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5G時代的AI技術應用詳解 ( 簡體 字) |
| 作者：亞信科技（中國）有限公司 | 類別：1. -> 程式設計 -> 人工智慧 |
| 譯者： |
| 出版社：清華大學出版社 |  3dWoo書號： 53706
詢問書籍請說出此書號！【有庫存】
NT售價： 395 元 |
| 出版日：11/1/2020 |
| 頁數：273 |
| 光碟數：0 |
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| 印刷：黑白印刷 | 語系： ( 簡體 版 ) |
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| ISBN：9787302565321 |
| 作者序 | 譯者序 | 前言 | 內容簡介 | 目錄 | 序 |
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前言：叢書序
2019年6月6日，工信部正式向中國電信、中國移動、中國聯通和中國廣電四家企業發放了5G牌照。這意味著中國正式按下了5G商用的啟動鍵。可以想見，在未來的若年干中，萬眾睢睢的5G將與人工智能、云計算、大數據、物聯網等新技術一起，改變個人生活，催生行業變革，加速經濟轉型，推動社會發展，真正打造一個“萬物智聯”的多維世界。
5G將帶來個人生活方式的迭代。更加暢快的通信體驗、無處不在的AR/VR、智能安全的自動駕駛……這些都將因5G的到來而變成現實，給人類帶來更加自由、豐富、健康的生活體驗。
5G將帶來行業的革新。受益于速率的提升、時延的改善、接入設備容量的增加，5G觸發的革新將從通信行業溢出，數字化改造得以加速，新技術的加持日趨顯著，新的商業模式不斷涌現，產業的升級將讓千行百業脫胎換骨。
5G將帶來多維的跨越。C端消費與B端產業轉型將共振共生。“4G改變生活，5G改變社會”，5G時代，普通消費者會因信息技術再一次升級而享受更多便捷，千行百業的數字化、智能化轉型也會真正實現，兩者互為表里，互
相助推，把整個社會的變革提升到新高度。
2019年是5G元年，也是亞信科技（中國）有限公司（簡稱亞信科技）上市后的第一個財年。作為國內領先的軟件與服務提供商、云網一體管理服務提供商，亞信科技緊扣時代發展節拍，積極擁抱5G、云計算、大數據、人工智能、物聯網等先進技術，與業界客戶、合作伙伴共同建設5G+X的生態體系，為5G賦能千行百業、企業數字化轉型、產業可持續發展積極做出貢獻。
在過去的一年中，亞信科技繼續深耕電信業務支撐軟件與服務（Business Supporting System，BSS）的優勢領域，為三大運營商的5G業務在中華大地全面商用提供了強有力的支撐。
亞信科技將能力延展到5G網絡Operation Supporting System（OSS）領域，公司打造的5G網絡智能化產品在三大運營商取得了多個商用局點的突破與落地實踐，在幫助運營商優化5G網絡環境、提升5G服務體驗的同時，公司也邁出了拓展OSS領域的堅實一步。
亞信科技在數字化運營的Data-Driven Software as a Service（DSaaS）這一創新業務板塊也取得了規模化突破。在金融、交通、能源、政府等多個領域，幫助行業客戶打造“數智”能力，用大數據和人工智能技術，協助他們獲客、活客、留客，改善服務質量，實現行業運營數字化轉型。
亞信科技在垂直行業市場服務領域進一步拓展，行業大客戶版圖進一步擴大，公司與云計算的各頭部企業達成云MSP合作，持續提升云集成、云SaaS、云運營能力，并與他們一起，幫助郵政、能源、交通、金融、零售等數十個大型行業客戶上云、用云，降低信息化支出，提升數字化效率。
亞信科技同時積極強化、完善了技術創新與研發的體系和機制。在過去的一年中，多項關鍵技術與產品獲得了國際和國家級獎項，諸多技術組合形成了國際與國家標準。5G+ABCDT的靈動組合，重塑了包括亞信科技自身在內的行業技術生態體系。“5G與AI技術大系”叢書是亞信科技在過去幾年中，以匠心精神打造我國5G軟件技術體系的創新成果與科研經驗的總結。我們非常高興能將這些階段性成果以叢書的形式與行業伙伴們分享與交流。
我國經歷了從2G落后、3G追隨、4G同步，到5G領先的歷程。在這個過程中，亞信科技從未缺席。在未來的5G時代，我們將繼續堅持以技術創新為引領，與業界合作伙伴們共同努力，為提升我國5G科技和應用水平、為國家新基建和全行業數字化轉型貢獻力量。
高念書
2020年9月于北京
前言
2019年6月6日，工信部正式向中國電信、中國移動、中國聯通、中國廣電發放了5G商用牌照，標志著我國開啟了真正意義上的5G商用元年。而2020年年初，國家新基建宏觀政策則更是將5G列為了需要重點發展的七大關鍵領域之首。
很顯然，5G的快速發展將使企業面臨全新的發展環境，新技術、新網絡和新業務的變革，為各行業帶來了全新的發展機遇和挑戰。如何在5G時代更好地規劃網絡以支撐業務應用？如何在降本增效的同時提升客戶服務感知？如何科學合理地設計多量綱的計費模式？如何實現業務決策和業務流程的智能化。這些單純依靠傳統的方式已經無能為力，將AI技術應用于5G業務場景中、解決企業的運營管理問題，已成為大勢所趨。但如何將AI技術應用到紛繁復雜的業務場景中，卻是企業面臨的一個非常實際的問題。
本書主要選取一些非常典型的5G業務場景，結合大量實際應用案例，系統介紹如何將AI與5G技術相結合，應用于5G智能網絡、5G切片、物聯網、Massive MIMO、CEM（客戶體驗管理）、CRM（客戶關系管理）、5G智能多量綱計費、商業智能分析、智能安防等5G業務場景中。希望通過理解AI技術在這些業務場景中的應用，為企業在5G時代通過AI技術賦能生產和運營管理提供有益的參考。同時，在本書的最后一章，特別介紹了在5G時代如何通過平臺建設的方式，將AI能力沉淀下來，更好地支持企業的業務管理運營。
全書共分為三篇：第一篇為基礎與網絡篇，包括第1∼4章，主要介紹如何將AI技術應用于網絡智能切片、物聯網和5G網絡多量綱計費業務場景中；第二篇為客戶與管理篇，包括第5∼8章，以客戶體驗管理、客戶關系管理、企業業務流程管理、企業商業智能決策四大典型應用場景為例，詳細介紹如何通過AI技術提升企業的管理效能；第三篇為運維與安全篇，包括第9∼12章，其中第9∼11章分別介紹AI技術應用于設備智能運維、機房智慧管控、智能安防的應用案例，第12章則對AI能力平臺化的建設、沉積等內容進行詳細論述，并給出AI平臺建設的理念、功能設計和技術設計建議。
與市場上介紹5G和AI的其他圖書相比，本書具有如下特色。
（1）定位明確。本書定位于AI技術與應用的有機結合，在應用中詳細分析技術。本書非常透徹地分析了企業在5G時代面臨的市場環境、發展機遇與挑戰，并通過大量的技術細節，詳細論述了企業在面對這些機遇和挑戰時，應如何通過技術手段注智業務場景，并輔以詳細的應用案例。
（2）技術先進。本書各章節中涉及的技術均為目前國內外AI領域的前沿技術，同時參考了亞信科技（中國）有限公司發表于國際權威學術刊物上的多篇學術論文，讀者通過閱讀本書，可以對5G時代AI前沿技術的應用有更加充分的了解。
（3）注重實戰。本書中的技術應用實例大部分是亞信科技（中國）有限公司近年來在生產過程中積累的實際應用案例，實踐性強。
本書由亞信科技通信人工智能實驗室編寫，編寫組成員包括歐陽曄博士、孟祥德、白世明、楊愛東博士、薛明博士、曾樹明博士、經琴、蔣煒、李紅霞、宋勇、龔福才等。
由于編者水平有限，更兼時間和精力所限，書中不足之處在所難免，若蒙讀者諸君不吝告知，必將不勝感激。
編者
2020年9月于北京 |
內容簡介：本書結合大量實際案例，全面且詳細地介紹了企業在5G時代應該如何應用AI技術來提升
生產、運營和管理能力。全書共分為三篇：第一篇為基礎與網絡篇，包括第1∼4章，主要介紹
如何將AI技術應用于網絡智能切片、物聯網和5G網絡多量綱計費業務場景中；第二篇為客戶
與管理篇，包括第5∼8章，以客戶體驗管理、客戶關系管理、企業業務流程管理、企業商業智
能決策四大典型應用場景為例，詳細介紹如何通過AI技術提升企業的管理效能；第三篇為運維
與安全篇，包括第9∼12章，其中第9∼11章分別介紹AI技術應用于網絡智能運維、機房智
慧管控、智能安防的應用案例，第12章則對AI能力平臺化的建設、沉積等內容進行詳細論述，
并給出AI平臺建設的理念、功能設計和技術設計建議。
本書可供通信行業和其他行業的IT從業人員，以及科研人員、高校師生閱讀和參考。 |
目錄：第一篇基礎與網絡篇
第1章“5G+AI”概述·································2
1.1新基建下的“5G+AI”技術發展·························3
1.1.1新基建的內涵和外延···············································3
1.1.2新基建對5G和AI發展的影響······························6
1.25G時代的AI技術趨勢······································10
1.2.1AI部署云邊協同····················································10
1.2.2AI注智實時持續····················································12
1.2.3AI應用民主靈活····················································13
1.2.4AI決策高度仿真····················································14
1.3我國5G產業與技術發展···································16
1.3.1我國5G技術發展歷程··········································16
1.3.25G改變社會···························································17
1.4我國AI產業與技術發展····································22
1.4.1人工智能發展概述·················································22
1.4.2我國人工智能技術的發展·····································24
第2章AI與5G網絡智能切片····················29
2.15G業務多樣化與網絡需求彈性化····················29
2.25G網絡智能切片概述········································31
2.2.15G網絡智能切片的概念與特征···························32
2.2.25G網絡智能切片端到端結構·······························33
2.2.35G網絡智能切片的RAN側技術挑戰················34
2.2.45G網絡智能切片的AI平臺和分析系統·············35
2.2.55G網絡智能切片的智能部署·······························36
2.2.65G網絡智能切片的標準化增強···························37
2.3應用于5G網絡切片中的AI技術·····················38
2.3.15G網絡智能切片的設計流程·······························38
2.3.2基于GA-PSO優化的網絡切片編排算法············43
2.3.35G網絡切片使能智能電網···································53
2.3.4應用于NWDAF中的聯邦學習技術····················59
第3章AI與智能物聯網······························63
3.15G時代IoT海量數據實時處理·························63
3.2邊緣計算與云邊協同··········································65
3.2.1邊緣計算·················65
3.2.2云邊協同·················67
3.3應用于智能IoT中的AI技術····························72
3.3.1聯邦遷移學習·························································72
3.3.2RPnet網絡與車牌識別··········································74
3.3.3對抗生成網絡與移動目標檢測·····························76
3.3.4Android手機去中心化的分布式機器學習···········78
3.3.5“AI＋移動警務”················································79
第4章AI與5G網絡多量綱計費················80
4.15G時代變得日益復雜的網絡計費····················80
4.25G多量綱計費概述············································82
4.2.1與4G計費量綱對標··············································83
4.2.25G計費因子確定···················································85
4.2.35G計費欺詐預防···················································86
4.2.45G流量異常監測···················································87
4.3應用于智能計費中的AI技術····························89
4.3.1ST-DenNetFus算法與網絡需求彈性分析············89
4.3.2強化學習（RL）與客戶意圖分析························92
第二篇?客戶與管理篇
第5章AI與客戶體驗管理··························98
5.1客戶感知網絡質量與客觀KPI指標差異··········98
5.2CEM概述···························································102
5.2.1CEM基本概念·····················································102
5.2.2客戶網絡體驗感知量化·······································104
5.2.3CEMC與端到端客戶服務體驗改善··················106
5.3應用于CEM中的AI技術·······························108
5.3.1ADS算法與用戶網絡感知原因定位··················109
5.3.2Chatbot技術與客服體驗優化·····························111
5.3.3基于KDtree、LSTM以及多算法融合的網絡容量預測··································113
5.3.4NPS度量與用戶業務感知提升··························114
第6章AI與客戶關系管理（CRM）·········118
6.15G需求差異化與服務精準化··························118
6.2CRM概述··························································120
6.2.1CRM基本概念·····················································120
6.2.2AI注智客戶差異化服務營銷······························121
6.3應用于CRM中的AI技術·······························122
6.3.1BERT技術在客服NLP中的應用······················122
6.3.2基于用戶單側通話記錄檢測的詐騙電話識別···················································127
6.3.3應用于用戶差異化營銷中的人臉識別應用技術···············································131
6.3.4應用于戶外廣告屏的人體屬性識別技術···········134
6.3.5MPMD加權回歸方法在客戶畫像中的應用實現··············································139
6.3.6“CRNN+OpenCV”與用戶身份證信息自動錄入···········································146
6.3.7基于OCR識別的用戶簽名信息核對·················148
6.3.8基于中心性和圖相似性算法的智能推薦應用···················································148
6.3.9基于LDA和MLLT的語音識別特征變換矩陣估計方法································150
6.3.10基于MFCC和Kaldi-chain聲學模型的語音情緒分析···································153
第7章AI與流程管理································156
7.1智能流程管理與企業降本增效························156
7.2AIRPA助力數字化轉型····································157
7.2.1RPA概述··············157
7.2.2RPA開發運行流程··············································161
7.2.3RPA開發工具······················································163
7.2.4RPA管控調度······················································164
7.2.5RPA任務執行引擎··············································166
7.3應用于智能流程管理中的AI技術··················167
7.3.1YOLO模型檢測和分類票據·······························167
7.3.2用OpenCV去除印章···········································169
7.3.3CRNN識別票據關鍵信息···································170
7.3.4基于模板的OCR識別·········································171
第8章AI與商業智能································173
8.15G與運營商業務決策和業務流程優化··········173
8.2構建基于通信AI的全面戰略管理決策體系··················································176
8.3應用于智能決策中的AI技術··························177
8.3.1納什均衡算法與攜號轉網最優市場決策···········177
8.3.2TransferLearning（遷移學習）技術與客戶攜轉風險識別······························183
8.3.3基于多源指標關聯分析的業務沙盤推演···········186
8.3.4基于社群發現的用戶轉網預警分析···················192
第三篇?運維與安全篇
第9章AI與網絡智能運維························198
9.15G網絡復雜化與運維模式創新······················198
9.2AIOps概述·························································200
9.2.1AIOps概念與關鍵業務流程·······························200
9.2.2AIOps與智能運維學件·······································202
9.3應用于智能運維中的AI技術··························204
9.3.1基于動態閾值的網絡運維異常檢測···················204
9.3.2基于DBSCAN和Apriori算法的傳輸網告警根因定位···································209
9.3.3集成學習算法與網絡故障預測···························214
9.3.4時序算法與網絡黃金指標預測···························216
9.3.5基于異構知識關聯的運維知識圖譜構建···········218
第10章AI與機房智慧管控·······················221
10.15G時代的中心機房智慧管控························221
10.2機房資源調度與監控管理概述······················223
10.2.1機房環境物理指標·············································223
10.2.2“IoT+AI”輔助機房管理自動化·····················224
10.2.3機房安防布控與違規預警·································225
10.3應用于機房智能化中的AI技術····················225
10.3.1機器學習方法輔助數據中心降低能源消耗·····················································225
10.3.2Faster-RCNN目標檢測算法監控機柜資源占用··············································229
10.3.3基于計算機視覺方法的機房火情監測·············233
第11章AI與智能安防······························235
11.1“5G+AI”安防發展趨勢·······························236
11.2應用于智能安防中的5G技術·······················239
11.2.1無線視頻監控部署·············································239
11.2.2三域一體立體化防控·········································241
11.2.3海量數據實時響應·············································242
11.3應用于智能安防中的AI技術························244
11.3.1AI安防模型························································244
11.3.2AI服務實現························································250
11.3.3資源混編調度·····················································252
第12章5G時代的AI能力平臺化············255
12.1AI平臺建設與能力沉積·································255
12.2AI平臺建設理念與思路·································256
12.3AI平臺建設功能設計····································261
12.3.1云化引擎設計·····················································261
12.3.2API算法體系······················································262
12.3.3AI能力生產方式················································262
12.3.4AI能力輸出方式················································265
12.3.5與生產環境對接·················································266
12.4AI平臺建設的技術設計·································267
參考文獻······················································269
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