6results about How to "Accurate border" patented technology

The present application relates to a kind of structure inference, automatic analysis and standardized output of multi-variant BOM table, belong to electronic manufacturing data processing, table analysis and structured data cleaning field.The present application constructs a set of analysis architecture for multi-variant BOM table by the modular processing link of "title row detection-column head normalization-multi-column identification-variant reconstruction-segment cleaning-intelligent inheritance-standardized output", robustly locates complex table head by strong and weak evidence weighting and verification under-probing mechanism, realizes the split and identification of multiple quantity columns using stop mark and semi-finished product number analysis, and introduces cross-row backfilling and position number inheritance rules under paragraph constraint, effectively solves the analysis problem caused by template difference, multi-column variant and information loss, realizes the automation, precision and standardization of BOM data from messy input to engineering level standardized output, significantly reduces manual intervention and improves data quality.

The invention discloses an automobile-oriented dynamic electronic fence generation method, which comprises the following steps of: acquiring vehicle information, denoising, mining a risk area and a resident place, and generating an initial risk area model with boundary coordinates and risk levels; then, administrative division and road vector data are fused, a three-dimensional hierarchical fence structure is constructed, and fence boundaries are optimized through a Douglas-Peucker algorithm and a cubic B-spline function; according to the real-time driving state of the vehicle, the fence radius is dynamically adjusted through a linear expansion algorithm, the alarm sensitivity is regulated and controlled in a graded mode, and dynamic fence configuration is generated; and finally, vehicle positioning is monitored based on a state machine model, fence switching and boundary jitter are processed by combining priority arbitration and a hysteresis comparison mechanism, redundant alarms are filtered through a time sliding window and behavior intention analysis, and an early warning report with a context is generated. According to the invention, dynamic and multi-level management and control of the electronic fence are realized, the accuracy and suitability of vehicle risk control monitoring are improved, and the false alarm rate is reduced.

The application discloses a remote sensing semantic change detection method based on mixed Mamba-Transformer and cross attention fusion, inputs a double-time-phase remote sensing image into a weight-shared mixed Mamba-Transformer encoder to extract multi-level double-time-phase features; a complementary space-time interaction fusion module performs space-time projection, parallel Mamba scanning and cross attention calculation on the features to generate change perception fusion features; a change detection decoder combines the fusion features and the multi-level features to output a binary change mask, and two parallel semantic segmentation decoders generate double-time-phase land cover semantic probability maps respectively; and the change mask is used to constrain the semantic probability maps in space to obtain a semantic change detection result. The application considers global modeling efficiency and local detail description ability through a mixed architecture, effectively suppresses environmental pseudo changes through parallel Mamba and cross attention fusion, and guarantees the consistency of change areas and semantic categories from the mechanism through cross-task collaborative decoding, so that the robustness and precision of remote sensing semantic change detection in a complex environment are significantly improved.

The invention belongs to the field of image region segmentation, and particularly relates to an AI-based colorectal tumor image region segmentation method and system, and the method comprises the following steps: S1, obtaining original 3D colorectal tumor image data; according to the method, the high-quality 3D colorectal tumor image data is processed through the surface parameterization technology, the obtained 2D feature map is more suitable for processing the hybrid FMU-attention network model, and the processing efficiency and the processing speed of the hybrid FMU-attention network model are greatly improved; in addition, an AI framework is formed by jointly fusing a hybrid FMU-attention network model, a Vision Transformers module and a DeepLabV3 + model, the AI framework enables the boundary to be more accurate and the multi-scale performance to be more excellent, and it is ensured that the tumor can be effectively recognized and segmented regardless of the size.

The application discloses a kind of dynamic electronic fence generation methods for car, this method first gathers vehicle information after denoising, excavates risk area and permanent site, generates initial risk area model with boundary coordinates and risk level;Again, administrative division, road vector data are fused, and three-dimensional hierarchical fence structure is constructed, and fence boundary is optimized by Douglas-Pork algorithm and cubic B-spline function;Then according to the real-time driving state of vehicle, the fence radius is dynamically adjusted by linear inflation algorithm, and the alarm sensitivity is controlled by classification, to generate dynamic fence configuration;Finally, based on state machine model monitoring vehicle positioning, combined with priority arbitration, hysteresis comparison mechanism handles fence switching and boundary jitter, filters redundant alarm through time sliding window and behavior intention analysis, generates warning report with context.The application realizes the dynamicization of electronic fence, multi-level management and control, improves the accuracy and adaptability of vehicle risk control monitoring, and reduces the false alarm rate.

The embodiment of the invention provides a three-dimensional scene text positioning method and device, equipment and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: acquiring a depth map corresponding to each view angle by using a three-dimensional Gaussian model, and acquiring a semantic coding vector of at least one object pixel block corresponding to the view angle; encoding the obtained text description to obtain a text embedding vector, for each view angle, obtaining a similarity matrix based on the text embedding vector and a semantic encoding vector, selecting an object pixel block corresponding to the maximum similarity in the similarity matrix as a target pixel block, and obtaining a three-dimensional center position corresponding to the target pixel block based on the depth map; and performing multi-view clustering voting on the three-dimensional center positions corresponding to all the views to obtain a multi-view center position, and obtaining a view cone region of the target object based on the multi-view center position. The method improves the accuracy of a positioning result, does not need to train scenes one by one, expands the application range of three-dimensional scene text positioning, and is especially suitable for online scenes.