Research on intelligent evaluation and measurement methods for power grid projects

This project addresses the shortcomings of general large models in project evaluation and proposes a solution with significant advantages. It precisely focuses on core pain points such as insufficient domain knowledge adaptation, deficiencies in long text processing, and hallucination risks, and builds an efficient and intelligent evaluation system through three research directions. In terms of domain adaptation, relying on professional knowledge extraction and enhancement technologies, it can deeply analyze evaluation norms and achieve intelligent matching of evaluation elements and standards, breaking through the domain knowledge barriers of general models and solving the problem of weak cross-domain knowledge correlation. The multi-agent system design is based on professional division of labor, taking into account centralized scheduling and distributed collaboration, balancing efficiency and flexibility. In collaborative reasoning, mixed strategies combined with reinforcement learning dynamically allocate tasks, allowing each agent to perform its duties precisely, achieving intelligent evaluation throughout the process; the trustworthy enhancement strategy uses feedback correction and causal tracing to suppress hallucinations, ensuring the reliability of the conclusion. Verified in the digital project scenario, the formed intelligent evaluation engine can break through the limitations of general models, achieve an upgrade from "general intelligence" to "domain expert", and provide interpretable, highly reliable, and low-risk intelligent support for project evaluation.