The invention relates to an
artificial intelligence-based cargo transportation path
planning method and
system for automatic warehouse logistics. The method comprises the steps of performing weighted scoring calculation based on customer historical
transaction data and cargo attributes, and generating service
label data for distinguishing a high-value hierarchy and a common hierarchy; a differentiation
algorithm strategy is called according to the service
label, an exclusive transportation path is distributed to the high-value goods,
obstacle avoidance is optimized in real time, and adjacent orders are matched for common goods to be merged and distributed; in combination with real-time road conditions and storage scheduling data, through path weight dynamic calculation and
genetic algorithm iterative optimization, generating a global transportation scheme considering both
time efficiency and cost; customer preference parameters are updated in a
closed loop mode based on execution feedback data, and
system self-
adaptive learning is achieved. According to the method, the problems that
differentiated services, dynamic response to environment changes and low-efficiency
resource utilization cannot be achieved in a traditional method are solved, the
punctuality rate of high-value orders and common orders can be increased, and meanwhile the
transportation cost of the common orders can be reduced.