Distributed energy trading has become an essential part of the energy trading market and provides a useful supplement to traditional centralized energy trading, but there are still problems such as opaque trading information and asymmetric user data. The blockchain technology has the advantages of traceability, trade openness, and data transparency, which is naturally suitable for distributed energy transactions. The electricity information data transmission represented by distributed energy transaction has the characteristics of real-time, which has a high-efficiency requirement on the selected blockchain technology. The consensus algorithm is the core of blockchain technology and affects the efficiency of the blockchain system. The efficiency of the existing consensus algorithms for energy transaction-oriented blockchain still needs to be improved. In this paper, a consensus resource slicing model(CRSM) is designed to meet the requirements of consensus efficiency in energy trading scenarios. Specifically, CRSM divides consensus nodes into different consensus domains for concurrent consensus, and the storage domain only stores block information without consensus. By building an experimental platform, the efficiency of CRSM was verified, the communication pressure of the blockchain system was reduced, and the consensus speed was effectively improved.INDEX TERMS Consensus mechanism, multi-consensus domain, blockchain, distributed energy trading.

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Spray deposition with following continuous extrusion (SD-CE) forming technique is a novel technology that combines spray forming and continuous extrusion. Optimization of test parameters for spray deposition is an important part of SD-CE. In this study, Al-20Si alloy was produced by spray forming at different melt temperature and gas pressure, and obtained grain diameter of 8 group primary silicon phase. Based on the experimental results, an Artificial Neural Network (ANN) with single hidden layers composing of 10 neurons was employed to simulate optimizing of test parameters for spray deposition. The inputs of the model are melt temperature and gas pressure. The output of the model is grain diameter. Finally, the minimum relative error of grain diameter is 0.09%, the maximum relative error is 8.38%, and error majority concentrate within 3.80%, the average absolute relative error(AARE) is 1.04%, R is 0.097, the error is small. The optimal test parameters for spray deposition are melt temperature(829 °C) and gas pressure(0.2 MPa). The results indicate that the ANN model is an easy and practical method to optimize the test parameters for spray deposition of Al-20Si alloy. Thereby this model is a useful reference for optimizing the test parameters of SD-CE