In　this　paper,　we　propose　a　joint　dynamic　pilot　and　data　power　allocation　scheme　for　time　division　duplex　(TDD)　massive　multiple-input　multiple-output　(MIMO)　systems,　so　as　to　both　adaptively　mitigate　pilot　contamination　and　balance　the　mutual　interference.　Due　to　the　unknown　of　instant　channel　state　information　before　pilots,　we　exploit　the　Gauss-Markov　process　of　temporally-correlated　channels　and　use　the　Kalman　filter　to　not　only　filter　out　the　pilot　contamination　but　also　provide　the　priori　estimation　values.　Subsequently,　the　deterministic　approximation　of　the　rate　is　derived　as　a　function　of　the　priori　channel　estimation　and　the　priori　estimate　errors,　and　accordingly　the　rate-profile　maximization　to　achieve　max-min　fairness　is　formulated.　To　deal　with　this　optimization　coupled　across　the　pilot　power　and　data　power　as　well　as　the　users,　we　give　an　iterative　alternating　rate-suboptimal　algorithm　composed　of　two　sub-problems,　both　of　which　are　further　solved　by　introducing　the　successive　convex　approximation　(SCA)　methods　and　slack　variables.　Numerical　results　confirm　the　improved　rate　provided　by　the　proposed　scheme.