Optimizing gas-steam combined cycle cogeneration (GSCCC) systems holds immense promise for energy conservation, yet it presents considerable complexity for researchers to address. In this study, we introduce an advanced real-time platform with a focus on GSCCC systems. Our data cleaning subsystem and parameter calculation subsystem are particularly adept at gathering critical real-time operational parameters essential for optimization. To ensure rapid response times for real-time optimization, we incorporate artificial neural network technology into the system optimization module. Additionally, we adopt a hybrid approach that combines the Harmony Search (HC) algorithm with the Genetic Algorithm (GA), aiming to continuously enhance optimization performance through iterative database updates. The efficiency of our platform is initially validated under usual conditions. Subsequently, it is deployed at a combined cycle cogeneration system to evaluate its practical application. The field test results underscore the platform's effectiveness, revealing a notable reduction of 0.415 g/kWh in the cogeneration system's standard coal consumption when utilizing the platform. These findings affirm the platform's practical viability and its potential to deliver significant energy efficiency improvements in real-world settings.