In order to solve the problem of environmental pollution control in the energy ecological economic zone, this paper proposes a technology to realize environmental pollution control by using big data. The main content of this technology is based on the characteristics and application of big data technology; through the collection of online monitoring data of ecological environment pollution, the construction of the online monitoring data quality model of ecological environment pollution, and finally through the experiments in the simulation environment, the feasibility of big data technology is obtained. The experimental results show that the average quality factor data of the quality control model in the environmental scientific data supervision is 2.16, the average quality factor data of the Internet of things in the marine pollution monitoring is 5.88, and the average quality factor data of the model in the ecological environment pollution online monitoring based on the big data technology is 8.64. Big data technology has good applicability. It is proven that the research of big data technology can meet the needs of environmental pollution control in the energy ecological economic zone.

Traditional electrical switching network faces the problems of limited bandwidth and large energy consumption. The optoelectronic hybrid data network fully combines the advantages of optical switching and electrical switching, and dynamically adjusts the topology and bandwidth in accordance to the demand, thereby effectively improving network performance and reducing power consumption. A high-performance distributed optoelectronic switching network architecture called DOIN_W was proposed in the study, aiming to solve the problems of the scalability of the optoelectronic switching network architecture and the insufficient flexibility of network resource allocation. The architecture was connected topologically according to the method of 2DTorus, thereby effectively improving the network scale of the photoelectric switching network and supporting the dynamic adjustment of the network scale. The optical switching of DOIN_W adopted the "broadcastselect" method to support different forms of broadcasting communication. A multi-dimensional optical signal switch was designed, and the optical signal broadcast of the switch can reach any of the optical switches in DOIN_W, thereby supporting the direct connection between optical switches. Considering the problems that the multi-broadcast service of DOIN_W was unable to access freely, stateful optical signal communication was hard to maintain and the single path weight resulted in lack priority of communication, the OpenFlow network protocol of the cloud computing network was introduced in the DOIN_W architecture. The anycast mechanism of DOIN_W was optimized based on this protocol. Based on the stream-level network simulator for network performance simulation analysis, the DOIN_W architecture can meet the requirements of network scalability and flexibility. Compared with the OVS architecture and Jellyfish architecture, the DOIN_W architecture can effectively reduce the average flow completion time by more than 30% and reduce average energy consumption by over 25%.

In order to discuss the application of economic management in database construction, an application research of big data and cloud computing in economic management database construction is put forward. This paper first summarizes the characteristics of big data and cloud computing, then analyzes the big data characteristics of economic management data, and finally explores the successful experience of building economic management database by using big data and cloud computing, and explores how economic management database can realize its continuous improvement and development by using big data and cloud computing. Keywords: big data • Cloud computing • Economic management • database 2 Characteristics of Big Data and Cloud Computing 2.1 The Meaning and Characteristics of Big Data Big data or large information group has the following three main characteristics: (1) Big data. Big Data Big data covers many dimensions of data files, including encoding techniques, data types, and data types. (2) High performance. (3) Data rate. Big data