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  • 2022 China’s Smart Park Market Report

    Market

    The deploymentof smart parks mainly follows the path from planning and design, constructionand deployment, to management and operation. In general, park property rightsholders or operation management agencies initiate projects. Entities supportingthe construction and development of smart parks can be roughly divided intothree layers, basic layer, platform layer and application layer. Theconstruction of smart parks attaches importance to systematic planning andconstruction, continuous business scenario permeation, and emphasis on platformsystem service functions and user experience. In the past year, the smart parkmarket had a high demand for construction, platform system building, and so on.Local suppliers or suppliers with local branches are preferred.

    Practice

    Smart park providers in China are mostly in theeastern region. They generally have high registered capital and attach greatimportance to technological innovation and patent application. From theperspective of industrial ecology, system integrators, solution providers, andprofessional platforms are the core players in this field. Professionalqualifications, knowledge of the industry, talent teams, cooperative ecology,technical level, and operational services are the key elements of smart parkmarket competition. As smart parks permeate professional business scenarios andoperation and maintenance become increasingly important, elements such asknowledge of the industry, talent teams, cooperative ecology, and operationservices will be more and more important.

    Outlook

    Theconstruction of smart parks in China extends from the east to the central andwestern regions. In the long run, as the demand for smart park construction incentral and western parks and smart park upgrading of eastern parks coexist,the market is still promising. Vertical expansion and refined development ofsmart scenarios in smart parks will go on. The operation and maintenance ofsmart parks will also require more digital talents. It is worth noticing thatunder the refined and vertical development trends of smart park scenarios,business development of smart parks still faces problems such as applicationbarriers, business segmentation, data security, management and operation, andso on.

    Development History andConnotation of Smart Parks

    Smart parksare an important part of smart cities. They integrate information technologiessuch as 5G, IoT, cloud computing, and artificial intelligence to gain newdevelopment capabilities such as interconnection, openness and sharing,collaborative operation, innovative development, and comprehensive perception.The construction of smart parks has three stages.  In the smart park 1.0 stage,  the primary digitalization of single-pointfacilities is realized mainly by improving the information infrastructureconstruction of the park; In the 2.0 stage, digital platforms are constructed,connecting diverse application scenarios. In the 3.0 stage, AI technologies arecompletely integrated into production, life and ecology of the parks, integrating the core systems of production,transportation, life, municipal administration, transportation, energy,commerce, and business in the parks to achieve intelligence upgrading.

    Construction Path and Blueprint of Smart Parks

    The threeperiods of smart park construction and operation, which are planning anddesign, construction and deployment, and management and operation, form asystematic project that gradually applies digital technology in all aspects ofthe parks, and realizes the system engineering of intelligent managementservices, operation and maintenance. Similar to the system architecture of asmart city, the ecosystem of a smart park can be divided into a foundationlayer, a platform layer, and an application layer. The foundation layerincludes perception terminals, network transmission equipment, etc. Theplatform layer mainly includes the foundation platform and management platform.The application layer is the application system and interactive interface forvarious scenarios.

    Smart Park Industry Map

    The core of the smart park industry is in themidstream and upstream of the industry chain

    Smart Park Company Ecology

    Systemintegrators 、solution providers 、professional platform providers are the three types ofcore players in the smart park industry. Among them, system integrators areusually leading technology companies. Thanks to their advantages in basichardware and software and core technologies, they have a great say and usuallycooperate with different solution providers; Solution providers usually playthe role of general integrator or general contractor. They are in charge of theconstruction and operation of smart parks; Professional platform providers areemerging vendors with expertise in some sectors. They compete with the firsttwo types of players, but also have a cooperative relationship with them insome professional sectors. Other software and hardware providers, design andconstruction organizations, and weak current engineering organizations providesupport for the core players.

    Competitive Elements of CoreCompanies

    From theperspective of the three types of core players, which are system integrators,solution providers, and professional platform providers, the key competitiveelements are professional qualifications, industry knowledge, talent teams,cooperative ecology, technological level, and operation services. As smart parks are permeating professional businessscenarios, the importance of operation and maintenance is increasinglyprominent. Elements such as industry knowledge, talent teams, cooperativeecology, and operation services will be more valued.

    New Challenges

    Thefundamental target of smart park business development is providing intelligentmethods for park operation and management to help asset holders and operatorsto achieve comprehensive management and operation objectives such as valuepreservation and appreciation of park assets, efficient operation of industrialecology, improvement of urban service functions, etc. With the continuous improvement of the intelligencelevel of the parks, smart scenarios cover every aspect of the parks. Smart parkconstruction and operation become harder. Both the demand and supply sides ofthe industry need to join hands to overcome difficulties such as high barriers to application inprofessional scenarios, business segmentation, increasingly important datasecurity, and low management and operation level.

    Table of Contents of the FullReport

    1.From Parks to Smart Parks  
    1.1 Concept Connotation and Functions
    1.2 Regional DistributionCharacteristics of Some National-level Parks
    1.3 Trends in the Development of Parks 
    1.4 Development History andConnotation of Smart Parks
    1.5 Technology Elements Driving SmartParks
    1.6 Policy Environment for Smart Parks 


    2. Insights into the Smart Park Market 
    2.1 Construction Path and Blueprint of Smart Parks 
    2.2 Smart Park Industry Map
    2.3 Smart Park Market Demand:Overview  
    2.4 Smart Park Market Demand:Price  
    2.5 Smart Park Market Demand:Procurement  
    2.6 Business Logic of Smart Parks: Focus on Planning and Construction
    2.7 Business Logic of Smart Parks:Penetration into Business Scenarios
    2.8 Business Logic of Smart Parks:Prominent Function of the Platform  
    2.9 Business Logic of Smart Parks: Focusing on User Experience 
    2.10 Distribution of Smart Park Providers 
    2.11 Ecology of Smart Park Providers
    2.12 Competitive Factors of CoreVendors
    2.13 Typical Vendor: Mingyuanyun
    2.14 Typical Vendor: Wanrui


    3. Development Trends of Smart Parks
    3.1 Promising Market Prospects
    3.2 Diversified Scenario Demands 

    3.3 Professional Operation Ecology
    3.4 New Challenges


    Jan 12 2023
  • 2022 Report on Big Data Analysis Platforms in China

    Market situation: The industry boundary is increasingly blurred. Thereare many players in the market. According to deployment model, architectureclassification and capacity replenishment, they can be divided into five types.1) Public cloud vendors focusing on cloud data lake solutions; 2) traditionalsoftware service providers focusing on local big data analysis platforms; 3)Database/data warehouse vendors that provide lightweight data warehousearchitecture; 4) Software vendors that provide service capabilities for thedata application layer; 5) AI vendors that improve data applicationcapabilities. The industry market is in a state of competition and cooperation.

    Architecture selection: Before building platforms, users need to have a clearunderstanding of their data volume and business scenario demand. After findingout the basic functions needed, determine the big data processing framework andtools used in the platform construction. Component selection and overallconstruction of the data analysis layer are crucial to the architecture. Inparticular, the choice of the storage engine directly determines the supportfor offline, online, and real-time scenarios and computing power efficiency.

    Trends: In thetraditional architecture, separated data lake and data warehouse lead to datasilos, resulting in problems in implementation, operation and maintenance, andcosts. The data lake and data warehouse integration forms an integratedarchitecture at the data and query levels, making breakthroughs in real-timeand concurrency, cluster scale, and unstructured data integration, and solvingproblems such as the long modeling path, and weak data consistency. Meanwhile,the platforms can integrate AI self-learning and adaptive capabilities, andenhance the analysis and decision-making capabilities of data users.

    Industry Definition

    Big dataanalysis platforms are used by enterprise to analyze and make decisions in abig data environment. From the perspective of technical architecture,  a big data analysis platform mainly consistsof three levels, data acquisition and storage, computation, analysis anddecision-making.  From the perspective ofservice boundary, the concept of big data analysis platform is smaller thanthat of data center. It emphasizes the data analysis and decision-makingcapabilities of the platform and attaches less importance to the planning,governance and services of the data. On the basis of OLAP, it integratestechnologies such as deep learning. While increasing the depth and breadth ofdata analysis, it also largely increases the friendliness of data services andlowers the threshold on the business side. It can meet enterprises' demand forreal-time enterprise-level wide table analysis, real-time BI report analysis,user behavior analysis, self-service analysis, AI analysis, and so on.

    Industry Map

    Products of vendors in the upstream, middle stream anddownstream of the industry chain overlap with products of midstream big dataanalysis vendors

    Types of Players

    Big dataanalysis platforms gradually shift from product form to integration form. Theindustry market has a lot of players and many service types. The boundary ofthe industry is increasingly blurred. The players can be divided into thefollowing types. 1) Public cloud vendors use cloud-native capabilities for thenatural evolution of disaggregated storage and compute architecture, andprovide data lake solutions that can facilitate access to various types of dataand reduce storage, operation and maintenance costs. 2) Different from cloudvendors that provide services in the form of PaaS, traditional software vendorsprovide integrated big data analysis platform solutions based on localdeployment. 3) Chinese domestic database and data warehouse vendors integrateinnovative technologies to independently research and develop products andarchitectures with excellent storage and analysis performance. 4) Softwarevendors, which provide the application layer of data analysis platforms withcapabilities such as BI analysis, user portrait, intelligent operation andvisual publishing, work with market participants mentioned above to establish acooperative ecology. In addition, AI vendors provide AI capabilities to extendthe application of data, making the process of data access, cleaning, storage,analysis, training, and visual output more automated, enhancing theadaptability of data analysis in different scenarios.

    Trend: Architecture Evolution

    With open dataarchitecture and management model, data lake and data warehouse integrationbuild data warehouses on data lakes to combine their advantages to improveenterprises’ basic technology stack. The integration connects underlyingheterogeneous data sources/platforms, supports the coexistence of differentdata types, and realizes data sharing. Data in the lake can be processeddirectly to reduce data computing, network and costs caused by data redundancyand flow. Compared to traditional data warehouse and data lake solutions, thelake and warehouse integration architecture can enhance the real-time businessprocessing ability and unstructured data governance. The advantages mainlyinclude 1) Perfect data management capability; 2) Strong computing enginesupport; 3) More real-time data; 4) Increased openness. In addition, necessaryfunctions for enterprise-level systems, such as data security, access control,and data exploration are all deployed, tested and managed in the integratedarchitecture of data warehouse and lake.

    Trend 2: AI Integration

    Big dataanalysis has been evolving thanks to the development of artificialintelligence, improving data users’ analysis and decision-making ability frommultiple levels and multiple dimensions. Although the business environment forenterprises has changed dramatically since the outbreak of Covid-19, AI andmachine learning have always been important. As business decision-makingbecomes more connected, more continuous and more scenario-oriented, enterprisesadapt, resist or absorb various factors through AI engineering orchestrationand system optimization to improve adaptive AI capabilities. In this way, theycan quickly adapt to scenario changes and realize faster and more flexibledecision-making. NLP enhances the accurate recognition, analysis and processingof natural language by computer systems, turning search-based analysis into anew visual interaction method. System intelligence converts questions ofnatural language structure into SQL statements for query, which improves easeof use and self-service level, and make it easier for business personnel touse.

    Architecture Selection

    In a broadsense, big data analysis platforms are no longer limited to the product form.They are increasingly like the integration of data application layer, storagelayer, scheduling layer, computing layer, interactive analysis layer, dataservice layer and so on. From the perspective of technical architecture, allbig data analysis platforms' architecture belongs to Lambda or Kappa. From theperspective of the scenario, the architecture can be divided into offline,online and real-time analysis architecture. In the bottom-up hierarchicalintegration state, the difference between the three analysis architectures ismainly caused by the selection of storage and computing engines in the dataanalysis layer. From the perspective of technology, the deployment of the dataanalysis layer is the most complex and innovative. It not only has the featuresof separation of storage and computing and elastic expansion and contraction ofcloud-native data lakes but also has platform decoupling based on docker technologyunder local deployment, which solves the problem of insufficient elasticity ofphysical server resource supply and supports the horizontal expansion ofstorage and computing capabilities. In terms of implementation, user analysisscenarios are converging. There are not only fusion framework of HTAP datawarehouse solutions but also big data analysis platforms that integrate AP andTP scenarios. Users can choose based on their needs.

    Table of Contents of the FullReport

    Abstract


    1 Overview of the Big Data Analysis Platform Industry
    1.1 Industry Definition
    1.2 Technology Evolution (1)
    1.3 Technology Evolution (2)
    1.4 Core Application
    1.5 Core Product (1)
    1.6 Core Product (2)
    1.7 Core Value
    1.8 Evaluation System


    2 Analysis of the Big Data Analysis Platform Market
    2.1 Develop History
    2.2 Driving Factors
    2.2.1 Policy Factors
    2.2.2 Macro Factors
    2.2.3 Micro Factors
    2.3 Industry Map
    2.4 Business Models
    2.5 Market Structure
    2.6 Comparison of Overseas andDomestic Markets
    2.7 Pain Points of Application
    2.8 Trend
    2.8.1 AI Integration
    2.8.2 Architecture Evolution
    2.8.3 Diversified Scenarios

    3 Suggestions on Building Big Data Analysis Platforms
    3.1 General Idea
    3.2 Capability Building
    3.3 Deployment Method
    3.4 Architecture Selection
    3.5 Component Selection
    3.6 Technology Trends


    4 IndustryApplication and Typical Cases  
    4.1 Industry-Overview
    4.2 Industry-GovernmentAffairs
    4.3 Industry-Finance
    4.4 Industry-Retail
    4.5 Industry-Healthcare
    4.6 Industry-Transportation
    4.7 Industry-Education
    4.8 Case Study-Arctic Data


    5 Analysis of Investment in theBig Data Analysis Industry
    5.1 Analysis of the OverallMarket 
    5.2 Analysis of InvestmentRounds 

    5.3 Analysis of Investment Cycle
    5.4 Analysis of Investment Risks

    Jan 05 2023
  • 2022 China’s Enterprise SaaS Industry Report

    In 2021, thesize of China's enterprise SaaS market was 72.8 billion yuan. Its YoY growthrate dropped from 48.7% in 2020 to 35.2% in 2021. The macroeconomic downturnhas led to great pressure. The reduced total demand for enterprise SaaS willcause a sharp decline in the industry's growth rate. It is estimated that in2022 the growth rate of the SaaS industry will fall below 10% for the firsttime. The growth of the SaaS industry in the next three years will rely on thespeed of macroeconomic recovery. According to neutral expectations, China'senterprise SaaS market is expected to reach 120.1 billion yuan by 2024, with acompound annual growth rate of 18.1% from 2021 to 2024.

    With deepeningbusinesses and the accumulation of service experience, the boundaries betweenSaaS products in some tracks are increasingly blurred. SaaS vendors begin todeploy multiple product lines at the same time. Business growth SaaS vendorsstart to expand their business layouts in marketing and CRM. Finance andtaxation and HRM tracks gradually mature. Vendors accelerate their integratedlayout. SaaS tools penetrate horizontally and are integrated with specificbusiness scenarios to exert their value.

    In the future,the opportunities for the SaaS tracks will lie intapping demand and technological dividends and taking advantage of hybridoffice, policy reform, and productivity reshaping. The Covid-19 pandemic leadsto the fast popularization of products such as enterprise collaboration andmobile office. Such products have low thresholds and application scenarios withrigid demand. Once the business model is formed, it is expected to growrapidly. SaaS vendors also need to focus on improving their own competitivenessmainly in four aspects: PLG subtractive thinking, enriching sales forms,accumulating industrial background, and exerting non-scale network effect.

    Overview of the Development ofSaaS Vendors in China

    Digitaltransformation is necessary for contemporary enterprises if they are tosurvive.  However, most of them don‘thave a plan for effectively carrying out digital transformation. From top-leveldesign to implementation, digital transformation faces many difficulties suchas high investment, high risk, and long cycle. Thus, SaaS with characteristicssuch as subscription payment, agile deployment and fast verification is worth atry. According to iResearch, in 2021 the size of China's enterprise applicationsoftware market was 259.2 billion yuan, of which SaaS accounted for 28.1%. SaaSplays a key role in the application scenarios in enterprises' digitaltransformation. It is usually the direct starting point for the digitalizationof specific links. Numerous enterprises in all kinds of industries find thevalue of digital technology/tools in actual business scenarios, Maximizing therole of SaaS as a digital scenario incubator.

    Size and Forecast of China'sEnterprise SaaS Market

    According to iResearch, the size ofChina's enterprise SaaS market reached 72.8 billion yuan in 2021. The  YoY growth rate dropped from 48.7% in 2020 to35.2%. The SaaS industry benefited from remote work at the beginning of theoutbreak of Covid-19. However, it is now affected by the macroeconomic downturnand the reduction of total demand. Its growth rate is estimated to drop below10% for the first time in 2022. The growth of the SaaS industry will rely onthe recovery of the macroeconomy in the next three years. Under neutralexpectations, the scale of China's enterprise SaaS market will reach 120.1billion yuan in 2024, with a compound annual growth rate of 18.1% from 2021 to2024.

    Size of the Enterprise SaaSMarket Segments in China 

    In 2021 the size of the businessvertical SaaS market in China reached 38.9 billion yuan, increasing by 33.2%year on year. In the category of business growth, CRM/SCRM benefits frombusinesses' open source needs and obvious benefit advantage. Tax and financemanagement in the category of operation and management benefited from paperlesspolicies. Collaborative synthesis in the category of collaborative office hasentered the stage of commercial monetization. Their growth rates are higherthan the average growth rate of the business vertical segment.

    China’s Enterprise SaaS MarketStructure and Forecast 

    According to iResearch, in 2021, thebusiness vertical segment and industry vertical segment accounted for 53.4% and46.6% of the scale of China's enterprise-level SaaS market, respectively. Withthe increasing uncertainty of the macroeconomy, some industries haveexperienced large fluctuations due to the pandemic or policies since 2021.Business vertical products have strong versatility among different industries.The industry vertical SaaS products are weaker in handling systemic risks. Itis forecasted that the proportion of the industry vertical segment willcontinue to grow in the next three years but at a lower rate.

    China'sEnterprise SaaS Industry Map

    China'sEnterprise SaaS Industry Map

    The Convergence Trend of SaaSTracks 

    With thedeepening of businesses and the accumulation of service experience, theboundaries between SaaS products in some tracks are more and more blurred. SaaSvendors have begun to deploy multiple product lines at the same time andconnect service links. Since the SaaS products for business growth are moremature and mostly target to increase the sales conversion rate directly orindirectly. This form of cross-track product line layout is obvious amongbusiness growth SaaS vendors. For example, some CRM, SCRM, enterpriselivestreaming and content creative vendors expand to the marketing field, andsome CRM vendors develop intelligent customer services or call center businessto increase customer reach rate.

    Business Layout Ideas of SaaSVendors

    Comprehensive cloud vendors try to better match marketsupply and demand through the form of cloud service complex

    Table of Contents of the FullReport

    Abstract


    1 The Chinese SaaS Market isClosely Related to the Global SaaS Market
    1.1 Overview of the Global SaaSMarket
    1.2 Status Quo of the SaaS Market inthe U.S.
    1.3 valuation of the SaaS Market in the U.S.  
    1.4 Enlightenment from Overseas Markets: India
    1.5 Enlightenment from OverseasMarkets: Deel
    1.6 Overseas Performance of Chinese SaaS Vendors
    1.7 Overview of the Development of Chinese SaaS Vendors

    2 Industry Chain Analysis 
    2.1 Size and Forecast of China‘sEnterprise SaaS Market
    2.2 Size of Enterprise SaaS Market Segments in China 
    2.3 Enterprise SaaS Market Structure andForecast in China  
    2.4 China’s Enterprise SaaS IndustryMap
    2.5 Integration Trend of SaaS Tracks
    2.6 Valuation of Enterprise SaaS inChina  
    2.7 Investment and Financing of Enterprise SaaS in China


    3 Analysis of the Core Players inthe Industry Chain
    3.1 Value Positioning of ChannelSystems of SaaS Vendors

    3.2 Cooperation between SaaS Vendors and Channel Providers

    3.3 Benefit Distribution Between SaaS Vendors and Channel Providers
    3.4 Application of SaaS inthe Cloud Market
    3.5 Analysis of Models in the Cloud Market 
    3.6 Alternative Relationship between SaaS and Low-code

    3.7 Combined Development of SaaS and Low-code

    3.8 Complementary Development of SaaS and Low-code

    4 Case Study of Typical SaaSVendors in China
    4.1 2haohr
    4.2 360 Enterprise Security Cloud
    4.3 Authing
    4.4 Lingque
    4.5 Qianmi
    4.6 SUNKE.COM
    4.7 Tencent TAPD
    4.8 Wangshang Guanjiapo
    4.9 Xuanwu Cloud


    5 Development Trends in China’s SaaS Industry
    5.1 Opportunities for SaaSTracks
    5.2 How Can SaaSVendors Improve Core Competitiveness  
    5.3 SaaS Vendors’Business Layout  


    Jan 04 2023
  • 2022 Insights into Computing Power Networks in China

    Computingpower network has a long construction cycle, and the construction maturityvaries depending on regions, industries, and scenarios. Based on research andinterviews, the report summarizes three factors for evaluating the maturity ofcomputing power network:(1) Basic performance: evaluate the adequacy ofcomputing power network resources, computing power network schedulingcapability and operational capability; (2) Stability: Evaluate the overallsecurity and operation and maintenance capabilities of the computing powernetwork; (3) Development potential: Evaluate whether the computing powernetwork has a scale effect based on the business performance and marketcoverage after the computing power network is put into industrial practice.

    With theadvancement of computing power network construction, the focus gradually shiftsfrom basic resource construction to scenario application.  As the AI capability and solutions are moreand more refined, intelligent computing power will become the key developmentdirection. Meanwhile, the application of computing power networks givesterminals unlimited computing power and deterministic network, which willfurther promote business scenario innovation and scientific research.


    The Connotation and Structure ofComputing Power Network

    A computingpower network is a resource network in the digital age. It takes computing asthe core, realizes connection through the network, and provides matching andscheduling services through perception. The Computing power network has threemajor elements: (1) Computing: the core resource of the computing powernetwork; (2) Perception: the perception of computing power demand in specificscenarios and computing power resources; (3) Connection: the integration ofdecentralized, heterogeneous, multi-level, and idle computing power. Thanks tothese three elements, the computing power network can have functionalattributes and service attributes, efficiently revitalize computing powerresources of the whole society and empower industry applications. Based on itstechnical architecture, thecomputing power network can be divided into three layers, the basic resourcelayer, the scheduling layer, and the operation layer. At the same time,computing power network operation& maintenance and security cover the wholeprocess. Ultimately, the computing power network will empower industryapplications in the form of products or capabilities.

    Development Stage andConstruction Status of Computing Power Network

    Theconstruction and development of the computing power network have three stages:(1) Pan-connection: strengthen the connection attribute of the computing powernetwork, and form an inclusive and interconnected computing power and networkbasic resource pool; (2) Perception fusion: a new operation model of integratedcomputing power and network scheduling is formed based on the perception systemof business scenarios and resource pools. (3) Imperceptible call: The computingpower network breaks the limitation of physical space, and establishes adeterministic computing power connection between terminals, allowing users touse unlimited computing power without feeling it. Currently, the constructionof computing power network is in the pan-connection stage. Led by operators,research institutes and related organizations jointly set projects to carry outresearch, and promote the completion of computing power network resources oflarge data centers and heterogeneous computing power. Computing power networkstill has a long way to go to before realizing mature application.

    Maturity Evaluation System ofComputing Power Network Construction 

    Evaluate stability, basic performance, and developmentpotential based on different construction stages

    Maturity Evaluation System ofComputing Power Network Construction 

    Evaluate stability, basic performance, and developmentpotential based on different construction stages

    Computing Power Network IndustryChain and Map 

    Operatorscooperate with providers of computing power network resources to providecomputing power support for the demand side

    Co-construction Direction ofComputing Power Network

    Constructing acomputing power network requires the full cooperation of all participants inthe industry chain. They need to complement each other in various fields,links, and levels and jointly promote the construction of the computing powernetwork through the penetration and integration of technologies and scenarios.During the construction of a computing power network, operators mainly providetop-level design guidance and basic resources. In terms of software-relatedresource management, scheduling, security, operation and maintenance, computingpower transaction business models and specific business scenarios need deepcooperation between all participants.

    Ecological Construction of theComputing Power Network

    Duringcomputing power network construction, it is necessary for operators to build acomputing power network ecology to gain advanced technologies and managementmodels. Guided by national strategies, the operators need to cooperate withplayers in industry, academia, and research, and promote the formulation ofindustrial policies and standards. At the same time, they closely cooperatewith end-user service providers to break the limitations of traditionalbusiness scenarios and innovate business models, jointly promoting the reformof computing power network business forms in the whole industry. With theadvancement of computing power network construction, the ecologicalconstruction of operators will tilt. However, generally speaking, the social attributeand strategic meaning of computing power networks will remain the same. In thefuture, operators will change their strategy from competition and cooperationto co-existance, and lowerthe threshold of computing power networks by ecological layouts. The computingpower network will benefit all walks of life.

    Table of Contents of the FullReport

    Abstract


    1 Development Background of the Computing Power Network
    1.1 Computing Power in the Era ofDigitalization
    1.2 Computing Power Network is aStage Product of Computing Power Development
    1.3 The Connotation and Structure ofComputing Power Network
    1.4 Value of Computing Power Network
    1.5 Policies Support theConstruction and Development of Computing Power Network 
    1.6 Development Stage andConstruction Status of Computing Power Network
    1.7 Maturity Evaluation System ofComputing Power Network Construction
    1.8 Main Difficulties in ComputingPower Network Construction


    2 Overview of Computing PowerNetwork Construction 
    2.1 Industry Chain and Industry Mapof Computing Power Network
    2.2 Co-construction Direction ofComputing Power Network
    2.3Basic Resource Layer: Construction Concerns
    2.4 Basic Resource Layer:Co-construction Model(1/2)
    2.5 Basic Resource Layer:Co-construction Model(2/2)
    2.6 Scheduling Layer of the ComputingPower Network: Construction Concerns
    2.7 Scheduling Layer of the ComputingPower Network: Co-construction Models and Solutions
    2.8 Operation Layer of the ComputingPower Network: Construction Concerns
    2.9 Operation Layer of the ComputingPower Network: Co-construction Models and Solutions
    2.10 Security Layer of Computing PowerNetwork: Construction Concerns
    2.11 Security Layer of Computing PowerNetwork: Co-construction Models and Solutions
    2.12 Maintenance Layer of theComputing Power Network: Construction Concerns
    2.13 Maintenance Layer of theComputing Power Network: Co-construction Models and Solutions
    2.14 Industry Application Layer: Concernsand Co-Construction Plans
    2.15 Tencent Product Matrix Empowersthe Construction of Computing Power Network

    3 Development Trends in Computing Power Network
    3.1 Ecological Construction ofthe Computing Power Network
    3.2 Demand-oriented Computing PowerNetwork Industry Solutions
    3.3 Computing Power Network IndustryCollaboration Based on AI
    3.4 Computing Power Network MakesScience and Technology More Attractive

    Jan 03 2023
  • 2022 China’s Medical Technology Industry Report

    This report roughly divides the medical technologyindustry into five categories, which are government planning & hospitalmanagement, product & service, service & user, product & user, andsmart payment. Then 15 segments that have made development achievements areselected for maturity evaluation. According to the evaluation results, thethree sub-tracks of digital health management, smart medical record, andmedical insurance informationization, are analyzedfrom the aspects of development status, business model, competition pattern,and development trend.

    The healthcare innovation services created by cloudcomputing, big data, IoT, AI, 5G and other digital technologies provide moreintelligent service for the government, hospitals, and patients, creating themost optimized big healthcare ecosystem—the smart healthcare model. Smartmedical care covers three main scenarios, which are smart hospital, regionalmedical service, family healthcare, reaching all links of medical services. Itscore goal is to enhance service capabilities, improve medical efficiency,improve patient experience and expand service coverage. The overall trend isfrom inside the hospital to outside of the hospital, from modularization tointegration.

    In terms of size, the penetration of medicaltechnology in the overall healthcare market is still low. In the future, withthe combination and convergence of all kinds of technologies, applications andservices, its penetration in the healthcare market will increase. In terms oftechnology, the combination between emerging technologies and medical scenarioswill increase, promoting the optimization of the healthcare service system. Interms of companies, the integration between medical companies and technologycompanies will speed up, contributing to the vertical development of currentservices. In general, the medical technology industry will drive the healthcareservice system to cover all areas, all diseases and the entire process.

    Scope of Medical Technology

    Due to theCovid-19 pandemic, intelligent medical service has sped up, accelerating thepenetration of Cloud Computing, Big Data, IoT, AI and 5G and other digital technologies in segmented medicalscenarios. Also, the smart medical care concept, which is full-cycle healthmanagement and borderless service extension, requires support from varioustechnologies. China’s medical industry has just started itsinnovative ‘medical + X’ growth model. Medicaltechnology refers to using the advanced network, communication, computer anddigital technologies to realize the intelligent collection, conversion,storage, transmission and post-processing of medical information, as well asthe digital operation of various medical services and processes. This reportfocuses on the application of digital technology in all links of the healthcareservice industry chain. It does not conduct research and analysis onbiotechnology such as gene editing, life science infrastructure such as CXO, orproduction and manufacturing of new materials.

    Development Basis of MedicalTechnology: Construction of Technology and Informationization

    Digitaltechnologies such as Cloud Computing, Big Data, IoT, AI and 5G started fastdevelopment around 2015. They gradually provide more support for theconstruction of the medical information system and become the key to improvingthe service and innovation capabilities of medical institutions in the contextof regular pandemic response. In addition, in 2020, over half of the businessesof Chinese hospitals have more than 500 applications (such as hospitalmanagement and guarantee information system, patient visit management, andservice information system, etc.) Most hospitals have accumulated experience inthe informationization of hardware,system, data, and so on. The informationization constructionof Chinese hospitals has achieved phased progress, and will probably experiencea new round of digital transformation supported by technologies.

    Value of Medical Technology

    In its longdevelopment, medical technology first pursues medical value, that is, it usestechnical characteristics to solve practical problems in medical scenarios,thereby meeting clinical needs, enhancing medical services and efficiency, aswell as improving quality management. Then it explores social value. Focusingon completing digital transformation and value reengineering of the medicalindustry at the macro level, it creates an ecology integrating hospitals,communities, governments, academic institutions, venture capital, publicmedical service platforms, local partners, mobile medical management platforms,and medical professionals, etc., solving the problem of uneven distribution ofmedical resources in different regions and cities, and large differences insupply and demand. When shifting from pursuing medical value to pursuing socialvalue, by understanding the demand of various stakeholders in the medicalecosystem, medical technology companies build various technology platforms tobalance interests among various subjects, thereby completing its business modelconstruction, clarifying commercial value, and building a complementary pathfor medical value and social value.

    Medical Technology Industry Map

    Overview of the Digital HealthManagement Industry

    Digital healthmanagement refers to using modern digital technologies for comprehensivemonitoring, evaluation and follow-up intervention on the health risk factors,trying to change from disease treatment to disease prevention. Report on theNutrition and Chronic Diseases Status of Chinese Residents 2020 pointed outthat China has a large sub-healthy population and a large elderlypopulation,  a lot of patients sufferingfrom chronic disease,  and manycritically ill patients. Thanks to the national strategy of “health China”people’s concept of health is shifting from centering on disease treatment tohealth promotion. The huge demand makes the digital health management marketpromising. Based on the concept of full coverage, digital health managementcenters on health management methods and application innovation such asbiomedical technology and information management technology. It is committed tofull life-cycle health management services for different groups, such ashealthy and sub-healthy groups, chronic disease groups, and rehabilitationgroups.

    Introduction to the IntegrationModels

    Horizontalintegration and intelligent development of innovative healthcare services

    A new round of informationrevolution represented by intelligent technology is promoting a profound reformof society and enhancing people’s living standards. “Intelligence” now plays animportant role in technological advancement, system innovation and economictransformation. Smartmedical care is involved in all aspects of healthcare services and has becomean increasingly important development path of medical service. Digitaltechnologies such as Cloud Computing, Big Data, IoT, AI and 5G” have createdinnovative healthcare services, providing more intelligent services forgovernments, hospitals, patients, and others, creating the most optimized bighealthcare ecosystem--smart medical care.

    The Scope and Division of SmartMedical Care

    Smart medical care consists ofsmart hospital systems, regional medical service systems and family healthcaresystems. The smart hospital can be divided into three parts, “smart medicalcare” targeting medical staff, intelligent management for hospital management,and smart services for patients. The regional healthcare system consists ofregional healthcare platforms and public healthcare systems, collecting,processing, and transmitting all information records of communities, hospitals,medical research institutions, and health regulatory departments. The familyhealthcare system is the closest health protection for people’s daily life. Itincludes video medical service for patients who cannot go to the hospital fortreatment; remote medical care for children, the elderly, and patientssuffering from chronic diseases, health monitoring of the mentally handicapped,the disabled, and people with infectious diseases, as well as the intelligentmedication system that can automatically remind patients of the time to takemedication, the contraindications and remaining dose of their medication, etc.

    Development Trends in the MedicalTechnology Industry

    It isestimated that the size of China’s medical care market exceeded 9 trillion yuanin 2021, increasing by 14.6% year on year. As the government and people attachincreasing importance to healthcare, the size is expected to exceed 15 trillionyuan by 2026. As mentioned above, the market size of the "service &user" segment in the medical technology industry is the largest. Thecombined market size of its main tracks, online diagnosis and treatment andpharmaceutical e-commerce, was about 240 billion yuan in 2021. However, it onlyaccounts for 2.6% of the overall healthcare market, which means the penetrationof medical technology in the overall healthcare market remains low.  In the future, with the linkage andintegration of various technologies, applications and services, the segments ofmedical technology will contribute to the systematic optimization of medicalservice models, achieving another leap-forward growth of the overall industryscale.

    Tableof Contents of the Full Report

    Abstract


    1 Overviewof the Medical Technology Industry
    1.1 Scope of Medical Technology
    1.2 Pain Points to be Solved
    1.3 Solid Foundation
    1.3.1 Economy and Policy 
    1.3.2 Technology and InformationConstruction
    1.4 Technological Changes Drive theAdvancement of Medical Service Models

    1.5 “Cloud Computing, Big Data, IoT,AI and 5G: Medical Cloud 
    1.6 “Cloud Computing, Big Data, IoT,AI and 5G: Medical Big Data
    1.7 “Cloud Computing, Big Data, IoT,AI and 5G: Medical IoT
    1.8 “Cloud Computing, Big Data, IoT,AI and 5G: Medical AI
    1.9 “Cloud Computing, Big Data, IoT,AI and 5G: Medical 5G
    1.10 Exploration of the Value of Medical Technology


    2 Analysisof Segments of the MedicalTechnology Industry
    2.1 The Medical Technology Industry Map  
    2.2 Maturity Evaluation of MainSegments of Medical Technology
    2.2.1 Maturity Evaluation Modeland Standard 
    2.2.2 Maturity Evaluation of theSub-Industries 
    2.3 Highly Mature Segments of Medical Technology
    2.3.1 Digital Health Management
    2.3.1.1 Overview of the Digital Health Management Industry
    2.3.1.2  Business Models of Digital Health Management
    2.3.1.3 Environment for the Digital Health Management Industry
    2.3.1.4 Development Direction of Digital Health Management
    2.3.2 Medical Record QualityControl 
    2.3.2.1 Smart Medical Record Concept andValue  
    2.3.2.2 Medical Record Quality ControlProblems and Solutions  
    2.3.2.3 DRGs Concept and Value
    2.3.2.4 Links and Differences BetweenDRGs and DIPs
    2.3.2.5 Relationship Between Smart Medical Record and DRGs

    2.3.2.6 Drivers of Smart Medical Record and DRGs
    2.3.2.7 Smart Medical Record and DRGs Competitive Landscape
    2.3.2.8 Development Directions ofSmart Medical Record and DRGs

    2.4 Overview of Medical Technology Segments with High Expectations  
    2.4.1 AI Pharmaceutical R&D
    2.4.2 Medical Robots
    2.4.3 Digital Treatment
    2.4.4 Digital Twin Medical Care
    2.4.5 Metaverse Medical Care 


    3 Analysis of Integration Models
    3.1 Introduction
    3.2 Concept and Development Historyof Smart Medical Care
    3.3 Architecture and Value of SmartMedical Care
    3.4 Core Goal of Smart Medical Care
    3.5 Data Management and ApplicationAre The Key to Smart Medical Care
    3.6 The Scope and Division of SmartMedical Care
    3.6.1 Smart Hospital

    3.6.2 Regional Medical Service
    3.6.3 Family Healthcare
    3.7 Smart Medical Care Companies


    4 Case Study 
    4.1 WOWJOY Technology
    4.2 Fangzhoujianke
    4.3 Unisound


    5 Insightsinto the Trend in the Medical Technology Industry
    5.1 Development Trends in the Medical Technology Industry
    5.1.1 Huge Development Room  

    5.1.2 New Technologies KeepDeveloping 
    5.1.3 Integration between MedicalEnterprises and Technology Enterprises Speeds Up
    5.1.4 Healthcare Service System Will Cover All Aspects, All Diseases and the Whole Process

    Dec 21 2022
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