5G 時代的 AI 技術應用詳解

亞信科技(中國)有限公司

  • 出版商: 清華大學
  • 出版日期: 2020-11-01
  • 售價: $474
  • 貴賓價: 9.5$450
  • 語言: 簡體中文
  • ISBN: 7302565325
  • ISBN-13: 9787302565321
  • 相關分類: 5G

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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技術應用於網絡智能切片、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技術應用於網絡智能運維、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, 並給出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。

作者簡介

亞信科技(中國)有限公司(簡稱亞信科技,股票代碼01675.HK)創建於1993年,是國內領先的軟件產品、解決方案和服務提供商,致力於成為5G時代大型企業數字化轉型的使能者。

公司積極擁抱5G、雲計算、大數據、AI、物聯網等先進的技術,依據“一鞏固、三發展”的戰略決策,依托產品、服務、運營和集成的能力,在傳統業務方面,以5G為契機,全面佈局,提升效能,鞏固BSS市場的領導地位;在新興業務方面,5G OSS網絡智能化、DSaaS數字化運營服務、企業上雲及垂直行業領域快速規模化發展。同時,公司將與業界夥伴共同建設生態體系,持續推動商業模式轉型,為企業數字化轉型和產業可持續發展貢獻力量。

亞信科技擁有行業領先的研發能力和豐富的電信級軟件產品,包括客戶關係管理、計費賬 務、大數據、物聯網及5G網絡智能化產品。大型企業客戶來自金融、交通、郵政、能源、廣電、零售、政務等行業。

目錄大綱

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

1.2 5G時代的AI技術趨勢······································10

1.2.1 AI部署雲邊協同····················································10

1.2.2 AI註智實時持續····················································12

1.2.3 AI應用民主靈活····················································13

1.2.4 AI決策高度模擬····················································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

1.3.2 5G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

2.1 5G業務多樣化與網絡需求彈性化····················29

2.2 5G網絡智能切片概述········································31

2.2.1 5G網絡智能切片的概念與特徵···························32

2.2.2 5G網絡智能切片端到端結構·······························33

2.2.3 5G網絡智能切片的RAN側技術挑戰················34

2.2.4 5G網絡智能切片的AI平臺和分析系統·············35

2.2.5 5G網絡智能切片的智能部署·······························36

2.2.6 5G網絡智能切片的標準化增強···························37

2.3  應用於5G網絡切片中的AI技術·····················38

2.3.1 5G網絡智能切片的設計流程·······························38

2.3.2  基於GA-PSO優化的網絡切片編排算法············43

2.3.3 5G網絡切片使能智能電網···································53

2.3.4  應用於NWDAF中的聯邦學習技術····················59

第3章  AI與智能物聯網······························63

3.1 5G時代IoT海量數據實時處理·························63

3.2  邊緣計算與雲邊協同··········································65

3.2.1  邊緣計算·················65

3.2.2  雲邊協同·················67

3.3  應用於智能IoT中的AI技術····························72

3.3.1  聯邦遷移學習·························································72

3.3.2 RPnet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

3.3.4 Android手機去中心化的分佈式機器學習···········78

3.3.5  “AI+移動警務”················································79

第4章  AI與5G網絡多量綱計費················80

4.1 5G時代變得日益復雜的網絡計費····················80

4.2 5G多量綱計費概述············································82

4.2.1  與4G計費量綱對標··············································83

4.2.2 5G計費因子確定···················································85

4.2.3 5G計費欺詐預防···················································86

4.2.4 5G流量異常監測···················································87

4.3  應用於智能計費中的AI技術····························89

4.3.1 ST-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

5.2 CEM概述···························································102

5.2.1 CEM基本概念·····················································102

5.2.2  客戶網絡體驗感知量化·······································104

5.2.3 CEMC與端到端客戶服務體驗改善··················106

5.3  應用於CEM中的AI技術·······························108

5.3.1 ADS算法與用戶網絡感知原因定位··················109

5.3.2 Chatbot技術與客服體驗優化·····························111

5.3.3  基於KDtree、LSTM以及多算法融合的網絡容量預測··································113

5.3.4 NPS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

6.1 5G需求差異化與服務精準化··························118

6.2 CRM概述··························································120

6.2.1 CRM基本概念·····················································120

6.2.2 AI註智客戶差異化服務營銷······························121

6.3  應用於CRM中的AI技術·······························122

6.3.1 BERT技術在客服NLP中的應用······················122

6.3.2  基於用戶單側通話記錄檢測的詐騙電話識別···················································127

6.3.3  應用於用戶差異化營銷中的人臉識別應用技術···············································131

6.3.4  應用於戶外廣告屏的人體屬性識別技術···········134

6.3.5 MPMD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  基於OCR識別的用戶簽名信息核對·················148

6.3.8  基於中心性和圖相似性算法的智能推薦應用···················································148

6.3.9  基於LDA和MLLT的語音識別特徵變換矩陣估計方法································150

6.3.10  基於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

7.2 AIRPA助力數字化轉型····································157

7.2.1 RPA概述··············157

7.2.2 RPA開發運行流程··············································161

7.2.3 RPA開發工具······················································163

7.2.4 RPA管控調度······················································164

7.2.5 RPA任務執行引擎··············································166

7.3  應用於智能流程管理中的AI技術··················167

7.3.1 YOLO模型檢測和分類票據·······························167

7.3.2  用OpenCV去除印章···········································169

7.3.3 CRNN識別票據關鍵信息···································170

7.3.4  基於模板的OCR識別·········································171

第8章  AI與商業智能································173

8.1 5G與運營商業務決策和業務流程優化··········173

8.2  構建基於通信AI的全面戰略管理決策體系··················································176

8.3  應用於智能決策中的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

8.3.2  Transfer Learning(遷移學習)技術與客戶攜轉風險識別······························183

8.3.3  基於多源指標關聯分析的業務沙盤推演···········186

8.3.4  基於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

9.1 5G網絡復雜化與運維模式創新······················198

9.2 AIOps概述·························································200

9.2.1 AIOps概念與關鍵業務流程·······························200

9.2.2 AIOps與智能運維學件·······································202

9.3  應用於智能運維中的AI技術··························204

9.3.1  基於動態閾值的網絡運維異常檢測···················204

9.3.2  基於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  基於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

10.1 5G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  應用於機房智能化中的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

10.3.2 Faster-RCNN目標檢測算法監控機櫃資源占用··············································229

10.3.3  基於電腦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  應用於智能安防中的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  應用於智能安防中的AI技術························244

11.3.1 AI安防模型························································244

11.3.2 AI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

12.1 AI平臺建設與能力沉積·································255

12.2 AI平臺建設理念與思路·································256

12.3  AI平臺建設功能設計····································261

12.3.1  雲化引擎設計·····················································261

12.3.2 API算法體系······················································262

12.3.3 AI能力生產方式················································262

12.3.4 AI能力輸出方式················································265

12.3.5  與生產環境對接·················································266

12.4  AI平臺建設的技術設計·································267

參考文獻······················································269