9results about How to "Good prediction accuracy" patented technology

This invention relates to the field of medical data-driven dose prediction technology, and discloses a method, system, device, and storage medium for individualized dose prediction of hypothyroidism based on medical data. The method includes: constructing a standardized feature vector based on the child's weight, age in days, corrected age in months, current L-T4 dose, TSH value, FT4 value, previous TSH value, TSH rate of change, feeding method, month of consultation, etiology of hypothyroidism, comorbidity status, previous dose adjustment magnitude, and age at which TSH first reached target levels; extracting TSH dynamic trajectory features from the child's TSH time-series data from previous follow-ups; obtaining a basic recommended dose using a gradient boosting decision tree model constructed with counterfactual filtering training data; and correcting the basic recommended dose to obtain an individualized recommended dose. This method improves the prediction accuracy of the gradient boosting decision tree model and allows the individualized recommended dose to simultaneously take into account multiple clinical confounding factors.

This invention proposes a method and system for fault early warning and prediction of rotating equipment in thermal power plants based on neural networks. The method includes: acquiring multi-source heterogeneous operating data of rotating equipment in thermal power plants, including vibration signals, temperature signals, rotational speed signals, oil quality signals, and current signals; performing denoising and standardization processing on the operating data to construct a multi-dimensional feature vector containing time-domain, frequency-domain, and time-frequency-domain features; based on the multi-dimensional feature vector, using a convolutional neural network to extract spatial correlation features, modeling long-term dependencies through a bidirectional long short-term memory network, and dynamically strengthening the weight allocation of fault-sensitive features using an attention mechanism to generate a fault identification model; and using the fault identification model to analyze the preprocessed feature data in real time, outputting fault type, remaining life prediction results, and risk level classification, wherein the risk level classification is generated based on a joint decision of fault severity and remaining life.

This invention belongs to the field of thermal energy storage control technology, specifically involving a thermal energy storage control method and control system based on flue gas parameter fluctuations. First, flue gas parameters from the boiler tail end are collected and preprocessed. Then, the parameters are decomposed and fluctuation characteristics are extracted using the EEMD algorithm. An LSTM-BP fusion model is used to predict the fluctuation range and trend. Next, a multi-objective optimization function is constructed based on relevant parameters, and the dynamic operating condition baseline value is obtained by solving it. Then, based on a fuzzy adaptive PID algorithm, the thermal energy storage device is adjusted by combining the deviation and fluctuation frequency. Finally, the actual fluctuations are monitored, and the model and optimization function are corrected to form a closed-loop control. This invention can accurately capture the fluctuation characteristics of flue gas parameters, achieve accurate prediction of fluctuation trends and dynamic adaptation to the operating condition baseline value, improve the accuracy and stability of thermal energy storage control, solve the pain points of existing methods such as adjustment lag and insufficient accuracy, and improve the efficiency of flue gas waste heat recovery.

This invention discloses a comprehensive energy load forecasting method based on a multi-scale graph conditional state-space model. The method includes constructing a comprehensive energy load forecasting dataset for a park, performing data preprocessing, and then using Pearson correlation analysis to select highly correlated features; dividing the dataset into training, validation, and test sets, and standardizing the data; constructing a joint prediction model based on dynamic graph learning, a multi-scale graph conditional state-space model, and a three-dimensional attention mechanism; training the joint prediction model using the training set; adjusting hyperparameters and selecting the optimal model using the validation set; inputting the test set into the trained model, and outputting the predicted electricity, cooling, and heating loads; restoring the actual predicted values ​​through inverse normalization; and evaluating the model performance using multiple indicators. This invention ensures the real-time requirement of the forecast and is suitable for online application scenarios in park energy dispatching.

The application discloses a soft-hard interbedded rock mechanical parameter prediction method and system based on a residual attention network, relates to the technical field of rock mechanical parameter prediction, and has the advantages that the traditional neural network is prone to gradient disappearance when processing a deep network, which influences the training effect; the existing method lacks an attention mechanism and cannot effectively identify and strengthen key features; and the modeling capability for interlayer interaction is insufficient; the application provides a soft-hard interbedded rock mechanical parameter prediction method based on a residual attention network, which comprises the following steps: obtaining structure parameters and target mechanical parameters of a soft-hard interbedded rock sample; converting the structure parameters into an enhanced feature vector; constructing a residual attention network model; inputting the enhanced feature vector into the residual attention network model; and outputting a mechanical parameter prediction result and reliability evaluation information.

The application relates to the technical field of tailing resource utilization, and discloses a tailing sand particle grading dynamic optimization processing method and system. The method comprises the following steps: collecting particle size data at a detection node of a conveying pipeline and dividing the particle size data into particle size groups; calculating dynamic weight coefficients according to data stability coefficients and flow influence coefficients and performing weighted fusion; predicting a grading deviation value through a long short-term memory neural network to trigger an optimization decision to obtain a target grading ratio; calculating accurate adjustment amounts of each bin to generate a batching control instruction; performing closed-loop adjustment and secondary correction to obtain a target grading product. The application solves the problems of tailing sand particle grading optimization response lag, insufficient control precision, difficulty in multi-target cooperation and lack of self-adaptive capability in the prior art, and improves the real-time performance and accuracy of grading control.

The utility model discloses an automobile auxiliary part with scale belongs to automobile manufacturing technical field, including automobile windshield glass, rear -view mirror and engine bonnet, wherein windshield glass and rear -view mirror are double -layer glass interlayer structure, and its interlayer interlayer bottom part is printed respectively the metric scale ruler of adaptation driver visual range, engine bonnet is printed along the length direction with the metric scale ruler of driver visual path matching. The utility model provides accurate visual reference for the driver, can promote trajectory pre -judge accuracy, and simple structure, high reliability, is applicable to various vehicle types, can effectively reduce the driving difficulty and accident rate.

The present application relates to the technical field of lithium ion battery, and provides a GRU-EC-GAPSO lithium ion battery SOH prediction method, which comprises the following steps: extracting a health factor from voltage and time data in a local SOC interval during charging and discharging of a lithium ion battery, inputting the GRU model to perform SOH prediction, calculating an error sequence of a predicted value and an actual value, training an EC model by using the error sequence, and obtaining a GRU-EC model; using a GAPSO algorithm to optimize parameters of the GRU-EC model, and obtaining a GRU-EC-GAPSO model; and applying the trained GRU-EC-GAPSO model to different types of lithium ion battery data to perform SOH prediction. The present application has high prediction accuracy and good robustness in lithium ion battery SOH prediction.