Recommending developers for open-source projects is of great significance to the construction of open-source ecology. Different from traditional software development， developers， projects， organizations and correlations in the open-source field reflect the characteristics of open collaborative projects， and their embedded semantics help to recommend developers accurately for open-source projects. Therefore， a Developer Recommendation method based on Collaborative Contribution Network （DRCCN） was proposed. Firstly， a CCN was constructed by utilizing the contribution relationships among Open-Source Software （OSS） developers， OSS projects and OSS organizations. Then， based on CCN， a three-layer deep heterogeneous GraphSAGE （Graph SAmple and aggreGatE） Graph Neural Network （GNN） model was constructed to predict the links between developer nodes and open-source project nodes， so as to generate the corresponding embedding pairs. Finally， according to the prediction results， the K-Nearest Neighbor （KNN） algorithm was adopted to complete the developer recommendation. The proposed model was trained and tested on GitHub dataset， and the experimental results show that compared to the contrastive learning model for sequential recommendation CL4SRec （Contrastive Learning for Sequential Recommendation）， DRCCN improves the precision， recall， and F1 score by approximately 10.7%， 2.6%， and 4.2%， respectively. It can be seen that the proposed model can provide important reference for the developer recommendation of open-source community projects.

Currently, the query of transmission lines monitoring system in smart grid is mostly aiming at the global query of Wireless Sensor Network (WSN), which cannot satisfy the flexible and efficient query requirements based on any area. The layout and query characteristics of network were analyzed in detail, and a query algorithm based on mesh structure in large-scale smart grid named MSQuery was proposed. The algorithm aggregated the data of query nodes within different grids to one or more logical query trees, and an optimized path of collecting query result was built by the merging strategy of the logical query tree. Experiments were conducted among MSQuery, RSA which used routing structure for querying and SkySensor which used cluster structure for querying. The simulation results show that MSQuery can quickly return the query results in query window, reduce the communication cost, and save the energy of sensor nodes.