In　the　active　distribution　network,　there　are　multi-source　measurement　data　with　varying　time　scales　and　measurement　accuracies.　The　full　fusion　of　multi-source　measurement　data　to　accurately　estimate　the　state　of　a　distribution　network　is　an　essential　prerequisite　for　its　efficient　operation.　In　this　paper,　a　novel　interval　state　estimation　method　for　multi-source　measurement　data　fusion　is　proposed.　Firstly,　the　measurement　bias　caused　by　inconsistent　timestamps　of　the　data　was　quantified　and　a　multi-source　interval　dataset　was　constructed.　On　this　basis,　a　method　for　multi-source　data　fusion　was　proposed,　which　incorporates　the　temporal　scale　characteristics　of　the　measurement　data.　Secondly,　an　adaptive　adjustment　technique　based　on　the　innovation　variance　was　employed　to　dynamically　adjust　the　weights　assigned　to　the　measurements,　aiming　to　achieve　accurate　state　estimation.　Finally,　the　feasibility　and　effectiveness　of　the　proposed　method　were　demonstrated　through　case　studies.