This invention belongs to the interdisciplinary field of digital twins and
artificial intelligence, specifically disclosing an automatic generation method for manufacturing equipment
control logic based on neural-symbolic
collaboration and intermediate representation
verification. This method utilizes a neural-symbolic
collaboration strategy, employing four-element semantic model constraints and thought chain reasoning to transform unstructured
natural language process instructions into structured hierarchical state
machine (HSM) intermediate representations. Through
linear temporal logic (LTL)
formal verification and
graph theory connectivity analysis, static security
verification of the
control logic is performed, effectively improving the
system's temporal
determinism and
deadlock resistance. Based on this, the
system performs dynamic physical
simulation in a digital twin environment, extracting spatial coordinate deviations when mechanical interference occurs, and inversely mapping these deviations into structured error correction prompts, which are then fed back to the large
language model for logic reconstruction. This invention constructs a cross-
modal "generation-
verification-correction" closed-loop mechanism, achieving
deep integration of model cognitive intent and underlying
physical space constraints, ultimately deterministically compiling the verified intermediate representation into highly reliable industrial standard control code.