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To address the problems of high communication complexity, the random selection of master nodes, and limited supported network size of the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm for consortium chains, an improved Byzantine Fault Tolerance (CBFT) algorithm based on grouping and credit hierarchy is proposed to optimize the nodes of large-scale consortium chains structure. First, the network nodes are divided into different consensus sets according to their response speed to the management nodes, and the consensus is carried out inside and outside the group respectively; based on this, a credit grading mechanism is proposed and a credit calculation formula is introduced to select the management nodes; finally, a simulation and performance testing system based on this improved scheme is built. The experimental results show that CBFT has less communication overhead, shorter latency, and higher throughput than PBFT, and it is more obvious with the increase in the number of nodes, which meets the needs of large consortium chains.

To improve the blockchain consensus algorithm practical Byzantine fault tolerance (PBFT) with random master node selection, which has high communication overhead and a small supported network size, this paper proposes a Byzantine fault tolerant consensus algorithm based on credit (CBFT) enhanced with a grouping and credit model. The CBFT algorithm divides the network nodes according to the speed of their response to the management nodes, resulting in different consensus sets, and achieves consensus within and outside the group separately to reduce communication overhead and increase system security. Second, the nodes are divided into different types according to the credit model, each with different responsibilities to reduce the probability that the master node is a malicious node. Experimental results show that the throughput of the CBFT algorithm is 3.1 times that of PBFT and 1.5 times that of GPBFT when the number of nodes is 52. Our scheme has latency that is 7.4% that of PBFT and 38.8% that of GPBFT; CBFT has communication overhead that is 6.4% that of PBFT and 87.3% that of GPBFT. The number of nodes is 300, and the Byzantine fault tolerance is improved by 59.3%. These improvements are clearer with the increase in the number of nodes.

The Manasi region is located in an arid and semi-arid region with fragile ecology and scarce resources. The land use change prediction is important for the management and optimization of land resources. We utilized Sankey diagram, dynamic degree of land use, and landscape indices to explore the temporal and spatial variation of land use and integrated the LSTM and MLP algorithms to predict land use prediction. The MLP-LSTM prediction model retains the spatiotemporal information of land use data to the greatest extent and extracts the spatiotemporal variation characteristics of each grid through a training set. Results showed that (1) from 1990 to 2020, cropland, tree cover, water bodies, and urban areas in the Manasi region increased by 855.3465 km2, 271.7136 km2, 40.0104 km2, and 109.2483 km2, respectively, whereas grassland and bare land decreased by 677.7243 km2 and 598.5945 km2, respectively; (2) Kappa coefficients reflect the accuracy of the mode’s predictions in terms of quantity. The Kappa coefficients of the land use data predicted by the MLP-LSTM, MLP-ANN, LR, and CA-Markov models were calculated to be 95.58%, 93.36%, 89.48%, and 85.35%, respectively. It can be found that the MLP-LSTM and MLP-ANN models obtain higher accuracy in most levels, while the CA–Markov model has the lowest accuracy. (3) The landscape indices can reflect the spatial configuration characteristics of landscape (land use types), and evaluating the prediction results of land use models using landscape indices can reflect the prediction accuracy of the models in terms of spatial features. The results indicate that the model predicted by MLP-LSTM model conforms to the development trend of land use from 1990 to 2020 in terms of spatial features. This gives a basis for the study of the Manasi region to formulate relevant land use development and rationally allocate land resources.