6results about How to "Solve choice problems" patented technology

The invention discloses an automatic algorithm feature processing method and system, and relates to the technical field of computer data processing. Data are collected and cleaned, then feature parameters are extracted, test cases are established based on the feature parameters, then a model is trained according to the feature parameters, test cases of online data feature parameters are established, and finally the test cases are input into a target server for testing, so that the whole test process is automatically carried out, and the test efficiency is effectively improved; and the testing process is suitable for artificial intelligence algorithm testing of various processing classes, and the universality is high. According to the automatic algorithm feature processing method and system, machine learning algorithms and deep learning algorithms are integrated, uniform file configuration and configurability are supported, when the data set reaches a certain amount, the deep learning strategy can be triggered, and the defect that the small data set modeling effect is poor is overcome.

The invention relates to the technical field of text processing, and discloses a manuscript generation method and device, equipment and a storage medium. The method comprises the following steps: constructing a template library containing a plurality of template frames and a knowledge base containing a plurality of domain knowledge bases; obtaining use features of a user and each template frame, receiving a free prompt input by the user, and constructing a task feature vector; matching a target template frame based on the first sorting model, and generating a task instruction based on a standard prompt word of the target template frame; if the matching target template frame does not exist, generating a task instruction based on the free prompt word; matching a target knowledge fragment from the knowledge base based on a second sorting model; and forming a large model generation manuscript called by a generation instruction in combination with the target knowledge fragment and the task instruction, correcting the manuscript in response to interaction between a user and the manuscript until a finalized manuscript is generated, and collecting interaction data to perform quality evaluation on the finalized manuscript. According to the method, high-quality, personalized and automatic generation of the manuscripts in the specific field is realized.

The invention relates to the technical field of natural language processing and medical information processing, and discloses a medical article writing method and device and a storage medium, and the method comprises the steps: collecting a to-be-processed data set, carrying out the preprocessing of the to-be-processed data set, and storing a preprocessing result in a preset time sequence medical knowledge base; constructing a graph community of the preset medical knowledge base according to the preset time sequence medical knowledge base; based on a hidden Markov model, performing different time point time sequence inference on a graph community of a preset medical knowledge base; the method comprises the following steps: establishing a preset retrieval system and setting a launch probability model to construct a launch probability matrix; according to the emission probability matrix, article fragments are selected, article writing and modification are carried out according to a preset model, manual auditing and revision are carried out, and medical article writing is completed. According to the method provided by the invention, global information capture, time consistency processing and mapping from the content features to the subjective points of the articles of the medical knowledge base are realized, and the writing quality and accuracy of the medical articles are improved.

The invention discloses a formation grouping optimization method for multi-node cluster cooperative positioning, and the method comprises the following steps: setting a constraint condition which is more suitable for multi-node cooperative work, and guiding multiple nodes to carry out formation grouping based on a clustering algorithm; designing a multi-node grouping optimization method combining a clustering algorithm and an improved wolf pack algorithm (K-WPA); optimizing the population process of the wolf pack algorithm according to the clustering result; a measurement model is established in combination with sensors equipped for nodes, and an evaluation function of formation grouping is designed based on a Fisher information matrix. The K-WPA algorithm provided by the invention shows a stronger capability of solving a globally optimal solution in a dynamic grouping process. The K-WPA grouping optimization algorithm solves the problem of selection of cooperative nodes, effectively improves the grouping efficiency of the unmanned aerial vehicle cluster, and improves the positioning precision of the unmanned aerial vehicle cluster.

The present invention discloses a method for predicting a ratio transfer value based on machine learning, which collects data of compounds, developing agents and ratio transfer values, establishes a ratio transfer value prediction model through a machine learning method, can quickly and accurately predict the ratio transfer value curve of a target compound under a target developing agent system, and gives an optimal developing agent ratio scheme according to the curve, so that the ratio transfer value obtained in the experiment is not too large or too small, greatly reducing the dependence of thin-layer chromatography analysis technology on the experience of the experimenter, solving the problem of developing agent selection, avoiding boring repeated experiments, greatly improving the efficiency of polarity determination, and saving time and labor costs. The present invention introduces machine learning technology into the field of experimental chemistry, making it possible to obtain the ratio transfer value of a compound without experimental conditions, and has wide applications and important significance in the fields of experimental chemistry, drug synthesis and analysis.

This invention discloses a method for evaluating the performance recovery of an electrolysis system after a pulsing operation. The method includes the following steps: Step 1: Collecting electrolysis system performance data for a baseline period before pulsing, the pulsing period, and the recovery evaluation period after pulsing; Step 2: Preprocessing the collected data; Step 3: Based on the preprocessed data from Step 2, fitting a natural decay trend within the baseline period before pulsing and extrapolating to obtain a non-interventional prediction curve; Step 4: Calculating four evaluation indicators within the recovery evaluation period after pulsing: recovery amount, recovery stability, improvement in decay slope, and recovery sustainability. The evaluation results can be used for selecting / recommending pulsing parameter combinations to achieve performance recovery and lifespan extension under energy consumption constraints.