Negative obstacle detection on open pit roads based on multi-feature fusion

With the gradual implementation of intelligent mine concept, intelligence and unmanned operation are gradually implemented in mining area. Unmanned driving of open pit mine trucks is increasingly becoming the main focus of intelligent mine construction. In order to solve the safety problems of the overturn of unmanned vehicles and heavy-duty trucks due to irregular negative obstacles appearing in some parts of road surface such as potholes and collapses in open pit mines, and to improve the safe driving coefficient in mines, a multi-feature fusion method of detecting negative obstacles in open pit mine roads is proposed. The method uses the BiFPN feature fusion module to improve the weight proportion of small-scale negative obstacle detection, introduces the spatial and channel dual attention mechanism to improve the feature extraction and feature fusion ability of negative obstacle edges, so as to improve the detection accuracy of small-scale negative obstacles on the road. Also, the SIoU Loss is adopted as the loss function of the model bounding box, the Anchor by using the K-means++ method is used to improve the convergence speed and boundary frame localization effect of the obstacle detection model, the hyperparameters are optimized based on genetic algorithm to make the model more suitable for the mine scene, and finally the fast and accurate recognition of negative obstacles on the mine road is realized. The experiments show that the detection model can quickly and accurately identify the negative road obstacle targets in the complex background of the open pit mine, and the detection accuracy, recall rate, and mAP of the negative road obstacle targets reach 96.9%, 89.9%, and 95.3%, respectively, and the size of the model is only 12.7 MB. Compared with other mainstream detection networks, the network model is more suitable for the safety needs of unstructured road driving in open pit mining areas under complex environment, and the robustness of the detection model is good, which can be adapted to a variety of situations in open pit mining areas, providing a feasible method for the detection of negative obstacles on unstructured roads in open pit mining areas where the actual environment is complex and variable, and providing some safety warnings for the safety of unmanned trucks in open pit mines

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

Sustainable facilities management (SFM) opens the door of opportunity for companies to evaluate the quality of resources and environment management at their facilities. It enables the principles of sustainable development. There is still inefficiency in quantitative research of integrating environmental factors, particularly multi-source data, to monitor and control complicated systems in buildings. The objective of this research is to develop an effective method to dynamically optimize energy efficiency in SFM plans and strategies. The research question is: can the integrated proactive method reduce energy consumption with dynamically adjustable controls? This paper proposes a coordinated proactive control method using dynamic time-series prediction (PCM-DTSP) for SFM, which optimizes system controls by integrating the prediction results and monitored environmental-data. The results show that, after optimization, the temperature fluctuations are reduced to 33.3%. The average temperature and maximum temperature are reduced by 8% and 13.1%, respectively. The instantaneous power consumption was reduced by 0.17 KW per hour for each cooling system unit. The PCM-DTSP method can significantly optimize energy efficiency, which paves the way for long-term comprehensive energy management. The contribution of the research lies in its optimized control of energy consumption, temperature stabilization, and improvement of environmental comfort solutions, which can be generalized to various types of buildings

A Real-Time Negative Obstacle Detection Method for Autonomous Trucks in Open-Pit Mines

Negative obstacles such as potholes and road collapses on unstructured roads in open-pit mining areas seriously affect the safe transportation of autonomous trucks. In this paper, we propose a real-time negative obstacle detection method for self-driving trucks in open-pit mines. By analyzing the characteristics of road negative obstacles in open-pit mines, a real-time target detection model based on the Yolov4 network was built. It uses RepVGG as the backbone feature extraction network, applying SimAM space and a channel attention mechanism to negative obstacle multiscale feature fusion. In addition, the classification and prediction modules of the network are optimized to improve the accuracy with which it detects negative obstacle targets. A non-maximum suppression optimization algorithm (CIoU Soft Non-Maximum Suppression, CS-NMS) is proposed in the post-processing stage of negative obstacle detection. The CS-NMS calculates the confidence of each detection frame with weighted optimization to solve the problems of encountering obscure negative obstacles or poor positioning accuracy of the detection boxes. The experimental results show that this research method achieves 96.35% mAP for detecting negative obstacles on mining roads with a real-time detection speed of 69.3 fps, and that it can effectively identify negative obstacles on unstructured roads in open-pit mines with complex backgrounds

A high-precision positioning method for open-pit mine vehicles based on improved HMM deviation correction algorithm

Vehicle location deviation could easily occur in complex road network of open-pit mines, which seriously affect production vehicle route planning and intelligent scheduling.In this light, this study proposed a method for high-precision positioning and rectification of open pit minecars based on an improved HMM(Hiddden Markov Model).Specifically, this study clipped the road section by the complex road network map of the constructed open-pit mine, and cleaned the positioning trajectory data of the minecart, whose density was sparsed and segmented; a buffer zone was established to search for candidate road points of the trajectory, so as to improve the efficiency of the minecart positioning and correction under the complex road network; the HMM optimization model of positioning deviation correction was established by calculating the positioning observation probability and transition probability of the minecart.The optimal deviation correctionwas conducted in combination with the Viterbi algorithm to achieve high-precision positioning and deviation correction of the open-pit minecart.Results indicate that the method produces a better correction effect than the original HMM positioning correction method, the correction accuracy can reach 89.2 %, and the average correction time is only 0.055 s.This could effectively correct the positioning coordinates of open-pit mine vehicles under complex backgrounds

Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open-Pit Mine Slope

With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform