  The advent of modern data processing has led to an increasing tendency
towards interdisciplinarity, which frequently involves the importation of
different technical approaches. Consequently, there is an urgent need for a
unified data control system to facilitate the integration of varying libraries.
This integration is of profound significance in accelerating prototype
verification, optimising algorithm performance and minimising maintenance
costs. This paper presents a novel functional programming (FP) paradigm based
on the Python architecture and associated suites in programming practice,
designed for the integration of pipelines of different data mapping operations.
In particular, the solution is intended for the integration of scientific
computation flows, which affords a robust yet flexible solution for the
aforementioned challenges.
