Jingkun Technology Leverages Ontology Technology to Solve Four Major Challenges Facing Urban Safety Agents
I. Four Underlying Pain Points in High-Density Public Security Scenarios That General Large Models Cannot Solve
Open public spaces such as urban waterfront scenic areas and core commercial districts often face the impact of hundreds of thousands of instantaneous passenger flows during holidays and large-scale events. The spatial structure with narrow passages and multiple interlaced entrances and exits can easily induce safety accidents such as stampedes and local congestion. Traditional manual management, general large models, and outdated automation systems cannot adapt to such dynamic decision-making scenarios. The industry's pain points are concentrated in four dimensions: data, semantics, rules, and real-time reasoning, which are also insurmountable underlying defects when general large models are implemented in the industry.
The first pain point is the fragmentation of multi-source heterogeneous data, which prevents unified perception of the overall situation. The public security monitoring system integrates more than ten independent business systems, including video surveillance, mobile LBS signaling, ticketing, transportation, and meteorology. Various types of data belong to different structures such as images, time series, space, and text, with update frequencies ranging from seconds to hours, and incompatible spatial statistical granularities. In the traditional mode, staff need to switch between multiple screens to view multiple systems. There is no unified spatio-temporal benchmark for data fusion, so managers cannot fully and in real time grasp the distribution of people flow across the entire domain, and local risk blind spots are very likely to occur. General large models do not have the ability to unify and weave data, cannot independently connect and align multi-source heterogeneous data sources, and can only passively receive standardized text organized manually, losing the ability of real-time perception.
The second pain point is the serious semantic gap in cross-system data, which makes AI unable to understand the real business meaning. Even if multi-source data aggregation is completed, general large models can only perform text generation and cannot establish associations between industry business concepts. For example, when the two sets of data "regional people flow density continues to rise + queues at subway entrances and exits exceed 200 meters" appear in parallel, the general model cannot automatically map the business risk of "imminent large-scale congestion", nor can it identify the spatial transmission causal relationship between the two sets of people flow data. Industry terms, regional spatial relationships, and risk judgment logic only exist in expert experience and paper plans, and there is a lack of standardized semantic carriers, so AI cannot independently make risk judgments with business value.
The third pain point is that business rules come from scattered sources and have highly coupled logic, making traditional systems unable to automatically execute complex linkage strategies. Passenger flow warning thresholds, hierarchical response plans, cross-departmental linkage mechanisms, and crowd evacuation strategies are scattered in policy documents, historical accident reviews, and security expert experience libraries. The rules have complex logics such as time sequence constraints, condition dependencies, and mutual exclusion conflicts. A typical scenario is "if the density in Area A exceeds 4 people/㎡ and lasts for 5 minutes, while the adjacent Area B's people flow continues to pour into Area A, then the Level 1 warning for Area A will be suspended". This judgment includes three constraints: space, time, and two-way people flow transmission. Traditional automation systems only support fixed if-then single-condition scripts, cannot handle multi-entity and multi-dimensional coupling rules, and can only rely on manual case-by-case judgment and manual activation of disposal measures.
The fourth pain point is that the decision-making link is passive and lagging, lacking real-time deduction and closed-loop iteration capabilities. The complete passenger flow safety disposal process includes six links: data fusion, passenger flow prediction, hierarchical warning, plan scheduling, execution evaluation, and strategy iteration. The traditional management mode completely relies on manual post-incident judgment, and the emergency plan is activated only after congestion forms, lacking the ability of early warning. General large models do not have the capabilities of time series simulation and crowd dynamics deduction, and cannot predict the arrival time of passenger flow peaks and the spread path of congestion; after the disposal measures are implemented, there is no quantitative effect evaluation mechanism, so the management strategy can only repeat trial and error, and cannot form a "prediction-disposal-optimization" closed-loop intelligent decision-making.
The essence of the above four pain points is that general large models lack a standardized and evolvable industry semantic base, only have generation and basic reasoning capabilities, and lack constraints, navigation, and verification mechanisms. They cannot safely, compliantly, and accurately complete dynamic decisions in complex public security scenarios, and the industry urgently needs ontology technology as underlying support.
II. As the Semantic Engineering Base, Ontology Targetedly Eliminates Four Implementation Challenges in Scenarios
Jingkun Technology has created computable and dynamically evolvable domain ontology modules, as the core semantic track of the Harness engineering, paired with four supporting capabilities of data weaving, rule engine, and digital twin sandbox, to solve the four pain points in public security scenarios in a layered manner, adding a business constraint framework to large models, enabling AI to complete safety decisions in an understandable, controllable, and traceable manner.
Aiming at the pain point of multi-source heterogeneous data silos, the ontology relies on the three-layer architecture of data weaving to realize unified semanticization of global data. The first layer processes structured time-series data such as ticketing and transportation in real time through data flow orchestration; the second layer uses intelligent document parsing tools to extract thresholds and linkage rules from plans and regulations, converting unstructured documents into structured indicators; the third layer relies on visual recognition models to parse video frames and output spatial data such as passenger flow density and people flow direction. On this basis, a master data model including regions, entrances and exits, police forces, and turnstiles is built, all entities, attributes, and spatial relationships are registered to the ontology semantic network, all heterogeneous data are uniformly mapped to the same spatio-temporal benchmark, realizing integrated perception of the overall situation on a single map, and completely breaking down system silos.
Aiming at the pain point of AI semantic gap, the ontology constructs a complete industry knowledge graph to open up the conversion channel between data and business concepts. The ontology has a built-in complex event processing engine, which can capture the spatio-temporal coupled complex risk state in real time and actively push high-risk warnings; it also supports natural language-driven ontology graph queries, enabling agents to independently traverse associations such as regional connectivity and people flow transmission, and automatically retrieve cross-system multi-dimensional indicators. The ontology solidifies scattered business concepts, spatial relationships, and risk causal logic into a computable semantic network. General large models rely on the ontology to complete business translation, no longer simply generate text, but output risk judgments with scenario value, bridging the semantic gap between AI and industry business.
Aiming at the pain point that complex coupled business rules cannot be automatically executed, the ontology is equipped with a two-layer reasoning engine and automated action modules to support global collaborative disposal. The single-object rule engine can complete simple judgments such as the density threshold of a single region in seconds; the global reasoner integrates temporal logic and entity dependencies, automatically handles complex rules with multi-condition, mutual exclusion, and time-series coupling, and balances conflicting disposal strategies. After the rules output disposal instructions, the action engine on the one hand retains a snapshot of the ontology data to fully record the decision-making process, ensuring that the entire process is auditable and traceable; on the other hand, it automatically links external systems such as turnstiles, police terminals, and traffic lights to complete fully automated closed-loop operations of flow restriction, evacuation, and police force scheduling, transforming complex rules scattered in documents and experts' minds into standardized logic that AI can independently execute.
Aiming at the pain point of lacking real-time decision deduction capabilities, the ontology is paired with digital twin snapshots and a deduction sandbox to build a proactive prediction closed loop. The system continuously stores full-time ontology snapshots in slices, embeds Prophet time series prediction and crowd dynamics physical models, relies on years of historical passenger flow data to identify the periodic passenger flow patterns of holidays and seasons, and deduces the scale of crowd aggregation and congestion propagation paths in future periods in advance. The isolated deduction sandbox supports parallel simulation of multiple sets of emergency response plans, quantitatively compares the mitigation effects of different evacuation and flow restriction measures without affecting the real production environment, and automatically selects the optimal disposal strategy. After the measures are implemented, the system updates the ontology snapshot, records the disposal effect and iterates the rule parameters, completing the full closed loop from prediction and warning, simulation deduction, automatic scheduling to strategy optimization, changing the traditional passive response management mode.
III. Ontology Technology Forms Differentiated Implementation Advantages, and Large-Scale Verification Has Been Completed in Public Security Scenarios
The core value of the ontology-centered solution lies in making up for the four major shortcomings of general large models in industry implementation, and targetedly solving the scenario pain points at the data, semantic, rule, and deduction levels. At present, it has been implemented and verified in the large passenger flow safety management scenarios of core landscape areas in many cities.
From the perspective of technical recognition, Jingkun Technology has been selected into Gartner's China Data and AI Related Technologies Hype Cycle for four consecutive years, covering the underlying core technology tracks of ontology such as data weaving and data governance, and has deeply participated in the formulation of multiple industry standards. The ontology semantic framework is standardized, compliant, and reusable, and can be quickly replicated to similar dynamic decision-making scenarios such as urban transportation, financial risk control, and emergency command. For government, cultural tourism, public security, and other government-enterprise customers, the entire solution takes the ontology semantic base as the differentiated core, avoiding the problems of general large models such as hallucinations, unexplainability, and poor business adaptation, and meeting the hard requirements of government scenarios for safety audit, full-process traceability, and compliant management.
From the perspective of implementation effect, in a closed waterfront scenic area scenario with hundreds of thousands of instantaneous passenger flows, the ontology-driven public security intelligent agent achieves millisecond-level risk identification, minute-level passenger flow prediction, and cross-system automatic scheduling, eliminating the need for a large number of manual multi-screen on-duty, transforming the traditional post-incident disposal mode into proactive risk prediction, and greatly reducing the probability of safety accidents such as stampedes and congestion. The entire technical architecture is designed around the four major scenario pain points. As the underlying unified semantic carrier, the ontology connects the entire process of data fusion, semantic understanding, rule reasoning, and simulation deduction, providing a implementable, quantifiable, and iterable engineering path for decision-making intelligent agents, and becoming the core digital base for refined governance of urban public safety.