3results about How to "Guaranteed implementability" patented technology

The pipe network robot path and data station deployment joint optimization method disclosed by the application belongs to the technical field of smart city infrastructure operation and maintenance, and specifically comprises the following steps: firstly, a pipe network-robot-data station system model is constructed, and the pipe network topology is mapped into an undirected graph; secondly, combined with EPANET hydraulic simulation data, the pipe monitoring time is calculated under the conditions of downstream flow and upstream flow, and a hybrid graph with time weight is constructed; then, a weighted optimization mathematical model with the double objectives of minimizing the maximum monitoring time and minimizing the maximum data delay is established, and constraint conditions such as pipe full coverage and robot collision-free are defined; finally, the NSGA-II algorithm is used to generate a Pareto frontier, and the best compromise scheme is selected through weight distribution. The application solves the technical problems that the robot path planning and data station deployment lack cooperation in the existing pipe network monitoring technology, and the pipe network hydraulic characteristics are not coupled, so that the monitoring time and data delay are difficult to consider.

The invention provides a wafer cutting depth control method, a wafer cutting depth control system and a wafer scribing machine, which are used for solving the problem of insufficient cutting depth precision caused by uneven wafer thickness and uneven cutting table surface. The method comprises the following steps: firstly, establishing a fixed compensation relation value between a height measurement value of a cutting knife body and a laser distance measurement value through calibration; secondly, multiple points on the surface of the wafer are scanned through a laser range finder before cutting, and a curved surface model of the surface appearance of the wafer is fitted through an interpolation algorithm; and finally, in the cutting process, the predicted height is obtained from the curved surface model according to the coordinates of the current cutting point, and the position of the cutter body is calculated and adjusted in real time in combination with the fixed compensation relation value and the preset cutting depth. The system comprises corresponding hardware and a control module. According to the method, constant control over the cutting depth is achieved through dynamic compensation, and the precision and consistency of the cutting technology are greatly improved.

The invention discloses a high-pressure inflatable tent anomaly detection method based on machine vision, and relates to the technical field of inflatable tent structure safety monitoring, and the method comprises the following steps: S1, continuously obtaining an image sequence of the surface of a tent lining through an image collection device disposed in a tent; and air pressure time sequence data in the tent are synchronously collected through an air pressure sensor arranged in the tent. According to the high-pressure inflatable tent anomaly detection method based on machine vision, dual information of machine vision and air pressure sensing is fused, a multi-mode collaborative sensing system is constructed, and the inherent defect that a traditional single air pressure threshold detection method is insufficient in sensitivity in a dynamic environment is effectively overcome. According to the method, the complementary characteristic of the visual deformation characteristic quantity and the air pressure trend characteristic quantity is utilized, and the active excitation verification mechanism and the iterative decision model are combined, so that the accurate identification of the early-stage tiny leakage of the inflatable tent is realized, the reliability of anomaly detection is improved, and the false alarm phenomenon caused by environmental interference is reduced.