The present invention discloses a neuromorphic brain-like decision-making
system, which relates to the field of
artificial intelligence technology. The present invention abstracts object information into symbols and marks them, combines them with position coordinates, takes the initial position of the
robot as the starting point, determines the coordinates of the task passing points and the end point, and uses a graph
search algorithm to generate an
executable path; then calculates the
walking distance of the path, and obtains an estimated
walking time in combination with the speed range set by the user; at the same time, a relationship between distance and
energy consumption is preset, and the estimated
energy consumption is determined by matching, and the
final energy consumption performance value is calculated on this basis, and the
final energy consumption performance value and estimated
walking time of the path are extracted. Then, combined with the user's
energy consumption preference habits and efficiency preference habits and their
weight coefficient sets, a comprehensive evaluation index of each path is calculated, and the path with the highest index is selected as the final walking path, thereby realizing
intelligent decision-making based on user preferences and multi-dimensional evaluation, balancing energy consumption and efficiency, and improving the
robot's operating efficiency and user experience.