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Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

China Telecom has launched a high-efficient and collaborative wide-area big data architecture system, the cloud edge computing big data platform, for large-scale governmental and enterprise organizations spanning multiple geographies and clusters. The platform logically abstracts data partitions through the cluster dimension, integrates multiple independent datasets into a "virtual dataset", and achieves many-to-one dataset mapping management. At the same time, the computing load dataset of the platform has generalized characteristics, which can flexibly cope with the data processing requirements in different scenarios. In addition, the platform also supports a variety of computing engines and scheduling systems using relational expressions as intermediate representations to achieve batch tasks for large-scale, complex data processing in highly fault-tolerant scenarios. At present, the cloud edge computing big data platform has been applied in a variety of application scenes. The platform has improved efficiency by 17% in 5G Core capacity scheduling subsystem (5GC) multi-centre big data job development and operation, and has achieved the collaborative scheduling of a total of 42 PB of storage, 84 TB of memory, and 24 984 VCore computing resources, with a daily average of 80 308 times of task scheduling between the front cluster and the core cluster.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the global food safety problem has increasingly become severe and supply chain reforms and food recalls are costly and challenging, it is particularly important to establish agricultural food traceability systems. Countries around the world are paying increasing attention to developing food safety traceability systems. Under the background of smart agriculture, information and communication technology can be further improved through blockchain infrastructure to achieve new farms and digital agriculture, thus ensuring end-to-end safety traceability of agricultural products from planting to sale. Through the analysis of the latest domestic and international research, this paper systematically elaborates on recent progress in research on agricultural food traceability based on blockchain technology. It dissects the processes of agricultural food supply chains, and proposes a basic architecture of blockchain in the field of agricultural food traceability. In addition, this paper summarizes the application of blockchain in agricultural food traceability, focusing mainly on blockchain combination with cloud-edge computing, encryption technology, storage optimization, consensus mechanism improvement, and smart contract design. It also points out the challenges in data security, storage scalability, regulatory difficulties, and practical applications. Finally, it proposes future direction for blockchain technology in agricultural food traceability such as strengthening security supervision, improving blockchain scalability, and empowering food traceability with emerging technologies. It emphasizes the opportunities and challenges in the application of blockchain technology in the agricultural food supply chains.

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper analyses the development status and technical characteristics of intelligent and networked technologies for conventional ground automatic driving. This paper also analyses the coal mine environmental characteristics, such as no GNSS (global navigation satellite system) signal coverage, low roadway illumination, lots of obstructions and obstacles and ubiquitous coal dust. In the context of the above technical and environmental characteristics, this study puts forward the key technologies of automatic driving research in underground coal mine. The technologies include mobile high-precision positioning technology without GNSS, laser radar technology, underground obstacle detection technology based on millimeter wave radar, underground low illumination video real-time enhancement and characteristic matching technology, underground environment high-precision map technology, underground autonomous vehicle decision planning technology, underground autonomous vehicle control execution technology, underground 5G communication technology and C-V2X direct connection communication technology. It is pointed out that the application of automatic driving in underground coal mine has the advantages of significant demand for fewer people or no people, clear operation management subject, closed scene, fixed route, slow speed, controllable permeability, good 5G construction foundation, easy open interface, etc. The reference architecture of 'human-vehicle-roadway-cloud' coal mine underground network connected automatic driving system is constructed. The system includes underground automatic driving vehicle, roadway infrastructure, personnel, coal mine cloud/edge computing platform and coal mine automatic driving application service platform. This paper designs the coal mine automatic driving vehicle architecture, which includes perception positioning system, network connected collaborative system, vehicle-mounted operating system and vehicle basic components. This paper puts forward three stages of the evolution of the network connected automatic driving in underground coal mines. The first stage is remote control and automatic driving, which realizes the transfer of vehicle drivers from underground to ground. The second stage is the automatic driving of vehicles with emergency takeover boundary. The vehicles are mainly driven by automatic driving, and remote emergency takeover is used as a safety guarantee method. The third stage is 'human-vehicle-roadway-cloud' collaborative control, the underground autonomous vehicles operate safely, efficiently and autonomously to realize highly unmanned intelligent transportation.

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The popularity of deep learning and cloud computing has promoted the widespread application of computer vision in various industries. However, centralized cloud inference services have problems such as high bandwidth resource consumption, image data privacy leakage, and high latency. It is hard that satisfy demand which requires diversified computer vision application. The dual gigabit upgrade of the communication network will promote depth collaboration of computer vision cloud-edge algorithms. Aiming to study the computer vision inference mechanism based on cloud-edge collaboration. Firstly, the advantages and disadvantages of the mainstream cloud and edge computer vision inference models in recent years were analyzed and explained, and on this basis, research on the cloud-edge collaborative computer vision inference model framework and deployment mechanism was carried out, model distributed reasoning model segmentation strategy, cloud-side collaborative network deployment optimization strategy was discussed in detail. In the end, the challenge and prospect of deep learning cloud-edge collaboration inference in future was discussed through data collaboration, network partition collaboration, and business function collaboration. [ABSTRACT FROM AUTHOR]