Most existing multi-view clustering algorithms suffer from issues such as incomplete fusion mechanisms， insufficient exploration of multi-view collaborative relationships， and weak robustness. These limitations result in low consistency in clustering results and unstable performance under noise and redundant information. To address these issues， a Multi-View Clustering algorithm based on Bipartite Graph and Consensus graph learning （BGC-MVC） was developed to enhance clustering consistency and complementarity by integrating information from multiple views. Specifically， BGC-MVC constructed a bipartite graph to capture neighborhood relationships across different views， and then learned a consensus graph to strengthen inter-view similarity. It integrated embeddings of the original multi-view data into a unified framework that combined graph learning with clustering process， thereby improving the overall clustering performance. Experimental results demonstrate that BGC-MVC achieves significant improvements in accuracy， F-score， Normalized Mutual Information （NMI） and purity under convergence conditions. Notably， on the MSRC_v1 dataset， BGC-MVC outperforms Large-scale Multi-View Subspace Clustering （LMVSC） by increasing the F-score by 19.48 percentage points and exhibits enhanced robustness and accuracy.

Concerning the challenges of complicated credential verification processes， limited credential sharing， and users' repeatedly applying for credentials during cross-department collaborative identity authentication in the digital transformation of government services， a blockchain-based cross-department collaborative identity authentication scheme for e-government was proposed. Firstly， a Verifiable Credential （VC） and its Proof of existence （VC Proof） mechanism were designed to store credential hash values and proof information on the blockchain to enable efficient multi-departmental credential verification. Secondly， an authorized credential mechanism was constructed to facilitate credential interactions between verifiers and relevant departments， thereby reducing the burden on users to repeatedly apply for credentials. Meanwhile， a smart contract-based non-interactive zero-knowledge proof technique was introduced to complete identity authentication while preserving the privacy of VCs. Experimental results show that the proposed scheme has low verification gas consumption， stabilizing at around 500 gas， while the gas consumption for contract deployment increases linearly with the scale of the contract. When the verification gas consumption is 140.55 Gwei， its throughput reaches the highest， about 7×10⁴ TPS （Transactions Per Second）， and when the verification gas consumption increases to 562.562 Gwei， the throughout drops to approximately 2×10⁴ TPS. In addition， compared to experimental results on Ethereum， the proposed scheme demonstrates better performance under the same concurrency conditions， with the average response time reduced by approximately 0.32 seconds.

为了克服传统密度估计方法受限于算法配置工作量高、高等级密度样本数量有限等因素无法大规模应用的缺点,提出一种基于监控视频的全景密度估计方法。首先,通过自动构建场景的权重图消除成像过程中射影畸变造成的影响,该过程针对不同的场景自动鲁棒地学习出对应的权值图,从而有效降低算法配置工作量;其次,利用仿真模拟方法通过低密度等级样本构建大量高密度等级样本;最后,提取训练样本的面积、周长等特征用于训练支持向量回归机(SVR)来预测每个场景的密度等级。在测试过程中,还通过二维图像与全景地理信息系统(GIS)地图的映射,实时展示全景密度分布情况。在北京北站广场地区的深度应用结果表明,所提全景密度估计方法可以准确、快速、有效地估计复杂场景中人群密度动态变化。

In order to solve the semantic conflict of emergency cases understanding in Case-Based Reasoning (CBR), an emergency case ontology model based on CBR was defined on the basis of the extended ABC ontology model according to the analysis of emergency cases, and the key elements of the model were described in detail, such as concepts, relations, axioms and instances. Finally, the model was validated by the analysis on the case of the nuclear crises of the first nuclear power station in Fukushima.

Multiprocessor scheduling algorithm is the key in the embedded real-time systems. According to the multiprocessor features, a new dynamic parallel scheduling algorithm of real-time multiprocessor, named Split-Parallel (SPara), was proposed. The algorithm solved the problem that the previous algorithms, such as Myopic, EDPF, only judge by the deadline to schedule the tasks, and it was also developed by adding the restriction of the urgency and an effective method as the task with long execution time and tight deadline. Furthermore, the multiprocessor scheduling algorithm which combined the theory of high-level coloured time Petri net was analyzed by modeling, and according to the model, an example of SPara algothrim was simulated and tested. The experimental results show that SPara performances are much better than the other algorithms like Myopic in processor utilization and scheduling success ratio.