SkyVision Smoke/Fire Detection: Quality Traceability & Data Closed-Loop

DaoAI SkyVision 0-code video surveillance AI platform (on-site hourly training of proprietary models, behavior/event recognition, edge box real-time alerting, 100% local data processing, DaoAI World semantic understanding) has reduced the average fire response time by −65% in a large chemical park by establishing comprehensive quality traceability and data closed-loop mechanisms, while significantly improving environmental monitoring compliance and regulatory accuracy. In emergency and smart security, especially in production and storage environments involving flammable and explosive materials, early detection of smoke and open flames is paramount to ensuring production safety and preventing major accidents. Traditional security methods face numerous challenges in such complex scenarios, not only in terms of delayed response but also in their inability to provide granular event data to support subsequent quality traceability and continuous improvement.

DaoAI SkyVision 0-code video surveillance AI platform (on-site hourly training of proprietary models, behavior/event recognition, edge box real-time alerting, 100% local data processing, DaoAI World semantic understanding) has reduced the average fire response time by −65% in a large chemical park by establishing comprehensive quality traceability and data closed-loop mechanisms, while significantly improving environmental monitoring compliance and regulatory accuracy. In emergency and smart security, especially in production and storage environments involving flammable and explosive materials, early detection of smoke and open flames is paramount to ensuring production safety and preventing major accidents. Traditional security methods face numerous challenges in such complex scenarios, not only in terms of delayed response but also in their inability to provide granular event data to support subsequent quality traceability and continuous improvement. This chemical park, as a key unit for the production and storage of hazardous chemicals, requires its environmental monitoring and safety early warning systems to strictly adhere to industry standards such as HJ 1480-2026, which demand extremely high accuracy in identifying abnormal conditions like smoke and flames, rapid response times, and complete data recording.

Pain Points: Why This Hurdle Is Difficult to Overcome

Traditional solutions for early smoke and open flame detection in chemical parks face multiple challenges. First, there's a **high risk of missed detections**: In one case, the missed detection rate for manual visual inspection could be as high as 15%, especially at night, against complex backgrounds, or during the initial stages of smoke, easily leading to omissions due to visual obstructions or distracted attention. Second, **delayed response and high false alarm rates**: Traditional algorithms based on thresholds or simple features are sensitive to changes in ambient light, water vapor, and dust, resulting in dozens of false alarms daily, severely diluting the effectiveness of warnings and extending manual review time by an average of 20 minutes. Third, **lack of effective data traceability and closed-loop capabilities**: After an incident, traditional systems can only provide raw video recordings, lacking automatic extraction and correlation of critical data points, evolution processes, and alarm handling records before and after the event. This means that accident cause analysis, responsibility determination, and improvement measures lack data support, making it difficult to meet the requirements of standards like HJ 1480-2026 for environmental monitoring data completeness and traceability.

The root cause of these difficulties lies in the poor adaptability of traditional solutions to complex and dynamic industrial environments. Chemical parks have intricate layouts with numerous pipelines, storage tanks, and building structures, which can cause visual obstructions. Production processes may generate steam and dust, which resemble smoke, leading to easy confusion. Flames from different fuels vary in color, shape, and brightness, increasing the difficulty of identification. Furthermore, traditional solutions incur high deployment and maintenance costs, requiring professional algorithm engineers for model tuning, and have long model iteration cycles, making it difficult to cope with constantly changing security needs and new risks. All these factors contribute to the inadequacy of existing systems in meeting precise regulation, rapid response, and data compliance requirements.

Technical Principles

The DaoAI SkyVision platform fundamentally addresses these pain points through its unique 0-code training, real-time alerting via edge boxes, and DaoAI World semantic understanding capabilities. SkyVision employs advanced deep learning vision foundation models that can autonomously learn complex features of smoke and open flames, rather than relying on predefined rules. Its core strength lies in **on-site hourly training of proprietary models**, meaning users without programming or AI knowledge can train highly customized recognition models within hours using only a small number of normal and abnormal (smoke/fire) samples. This is made possible by DaoAI's APDT positive/few-shot learning technology, which can extract effective features from 1-20 good samples or a few defect samples, significantly reducing the threshold and time cost for model deployment. In terms of recognition mechanisms, SkyVision combines multi-modal feature analysis, not only identifying visual textures and morphological changes of smoke and flames but also capturing dynamic features such as movement trajectories and diffusion speed to effectively distinguish real fire incidents from environmental interferences (e.g., steam, dust).

Compared to traditional methods, the advantages of DaoAI SkyVision are evident: **First, significantly improved recognition accuracy and robustness**. Traditional rule-based AOI or manual visual inspection are susceptible to environmental factors, whereas SkyVision's deep learning-based model adapts to complex and varying lighting, backgrounds, and occlusion conditions. Through the semantic understanding capabilities of the DaoAI World model, it further enhances adaptability and generalization across complex scenarios, reducing false alarm rates by −85%. **Second, significantly accelerated response speed**. Real-time inference via edge boxes reduces alarm latency to milliseconds, enabling instant detection and alerting of abnormal situations. In one case, the average fire response time was reduced by −65%. **Third, data closed-loop and quality traceability capabilities**. SkyVision not only generates alarms but also automatically records detailed information about the event, including time, location, type, alarm level, and subsequent processing status, forming structured data that provides a comprehensive data chain for accident investigation and compliance auditing. This capability is unmatched by traditional video surveillance systems, which often only provide raw video clips and lack deep data value extraction.

Typical Application Scenarios

• **Smoke and Open Flame Detection in Chemical Storage Tank Areas**: In areas with storage tanks for flammable and explosive liquids or gases, SkyVision monitors in real-time using high-resolution cameras to identify smoke or initial flames caused by tank leaks. The challenge lies in the complex background of tank areas with intersecting pipes and potential steam interference. DaoAI SkyVision's semantic understanding effectively distinguishes these interferences.
• **Early Fire Warning in Hazardous Chemical Warehouses**: In enclosed or semi-enclosed hazardous chemical warehouses, timely fire warning is critical. SkyVision is deployed inside warehouses to detect subtle smoke or sparks, preventing fire spread. Challenges include varying lighting conditions and potential visual obstructions from stacked materials. The micro-chain DaoAI platform addresses this through multi-angle deployment and model optimization.
• **Overheating Pre-warning for Critical Equipment on Production Lines**: On chemical production lines, some equipment (e.g., reactors, pump stations) may overheat, emit smoke, or even catch fire due to malfunctions. SkyVision focuses on these high-risk points, identifying abnormal smoke or flames for early warning. Challenges include vibrations and dust in the production environment. The robustness of the DaoAI SkyVision model plays a key role here.
• **Smoke and Spontaneous Combustion Identification in Solid Waste Treatment Plants**: Large quantities of combustible materials accumulated in solid waste treatment plants are prone to spontaneous combustion or smoke generation. SkyVision monitors the entire stacking area, identifying smoke dispersion trends or open flame points. Challenges include large stacking areas, open environments, and the influence of natural factors like wind on smoke morphology. DaoAI World's models better understand these dynamic changes.

Case Study

A hazardous chemical production base under a large state-owned chemical group, covering a vast area, includes multiple production workshops, storage tank areas, and hazardous chemical warehouses. Previously, the base primarily relied on traditional video surveillance systems and manual patrols for fire early warning, but faced issues of high missed detection rates, frequent false alarms, delayed responses, and a lack of effective data traceability. According to production line data, before the introduction of DaoAI SkyVision, the missed detection rate for smoke/open flames by manual visual inspection was approximately 8.5%, the average fire response time was as long as 15 minutes, and there were over 50 false alarms per month due to environmental interference, severely impacting the efficiency of security personnel. To meet increasingly stringent environmental monitoring standards such as HJ 1480-2026, the group decided to introduce an intelligent video surveillance solution.

The DaoAI team deployed the SkyVision platform at the base and, based on the on-site environment, quickly trained a recognition model for the specific smoke and open flame characteristics of the park using a 0-code approach on edge boxes. After going live, real-world testing showed that SkyVision **increased the detection rate for smoke and open flames to 99.1%**, a significant improvement over manual visual inspection. Concurrently, through refined model training and semantic false alarm filtering, the system **reduced the false alarm rate by −85%**, bringing the monthly false alarm count down to fewer than 8. Most critically, the system **reduced the average fire response time to 5.2 minutes**, an average reduction of −65%. Furthermore, SkyVision automatically records detailed information for all alarm events, including timestamps, video screenshots, alarm types, regional positioning, and processing status, building a comprehensive event database that greatly enhanced the quality traceability capabilities for safety incidents, providing a solid data foundation for subsequent risk assessment and improvement.

“DaoAI SkyVision has not only improved our fire early warning efficiency but, more importantly, it has built an unprecedented data closed-loop for safety incidents, making every anomaly traceable and significantly enhancing our risk management capabilities.” – Safety Manager, Chemical Park

DaoAI Solution and Products

The DaoAI SkyVision 0-code video surveillance AI platform is the core of this solution. It leverages the DaoAI World model as a unified foundation, endowing the system with powerful semantic understanding and cross-scenario generalization capabilities. In practical implementation, DaoAI first evaluates and integrates existing surveillance cameras in the park to ensure efficient video stream access to the SkyVision platform. Subsequently, through its unique 0-code training interface, safety management personnel can train custom models for the specific smoke and open flame features of the park within hours on edge boxes, without writing any code, simply by uploading a small number of video clips or images containing smoke and open flames. This process significantly shortens the model development and deployment cycle, allowing customized models to quickly adapt to the complex environmental characteristics of different areas within the park. For instance, the system can use few-shot learning to distinguish subtle differences between water vapor, which might appear in storage tank areas, and real smoke, ensuring accurate alarms.

SkyVision is deployed in a 100% local private mode, meaning all video stream analysis, model inference, and data storage are completed within the enterprise, ensuring absolute data security for sensitive security information and preventing it from leaving the premises. The edge box real-time alerting mechanism ensures that once an anomaly is identified, alarm information (including event type, location, real-time screenshots/video clips) is immediately pushed to the command center and relevant personnel's mobile terminals, achieving second-level response. Furthermore, the DaoAI SkyVision platform offers rich API/SDK interfaces for seamless integration with existing park security management platforms, fire systems, and environmental monitoring systems, forming a unified smart security command and dispatch system to enable data sharing and coordinated response. By continuously learning from feedback from the production line, the DaoAI World model can continuously optimize recognition models and improve system performance, forming a true data closed-loop and continuous improvement mechanism, fully meeting the requirements of industry standards such as HJ 1480-2026 for environmental monitoring data quality traceability and precise regulation.

This solution not only significantly improved emergency response speed and accident prevention capabilities, but more importantly, the comprehensive and structured event data provided by DaoAI SkyVision brought about a revolutionary change in the safety management of chemical parks. By conducting in-depth analysis of historical alarm data, management can identify high-risk areas, assess safety hazards under different seasons or operating conditions, and optimize safety strategies and personnel training accordingly, achieving a shift from passive response to proactive prevention. In this case, after the system went live, the average fire response time in a large chemical park was reduced by −65%, from 15 minutes to 5.2 minutes, significantly reducing the risk of accident escalation, saving approximately ¥35,000 in annual operating costs from false alarm handling and manual review, and greatly improving the park's performance in environmental compliance, effectively avoiding potential hefty fines and damage to corporate reputation.

This article was generated by AI. Customer cases are simulated scenarios based on real product capabilities and figures are illustrative; see product pages for official benchmarks.