Spatial-temporal Evolvement Characteristics of Climate Productivity for the Plants on Inner Mongolia Desert Steppe

Thornthwaite Memorial model and other statistic methods were used to calculate the climate-productivity of plants with the meteorological data from 1961 to 2007 at 9 stations distributed on Inner Mongolia desert steppe.The spatial and temporal variation characteristics of climate-productivity were analyzed by using the methods of the tendency rate of the climate trend,accumulative anomaly,and spatial difference and so on.The results showed that the climate-productivity kept linear increased trend over Inner Mongolia desert steppe in recent 47 years,but not significant.In spatial distribution,the climate-productivity reduced with the increased latitude.The climate-productivity in southwest part of Inner Mongolia desert steppe was growing while that in the southeast was reducing.The variation rate of the climate-productivity increased from the northwest part to the southeast part of Inner Mongolia desert steppe.In recent 47 years,the climate-productivity in southeast Jurh underwent the greatest decreasing extent,and the region was the sensitive area of the climate-productivity variation.

Spatial-temporal distribution and geochemistry of highly evolved Mesozoic granites in Great Xing’an Range,NE China:Discriminant criteria and geological significance

Highly evolved granite is an important sign of the mature continent crust and closely associated with deposits of rare metals.In this work,the authors undertake systematically zircon U-Pb ages and whole rock elemental data for highly evolved granitic intrusions from the Great Xing’an Range(GXR),NE China,to elucidate their discriminant criteria,spatial-temporal distribution,differentiation and geodynamic mecha-nism.Geochemical data of these highly evolved granites suggest that high w(SiO_(2))(>70%)and differentiation index(DI>88)could be quantified indicators,while strong Eu depletion,high TE_(1,3),lowΣREE and low Zr/Hf,Nb/Ta,K/Rb could only be qualitative indicators.Zircon U-Pb ages suggest that the highly evolved gran-ites in the GXR were mainly formed in Late Mesozoic,which can be divided into two major stages:Late Ju-rassic-early Early Cretaceous(162-136 Ma,peak at 138 Ma),and late Early Cretaceous(136-106 Ma,peak at 126 Ma).The highly evolved granites are mainly distributed in the central-southern GXR,and display a weakly trend of getting younger from northwest to southeast,meanwhile indicating the metallogenic potential of rare metals within the central GXR.The spatial-temporal distribution,combined with regional geological data,indicates the highly evolved Mesozoic granites in the GXR were emplaced in an extensional environ-ment,of which the Late Jurassic-early Early Cretaceous extension was related to the closure of the Mongol-Okhotsk Ocean and roll-back of the Paleo-Pacific Plate,while the late Early Cretaceous extension was mainly related to the roll-back of the Paleo-Pacific Plate.

Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

Adaptive spatial-temporal graph attention network for traffic speed prediction

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

Spatial-temporal Variation Characteristics of Water Quality in the Lower Reaches of the Nenjiang River

As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.

Spatial-temporal difference between nitrate in groundwater and nitrogen in soil based on geostatistical analysis

The study of temporal and spatial variations of nitrate in groundwater under different soil nitrogen environments is helpful to the security of groundwater resources in agricultural areas.In this paper,based on 320 groups of soil and groundwater samples collected at the same time,geostatistical analysis and multiple regression analysis were comprehensively used to conduct the evaluation of nitrogen contents in both groundwater and soil.From May to August,as the nitrification of groundwater is dominant,the average concentration of nitrate nitrogen is 34.80 mg/L;The variation of soil ammonia nitrogen and nitrate nitrogen is moderate from May to July,and the variation coefficient decreased sharply and then increased in August.There is a high correlation between the nitrate nitrogen in groundwater and soil in July,and there is a high correlation between the nitrate nitrogen in groundwater and ammonium nitrogen in soil in August and nitrate nitrogen in soil in July.From May to August,the area of low groundwater nitrate nitrogen in 0-5 mg/L and 5-10 mg/L decreased from 10.97%to 0,and the proportion of high-value area(greater than 70 mg/L)increased from 21.19%to 27.29%.Nitrate nitrogen is the main factor affecting the quality of groundwater.The correlation analysis of nitrate nitrogen in groundwater,nitrate nitrogen in soil and ammonium nitrogen shows that they have a certain period of delay.The areas with high concentration of nitrate in groundwater are mainly concentrated in the western part of the study area,which has a high consistency with the high value areas of soil nitrate distribution from July to August,and a high difference with the spatial position of soil ammonia nitrogen distribution in August.

STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.

Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Study on the Relationship among Forest Fire,Temperature and Precipitation and Its Spatial-temporal Variability in China

[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in forest area of each province in fire season were obtained based on meterological data,forest distribution data,seasonal and monthly data of forest fire in China.Secondly,the relationship among forest fire area,precipitation and temperature was discussed through temporal and correlation analysis.[Result] The changes of precipitation and temperature with time could reflect the annual variation of fire area well.Forest fire area went up with the decrease of precipitation and increase of temprature,and visa versa.Meanwhile,there existed diffirences in the relationship in various regions over time.Correlation analyses revealed that there was positive correlation between forest fire area and temperature,especailly Northwest China （R=0.367,P〈0.01）,Southwest China （R=0.327,P〈0.05）,South China （R=0.33,P〈0.05）,East China （R=0.516,P〈0.01） and Xinjiang （R=0.447,P〈0.05） with obviously positive correlation.At the same time,the correlation between forest fire area and precipitation was significantly positive in Northwest China （R=0.482,P〈0.01）,while it was significantly negaive in South China （R=-0.323,P=0.03）,but there was no significant correlation in other regions.[Conclusion] Relationships between forest fire and meteorological elements （precipitation and temprature） revealed in the study would be useful for fire provention and early warning in China.

Spatial-Temporal Distribution Characteristics and Limiting Factors of Medium-low Yield Farmland in Tianjin

[Objective] This paper aimed to understand the area change and distribu- tion of medium-low yield farmland, and offered basis to the improvement of mediumlow farmland and its increase of grain production in Tianjin. [Method] Based on the statistical date of Tianjin and its relevant counties and districts, the yield standard was set up to classify high-yield, medium-yield and low-yield farmland in Tianjin. The author analyzed area change of medium-low yield farmland in six agricultural counties and districts （including Jixian County, Wuqing District, Baodi District, Ninghe County, Jinghai County and Dagang district of Binghai New Area） from 1980 to 2010. [Result] The results showed that the average yield of grain rose from 2 445 kg/hm^2 in 1980 to 5 130 kg/hm^2 in 2010, increasing 109.82%. The area of mediumlow yield farmland was reduced from 291 250.13 hm^2 in 1985 to 76 489.87 hm^2 in 2010, coming down 74%. In Tianjin, the area of medium-low yield farmland of 2010 accounted for 19% of the total farmland, of which the ratios of medium-low yield farmland of Jinghai County, Jixian County, Dagang district of Binghai New Area, Wuqing District, Baodi District and Ninghe County were 43.12%, 18.59%, 17.23%, 14.01%, 7.05% and 0, respectively. Low soil nutrient content, drought and water shortage, as well as soil salinization were the main yield limiting factors to mediumlow yield farmland in Tianjin in 2010. [Conclusion] The countermeasures to improve the medium-low yield farmland were proposed, involving enhancing the investment of the government, strengthening the construction of water conservancy infrastructure, further improving the soil fertility, as well as saline and alkaline land, optimizing the farming system and planting drought and salt tolerance crops, etc.

Comparison of the Evolvement Structure of Chenopodium album L.under the Stress of Different Saline Environments

[Objective] The aim of this study was to reveal the evolvement structures,especially the crystal characteristics of Chenopodium album L.under saline stress,so as to providing the first-hand data for utilizing biological techniques to control saline environment.[Method] Employing high definition display method of plant crystal structure and paraffin-section method,we performed a comparative study on the evolvement structures of C.album growing in high salinity areas in the coast of Egyptian Red Sea and common salinity areas in the grasslands in Changling County of Jilin Province.[Result] The regionally distributed crystal and the developed assimilating tissue of C.album are the key structural characteristics to antagonize the saline stress during the evolving process.Stem cortex of C.album growing in both the high salinity areas in coast of Egyptian Red Sea and common salinity areas in the grasslands in Changling County of Jilin Province has similar discontinuous crystal rings.Assimilating tissue in C.album growing in high salinity areas is highly developed than that in common salinity environment.Comparative analysis indicates that the developed stratum corneum and marrow is also the key structural characteristics to antagonize the saline stress.[Conclusion] Our results provide a valuable approach to study the salt-tolerance mechanism of plant using structural botanical techniques,i.e.,crystal may become the identification characteristics of salt tolerant plant.

Spatial-temporal Evolution and Driving Force of Cultivated Land Quality in Henan Province

The purpose of this study was to find out the spatial-temporal rules and driving force of cultivated land quality in Henan Province in the last ten years. Agricultural land grading factor evaluation was used to evaluate the cultivated land quality of 2002 and 2012 in Henan Province, and to research the change laws. Method of correlation coefficient was employed to select the driving forces affecting cultivated land quality evolution. The results indicated that the cultivated land quality in Henan Province increased slightly in the last ten years in general, and in spatial there were unchanged regions, increased regions and decreased regions. The cultivated land quality in spatial presented the trend of good becoming better, bad becoming worse, which should be highly valued in cultivated land quality protection and management. Land development and consolidation projects had significant contributions to increasing the cultivated land quality. Driving forces between the sudden change regions and gradual change regions were significantly different. The paper concluded that the research on the spatial-temporal evolution and driving force of cultivated land quality based on cultivated land quality evolution had important academic significance and practical value.

英国高中地理教材栏目设置的特点及启示——以Geography B Evolving Planet为例

以英国中学地理教材Geography B Evolving Planet中的教材栏目为研究对象,结合其在教材中实际发挥的功能,将八种主要的教材栏目划分为问题思考类、技能训练类、信息提示类和考试导向类四大类。这四大类教材栏目体现出了不同的特点。问题思考类栏目指向不同教学目标,技能训练类栏目凸显实用性和应用性的学科特色,信息提示类栏目关注学生心理特点,考试导向类栏目提高应试答题技巧。受此启发,未来我国中学地理教材栏目设置应有所侧重,即教材栏目在内容设置上要注重针对性,在形式设置上要增加多样性,在外观设置上要注重醒目性。

Spatial-temporal Patterns and Driving Forces of Water Retention Service in China

Overwhelming water-deficiency conditions and an unbalanced water supply and demand have been major concerns of both the Chinese government and the general public during recent decades. Studying the spatial-temporal patterns and impact factors that influence water retention in China is important to enhance the management of water resources in China and other similar countries. We employed a revised Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST) model and regression analyses to investigate the water retention service in China. The results showed that the southeastern China generally performed much better than Northwest China in terms of the spatial distribution of water retention. In general, the efficacy of the water retention service in China increased from 2000 to 2014; although some areas still had a downward trend. Water retention service increased significantly(P < 0.05) in aggregate in the Qinghai-Tibet Plateau, and the Da Hinggan Mountains and Xiao Hinggan Mountains. However, the service in southwestern China showed a decreasing trend(P < 0.05), which would have significant negative impact on the downstream population. This study also showed that in China the changes in water retention service were primarily due to climate change(which could explain 83.49% of the total variance), with anthropogenic impact as a secondary influence(likewise the ecological programs and socioeconomic development could explain 9.47% and 1.06%, respectively). Moreover, the identification of water retention importance indicated that important areas conservation and selection based on downstream beneficiaries is vital for optimization protection of ecosystem services, and has practical significance for natural resources and ecosystem management.

Research on Spatial-Temporal Characteristics and Driving Factor of Agricultural Carbon Emissions in China

Macroscopic grasp of agricultural carbon emissions status, spatial-temporal characteristics as well as driving factors are the basic premise in further research on China’s agricultural carbon emissions. Based on 23 kinds of major carbon emission sources including agricultural materials inputs, paddy ifeld, soil and livestock breeding, this paper ifrstly calculated agricultural carbon emissions from 1995 to 2010, as well as 31 provinces and cities in 2010 in China. We then made a decomposed analysis to the driving factors of carbon emissions with logarithmic mean Divisia index （LMDI） model. The results show：（1） The amount of agricultural carbon emissions is 291.1691 million t in 2010. Compared with 249.5239 million t in 1995, it increased by 16.69%, in which, agricultural materials inputs, paddy ifeld, soil, enteric fermentation, and manure management accounted for 33.59, 22.03, 7.46, 17.53 and 19.39%of total agricultural carbon emissions, respectively. Although the amount exist ups and downs, it shows an overall trend of cyclical rise; （2） There is an obvious difference among regions：the amount of agricultural carbon emissions from top ten zones account for 56.68%, while 9.84%from last 10 zones. The traditional agricultural provinces, especially the major crop production areas are the main source regions. Based on the differences of carbon emission rations, 31 provinces and cities are divided into ifve types, namely agricultural materials dominant type, paddy ifeld dominant type, enteric fermentation dominant type, composite factors dominant type and balanced type. The agricultural carbon emissions intensity in west of China is the highest, followed by the central region, and the east zone is the lowest; （3） Compared with 1995, efifciency, labor and structure factors cut down carbon emissions by 65.78, 27.51 and 3.19%, respectively;while economy factor increase carbon emissions by 113.16%.

Spatial-Temporal Changes of Soil Organic Carbon During Vegetation Recovery at Ziwuling, China

To probe the processes and mechanisms of soil organic carbon (SOC) changes during forest recovery, a 150-yearchronosequence study on SOC was conducted for various vegetation succession stages at the Ziwuling area, in the centralpart of the Loess Plateau, China. Results showed that during the 150 years of local vegetation rehabilitation SOC increasedsignificantly (P < 0.05) over time in the initial period of 55-59 years, but slightly decreased afterwards. Average SOCdensities for the 0-100 cm layer of farmland, grassland, shrubland and forest were 4.46, 5.05, 9.95, and 7.49 kg C m-3,respectively. The decrease in SOC from 60 to 150 years of abandonment implied that the soil carbon pool was a sink forCO2 before the shrubland stage and became a source in the later period. This change resulted from the spatially variedcomposition and structure of the vegetation. Vegetation recovery had a maximum effect on the surface (0-20 cm) SOCpool. It. was concluded that vegetation recovery on the Loess Plateau could result in significantly increased sequestrationof atmospheric CO2 in soil and vegetation, which was ecologically important for mitigating the increase of atmosphericconcentration of CO2 and for ameliorating the local eco-environment.

Product Model Evolvement and Configuration Knowledge Reuse Method

Configuration knowledge is a dynamic information set which is evolving and enriching on and on. Product model is the instantiation of configuration knowledge and the evolution of configuration knowledge is the essential inherent reason which causes the models dynamic evolvement. In the traditional model evolvement process, the inheriting and reuse of configuration knowledge was always ignored. Aim at solving the above problem, the multistage rhombus evolution mode of configuration knowledge is discussed in this paper. The product model based on configuration knowledge is put forward in different levels to achieve the models dynamic evolvement and automatic upgrading. The evolving configuration knowledge drives the product model to evolve directly according to the rule of up-layer evolvement. Furthermore, a new configuration knowledge reuse and optimization technology is presented to inheriting and reuse the foregone configuration knowledge in the course of model evolvement. At last, the air separation equipment which is related with the project is taken as an example to illuminate that the presented model evolvement and configuration knowledge reuse technology are validity and practical.