4results about How to "Clear data structure" patented technology

The invention belongs to the technical field of medical internet of things, and discloses a lung rehabilitation detection intelligent evaluation system, which comprises a database construction module used for constructing an evaluation reference database according to historical evaluation reference data, and the evaluation reference data comprises historical reference patient physiological parameter data and historical lung rehabilitation detection evaluation data; the data collection module is used for collecting physiological parameter data, chest image data and air quality data of the pre-evaluated patient; the data processing module is used for acquiring the physiological parameter data of the patient to be evaluated through the pre-evaluated physiological parameter data of the patient and the evaluation reference database; processing the acquired physiological parameter data, chest image data and air quality data of the to-be-evaluated patient to obtain a comprehensive feature data set of the to-be-evaluated patient; through systematic evaluation and dynamic adjustment, rehabilitation of the patient is more effectively promoted, an evaluation result provides a scientific basis for decision making of a doctor, and a treatment scheme can be more reasonably formulated.

The present invention discloses a graph-based multiple sequence alignment method, system, device and computer-readable storage medium, the method comprising: obtaining a set of pan-genome sequence data, mapping the pan-genome sequence data, and obtaining a pan-genome colored graph; marking and obtaining the characteristics of the access status of a single node of the colored graph, traversing the colored graph to obtain a cSupB data model after decomposing the colored graph; obtaining the cSupB data model after the colored graph is decomposed, extracting the offset value data characteristics of the nodes in the cSupB data model; performing a first preprocessing on the cSupB data model based on the offset value data characteristics, and obtaining the initial alignment result of the cSupB data model. The present invention overcomes the problem that the traditional multiple sequence alignment method cannot meet the current situation when facing a large number of sequenced sample genomes.

The application discloses a KASP molecular marker related to quality traits of Brassica napus and application thereof, and belongs to the technical field of molecular genetics.The application discloses the KASP molecular marker related to quality traits of Brassica napus and a primer thereof, and application of the KASP molecular marker and the primer in genotyping or quality trait identification of Brassica napus.The results show that the application can accurately identify genotypes, has high sensitivity and specificity, and effectively reduces the error rate.The application further discloses a Brassica napus genotyping / quality identification method.The results show that a material with a genotyping result of AA has the characteristics of low erucic acid, low glucosinolate, high oleic acid, high linoleic acid, low arachidic acid and low protein content; a material with a GG type shows high erucic acid, high glucosinolate, low oleic acid, low linoleic acid, high arachidic acid and high protein content.The application can quickly screen out individuals with excellent quality traits in early generations, and avoids the long process of identifying traits after plants mature in traditional breeding.

The invention provides a spacecraft environment test sensor clipping method, which comprises the following steps of: pasting sensors inside and outside a cabin of a preorder test spacecraft in a spacecraft to be tested, and executing an environment test to obtain response data; according to the sensor pasting positions, spacecraft measuring point classification is carried out; cutting off corresponding in-cabin test points and out-cabin necessary test points in a subsequent test spacecraft; a KMeans clustering model with the preset number of reserved measuring points as the classification number is established, and the corresponding outboard optional measuring points in the spacecraft are cut in the mode that one measuring point is reserved for each category through the KMeans clustering model; establishing a spacecraft response prediction model based on machine learning, and taking the response data in the preorder test spacecraft as a test set and a training set; and the average error and the maximum error of response prediction of the measurement points output in the test set are adopted as model evaluation parameters, and the number of the preset reserved measurement points is adjusted to determine the reserved measurement points of the subsequent test spacecraft. Therefore, the response of each part of the spacecraft can be accurately obtained, the spacecraft environment test can be safely and quickly carried out, and the thermal control measures on the outer surface of the spacecraft are protected.