The invention discloses a
deep learning programming method and
system based on a digital twin DaaS platform, and the method comprises the steps: obtaining the equipment operation data of each sub-operation process in the business processes of different terminal devices, and enabling the equipment operation data to comprise the process number of each sub-operation process, and performing
data modeling on the equipment operation data of each sub-operation process in the different
terminal equipment, performing comparative analysis, judging whether the
terminal equipment needs to be upgraded and optimized or not based on an analysis result, performing
deep learning programming on the equipment operation data of each sub-operation process based on a trained
time sequence programming model, and if not, performing
deep learning programming on the equipment operation data of each sub-operation process. A target operation code is automatically generated, the target operation code is upgraded to the corresponding
terminal equipment in a distributed mode, and the
time sequence programming model is constructed based on equipment operation data, collected in advance, of all sub-operation processes corresponding to service processes in different terminal equipment. According to the method and the device, the problems of relatively low
business process and equipment optimization upgrading efficiency and relatively high labor cost are solved.