3results about How to "High computational complexity" patented technology

This invention discloses a fall detection method, apparatus, device, and medium. The method includes: determining target noise point detection information corresponding to the target radar detection information of a target person based on personnel radar detection information detected by millimeter-wave radar and noise point detection information detected by an acoustic detection device; determining the detection mode of the target person based on the target radar detection information, and determining target detection information from the target radar detection information and target noise point detection information based on the detection mode; wherein the detection mode includes an obstruction mode, a stationary mode, and a motion mode; and detecting the fall state of the target person based on the target detection information. This invention uses a combination of millimeter-wave radar and acoustic detection for fall detection, enabling continuous fall state detection even when the target person is obstructed by objects indoors, improving the accuracy and efficiency of fall detection; and since both millimeter waves and acoustic waves are invisible waves, user privacy can be effectively protected.

The application discloses a decision tree binning calculation optimization method, equipment, medium and computer program product. The decision tree binning calculation optimization method comprises the following steps: obtaining memory storage data corresponding to a target decision tree and an index table corresponding to the memory storage data, wherein the index table at least comprises a node binning label corresponding to each decision tree binning; then, based on the size of each decision tree binning, the node binning label is segmented to obtain a segmented batch of each binning label; then, based on the index table, each decision tree binning batch data is extracted; then, based on the decision tree binning size information corresponding to each segmented batch of binning label, a corresponding number of parallel computing threads is matched for each decision tree binning batch data; then, based on the number of parallel computing threads, parallel decision tree binning calculation is performed on each decision tree binning batch data to obtain a target decision tree binning calculation result. The application solves the technical problem of low calculation efficiency during decision tree binning.