As artificial intelligence accelerates its connection between the digital world and the real economy, industrial intelligence is expanding beyond information processing to include continuous perception and understanding of actual physical conditions. Multi-spectral AI, based on multi-band light and heat perception such as ultraviolet, visible light, near-infrared, and thermal infrared, uses signal alignment, band fusion, and AI model analysis to convert physical states like temperature, radiation, discharge, overheating, combustion, and surface or structural abnormalities into quantifiable digital features. This provides a foundation for state assessment, risk early warning, and decision support. Currently, multi-spectral AI has been widely applied in various security scenarios such as data centers, power systems, new energy, public safety, and urban governance. The multi-spectral AI industry in China is moving from commercial validation to a stage of large-scale deployment.
Recently, Frost & Sullivan (hereinafter referred to as "Frost & Sullivan") officially released the "2026 Multi-Spectral AI Industry Development White Paper" (hereinafter referred to as "The White Paper"). The White Paper analyzes the multi-spectral AI industry in terms of definition, technical principles, application scenarios and customer value, industrial chain structure, business models, market size, competitive landscape, industry development trends, and industry value model, systematically presenting the current development status and future evolution direction of the multi-spectral AI industry.
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"The White Paper" starts with defining and positioning multi-spectral AI, clarifying its relationship with physical AI, perceptual intelligence, and other sensing technologies. It analyzes the application value of multi-spectral AI in typical scenarios such as data centers, power systems, new energy, public safety, and urban governance. On this basis, the White Paper further outlines the industry's industrial chain and business model, calculates the global and Chinese multi-spectral AI industry market size, analyzes the performance of manufacturers in the Chinese market and the industry competition situation, and explores the future development of the industry around multi-modal fusion sensing, end-cloud collaboration, product miniaturization, low cost, and potential application prospects.
The following is a selection from the white paper. For detailed content, scan the QR code:
01
Definition and Scope of the Multi-Spectral AI Industry
01
Definition and Positioning of Multi-Spectral AI
Multi-spectral AI is a technical capability that based on multi-band light and heat perception such as ultraviolet, visible light, near-infrared, and thermal infrared, uses signal alignment, band fusion, and AI model analysis to convert physical states like temperature, radiation, discharge, overheating, combustion, and surface or structural abnormalities into quantifiable digital features, supporting state assessment, risk early warning, and decision support.
In the artificial intelligence technology system, multi-spectral AI is one of the core implementations of the optical sensing path in the physical AI sensing layer, but not the whole of it. Sensing methods such as heat, sound, electricity, and force can be used to fill the optical sensing blind spots or cooperate with physical signals. The outputs of multi-spectral AI mainly include early warnings, diagnoses, assessments, handling suggestions, and linkage signals. Actual response and handling are still carried out by business systems, personnel, or controlled execution units.
Source: Literature search, Frost & Sullivan analysis
02
Business Model and Customer Value of Multi-Spectral AI
Multi-spectral AI uses multi-channel spectral sensing as an entry point. Through AI computing at the end, cloud, or end-cloud collaboration, it completes calibration, alignment, cross-band fusion, and identification analysis. It connects to business systems, personnel, or controlled execution units in scenarios such as data centers, power, new energy, urban emergency and border protection, and marine areas, forming operable services.
Its customer value is mainly reflected in obtaining more invisible physical information, detecting fault signs in advance, reducing misjudgments and omissions through mutual verification of multi-channel data, shortening judgment time, and reducing unplanned downtime and emergency repairs through early warning and predictive maintenance. As multi-spectral sensing and AI analysis capabilities are further integrated into infrastructure, equipment, and operating environments, the industry is expected to evolve towards an intelligent environment with low disturbance and continuous online operation.
02
Application Scenarios and Value of Multi-Spectral AI
01
Large-scale applications have been formed in pan-security scenarios
Pan-security is the core area where multi-spectral AI has achieved large-scale implementation earlier. Applications in data centers mainly include health monitoring of key power equipment, space perception and mobile inspection in server rooms, as well as dynamic asset perception and location verification. Applications in power systems cover the transmission side, transformation side, and distribution side, and can be used for equipment status monitoring, channel hazard identification, and station safety early warning. Applications in new energy cover battery manufacturing, charging piles, photovoltaics, wind power, and energy storage stations. Applications in public safety and urban governance include forest fire prevention, urban security, fire prevention of cultural relics and ancient buildings, border protection, and marine situation perception.
02
Comprehensive Analysis of the Industrial Chain
Multi-spectral AI has formed a relatively complete industrial chain from upstream sensing components, midstream system integration and intelligent empowerment, to downstream industry applications.
Upstream is the source of industrial innovation, mainly including image sensors, optical components, imaging modules, AI computing chips, precision machinery and structural parts, and other electronic components. Midstream is the core and value-condensing part of the industrial chain. Enterprises integrate upstream components and modules, and inject self-developed algorithms and software to form intelligent products and delivery capabilities for specific scenarios. Downstream is the value realization end of the industrial chain, covering applications in data centers and computing centers, power and energy, industry and public safety, agriculture and resources, consumer and medical fields.
Source: Frost & Sullivan analysis
03
Market Size of Multi-Spectral AI
01
Global and Chinese Multi-Spectral AI Industry Market Size
The global multi-spectral AI industry market size increased from 34.8 billion yuan in 2020 to 58.5 billion yuan in 2024, with a compound annual growth rate of 13.9%. It is expected to grow from 85 billion yuan in 2025 to 305.4 billion yuan in 2030, with a compound annual growth rate of 29.1%.
The Chinese multi-spectral AI industry market size increased from 6.3 billion yuan in 2020 to 13.5 billion yuan in 2024, with a compound annual growth rate of 21.0%. It is expected to grow from 20 billion yuan in 2025 to 79.4 billion yuan in 2030, with a compound annual growth rate of 31.8%, higher than the global market growth rate.
The growth of the Chinese market is driven by factors such as full-stack technology, miniaturized sensors and computing power, continuous release of high-density scenario demands, and increased customized demand. Demands for early signal recognition such as temperature, discharge, and equipment aging in scenarios such as power grids, data centers, new energy, and urban security continue to rise, and the industry service model is gradually evolving towards a hardware-software integration direction.
Source: Frost & Sullivan analysis
02
Sub-sectors of the Chinese Multi-Spectral AI Industry
According to product and service types, the Chinese multi-spectral AI industry includes multi-spectral AI sensing terminals, multi-spectral AI modules, and multi-spectral AI large model services. From 2025 to 2030, the compound growth rates of the above three sub-sectors are expected to be 31.5%, 29.7%, and 41.1% respectively, among which large model services are expected to become a fast-growing sub-sector.
Note: There may be slight differences between the total of multiple sub-sectors and the total due to rounding.
Source: Public information compilation, expert interviews, Frost & Sullivan analysis
04
Competitive Landscape of Multi-Spectral AI
01
Performance of Manufacturers in the Chinese Multi-Spectral AI Industry
From the perspective of competition composition, the Chinese multi-spectral AI market has formed a diversified pattern with domestic and foreign manufacturers competing on the same stage. Overseas manufacturers such as Teledyne, Olympus, and Honeywell still occupy important positions in modules and sensing terminals. At the same time, domestic manufacturers such as SITWE, Tracr, Haiqing Zhiyuan, Qiushi Spectra, SenseTime, Zhipu Huazhang, Gaode Infrared, and Dali Technology have widely entered modules, large model services, sensing terminals, and comprehensive markets. With the continuous development of domestic optics and standardized modules, the application of domestic infrared lenses, filters, and AI chips is deepening. China's multi-spectral AI industry's independent supply and local delivery capabilities are gradually strengthening, and the industry competition focus is also evolving from a single hardware product to the collaborative ability of "module - terminal - model - scenario".
Under this pattern, Haiqing Zhiyuan shows a relatively prominent multi-link business coverage ability. Based on the revenue related to multi-spectral AI in China in 2025, Haiqing Zhiyuan's relevant revenue was 657 million yuan, ranking first. At the same time, Haiqing Zhiyuan was included in the list of key players in multi-spectral AI modules, large model services, and sensing terminals, and used multi-spectral AI modules and power smart inspection solutions as representative cases of the industry business model, reflecting its comprehensive ability to extend from underlying sensing modules to intelligent terminals, model services, and industry scenarios.
Source: Haiqing Zhiyuan's prospectus, expert interviews, Frost & Sullivan analysis
Note: The scope of multi-spectral AI companies is defined as having 50% or more of the revenue related to multi-spectral. The revenue in the multi-spectral AI market ranking does not equal the total revenue of the company.
05
Development Trends of the Multi-Spectral AI Industry
01
Multi-modal Fusion Sensing and End-Cloud Collaboration
Multi-spectral AI is evolving from single sensing to multi-dimensional mutual verification. Based on multi-band sensing such as infrared, ultraviolet, and visible light, some solutions can also collaborate with other sensing modalities such as radar and acoustics, enhancing the judgment ability in complex scenarios through multi-dimensional information mutual verification. At the same time, the computing architecture is gradually evolving towards end-cloud collaboration, with the end side responsible for low-latency reasoning and real-time response, and the cloud combining industry knowledge bases for root cause analysis, situation judgment, and handling suggestion support.
Source: Frost & Sullivan analysis
02
Product Miniaturization
Through SoC heterogeneous computing, super surfaces and computational optics, wafer-level packaging, model lightweighting, and end-side real-time reasoning, multi-spectral AI is evolving from laboratory desktop devices to portable forms and even millimetre-level embedded sensing units, and is gradually integrated into mobile phones, smart watches, drones, and robots.
Source: Frost & Sullivan analysis
03
Low Cost
Cost optimization of multi-spectral AI comes from improving the hardware cost structure and enhancing software deployment efficiency. Technologies such as quantum dot CMOS, non-thermal infrared, and computational spectral imaging help reduce the cost of sensing components; automated calibration, transfer learning, model lightweighting, and end-cloud collaborative iteration help reduce deployment, adaptation, and continuous operation costs, promoting multi-spectral AI from benchmark projects to large-scale applications.
Source: Frost & Sullivan analysis
07
Outlook on the Multi-Spectral AI Market
01
Industry Value Model of Multi-Spectral AI
The value of the multi-spectral AI industry can be explained by the "V=P×S×n" model. Here, P represents the spectral coverage width, reflecting the spectrum range that can be covered and utilized; S represents the number of valid samples, reflecting the scale of valid sampling available for identification and analysis; n represents the number of online connections across the network, reflecting the scale of sensing nodes that actually connect to the system and remain online.
As the spectral coverage expands, multi-spectral AI can obtain more physical characteristic information invisible to the naked eye. As the number of valid samples increases, the industry's observation accuracy of target objects and scenarios, as well as the foundation of AI analysis, are further enhanced. As the number of online nodes and connection forms increases, multi-spectral sensing capabilities will continuously cover more real physical nodes. Thus, the industry value will continue to be released along the direction of "the wider the spectrum, the deeper the identification; the more samples, the finer the data; the broader the connection, the more comprehensive the penetration."
Source: Frost & Sullivan analysis
