Aiming at the possible fraud problem of maintenance service picture information in industry chain collaboration Software as a Service (SaaS) platform's after sales maintenance service, a scheme for collecting and dealing with the after-sales service information by mobile intelligent terminal equipment based on Android platform was proposed. Firstly, the proposed solution collected maintenance service information through processing of digital image of mobile intelligent terminal. Secondly, the proposed solution utilized the recognition technology of images and characters for key information contained in maintenance service information such as chassis number and odometer. Thirdly, the proposed solution embedded the above key information in the collected images via digital watermarking technology. Lastly, mobile intelligent terminal and the after-sales service system were integrated through Web service. The feasibility and effectiveness about the scheme of after-sales service information acquisition based on the Android platform are verified by specific use of after-sales service image information acquisition to prevent fraud in industry chain collaboration SaaS platform's after sales maintenance service.

To improve forecast accuracy of water demand when using Projection Pursuit (PP) model which are high-dimensional, non-normality and nonlinear, an Ant Colony Algorithm (ACA) was used for the parameter optimization of the model. ACA was improved to self-adaptive control pheromone on the grids divided by definitional domains of the model parameters. A case for water demand prediction was emulated according to the improved ACA and PP model. Then prediction accuracy from the improved ACA was compared with the results from Artificial Immune Algorithm (AIA) and BP Artificial Neural Network (BPANN) model, respectively. It is shown that: 1) the absolute relative errors of fitting accuracy are less than 2% from ACA and less than 10% from AIA and BPANN; 2) the absolute relative errors of prediction accuracy are less than 6%, 11% and 12% from ACA, AIA and BPANN, respectively; 3) ACA can converge to global optimal solution with higher convergence rate. Therefore, the improved ACA for optimizing the parameters of PP water demand prediction model is significantly better than the AIA and BPANN. This method can be applied to other similar high-dimensional and nonlinear problems.

A mixed compression algorithm for error-diffusion image that could overcome the disadvantages of the conventional binary image loss coding techniques and combined with the inverse half toning method was proposed. Look-Up-Table (LUT) inverse half toning method was used to convert error-diffusion image back to the contone image, then an improved Discrete Cosine Transform (DCT) coding algorithm was constructed to get higher compression rate. The experimental results indicate that the proposed algorithm fits to the error-diffusion image, and the quality of the decoding image is well.