7results about How to "Avoid premature" patented technology

This invention belongs to the field of fire extinguishing materials and their preparation technology, and provides a fire extinguishing microcapsule and its microfluidic controllable preparation method. The preparation method is characterized by the following steps: (1) preparing fluids of each phase, (2) preparing a dual emulsion using a microfluidic device, (3) collecting and sealing the dual emulsion in a collection container, controlling the axis of the collection container to be horizontal, and controlling the collection container to rotate around its axis for 1 to 15 minutes, applying light to solidify the outer shell of the dual emulsion droplets while maintaining the rotation of the collection container, cleaning, and drying to obtain the final product. The fire extinguishing microcapsule prepared by this method consists of an outer shell and a liquid fluorine-containing fire extinguishing agent encapsulated within the outer shell. The outer shell of the fire extinguishing microcapsule has a uniform thickness, and the eccentricity between the outer shell and the core is less than 0.1. The method of this invention can achieve precise customization of fire extinguishing microcapsules according to the differentiated fire characteristics of different application scenarios, and can improve the concentricity between the core and the outer shell of existing fire extinguishing microcapsules.

This invention belongs to the field of sensor scheduling and discloses a cross-domain sensor resource scheduling method and system based on adaptive particle swarm optimization. The method includes initializing the particle swarm; updating the initialized particle swarm using a combination of swarm optimization and particle update algorithms; in the leader bee neighborhood search phase, updating the initialized particle swarm using the particle update algorithm to generate updated particles and calculating the target fitness corresponding to each updated particle; in the follower bee follow search phase, updating the particle swarm again using the roulette wheel algorithm based on the target fitness corresponding to each updated particle to obtain the update result; repeating the processes corresponding to the leader bee neighborhood search phase and the follower bee follow search phase until the maximum number of iterations is reached, and outputting the current update result as the optimal sensor scheduling scheme. This method improves detection accuracy and reduces radiation exposure risk and the number of sensor task switching operations.

This invention discloses a real-time adjustable automatic lighting device and strategy. The lighting device includes an optical imaging module, a multi-channel light source module, a control unit, and a software control module. The multi-channel light source module includes multiple light sources and a light source controller, with each channel capable of independent adjustment. The software control module controls the channel combination parameters of the multi-channel light sources. The lighting strategy includes: a camera acquiring the current product image; a real-time dimming algorithm equipped with an image quality real-time evaluation module, a dimming decision module, and a parameter storage module to achieve closed-loop calculation and output control, adjust the light source combination scheme, generate the optimal lighting scheme, and store it. This invention achieves multi-dimensional independent control of the light source, real-time evaluation of imaging quality, automatic optimization of lighting parameters, and automatic generation of the optimal lighting scheme, improving the robustness, versatility, and automation level of the machine vision system. It can be used to quickly and automatically iterate the light source scheme intelligently when there are variations in product manufacturing processes or complex product types.

This application relates to an automatic cooking device, including a pot body assembly, a pot lid assembly, a dispensing box, a drive mechanism, and a control mechanism. The pot body assembly is equipped with a first temperature detection device for detecting the pot's temperature during cooking. The pot lid assembly includes a stirring element, on which a second temperature detection device is provided for acquiring the temperature of the ingredients inside the pot during cooking. The dispensing box is detachably disposed within the pot lid assembly and includes multiple seasoning compartments, each with a corresponding seasoning dropping component below it. The drive mechanism is disposed within the pot lid assembly and drives the corresponding seasoning dropping components to control the dispensing of seasonings from different compartments. The control mechanism receives and processes temperature data, combines it with preset recipe information, determines the cooking status of the ingredients, and, based on the determined cooking status, controls the dispensing of seasonings from the corresponding compartments via the drive mechanism. This improves the accuracy of seasoning dispensing timing and enhances the level of automation in cooking.

The invention provides a multi-constraint particle swarm optimization electric power overhaul scheduling method and device and a medium, and the method combines a dynamic inertia weight adjustment mechanism and a midpoint guide variation strategy to enhance the global search capability. And a genetic evolution mechanism of elite retention, random mask crossover and adaptive probability variation is introduced to improve population diversity and optimization stability. In addition, a multi-neighborhood local hill-climbing search and multi-constraint feasibility repair function is designed, and it is ensured that the scheduling scheme has high quality on the premise that constraints are met. Experimental results show that the method is superior to a traditional algorithm in the aspects of convergence speed, solution feasibility and scheduling quality, and has good engineering applicability and optimization effect.

The invention discloses a sintering end point prediction method based on big data. The method comprises the following steps: collecting historical data in a sintering process; cleaning and preprocessing the collected historical data; effective features are extracted from the preprocessed historical data, and an input variable with the most predictive capacity is determined through a feature selection method; training a sintering end point prediction model based on the extracted features by using a support vector machine (SVM) algorithm; and carrying out sintering end point prediction based on the trained prediction model. According to the method, the sintering process parameters are comprehensively analyzed through a big data technology and a machine learning algorithm, so that accurate prediction of the sintering end point is realized, and the prediction accuracy and reliability are greatly improved.

This invention discloses an optimization method for a natural gas pressure energy system based on a multi-objective frost algorithm, relating to the field of energy utilization technology. The specific steps are as follows: A comprehensive optimization model is established for the natural gas pressure energy system from three aspects: system efficiency, economic benefits, and environmental benefits. Then, algorithm parameters are initialized and the population is uniformly initialized. Next, a neighborhood structure is constructed by calculating Euclidean distance, and the fitness of each individual is calculated using a weighted sum decomposition method. The quality of local solutions is optimized by combining a frost neighborhood perturbation mechanism. In the stage of generating new individuals, simulated binary crossover and polynomial mutation operators are used, and a hard frost puncture mechanism is introduced to avoid local optima. Subsequently, the population and solution set are updated, retaining non-dominated solutions and maintaining the convergence and diversity of the solution set. Finally, the optimal result is selected by comprehensively evaluating the optimized solution set using the analytic hierarchy process (AHP)-fuzzy decision method. This invention can optimize and improve the performance of a natural gas pressure energy system.