The application relates to an AI-driven conversational ERP order generation method and
system, and belongs to the field of
resource planning systems. The method comprises the following steps: loading user permissions and collecting historical behavior sequences in response to a wake-up instruction; predicting an order intention and generating an active guidance prompt based on a
time sequence coding model; extracting initial order elements by using a large
language model, combining a static permission vector and a dynamic
context vector for joint constraint optimization to generate accurate order elements; calling an ERP
master data interface to obtain real
business data after permission
verification to generate a basic order
data set; performing conflict resolution through a structured constraint rule
library and a model output-rule bidirectional
verification mechanism to generate a pre-order data object; and writing into a
database to generate an official order after multi-
modal fusion confirmation and
risk control pre-checking. The application has the advantages that permission
verification is pre-positioned, data is accurate and reliable, active guidance is provided, and multi-
modal risk pre-checking is performed, so that
order entry efficiency and compliance are significantly improved.