Emotional computing based on cross-modal fusion and edge network data incentive

In large-scale emotional events and complex emotional recognition applications, how to improve the recognition accuracy, computing efficiency, and user experience quality becomes the first problem to be solved. Aiming at the above problems, this paper proposes an emotional computing algorithm based on cross-modal fusion and edge network data incentive. In order to improve the efficiency of emotional data collection and the accuracy of emotional recognition, deep cross-modal fusion can capture the semantic deviation between multi-modal and design fusion methods through non-linear cross-layer mapping. In this paper, a deep fusion cross-modal data fusion method is designed. In order to improve the computational efficiency and user experience quality, a data incentive algorithm for edge network is designed in this paper, based on the overlapping delay gaps and incentive weights of large data collection and error detection. Finally, the edge network is mapped to a finite data set space from the set of emotional data elements inspired by heterogeneous emotional events. In this space, all emotional events and emotional data elements are balanced. In this paper, an emotional computing algorithm based on cross-modal data fusion is designed. The results of simulation experiments and theoretical analysis show that the proposed algorithm is superior to the edge network data incentive algorithm and the cross-modal data fusion algorithm in recognition accuracy, complex emotion recognition efficiency, and computation efficiency and delay.