In the greenhouse environment, the application of complex event processing (CEP) technology can effectively tackle the problem of recognition of the complex patterns that appeared in greenhouse conditions. In the existing research, few scholars have proposed a scheme to integrate complicated scenes within the greenhouse environment with high efficiency, convenience, and low coupling. Therefore, in order to solve the problem of hard recognition and fusion of complex patterns in the greenhouse environment, based on the characteristics of the greenhouse, this paper proposes a complex event processing method for greenhouse control. Our method has high applicability and high expansibility, including 13 types of event processing agents and 21 types of typical events involved in greenhouse automatic control. This method has the advantages of low information coupling and multi-domain integration, which can be directly used by agricultural experts and related workers and is of great significance to promote the extensive application of CEP technology in the greenhouse field. Our experiment successfully realized a greenhouse intelligent control system based on CEP technology is successfully realized in our experiment. The experimental statistics shows that the structure of the control system was accessible and effective.

Social influence analysis is a very popular research direction. This article analyzes the social network of musicians and the many influencing factors when musicians create music to rank the influence of musicians. In order to achieve the practical purpose of the model making accurate predictions in the broad music market, the algorithm adopts a macromodel and considers the social network topology network. The article adds the time decay function and the weight of genre influence to the traditional PageRank algorithm, and thus, the MRGT (Musician Ranking based on Genre and Time) algorithm appears. Considering the timeliness of social networks and the continuous development of music, we realized the importance of evolving MRGT into a dynamic social network. Therefore, we adopted audio data analysis technology and used Gaussian distance to classify and study the evolution of music properties at different times and different genres and finally formed the dynamic influence ranking algorithm based on musicians’ social and personal information networks. As a macromodel heuristic algorithm, our model is explanatory, can handle batch data and can avoid unfavorable factors, so as to provide fast speed and improved accuracy. The network can obtain an era indicator DMI (Dynamic Music Influence) that measures the degree of music revolution. DMI is the indicator we provide for music companies to invest in musicians.

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In the farmland Internet of Things, to achieve precise control of production, it is necessary to obtain more data support, which requires the deployment of many sensors, and this will inevitably bring about high investment and high-cost problems. This paper mainly studies the optimization of sensor placement in the agricultural field. Through compressed sensing and algorithm optimization, the number of sensors used is reduced and the cost is reduced on the premise of ensuring the effect. At present, there are many mature sensor layout optimization methods, but these methods will have incomplete parameters due to experimental conditions and environmental factors. They are more suitable for structural health monitoring and lack research in agricultural applications. Considering that the sensor layout optimization can be converted into the characteristics of image compression selection and the compression effect of the compressed sensing theory is better, therefore, this paper proposes a sensor layout optimization method based on compressed sensing. Due to the structural characteristics of the existing measurement matrix in the compressed sensing theory, the specific position distribution of the optimized sensor layout cannot be obtained directly. This paper improves the existing sparse random measurement matrix to determine the number of sensors required for a given region and the function of the specific location of each sensor. The experimental results show that soil moisture can be measured with a small error of 0.91 by using 1/3 of the original sensor number. The result of data reconstruction using 1/6 of the original sensor is average, and the average error is up to 1.68, which is suitable for the environment with small data fluctuation.