5results about How to "Optimize forecast results" patented technology

This invention relates to a landslide prediction method based on a multi-scale hierarchical attention mechanism, comprising: acquiring rainfall data to be predicted for a target area; inputting the rainfall data to be predicted into a landslide prediction model for multi-scale feature decomposition to obtain several frequency scales; extracting and fusing features from the several frequency scales through an attention mechanism; and outputting the landslide prediction result for the target area. The landslide prediction model is obtained through training on a training set, which consists of landslide-related rainfall data. This invention uses univariate rainfall time-series data as input and utilizes wavelet transform for multi-scale feature decomposition to extract short-term, medium-term, and long-term rainfall features. Based on this, a hierarchical attention mechanism including expanded attention and sampling channel attention is constructed to achieve the extraction of key time-series features and the dynamic fusion of multi-scale features, respectively. This invention significantly outperforms traditional models in prediction accuracy, providing an effective solution for regional landslide early warning.

The invention relates to an e-commerce user re-purchase prediction method and system based on big data, and the method comprises the steps: 1, obtaining the original behavior data of a user, and carrying out the preprocessing of the original behavior data, and obtaining the behavior data of the user; 2, constructing user behavior characteristics, and obtaining corresponding user behavior characteristic data according to the behavior data of the user; 3, inputting the user behavior characteristic data into a user re-purchase prediction model for user re-purchase prediction to obtain a user re-purchase prediction result; wherein the user re-purchase prediction model is obtained by fusing an XGBoost prediction model, a Light GBM prediction model and a CatBoost prediction model which are trained on the basis of a voting method. Compared with previous logistic regression models and the like, the e-commerce user re-purchase prediction method based on the big data has the advantages that the prediction efficiency is greatly improved, the prediction precision is higher, related merchants can make corresponding marketing schemes, and accurate pushing to the users is realized.

The application discloses a method for establishing a short-term building cold load prediction model based on a Transformer model, a prediction method and computer equipment, belongs to the technical field of building cold load prediction, and solves the problem of low prediction accuracy of existing building cold load prediction. The method comprises the following steps: selecting feature variables required for building cold load prediction, wherein the feature variables comprise weather feature variables and time feature variables; obtaining linear correlation of each two feature variables, and selecting feature variables linearly independent with each other as input data of a prediction model; establishing a data set with a sequence information mode according to the input data of the prediction model, and generating a sequence input data set according to the data set with the sequence information mode; dividing the sequence input data set into training data and test data, training a model based on a Transformer network; and obtaining the short-term building cold load prediction model based on the Transformer model according to the model based on the Transformer network after training. The application is suitable for predicting short-term building cold load.

The present disclosure relates to the technical field of bone density value prediction, and proposes a multi-model fusion bone density value prediction method and system, including the following steps: acquiring clinical feature data for bone density prediction; constructing a multi-model fusion prediction model including sequentially connected base learners and meta learners, inputting the acquired features into multiple base learners for prediction and recognition to obtain primary prediction results; adaptively calculating fusion weights according to the prediction accuracy of the base learners, and fusing the primary prediction results of each base learner based on the calculated fusion weights; and transmitting the fused primary prediction results to the meta learners for prediction to obtain the prediction results of the bone density. The present disclosure designs multiple machine learning models as a multi-stage prediction architecture, and each stage of the model learns and optimizes the prediction results of the previous stage and the original input features, thereby gradually improving the accuracy of the prediction.

The application relates to a commodity sales prediction method and system, and belongs to the technical field of sales prediction. The method comprises the following steps: obtaining a historical sales sequence and attribute information of a target commodity, identifying a cold-start commodity based on sequence length; training a time sequence representation encoder through self-supervised learning based on the sales sequence of a non-cold-start commodity, and extracting a time sequence representation representing sales change rules; for the cold-start commodity, extracting the time sequence representation through the encoder and calculating a quality score, determining a similar commodity set based on the attribute information and generating a migration representation, calculating a dynamic historical information proportion according to the historical sales sequence length and the quality score, fusing the time sequence representation and the migration representation to obtain a fused representation, and outputting a future sales prediction value that has been trend calibrated based on the fused representation. Through self-supervised learning, general time sequence rules are extracted, external migration information is dynamically fused based on the historical sales sequence, and the accuracy and stability of commodity sales prediction in a cold-start scenario are effectively improved.