Data flow analysis plays an important role in binary code analysis. Due to consuming too much time and space, constructing the traditional data dependence graph (DDG) limits the size of the analyzed code thoroughly. This study introduces a novel graph model, function-level data dependence graph (FDDG), and proposes a corresponding construction method. The key insights behind FDDG lie in the following two points. First, FDDG focuses on the relationships between function parameters; Second, FDDG treats a function as a whole and ignores the details inside the function. As a result, the size of the data dependence graph is reduced significantly. Also, the time and space are saved greatly. The experimental results show the time performance of the method is improved by about three orders of magnitude compared to the method in angr. As an instance, FDDG is employed to analyze the vulnerability of embedded firmwares, and a firmware vulnerability analysis prototype system called FFVA is implemented. The implemented FFVA system is used to analyze firmwares from real embedded devices, and find a total of 24 vulnerabilities in the devices from D-Link, NETGEAR, EasyN, uniview, and so on, among which 14 are unknown vulnerabilities, thus validating the effectiveness of function-level data dependence graph in static vulnerability analysis.