5results about How to "Mitigation mismatch" patented technology

The invention provides a bidding method for virtual power plants participating in a joint market based on a master-slave non-cooperative game. The bidding method comprises the following steps: constructing a double-layer game framework in which multiple virtual power plants participate in a day-ahead joint market; carrying out Wasserstein distance uncertainty modeling on the wind power, the photovoltaic power and the electric load power; based on the multi-source load power uncertainty set, an upper-layer multi-virtual power plant two-stage distribution robust bidding model is constructed, and the bidding model comprises a first objective function with the minimum sum of pre-scheduling cost for making a bidding strategy in a day-ahead stage and rescheduling cost expectation in an intra-day stage and a corresponding first class of constraint conditions; constructing a lower-layer joint market clearing model; the clearing model comprises a second objective function with the minimum day-ahead electricity-peak regulation-hydrogen combined market energy purchase cost and a corresponding second class of constraint conditions; performing equivalent conversion on the bidding model and the clearing model; and solving the converted bidding model and the converted clearing model to obtain a bidding strategy.

The invention provides an adaptive knowledge contrast learning recommendation method based on a dynamic attention network. The method comprises the following steps: step 1, acquiring a user-project interaction graph and a knowledge graph; wherein the user-project interaction graph is used for describing historical interaction records of a user on projects, and the knowledge graph describes a relationship between the projects in a triple form; step 2, constructing an AKCL-DA network framework, and training the AKCL-DA network framework by using the user-project interaction graph and the knowledge graph to obtain a recommendation model; 3, inputting the target user-project interaction diagram into the recommendation model, and generating a recommendation result of the project of the target user; the item recommendation result is an item set with a high matching score output by the recommendation model. According to the method, the AKCL-DA network framework is provided to obtain the recommendation model, and the problem that sparse information and personal interests are not matched with general knowledge in the knowledge graph can be solved.

The utility model relates to a conversion component, a dual-voltage battery pack and an electric tool system.The technical scheme includes that the conversion component is used for being connected with the dual-voltage battery pack and a first electric tool and converting the voltage of the dual-voltage battery pack into the voltage matched with the first electric tool, and the dual-voltage battery pack is provided with a first guide groove and comprises a main body part and a second guide groove; a terminal groove is formed in the housing; the male terminal is connected to the main body part and at least part of the male terminal is exposed out of the main body part; the female terminal is positioned in the terminal groove; and the second guide groove is formed in the side wall of the main body part and can be communicated with the first guide groove, so that the effect of improving the compatibility between the battery packs is achieved.

The application discloses a medical image segmentation method capable of learning a spectrum prior, and belongs to the technical field of medical image processing. The method acquires a medical image and performs pretreatment, and then inputs a double-branch diffusion model trained to extract image condition features; a mask noise state and a spectrum noise state are initialized, and joint reverse diffusion denoising is performed on the two states based on the image condition features; a mask diffusion branch in the double-branch diffusion model is used to generate a mask prediction result, and a spectrum diffusion branch is used to generate a learnable spectrum prior; in the joint reverse diffusion denoising process, the learnable spectrum prior dynamically guides the generation of the mask prediction result through space-spectrum collaborative updating, and finally a medical image segmentation result is obtained according to the denoised mask prediction result. In the training stage, the double-branch diffusion model uses an analytical spectrum anchor point and a structured spectrum statistical target to constrain the spectrum diffusion branch, so that the generated learnable spectrum prior is different from a fixed analytical spectrum.

The present application relates to the field of image processing technology, and specifically discloses a method and system for super-resolution reconstruction of remote sensing images based on a priori diffusion model. The method comprises extracting shallow feature information from a low-resolution image to obtain a shallow feature map, performing multi-scale downsampling and convolution fusion processing on the shallow feature map and the high-resolution image, extracting high-frequency information from the processed feature map and performing adaptive high-frequency enhancement processing to obtain conditional information. In the denoising process of the diffusion model, high-frequency information of the low-resolution image used as the conditional information input is deeply mined, so that the high-frequency information in the low-resolution image gradually approaches the high-resolution image, thereby improving the reconstruction accuracy. In addition, a noise prediction network model is introduced in the reverse diffusion process, and the noise prediction network model guides the gradual removal of noise in the reverse diffusion process, thereby improving the detail restoration capability and accuracy of super-resolution reconstruction of natural disaster remote sensing images.