78results about How to "Good explanatory" patented technology

The embodiment of the invention provides a medical knowledge graph construction method and device. The method comprises: acquiring diagnosis evidence information, clinical manifestation information and disease information; according to the diagnosis evidence information, the clinical manifestation information and the disease information, utilizing a preset diagnosis standard, a clinical standard and a disease diagnosis method to establish an association relationship between the disease and the multi-layer clinical manifestation and an association relationship between the clinical manifestationof each layer and the diagnosis evidence; and according to the association relationship between the disease and the multi-layer clinical manifestation and the association relationship between the clinical manifestation of each layer and the diagnosis evidence, constructing a medical knowledge graph containing the association relationship among the diagnosis evidence entity, the clinical manifestation entity and the disease entity. According to the method, clinical manifestation information is added into the medical knowledge graph, and the association relationship between diagnosis evidences and diseases is established through multi-layer clinical manifestation, so that the medical knowledge graph can fully reflect the differential diagnosis thought of doctors.

The invention mainly discloses a method and device for generating a prediction model for predicting pneumonia severity. The method comprises the following steps: acquiring clinical information of a subject; acquiring image information of the subject; screening N features out from clinical information and image information, wherein N is a natural number; constructing a corresponding classificationmodel by taking at least one of the N features as an input and the pneumonia severity as an output; training corresponding classification models by taking the N features of the plurality of subjects as training samples, and determining the classification model with the optimal fitting precision and parameter number in the plurality of classification models as the prediction model based on a presetcriterion. By adopting the scheme provided by the invention, the prediction model with low model complexity and high prediction accuracy is generated.

The invention provides a vehicle travel energy consumption prediction method and device. The vehicle travel energy consumption prediction method comprises the steps that route condition data of a target vehicle and vehicle condition data, driving behavior data and energy consumption data corresponding to the route condition data are recycled; extracting and training the relationship between the driving behavior data and the route condition data to establish a driving behavior prediction model; extracting and training relationships between the energy consumption data and the vehicle condition data, the route condition data and the driving behavior data to establish an energy consumption prediction model; when a target driving route is selected; acquiring route condition data of the target driving route, the driving behavior prediction model is utilized to predict the vehicle owner driving behavior under the target driving route according to the route condition data of the target drivingroute, and then the energy consumption prediction model is utilized to predict the energy consumption under the target driving route according to the route condition data of the target driving routeand the predicted vehicle owner driving behavior. Therefore, the vehicle travel energy consumption can be accurately predicted, so that a user can reasonably plan the vehicle power mode.

The invention discloses a calculating method and device for traffic transportation capacity based on track analysis, and belongs to the technical field of calculation of traffic transportation capacity. The device comprise a floating vehicle data cleaning module, a floating vehicle track clustering module, a traffic transportation capacity calculating module and a traffic transportation capacity scale evaluation module. Output information of all vehicles passing through an area in some time period is recorded, a track data set composed of single-point data is obtained for the n floating vehicle, and a complete data set is obtained through the floating vehicle data cleaning module; by utilizing the floating vehicle track clustering module, the directed space-time clustering volume meeting the requirements of a specific time sequence and space movement is obtained, a model of the relationship between the transportation capacity and common factors influencing the traffic transportation capacity is constructed, and an association coefficient of the model is extracted to obtain a transportation capacity factor loading matrix; the traffic transportation capacity calculation module isused for solving to obtain the total traffic transportation capacity, and the traffic transportation capacity is graded. The calculating method and device have reliability and scientificity and provide powerful basis for related decisions.

The embodiment of the invention relates to the field of artificial intelligence, and provides a label classification method of corpora, which comprises the following steps of: carrying out word segmentation on multiple sections of text data of multiple sections of corpus data to obtain corresponding multiple sections of word segmentation results; inputting the multiple sections of word segmentation results into a probability model, and analyzing the word segmentation results by modeling of the probability model to obtain a plurality of K values; calculating the confusion degree of the plurality of K values, and taking the K value with the minimum confusion degree to obtain a corresponding first-level label; and inputting the corresponding multi-segment word segmentation results into a deformed bidirectional encoder representation model corresponding to the first-level label, and obtaining sub-labels under the first-level label through the deformed bidirectional encoder representation model. In addition, the invention further relates to blockchain technology, and the multiple segments of text data can be stored in the blockchain. The invention further provides a label classificationdevice for the corpora, computer equipment and a storage medium. Label classification accuracy of the corpora is improved.

The invention provides an intelligent subjective question marking method, system and device based on transfer learning, and the method comprises the steps: constructing a semantic feature extraction network based on an occlusion language model, and carrying out pre-training of the semantic feature extraction network in a self-supervised learning mode; constructing a multi-score evaluation model onthe basis of the pre-trained semantic feature extraction network, selecting examinee data evaluated by experts, and performing supervised fine adjustment training on the multi-score evaluation model;and scoring the examinee data to be scored by using the multi-score-point scoring model after fine adjustment training. According to the method, the problem of small sample learning can be solved, and the intelligent marking system constructed according to the method has high accuracy, high marking efficiency and certain interpretability.

The invention discloses a machine learning interpretable method and apparatus, and a computer readable storage medium. The method comprises the steps of firstly obtaining a to-be-interpreted sample; sampling the to-be-interpreted sample based on the feature correlation of the to-be-interpreted sample to obtain a sampling set comprising a plurality of sampling values; carrying out model training byutilizing the plurality of sampling values to obtain an interpretable model; and finally, explaining the to-be-explained sample by utilizing the explainable model to obtain an explaining result.

The invention discloses a color face recognition method based on quaternion non-convex penalty sparse principal component analysis, and belongs to the field of mode recognition and artificial intelligence. The method comprises the following steps: firstly, encoding red, green and blue channels of a color image by utilizing quaternion, constructing a complex representation form of a quaternion matrix, then calculating a quaternion covariance matrix, and carrying out characteristic decomposition on the quaternion covariance matrix to obtain a principal component vector; and introducing a non-convex penalty L1 / 2 norm as a sparse constraint term to obtain a new quaternion non-convex penalty sparse principal component analysis (QHSPCA) optimization model, and solving a sparse solution of the model by adopting a coordinate descent method and an immobile point iteration method, and finally realizing face recognition by using a nearest neighbor classifier. Experiments on a Geoga Tech face database show that the QHSPCA method provided by the invention has relatively good recognition performance, and the calculation efficiency is also improved.

The invention discloses an interpretive information determination method and device, equipment and a readable storage medium, and belongs to the technical field of computers. According to the technical scheme provided by the embodiment of the invention, in response to the search instruction, at least one piece of target information is determined based on the query word carried by the search instruction. On the basis of a coding model, the query word is coded to obtain a query vector, the coding model is obtained through training on the basis of the sample query word and the positive example interpretation information and the negative example interpretation information under the same historical sample information, and the historical sample information is a query result determined on the basis of the sample query word. And determining target explanatory information corresponding to each piece of target information in an explanatory information set corresponding to the at least one piece of target information based on the query vector. And outputting at least one piece of target information and target interpretive information corresponding to each piece of target information based on the search instruction. According to the technical scheme, the to-be-displayed explanatory information can be determined from the explanatory information more quickly.