The new hardware and its built environment have changed the traditional computing, storage and network systems, and also changed the previous design assumptions of the upper-level software. In particular, the heterogeneous computing architecture composed of general-purpose processors and dedicated accelerators has changed the design of the underlying framework of the database system and the cost model of query optimization. The database system needs to make adaptive adjustments to the characteristics of the new hardware to give full play to the potential of the new hardware. A cost-based query optimizer Geno for CPU/GPU/FPGA heterogeneous computing fusion is proposed, which can flexibly schedule and optimize the use of various computing resources. The main contribution is: finding that adjusting the cost parameters according to the actual hardware capabilities of the system environment can significantly improve the accuracy of the query plan, and proposing a calculation method and calibration tool for the cost of heterogeneous resources; through the estimation of the capabilities of heterogeneous hardware such as GPU and FPGA and the calibration of the actual capabilities of the database system hardware, establishing a cost model for query processing in a heterogeneous computing environment; implementing GPU operators and FPGA operators that support selection, projection, join and aggregation, realizing GPU operator pipeline design and FPGA operator pipeline design; solving the operator assignment and scheduling through cost-based evaluation, and generating a heterogeneous collaborative execution plan to realize the collaborative optimization of heterogeneous computing resources to makes full use of the advantages of each heterogeneous resource. Experiments show that the parameter values calibrated by Geno are more compatible with the actual hardware capabilities. Compared with PostgreSQL and GPU database HeteroDB, Geno can generate a more reasonable query plan. In the TPC-H scenario, the execution time of Geno in the case of row storage is 64%-93% less than that of Postgresql, and 1% to 39% less than that of Hetero-DB; in the case of column storage, Geno’s execution time is 87%-92% less than that of Postgresql, and 1%-81% less than that of Hetero-DB; Compared with row storage, Geno reduces query execution time 32%-89% in the case of column storage.