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Science research has general rules of development, is like any other social activity. With the improvement of science and technology, scientific problems have become more complex and systematic, individual approach has been replaced by teamwork in scientific research. This paper takes scientific research team cooperative network as research object, analyzes the influence of scientific research teams in the cooperative network from the aspect of node heterogeneity and node similarity of content and structure, and puts forward the influence evaluation method of scientific research team. A scientific research team cooperation network is constructed as the unweighted and undirected graph by the cooperation relationship data of scientific research teams, including co-author, citation, project cooperation and son on. In this network, the scientific research teams are take nodes, and the cooperative relationships between scientific research teams are take as edges. The major factors of scientific research team influence are analyzed, including node heterogeneity and relationship strength between nodes, then a weight and attributed graph is constructed by the research direction of scientific research team and is weighted based on the similarity of nodes’ content and structure by the SimRank model and the Jaccard similarity method. An influence evaluation method was proposed based on the impact of node subjective heterogeneity and node domain heterogeneity, and An influence spread model based on SIR model was given for verifying the proposed influence evaluation method.

Currently, large data sets are deployed on large-scale clusters, which require a large amount of physical resources. However, current network architecture does not have flexible deployment, making it difficult to adjust physical resources after deployment. Based on software definition network, this article proposes a framework for building virtual data domain, which establishes a multi-attribute decision model by network nodes for optimizing the deployment of control layer, so as to realize large-scale deployment. By analyzing the actual usage and virtual distribution of the underlying data resources, a mapping algorithm of network resource overhead–allocation ratio is proposed for adjusting the mapping space of network flow based on the mapping result, so as to meet more virtual domain applications. At the same time, the reasonable utilization of resources is helpful to reduce the communication delay in domain. Simulation results show that compared with the shortest path mapping and greedy resource mapping algorithms, the virtual domain established by the network resource overhead–allocation algorithm can improve the resource utilization rate by 10% and reduce the intra-domain communication delay by 30%. Therefore, under the background of the expanding scale of data domain, this framework can solve the problem that the current backward network architecture cannot adapt to the development trend of the information field.

The traditional soil moisture sensors need to be recalibrated for different soil types. It is not conducive to large-scale applications because of the complex calibration algorithm and expensive equipment. In this paper, a capacitive sensor was designed based on the difference of dielectric constant caused by the variation of water content in soil. The effect of structural parameters of sensor on its performance was analyzed by finite element method. The optimized sensor was evaluated in the air-drying experiment of five soil samples for four cycles. According to the relationship between capacitance-water model and sand content, a simple calibration-free detection model was proposed for multiple soil environments. The accuracy of the model was discussed by three repetitive experiments, and compared with the standard oven-drying method. The results show that MAME and RMSE of CWM-four model are 3.8% and 2.0%, respectively, which can basically meet the accuracy requirement for the field application.

The detection of soil moisture is one of the most important researches in the development of precision agriculture. At present, the detection of soil dielectric constant is the most effective and convenient method to measure soil moisture content. Most of the dielectric type soil moisture sensors reduce the influence of soil salinity, texture and other factors on the measurement by detecting at high frequency. However, this will increase the production cost of the measurement circuit and fail to meet the requirements for large-scale deployment of sensors in agricultural production. The method of low-frequency capacitance detection has a price advantage but a low measurement accuracy, thus the application is very limited. This paper intends to improve the measurement accuracy and application range of low-frequency capacitance moisture sensors by correcting the relationship between low-frequency capacitance and moisture through the detection of soil conductivity. Three modified models (logistic, exponential and polynomial) to estimate the moisture content was verified by comparing with the oven-drying method calibration value. The results indicated that the MAME and MAE of the logistic model was less than 3.55% and 2.50%, respectively, which can satisfy most agricultural production requirements for soil moisture detection. Therefore, the study provided an effective detection method for low-cost soil moisture detection in low salinity and low organic matter soils.