The increasing importance of mineral resources in contemporary society is becoming more prominent, playing an indispensable and crucial role in the global economy. These resources not only provide essential raw materials for the global economic system but also play an irreplaceable role in supporting the development of modern industry, technology, and infrastructure. With the rapid development of intelligent technologies such as Industry 5.0 and advanced Large Language Models (LLMs), the mining industry is facing unprecedented opportunities and challenges. The development of smart mines has become a crucial direction for industry progress. This article aims to explore the strategic requirements for the development of smart mines by combining advanced products or technologies such as Chat-GPT (one of the successful applications of LLMs), digital twins, and scenario engineering. We propose a comprehensive architecture consisting of three different levels, the mining industrial Internet of Things (IoT) platform, mining operating systems, and foundation models. The systems and models empower the mining equipment for transportation. The architecture delivers a comprehensive solution that aligns perfectly with the demands of Industry 5.0. The application and validation outcomes of this intelligent solution showcase a noteworthy enhancement in mining efficiency and a reduction in safety risks, thereby laying a sturdy groundwork for the advent of Mining 5.0.

Accurate localization of the shearer is key to achieving efficient, automated, unmanned mining. Unfortunately, the existing positioning algorithms usually yields low-precision, insufficient applicability and unreliable final estimation due to the existence of non-line-of-sight (NLOS) error and because they ignore the position error of anchor nodes (ANs). To bridge this technology gap, we developed a novel localization strategy by incorporating calibration, variational Bayesian unscented Kalman filter (VBUKF), total least squares (TLS) and water cycle algorithm (WCA) to enhance the location estimation accuracy of Ultra-wideband (UWB) positioning system in complicated underground environment. Firstly, calibration was implemented based on the selection of appropriate reference nodes (RNs) in line-of-sight (LOS) scenarios to estimate the scale-factor error, bias, and the position errors for each AN; then the VBUKF smoothing with the consideration of time-variant measurement noise was used to reduce the interference of NLOS owing to the difficult separation of bias and NLOS error. Secondly, the TLS method was implemented to calculate the target node’s position based on the smoothed distances and calibrated positions of ANs. Lastly, the WCA embedded scheme was executed to further ameliorate estimation accuracy. The experimental results demonstrated that the proposed TLS-WCA approach, after the implementation of calibration and VBUKF, was best able to improve the localization accuracy remarkably, and outperformed the other compared methods, which can efficiently attain higher estimation accuracy, highlighting the outstanding localization performance.

The achievement of accurate and reliable localization is invariably a predominant bottleneck to the realization of shearer automation. Unfortunately, the positioning accuracy may significantly degrade and the yield may be unsatisfactory due to the existence of the line-of-sight (LOS), non-line-of-sight (NLOS) along with mixed LOS-NLOS situations in complicated under-ground environments. To address this issue, this article proposes a novel localization algorithm called GMM-NNIMM-CLMFO, which is a combination of the Gaussian mixture model (GMM), neural network (NN)-based interacting multiple model (NNIMM), Caffery localization (CL), and moth-flame optimization (MFO). The GMM is first used to re-estimate the range, and the variational Bayesian cubature Kalman filter with the assistance of NN under the IMM framework is then respectively employed to diminish the LOS and NLOS errors. Then, based on the result of GMM-NNIMM, the CL method is used to compute the position coordinates of the target node. Finally, the MFO algorithm is implemented to optimize the positioning outcome sourced from the GMM-NNIMM-CL method. The proposed method is evaluated by using P440 sensors, and the experimental results demonstrate that the proposed technique is able to remarkably enhance the overall positioning accuracy, and outperforms other approaches. It can therefore efficiently alleviate the influence of NLOS errors and achieve more accurate position estimation, thereby manifesting better positioning performance and higher robustness in mixed LOS/NLOS situations.

Appropriate deployment of anchor nodes (ANs) is an essential prerequisite for achieving high-precision positioning. To ameliorate the comprehensive positioning performance, the weight position dilution of precision (WPDOP) that is utilized to evaluate the appropriateness of the ANs placement geometry is proposed by taking into account different error variances for each AN. A simple closed-form calculation WPDOP method is theoretically derived for ultrawideband (UWB) positioning system by utilizing elementary symmetric polynomials and Newton’s identity, which is applied to evaluate the UWB deployment at the end of coal mine working face (CMWF). Several basic ANs layout strategies, based on the projection shape in the $xy$ plane, are designed for the end of CMWF, and the modified gray wolf optimizer (MGWO) algorithm is executed to optimize the ANs deployment scheme to attain the better configuration and higher positioning accuracy. Simulation and experiments verification are simultaneously performed via the UWB positioning system. The experimental results sufficiently demonstrate that MGWO deployment scheme offers lower average WPDOP values than the obtained ones when applying random placement, and the lowest average improvement percentage was approximately equal to 37.6%. Furthermore, after implementing the MGWO algorithm, the V-shape deployment configuration is capable to provide the lowest average WPDOP values and outperforms the other arrangements, keeping the lowest localization error. This is regarded as the best deployment and it gives significant guidance for the ANs sensor deployment at the end of CMWF.

In this article, selective harmonic elimination (SHE)-based torque ripple optimization method is proposed for permanent magnet synchronous motors (PMSMs). First, the torque harmonic model for PMSMs is derived, and the influence of inverter voltage harmonics on torque harmonics is quantified. Second, multiple precise solutions of SHE, obtained through the algebraic methods, are employed to optimize the torque harmonics. By exploiting the multiple solution characteristics of SHE and selecting the optimal solution based on the torque harmonic model, torque harmonics are effectively optimized. Finally, to implement a closed-loop control system based on SHE modulation, the concept of model predictive pulse pattern control (MP$^{3}$C) is incorporated, and a improved SHE-based MP$^{3}$C strategy is designed. Compared to traditional methods, the proposed control system shows improved steady-state performance. The results demonstrate that the proposed method successfully reduces torque ripple and ensures fast dynamic response for PMSMs, providing a robust solution for high-performance motor control. Both simulation and experimental results validate the effectiveness and accuracy of the proposed method.