In the past thirty years, China experiences fast development in terms of economic and technology. This leads to the frequent exchange and communication among different cities. Specifically, meetings, sports, and industry exchanges at various levels are becoming more frequent and popular. Above events have significant impact on the city, hence, government/organizer should quantitatively evaluate the impact of events. Based on the various data of telecom operators, this paper proposes an insight scheme for large-scale events of the city. The proposed scheme can analyze the changes of the users scale and the business volume. In addition, this scheme also analyzes the aggregation areas of users before and after events and other characteristics. Especially, for the characteristics of external users from other cities, the proposed scheme can provide the data analysis results, in this way to support both the organizer and the government of the host city. Finally, the organizer and the government can provide good services.
In view of the current issue that the shearer in coal mine fully mechanized working face can't accurate automatic identification of coal rock interface, the method of using the shearer motor working parameters to reflect the hardness of coal and rock is put forward.The stress of roller cutter pick, via the rocker retarding mechanism, is delivered to drag torque of cutting motor output shaft.Mathematical model between the shearer motor working parameters and the hardness of coal and rock is established, by analysis of the stress combined with the mechanical model of the pick cutting coal and rock.It provides mathematical basis about the test data to verify the result of the experiment.
The working environment in coal mines is complex and constantly changing. Harsh conditions, such as narrow passageways, irregular road shapes, and unpredictable obstacles, pose significant challenges for the precise perception of unmanned mining vehicles. To address these issues, this paper proposes a positioning method that tightly couples an Inertial Measurement Unit (IMU) with LiDAR to overcome the challenge of precise positioning in degraded underground spaces where a single LiDAR alone cannot achieve accurate localization.First, the LiDAR point cloud is segmented, and IMU pre-integrated poses are used to eliminate nonlinear motion distortions. Line and surface features are extracted from the obtained point cloud. Next, the line and surface features of adjacent frames are matched, and in the layered pose estimation process, IMU pre-integrated pose initial values are fused to reduce the number of computational iterations, improve feature point matching accuracy, and compute the current frame's pose. Finally, local map factors, IMU factors, and keyframe factors are inserted into the factor graph to optimize the pose. Keyframes are matched with local maps, and map construction is achieved through an octree structure.Considering the specific limitations of coal mining operations in the real environment, which are characterized by high experimental difficulty, cost, limited and hard-to-obtain data, this paper combines the ACP method to simulate various coal mine tunnel scenarios within an artificial system. It includes features such as uneven ground, curves, slopes, and other complex parameters to experimentally validate the proposed method. This approach not only reduces experimental risk and costs but also significantly enriches the experimental data.The experimental results demonstrate that the method presented in this paper exhibits strong robustness and accuracy in various scenarios. The positioning accuracy of unmanned mining vehicles in actual scenarios is improved by 74% to 91% compared to traditional methods.
The level of intelligence across various industries, including mining, has experienced a notable advancement since the introduction of Industry 4.0. However, the societal demands for employee well-being, resource conservation, and environmental preservation continues to be unavoidable challenges. Thus, the parallel system theory based Industry 5.0 is proposed, which augments industries with human-machine collaboration, virtual-real interaction and local-global balanced resources. In this letter, we proposed the concept of Mining 5.0. Mining 5.0 aims to realize green and sustainable development goals while prioritizing a human-centric perspective. This letter begins by elucidating the prerequisites, characteristics, and construction objectives of Mining 5.0. It then introduces the "4I", "5O" and "6S" concluded framework of "Intelligent Mining Using Parallel Intelligence" for achieving Mining 5.0. In the last part, the efforts made by Chinese mining research institutions and leading enterprises in the development of mining intelligence standards, and the practical application of parallel mining within the mining sector are outlined. This letter is a summary of recent Distributed/Decentralized Hybrid Workshop on Autonomous Mining (DHW-AM) and aims at enhancing the intelligence of future mining operations.
In order to solve the problem of indirect identification of cutting load, a method of feature extraction based on vibration signal analysis and processing is presented. This method takes the shearer rocker arm level vibration signal as the object, using the empirical formula to improve the vibration signal is decomposed into several effective time-frequency features, improved the classical wavelet packet method of aliasing and endpoint effect phenomenon, selects the energy characteristics of each order value, explore the relationship between the characteristic value and the coal mining machine payload the. The results show that the analysis of the signal acquisition system of a coal mining machine structure does not change the method, not affect the normal operation of coal mining machine, easy to implement long-term online monitoring, for a large number of basic data collection, in order to improve the quantitative load cutting hardness recognition accumulated data.
The method of laneway environment modeling and roadheader positioning based on Self-coupling and HectorSLAM was proposed to solve the problems of difficult extraction of environmental information of coal mine laneway and difficult determination of the position of roadheader and realization of autonomous mobilization of mine roadheader. The optimality of HectorSLAM was verified by experiments, and the deficiencies were pointed out. Then the self - coupling HectorSLAM algorithm was proposed. Finally, the self-coupling and Hector SLAM algorithms were run in ROS system. Environmental modeling of coal mine laneway was completed. The shaft bottom visualization positioning function of roadheader was realized. The comparative experiment proves that: Compared with the original algorithm, the self-coupling and Hector SLAM algorithms were more adaptive and more accurate in the simulated laneway environment.
This study proposes a novel method of optimal path planning in stochastic constraint network scenarios. We present a dynamic stochastic grid network model containing semienclosed narrow and long constraint information according to the unstructured environment of an underground or mine tunnel. This novel environment modeling (stochastic constraint grid network) computes the most likely global path in terms of a defined minimum traffic cost for a roadheader in such unstructured environments. Designing high-dimensional constraint vector and traffic cost in nodes and arcs based on two- and three-dimensional terrain elevation data in a grid network, this study considers the walking and space constraints of a roadheader to construct the network topology for the traffic cost value weights. The improved algorithm of variation self-adapting particle swarm optimization is proposed to optimize the regional path. The experimental results both in the simulation and in the actual test model settings illustrate the performance of the described approach, where a hybrid, centralized-distributed modeling method with path planning capabilities is used.
For this kind of complex system equipment, parts fault detection of single large difficulty and low efficiency from the whole system considering many factors can lead to failure, and these factors affect each other, and the fault also exists between some logical relations, because of the condition monitoring and fault diagnosis accurately and effectively is very difficult. Based on the analysis of the traditional Petri Net application in the field of fault diagnosis, this paper gives fault diagnosis modeling expression of different classification problem & a kind of theory, and introduces transition token matrix representation of traces transition in order to express path of fault propagation. On this basis, from several aspects which are the TTFPN definition, this paper discusses the theory and technology of fault definition of seafloor drilling based on traces transition. Finally, the reasoning between the intelligent diagnosis function and the fault propagation path is verified by the mathematical model reasoning of the fault data.Yang, J.; Tang, Z.; Wang, Z.; Wang, X.; Huang, Q., and Wu, M., 2018. Improved fault petri nets of unmanned seafloor drilling based on traces transition. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 479–485. Coconut Creek (Florida), ISSN 0749-0208.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)