Intelligent Fireworks Governance Addresses Six Major Pain Points in Fireworks Safety Supervision with Ontology Technology
I. Inadequate Adaptability of Regulatory Models, Numerous Shortcomings in Retail Safety Governance for Fireworks and Firecrackers
During the 2026 Spring Festival, a series of safety accidents occurred at fireworks and firecracker retail outlets across multiple regions in China, resulting in casualties and property losses, which exposed systematic loopholes in the end-point supervision of this sector. As a high-risk flammable and explosive category, fireworks and firecracker retail outlets are characterized by scattered layouts, huge quantities, uneven operating conditions, and weak safety awareness among practitioners. Traditional manual inspection models and general AI technologies are difficult to meet the all-weather, dynamic, and high-precision regulatory requirements, making intelligent and digital transformation a core rigid demand for industry safety governance.
From the perspective of front-line regulatory practices, there are six core pain points in fireworks and firecracker retail safety governance, which form an efficiency bottleneck that traditional human-based supervision cannot break through, and are also the key reason why general large models struggle to adapt to such scenarios. The details are as follows:
1. Insufficient Coverage of Regulatory Manpower, Massive Blind Spots in Full-Domain Supervision
The staffing of grassroots emergency regulatory personnel is generally tight, while the number of fireworks and firecracker retail points in the jurisdiction is large and scattered. On average, a single regulator is responsible for 50 to 80 retail outlets. Under the traditional manual inspection model, completing a full-coverage inspection round every week is already the limit of manpower, and the effective coverage rate of overall manual inspections is less than 20%. A large number of scattered retail points in towns, townships, and suburban areas have long been in a state of insufficient and weak supervision, where hidden risks cannot be detected in a timely manner, making full-domain closed-loop supervision difficult to implement.
2. Gaps in Inspection Time, Lack of All-Weather Supervision Capabilities
Manual inspections are highly dependent on offline on-duty presence, which can only cover daytime hours on working days. During peak periods of public procurement and high accident occurrence, such as from 18:00 at night to 8:00 the next day, weekends, and statutory holidays, supervision is basically in a blank state. Most fires and explosion accidents involving fireworks and firecrackers occur during non-working hours. The traditional regulatory model cannot achieve 7×24-hour uninterrupted monitoring, leading to severe delays in risk detection and a near-complete lack of proactive prevention capabilities.
3. Strong Randomness of Violations, Great Difficulty in Evidence Collection for Post-Incident Intervention
Typical violations such as open-flame smoking inside stores, illegal hot work, and temporary goods placement outside stores are characterized by instantaneity, randomness, and guerrilla-style occurrence. Most operators will operate in a standardized manner during regulatory inspection hours, but conduct illegal operations during unmonitored periods. By the time regulators arrive for on-site verification, most violations have already ceased and the scene has been restored, making evidence collection difficult, missing the best opportunity for risk intervention, and resulting in repeated violations that cannot be effectively curbed.
4. Extensive Control of Goods Stacking, Lack of Regular Compliance Monitoring
In accordance with industry specifications, the stacking height of fireworks and firecrackers must not exceed 2 meters, and it is strictly forbidden to block fire exits or occupy public spaces outside stores for goods placement. However, in offline operations, problems such as over-height goods stacking, blocked passages with debris, and illegal goods display outside stores are very common. Traditional supervision relies on manual visual judgment and on-site measurement, which is inefficient and highly subjective, making it impossible to achieve high-frequency, regular, and standardized monitoring, thus hindering the implementation of normalized compliance control.
5. Difficult to Clarify Business Status, Low Efficiency in Compliance Time Period Control
Fireworks and firecracker retail outlets have flexible and independent business hours, and some stores have problems such as over-time operation and illegal night operation. Regulatory authorities cannot grasp the real business status of all outlets in real time, nor can they accurately verify the operational compliance of each time period. They can only rely on traditional methods such as random on-site spot checks and telephone inquiries for verification, which is time-consuming, labor-intensive, and limited in coverage, making it difficult to achieve precise and dynamic time-period compliance supervision.
6. High Failure Rate of Monitoring Equipment, Lack of Closed-Loop Equipment Operation and Maintenance
Most retail outlets have long neglected the operation and maintenance of monitoring equipment, leading to frequent problems such as cameras being blocked by goods, artificially displaced, equipment aging and damage, network disconnection, and power outages, which directly form blind spots in video surveillance. The traditional model relies on regular manual on-site inspections to troubleshoot equipment failures, resulting in delayed failure detection and untimely rectification, which renders a large number of monitoring points ineffective and cannot guarantee the completeness and effectiveness of the entire video supervision system.
The six core regulatory pain points overlap and restrict each other, pushing the traditional human-based supervision model to its absolute efficiency ceiling. Meanwhile, the technical limitations of general large models further hinder the intelligent transformation of the industry. General large models lack a dedicated semantic base adapted to high-risk safety supervision, cannot understand the exclusive business rules, risk logic, and regulatory standards of the fireworks and firecracker industry, and can only complete basic image recognition and text generation. They are unable to identify composite risk events with multiple coupled conditions, and their output judgment results have no compliance basis and cannot be implemented, making it difficult to solve real front-line regulatory problems. The industry is in urgent need of exclusive, professional, and actionable intelligent solutions.
II. Building a Five-Layer Intelligent Architecture, Ontology Technology Creates a Dedicated Regulatory Solution
Targeting the core regulatory pain points of the industry, KINGEL Cloud Technology has launched the "Smart Fireworks Governance" intelligent agent platform. Adhering to the lightweight construction concept of "fully leveraging existing resources and implementing intelligent upgrades", the platform takes ontology semantic technology as the core to build a full-link technical architecture covering perception, cognition, action, evolution, and supervision, providing a standardized, iterable, and auditable intelligent solution for fireworks and firecracker retail safety supervision, which fills the dual gaps of traditional human-based supervision and general AI.
At the basic perception level, the platform builds a multi-source integrated data access system to minimize hardware transformation costs. The system can directly reuse existing video equipment at retail outlets that meets the GB/T 28181 standard without large-scale replacement and update; at the same time, it flexibly accesses IoT devices such as smoke sensors, temperature sensors, and access control systems to construct a multi-dimensional on-site perception network, and connects to data from government business systems such as business licensing and credit supervision, breaking data silos between different platforms to achieve unified integration of full-domain data, providing complete data support for intelligent supervision.
The cognitive layer is the core differentiated capability of the platform, which relies on domain ontology modeling to build an industry-exclusive knowledge graph, completely solving the AI semantic gap problem. The platform converts scattered expert implicit experiences such as regulatory standards, equipment specifications, disposal processes, and risk levels into machine-recognizable structured semantic networks, clarifying exclusive concepts and their associations such as fire hazards, warehousing risks, and personnel violations, breaking down semantic barriers in data from multiple departments. Relying on the composite event processing engine and dual-layer reasoning engine, the platform can accurately identify composite high-risk events such as "smoking inside the store + over-height goods + missing fire extinguisher", and automatically match regulatory bases and disposal plans. All judgment results are interpretable and traceable, fundamentally avoiding the problem of AI hallucinations.
On this basis, the platform realizes automated closed-loop disposal through multi-agent collaboration. For different scenarios such as fires and illegal operations, it automatically schedules agents for fire communication, video tracking, access control management, and broadcast guidance to work collaboratively, achieving second-level response and minute-level disposal. At the same time, it is equipped with a memory evolution module that archives and precipitates the full-process data of each risk disposal, automatically refining and optimizing regulatory rules and plan templates. Combined with the real-time monitoring function of equipment status, the platform has independent iteration capabilities, and the equipment availability rate has steadily increased to over 98%. In addition, the platform builds exclusive functional portals for provincial, municipal, and enterprise-level users, supporting natural language interaction, situational large-screen visualization, and lightweight mobile operations, greatly lowering the threshold for regulatory operations.
III. Fully Highlighted Implementation Effects, the Intelligent Supervision Model Achieves Large-Scale Reuse
Compared with traditional computer vision (CV) solutions, the "Smart Fireworks Governance" intelligent agent has essential technical advantages. Traditional CV can only recognize preset training targets, prone to missed and false reports for complex scenarios and new types of violations, and only has pixel-level recognition capabilities without the ability to understand business logic. In contrast, the KINGEL Cloud Technology intelligent agent integrates multi-modal understanding and ontology knowledge graph, with capabilities of scenario semantic interpretation, logical reasoning, and risk generalization, greatly reducing the false report rate. At the same time, the project adopts the architecture of "ordinary video equipment + cloud intelligent agent", reducing front-end hardware costs by more than 50%, and the cloud-based centralized operation and maintenance model also greatly optimizes long-term operating costs.
At present, this solution has been fully implemented on a large scale in a provincial-level fireworks and firecracker intelligent safety management project, covering nearly 3,000 fireworks and firecracker retail enterprises across the entire prefecture. Only a small number of non-standard equipment has been replaced, maximizing the retention of original hardware resources, with significant cost-effectiveness advantages. After the project is implemented, a three-level efficient supervision system has been constructed, with prominent quantifiable results. Provincial emergency management departments can achieve a unified overview of the situation of all outlets on a single screen, with AI identifying high-risk events in seconds, improving emergency response efficiency by 80%; municipal and county-level regulatory departments realize 7×24-hour uninterrupted intelligent inspections, reducing manual inspection costs by more than 85%, automatically identifying 8 types of high-frequency violations, and increasing regulatory work efficiency by more than 20 times; retail enterprises realize regular safety self-inspection and self-correction, effectively reducing the probability of safety accidents such as fires and explosions.
As a technology enterprise deeply engaged in the fields of enterprise-level intelligent agents and data intelligence, KINGEL Cloud Technology has been selected in multiple Gartner core technology hype cycles for four consecutive years, deeply participating in the formulation of industry standards, with its technical compliance and reusability verified through multiple scenarios. The implemented "Smart Fireworks Governance" solution, with ontology technology as the core, builds a practical, quantifiable, and iterable safety governance engineering system, providing a new model for the digital and intelligent supervision transformation of the high-risk retail industry.