5G 時代的 AI 技術應用詳解
亞信科技（中國）有限公司
買這商品的人也買了...
-
$265Web API 的設計與開發 (Web API : the Good Parts) -
$267深入淺出 SSD：固態存儲核心技術、原理與實戰 -
從零開始學架構：照著做，你也能成為架構師$594$564 -
Windows 黑客編程技術詳解$648$616 -
$403Cloud Native 分佈式架構原理與實踐 -
微服務架構設計模式 (Microservices Patterns: With examples in Java)$834$792 -
$284邊緣計算 5G時代的商業變革與重構 -
$653國之重器出版工程 SDN/NFV重構下一代網絡 -
深入淺出神經網絡與深度學習$534$507 -
機器學習的數學：用數學引領你走進AI的神秘世界$580$452 -
$6535G 車聯網技術及應用 -
$4035G 時代邊緣計算：lf edge 生態與 edgegallery 技術詳解 -
$658國之重器出版工程 5G網絡安全實踐 -
$352邊緣計算2.0：網絡架構與技術體系 -
邊緣計算與算力網絡 — 5G + AI 時代的新型算力平臺與網絡連接$528$502 -
$713物聯網(IoT)基礎 : 網絡技術 + 協議 + 用例 -
使用 AWS 在雲端建置 Linux 伺服器的 20堂課$500$375 -
5G時代的場景計費詳解：從流量計費到價值計費$474$450 -
$6115G + AI 融合全景圖 -
比 VBA 更強的 RPA 來了！Power Automate Desktop 零程式打造辦公室流程自動化$560$442 -
$611深入淺出 Windows API 程序設計：編程基礎篇 -
AWS 職場實戰手冊 - 企業架站、安全防護、費用監控，用最省錢的方式紮實學會！$620$490 -
Python 資料科學實戰教本 - 爬蟲、清理、資料庫、視覺化、探索式分析、機器學習建模，數據工程一次搞定！$680$537 -
軟體專案估算$620$484 -
Google 的軟體工程之道｜從程式設計經驗中吸取教訓 (Software Engineering at Google)$880$695
相關主題
商品描述
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
