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With the rapid development of China's economy, the scale of the city has been continuously expanding, industrial enterprises have been increasing, the discharge of multiple pollutants has reached the top of the world., and the environmental problems become more and more serious. The air pollution problem is particularly prominent. Air quality has become a daily concern for people. In order to control air pollution, it is necessary to grasp the air quality situation in an all-round way. It is necessary to evaluate air quality. Accurate results of air quality evaluation can help people know more about air quality. In this paper, refers to previous research results and different evaluation methods, combined with artificial neural network, fuzzy theory, genetic algorithm, GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality. At the same time, for the problem that the two-level standard of air quality annual evaluation is not suitable for practical application, the four-level standard for annual air quality evaluation has been proposed, and its practicality has been verified through experiments. By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm, it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model, both in efficiency and accuracy.

In this paper, we first provide some properties of truth tables of bent functions. Furthermore, a upper bound of truth table’s runs length of a bent function is presented. Based on these results, we propose a new algorithm for enumerating bent functions. At last, we find that our algorithm requires lower storage complexity and is easier to implement with parallel/distributed computing infrastructures by comparing with some known searching algorithms.

With the gradual maturity of the Internet of Things technology, production intelligence and information has become more common. The level of automation in irrigation systems represents a major factor in the efficiency of agricultural development. This article has studied the intelligent automatic irrigation system of balcony flowers based on Internet of Things. MSP430F5438A single-chip microcomputer is used as the core microcontroller to collect soil moisture information through soil moisture sensors. The humidity information is uploaded to the server by the wireless communication module. The server responds to the client’s instructions and the real-time soil temperature and humidity to water the balcony flowers. The GPRS module sends the control command to the lower position machine, and the microcontroller transfers the instructions through the wireless transmission module. And to control the operation of the switch of solenoid valve. The entire irrigation system is based on the MSP430 single-chip microcomputer and has good features such as low power consumption and high stability.

Aiming at the problem of strong decentralization, poor compatibility and low sharing in the traceability system of domestic agricultural products, combined with the industrial chain of agricultural products, three general traceability models of agricultural products were proposed and established. Through the research of the model, it is concluded that the establishment of the traceability information flow model of agricultural products ensures seamless connection of all links in the agricultural product chain, and facilitates the traceability of agricultural products smoothly. The information sharing model of the traceability information of agricultural products improves the sharing of traceability information and is beneficial to the establishment of a comprehensive traceability system. The traceability function model of agricultural products provides a more general model for the traceability system. The validity of the model was proved by applying the model to the traceability system of winter jujube, providing a theoretical basis and a feasible solution for the construction of agricultural products.

In recent years, garlic prices have fluctuate drastically, and garlic price prediction has always been the focus of attention. In order to study the price fluctuations of garlic and predict garlic prices, the most commonly used time series forecasting method autoregressive integrated moving average (ARIMA) model is used in this article to predict garlic prices. Combining the monthly average price data for 2010–2017 in Shandong, China, which is representative of the world. Using the powerful data analysis function of R language, forecast the monthly average price of garlic in the first half of 2018 in Shandong province. The results of experiment show that the ARIMA model has good effect in predicting the short-term garlic price fluctuation, and the garlic price fluctuation trend in the first half of 2018 is to rise first and then fall. Finally, according to several major factors which affecting the price fluctuation of garlic, some suggestions such as the establishment of garlic growth model, the improvement of forecast methods, and the strengthening of market supervision are proposed.

Informationization is a propellant for promoting the development of traditional industries. The comprehensive application of informatization technology also plays an important role in improving the macroeconomic decision-making level of agriculture and improving scientific management capabilities. The growth status of wheat in each growth period is closely related to the yield, and the wheat growth process is complicated and changeable. It is very important to make full use of information technology to monitor, manage and analyze wheat field environment and wheat growth information, which is of great significance for improving the scientific management level of wheat production and increasing grain yield. On the basis of fully analyzing the demand of Bohai granary technology demonstration project and the key influencing factors of wheat production, the wheat production information management system was developed by using Internet of things technology, data analysis technology and data visualization technology. It realized the collection and storage of wheat field environmental information, field management information and wheat growth information, and provided information service and technical support for scientific planting and management of Wheat by analyzing and processing related data.

To analyse the long-term effects and disturbances of other meteorological factors on precipitation, the historical information of the Ewenki Banner from 1991 to 2010 for a total of 20 years is taken as an example to construct the dynamic relationship between precipitation and temperature, humidity, wind speed and sunshine hours. The results show that each variable has a first-order single integer sequence at the 1% level of significance and has a stable long-term equilibrium relationship at the 5% level of significance. The vector autoregressive model (VAR) also passes the stability test to satisfy the preconditions for running. According to the analysis of impulse response and variance decomposition, the influence of humidity is greatest except for 43.29% of precipitation subject to its influence. The contribution of humidity reaches 43.85%, exceeding the impact of precipitation itself. The impact of sunshine hours on precipitation has a certain lag period and the fluctuations in the later period are relatively large. The impact of wind speed and temperature on the precipitation is not obvious. The model better simulates the impact of various influencing factors on precipitation and proves its feasibility in the analysis of the influencing factors of desert precipitation, which has certain practical significance.

In response to the increasing food safety problems of the meat product in the industry chain of livestock and poultry, and the actual demand of enterprises and consumers, combining the development status of the enterprises, considering the safety factors of animal breeding and product processing, this paper designed a food safety traceability system, based on modern internet of things technologies such as RFID, QR code, sensor technology and wireless communication technology, of which the traceability information covers all links of the whole industry chain. Compared with other traceability systems, the system in this paper has improved greatly in terms of its breadth, depth, and accuracy, which is an effective way to solve the food safety problem. The system provides the customers, enterprises, and the government department with multi-channel information traceability service, which has the real significance of the reduction food safety risk and improvement of recall efficiency of defective products.

The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

To analyse the correlations among the five factors of temperature, humidity, precipitation, sunshine and wind speed in the desert, the meteorological data of Hangjin Banner for a total of 52 years from 1959 to 2010 are used as the experimental data. Through correlation analysis and regression analysis, a regression equation is established between any factor as the dependent variable and the other four factors as independent variables. The test results show that each coefficient in the equation passes the 95% significance test. Among them, the regression equation has the best fitting degree when humidity and temperature are used as dependent variables, which are 0.520 and 0.514, respectively. Using the data from 2011–2016 of Hangjin Banner to test the regression equations of humidity and temperature, it is found that the model has better prediction ability. Therefore, it is feasible to apply the regression equation to the analysis and prediction of desert meteorological data.

Agroclimatological resources provide material and energy for agricultural production. This study is aimed to analyze the impact of selected climate factors change on wheat yield over the different growth period applied quantitatively method, by comparing two different time division modules of wheat growth cycle- monthly empirical-statistical multiple regression models ( From October to June of next year ) and growth stage empirical-statistical multiple regression models (Including sowing stage, seedling stage, tillering stage, overwintering period, regreening period, jointing stage, heading stage, maturity stage) analysis of relationship between agrometeorological data and growth stage records and winter wheat production in Yanzhou, Shandong Province of China. Correlation analysis(CA)was done for 35 years (from 1981 to 2015) between crop yield and corresponding weather parameters including daily mean temperature, sunshine duration, and average daily precipitation selected from 18 different meteorological factors. The results shows that the greatest impact on the winter wheat yield is the precipitation overwintering period in this area, each 1mm increase in daily mean rainfall was associated with 201.64 kg/hm2 lowered output. Moreover, the temperature and sunshine duration in heading period and maturity stage also exert significant influence on the output, every 1°C increase in daily mean temperature was associated with 199.85kg/hm2 adding output, every 1h increase in mean sunshine duration was associated with 130.68kg/hm2 reduced output. Comparing with the results of experiment which using months as step sizes and using farming as step sizes was in better agreement with the fluctuation in meteorological yield, offered a better explanation on the growth mechanism of wheat. Eventually the results indicated that 3 factors affects the yield during different growing periods of wheat in different extent and provided more specific reference to guide the agricultural production management in this area.