Dynamic scheduling method for greening services based on spatiotemporal big data

Abstract

The invention relates to a dynamic scheduling method for greening services based on spatiotemporal big data, including: collecting multi-source environmental information of greening areas by means of information collection equipment and determining the location of emergency occurrences; acquiring marked greening demand areas by means of demand determination equipment based on the multi-source environmental information and the location of emergency occurrences, and determining the required amount of responsive greening resources; performing grouping processing on the marked greening demand areas by means of area grouping equipment using the K-means clustering algorithm to generate resource allocation clusters; calculating the optimal scheduling path of greening equipment from the resource allocation clusters by means of path generation equipment through the A path planning algorithm, and obtaining path data to determine the dynamic scheduling scheme. On the whole, the invention improves the real-time response capability to emergencies, ensures the efficient advancement of ecological restoration progress, significantly reduces resource waste and enhances the intelligence level of greening management.

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