Abstract: Fracture-Driven Interactions (FDIs) in tight reservoirs has a significant impact on production, and accurate diagnosis of the extent of FDI is the basis for formulating sound strategies. However, to date, a systematic method for quantitatively diagnosing FDI has not yet been established. To address this issue, this paper categorizes FDI into stages based on interference characteristics, identifies the influencing factors at different stages, and systematically summarizes the primary diagnosis methods for FDI. The results indicate that: firstly, the study of FDI can be divided into four stages: fracturing, soaking, flowback, and production. During the fracturing stage, FDI manifests as pressure fluctuations at the wellhead of adjacent wells; during the soaking stage, it appears as a slow decline in pressure within the high-pressure fracture system of adjacent wells; during the flowback stage, it is characterized by a rapid pressure drop in adjacent wells or a synchronous pressure decline when both wells are started up simultaneously; during the production stage, adjacent wells exhibit deviations from normal production trends. Secondly, FDI diagnosis methods include microseismic monitoring, water hammer pressure wave monitoring, DAS/DTS monitoring, tracer monitoring, well testing analysis monitoring, and machine learning-assisted monitoring. Among these, machine learning-assisted monitoring can effectively process large amounts of complex FDI data and automatically identify interference characteristics and patterns through model training. And thirdly, FDI at different stages corresponds to distinct physical processes. A physics-data dual-driven diagnosis approach is essential to closely integrate diagnosis methods with field applications. In addition, throughout the full lifecycle management of oil and gas wells, FDI should be fully considered, and production regimes should be dynamically adjusted. This study establishes a theoretical framework for staged diagnosis and provides an integrated diagnostic strategy based on multi-source information fusion for the development of diagnosis methods for FDI in tight reservoirs, as well as for process design that considers such interference.