Data-driven　soft　sensor　methods　have　been　widely　used　in　municipal　wastewater　treatment　processes　to　achieve　efficient　monitoring　of　effluent　indicators.　However,　the　complex　biochemical　reaction　mechanisms　in　the　wastewater　treatment　process　lead　to　process　data　with　strong　nonlinear　and　time　correlation　characteristics,　which　causes　the　performance　of　the　current　state-of-the-art　soft　sensor　techniques　to　be　limited.　Therefore,　in　this　article,　a　novel　Transformer　network　is　introduced　to　construct　a　soft　sensor　model.　The　model　structure　utilizes　a　positional　encoding　mechanism　combined　with　a　multihead　attention　mechanism　for　the　parallel　processing　of　data,　which　can　establish　global　interdependencies　in　the　time　series　to　fully　extract　the　long-term　time　correlation　of　the　time　series　data.　Subsequently,　the　model　is　introduced　with　a　residual　connection　module　to　successfully　ensure　the　extraction　capability　of　the　model　for　nonlinear　characteristics　while　also　avoiding　the　problem　of　gradient　disappearance　and　ensuring　the　performance　of　the　model.　Finally,　the　effectiveness　and　feasibility　of　the　proposed　method　were　verified　on　the　benchmark　simulation　model.