Stochastic and high-power fluctuations of large-scale photovoltaic generations in distribution networks lead to complex power flow variations and voltage violations, posing significant challenges to voltage control. To address these challenges, this paper puts forward a knowledge-data driven centralized-decentralized coordinated four-step voltage control strategy to effectively dispatch heterogeneous voltage regulation devices. Step 1 proposes an optimal power flow model to determine the day-ahead voltage control results by regulating the taps of the on-load tap changer, the number of capacitor banks, and the charging/discharging power of battery energy storage systems, thereby minimizing daily network loss and preventing slow-time-scale voltage violations. Step 2 generates the voltage-regulation dataset through power flow and volt/var optimization calculations, establishing the data foundation for data-driven learning. Step 3 develops an intelligent inverter-based voltage controller by using fuzzy control theory for photovoltaic generations and battery energy storage systems, with voltage regulation knowledge embedded. Furthermore, a data-driven gradient descent learning method is presented for controller parameter optimization, enhancing global voltage regulation performance. Step 4 forms an online decentralized voltage control strategy with optimized voltage controllers to perform effective reactive power control adaptively according to operation states, thereby addressing frequent voltage violations and optimizing network power loss. Simulation results based on the IEEE33-bus system and a large-scale Caracas 141-bus system show that the proposed strategy can effectively maintain bus voltages within a secure range and reduce the network power loss by approximately 49% and 37%, respectively for the two systems, thereby validating its effectiveness and superiority.