Focused on the current lack of fair and reasonable incentive mechanism for federated learning， and the difficulty in measuring the contribution to federated learning by participant nodes with different data volumes， different data qualities， and different data distributions， a new incentive mechanism for federated learning based on Generative Adversarial Network （GAN） was proposed. Firstly， a GAN with Trained model （GANT） was proposed to achieve high-precision sample generation. Then， the contribution evaluation algorithm of the incentive mechanism was implemented based on GANT. The algorithm filtered samples and generated data labels through the joint model， and introduced the local data labels of the participant nodes to balance the impact of non-independent identically distributed data labels on the contribution evaluation. Finally， a two-stage Stackelberg game was used to realize the federated learning incentive process. The security analysis results show that the proposed incentive mechanism ensures data security and system stability in the process of federated learning. The experimental results show that the proposed incentive mechanism is correct， and the contribution evaluation algorithm has good performance under different data volumes， different data qualities and different data distributions.

Focusing on coarse granularity of access control， low sharing flexibility and security risks such as data leakage of centralized medical data sharing platform， a blockchain-based hierarchical access control and sharing system of medical data was proposed. Firstly， medical data was classified according to sensitivity， and a Ciphertext-Policy Attribute-Based Hierarchical Encryption （CP-ABHE） algorithm was proposed to achieve access control of medical data with different sensitivity. In the algorithm， access control trees were merged and symmetric encryption methods were combinined to improve the performance of Ciphertext-Policy Attribute-Based Encryption （CP-ABE） algorithm， and the multi-authority center was used to solve the key escrow problem. Then， the medical data sharing mode based on permissioned blockchain was used to solve the centralized trust problem of centralized sharing platform. Security analysis shows that the proposed system ensures the security of data during the data sharing process， and can resist user collusion attacks and authority collusion attacks. Experimental results also show that the proposed CP-ABHE algorithm has lower computational cost than CP-ABE algorithm， the maximum average delay of the proposed system is 7.8 s， and the maximum throughput is 236 transactions per second， which meets the expected performance requirements.

The meteorological ground minute data has characteristics including various elements, large amounts of information and high frequency generation, therefore the traditional relational database system has some problems such as server overload and low read and write performance in data storage and management. With the research of storage model of distributed databases HBase, the database model of the meteorological ground minute data was proposed to achieve distributed storage of massive meteorological data and meta-information management, in which the row key was designed by the method of time plus station number. When processing the complex meteorological query case, the response time of unique index in HBase is too long. To address this defect and meet the requirements of retrieval time efficiency, with considering the query case, API interface offered by search engine solr was used to establish secondary index for related field. The experimental results show that this system has high efficiency of storage and index, the maximum storage efficiency can be up to 34000 records/s. When generic query cases return, the time consuming can be down to millisecond level. This method can satisfy the performance requirements of large-scale meteorological data in business applications.

A new variant of Boyer-Moore (BM) algorithm was proposed on the basis of analyzing BM algorithm. The basic idea of the improvement was to form match heuristic (i.e. good-suffix rule) for the expanded pattern Pa in preprocessing phase, where P was the pattern and a was an arbitrary character that belonged to the alphabet, so both to increase length of the matched suffix and to imply Sunday's occurrence heuristic (i.e. bad-character rule), therefore a larger shift distance of scanning window was obtained. The theoretical analyses show that the improvement has linear time complexity even in the worst case and sublinear behavior on the average case, and space complexity of O(m(σ+1)). The experimental results also show that implementation performance of the improved one is significantly improved, especially in the case of small alphabet.

For the research and improvement of Boyer-Moore (BM) algorithm and its variants, it is very necessary to establish strict formal theory of the function shift in Boyer-Moore's and its construction algorithm. A clear formal definition of shift was given. Then, characteristic sets of the pattern suffixes and its minimum value function were introduced, and the construction of shift was described by the characteristic sets, thus theoretical basis of shift and its construction algorithm were strictly established. Finally, a new construction algorithm of shift was presented based on the construction theorem of shift and iterative computing method of the minimum value function. It is proved that the algorithm has linear time and space complexity. The theoretical analysis and computing results show that the algorithm is simpler, and its complexity of computation is lower, so it is more suitable for hardware implementation compared with several existing algorithms.