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市場調查報告書
商品編碼
1916340
人形機器人:全球市場佔有率和排名、總收入和需求預測(2025-2031年)Humanoid Robot - Global Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031 |
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本報告將全球人形機器人產業檢驗更廣泛的「實體人工智慧」領域內的一個策略性新興產業。
本文所定義的類人機器人是指具有類似人類形態(頭部、軀幹、手臂,通常還包括腿部)、多自由度肢體和靈巧末端執行器的人形人工智慧機器人系統。它們的設計旨在適應最初為人類工作者建造的空間、工作流程和工具集。類人機器人融合了先進的機電一體化技術、高效能運算、大規模人工智慧模型和雲端連接,從而創造出能夠在非結構化環境中感知、推理和行動的具身智慧體。從這個意義上講,類人機器人代表了實體人工智慧最先進的應用之一,展現了數位智慧如何與現實世界中的實體互動緊密結合。
從產品觀點來看,本報告從三個核心維度對人形機器人進行了描述。首先,它們具備「擬人智慧」:高參數感知和決策模型使機器人能夠理解場景、識別物體和人,並透過數據驅動的學習循環不斷提升任務績效。其次,它們具有「形態擬人」的特徵。類人的身體結構,包括雙手操作以及(在許多平台上)雙足或混合腿式運動,都經過精心設計,旨在復用現有的人體基礎設施、工具和人體工學設計(例如,螺絲刀握柄、門的開關以及標準控制面板的操作),從而最大限度地減少對工廠和設施進行待開發區設計的需要。第三,它們具有「廣泛的適用性」。同一基礎平台,配備相應的軟體和工具,即可應用於工廠組裝、倉庫作業、老年護理、檢測,甚至危險環境中的公共任務,從而實現傳統單一用途工業機器人難以企及的規模經濟效益。
在需求方面,報告估計2024年全球人形機器人市場規模約為4.1579億美元,並預測2031年將成長至約290.8億美元。這意味著在2025年至2031年的預測期內,該市場價值的複合年成長率將高達約59.3%,反映出市場正從小規模項目轉向多個行業的早期規模化部署。在銷售方面,預計2024年出貨量約為73,014台,至2031年將成長至約1,290,266台,同期銷量複合年成長率約為50.6%。隨著銷售和製造經驗的積累,該報告預測,致動器、傳動裝置、電子元件、電池和系統整合方面的學習曲線效應將提升機器人的性能和可靠性,同時也將導致平均售價的結構性下降。
本研究的量化基準是基於專有的自下而上和自上而下的建模方法,但與更廣泛的外部共識相符,即人形機器人正從概念階段邁向商業化階段。多項第三方市場研究(儘管其範圍定義和時間跨度各不相同)表明,到2030年,人形機器人的各個細分市場將保持30-40%以上的複合年成長率,並持續高速成長。隨著技術和成本曲線的成熟,預計2030年代和2040年代的市場規模將更大。一家全球投資銀行的長期情境分析表明,到2050年,人形機器人及其相關供應鏈有望創造數兆美元的市場機遇,屆時全球工業、服務和家庭環境中將部署數十億台機器人。
本報告指出的需求促進因素是結構性的,而非週期性的。首先,主要經濟體人口老化和持續的勞動力短缺導致製造業、物流、醫療保健和護理等行業中體力勞動強度大、重複性高且危險性高的職位短缺。其次,人工智慧(尤其是大規模視覺、語言和行為模型以及強化學習)的持續進步顯著提升了機器人識別複雜情境、跨任務泛化和快速適應的能力。這使得人形機器人更加實用,即使在傳統上由人類主導的非結構化環境中也能發揮作用。第三,企業面臨著提高營運安全性、韌性和柔軟性的壓力,這迫使它們考慮採用可快速重新部署和重新訓練的機器人,而不是重新配置成本高昂的固定用途自動化設備。最後,中國、北美、歐洲和亞太部分地區的政府產業政策和資助計畫明確地將機器人和物理人工智慧作為戰略領域加以推廣,進一步加速了相關領域的研發和早期商業化進程。
該報告按細分市場將人形機器人市場分為五大應用領域:工業製造、倉儲物流、服務與護理、教育與研究以及公共與救援,並補充探討了早期商業和娛樂應用案例。在工業製造領域,人形機器人專注於傳統工業機器人和協作機器人柔軟性或重新配置成本過高的任務。例如,軟性組裝、機器監控與輔助、工廠物料搬運、生產線切換以及人性化的工廠中現有設備的現代化改造。人形機器人外型酷似人類,可以與人類共用工作單元、工具和夾具,從而支持漸進式自動化,而非徹底的流程重塑。報告預測,在預測期內,隨著汽車、電子、電池和機械製造商從試點部署轉向在特定工作站和生產線上進行系統性部署,工業製造將成為最大、成長最快的應用領域之一。
在倉儲物流環境中,人形機器人可以作為移動機器人和自動化倉庫系統的補充,在某些情況下甚至可以取代它們。它們非常適合處理複雜的揀貨和放置、箱體搬運、碼垛和拆垛、拖車卸貨以及異常處理等任務,這些任務仍然需要類似人類的觸及範圍、靈巧性和情境判斷能力。該報告強調了履約中心、第三方物流供應商和小包裹中心對人形機器人的濃厚興趣,這些機構面臨著對體力勞動強度大、重複性高的工作崗位的快速需求成長以及人才招聘和留任方面的挑戰。隨著安全標準、吞吐量和整體擁有成本 (TCO) 的提高,倉儲物流有望成為人形機器人大規模應用最快的領域之一,並且是在現有自動化基礎設施的基礎上進行擴展,而不是取代現有基礎設施。
服務和護理行業涵蓋酒店、零售、醫療保健和住宅等環境。在這些行業中,人形機器人將扮演多種角色,從接待員職責(接待、禮賓和資訊諮詢)到基本的配送、手工行政工作,以及在醫院和養老機構提供有限的非臨床支援。報告指出,中期來看,重點將放在協助人類員工(承擔單調且體力消耗大的子任務)上,而不是完全自主地提供護理或與客戶互動。該行業的採用不僅取決於技術能力,還取決於監管因素、倫理和隱私,以及維持人性化的高標準護理的必要性。
教育和研究領域也是重要的早期採用者。大學、技術研究所和企業研發部門正在使用人形機器人平台作為標準化測試平台,探索具身智慧、人機互動 (HRI)、運動和操控技術。報告預測,人形機器人將發展成為學生和研究人員的綜合性“物理人工智慧實驗室”,並將其與模擬環境、課程內容和雲端開發平台整合在一起。這將減少整合摩擦,並創建一個基於領先硬體和軟體堆疊的全球開發者生態系統,從而加速創新,並為平台領先創造鎖定優勢。
公共和救援領域雖然規模較小,但戰略意義至關重要。人形機器人非常適合在危險區域作業,例如震後搜救、工業事故響應、核能設施退役以及危險環境中的基礎設施檢查。它們能夠上下樓梯、操作標準工具並與人機互動介面互動,這些特性對於希望降低第一線救援人員風險的緊急應變機構來說極具吸引力。報告指出,雖然短期內人形機器人的應用可能有限,但公共領域的部署將推動機器人在穩健性、遠端操控、自主性和認證標準等方面取得重要進展,這些進步最終將惠及商業產品。從長遠來看,隨著性能的驗證和法規結構的完善,人形機器人有望成為某些高風險任務的標準配備。
從產品架構的角度來看,本報告將市場分為三大類:雙足人形機器人、輪式人形機器人、輪腿式或可變形平台。雙足設計最接近人類的運動方式,非常適合有樓梯、不平坦地形和複雜佈局的環境,但同時也對機械和計算資源提出了很高的要求。輪式人形機器人將人形上半身與輪式底盤結合,犧牲了一定的地形適應性,以實現高能源效率、簡化控制和低成本。預計它們將主要部署在地面平坦的工廠和倉庫中。輪腿式系統則介於兩者之間,透過複雜的可重建腿輪模組,兼具滾動效率和行走能力。報告預測,雙足平台將主導高性能通用應用領域,而輪式和混合式設計將佔據對成本敏感的特定應用細分市場,尤其是在物流和室內服務行業。
本報告從區域角度涵蓋全球市場,重點關注中國、北美、歐洲、日本和韓國、亞太其他地區、拉丁美洲以及中東和非洲。早期收入來源和試點活動主要集中在中國、北美和歐洲部分地區,這得益於強大的機器人生態系統、對工業自動化的需求以及充足的資金。根據外部對人工智慧人形機器人的市場預測,北美目前在全球收入佔有率中領先(預計在2024年將佔約一半),這反映出該地區在製造業和物流領域的高投資水平和早期應用。同時,在產業政策、本土供應鏈和人口壓力等因素的支撐下,中國預計將在長期內成為最大的單一市場。日本、韓國和新興的亞洲製造地也被視為關鍵成長區域,尤其是在出口導向產業叢集和高階消費性電子領域。
報告指出,在供應方面,人形機器人產業雖然集中度較高,但發展迅速。包括綜合性科技巨頭和專業機器人公司在內的核心廠商群體,將在2024年佔據大部分市場。在高階全尺寸通用型機器人領域,特斯拉、波士頓動力、Figure AI和Agility Robotics等公司正在建構以垂直整合硬體、自主研發的人工智慧技術堆疊以及與藍籌工業客戶進行的大規模試驗計畫為核心的平台策略。在中低階市場,優必選、優創科技和Fourier等新興的中國及全球廠商正積極推動成本削減藍圖,並針對物流、服務和教育等應用領域開發特定應用模式。報告強調,競爭優勢正從純粹的硬體效能轉向全端能力,包括運動規劃和控制軟體、安全系統、雲端協作、模擬和訓練流程,以及服務、維護和融資方案。現實世界中的一些消息,例如某大型汽車集團計劃從 2028 年左右開始在其生產設施中部署人形機器人,初步表明,一旦安全、可靠性和投資回報閾值得到滿足,工業客戶正在為大規模採用人形機器人做準備。
該報告對生態系統進行了廣泛的分析,揭示了一條全新價值鏈的形成,這條價值鏈從關鍵零件供應商(包括高扭矩致動器、精密齒輪、感測器、電池、計算模組和通訊系統)延伸至系統整合商和平台供應商,最終到達終端用戶產業。在上游工程,關節致動器、輕質結構材料、高密度電池和邊緣人工智慧晶片的進步是提升效能、降低成本和功耗的關鍵。在中游,平台公司正在探索各種經營模式,從一次性硬體銷售到機器人即服務、車隊管理訂閱以及面向任務技能和應用的軟體/內容市場。在下游,早期採用者包括汽車製造商及其一級供應商、大型電子商務和物流運營商、尋求「無人化」或高混合、小批量生產的工業製造商、大型醫院和養老院以及頂尖研究型大學。儘管這個多層次生態系統仍在不斷變化,但該報告預測,未來十年內,它將圍繞幾個關鍵的硬體和軟體平台進行整合。
在方法論上,本研究採用自下而上和自上而下的混合方法。在自下而上層面,調查團隊彙整了來自年度報告、投資者報告、產品公告、訪談和其他一手資料的公司層面的人形機器人出貨量、收入和定價數據。我們將這些數據標準化為出廠價(或最佳近似值),並仔細排除非人形產品線。在自上而下層面,我們將這些估計值與宏觀指標進行三角驗證,例如工業自動化支出、機器人應用基準、資本投資計畫、政策框架和公開的試點計畫資訊。我們的模型報告了自2020年以來的歷史數據,並以2024年為基準年,計算了2025年至2031年按細分市場(類型、應用、地區和國家)分類的預測值。這反映了明確的情境假設以及採用德爾菲法收集的行業專家意見。
最後,報告承認了一些關鍵的不確定性和風險,包括人工智慧的發展速度及其轉化為強大的實體能力的程度、組件和系統成本趨勢、可能延緩應用普及的安全事故或監管障礙、客戶接受度和勞資關係方面的考量,以及由於過度投資可能導致供應商在短期內經歷洗牌。然而,報告的核心觀點是,如果將人形機器人視為通用實體人工智慧平台而非獨立產品,它們有可能成為全球自動化基礎設施中具有戰略意義的重要層級。在未來十年,人形機器人的應用將重塑製造業、物流業、服務業、教育業和公共業,同時為它們在家庭環境和日常生活中的更廣泛應用奠定基礎,而這項應用的擴展時間範圍將超過本研究設定的2031年。
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This report examines the global humanoid robot industry as a strategic emerging segment within the broader "physical AI" landscape. Humanoid robots are defined here as anthropomorphic, AI-enabled robotic systems with human-like morphology (head, torso, arms, and in many cases legs), multi-degree-of-freedom limbs, and dexterous end-effectors, designed to operate in spaces, workflows and toolsets originally built for human workers. They embody the convergence of advanced mechatronics, high-performance computing, large-scale AI models and cloud connectivity into embodied agents capable of perception, reasoning, and action in unstructured environments. In this sense, humanoid robots represent one of the most advanced implementations of physical AI, where digital intelligence is tightly coupled with physical interaction in the real world.
From a product perspective, the report characterizes humanoid robots along three core dimensions. First, they are "anthropomorphically intelligent": high-parameter perception and decision models allow robots to understand scenes, recognize objects and people, and continuously improve task performance via data-driven learning loops. Second, they are "humanoid in morphology": the human-like body plan, including bimanual manipulation and (for many platforms) bipedal or hybrid legged mobility, is explicitly chosen to reuse existing human infrastructure, tools and ergonomics-such as gripping a screwdriver, opening doors, or operating standard control panels-minimizing the need for greenfield redesign of factories and facilities. Third, they are "broadly applicable": the same base platform, with appropriate software and tooling, can span factory assembly, warehouse handling, eldercare, inspection and even public-safety tasks in hazardous environments, enabling scale economics that traditional single-purpose industrial robots have struggled to achieve.
On the demand side, the report sizes the global humanoid robot market at approximately US$415.79 million in 2024 and projects it to grow to around US$29.08 billion by 2031. Over the 2025-2031 forecast period this implies a very high compound annual growth rate (CAGR) of roughly 59.3% in value terms, reflecting the transition from small-batch pilots to early scaled deployments in multiple industries. In volume terms, shipments are estimated at about 73,014 units in 2024, rising to approximately 1,290,266 units by 2031, corresponding to a volume CAGR of about 50.6% over the same period. As both volumes and manufacturing experience scale, the report anticipates a structural decline in average selling prices, driven by learning-curve effects in actuators and transmissions, electronics, batteries, and system integration, even as performance and reliability improve.
While the quantitative baseline in this study is built from proprietary bottom-up and top-down modeling, it is consistent with a broader external consensus that humanoid robotics is moving from speculative concept toward commercial reality. Multiple third-party market studies, although using different scope definitions and time horizons, point to sustained high growth-often in the range of 30-40%+ CAGR through 2030 for various humanoid segments-and envision a much larger addressable market by the 2030s and 2040s as technology and cost curves mature. Longer-term scenario work from global investment banks suggests that humanoid robots and their associated supply chains could represent a multi-trillion-dollar opportunity by mid-century, with tens or even hundreds of millions of units deployed worldwide in industrial, service and household settings.
The underlying demand drivers identified in the report are structural rather than cyclical. First, demographic aging and persistent labor shortages in key economies create gaps in physically demanding, repetitive and hazardous roles across manufacturing, logistics, healthcare and care work. Second, continuous advances in AI-especially large vision-language-action models and reinforcement learning-substantially improve robots' ability to perceive complex scenes, generalize across tasks and adapt on the fly, making humanoid formats more viable in unstructured environments that were previously reserved for humans. Third, enterprises face increasing pressure to improve safety, resilience and flexibility of operations, pushing them to consider robots that can be rapidly redeployed and retrained, rather than fixed-purpose automation that is costly to reconfigure. Finally, government industrial policies and funding programs in China, North America, Europe and parts of Asia-Pacific explicitly encourage robotics and physical AI as strategic sectors, further accelerating R&D and early commercialization.
Segmentally, the report structures the humanoid market around five primary application clusters: industrial production, warehouse & logistics, service & care, education & research, and public safety & rescue, with additional discussion of early commercial and entertainment use cases. In industrial production, humanoid robots target tasks where conventional industrial robots or cobots are either too rigid or too costly to reconfigure-examples include flexible assembly, machine tending, intra-factory material flow, line changeovers and brownfield modernization in plants built around human workers. Their human-like form factor allows them to share workcells, tools and fixtures with people, supporting gradual automation rather than wholesale process redesign. Over the forecast period, the report expects industrial production to emerge as one of the largest and fastest-growing application segments, as automotive, electronics, battery and machinery manufacturers move from lighthouse pilots to systematic deployment in selected workstations and lines.
In warehouse & logistics settings, humanoid robots complement or, in some scenarios, substitute mobile robots and automated storage systems. They are positioned for complex pick-and-place, case handling, palletizing and depalletizing, trailer unloading, and exception-handling tasks that still require human-like reach, dexterity and situational judgment. The report highlights strong interest from e-commerce fulfillment centers, third-party logistics providers and parcel hubs that face both rapid demand growth and difficulty recruiting and retaining staff for physically intensive, repetitive roles. As safety standards, throughput performance and total cost of ownership (TCO) improve, warehouse & logistics is expected to become one of the earliest domains where humanoids achieve large-fleet deployment, building on existing automation infrastructure rather than replacing it.
The service & care segment spans hospitality, retail, healthcare and residential care environments. Here, humanoid robots perform roles ranging from front-of-house reception, concierge and wayfinding to basic delivery, tedious administrative workflows, and selected non-clinical assistance tasks in hospitals and senior-care facilities. The report notes that in the medium term, the emphasis is more on augmenting human staff-taking over mundane or physically taxing subtasks-than on fully autonomous caregiving or customer interaction. Adoption in this segment will be shaped not only by technical capabilities but also by regulatory considerations, ethics and privacy, and the need to maintain high standards of human-centered care.
Education & research represents another important early adopter group. Universities, technical institutes and corporate R&D labs deploy humanoid platforms as standardized testbeds for embodied intelligence, human-robot interaction (HRI), locomotion and manipulation research. The report anticipates that humanoid robots will increasingly be bundled with simulation environments, curriculum content and cloud-based development platforms, turning them into holistic "physical AI labs" for students and researchers. This, in turn, should reduce integration friction and seed a global developer ecosystem around leading hardware and software stacks, accelerating innovation and creating lock-in advantages for early platform leaders.
Public safety & rescue is a smaller but strategically critical segment. Humanoid robots are well suited to operate in hazardous domains such as post-earthquake search and rescue, industrial accident response, nuclear decommissioning, and infrastructure inspection in dangerous environments. Their ability to navigate stairs, manipulate standard tools and interact with human-oriented interfaces makes them attractive for emergency agencies seeking to reduce risks to first responders. The report argues that although near-term volumes will be modest, public-safety deployments can drive important advances in robustness, teleoperation, autonomy and certification that will spill over into commercial products. Over the longer term, as performance is proven and regulatory frameworks mature, humanoids could become standard equipment for certain classes of high-risk missions.
From a product-architecture standpoint, the report segments the market into three main types: biped humanoid robots, wheeled humanoid robots, and wheel-legged or transformable platforms. Biped designs most closely mirror human locomotion and are well suited to environments with stairs, uneven ground and complex layouts, but are mechanically and computationally demanding. Wheeled humanoids combine a humanoid upper body with a wheeled lower chassis, trading off some terrain versatility for higher energy efficiency, simpler control and lower cost-making them attractive for factories and warehouses with mostly flat floors. Wheel-legged systems occupy an intermediate position, offering both rolling efficiency and stepping capability via complex, reconfigurable leg-wheel modules. The report expects biped platforms to dominate high-end, general-purpose deployments, while wheeled and hybrid designs capture cost-sensitive and application-specific niches, especially in logistics and indoor services.
Geographically, the report covers the global market with a focus on China, North America, Europe, Japan & Korea, the rest of Asia-Pacific, Latin America, and the Middle East & Africa. Early revenue pools and pilot activity are concentrated in China, North America and parts of Europe, driven by strong robotics ecosystems, industrial automation demand and access to capital. External market estimates for AI-powered humanoids suggest that North America currently commands a leading share of global revenue-over half in some 2024 snapshots-reflecting high levels of investment and early deployments in manufacturing and logistics, while China is widely expected to emerge as the largest single market over the longer term, supported by industrial policy, local supply chains and demographic pressures. Japan & Korea, along with emerging Asian manufacturing hubs, are also highlighted as important growth regions, particularly for export-oriented industrial clusters and high-end consumer electronics.
On the supply side, the report finds that the humanoid robot industry is already relatively concentrated, though highly dynamic. A core group of leading vendors-including both integrated technology giants and specialist robotics companies-accounts for a substantial share of 2024 revenues. In the high-end, full-size general-purpose segment, companies such as Tesla, Boston Dynamics, Figure AI and Agility Robotics are building platform strategies around vertically integrated hardware, proprietary AI stacks and large-scale pilot programs with blue-chip industrial customers. In the mid-range and value segments, vendors such as UBTECH, Unitree, Fourier and various emerging Chinese and global players are pushing aggressive cost-down roadmaps and application-specific variants targeted at logistics, services and education. The report emphasizes that competitive differentiation is shifting from pure hardware performance toward full-stack capabilities: motion planning and control software, safety systems, cloud orchestration, simulation and training pipelines, as well as service, maintenance and financing offerings. Real-world announcements-such as large automotive groups planning to deploy humanoid robots in production facilities from around 2028-provide early evidence that industrial customers are preparing for scaled adoption once safety, reliability and ROI thresholds are met.
The broader ecosystem analysis in the report maps an emerging value chain from key component suppliers (high-torque actuators, precision gears, sensors, batteries, compute modules and communication systems) through system integrators and platform providers to end-user industries. Upstream, advances in joint actuators, lightweight structural materials, high-density batteries and edge AI chips are critical to improving performance while lowering cost and power consumption. Midstream, platform companies are experimenting with business models ranging from one-off hardware sales to robotics-as-a-service, fleet-management subscriptions and software/content marketplaces for task skills and applications. Downstream, early adopters include automotive OEMs and their tier-1 suppliers, major e-commerce and logistics operators, industrial manufacturers pursuing "lights-out" or high-mix production, large hospitals and care institutions, and leading research universities. This multi-layered ecosystem is still in flux, but the report anticipates increasing consolidation around a small number of dominant hardware and software platforms over the next decade.
Methodologically, the study adopts a hybrid bottom-up and top-down approach. At the bottom-up level, the research team aggregates company-level data on humanoid robot shipments, revenues and prices from annual reports, investor presentations, product announcements, interviews and other primary sources, normalizing values to ex-factory prices (or best available proxies) and carefully excluding non-humanoid product lines. At the top-down level, these estimates are triangulated against macro indicators such as industrial automation spending, robotics penetration benchmarks, capital expenditure plans, policy frameworks and disclosed pilot pipeline information. The model reports historical data from 2020 onward, uses 2024 as the base year, and produces forecasts for 2025-2031 by segment (type, application, region and country), with explicit scenario assumptions and Delphi inputs from industry experts.
Finally, the report acknowledges key uncertainties and risks. These include the pace of AI progress and its translation into robust embodied capabilities, the trajectory of component and system costs, safety incidents or regulatory setbacks that could delay deployments, customer acceptance and labor relations considerations, and the possibility of over-investment leading to near-term shake-outs among vendors. Nevertheless, the central finding is that humanoid robots-when viewed as general-purpose physical AI platforms rather than isolated products-are poised to become a strategically important layer in the global automation stack. Over the coming decade, their deployment is likely to reshape segments of manufacturing, logistics, services, education and public safety, while laying the groundwork for even broader adoption in household and everyday environments beyond the 2031 horizon of this study.
Market Segmentation
By Company
Segment by Type
Segment by Application
By Region
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size (valve, volume and price). This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Solar Thermal (CSP) manufacturers competitive landscape, price, sales and revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Sales, revenue of Solar Thermal (CSP) in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 7: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 8: Conclusion.