Honeypots and lightweight honeypoints record scanning, reconnaissance, and exploitation attempts against deliberately exposed services. In operational exports, however, observations from the same source are often split across dates, decoys, alerts, threat intelligence fields, and historical records. Ranking sources only by interaction count shows activity volume but not where a source appeared, when it returned, or which contextual signals accompanied it. We present Mizong, a source-level analysis protocol that converts these fragmented records into auditable profiles for longitudinal and cross-site analysis. Mizong links all records belonging to the same source and characterizes each resulting trace by interaction intensity, honeypoint breadth, observed date persistence, alert linkage, threat intelligence context, and historical activity. Each dimension has an explicit aggregation rule; missing and conflicting states remain visible, and every profile retains its contributing rows. We evaluate source profiles using 20 detailed Shenzhen-side exports containing 10,257 sources and 3,124,252 interactions from the 2025 National Games and Para Games assurance task. A top 5% interaction count queue covered 5.4% of multi-point sources, 14.5% of multi-export sources, and 2.4% of alert-linked sources. Source-set comparison found 778 identifiers in both assurance phases; a separate 76-day operational summary reported 603 identifiers at all three protected sites. These results show that activity volume and the other profile dimensions answer different analytical questions. Mizong provides a reproducible way to move from disconnected export rows to source profiles that can be inspected across time, phases, and sites.