To address characteristics of government collaborative scenarios， such as diverse and complex requirements， difficulty in managing personnel turnover， high data privacy level， and large data size， a Government-Based Access Control （GBAC） model for government collaboration was proposed. Access control in government collaborative scenarios must meet requirement for multiple departments performing different operations to the same resource. The existing access control technologies face issues of inadequate granularity and high maintenance costs， lacking secure， flexible， and precise access control model. Therefore， combining operating mechanisms of government departments， firstly， government organizational structure and administrative division structure were integrated into the access control model， and a belonging relationship tree of government staff， organizations， resources， and administrative divisions was constructed. Secondly， combined with attributes of organizations and positions which the government staff belongs to， a joint subject was constructed to achieve automatic granting and revoking permission. Thirdly， based on organizing functions and administrative division levels， a subject-object attribute matching strategy was designed to break data barriers and improve authentication efficiency. Finally， by introducing idea of permission hierarchy， data levels and functional levels were set for resources to control the access threshold of the subject， which enhanced model flexibility and further ensured data security. Experimental results show that compared with benchmark models such as Role-Based Access Control （RBAC） and Attribute-Based Access Control （ABAC）， GBAC model reduces memory consumption and access latency significantly. It can be seen that the proposed model implements access management in government collaborative scenarios securely， effectively and flexibly.

Lithology identification is the most critical procedure in the logging data interpretation field, while the traditional lithology identification methods have a lot of defects such as slow explain efficiency, low accuracy, and big influenced human factors. To resolve these problems, a new kind lithology identification method was put forward using genetic optimized Radial Basis Probability Neural Network (RBPNN). Probabilistic Neural Network (PNN) and the Radial Basis Function Neural Network (RBFNN) were combined to construct RBPNN. To optimize network structure, upgrade convergence speed and accuracy, Genetic Algorithm (GA) was used to search for the optimal hidden center vector and matching kernel function control parameters of the RBPNN structure which must satisfy minimum error of RBPNN training and form genetic optimized RBPNN network model. The case study shows that lithology identification based on genetic optimized RBPNN can achieve the actual application standards, and it is feasible and effective, it also can provide scientific theoretical supports and dependences for oil geological exploration field.