5results about How to "Implement initialization" patented technology

The invention discloses an array antenna beam pointing initialization method based on a baseband chip and MEMS inertial navigation, and the method comprises the steps: calculating an elevation angle and an azimuth angle of a pointing vector of a pointing satellite of an array antenna in an array antenna carrier coordinate system through a calculation processing module under a static or quasi-static condition; and controlling the beam of the array antenna to point to the elevation angle and the azimuth angle, and gradually screening out the optimal antenna carrier azimuth angle from the generated multiple groups of azimuth angles by evaluating the signal strength of a specified satellite corresponding to different azimuth angles of the array antenna in the navigation system. And calculating and determining an antenna carrier pitch angle and a roll angle of the array antenna in combination with information reported by MEMS inertial navigation, completing the initialization of the attitude of the array antenna, and realizing the initialization of the beam pointing of the array antenna.

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.

The embodiments of the present specification provide an engine processing method, device, medium and program product, wherein the method comprises: receiving an access instruction sent by a target platform, and deploying a kernel module and a target application module of a target engine on the target platform, wherein the target application module is an application module corresponding to the target platform among a plurality of application modules of the target engine; creating at least one application component through the target application module deployed on the target platform; initializing the application component and an engine function, and controlling the target platform to display an engine interface of the target engine. By deploying the kernel module and the target application module on the target platform, the engine can be accessed to the target platform, so that the engine can provide support for the target platform; by creating the application component, initializing the application component and the engine function, and completing engine starting, project development based on the target platform can be implemented in the running environment of the target platform, and the product developed can also have compatibility for the target platform.

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.

The invention provides a writing method and a writing circuit based on an RRAM chip, and relates to the technical field of memory circuit design, the method comprises the following steps: when a writing instruction for a target RRAM chip is received, reading a formation state flag of the target RRAM chip; wherein if the formation state flag is not formed, the target RRAM chip does not execute one-time initialization operation; and when the formation state flag is not formed, executing an initialization operation on the target RRAM chip, and setting the target RRAM chip in the first logic state through the initialization operation to complete the write-in instruction. According to the method and the device, the optimal writing reliability and data stability can be provided for key application scenes, such as a solidified test program code, a storage equipment calibration parameter or a secure root key, which only need to write data at one time.