Abstract: A soft gel product of surimi and a preparation method thereof are provided in the present disclosure, belonging to the technical field of aquatic products, and including surimi, alkaline protease, fish oil Pickering emulsion and auxiliary materials. The preparation method includes the following steps: thawing the surimi, then adding protease for pounding for 2 to 4 minutes, then adding salt for pounding for another 2 to 4 minutes, and then adding Pickering emulsion and remaining auxiliary materials to continue pounding for 3 to 5 minutes, and finally, molding the surimi, gelatinizing the surimi by two-stage heating, and cooling to obtain the soft gel product of surimi.

Abstract: The disclosure provides a feature compression algorithm based on neural network, including the following steps: S1, image data preparation: collecting facial images, and uniformly performing map processing to the facial images collected; S2, feature data acquisition: delivering the facial images processed into a face recognition system for face detection and feature extraction, and saving facial feature data; S3, setting up a neural network model; S4, model iteration training; S5, storing a parameter model; and S6, feature compression. The feature compression algorithm based on neural network of the disclosure can not only achieve compression of original feature data, but also retain its original semantic feature, which belongs to a higher-dimensional feature abstraction. The compressed feature data can be directly used.

Abstract: The present invention provides a method of producing a product for waste degradation, a microbial consortium or a mixed microbial composition, a product for waste degradation, and a waste degradation method. The method of producing the product for waste degradation comprises providing at least one isolated microbial strain from excreta, providing at least one lactic acid and/or acetic acid producing microbial strain, providing at least one yeast strain, providing at least one Bacillus sp., and combining the microbial strains to produce a microbial consortium for waste degradation.

Abstract: The disclosure provides a feature compression algorithm based on neural network, including the following steps: S1, image data preparation: collecting facial images, and uniformly performing map processing to the facial images collected; S2, feature data acquisition: delivering the facial images processed into a face recognition system for face detection and feature extraction, and saving facial feature data; S3, setting up a neural network model; S4, model iteration training; S5, storing a parameter model; and S6, feature compression. The feature compression algorithm based on neural network of the disclosure can not only achieve compression of original feature data, but also retain its original semantic feature, which belongs to a higher-dimensional feature abstraction. The compressed feature data can be directly used.

Abstract: A substation-control center tow-level distributed grid nonlinear state estimation method is provided. RTU measurement data and PMU measurement data at each voltage level in each substation are collected. Performing a first three-phase state estimation is performed to obtain a first estimation result. Bad data from the RTU measurement data and the PMU measurement data are removed to estimate a state of a CB. A new CB island at each voltage level is formed, and a second three-phase state estimation for the new CB island is performed to obtain a final estimation result, in which the new CB island is a bus. The final estimation result is processed to obtain ripe data. And the ripe data, a part of the RTU measurement data and a part of the PMU measurement data are collected and a whole grid state estimation is performed.

Abstract: A substation-control center tow-level distributed grid nonlinear state estimation method is provided. The method comprises steps of: collecting RTU measurement data and PMU measurement data at each voltage level in each substation; performing a first three-phase state estimation to obtain a first estimation result, and removing bad data from the RTU measurement data and the PMU measurement data to estimate a state of a CB; forming a new CB island at each voltage level, and performing a second three-phase state estimation for the new CB island to obtain a final estimation result, in which the new CB island is a bus; processing the final estimation result to obtain ripe data; and collecting the ripe data and/or a part of the RTU measurement data and a part of the PMU measurement data and performing a whole grid state estimation.