Edge　computing　brings　computing　resources　closer　to　the　Internet　of　Things　(IoT)　devices,　significantly　reducing　transmission　latency　and　bandwidth　usage.　However,　the　limited　resources　of　edge　servers　require　efficient　management.　Serverless　computing　meets　this　demand　through　its　elastic　resource　provisioning,　leading　to　the　emergence　of　serverless　edge　computing-a　promising　computing　paradigm.　Despite　its　potential,　realtime　task　dispatching　and　scheduling　in　the　highly　complex　and　dynamic　environment　of　serverless　edge　computing　present　significant　challenges.　On　the　one　hand,　task　execution　requires　not　only　sufficient　CPU　resources　but　also　free　containers;　on　the　other　hand,　tasks　are　typically　event-driven,　with　strong　burstiness　and　high　concurrency,　and　impose　stringent　demands　on　fast　decision-making.　To　address　these　challenges,　we　propose　a　real-time　task　dispatching　and　scheduling　method,　aiming　to　maximize　the　satisfaction　rate　of　Service　Level　Objectives　(SLOs)　for　tasks.　First,　we　design　a　task　dispatching　algorithm　named　Adaptive　Deep　Reinforcement　Learning　(ADRL).　This　algorithm　can　quickly　decide　the　execution　position　of　tasks　based　on　coarse　information　and　effectively　adapt　to　the　changes　in　available　servers　in　dynamic　environments.　Second,　we　propose　a　task　scheduling　algorithm　named　Warm-aware　Shortest　Remaining　Idle　Time　(WSRIT),　which　guides　the　edge　servers　to　schedule　the　tasks　in　the　request　queue　based　on　the　tasks＇　remaining　idle　time　and　the　state　of　the　warm　containers.　Considering　the　limited　storage　space　of　the　edge　servers,　we　further　introduce　a　container　replacement　algorithm　named　Low　Priority　First　(LPF)　to　ensure　smooth　container　launches.　Extensive　simulation　experiments　are　conducted　based　on　Azure　datasets.　The　results　show　that　our　methodcan　improve　the　satisfaction　rate　of　SLOs　by　12.57　similar　to　41.87%　and　achieve　the　lowest　cold　start　rate　compared　to　existing　methods.