Aiming at the problems of low efficiency and high resource consumption in the existing full-text search schemes of Relational DataBase （RDB）， a lightweight full-text search model for relational databases with enhanced secondary cache was proposed. Firstly， an inverted index based on Redis was built in the proposed model and cache index was used to reduce the search scope， which solved the I/O bottleneck of relational database with efficient data processing capacity in memory， and the overall performance of the system was improved. Secondly， in order to ensure the accuracy and real time performance of the search results， the index synchronization strategy was further proposed， and the incremental index component was designed and implemented to hide the index processing details， so as to improve the usability and universality of the model. Finally， an index update mechanism based on access heat was provided for hotspot data to reduce memory usage of the inverted index. Experimental results show that on the premise of ensuring the response speed and accuracy of full-text search in relational databases， the space resource consumption of the proposed model is 48.8% - 60.9% lower than that of MySQL full-text index and 85.2% - 96.2% lower than that of Elasticsearch， verifying that the proposed model is feasible and effective in practical applications.

In order to meet the requirements of safe storage and effective distribution of content encryption key in digital rights protection， a new digital rights protection scheme based on secret sharing in blockchain environment was proposed， including 4 protocols： system initialization， content encryption， license authorization and content decryption. The Pedersen’s verifiable secret sharing scheme and Attribute-Based Encryption （ABE） algorithm were used to protect and distribute the content encryption key. The content providers were freed from the task of managing content encryption keys， which ensured the security and flexibility of key management. In addition， the digital rights protection scheme based on blockchain has the characteristics of information openness and transparency， and is tamper-resistant. Security analysis show that the proposed scheme is safe and feasible in the blockchain environment； simulation results show that the proposed scheme can achieve the rights protection of digital content with low cost.

Concerning the lack of tagged data resources in machine translation tasks of low-resource languages， a new neural machine translation corpus expansion method based on language similarity mining was proposed. Firstly， Uyghur and Kazakh were considered as similar language pairs and their corpora were mixed. Then， Byte Pair Encoding （BPE）， syllable segmentation and BPE based on syllable segmentation were carried out on the mixed corpus respectively to explore the similarity between Kazakh and Uyghur deeply. Finally， the “Begin-Middle-End （BME）” sequence tagging method was introduced to tag the segmented syllables in the corpus in order to eliminate some ambiguities caused by syllable input. Experimental results on CWMT2015 Uyghur-Chinese parallel corpus and Kazakh-Chinese parallel corpus show that， compared with the ordinary models without special corpus processing and trained by BPE corpus processing training， the proposed method increases the Bilingual Evaluation Understudy （BLEU） by 9.66， 4.55 respectively for the Uyghur-Chinese translation and by 9.44， 4.36 respectively for the Kazakh-Chinese translation. The proposed scheme achieves cross-language neural machine translation from Uyghur and Kazakh to Chinese， improves the translation quality of Uyghur-Chinese and Kazakh-Chinese machine translation， and can be applied to corpus processing of Uyghur and Kazakh.

Maximizing customer satisfaction is directly related to the enterprise profit and market competitiveness for the supermarket as a service enterprise, so it is important to optimize the retail checkout operation. Firstly, the retail checkout scheduling problem was described by a triplet of α/β/γ, maximizing customer satisfaction was taken as the first goal and minimizing operating cost was taken as the second goal with machine usage restriction and the rule of First In First Out (FIFO). The corresponding mathematical model was established, and then an algorithm was designed using plant growth simulation algorithm. 〖BP(〗Finally, the actual data was used to simulate, and the results prove that the study has effectiveness and feasibility. 〖BP)〗Finally, a numerical simulation of actual cases was used to verify the effectiveness and feasibility of the method.

An improved Differential Evolution (DE) algorithm was proposed to solve the problem of premature convergence and improve the computational efficiency of DE. Firstly, different strategies with different parameter values were adopted to enrich the population diversity. Secondly, a new evaluation index was established to determine the suitable combination to match different phases of the search process. Finally, the evolution process was divided into many subprocesses to eliminate the negative effect of the previously selected combination. The contrast experimental results on ten classical Benchmark functions show that the proposed algorithm has a relatively better performance.