A smart fully mechanized coal mining working face is comprised of various heterogeneous equipment that work together in unknown coal seam environments.The goal is to form a smart operational system with comprehensive ...A smart fully mechanized coal mining working face is comprised of various heterogeneous equipment that work together in unknown coal seam environments.The goal is to form a smart operational system with comprehensive perception,decisionmaking,and control.This involves many work points and complex coupling relationships,indicating it needs to be performed in stages and coordinated to address key problems in all directions and along multiple points.However,there are no existing unifed test or analysis tools.Therefore,this study proposed a virtual test and evaluation method for a fully mechanized mining production system with diferent smart levels.This is based on the concept of“real data processing–virtual scene construction–setting key information points–virtual operation and evaluation.”The actual operational data for a specifc working face geology and equipment were reasonably transformed into a visual virtual scene through a movement relationship model.The virtual operations and mining conditions of the working face were accurately reproduced.Based on the sensor and execution error analyses for diferent smart levels,the input interface for sensing,decision-making,and control was established for each piece of equipment,and an operation evaluation system was constructed.The system comprehensively simulates and tests the key points of sensing decision-making and control with various smart levels.The experimental results showed that the virtual scene constructed based on actual operational data has a high simulation degree.Users can simulate,analyze,and evaluate the overall operations of the smart mining 2.0–4.0 working face by inputting key information.The future direction for the smart development of fully mechanized mining is highlighted.展开更多
Periodic pattern mining is of great significance for understanding passenger travel behav-ior,but the previous works mainly focused on the trajectory data and the dimension of the spot/point.Besides,many uncertain fac...Periodic pattern mining is of great significance for understanding passenger travel behav-ior,but the previous works mainly focused on the trajectory data and the dimension of the spot/point.Besides,many uncertain factors(severe weather,traffic accident,etc.)may interfere with discovering original and accurate periodic travel patterns.This paper pro-poses a novel type of travel pattern called motif periodic frequent pattern(MPFP),which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics considering,uncertain disturbances.We also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data(SCD),and apply the real long-time-span experimental data from a large-scale metro system is applied.Results show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of“week”.Only the top 10 types of all 4624 types account for about 95%of all motifs and the top 5 types constitute about 90%,and the MPFP of the top 3 types of motifs account for nearly 80%of all periodic patterns,in which Mono-MPFP and 2-MPFP are the main ones.The relatively stable time range of MPFP is three months,and the threshold for the optimal uncertain disturbance factor should be set at 5%.Additionally,several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.展开更多
基金Funding National Natural Science Foundation of China,52004174Major Science and Technology Projects in Shanxi Province,202101020101021+2 种基金Fund for Shanxi“1331”ProjectKey Project of the Chinese Society of Academic Degrees and Graduate Education,2020ZDA12Natural Science Foundation of Shanxi Province,201901D211022.
文摘A smart fully mechanized coal mining working face is comprised of various heterogeneous equipment that work together in unknown coal seam environments.The goal is to form a smart operational system with comprehensive perception,decisionmaking,and control.This involves many work points and complex coupling relationships,indicating it needs to be performed in stages and coordinated to address key problems in all directions and along multiple points.However,there are no existing unifed test or analysis tools.Therefore,this study proposed a virtual test and evaluation method for a fully mechanized mining production system with diferent smart levels.This is based on the concept of“real data processing–virtual scene construction–setting key information points–virtual operation and evaluation.”The actual operational data for a specifc working face geology and equipment were reasonably transformed into a visual virtual scene through a movement relationship model.The virtual operations and mining conditions of the working face were accurately reproduced.Based on the sensor and execution error analyses for diferent smart levels,the input interface for sensing,decision-making,and control was established for each piece of equipment,and an operation evaluation system was constructed.The system comprehensively simulates and tests the key points of sensing decision-making and control with various smart levels.The experimental results showed that the virtual scene constructed based on actual operational data has a high simulation degree.Users can simulate,analyze,and evaluate the overall operations of the smart mining 2.0–4.0 working face by inputting key information.The future direction for the smart development of fully mechanized mining is highlighted.
基金supported by the National Natural Science Foundation of China(No.52372332)the Fundamental Research Funds for the Central Universities of China(No.2022-5-YB-04)the Shanghai Shentong Metro Group Co.,Ltd.(Nos.JSKY21R005-1-WT-21064,and JS-KY22R033-2).
文摘Periodic pattern mining is of great significance for understanding passenger travel behav-ior,but the previous works mainly focused on the trajectory data and the dimension of the spot/point.Besides,many uncertain factors(severe weather,traffic accident,etc.)may interfere with discovering original and accurate periodic travel patterns.This paper pro-poses a novel type of travel pattern called motif periodic frequent pattern(MPFP),which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics considering,uncertain disturbances.We also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data(SCD),and apply the real long-time-span experimental data from a large-scale metro system is applied.Results show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of“week”.Only the top 10 types of all 4624 types account for about 95%of all motifs and the top 5 types constitute about 90%,and the MPFP of the top 3 types of motifs account for nearly 80%of all periodic patterns,in which Mono-MPFP and 2-MPFP are the main ones.The relatively stable time range of MPFP is three months,and the threshold for the optimal uncertain disturbance factor should be set at 5%.Additionally,several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.
