The application of the software covers more and more widely with the improvement of the theory of software engineering methodologies and the ripeness of the software developer 隨著軟件工程方法學(xué)理論的進(jìn)步、軟件開發(fā)人員的成熟,計(jì)算機(jī)軟件的應(yīng)用覆蓋面越來越大。
According to the requirements of operation of blast furnace and software engineering methodology, the properties and function of prototype of prediction system have been analyzed and hierarchical structure and data flow and data processing in the system have been outlined by using data flow diagram 根據(jù)高爐生產(chǎn)的需求以及軟件工程理論,系統(tǒng)分析了預(yù)測(cè)軟件的性能要求與模塊組成,以系統(tǒng)數(shù)據(jù)流程圖逐層分析了信息流在軟件系統(tǒng)中的流動(dòng)、變換和處理情況。
Uml is the convergence of best practices in the object-technology industry . and it is a rich; precise, extensible modeling language for object-oriented system development and software developing automation environments . uml is the representation of excellent software engineering methodology which is approbatory in large-scale and complex modeling field 它涵蓋了面向?qū)ο蟮姆治?、設(shè)計(jì)和實(shí)現(xiàn),融合了早期面向?qū)ο蠼7椒ê透鞣N建模語言的優(yōu)點(diǎn);為面向?qū)ο笙到y(tǒng)的開發(fā)、軟件自動(dòng)化工具與環(huán)境提供了豐富的、嚴(yán)謹(jǐn)?shù)?、擴(kuò)充性強(qiáng)的表達(dá)方式。
In this thesis, a new model used for prediction of silicon content in hot metal based on self-organized experience evolution approach has been investigated by developing prototype of the model with software engineering methodology, optimizing model parameters and testing it with process data of blast furnace in tianjin iron plant 針對(duì)目前鐵水硅含量預(yù)測(cè)方法尚不能滿足高爐過程控制需要的現(xiàn)狀,根據(jù)所提出的高爐鐵水硅含量自組織經(jīng)驗(yàn)進(jìn)化預(yù)測(cè)模型原理,用軟件工程方法學(xué)設(shè)計(jì)和開發(fā)了相應(yīng)軟件原型,并從理論和實(shí)踐角度對(duì)這種新的智能預(yù)測(cè)模型進(jìn)行了研究。
However, existing models till now which used for the prediction and controlling of silicon content in hot metal could n't meet the requirement to control such a complex processing of blast furnace . in this thesis, a new model used for predicting and controlling silicon content in hot metal based on artificial neural networks ( anns ) and expert system has been investigated by developing prototype of the model with software engineering methodology, after inquiring about the parameters which could affect silicon content in hot metal with the operators of blast furnace 本研究即是針對(duì)目前硅含量的預(yù)報(bào)和控制方法不能滿足實(shí)際高爐生產(chǎn)過程控制需要,在對(duì)高爐鐵水硅含量影響因素及控制知識(shí)進(jìn)行收集、分析的基礎(chǔ)上,結(jié)合鐵水硅含量控制過程中的諸多不確定因素,提出了將神經(jīng)網(wǎng)絡(luò)、專家系統(tǒng)共同作用于高爐鐵水硅含量的預(yù)報(bào)、控制模型。