17 results about "Background distribution" patented technology

The invention provides a deep space background-oriented space target sequence image generation method and device, electronic equipment and a medium, and relates to the field of space target optical image simulation. The method comprises the steps of obtaining pose data of a space target in an observation time period through simulation based on pre-defined initial parameters; screening imageable fixed stars from the reference star library according to the field angle and the detectability of the on-orbit optical camera; based on the position data of the imageable fixed star, generating a deep space background image matched with the observation view field of the space target through brightness simulation and coordinate system transformation; generating a time sequence rendering image through a rendering engine by using the three-dimensional model and the pose data of the space target; and superposing the deep space background image and the time sequence rendering image according to foreground and background distribution, and adding noise to obtain a simulation sequence image.

The invention discloses a background analysis method and system based on atmospheric greenhouse gas monitoring data. The method comprises the following steps: collecting greenhouse gas monitoring data through monitoring equipment; generating an intelligent agent about the monitoring space according to the distribution condition of the monitoring data; constructing the monitoring data of the intelligent agent into a composite state formed by superposing background data and interference data; and carrying out complex state evolution analysis among different intelligent agents, and carrying out background data reconstruction on the monitoring space to obtain a background analysis result of the atmospheric greenhouse gas monitoring data. The background distribution of the atmospheric greenhouse gas can be reconstructed more accurately and stably under the scenes of complex interference, multi-source isomerism and remarkable boundary effect, and the scientificity and precision of background judgment are improved.

The application discloses a small sample point cloud semantic segmentation method based on difference enhancement and related equipment, and the method comprises the following steps: obtaining initial prototypes of various categories according to support set features and corresponding support set masks; calculating the similarity between the initial prototypes and query set features to obtain the probability distribution of the query set features belonging to foreground and background categories; taking the inverse of the foreground distribution according to foreground and background difference enhancement, combining the current background distribution to obtain accurate background probability distribution, and integrating to obtain reliable probability distribution of the entire category; the query set features are subjected to transformation processing through a feature aggregation module to obtain aggregated query set features; the reliable probability distribution and the aggregated query set features are subjected to aggregation processing through a prototype correction module to obtain pseudo prototypes; the support set data is expanded according to the pseudo prototypes through the prototype correction module to obtain corrected prototypes, and the segmentation result of the point cloud is obtained according to the corrected prototypes. The application obtains better segmentation effect.

This invention relates to the field of defect detection technology, specifically to a PCB defect detection method for industrial incremental scenarios. It includes a feature fusion method based on a lightweight segmentation strategy that combines random pruning with separate processing of strong and weak features. This strategy simplifies redundant computation by using a feature selector, and differentiates and recombines defect features of varying saliency during the feature fusion stage. This significantly improves inference speed while maintaining the precision of segmentation, solving the problems of high computational cost and difficulty in capturing minute defects in existing methods. The method also includes an incremental learning approach employing a background classifier adaptation mechanism and local semantic distillation. Class-specific regularization and spatially weighted logical alignment distillation work synergistically. By dynamically calibrating the background prediction logic and constructing a pixel-level semantic relevance matrix, it achieves deep alignment between new and old knowledge and background distribution. This effectively solves the catastrophic forgetting problem caused by the evolution of PCB background texture in existing incremental learning methods, significantly enhancing detection stability and the adaptability of the enhanced model.

The application discloses a polarimetric SAR ship detection method based on fusion of statistics of Wasserstein distance, and relates to the field of polarimetric synthetic aperture radar ship target detection and identification. The method comprises the following steps: S1, acquiring polarimetric synthetic aperture radar data of an original ship and performing filtering processing; S2, performing feature extraction on the polarimetric synthetic aperture radar data after filtering processing to obtain three polarization features; S3, calculating a statistics map of Wasserstein distance of each pixel of a to-be-detected image by using the three polarization features; S4, screening a sea surface background on the statistics map by using a feature prior method, estimating a sea surface background distribution by using a kernel density method combined with a constant false alarm rate detection method, obtaining a threshold according to a preset false alarm rate, and completing pixel-by-pixel judgment on the sea surface background. The application significantly enhances the target / background contrast, and can still achieve high recall, low missed detection and good robustness in complex scenes such as strong sidelobes, strip artifacts and dense targets.

The application discloses a kind of intelligent behavior detection method and system based on motion feature enhancement, construct based on adaptive mixture Gaussian model, the Mahalanobis distance of pixel point and background distribution is calculated and model weight is dynamically updated, realize the accurate decoupling and separation of foreground motion region in video frame;Then, the foreground mask obtained initially is based on the connected domain analysis of area threshold to filter out discrete noise, and the morphological closing operation of structure element adaptation is used to fill target internal cavity and repair edge fracture, to generate the high signal-to-noise ratio of motion region binary graph;Again, the purified binary graph is mapped as high visual saliency color heat map, it is weighted linearly fused with original RGB video frame, the fusion feature map is used to guide deep learning model to pay attention to motion region, and through double threshold detection strategy, the perception and identification ability of system to small target and hidden uncivilized behavior under complex dynamic scene is greatly improved.

The invention discloses a foreground object recognition and correction method based on background distribution self-adaption, which realizes accurate quantification of background prejudice and self-adaption data construction, and improves the pertinence and effectiveness of correction from the source. By analyzing a small number of real scene samples, the method can accurately describe the intensity and diversity of background prejudice features in a specific scene. The correlation and effectiveness of the training data are ensured, subsequent model training can respond to indications, and a solid foundation is laid for fundamentally correcting background prejudice and improving model robustness. An original feature destruction and double-model cooperative training mechanism significantly enhances the recognition and stripping capability of background prejudice features. According to the method, the performance and reliability of a foreground object recognition model in a real deployment scene are remarkably improved, and powerful technical support is provided for robustness application of artificial intelligence in key fields such as safety, automatic driving and medical image analysis.

The application discloses a foreground object recognition and rectification method based on background distribution self-adaption, realizes accurate quantification and self-adaptive data construction of background bias, and improves the pertinence and effectiveness of rectification from the root. Through analysis of a small amount of real scene samples, the method can accurately depict the intensity and diversity of the background bias characteristics in a specific scene. This not only ensures the relevance and effectiveness of the training data, but also enables the subsequent model training to be targeted, laying a solid foundation for fundamentally correcting the background bias and improving the robustness of the model. The unique feature destruction and double-model collaborative training mechanism significantly enhances the recognition and stripping ability of the background bias characteristics. It significantly improves the performance and reliability of the foreground object recognition model in real deployment scenarios, and provides strong technical support for the robust application of artificial intelligence in key fields such as safety, autonomous driving and medical image analysis.

The invention discloses a vehicle-mounted underway high-precision greenhouse gas two-dimensional distribution monitoring system and method, and relates to the technical field of environment monitoring. According to the vehicle-mounted underway high-precision greenhouse gas two-dimensional distribution monitoring method, gas is alternately collected through double-path sampling probes arranged at different heights of a vehicle roof, and instantaneous interference of exhaust emission of motor vehicles on a road is removed by combining an underway position and real-time meteorological data which are synchronously obtained and based on concentration difference and meteorological information by utilizing a specific algorithm, so that the real-time distribution of the greenhouse gas is realized. Therefore, the real environment background greenhouse gas concentration is extracted. According to the method, the high-precision two-dimensional greenhouse gas concentration distribution diagram of the underway area is inverted through spatial interpolation processing according to the purified background concentration data, credible quantification of regional scale greenhouse gas spatial distribution is realized, a key technical means is provided for accurate measurement and traceability of carbon emission, and the method is suitable for popularization and application. The core problem that the existing mobile monitoring technology is difficult to accurately reflect regional background distribution due to self-path pollution is effectively solved.

The application discloses a weakly supervised semantic segmentation method and device based on commonality-specificity supervision mechanism, establishes a contrast convolution module, utilizes the convolution cognitive difference of different receptive fields in an image to identify the boundary area with ambiguity in the image, and overcomes the problem of fuzzy segmentation boundary in the weakly supervised semantic segmentation task; a commonality-specificity supervision module is established, a commonality supervision mechanism is utilized to find the similar structural background distribution between different category images, a specificity supervision mechanism is utilized to identify the prominent area in the image distribution, semantic segmentation of a target object is realized, the positioning area is improved to be sparse, and the segmentation boundary is optimized; a knowledge gap module constructs a structure distribution enhanced contrast generated image, and the knowledge gap between the contrast generated image and the category image effectively overcomes the incomplete activation corresponding relationship in mainstream methods, and improves the image level weakly supervised semantic segmentation performance.

The invention discloses a ship detection method based on Warsestein distance fusion statistics, and relates to the field of polarized synthetic aperture radar ship target detection and identification. The method comprises the following steps: S1, acquiring polarized synthetic aperture radar data of an original ship and carrying out filtering processing; s2, performing feature extraction on the filtered polarized synthetic aperture radar data to obtain three polarized features; s3, using the three polarization features to calculate a statistical diagram of the Warisstein distance of each pixel of the image to be detected; and S4, screening a sea surface background on the statistical magnitude map by using a feature prior mode, estimating the distribution of the sea surface background by using a kernel density method in combination with a constant false alarm rate detection method, solving a threshold value according to a preset false alarm rate, and completing pixel-by-pixel judgment on the sea surface background. According to the method, the target / background contrast is remarkably enhanced, and high recall, low leak detection and good robustness can still be realized in complex scenes such as strong side lobes, stripe artifacts and dense targets.

The invention discloses a three-dimensional space-time kernel density estimation method based on adaptive bandwidth, and the method comprises the steps: determining the boundary contour of a research region in a geographic space, determining the lower bound and upper bound of the occurrence time of a target event, constructing an effective space-time cube, and collecting a target event sample in the interior, thereby obtaining a target event data set. And inputting the data set to calculate and fit the real background distribution of the target event, namely a joint probability density model. And carrying out time-by-time slicing processing on the joint probability density model to obtain a space condition probability density model. And respectively carrying out qualitative analysis and quantitative analysis on the two models according to requirements, and analyzing a spatial distribution rule and a time evolution characteristic of a high-incidence area (hot area) of a target event. The adaptive bandwidth change is introduced into the traditional algorithm, so that the algorithm provided by the invention has better fitting result, more accurate prediction performance, more concentrated hot area identification capability and more robust extreme event impact absorption capability.

The invention discloses a daytime star point detection method based on spatio-temporal feature context enhancement, which comprises the following steps: designing a frame difference-optical flow guided spatio-temporal feature context enhancement module, extracting spatial features, frame difference features and optical flow motion features of continuous frame star point targets through a sliding observation window, and supplementing the spatial features, the frame difference features and the optical flow motion features to a data-driven network through multi-flow feature fusion; constructing a synthetic star map data set covering various noise and background distributions, and adopting U-Net as a backbone network to realize a coding and decoding architecture to carry out star point segmentation training; the performance of the method is verified through a simulation experiment and an external field star observation experiment. According to the method, the receptive field of the star points in the time dimension is expanded, the dark and weak star point features are enhanced, non-stationary background interference is restrained, the detection precision and robustness of the dark and weak star points under the complex background in the daytime are remarkably improved, and the method is suitable for daytime dark and weak star point detection of an all-day star sensor.

The application provides a spatial domain division-based variational autoencoder and spatial clustering anomaly identification method, and belongs to the field of marine mineral resources exploration and geochemical anomaly identification. Multivariate features and spatial adjacency relationship are adopted to divide local background domains, a variational autoencoder is used to learn local background distribution in each spatial domain, a Mahalanobis distance is used to comprehensively form multi-dimensional reconstruction errors to form an anomaly score, and finally, discrete anomaly points are converted into continuous anomaly areas through density clustering and boundary processing, so as to significantly improve the anomaly identification precision under complex sea area background conditions, and to realize the purposes of reducing the influence of artificial interpretation and improving the accuracy of regionalization results of ore-prospecting target area delineation.

The present application relates to the technical field of carbon footprint uncertainty analysis, and particularly relates to a carbon footprint uncertainty calculation method and electronic equipment, the method comprising: automatically generating a plurality of rules from unstructured text comprising carbon footprint historical data; fuzzy weighted synthesis of the plurality of rules in a scenario to generate a credibility distribution; extracting a global range and a scenario range according to a credibility threshold, and truncating the probability density function of the obtained truncated normal distribution on the global range to obtain a background distribution; equally dividing the scenario range into three scenario sub-intervals, and calculating the scenario weights using the background distribution; conditionally truncating sampling for each scenario sub-interval, and independently running Monte Carlo simulation to output the corresponding mean and variance; and weighting and synthesizing the Monte Carlo simulation results of each scenario through the corresponding scenario weights to obtain the uncertainty of the carbon footprint. Compared with the prior art, the present application has the advantages of efficient and accurate analysis of carbon footprint uncertainty, etc.

The application discloses a kind of ultra-high sensitivity sample component evaluation and traceability method and device, the method comprises: obtaining sample, carries out nucleic acid extraction, library construction, capture enrichment and sequencing, generates sequencing data;Sequencing data is preprocessed, and high-quality sequencing data is obtained;Identify the SNP site and its genotype that exist in high-quality sequencing data, construct SNP combination database;Quantitative statistics is carried out to the double allele genotype of SNP site, and the variation abundance of suballele is calculated;According to the genotype information and allele variation abundance of SNP site, construct SNP site variation abundance background distribution model;Using the SNP combination database and variation abundance background distribution model constructed, cross contamination evaluation is carried out to sample.The method of the application realizes the qualitative evaluation of multiple source components in sample, the quantitative calculation of cross contamination proportion and the traceability analysis of non-self source.

The invention relates to the technical field of carbon footprint uncertainty analysis, in particular to a carbon footprint uncertainty calculation method and electronic equipment, and the method comprises the steps: automatically generating a plurality of rules from an unstructured text comprising carbon footprint historical data; performing fuzzy weighted synthesis on the plurality of rules in one scene to generate credibility distribution; extracting a global range and a scene range according to a credibility threshold, and truncating the probability density function of the sampled truncation normal distribution in the global range to obtain background distribution; equally dividing the scene range into three scene subintervals, and calculating to obtain the weight of each scene by using background distribution; performing conditional truncation sampling on each scene subinterval, independently running Monte Carlo simulation, and outputting a corresponding mean value and variance; and performing weighted integration on Monte Carlo simulation results of the scenes through corresponding scene weights to obtain the uncertainty of the carbon footprint. Compared with the prior art, the method has the advantages of efficiently and accurately analyzing the carbon footprint uncertainty and the like.