Traditional Mathematical Morphology （TMM） is not well in structure-preserving， and the existing adaptive modified methods usually miss mathematical properties. To address the problems， a Guided Adaptive Mathematical Morphology （GAMM） for multimodal images was proposed. Firstly， the structure elements were constructed by considering the joint information of the input and the guidance images， so that the corresponding operators were more robust to the noise. Secondly， according to 3σ rule， the selected members of structure elements were able to be adapted to image contents. Finally， by using the Hadamard product of sparse matrices， the structure elements were imposed with a symmetry constraint. Both of the theoretical verification and simulation show that the corresponding operators of the proposed mathematical morphology can have important mathematical properties， such as order preservation and adjunction， at the same time. Denoising experimental results on multimodal images show that the Peak Signal-to-Noise Ratio （PSNR） of GAMM is 2 to 3 dB higher than those of TMM and Robust Adaptive Mathematical Morphology （RAMM）. Meanwhile， comparison of subjective visual effect shows that GAMM significantly outperforms TMM and RAMM in noise removal and structure preservation.

In order to achieve complementary advantages and information sharing of heterogeneous workflow systems among enterprises, concerning the workflow showing heterogeneous distribution and other characteristics, an interface design of heterogeneous workflow processes interconnection based on Web service was proposed. For the interconnection of heterogeneous processes, the solution of heterogeneous workflow interconnection was described from call interface，call mode and call return respectively. Taking example of SynchroFlow workflow process described by XPDL and ODE (Open Dynamic Engine) workflow process described by BPEL, the process calls between the workflows was achieved.

Aiming at the issues in fault diagnosis of energy Internet such as long model training time， insufficient extraction of fault features， and low diagnostic accuracy with limited training sample size， a Hierarchical Clustering and Multi-Head attention based Convolutional neural network （HCMHC） model was proposed. In the model， the novel Hierarchical Clustering （HC） model was adopted to reduce data redundancy effectively， while Convolutional Neural Network （CNN） and multi-head attention were combined for more accurate and comprehensive fault feature extraction. Furthermore， a contrastive learning model was employed to enhance the complementarity among features with limited training sample size， thereby improving model generalization ability and diagnostic accuracy on new data. Experimental verification results on the New England test system with 39 buses and 10 generators demonstrate that the HCMHC model achieves accuracies of 99.8% and 99.5% on two different datasets respectively， which have improvements of 4.3 and 4.5 percentage points approximately and respectively compared to the Multiple-Input CNN （MI-CNN） model. Additionally， even with a training set/validation set ratio of 20/80， this model still has accuracies of 98.3% and 95.8% on two datasets respectively. The above proves the significant effectiveness and superiority of the proposed model in the field of fault diagnosis.