6results about How to "Strong technical support" patented technology

This invention relates to the field of pathogen detection technology, specifically to an intelligent adaptive virus detection and optimization method and system. The method includes: acquiring raw sequence data generated by probe capture sequencing; performing real-time analysis of the sequence data using a deep learning model, outputting analysis results including the virus's presence state and sequence characteristics; dynamically adjusting control parameters of the hybridization capture process based on the analysis results through an adaptive control system; and completing hybridization capture according to the adjusted parameters and outputting a detection report. The system includes a sequencing data acquisition module, an analysis module, a sensor data acquisition module, an adaptive control module, and a detection report generation module. This invention, by combining artificial intelligence analysis with real-time process control, achieves intelligent optimization of the virus detection process, significantly improving the accuracy, sensitivity, and adaptability of detection. It is particularly suitable for detecting highly variable viruses and low-viral-load samples, and has significant clinical application value.

The invention belongs to the technical field of biomedicine, and provides a breast pad in-situ tumor animal model blood flow velocity prediction method for solving the problem that an existing solid tumor blood flow velocity detection method is difficult to be widely applied to conventional research, and the method comprises the steps that the blood flow velocity and the tumor volume of in-situ tumor blood vessels are continuously collected; analyzing the animal plasma sample to obtain the plasma cell factor concentration related to the blood vessel function; and fitting the morphological characteristics of the tumor tissue, the cell factor concentration and the blood flow velocity of the tumor blood vessel to construct a blood flow velocity prediction model based on the tumor volume and a blood flow velocity prediction model based on the plasma cell factor concentration. Through the constructed prediction model, a researcher can conveniently predict the blood flow velocity of the tumor blood vessel through the obtained data only by obtaining tumor volume data or concentration data of a certain plasma cell factor, additional animal experiments are not needed for measurement, and the method can be widely applied to conventional research.

The invention relates to the technical field of forestry dynamic monitoring, and discloses a forestry dynamic monitoring method and system based on cloud network collaboration. The method comprises the following steps: acquiring forestry environment data through Internet of Things sensor nodes deployed in a forest region, and carrying out real-time data cleaning and format normalization by a built-in edge calculation unit of the nodes to generate a standardized compressed data packet; a transmission path is dynamically selected by using a wireless communication network, a data subpackage and retransmission mechanism is adaptively adjusted, and reliable transmission of data is ensured. The cloud platform analyzes the data and performs space-time correlation analysis, and generates monitoring information through abnormal mode recognition, thereby dynamically adjusting the data acquisition frequency of the sensor node and the working mode of the edge computing unit, and improving the forestry disaster early warning capability and the resource management efficiency.

This invention belongs to the interdisciplinary field of neuroscience, sensory science and technology and food science. It provides a method for objectively quantifying taste perception using electroencephalography. The invention aims to separate and decode the neural dynamics of the brain in processing taste stimuli of different intensities, and to objectively evaluate and classify them based on individual differences in innate taste sensitivity.

The present application relates to a kind of data-driven mode under the method and system for predicting net pressure in fractured well fracture, comprising: collecting oilfield field data, and calculating fracture net pressure;Correlation between well logging parameters and fracture net pressure is calculated and screened, and the well logging parameters after screening are pretreated;Fracture net pressure prediction model is constructed, part of data set is input into fracture net pressure prediction model for training, and the hyperparameters of fracture net pressure prediction model are optimized;Optimized fracture net pressure prediction model is trained using all data sets;When obtaining incremental data, the trained fracture net pressure prediction model is used as submodel, the structure and parameters of submodel are replicated to generate new submodel, and submodel is trained to form submodel for different well sections;The comprehensive prediction value of all submodels is calculated.The present application effectively overcomes the limitation of traditional prediction method, significantly improves the prediction accuracy and model adaptability, and has important significance for oil and gas exploitation engineering.

The application discloses a laser photolysis-infrared spectrum combined technology-based atmospheric cl intermediates generation and detection system and a detection method, and the system comprises a low-pressure flow tube sampling module, a excimer laser photolysis module, a high-vacuum ultralow-temperature reaction cell module and an in-situ Fourier transform infrared spectrum detection module; the low-pressure flow tube sampling module is used for conveying mixed gas to the high-vacuum ultralow-temperature reaction cell module; the excimer laser photolysis module is used as a photolysis source and is guided to the high-vacuum ultralow-temperature reaction cell module through an optical path; and the in-situ Fourier transform infrared spectrum detection module is used for collecting and analyzing infrared spectrum of a captured sample on a cesium iodide cold window surface of the high-vacuum ultralow-temperature reaction cell module. Through the low-pressure flow tube premixing and the working mode of "photolysis and deposition simultaneously", the application can efficiently and in-situ generate CIs in an ultralow-temperature matrix isolation environment, and realize high-sensitivity and high-specificity infrared detection.