本文選題：可追溯 + 物聯(lián)網(wǎng)��； 參考：《中國農(nóng)業(yè)大學(xué)》2014年博士論文

【摘要】：可追溯系統(tǒng)是保證生鮮農(nóng)產(chǎn)品質(zhì)量安全的重要手段,當(dāng)前技術(shù)構(gòu)架下可追溯系統(tǒng)面臨的感知數(shù)據(jù)采集能力欠缺、追溯數(shù)據(jù)粒度輸出單一、追溯平臺體系結(jié)構(gòu)薄弱等瓶頸,阻礙了系統(tǒng)的規(guī)模化應(yīng)用。物聯(lián)網(wǎng)技術(shù)的發(fā)展,使生鮮農(nóng)產(chǎn)品質(zhì)量安全可追溯系統(tǒng)突破技術(shù)與應(yīng)用的瓶頸成為可能。 本研究從可追溯系統(tǒng)的三個技術(shù)瓶頸出發(fā),緊緊圍繞物聯(lián)網(wǎng)“無處不在的數(shù)據(jù)采集、可靠的數(shù)據(jù)傳輸與信息處理、智能化的信息應(yīng)用”三個核心內(nèi)涵,以動、植物源性農(nóng)產(chǎn)品可追溯供應(yīng)鏈為研究對象,構(gòu)建了物聯(lián)網(wǎng)環(huán)境下可追溯系統(tǒng)數(shù)據(jù)采集與建模方法,研發(fā)了基于WSN的生鮮農(nóng)產(chǎn)品質(zhì)量安全可追溯感知數(shù)據(jù)采集硬件與嵌入式軟件,設(shè)計了基于SPC的可追溯感知數(shù)據(jù)時域壓縮方法,針對用戶數(shù)據(jù)粒度需求的差異,進行了可追溯數(shù)據(jù)兼容建模和面向粒度分級的規(guī)約,設(shè)計和實現(xiàn)了基于云計算的可追溯綜合服務(wù)平臺。研究的主要貢獻與創(chuàng)新之處是： (1)提出了基于WSN的可追溯感知數(shù)據(jù)采集方法和基于SPC的時域壓縮方法,提高了感知數(shù)據(jù)采集效率,并延長了監(jiān)測網(wǎng)絡(luò)壽命。基于WSN所研發(fā)的可追溯感知數(shù)據(jù)采集方法軟、硬件原型,測試結(jié)果表明通信鏈路可靠,感知節(jié)點對生鮮農(nóng)產(chǎn)品供應(yīng)鏈保鮮工藝環(huán)境的兼容性、傳感器硬件兼容性好：基于SPC所設(shè)計的改進X-Rs感知數(shù)據(jù)時域壓縮算法與閾值、K-滑動均值算法對比,能耗在同一數(shù)量級,平穩(wěn)時間序列的Se為最優(yōu),2種時間序列平穩(wěn)性上tc值均接近最優(yōu),算法的平衡性和適應(yīng)性好。 (2)提出了面向粒度分級的可追溯系統(tǒng)建模方法,滿足了不同用戶的數(shù)據(jù)粒度需求�；诮Y(jié)構(gòu)模式識別,構(gòu)造了描述追溯單元轉(zhuǎn)化的12種模式基元；基于關(guān)系代數(shù),設(shè)計了模式基元的數(shù)據(jù)存儲結(jié)構(gòu)與數(shù)據(jù)采集算法；構(gòu)建了基于2型文法的可追溯數(shù)據(jù)形式化描述文法和文法句子生成算法；基于改進下推自動機建立了粒度分級規(guī)約方法；以凍羅非魚片加工、半滑舌鰨養(yǎng)殖、肉牛養(yǎng)殖與屠宰加工業(yè)務(wù)流程為實例進行了方法驗證,結(jié)果表明在以上供應(yīng)鏈,數(shù)據(jù)分級規(guī)約強度為44.8-99.4%,在供應(yīng)鏈結(jié)構(gòu)信息少的初級農(nóng)產(chǎn)品生產(chǎn)流程中,規(guī)約強度最高。 (3)設(shè)計了基于云計算的可追溯綜合服務(wù)平臺,實現(xiàn)了平臺級可追溯服務(wù)。識別了可追溯數(shù)據(jù)在生鮮農(nóng)產(chǎn)品供應(yīng)鏈上各階段的潛在價值,包括文檔標準化、危害溯源、精確召回、物流監(jiān)控、關(guān)鍵點預(yù)警、質(zhì)量預(yù)測、貨架期管理和庫存優(yōu)化；基于Hadoop設(shè)計了平臺的技術(shù)架構(gòu)、服務(wù)引擎、體系結(jié)構(gòu),基于Map/Reduce實現(xiàn)了決策模型并行化；在Ubuntu10.0.4操作系統(tǒng)和Hadoop0.20.0并行計算環(huán)境上進行了平臺實現(xiàn)；以工廠化水產(chǎn)養(yǎng)殖、水產(chǎn)品冷鏈物流為例的系統(tǒng)評價表明平臺在數(shù)據(jù)采集、信息追溯和智能決策等方面改善了生鮮農(nóng)產(chǎn)品供應(yīng)鏈管理水平。
[Abstract]:Traceability system is an important means to guarantee the quality and safety of fresh agricultural products. Under the current technical framework, traceability system is faced with the bottleneck of lacking of perceptual data collection ability, single output of traceability data granularity, weak architecture of traceability platform, etc. It hinders the large-scale application of the system. With the development of Internet of things, it is possible to break through the bottleneck of technology and application in the traceability system of fresh agricultural products. This research starts from the three technical bottlenecks of traceability system, tightly revolves around "everywhere data collection, reliable data transmission and information processing, intelligent information application" three core connotations, in order to move, Based on the traceability supply chain of plant-derived agricultural products, the data acquisition and modeling method of traceability system in Internet of things environment is constructed, and the hardware and embedded software of traceability sensing data acquisition for fresh agricultural products based on WSN are developed. A time-domain compression method of traceability perceptual data based on SPC is designed. According to the difference of user's data granularity requirements, the compatible modeling of traceability data and granulity-oriented hierarchical specification are carried out. Design and implementation of a cloud-based traceability integrated service platform. The main contributions and innovations of the research are: 1) A method of traceable perceptual data acquisition based on WSN and a time-domain compression method based on SPC are proposed to improve the efficiency of perceptual data acquisition and prolong the lifetime of monitoring network. Based on the software and hardware prototype of traceable perceptual data acquisition developed by WSN, the test results show that the communication link is reliable and the perception node is compatible with the fresh agricultural product supply chain fresh-keeping technology environment. The sensor hardware compatibility is good: the improved X-Rs perceptual data time domain compression algorithm based on SPC is compared with the threshold K- sliding mean algorithm. The energy consumption is in the same order of magnitude. The se of stationary time series is optimal and the values of TC are close to optimal on the stationarity of two kinds of time series, and the algorithm has good balance and adaptability. (2) the modeling method of traceability system for granularity classification is proposed to meet the data granularity requirements of different users. Based on structural pattern recognition, 12 kinds of pattern primitives are constructed to describe the transformation of tracing units, and data storage structures and data acquisition algorithms of pattern primitives are designed based on relational algebra. A formal description grammar and sentence generation algorithm for traceable data based on 2 type grammar is constructed, a granularity classification method based on improved push-down automata is established, and frozen tilapia slices are processed, and semi-smooth tongue sole is cultured. The results showed that the strength of the data classification specification was 44.8-99.4 in the supply chain, and the intensity of the specification was the highest in the primary agricultural production process with little information on the supply chain structure. The platform of traceability integrated service based on cloud computing is designed, and the platform level traceability service is realized. The potential value of traceable data in all stages of supply chain of fresh agricultural products was identified, including document standardization, hazard traceability, accurate recall, logistics monitoring, key point early warning, quality prediction, shelf life management and inventory optimization. The technology architecture, service engine, architecture of the platform are designed based on Hadoop, and the decision model is parallelized based on Map/Reduce. The platform is implemented on the Ubuntu10.0.4 operating system and Hadoop0.20.0 parallel computing environment. The systematic evaluation of the cold chain logistics of aquatic products shows that the platform improves the supply chain management level of fresh agricultural products in the aspects of data collection, information tracing and intelligent decision making.
【學(xué)位授予單位】：中國農(nóng)業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TS205

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