The multifunctional communication, positioning, and search-and-rescue interactive intelligent command system, jointly developed by Kaichang and the Shanghai Fire Research Institute of the Ministry of Emergency Management, is equipped with high-performance computing servers, significantly improving rescue response efficiency and reducing accident risks.

I. Addressing the Industry’s Four Major Pain Points

1. Information silos: Wearable smart devices, walkie-talkies, detectors, and other equipment operate independently, preventing commanders from gaining a comprehensive view of the situation on the ground.

2. Fragile communications: Signals are prone to disruption in extreme environments such as fire scenes and underground locations, frequently causing a loss of communication between the front lines and the rear.

3. Lack of Safety Alerts: There is a lack of real-time monitoring and proactive alerts for vital signs, remaining air supply, and toxic gases.

4. Inability of AI Models to Continuously Evolve: Computing power bottlenecks result in long model training cycles and low iteration rates, preventing real-world operational data from being rapidly translated into improved model capabilities.

II. Kaichang’s End-to-End Solution

The true core of Kaichang’s Multifunctional Communication, Positioning, Search and Rescue Interactive Intelligent Command System lies in its continuously iterated and optimized core algorithms. Computing servers are the algorithms’ “running shoes,” but the algorithms themselves are the true “brain.” The algorithms are the key factor determining the level of intelligence in rescue operations.

(1) Smart Wearable Device Matrix

Smart wearable individual soldier devices and AR breathing mask-integrated units combine thermal imaging, gas detection, and wireless communication to enable fire source localization, personnel search and rescue, and first-person view transmission; vital signs wristbands and electronic pressure gauges monitor heart rate, blood oxygen levels, and remaining oxygen supply in real time; Bluetooth audible and visual alarm lights automatically trigger high-decibel alarms and pinpoint locations when personnel are in distress.

(2) Converged Communication Networking

Multiple communication channels—including public networks and ad-hoc networks—ensure real-time interoperability of voice, data, and location information.

(3) Computing Power Servers

① Model Training

Operational data from every emergency response is automatically fed into the server, forming a training corpus based on real-world scenarios. This supports continuous 24/7 online training, enabling core algorithms—such as deflagration prediction and personnel posture recognition—to become increasingly intelligent with use. At the same time, GPU computing power is utilized for large-scale disaster simulations, significantly reducing the cost of live-force drills.

② Model Inference

The computing server simultaneously processes dozens of AR video streams from the front lines, identifying suspected hazards and trapped individuals in real time; it integrates multi-source data—including location, vital signs, and gas concentration—to generate a comprehensive situational map; AI alerts are pushed within seconds, shifting the response from reactive to proactive prevention and control.

③ Back-End Command Center

A single dashboard integrates data from all dimensions, supported by real-time rendering from computing servers, ensuring low-risk on-site decision-making.

III. Key Achievements

★ Efficiency Leap: Rescue response efficiency has significantly improved, with command and decision-making evolving from “listening” to “seeing.”

★ Risk Reduction: The active early-warning mechanism has lowered the risk of accidents.

★ Seamless Communication: Even in fire environments, the command chain remains uninterrupted, data is not lost, and location tracking remains accurate.

★ Computing Power Enables Model Evolution: High-performance computing servers support continuous online training and iteration of AI models, with the accuracy of deflagration predictions improving month by month as real-world operational data accumulates.

★ Cost Reduction Through Simulation: GPU-accelerated virtual simulations replace a large number of live-force drills, reducing training costs.

★ Digital-Intelligence Closed Loop: Operational data → model training → AI inference → precise early warning → data re-accumulation, forming a positive feedback loop for continuous evolution.