For effectively early warning the marketing risk caused along with the varied environment, a BP neural network method was introduced on the basis of analyzing the shortcomings of the risk early warning method, and com...For effectively early warning the marketing risk caused along with the varied environment, a BP neural network method was introduced on the basis of analyzing the shortcomings of the risk early warning method, and combined with the practical conditions of dairy enterprises, the index system caused by the marketing risk was also studied. The＇principal component method was used for screening the indexes, the grades and critical values of the marketing risk were determined. Through the configuration of BP network, node processing and error analysis, the early warning resuits of the marketing risk were obtained. The results indicate that BP neural network method can be effectively applied through the function approach in the marketing early warning with incomplete information and complex varied conditions.展开更多
The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly sp...The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly spread wildly across coastal wetlands,challenging resource managers for control of its further spread.An investigation of S.alterniflora invasion and associated ecological risk is urgent in China's coastal wetlands.In this study,an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework.Predictions were made of'warning degrees':zero warning and light,moderate,strong,and extreme warning,by developing a back propagation(BP)artificial neural network model for coastal wetlands in eastern Fujian Province.Our results suggest that S.alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions.An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas.Areas of light warning and extreme warning accounted for43%and 7%,respectively,suggesting the BP neural network model is reliable prediction of the ecological risk of S.alterniflora invasion.The model predicts that distribution pattern of this invasive species will change little in the next 10 years.However,the invaded patches will become relatively more concentrated without warning predicted.We suggest that human factors such as land use activities may partially determine changes in warning degree.Our results emphasize that an early warning system for S.alterniflora invasion in China's eastern coastal wetlands is significant,and comprehensive control measures are needed,particularly for K.candel beach.展开更多
Relying on the advanced information technologies, such as information monitoring, data mining, natural language processing etc., the dynamic technology early-warning system is constructed. The system consists of techn...Relying on the advanced information technologies, such as information monitoring, data mining, natural language processing etc., the dynamic technology early-warning system is constructed. The system consists of technology information automatic retrieval, technology information monitoring, technology threat evaluation, and crisis response and management subsystem, which implements uninterrupted dynamic monitoring, trace and crisis early-warning to the specific technology. Empirical study testifies that the system improves the accuracy, timeliness and reliability of technology early-warning.展开更多
The multidimensional analysis engine data management platform is constructed using big data distributed storage and parallel computing,data warehouse modeling technology,realizing the optimal management and instant qu...The multidimensional analysis engine data management platform is constructed using big data distributed storage and parallel computing,data warehouse modeling technology,realizing the optimal management and instant query of distributed oil and gas production dynamic big data.The centralized management and quick response of the production data of more than 36×10^4 oil,gas and water wells is realized.Multidimensional analysis subject model of oil,gas and water well production is built to pretreat the relevant data.At the level of China National Petroleum Corporation(CNPC),the rapid analysis and applications such as oil and gas production tracking,early production warning of key oilfields,analysis of low production wells and long shutdown wells,classification of reservoir development laws have been realized,and the processing time has been shortened from 1 d to 5 s.The basic unit of oil and gas production analysis is refined from oilfield to single well,making the production management more detailed.The process can be traced step by step according to CNPC,oil field company,field,block and single well,and the oil and gas production performance of each unit can be mastered in real time.展开更多
文摘For effectively early warning the marketing risk caused along with the varied environment, a BP neural network method was introduced on the basis of analyzing the shortcomings of the risk early warning method, and combined with the practical conditions of dairy enterprises, the index system caused by the marketing risk was also studied. The＇principal component method was used for screening the indexes, the grades and critical values of the marketing risk were determined. Through the configuration of BP network, node processing and error analysis, the early warning resuits of the marketing risk were obtained. The results indicate that BP neural network method can be effectively applied through the function approach in the marketing early warning with incomplete information and complex varied conditions.
基金funded by Forestry Peak Discipline Construction Project of Fujian Agriculture and Forestry University (72202200205)Fujian Province Natural Science (2022J01575)Science and Technology Innovation Project of Fujian Agriculture and Forestry University (KFA20036A)。
文摘The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly spread wildly across coastal wetlands,challenging resource managers for control of its further spread.An investigation of S.alterniflora invasion and associated ecological risk is urgent in China's coastal wetlands.In this study,an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework.Predictions were made of'warning degrees':zero warning and light,moderate,strong,and extreme warning,by developing a back propagation(BP)artificial neural network model for coastal wetlands in eastern Fujian Province.Our results suggest that S.alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions.An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas.Areas of light warning and extreme warning accounted for43%and 7%,respectively,suggesting the BP neural network model is reliable prediction of the ecological risk of S.alterniflora invasion.The model predicts that distribution pattern of this invasive species will change little in the next 10 years.However,the invaded patches will become relatively more concentrated without warning predicted.We suggest that human factors such as land use activities may partially determine changes in warning degree.Our results emphasize that an early warning system for S.alterniflora invasion in China's eastern coastal wetlands is significant,and comprehensive control measures are needed,particularly for K.candel beach.
基金Sponsored by Excellent Young Scholars Research Fund of Beijing Institute of Technology (c2007Y0820)Program for New Century Excellent Talents in University (NCET)"985" Philosophy and Social Science Innovation Base of the Ministry of Education(107008200400024)
文摘Relying on the advanced information technologies, such as information monitoring, data mining, natural language processing etc., the dynamic technology early-warning system is constructed. The system consists of technology information automatic retrieval, technology information monitoring, technology threat evaluation, and crisis response and management subsystem, which implements uninterrupted dynamic monitoring, trace and crisis early-warning to the specific technology. Empirical study testifies that the system improves the accuracy, timeliness and reliability of technology early-warning.
基金Supported by the China National Science and Technology Major Project(2016ZX05016-006).
文摘The multidimensional analysis engine data management platform is constructed using big data distributed storage and parallel computing,data warehouse modeling technology,realizing the optimal management and instant query of distributed oil and gas production dynamic big data.The centralized management and quick response of the production data of more than 36×10^4 oil,gas and water wells is realized.Multidimensional analysis subject model of oil,gas and water well production is built to pretreat the relevant data.At the level of China National Petroleum Corporation(CNPC),the rapid analysis and applications such as oil and gas production tracking,early production warning of key oilfields,analysis of low production wells and long shutdown wells,classification of reservoir development laws have been realized,and the processing time has been shortened from 1 d to 5 s.The basic unit of oil and gas production analysis is refined from oilfield to single well,making the production management more detailed.The process can be traced step by step according to CNPC,oil field company,field,block and single well,and the oil and gas production performance of each unit can be mastered in real time.
