Computer Information Technology Application Practice systematically 

explains cutting-edge technology systems and practical approaches in areas like 

Artificial Intelligence, Software Engineering, and Medical Informatization.

Chapter 1: Core Artificial Intelligence Technology Systems. This chapter 

focuses on the principles of machine learning algorithms (supervised, unsupervised, 

and reinforcement learning), combining them with feature engineering and 

financial risk control case studies. It delves into TensorFlow/PyTorch framework 

development, covering model compression, GAN applications, and explainable AI 

research. It analyzes NLP word embedding, text classification, machine translation, 

and intelligent Q&A technologies. It dissects the practical application of computer 

vision in object detection (YOLO optimization), medical image segmentation, 

and industrial quality inspection. Finally, it outlines the construction of intelligent 

decision-making systems using knowledge graphs and multi-source data fusion, 

encompassing recommendation algorithms, quantitative finance, and smart 

transportation scheduling.

Chapter 2: Software Engineering Development Practices. This chapter covers 

the practice of agile development (Scrum/Kanban processes)and microservices 

architecture (DDD/Spring Cloud / service mesh), including the design of CI/CD 

pipelines (Jenkins / GitLab / containerization / IaC). It details the establishment 

of a software quality assurance system (automated testing, performance tuning, 

security scanning, chaos engineering). It also analyzes large-scale project 

development case studies such as e-commerce platforms, banking systems, and 

government clouds.

Chapter 3: Medical Informatization System Construction. This chapter 

focuses on developing Electronic Health Record (EHR)systems based on HL7 

FHIR standards, enabling structured storage and multi-center sharing. It explores 

AI analysis of medical images (DICOM preprocessing, 3D reconstruction, 

pathology assisted diagnosis). It covers the design of telemedicine platforms (low￾latency consultations, IoT integration, electronic prescription review). Finally, it 

outlines the construction of a medical data security system (HIPAA compliance, 

differential privacy, blockchain-based evidence).

Chapter 4: Smart Hospital Management Innovation. This chapter discusses 

the development of RFID smart warehousing and high-value consumables 

traceability systems. It covers the optimization of medical resource scheduling 

(emergency triage, surgical scheduling, bed allocation). It explores enabling 

cardless medical treatment for the entire patient journey and smart navigation. It 

also delves into the practical application of DRG payment systems (medical record 

quality control, medical insurance review, cost accounting).

Chapter 5: Big Data Technology Applications. This chapter analyzes Spark/

Flink/Hadoop distributed computing frameworks. It covers the design of lakehouse 

architecture and metadata governance engines. It explains the implementation of 

Kafka stream processing and real-time data warehousing. It also discusses the 

application of government big data in population forecasting, urban vital signs, 

and epidemic prevention and control.

Chapter 6: Ethical and Security Aspects of Intelligent Systems. This chapter 

explores algorithm explainability (SHAP values/counterfactual explanations). 

It covers the practice of privacy-preserving computation (federated learning/

homomorphic encryption). It outlines the establishment of an AI ethics review 

mechanism (evaluation matrix/penetration testing). Finally, it describes the 

construction of a system offense and defense framework (adversarial example 

defense/red team-blue team).

Chapter 7: Edge Computing and Internet of Things. This chapter covers 

the development of edge intelligent devices (model quantization/secure boot). It 

discusses the application of 5G network slicing and URLLC latency optimization. 

It details the construction of industrial IoT platforms (time-series databases/OPC 

UA gateways/predictive maintenance). It also covers the implementation of multiprotocol interconnection and energy scheduling in smart homes.

Chapter 8: Blockchain Technology Practices. This chapter focuses on the 

development of secure smart contracts (Gas optimization/vulnerability prevention). 

It covers the implementation of multi-level credential circulation and on-chain 

rights confirmation in supply chain finance. It discusses the design of DID digital 

identity authentication and judicial evidence preservation systems. Finally, it 

explores the application of blockchain in carbon emission rights confirmation and 

pollution traceability.

《计算机信息技术应用实践》系统阐述人工智能、软件工程、医疗信息

化等前沿技术体系与实践路径。

第一章人工智能核心技术体系。聚焦机器学习算法原理（监督 / 无监督 /

强化学习），结合特征工程与金融风控案例；详解 TensorFlow/PyTorch 框架

开发，涵盖模型压缩、GAN应用及可解释性研究；解析NLP词嵌入、文本分类、

机器翻译与智能问答技术；剖析计算机视觉在目标检测（YOLO 优化）、医

疗影像分割及工业质检的实践；构建知识图谱与多源数据融合的智能决策系

统，覆盖推荐算法、量化金融及智慧交通调度。

第二章软件工程开发实践。实践敏捷开发（Scrum/Kanban 流程）与微

服务架构（DDD/Spring Cloud/ 服务网格），设计 CI/CD 流水线（Jenkins/

GitLab/ 容器化 /IaC）；建立软件质量保障体系（自动化测试、性能调优、

安全扫描、混沌工程）；剖析电商平台、银行系统、政务云等大型项目开发

案例。

第三章医疗信息化系统构建。基于 HL7 FHIR 标准开发电子病历系统，

实现结构化存储与多中心共享；研究医学影像 AI 分析（DICOM 预处理、三

维重建、病理辅助诊断）；设计远程诊疗平台（低延时会诊、物联网接入、

电子处方审核）；构建医疗数据安全体系（HIPAA 合规、差分隐私、区块链

存证）。

第四章智慧医院管理创新。开发 RFID 智能仓储与高值耗材追溯系统；

优化医疗资源调度（急诊分级、手术排程、床位分配）；实现患者全流程无

卡就医与智能导诊；实践 DRG 支付系统（病案质控、医保审核、成本核算）。

第五章大数据技术应用。解析 Spark/Flink/Hadoop 分布式计算框架；设

计湖仓一体架构与元数据治理引擎；实现 Kafka 流处理与实时数仓；应用政

务大数据于人口预测、城市体征及疫情防控。

第六章智能系统伦理与安全。研究算法可解释性（SHAP 值 / 反事实解

释）；实践隐私计算（联邦学习 / 同态加密）；建立 AI 伦理审查机制（评

估矩阵 / 穿透测试）；构建系统攻防体系（对抗样本防御 / 红蓝对抗）。

第七章边缘计算与物联网。开发边缘智能设备（模型量化 / 安全启动）；

应用 5G 网络切片与 URLLC 时延优化；构建工业物联网平台（时序数据库 /

OPC UA 网关 / 预测性维护）；落地智能家居多协议互联与能源调度。

第八章区块链技术实践。开发安全智能合约（Gas 优化 / 防漏洞）；实

现供应链金融多级凭证流转与链上确权；设计 DID 数字身份认证与司法存

证系统；探索区块链在碳交易确权与污染溯源的应用。