adaptively adjusted according to prediction deviation, a measurement-prediction-allocation-evaluation-optimization closed loop is formed, and bandwidth prediction precision, the resource utilization rate and rendering stability are improved.

The present invention discloses a single-station multi-satellite signal synchronous acquisition device for regional rainfall monitoring, comprising an antenna array unit, an antenna servo unit, a multi-channel satellite receiving unit, a signal processing unit, a signal transmission unit and a power supply unit. The power supply unit is used to supply power to the remaining units; the antenna array unit array is arranged on the ground for directionally receiving satellite signals; the antenna servo unit position information and the satellite orbit parameters sent by the receiving station calculate the receiving parameters of the corresponding satellite and adjust the angle of the antenna in the antenna array unit to achieve the reception of the corresponding satellite signal; the multi-channel satellite receiving unit synchronously acquires the satellite signals received by each antenna array unit, and the signal processing unit extracts the satellite signal parameters from the satellite signals acquired by the multi-channel satellite receiving unit, and transmits them to the receiving station through the signal transmission unit. This device realizes the synchronous acquisition of multiple satellite signals at different angles and frequency bands at a single station.

The present application relates to the technical field of data processing, and more particularly to a rescue protective helmet data acquisition method based on multi-source sensing, which comprises the following steps: collecting a plurality of sensing data and a plurality of vital sign data in real time, and constructing a time neighborhood at the current time; calculating a safety correlation degree for any kind of sensing data at the current time; calculating a risk feature to obtain a cooperative risk degree according to the difference of the relative mean value and the change of the local slope relative to the overall slope of the sensing data within the time neighborhood, and combining the risk feature sequence correlation and the safety correlation degree difference to calculate an auxiliary risk reference degree; weighting and fusing the cooperative risk degree by using the auxiliary risk reference degree to obtain a safety risk index, and multiplying the safety risk index by the obtained safety core degree to obtain a safety criticality; and weighting and correcting the basic sampling frequency by using the safety criticality to obtain a dynamic sampling frequency. The present application realizes the cooperative scheduling of multi-source sensing data, and solves the problems of sampling redundancy and risk identification lag.