Wireless sensor networks (WSN), which consist of randomly deployed tiny sensors, data processing unit, and communicating components, have a wide range of applications, such as industry, agriculture, business, military, and health. Because power supply might be impossible, it is the key problem to find an efficient energy strategy for prolonging network's lifetime. This article made a detailed introduction for energy management of WSN, analyzing energy strategies used for WSN.

Solenoid valves are important control components in industrial production and are used in various aerospace vehicles. In view of the characteristics and requirements of the solenoid valve polarity test of the launch vehicle power system, combined with some current research on magnetic flux leakage detection, and considering the difficulty of operation, a design scheme of a contact-less solenoid valve polarity test system based on Wireless Sensor Networks (WSN) is proposed. The solenoid valve of the launch vehicle was tested and verified. The system organically combines wireless sensor network technology, leakage magnetic field measurement technology, and rapid monitoring and interpretation technology. It has the characteristics of miniaturization, portability and cable-free. The software interface of the test instrument is more concise and beautiful, and it is accurate to the solenoid valve polarity system. Degree, portability, test coverage and user experience have all been significantly improved, which has important reference significance for the application of subsequent wireless test technology in other space vehicle tests.

With the extensive use of converters in high-power ventilator, shearer, road header, conveyor, hoist, and other coal mine equipment, the electromagnetic disturbance problem of the converter is worthy of attention. In this paper, the field measurement and statistical method are used to analyze the radiation interference characteristics of frequency converter. Then, a typical fan converter is taken as an example, and the electromagnetic disturbance of the converter is tested in four key positions and two working frequencies, respectively. Multiple sets of data and spectrum are obtained by using a spectrum analyzer and other instruments, and the dominant frequency characteristic parameters of the converter are analyzed emphatically. The small sample data adopts the ShapiroWilk test, and the 80/80 rule was used for statistical analysis. Finally, we got five common frequencies of electromagnetic interference generated by the converter (electric field dBμV/m). The CH₄ sensor and other sensors work near these five dominant frequencies, which may affect the normal operation of the sensor and cause alarm. The test and analysis method proposed in this paper can be used to obtain the characteristic parameters of the converter electromagnetic disturbance, which can be used as a reference for the design of the immunity of sensors or control instruments.

Sales forecast is the basis and premise of supply chain management. It affects enterprise inventory level and supply chain operational efficiency. With the rapid development of online shopping, B2C e-commerce enterprises have an increasingly strong demand for accurate and efficient sales forecasting. Influenced by various promotional activities, B2C e-commerce sales in China are characterized by big fluctuations, fast pace and difficult prediction. The problem of sales forecast considering the influence of promotion is an important problem to be solved. This paper combines the characteristics of B2C e-commerce promotion in China and a quantitative model of the influence of promotion on B2C e-commerce sales is established. Based on promotion factors and historical data, we constructed a BP neural network model for B2C e-commerce sales forecast to solve the problem that e-commerce sales are difficult to predict accurately under promotional activities. Through the verification of Alibaba's actual case, it is concluded that the GA-BP algorithm model has a good adaptability to sales forecasting and it achieved 94 percent accuracy. The B2C e-commerce sales forecast model based on sales promotion and historical data established in this paper has high application value.

Distributed data stream processing system is NP-complete problem to assign tasks to any number of nodes handling the task scheduling. Even for substantially reducing scheduling scale, the problem still cannot be avoided. This paper takes advantage of the classical algorithm (ant colony optimization) of heuristic methods to simulate the global task scheduling problem of distributed system. Rational improvement on ant colony optimization path-finding for the memory and CPU usage of each node achieves load balancing in a short time. It gives the sub-optimal solution of the global task scheduling. The experiments show that the data stream processing system we proposed has good real-time characteristics and stability.