The information interaction between operation anomalies and treatments is usually completed by operators to ensure the stable operation of distribution networks， but it is vulnerable to the subjectivity of operators， resulting in the mismatch between treatments and operation anomalies， and the lack of guarantee of information security for the interaction process. Therefore， a blockchain-based network model for distribution network exception management， Exception Management Blockchain Network （EMBN）， was proposed， as well as an improved three-line defense model for distribution network. Firstly， according to the tamper-proof and traceable characteristics of blockchain， an Anomaly Index Blockchain （AIB） was constructed， and appropriate treatments were found to solve operation anomalies based on the latest information in the block. Secondly， an Exception Interact Blockchain （EIB） was constructed to monitor the interaction process of operation anomalies and treatments， and ensure the implementation of treatments. Finally， the EMBN was applied to the three-lines of defense in traditional distribution network， and the intelligent contract was combined to realize adaptive detection and anomaly response of the distribution network. Simulation results show that， facing the complicated distribution network environment， EMBN can match treatments and operation anomalies without the influence by subjectivity of operators； compared with the traditional distribution network， EMBN has the advantage in the information security of information interaction.

For Minimum Phone Error based Maximum A Posteriori (MPE-MAP) adaptation, in order to accurately estimate the center of prior distribution and to improve the recognition performance, the Maximum Mutual Information based MAP (MMI-MAP) adaptation and H-criterion, which was the interpolation of MMI and Maximum Likelihood (ML) criterion, based on MAP (H-MAP) adaptation were used for the estimation of the center of prior distribution, which led to MMI-MAP prior based MPE-MAP (MPE-MMI-MAP) and H-MAP prior based MPE-MAP (MPE-H-MAP). The experimental results of task adaptation show that the two proposed methods both can obtain better recognition performance than MPE-MAP, MMI-MAP and MAP adaptation. MPE-MMI-MAP and MPE-H-MAP can obtain 3.4% and 2.7% relative improvement over MPE-MAP respectively.