2results about How to "Avoid getting stuck in a local optimum" patented technology

This invention discloses a method and system for processing personnel information for business administration based on big data, relating to the field of personnel information data processing technology. The method includes the following steps: acquiring personnel information; preprocessing the personnel information to obtain preprocessed text information; processing the preprocessed text information through an embedding layer based on a CNN-BERT model to obtain a word vector matrix; processing the word vector matrix sequentially through a CNN convolutional layer, pooling layer, and fully connected layer to obtain structured data; using an improved ant colony optimization algorithm to perform path optimization for multi-condition combined queries on the structured data to obtain the globally optimal retrieval path; retrieving personnel information that satisfies the multi-condition combined query from the structured data based on the globally optimal retrieval path; and adaptively updating the global pheromone of the improved ant colony optimization algorithm after each round of multi-ant colony collaborative search to accurately locate the target personnel information.

The invention discloses an improved whale optimization algorithm and SVRM short-term icing prediction method based on a random forest, and the method comprises the steps: (1) obtaining all micrometeorological data and the equivalent icing thickness thereof, carrying out the mapping along with a time sequence, preprocessing the data, and dividing the data into a training set and a test set; (2) analyzing the influence of each meteorological factor on icing growth based on a random forest algorithm, obtaining an icing influence rate through the random forest algorithm, and taking the characteristic quantity with the high influence rate as an input characteristic quantity; (3) initializing a support vector machine regression model; (4) adding stage position updating in the basic whale optimization algorithm, and adding adaptive weight for improvement; (5) optimizing a kernel parameter and a penalty factor of the support vector machine regression model by using an improved whale algorithm; and (6) reconstructing a support vector machine regression model by using the obtained optimal parameters, training the support vector machine regression model by using the training set, testing the input of the test set, obtaining a final icing prediction value, and performing error calculation on a prediction result.