The application discloses a tendering and
purchasing process optimization method and
system based on self-optimization instruction
engineering, and relates to the fields of
artificial intelligence and browser
automation. The method comprises the following steps: deploying an agent basic model and a browser execution environment, and establishing a
memory bank and an instruction
bank; recording user browser interaction events and
context data, mapping the events and data into a tendering and
purchasing business intention, and generating a structured operation instruction; generating a candidate positioning method for the same business intention; generating an anti-forgetting instruction to maintain business constraints and process states; constructing a preference pair based on operation results, user corrections and
business rule satisfaction conditions, optimizing a strategy model by using a direct preference optimization
algorithm, and updating instruction priorities; and executing a process according to the updated instruction
bank and using execution feedback for
continuous optimization. The application can improve the stability, accuracy and adaptability of tendering and
purchasing automation execution in the case of page changes and long processes.