2results about How to "Meet the complexity" patented technology

The utility model relates to a luminous shoelace and a shoe with the luminous shoelace, the luminous shoelace comprises a strip-shaped piece coupled with a shoelace body and a luminous device arranged between the shoelace body and the strip-shaped piece, and the luminous device comprises a circuit board module and a battery. The plurality of predetermined colored light emitting diodes are electrically coupled with the circuit board module and the battery and comprise a first predetermined colored light emitting diode and a second predetermined colored light emitting diode; a sensor detects an external force to generate a control signal; the control chip is arranged on the circuit board module, receives a control signal and drives the light-emitting diodes with the preset colored light to emit light in a light-emitting mode, and the circuit board module is a flexible circuit board or a hard circuit board with a flexible flat cable; therefore, the flexible circuit board or the circuit board with the flexible flat cable is combined with the high polymer material, so that the lightweight design is realized, the overall volume is greatly reduced, the diversified requirements of different age groups and foot types on the size of the shoelace are met, and diversified light-emitting modes and practicability are provided.

The application provides a database intelligent index recommendation method and system based on reinforcement learning, which comprises the following modules: (1) an initialization module; (2) an action space definition module; (3) a reward function optimization module, which selects appropriate index operations to minimize the reward function value; (4) a model training module; and (5) a deployment and optimization module. The application introduces a reward function calculation method that comprehensively considers multiple key factors such as the optimization effect and size of the index, and the reward function can more comprehensively evaluate the good and bad of an action (index operation). Compared with a single index, this multi-factor comprehensive consideration method is more in line with the complexity of the actual database performance, and is helpful to improve the accuracy and applicability of the index recommendation algorithm. The introduction of the evaluation of the index size avoids excessive consumption of system resources such as memory and disk space. Considering the resource consumption factors such as the index size in the reward function helps to avoid performance problems caused by excessively large or small index configurations, and improves the robustness and practicality of the system.