To solve the competitive facility location problem of new energy vehicle battery recycling outlets considering queuing theory， an Improved Human Learning Optimization （IHLO） algorithm was proposed. First， the competitive facility location model of new energy vehicle battery recycling outlets was constructed， which included queuing time constraints， capacity constraints， threshold constraints and other constraints. Then， considering that this problem belongs to NP-hard problem， in view of the shortcomings of Human Learning Optimization （HLO） algorithm， such as low convergence speed，optimization accuracy and solving stability in the early stage， IHLO algorithm was proposed by adopting elite population reverse learning strategy， group mutual learning operator and adaptive strategy of harmonic parameter. Finally， taking Shanghai and the Yangtze River Delta as examples for numerical experiments， IHLO was compared with Improved Binary Grey Wolf Optimization （IBGWO） algorithm， Improved Binary Particle Swarm Optimization （IBPSO） algorithm， HLO and Human Learning Optimization based on Learning Psychology （LPHLO） algorithm. For large， medium and small scales， the experimental results show that IHLO algorithm has the best performance in 14 of the 15 indicators； compared with IBGWO algorithm， the solution accuracy of IHLO algorithm is improved by at least 0.13%， the solution stability is improved by at least 10.05%， and the solution speed is improved by at least 17.48%. The results show that the proposed algorithm has high computational accuracy and fast optimization speed， which can effectively solve the competitive facility location problem.

For the problems of existing Attribute-Based Encryption （ABE） schemes， such as low efficiency of attribute revocation and difficulty in coordinating the distribution and revocation of user attribute keys， a Blockchain-based Decentralized Attribute-Based Encryption for Revocable attributes （BRDABE） scheme was proposed. Firstly， the consensus-driven blockchain architecture was used to map the trust issue of key distribution from the attribute authority to the distributed ledger， and smart contracts were used to record the status of user attributes and data sharing and assist the attribute authority to realize the user attribute revocation. When revoking a user’s attribute， the smart contracts were used by the attribute authority to automatically screen out the involved data owners and non-revoked authorized users and computed the ciphertext update key and key update key related to the revoked attribute， and the off-chain ciphertext and key update was realized. Then， the version key and the user’s global identity were embedded in the attribute private key， so that the identities in the session key ciphertext and the user’s attribute private key were able to cancel each other out when the user decrypted. Based on reasonable assumptions， BRDABE scheme was proved to resist the collusion attack of users and satisfy the forward and backward security of user attribute revocation. Experimental results show that with the increase of the number of user attributes， the time of user key generation， encryption and decryption and attribute revocation increase linearly. In the case of the same number of attributes， compared with DABE （Decentralizing Attribute-Based Encryption） scheme BRDABE scheme has the decryption time reduced by 94.06% to 94.75%， and compared with EDAC-MCSS （Effective Data Access Control for Multiauthority Cloud Storage Systems） scheme， BRDABE scheme has the attribute revocation time reduced by 92.19% to 92.27%. Therefore， BRDABE scheme not only improves the efficiency of attribute revocation， but also guarantees the forward and backward security of shared data.

Aiming at the limited scalability of medical data sharing based on traditional blockchains， a scale-out and sharing scheme of blockchain based on sharding technology was proposed. Firstly， the periodic network sharding was performed based on the jump consistent hash algorithm， and the risk of Sybil attacks in a single shard was greatly reduced by randomly dividing the network nodes. Then， the Scalable decentralized Trust inFrastructure for Blockchains （SBFT） consensus protocol was used in the shards to reduce the high communication complexity of the Pratic Byzantic Fault Torent （PBFT） consensus protocol， and the two-layer architecture was used between the physical multi-chain of shards and the logical single chain of the main chain to reduce the storage pressure of the members of shards. Finally， a multi-keyword association retrieval searchable encryption sharing scheme based on Public key Encryption with Conjunctive field Keyword Search （PECKS） was proposed on the medical consortium blockchain， so as to improve the patients’ control over their sensitive data， and realize the fine-grained search of sensitive data under encryption. Through performance analysis， it can be seen that under the parallel sharding structure， the throughput of blockchain is significantly increased with the increase of shards， and the retrieval efficiency is also significantly improved. Experimental results show that the proposed scheme can greatly improve the efficiency and scalability of the blockchain system.

The traditional sparse adaptive filtering has the problems of poor steady-state performance and even unable to converge in impulse noise interface environment. In order to solve the problems and improve the accuracy of sparse parameter identification without increasing too much computational cost， a sparse adaptive filtering algorithm based on Generalized Maximum Versoria Criterion （GMVC） was proposed， namely the GMVC with CIM constraints （CIMGMVC）. Firstly， the generalized Versoria function was employed as the learning criterion， which contained the reciprocal form of the error p-order moment. And thus the purpose of suppressing impulse noise was able to be achieved because the GMVC would approach to 0 when the error caused by the impulse interference was very large. Then， a novel cost function was constructed by combining the Correntropy Induced Metric （CIM） used as the sparse penalty constraint and the GMVC， where the CIM was based on the Gaussian probability density function， and it was able to be infinitely close to l_{\mathrm{0}} -norm when the appropriate kernel width was selected. Finally， the CIMGMVC algorithm was derived by using the gradient method， and the mean square convergence of the proposed algorithm was analyzed. The simulation was performed on Matlab platform， and the \alpha -stable distribution model was used to generate impulse noise. Experimental results show that， the proposed CIMGMVC algorithm can effectively suppress the interference of non-Gaussian impulse noise， it has the better robustness than the traditional sparse adaptive filtering， and has the steady-state error lower than the GMVC algorithm.

A new approach using spatial indexing mechanism called z-values was proposed in order to improve the efficiency of Web cartographic generalization and represent spatial data with different scales. The method can effectively filter unrelated spatial data and derive spatial data with proper level of details. So this approach can effectively improve the efficiency of spatial data transmission across the Internet. The application server pushes vector descriptions to an SVG client for displaying. An SVG file can be edited effectively in simple text editors and can be generated easily from server-side tools. In addition to faster download speeds, the SVG’s interoperability is also demonstrated. The experiment shows our approach is more efficient than the traditional one.