3results about How to "Maximize transfer efficiency" patented technology

This invention belongs to the field of communication and network control technology. To address the problems of poor adaptability in non-ideal observation environments and the inability of a single optimization objective to meet various differentiated service requirements, and to overcome scheduling failures caused by network jitter or node failures leading to data loss, this invention provides: a time-sensitive network traffic scheduling method based on causal data completion. This method collects real-time traffic data from the current time-sensitive network (TSN) and constructs an irregular observation matrix; it uses a state reconstruction module based on neural Granger causality to complete the missing data in the observation matrix; it extracts robust spatiotemporal features with noise resistance from the completed observation matrix using a variational graph attention network; and it inputs these robust spatiotemporal features into a reinforcement learning decision network based on hierarchical optimization. Under the hard real-time constraint of priority critical time-triggered TT flow, it optimizes the best-effort BE flow transmission efficiency, ultimately generating a gated control list for execution. This invention is primarily applied in design and manufacturing applications.

This invention relates to the field of cloud computing and intelligent image data processing technology, specifically to a customized data management system for image products based on a cloud platform. It comprises a cloud platform data management center, semantic monitoring, feature drift analysis, transmission strategy, model matching, and data reconstruction units. The semantic monitoring unit calculates a semantic entropy evaluation value and identifies the chaotic state of model understanding through adversarial operations between image feature complexity and model confidence. It identifies the image to be reconstructed and determines the signal. If it is a drift risk signal, the feature drift unit verifies atypical features and outputs intervention or calibration instructions to prevent blind reconstruction beyond cognitive boundaries. If it is a steady-state signal, the transmission strategy unit analyzes throughput requirements, the model matching unit quantifies reconstruction decisions, and outputs a highly compressed or high-fidelity reconstructed signal. This invention addresses the problem of semantic illusions easily generated by generative AI when reconstructing complex textures; the semantic monitoring unit calculates a semantic entropy evaluation value.

The utility model discloses a coupling chip biological frequency modulation module which comprises a chip and a coupling antenna, the coupling antenna comprises a coupling line unit and a matching ring unit which are separated from each other, and the matching ring unit is connected with the chip; the coupling line unit is of a bending line structure with a plurality of square bending parts, the matching ring unit is of a double-layer coil structure, the matching ring unit is arranged in the square bending part in the middle of the coupling line unit, and a coupling gap is formed between an outer-layer coil in the double-layer coil structure of the matching ring unit and the bending line of the coupling line unit. The utility model provides a coupling chip biological frequency modulation module, which can improve the transmission efficiency.