5results about How to "Address underutilization" patented technology

This invention proposes a method and system for monitoring electricity trading risks based on a multi-source signal mechanism and a sparse hybrid expert model. The method includes: collecting historical day-ahead electricity market price data and raw data on influencing factors; performing data preprocessing to construct a multi-source fusion signal from price spread signals and bidding space signals; using a random forest algorithm for feature selection; constructing a set of similar day data based on the bidding space signal and incorporating it into the training set; using a sparse hybrid expert model as the model framework, with the price spread signal and bidding space signal as targets, training different prediction models; and searching for an optimal declaration curve based on the Northern Eagle optimization search algorithm by predicting the price spread signal and bidding space signal, using the short-term power prediction curve as a benchmark. This invention effectively solves the problem of insufficient model generalization ability, realizes risk monitoring in electricity spot trading, and improves the accuracy of electricity spot trading risk assessment while enhancing the stability of the power system.

The application discloses a kind of nonlinear carrier phase removal methods and systems of fringe projection image.The method obtains the fringe projection image of first and second view angle with nonlinear carrier;Including the following steps: (1) the fringe projection image of first and second view angle is fused using image splicing algorithm;(2) using singular value decomposition, the first principal component obtained is regarded as background component, and other components are regarded as foreground component;(3) detect foreground region and background region;(4) polynomial curved surface fitting is carried out to the background region of first and / or second view angle, and the first and / or second nonlinear phase is obtained;(5) subtract respective carrier phase and the respective nonlinear phase obtained in step (4).The application obtains global fusion fringe projection image without obstruction by double camera fringe imaging system, and detects background region based on singular value decomposition, so that nonlinear carrier phase is accurately removed.

This invention discloses a method and system for reusing discarded convolutional information in small-sample fine-grained image classification, belonging to the field of computer vision. The method includes the following steps: acquiring images of a support set and a query set, and extracting the backbone feature descriptors of the images using a convolutional neural network; after at least one convolutional block in the convolutional neural network, retrieving the discarded feature information of the convolutional block through a feature retrieval path to generate a retrieved feature descriptor; fusing the backbone feature descriptor with the retrieved feature descriptor to obtain a high-dimensional feature descriptor in a unified format; calculating the similarity between the images of the query set and the support set based on the high-dimensional feature descriptor, and performing small-sample fine-grained image classification based on the similarity, thereby realizing the reuse of discarded convolutional information. This invention can effectively retrieve discarded feature information in convolutional neural networks, improving the performance of small-sample fine-grained image classification.

This invention relates to the field of intelligent decision-making technology and discloses an intelligent decision-making system for agricultural irrigation based on artificial intelligence. The system constructs a collaborative architecture comprising a field perception layer, an edge computing layer, a cloud-based decision-making layer, and an irrigation execution layer, forming an irrigation task context through multi-source perception data. Based on this, a meta-reinforcement learning-driven multi-round agent-based search decision-making mechanism is introduced, combined with explicit self-reflection to achieve iterative optimization of the decision-making process. Simultaneously, a preference-aware reward evaluation model is constructed, achieving multi-objective trade-off modeling through latent variable mirror constraints and inverse autoregressive flow transformation, and globally optimizing the strategy using a cross-round benefit attribution mechanism to generate an irrigation scheduling scheme that meets water-saving, crop water requirement, and energy consumption constraints. This invention can improve the accuracy, stability, and adaptability of irrigation decisions.

The application discloses a medical image segmentation method combining a double attention mechanism and a U-Net++, and related devices. The method comprises the following steps: acquiring a medical image and inputting the medical image into a segmentation network; extracting multi-scale coding features through an encoder, and generating multi-scale decoding features through a decoder; inputting the same-scale features into a double attention module, and sequentially executing channel attention and spatial attention; and finally, fusing the spatially weighted coding features to the decoder, and outputting a segmentation result. The application can be deployed in an artificial intelligence optimization operating system or an artificial intelligence middleware platform, high-efficiency calculation is realized by using a related function library, is suitable for computer audio-visual software, biological feature recognition software and other artificial intelligence application software, and the double attention mechanism and dense skip connection are combined, so that the feature expression capability is effectively enhanced, and the precision and robustness of medical image segmentation are improved.