6results about How to "Excellent quality" patented technology

This invention belongs to the technical field of aircraft manufacturing engineering and intelligent scheduling optimization. It discloses an aircraft final assembly scheduling method and equipment based on an improved genetic algorithm. The steps are as follows: (1) Construct a scheduling model based on process and worker data with the optimization objectives of minimizing the final assembly completion time and minimizing the number of workers involved; (2) Encode the process priority and worker skill slots obtained from the scheduling model using an improved genetic algorithm in the encoding stage, and use the encoding as chromosomes to form an initial population; (3) Perform event-driven decoding on the individuals in the initial population to obtain an initial scheduling scheme; (4) Calculate the fitness value of each chromosome according to the total completion time and the number of workers involved corresponding to the initial scheduling scheme, and screen all chromosomes according to the fitness value to obtain a new population, and then finally obtain the aircraft final assembly scheduling scheme. This invention realizes the collaborative optimization of completion time and the number of workers involved.

This invention discloses a goal-oriented method for the targeted cultivation of selenium-enriched shiitake mushrooms, belonging to the technical field of edible fungi cultivation and functional foods. The method includes the following steps: adding exogenous selenium to the cultivation substrate during the fruiting body cultivation stage of shiitake mushrooms; the exogenous selenium is selected from nano-selenium or sodium selenite; different selenium sources are selected according to the target selenium enrichment requirements: (a) when the goal is to increase the yield of shiitake mushroom fruiting bodies, nano-selenium is selected; (b) when the goal is to increase the total selenium content or bioavailability of selenium in shiitake mushroom fruiting bodies, sodium selenite is selected. This invention provides clear selenium source selection schemes for three different commercial needs: "high yield," "high selenium," and "high absorption," filling a technological gap in the shiitake mushroom field. Producers can flexibly choose according to market demand, realizing precise and customized production of selenium-enriched shiitake mushrooms.

The invention belongs to the field of pharmaceutical preparations, and particularly relates to a diphenhydramine hydrochloride composition and a preparation method thereof. According to the diphenhydramine hydrochloride injection disclosed by the invention, the pH of the diphenhydramine hydrochloride solution is adjusted by adopting carbon dioxide, and terminal sterilization is performed on the diphenhydramine hydrochloride solution, so that the sterile guarantee level of the diphenhydramine hydrochloride injection is improved, and the clinical use safety is greatly improved.

This invention relates to the field of data processing, and more specifically, to a method and system for intelligent parameter optimization of core coating equipment. The method includes: statistically cleaning historical process data to construct a tree-structured prediction model that can simultaneously predict bonding strength and energy consumption; using this model to select high-quality samples to construct a reference sample library, and adaptively constructing a parameter coupling structure by analyzing the global correlation matrix between parameters; employing a structure-aware genetic algorithm for parameter optimization, maintaining the integrity of strongly correlated parameter groups during recombination operations, and using a time-varying decay strategy during perturbation operations for efficient parameter optimization; when the prediction deviation exceeds a dynamic trigger threshold, fusing recent data with the reference sample library to establish a current operating condition model, and selecting highly correlated historical data and assigning weights to update the model, thereby continuously adapting to process changes. This invention achieves a significant reduction in energy consumption while ensuring coating quality.

The invention provides a low-adhesion long-life crust breaking hammer based on cathode scrap steel reinforcement and a preparation method thereof, and belongs to the technical field of aluminum electrolysis. The preparation method comprises the following steps: taking cathode scrap steel generated by electrolytic aluminum overhaul as a main raw material, adding chromium, nickel, boron, silicon iron and lanthanide rare earth metal, and smelting to prepare an alloy melt; a pre-coating layer is formed on the surface of a preheated crust breaking hammer base body through a dip plating process, then hot extrusion is conducted through a special mold, and a compact and shaped reinforced protection layer which is in metallurgical bonding with the base body is obtained at a time. According to the prepared crust breaking hammer head, the protective layer generates a carbide hard phase in situ by utilizing carbon in cathode waste steel, and the crust breaking hammer head has high hardness and extremely low electrolyte molten salt adhesion and is high in bonding strength and good in impact resistance. According to the method, high-value utilization of the cathode scrap steel is achieved, the process is simple and efficient, the service life of the obtained product under the complex working conditions of electrolytic aluminum high temperature, corrosion and impact is remarkably prolonged, and the important economic and environment-friendly value is achieved.

The invention discloses a training method of a deep reinforcement learning model, a computed tomography image reconstruction method, equipment, a medium and a product, and relates to the technical field of medical imaging, and the training method comprises the steps: extracting the features of a current state image through an upper path and a lower path which are processed in parallel by using a structure-detail collaborative feature extraction module, fusing the outputs of the two paths to obtain a fused feature map; outputting the action probability distribution of each predefined image processing operation selected by each pixel according to the input fusion feature map by utilizing a strategy network; and based on the motion probability distribution, determining a predefined image processing operation applied to each pixel by the current time step, and then applying the predefined image processing operation to the current state image to obtain a reconstructed image. According to the method, through adaptive sequential decision and multi-target composite reward optimization based on deep reinforcement learning, dynamic balance noise suppression and anatomical detail retention, the quality, adaptive ability and calculation efficiency of low-dose CT image reconstruction are significantly improved.