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In order to solve the problem of our country in the construction of agricultural information, such as a single mode of information services, guidance and applicability of the information is not enough and so on, research and design agricultural information service platform based on cross-platform Technology. The mobile terminal as the carrying platform and take advantage of its portability, the popularity and relatively low price, convenient on-site processing ability. The platform integrating agricultural information, agricultural policy, agricultural knowledge, agricultural pre-warning, characteristic agriculture, rich experience, experts inquiry in one, zero distance contact farmers and agricultural information, it has high practical value and broad application prospects.

Realization of big Deep Web database optimized access with low sample deviation was researched. With the sharp increasing of the storage of Deep Web database geometrically, the problem of optimized access to the database was becoming difficult. On the basis of correlation feature rule extraction, an improved optimized access method for the Deep Web database was proposed based on Graph Model Sample process and correlation rule. The access was realized by computer simulation. The access process was started with the arbitrary effective result as the access starting line. The access result was returned, and the records were obtained in the returned web page. The next query and access was implemented with the local sample database for the nest access start line. The method avoided the effects of the query interface properties. And the Graph Model Sample method could overcome the limitation of the query interface properties. Also the correlation feature extraction algorithm was researched, and it was used as the access rule in the database for the realization of optimized and efficient query. The simulation result shows that the sample deviation is stable with the value about 10% for the simulated Deep Web database. The relevant sample deviation value is trended to convergence and is near to the real value with the increasing of the access number. According to the realistic Web database, the sample bias estimation mean value is about 21% which is higher than the simulated database. Two typical Deep Web databases are taken as the researching objects, with the new method, and the access bias is lower than another Web database which without using the proposed method, result shows nice performance of the proposed optimized database access method in application.

The precise classification of massive information in big database was researched in this paper, and also the redundant information as the interference should be filtered in the subject. According to the traditional data classification method, the frequency points were concentrated and the data classification frequent points were not easy to be eliminated. The nodes classification technology with low self-adaptive property refused the nodes in high disturbance and in the deep attenuation parts, and then the classification precision and the immunity of the disturbance property were limited greatly. A new optimum data classification method and the redundant information model were proposed based on the chaotic probability analysis. The classification error rates was mapped as a probability density function based on the channel mapping function method, the classification probability was allocated with this probability density function. The random series which could reflect the essential feature was produced based on the chaotic probability analysis method which could meet to the demands of the random frequency classification. And the data clustering and optimization classification was realized finally. Simulation was taken with the KDD_CUP2009 experimental big database, and simulation result shows that the proposed method can classify each type of the data effectively. The performance of the data classification is perfect, comparing to the traditional neural net fuzzy c-means method, the classification precision rate was improved by 17.8% It show that the model and algorithm has excellent classification performance and can be taken in the application such as data mining, fault diagnosis and target recognition as engineering practice.

The CR/OMMT nanocomposites were prepared by mechanical blending, and the microstructure and properties of nanocomposites were studied. The half-exfoliated organoclay nanocomposites with uniform dispersion were confirmed by transmission electron microscopy (TEM) and X-ray diffraction (XRD), and the cure characteristic, physics mechanical and anti-aging properties of CR/OMMT nanocomposites due to the percent of OMMT were discussed. The results showed that the tear strength and the tensile strength of the nanocomposites with 5 phr OMMT were improved by 49% and 53.3%, respectively. The anti-aging properties of nanocomposites were improved greatly. OMMT could participate in the vulcanization reaction, reduced the cure time (T90) of nanocomposites, and raised the production efficiency.