In coal mining technology systems, it is very important to acquire, store, and represent basic geological data comprehensively and accurately. Based on the current working mode and information level in mining geology at coal mines, this paper proposes a process of building basic geological database for modeling of coal mines by using existing results’ data of mining geology and develops the efficient program for getting the basic geological data from the important 2D plane drawings’ achievement at mines, such as the (...) contour maps of mine coal seam floors, geological cross-sections, underground drilling results, and geological survey results, based on AutoLISP, which is a programming language for the secondary development of AutoCAD. The obtained data in general text format is stored and managed by the MongoDB database, which realizes the storage, query, analysis, and correction of massive data of geological objects in the space of the underground coalmine. The application results show that compared with the previous data acquisition methods such as manual input and graphic transformation attribute, the extraction of spatial and attribute data from the existing mine 2D plane drawings by programming can effectively avoid the prominent problems such as artificial gross error, distortion of graph conversion, and different database structure, make the obtained spatial geological data more comprehensive, accurate, and effective, and, meanwhile, increase the rate by more than 60%, which plays an important role in data support for the construction of the geological modeling systems for transparent mines. (shrink)

In this paper, we investigate the model of decentralized groupwise e-commerce through regional chains combined with edge computing algorithms and design and implement it. A blockchain-based architecture for remote sensing resource and service transactions is proposed. The architecture utilizes the characteristics of nontamperable and traceable data on the blockchain to solve the problem of mutual distrust between the transaction parties, uses MongoDB for data retrieval and filtering to solve the problem that it is difficult to query flexibly on the (...) blockchain, uses the file platform to save public resources to solve the problem that it is very expensive or even infeasible to store images or large texts in the blockchain, and ensures remote sensing resources and services related to the effective storage of images and description texts. The Ethernet features are described and analyzed to assess their suitability as a technical basis for the corresponding solutions. To assess the ability of the Ethernet-based system to solve solvable problems, its practical use cases are analyzed. The results of the analysis show that five main issues need to be addressed when applying Ethernet to e-commerce, namely, low scalability, lack of distributed storage, lack of convenience, reliability, and functional integrity. To exploit the idea of using blockchain technology to solve complex problems and to overcome the challenges identified in the existing Ethernet-based implementation solutions, an optimization idea is proposed by creating a multiblockchain architecture based multimodule solution in the Ethernet framework. Finally, the application prospects of the proposed information system are discussed. (shrink)

Precision agriculture is a breakthrough in digital farming technology, which facilitates the application of precise and exact amount of input level of water and fertilizer to the crop at the required time for increasing the yield. Since agriculture relies on direct rainfall than irrigation and the prediction of rainfall date is easily available from web source, the integration of rainfall prediction with precision agriculture helps to regulate the water consumption in farms. In this work, an edge computing model is developed (...) for predicting soil moisture in real time and managing the water usage in accordance with rain prediction. A soil moisture prediction hybrid algorithm has been developed that revolves around the decision-making techniques with live environmental parameters including weather parameters for the prediction of soil moisture through the impact of precipitation. Numerous algorithms with the combination of regression + clustering are estimated, and it is inferred that XGBoost + k-means outperforms other algorithmic combinations that is deployed in edge model. This model is used as an intermediary between the end IoT devices and cloud that results in the saving of computationally intensive processing performed on cloud servers. The servers located on a local edge network perform the developed algorithmic computations. Avoiding transmission over the cloud results in significant latency, response time, and computation power savings and therefore increases the efficiency of data transfer. The proposed edge computing model is implemented in Raspberry Pi as an edge, Heroku as cloud, and edge nodes as the combination of Pi with actuators and sensors. The monitored data from Pi are stored in MongoDB webserver that is controlled by Web dashboard. Finally, the developed model is implemented in cloud and edge where the edge server implementation performs better in terms of latency, bandwidth, throughput, response time, and CPU memory usage. (shrink)