3results about How to "Done successfully" patented technology

The application discloses a kind of unmanned aerial vehicle hoisting task optimization method and device, and the application relates to unmanned aerial vehicle scheduling technical field, comprising the following steps: based on hoisting starting point and target construction site determines hoisting path boundary, and the point cloud data of this area is collected, set along the way node, obtain the signal strength of each node and analyze its fluctuation, filter out the effective path point of signal stability, according to the distance of effective path point and target construction site Priority is set, with hoisting starting point as center, determine search range, select several candidate nodes from it, and collect wind speed variation, terrain complexity and cargo swing angle, calculate the risk cost of each node, select the candidate node with the lowest risk cost as the next waypoint, update the current effective path point of unmanned aerial vehicle, until unmanned aerial vehicle reaches target construction site, complete hoisting transport task, realize more safe and reliable transport movement.

The invention relates to the technical field of energy-saving control, in particular to a power supply energy-saving control method and system applied to the ultrasonic field, the method is applied to an ultrasonic detection system, and the ultrasonic detection system comprises an ultrasonic master control device and a plurality of movable ultrasonic detection terminals. The method comprises the steps of generating a detection task energy-saving sorting list according to a preset energy-saving priority evaluation strategy based on task information of all ultrasonic detection tasks in a preset detection period of the ultrasonic detection system; wherein the task information comprises detection task complexity and detection task time planning information. The energy-saving operation modes of the ultrasonic master control device and the movable ultrasonic detection terminal are dynamically adjusted based on the task priority, the energy consumption information and the equipment operation state, and energy consumption can be effectively reduced in different operation environments.

This invention relates to the field of image defect detection technology, specifically a deep learning-based method for detecting defects in watch strap images. The method includes: monitoring the surface image state of the watch strap, analyzing texture change trends, and generating real-time image state data; predicting material texture trends based on the data, adjusting the convolutional feature extraction depth and strengthening high-contrast regions to generate a dynamic feature extraction strategy; analyzing the differences in feature representation between convolutional layers of the model, optimizing feature propagation paths, and adjusting layer weights; and dynamically allocating feature routes based on the extraction strategy and weight adjustment results to optimize defect judgment. This invention effectively suppresses high-noise background interference, solves the problem of missing small defects under complex textures, significantly enhances the stability and recognition accuracy of the detection system while improving the efficiency of computational resource utilization, and ensures the efficient completion of quality inspection tasks.