3results about How to "Implement initialization" patented technology

Embodiments of the present application provide an application initialization method and device, a storage medium and a program product. In the embodiments of the present application, a hook function is embedded in an execution initialization function in a runtime component to hijack an original function initialization process, and the hook function is used to set a function initialization order of a target application as a target initialization order in which initialization of a target function's embedded code is performed first and then initialization of the target function is performed. In this way, when the executable file of the target application is initialized according to the target initialization order, it is ensured that the initialization of the target function's embedded code is completed before the initialization of the target function, and the initialization of the instrumented application is implemented. In this way, the target function can run normally during runtime, and the embedded code can ensure performance monitoring or state tracking of the target function.

This invention relates to the fields of clinical laboratory medicine and artificial intelligence, and provides a risk prediction method and device based on machine learning for clinical mass spectrometry. The risk prediction method includes: defining N+M dimensions of features based on a liquid chromatography-tandem mass spectrometry system to obtain an N+M dimension feature vector structure; obtaining a virtual training dataset based on the N+M dimension feature vector structure, acquiring parameter values ​​of each dimension of the current batch through a data acquisition interface, and assembling them into N+M dimension feature vector data; performing format verification and invalid value filtering on the N+M dimension feature vector data using the N+M dimension feature vector structure to obtain a feature matrix; and obtaining a standardized real-time risk score based on the feature matrix and a trained fusion-integrated risk prediction model. This invention utilizes a machine learning model to uncover the complex nonlinear relationship between configuration parameters and dynamic parameters, thereby achieving more accurate and forward-looking risk warnings than traditional single-threshold methods.