Considering that the microphone array is mostly used as a sound pickup device in the smart speaker,and there are distortion and complexity of the acoustic echo cancellation in the adaptive filtering technology on single channel,a fast echo cancellation algorithm for microphone array was proposed. First of all,the adaptive filtering technology was used to estimate the first channel echo,then estimate the relative echo transfer function between the arrays,and the echoes of other channels were obtained by multiplying the above two. Secondly,the estimated echo and the noise were regarded as the noise reference signals of the Generalized Sidelobe Canceller(GSC)beamforming lower branch,which were removed by the GSC beamforming algorithm. The simulation results show that the proposed algorithm has good echo cancellation and noise suppression performance under moderate reverberation,long distance,low echo to noise ratio and using music as echo environment. And the algorithm not only has small computational complexity,but also makes target speech signals have high signal distortion ratio and intelligibility.

In view of the optimization of high-speed train connection in passenger transport hub under the condition of high-speed railway network, the concept of transfer satisfaction of medium and long distance passenger flow was proposed by analyzing the passenger transfer process in hub, and a high-speed train connection optimization model for large passenger transport hub based on transfer orientation was proposed with the average transfer satisfaction and the arrival and departure equilibrium of trains at hub stations as the optimization objective and with the constraint conditions of reasonable originating time of large stations, reasonable terminating time, station operation interval time, passenger transfer time and station arrival and departure line capacity. A genetic algorithm with improved chromosome coding mode and selection strategy was designed to solve the example. Compared with the basic genetic algorithm and the basic simulated annealing algorithm, the improved genetic algorithm increases the average transfer satisfaction in the objective function by 5.10% and 2.93% respectively, and raises the equilibrium of arrival and departure of trains at hub stations by 0.27% and 2.31% respectively. The results of the example verify the effectiveness and stability of the improved genetic algorithm, which indicates that the proposed method can effectively optimize the quality of the high-speed train connection in large passenger transport hub.

In order to improve the quality and efficiency of railway crew routing plan, the problem of crew routing plan was abstracted as a Multi-Traveling Salesman Problem (MTSP) with single base and balanced travel distance, and a equilibrium factor was introduced to establish a mathematical model aiming at less crew routing time and balanced tasks between sub-crew routings. A dual-strategy ant colony optimization algorithm was proposed for this model. Firstly, a solution space satisfying the space-time constraints was constructed and pheromone concentration was set for the node of the crew section and the continuation path respectively, then the transitional probability of the dual-strategy state was adopted to make the ant traverse all of the crew segments, and finally the sub-crew routings that meet the crew constraint rules were found. The designed model and algorithm were tested by the data of the intercity railway from Guangzhou to Shenzhen. The comparison with the experimental results of genetic algorithm shows that under the same model conditions, the number of crew routing in the crew routing plan generated by double-strategy ant colony optimization algorithm is reduced by about 21.74%, the total length of crew routing is decreased by about 5.76%, and the routing overload rate is 0. Using the designed model and algorithm to generate the crew routing plan can reduce the crew routing time of crew plan, balance the workload and avoid overload routing.

In order to improve the quality and efficiency of railway crew rostering plan arrangement, the problem of crew rostering plan arrangement was abstracted as a Multi-Traveling Salesman Problem (MTSP) with single base and considering mid-way rest, a single-circulation crew rostering plan mathematical model aiming at the smallest rostering period and the most balanced distributed redundant connection time between crew routings was established, and a new amended heuristic ant colony optimization algorithm was proposed aiming at the model. Firstly, a solution space satisfying the spatial-temporal constraints was constructed and the pheromone concentration was set for the crew routing nodes and the continued paths respectively. Then, the amended heuristic information was adopted to make the ants start at the crew routing order and go through all the crew routings. Finally, the optimal crew rostering plan was selected from the different crew rostering schemes. The proposed model and algorithm were tested on the data of the intercity railway from Guangzhou to Shenzhen. The comparison results with the plan arranged by particle swarm optimization show that under the same model conditions, the crew rostering plan arranged by amended heuristic ant colony optimization algorithm has the average monthly man-hour reduced by 8.5%, the rostering period decreased by 9.4%, and the crew overwork rate of 0. The designed model and algorithm can compress the crew rostering cycle, reduce the crew cost, balance the workload, and avoid the overwork of crew.

In order to improve the robustness of Non-negative Matrix Factorization (NMF) algorithm for speech enhancement in different background noises, a speech enhancement algorithm based on Sparsity-regularized Robust NMF (SRNMF) was proposed, which takes into account the noise effect of data processing, and makes sparse constraints on the coefficient matrix to get better speech characteristics of the decomposed data. First, the prior dictionary of the amplitude spectrum of speech and noise were learned and the joint dictionary matrix of speech and noise were constructed. Then, the SRNMF algorithm was used to update the coefficient matrix of the amplitude spectrum with noise in the joint dictionary matrix. Finally, the original pure speech was reconstructed, and enhanced. The speech enhancement performance of the SRNMF algorithm in different environmental noise was analyzed through simulation experiments. Experimental results show that the proposed algorithm can effectively weaken the influence of noise changes on performance under non-stationary environments and low Signal-to-Noise Ratio (SNR) (<0 dB), it not only has about 1-1.5 magnitudes improvement in Source-to-Distortion Ratio (SDR) scores, but also is faster than other algorithms, which makes the NMF-based speech enhancement algorithm more practical.