The original article "Research on 5G Networking in Multi-machine Collaborative Operation of Suburban Smart Farms" was published in "Wireless Communications" magazine. The authors are Li Yong, Xi Lei, Wang Wenlong, Shi Lei, Che Yinchao, Ma Xinming, and Li Chao of CITIC Mobile Communication Technology Co., Ltd. from the School of Information and Management Science of Henan Agricultural University.
The research on the operation technology of a single agricultural machine is gradually becoming mature, and there are few reports on the research on the communication technology of the joint operation of multiple agricultural machines. During the operation of multiple agricultural machinery, a large amount of different types of data (images, videos, etc.) are continuously collected through on-board sensors. Whether 5G can meet the transmission requirements of these data on bandwidth, delay, and speed to realize collaborative operation of the machine group is a science worth studying. question.
After carefully sorting out the research status and dynamic analysis at home and abroad, this paper proposes that wireless communication technology supporting multi-machine collaborative operation is a key technical feature of smart farms; whether the current coverage and signal strength of 4G and 5G in farms support multi-machine collaborative operation is worth studying . This paper selects a national agricultural science and technology park in Xinxiang, Henan Province as the experimental base. Through design experiments, the reference signal received power and signal-to-interference-plus-noise ratio indicators are tested, and the coverage and strength of 4G and 5G signals are actually measured by using vehicle-mounted instruments and handheld instruments. , and visually display and analyze the measurement data, providing scientific basis and guidance for how to use 4G and 5G hybrid networking for multi-machine collaborative operations in smart farms.
Text | Li Yong et al.
Full text 70 00 , expected to read 18 minutes
01. Introduction
With the continuous advancement of industrial informatization and urbanization, the labor force engaged in traditional agricultural production in my country has gradually decreased, and the aging and shortage of agricultural labor force have gradually become prominent, which has become an important factor restricting agricultural development. Who will farm the land in the future and what equipment will be used for farming is an urgent problem in our country. Agricultural machinery operations are repetitive and heavy operations, and the operating environment is relatively closed, which provides natural conditions for the landing of unmanned agricultural machinery. As the national policy tilts toward the demand for smart agriculture and unmanned farms, the domestic demand for unmanned driving continues to increase. All over the country are building unmanned demonstration farms and smart agriculture to try and open up the process of unmanned agriculture. . The demand for the development of modern smart agriculture characterized by "machine replacement" for agricultural production is increasing [1-2] ; smart agriculture is the development direction of future agriculture and an advanced form of modern agriculture [3-4] . For crop planting areas, different types of self-driving agricultural machinery (tractors, harvesters, grain trucks, etc.) are required to realize unmanned operations in the links of "ploughing, planting, management, harvesting, and transportation". Operational accuracy and efficiency, improving farmers' operating experience, reducing farmers' labor intensity and input costs per unit area, etc.
The daily workload of a single unmanned tractor can reach 16hm 2 , and the driving speed can also reach 12km/h. The high-precision operation and all-terrain adaptability make the daily workload and driving speed of automatic driving increase by about 33.3% compared with manual driving. and 50% [5] ; with the assistance of infrared, navigation and positioning, millimeter-wave radar and other sensing technologies, tractors can ensure operating efficiency even in the state of unmanned driving at night; automatic driving agricultural machinery reduces unnecessary Necessary travel, while effectively reducing fuel consumption and environmental pollution, it also reduces soil compaction [6-7] .
Automatic driving of agricultural machinery refers to the technology that agricultural machinery equipment obtains the surrounding environment information of the vehicle and its own spatial position through various sensors and communication equipment installed on itself, and autonomously follows the preset driving route and actively avoids obstacles to complete the operation [8 ] . The global navigation satellite system (Global Navigation Satellite System, GNSS) and China Beidou Navigation Satellite System (BeiDou Navigation Satellite System, BDS) positioning technology based on absolute position measurement in geospatial space are applied in the automatic driving of agricultural machinery [9 ] .
Until 1992, Trimble of the United States developed the RTK system and commercialized it, which cleared the obstacles for the application of GNSS in agriculture [10] . China's research on agricultural machinery automatic driving technology began in 2004 with Luo Xiwen's team. After more than ten years of research, they have broken through more than 10 key technologies such as speed wire control, master-slave navigation, path tracking, and automatic obstacle avoidance, and developed a planter. , Rotary tillers, harvesters, rice transplanters and other unmanned agricultural machinery are at the international leading level in automatic navigation of paddy field scenes [11-12] ; while the intelligent network unmanned agricultural machinery technology has not yet made new technological breakthroughs abroad .
South China Agricultural University, Shanghai Jiaotong University, National Agricultural Information Engineering Technology Research Center, Shanghai Lianshi Navigation Technology Co., Ltd. and Weichai Lovol Heavy Industry Co., Ltd. participated in the research work of agricultural machinery navigation technology [13-17 ] . Although all countries attach great importance to the standardization of automatic or unmanned agricultural machinery, there is currently no unified international norm or standard to classify the degree of automatic driving of agricultural machinery, and there is no communication protocol standard and failure rate for automatic driving agricultural machinery. , operating efficiency, land use efficiency, operating accuracy, etc. to test and evaluate or unify equipment interface protocols and specifications.
Vehicular Communication (VC) has attracted the attention of scholars and engineers from all over the world. Vehicle communication is composed of various communication protocols. At present, there is no unified standard in various countries. The mainstream is the Dedicated Short Range Communication (DSRC) technical standard represented by the United States. As early as 1992, the American Society for Testing and Materials (ASTM) had begun to develop a DSRC technical draft for vehicle communication [18] . The DSRC technical standard is essentially a low-overhead adjustment of the IEEE 802.11a standard on the 5.9GHz spectrum. Therefore, in the IEEE 802.11 protocol suite, DSRC is called IEEE 802.11p [19], which belongs to the wireless local area network technology. Follow-up in-depth research on DSRC There are fewer studies [20] . However, the LTE-V2X standard based on cellular communication technology represented by China has been formulated. Unfortunately, vehicle communication technology has not yet been used on a large scale to implement safety-related applications, and has not yet entered the stage of large-scale commercial application. Compared with the unmanned driving technology of automobiles, the automatic driving technology of agricultural machinery has the characteristics of low driving speed, high operation accuracy (±2.5 cm), low cost and strong durability.
In summary, based on the research status and development trends at home and abroad, it can be seen that the wireless network communication technology standards for agricultural machinery group operations at home and abroad have not yet been unified, and there are few existing studies involving wireless network communication technology and data transmission methods for agricultural machinery group operations [ 21-23] ; The research on the operation technology of a single agricultural machine is gradually becoming mature, and there are few research reports on the network communication technology for the joint operation of multiple agricultural machines, and there are no research reports on the new communication technologies used.
Therefore, the research on communication technology and data transmission method of wireless network has important theoretical significance and application value. Under modern large-scale production conditions, in order to improve operating efficiency, it is necessary to cooperate with multiple autonomous driving agricultural machinery and equipment [5] [23] . Driven by national policies, rural land is rapidly transferred and concentrated; agricultural production is about to enter a new era of informatization, intelligence, and unmanned operations, and the era of smart agriculture in China has gradually opened.
In order to realize the cooperative operation of multiple agricultural machinery, a large amount of different types of data (video, image, etc.) Quality (Quality of Service, QoS) and other requirements [24-26] , these data have different requirements on priority, delay, bandwidth, QoS, etc.; how to meet the specific transmission requirements of these data to realize the cooperative operation of the cluster is A question worth studying. 4G, 5G, and Wireless Local Area Network (WLAN) have developed rapidly in recent years and have been widely deployed. It is unclear whether multiple self-driving agricultural machinery and equipment can recognize each other and exchange information through 4G, 5G, and WLAN. In this paper, aiming at the actual needs of communication technology for multi-machine collaborative operation in wheat and corn rotation, and aiming at solving the bottleneck problem of communication technology, this paper studies how to use 4G, 5G, and WLAN hybrid networking to provide theoretical basis and technical support for multi-machine collaborative operation It is of great value and practical significance to improve the research and development level of my country's agricultural machinery equipment communication technology and the development of smart agriculture.
02. Farm actual measurement experiment and result analysis
In this research on the wireless network communication technology and data transmission method of agricultural machinery group operation, it is first necessary to clarify whether the existing hardware conditions of wireless communication in the farm environment meet the requirements of agricultural machinery operation. Design wireless test experiments for farm location, land area, crop plant height, density, green tree distribution, tree height, 4G, 5G base station location and height near the farm, model agricultural machinery moving speed, test reference signal receiving power (Reference Signal Receiving Power, RSRP), average Signal to Interference plus Noise Ratio (Signal to Interference plus Noise Ratio, SINR), the communication environment test conditions of Yuanyang Test Base are as follows:
(1) Test time: March 19, 2022, 9:30~11:50, the weather is cloudy and slightly windy;
(2) Test terminal: Huawei p40 (11.0.0.990SP11), self-developed Cool Test software (DT_Cool Test 3.0);
(3) Test speed: the average speed of the walking test is 8km/h, and the average speed of the driving test is 30km/h;
The test results are shown in Figures 1 to 4:
Figure 1. 4G Reference Signal Received Power
Figure 2. 4G Signal-to-Interference-plus-Noise Ratio
Figure 3. 5G reference signal received power
Figure 4. 5G Signal-to-Interference-plus-Noise Ratio
RSRP and SINR are the two most basic indicators of wireless signal coverage; RSRP is a key parameter that can represent wireless signal strength in a 4G network and one of the physical layer measurement requirements. It is received on all resource elements that carry reference signals in a certain symbol. The average value of the received signal power; SINR refers to the ratio of the strength of the received useful signal to the strength of the received interference signal (noise and interference), also known as the "signal-to-noise ratio". From Figures 1 to 4, it can be seen that the overall coverage rate of 4G signals on the farm is about 95.82%, the average 4G RSRP is about -85dbm, and the average SINR is about 7db. In the field test, the 5G base station is about 5 kilometers (Km) away from the farm. The overall 5G coverage rate of the farm is about 35.77%, the average 5G RSRP is about -104dbm, and the average SINR is about -0.94db.
The test data in Figures 1 to 4 show that it is necessary to study and design a multi-modal network and communication model that is suitable for the farm. For the wireless signal coverage environment of the farm, a mixed multi-mode network structure of 4G and 5G is adopted. The data collected by the agricultural machinery sensor is transmitted to the base station side through 4G and 5G, and aggregated at the base station side. The aggregated data is transmitted through the operator's public network Go to the cloud for processing, and after cloud analysis, feed back to the agricultural machinery equipment along the original route, and the agricultural machinery equipment performs corresponding operations according to the instructions.
03. 5G support and guidance for multi-machine collaborative operation
In terms of speed, the multi-machine cooperative operation of wheat and corn rotation, the average running speed of wheeled agricultural machinery is lower than 30km/h during driving and operation, and the speed is lower than 20km/h in the process of rotary tillage, plowing and other operations. It belongs to medium and low speed operation; in terms of delay, wheeled agricultural machinery does not have strict requirements on delay in medium and low speed driving scenarios. In the daily public network communication environment, the service requires that the delay of large data The delay is within 15ms, which can meet the needs of wheeled agricultural machinery operations; the flying speed of drones for spraying pesticides, pollination and breeding purposes is higher than 30km/h, which belongs to the high-speed flight environment; unmanned aerial vehicles for spraying pesticides, pollination and breeding purposes Machines have stricter requirements on delay. In the daily public network communication environment, the business requires the delay of large data packets to be within 17ms, and the delay of small data packets to be within 15ms. Therefore, the existing public network communication technology is difficult to meet the requirements of drones. Operation requirements, according to the UAV operation path, on the one hand, it is necessary to adjust the antenna angle of the existing public network 5G base station to increase the signal strength of the area where the UAV operation path is located; on the other hand, increase the number of 5G base station deployments to expand 5G signal coverage.
The overall coverage of 4G signals on the farm is about 95.82%, the average 4G Reference Signal Received Power (Reference Signal Receiving Power, RSRP) is about -85dbm, and the average Signal to Interference plus Noise Ratio (Signal to Interference plus Noise Ratio, SINR) is about 7db. Therefore, 4G communication mode can be used for medium and low-speed wheeled agricultural machinery with low real-time requirements; because WIFI-6 in WLAN has the advantages of low cost, large bandwidth, fast speed, unlicensed frequency band, self-organization, and coexistence with other networks For fixed image and video sensors in the field, WIFI-6 can be used for website deployment, so that images and videos collected by multiple sensors can be transmitted to the central node, and the central node can be transmitted to the cloud through the operator's broadband.
04. Epilogue
Through the experimental design, a single 5G base station is tested at a location about 5Km away from the farm. The overall 5G coverage of the farm is about 35.77%, the average 5G RSRP is about -104dbm, and the average SINR is about -0.94db. For the coverage of 4G and 5G wireless signals in farms, a multi-mode network structure of 4G, 5G and WIFI-6 in WLAN should be adopted.
The data collected by the on-board sensors of agricultural machinery is transmitted to the edge of the network through 4G; the unmanned aerial vehicle is transmitted to the edge of the network through the coverage area of 5G; the fixed image and video sensors in the field are transmitted to the edge of the network through WIFI-6. Carry out data aggregation, the aggregated data is transmitted to the cloud through the operator's public network for processing, and then fed back to the agricultural machinery equipment along the original path after cloud analysis, and the agricultural machinery equipment performs corresponding operations according to the instructions to achieve multi-machine collaborative operation and various sensors. communication support.
references
Luo Xiwen, Liao Juan, Hu Lian, et al. Research progress of intelligent agricultural machinery and practice of unmanned farms in my country [J]. Journal of South China Agricultural University, 2021, 42(6): 8-17.
Zhao Chunjiang, Li Jin, Feng Xian. Research on the Development Strategy of Smart Agriculture Facing 2035 [J]. Engineering Science of China, 2021, 23(04): 1-9.
Li Daoliang, Li Zhen. Analysis and Development Prospect of Unmanned Farm System [J]. Journal of Agricultural Machinery, 2020, 51(7): 1-12.
Huang Jikun, Xie Wei, Sheng Yu, Wang Xiaobing, Wang Jinxia, Liu Chengfang, Hou Lingling. Global Agricultural Development Trends and Prospects for China's Agricultural Development in 2050[J]. Engineering Science of China, 2022, 24(1): 29-37.
Yang Tao, Li Xiaoxiao. Research progress of agricultural machinery automatic driving system and analysis of industry competition environment [J]. Chinese Journal of Agricultural Mechanization, 2021, 42(11): 222-231.
Zhang Wenyu, Zhang Zhigang, Luo Xiwen, et al. Cooperative control method and experiment of longitudinal relative position and velocity coupling between harvester and grain transport vehicle [J]. Journal of Agricultural Engineering, 2021, 37(9): 1-11.
Yang Yang, Zha Jiayi, Li Yankai, Wang Weiwei, Xu Liangyuan, Chen Liqing. Design and experiment of tractor unit unmanned operation cooperative control system[J/OL]. Journal of Agricultural Machinery: 1-11. [2022-03-10]. http ://kns.cnki.net.
Zhong Yin, Xue Mengqi, Yuan Hongliang. Design of GNSS/INS Integrated Navigation System for Intelligent Agricultural Machinery [J]. Journal of Agricultural Engineering, 2021, 37(9): 40-46.
Xie Binbin, Liu Jizhan, He Meng, et al. Design of detection system for unmanned driving navigation parameters of field agricultural machinery with improved AOA mode [J]. Journal of Agricultural Engineering, 2021, 37(14): 40-51.
Ar. A., Nn. B., Ho. B., et al. A review of autonomous agricultural vehicle (The experience of Hokkaido University)-Science direct[J].Journal of Terramechanics,2020,91:155-183.
Wang Hui, Wang Guimin, Luo Xiwen, Zhang Zhigang, Gao Yang, He Jie, Yue Binbin. Path tracking control method for agricultural machinery navigation based on preview tracking model [J]. Journal of Agricultural Engineering, 2019, 35(4): 11-19.
He Jie, Zhu Jinguang, Luo Xiwen, et al. Design of Steering Control System for Rice Transplanter with Electric Steering Wheel [J]. Journal of Agricultural Engineering, 2019, 35(6): 10-17.
Luo Xiwen, Zhang Zhigang, Zhao Zuoxi, et al. DGPS automatic navigation control system of Dongfanghong X-804 tractor[J]. Journal of Agricultural Engineering, 2009, 25(11):139－145．
Zeng Hongwei, Lei Junbo, Tao Jianfeng, et al. Extraction method of combine harvester navigation line under low contrast conditions [J]. Journal of Agricultural Engineering, 2020, 36(4):18-25.
Huang Peikui, Zhang Zhigang, Luo Xiwen, et al. Development of External Acceleration Identification and Attitude Measurement System for Field Vehicles [J]. Journal of Agricultural Engineering, 2019,35(3): 9-15.
Huang Huang, Chen Yanyan, Zhu Ming, et al. Multi-site and multi-machine collaborative real-time response scheduling system based on fuzzy membership [J]. Journal of Agricultural Engineering, 2021, 37(21): 71-79.
Chen Xuegeng, Wen Haojun, Zhang Weirong, Pan Fochu, Zhao Yan. Current Situation and Direction of Agricultural Machinery and Information Technology Integration [J]. Smart Agriculture (Chinese and English), 2020, 2(4): 1-16.
Kenney J. B. Dedicated Short-Range Communications (DSRC) Standards in the United Stated[J].Proceedings of the IEEE,2011,99(7):1162-1182.
Wang J., Yan Y., Yang X., et al. Situations and Tendency of Intelligent Transportation System in Europe[C]. International Conference on Optoelectronics and Image Processing. IEEE, 2010: 396-401.
Eichler S., Performance Evaluation of the IEEE 802.11p WAVE Communication Stardard[C]. IEEE, vehicular Technology Conference. IEEE, 2007: 2199-2203.
Bilstrup K., Uhlemann E., Strom E. G., et al. Evaluation of the IEEE802.11p MAC Method for Vehicle-to-Vehicle Communication[C]. Vehicle Technology Conference, 2008.Vtc 2008-Fall. IEEE, 2008: 1-5.
Murray T., Cojocari M., et al. Measuring the performance of IEEE 802.11p using ns-2 simulator for vehicular networks[C]. IEEE International Conference on Electro/information Technology. IEEE, 2008: 498-503.
Zhang Man, Ji Yuhan, Li Shichao, etc. Research progress of agricultural machinery navigation technology [J]. Journal of Agricultural Machinery, 2020, 51(4): 1-18.
Li Yong, Li Bo, Yang Mao, Yan Zhongjiang. A spatial clustering group division-based OFDMA access protocol for the next generation WLAN[J]. Wireless Networks, 2019, 25(8): 5083-5097.
Li Yong, Li Bo, Yang Mao, Yan Zhongjiang. A Spatial Aggregation Group Multiple Access Protocol for Associated Multiple Cells [J]. Journal of Northwestern Polytechnical University, 2019, 37(4): 809-815.
Li Yong, Li Bo, Yang Mao. Unsaturated traffic oriented spatial clustering multiusers MAC protocol for the next generation WLAN[J]. International Journal of Internet Protocol Technology, 2021, 14(2): 77-85.
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