4results about How to "Excellent performance" patented technology

The deep learning-based video multi-level adaptive compression cross-layer transmission optimization method and system of the application relates to the technical field of video transmission. By designing a compression level selection model based on deep learning, the optimal compression level is output based on the original video data and network environment parameters, and the corresponding compression strategy of the optimal compression level is obtained. A transmission strategy selection model is designed to generate a transmission strategy based on video quality parameters and network environment parameters. A global utility function is defined to calculate the global utility value when the compression strategy and the transmission strategy are assumed to be executed. An optimization objective function is designed, which inputs the compression strategy and the transmission strategy and outputs the optimized compression strategy and the transmission strategy. A condition judgment logic is set to select the final strategy based on the compression strategy and the transmission strategy before and after optimization, thereby achieving global optimization of video compression and transmission.

The invention discloses a high-heat-resistance melt-blown cotton production device and high-heat-resistance melt-blown cotton prepared through the high-heat-resistance melt-blown cotton production device, the device comprises a collecting net system, and a grid base cloth unwinding system is arranged in front of the collecting net system and is responsible for releasing wound grid base cloth to the collecting net system; grid base cloth tightening devices are arranged on the left side and the right side of the collecting net system and are used for tightening grid base cloth above the net surface of the collecting net system; a melt-blowing / spinning system is arranged above the collecting net system and is responsible for spraying melt-blowing fibers to the grid base cloth on the net surface; the collection net system promotes the sprayed melt-blown fibers to be gathered on the grid base cloth through negative pressure air in the collection net system. The three-dimensional cross warp spacer fabric is used as the grid base cloth, the superfine fibers are melt-blown on the grid base cloth, the superfine fibers are mutually bonded in gaps of the spacer fabric to form a special capillary structure attached to the three-dimensional grid yarn, mutual overlapping and pressing due to dead weight are avoided, melt-blown cotton with large thickness can be manufactured, and the high-thermal-resistance material is obtained.

The utility model discloses a silicone sealant production device relates to sealant production technical field, including the reation kettle, the inlet of reation kettle is respectively through pipeline communication to have silica rubber jar, white carbon black jar, calcium carbonate jar, anti -settling agent jar, plasticizing agent jar, crosslinking agent jar and catalyst jar, the outlet of reation kettle is through pipeline communication to have double screw extruder, the outlet of double screw extruder is through pipeline communication to have product jar. Through adding anti -settling agent (such as organic bentonite) to reation kettle, with silica rubber, filler (white carbon black, calcium carbonate), plasticizing agent synchronous mixing, anti -settling agent can form three -dimensional net structure, prevent filler and settle in the subsequent storage and construction process, and the uniformity of silicone sealant is good, and the product quality is high.

The application discloses a kind of forging step beam servo control system and method, solve the problem of poor performance of prior art step beam control system, with better security, faster response speed Advantage, specific scheme is as follows: a kind of forging step beam servo control system, including industrial computer, controller, data acquisition module, control module, industrial computer is connected with controller, controller is separately connected with data acquisition module, motor control module, motor control module includes driver and motor, driver is connected with motor, motor is connected with ball screw mechanism to distinguish step beam or gripper;Industrial computer is installed with the motion control software for parameter setting and motor motion control program writing;Controller receives instruction from industrial computer and real-time information transmitted by data acquisition module and handles, judges the state and position of motor, compares with motor setting track, state, finally transmits real-time monitoring information to industrial computer.