3results about How to "Comprehensive forecast" patented technology

This application provides a method, apparatus, storage medium, and processor for fault prediction of power modules. The method includes: acquiring historical operating data of the power module under multiple states; analyzing and selecting historical operating data that meets preset conditions as positive sample data; processing the positive sample data and inputting the processed positive sample data into a fault prediction data model to train the fault prediction data model and obtain a trained fault prediction data model; acquiring real-time operating data of the power module to be predicted; inputting the real-time operating data into the trained fault prediction data model to output the fault type of the power module to be predicted through the trained fault prediction data model, thereby realizing state monitoring and fault prediction of internal components of the power module, constructing an accurate fault prediction data model, and timely and accurately predicting potential faults of the power module, with more comprehensive predictions and a greater reduction in losses caused by faults.

The present application relates to the field of image processing, and specifically to an intestinal necrosis feature recognition system based on image analysis and a storage medium, wherein a computer program in the storage medium performs the following steps: obtaining medical images of a patient and dividing them into an expiration phase subset and an inspiration phase subset; performing spatial registration and motion artifact correction on the expiration phase subset and the inspiration phase subset to obtain multi-modal images; extracting a blood vessel obstruction feature, a perfusion injury feature and a cell necrosis feature in the multi-modal images; constructing a causal hierarchical relationship among the blood vessel obstruction feature, the perfusion injury feature and the cell necrosis feature based on an intestinal necrosis pathological mechanism; and determining a risk level of the patient having intestinal necrosis and a probability corresponding to the risk level based on the causal hierarchical relationship, the blood vessel obstruction feature, the perfusion injury feature and the cell necrosis feature using a random forest algorithm. The present application solves the problem of insufficient accuracy in recognizing intestinal necrosis features in the prior art.

The application relates to an oral squamous cell carcinoma auxiliary chemical immunotherapy response method and device, which comprises the following steps: acquiring image data and baseline clinical factors of a target user, the image data comprising metabolic parameters and image features of a tumor region; performing clinical parameter standardization processing on the metabolic parameters to obtain a standardized metabolic parameter set related to treatment response; performing stability screening on the image features by using an intragroup correlation coefficient to obtain key features related to treatment response; performing regression analysis on the baseline clinical factors to identify independent prognostic factors related to treatment response in the baseline clinical factors; and performing feature fusion on the standardized metabolic parameter set, the key features and the independent prognostic factors to obtain a response rate and a prognosis prediction result of the target user. The method can more accurately and comprehensively predict the response rate and the prognosis of the target user to the auxiliary chemical immunotherapy.