6results about How to "Improve output" patented technology

The application provides a FAERS data standardization method, which comprises the following steps: A, obtaining a FAERS data set and integrating the data set; B, performing missing value filling and deduplication operation on the integrated DEMO table; C, performing deduplication on the rest of the tables in the FAERS data set; D, performing standardization on the REAC table; E, performing standardization on the drug name in the DRUG table; and F, generating an adverse drug reaction 2*2 contingency table. The application solves the problems of data repetition, data omission and non-standard drug name in the FAERS data set, improves the proportion of drug name standardization, makes each value in the generated adverse drug reaction 2*2 contingency table more accurate, and improves the accuracy of subsequent mining of adverse drug reaction signals.

The invention provides a yaw multi-transmission control system, method and device for a wind generating set, a terminal and a medium. The system comprises a fan controller, a fan rectification unit and k inversion units, the fan rectification unit and the k inversion units are connected with the fan controller through communication lines; the fan rectification unit is connected in parallel with the k inversion units through a bus; and the k inversion units are respectively connected with a yaw motor. According to the method, the rotating speed and torque data of a single yaw motor are obtained in the mode that one inverter controls one yaw motor, and the output condition of the whole yaw motor is controlled in the mode that the target rotating speed of the motor with too large output is reduced and the target rotating speed of the motor with too small output is increased in the whole yaw process. And the suppression of the unbalanced output of the yaw motor is realized.

The application discloses a data monitoring method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: obtaining a data original message; extracting key data in the data original message, and storing the key data in a data set according to a predetermined level; receiving an abnormal data query operation, determining abnormal data in the data set; and performing timing monitoring on the abnormal data. The application solves the technical problem that it is difficult to comprehensively monitor abnormal data in related technologies.

The invention relates to the field of natural language processing, and discloses a verbal skill classification model optimization method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining an initial sample set composed of a plurality of verbal skill texts, and determining a reference classification result of each verbal skill text in response to manual marking of each verbal skill text; classifying the initial sample set by utilizing a verbal skill classification model to obtain a model classification result of each verbal skill text; screening quality inspection texts with wrong classification results according to the model classification results and the reference classification results; performing data enhancement on the quality inspection text to form a newly added sample set; and training the verbal skill classification model by using the initial sample set and the newly added sample set to obtain an optimized verbal skill classification model. And fixed-point data enhancement is performed on the samples corresponding to the error classification results output by the verbal skill classification model, and the verbal skill classification model is optimized in combination with the new samples after data enhancement, so that the accuracy of the verbal skill classification model is improved.

The invention relates to the technical field of image recognition and financial science and technology, and discloses an image forgery detection method, device and equipment based on granularity grading and a medium, and the method comprises the steps: determining a detection level of a pre-obtained target image according to a user demand, and recognizing a to-be-detected level image corresponding to the target image according to the detection level; determining a detection branch according to the detection hierarchy, and mapping the to-be-detected hierarchy image and the detection branch to obtain an image branch mapping relationship; inputting the to-be-detected hierarchical images into the detection branches one by one through an image branch mapping relation, and outputting tampering masks of the to-be-detected hierarchical images; fusing the tampering mask and the target image to obtain a fused image, and positioning the position of a forged image in the target image according to the fused image; and determining a forgery behavior in the target image through the position of the forgery image. According to the invention, the accuracy of image forgery detection can be improved.

This invention discloses a multi-objective optimization method based on a GCN proxy model, which mainly addresses the problems of high computational complexity, low optimization efficiency, and low prediction accuracy in cascading failure simulation during critical node detection in complex networks. First, initialization and objective function construction are performed, setting relevant algorithm and model parameters, and constructing a dual objective function for attack cost and attack failure effect. A complex network dataset is generated, downloaded, and preprocessed. Second, addressing the low computational and optimization efficiency caused by multiple traversals of the entire network in each evaluation of cascading simulations during critical node detection in complex networks, this invention constructs and trains a multi-branch attention GCN proxy model. Through multi-dimensional feature extraction, multi-task learning, and group calibration, the prediction accuracy of the number of cascading failure nodes is ensured. Then, a multi-objective optimization algorithm fusing GA and PSO is used iteratively, combined with the GCN proxy model to predict the objective function, improving optimization efficiency. The convergence and diversity of solutions are balanced through GA global search and PSO local optimization. Next, the Pareto optimal solution is calibrated and verified to ensure the relative error is within a reasonable range and to verify accuracy. Finally, the experimental results are output and archived. This invention utilizes the GCN proxy model to assist in the fusion of multi-objective optimization algorithms, significantly improving optimization efficiency and prediction accuracy, and enabling precise detection of key nodes in complex networks.