The evolution of Unmanned Aerial Vehicles (UAVs) has profoundly transformed emergency response paradigms worldwide. In China, the rapid advancement of AI, 5G, smart sensing, and new energy technologies has accelerated the development of sophisticated China UAV drone platforms. These drones are increasingly deployed for critical tasks such as aerial imaging for disaster assessment, search and rescue using thermal imaging, and the delivery of essential supplies like AEDs and medical kits to isolated areas. Despite this progress, a significant challenge persists. Many rescue drones currently in operation are modified from commercial or industrial models, leading to inherent shortcomings such as low equipment integration, weak functional adaptability, and insufficient professional specialization for complex rescue environments. This gap hinders the optimal efficiency and reliability of China UAV drone operations in life-saving scenarios. Therefore, the dedicated, systematic design of rescue drones is paramount for enhancing operational efficiency and reducing risks. This study establishes an integrated innovative design process based on the Analytic Hierarchy Process (AHP), Quality Function Deployment (QFD), and the Theory of Inventive Problem Solving (TRIZ) to optimize the equipment configuration and exterior design of rescue drones, thereby enhancing their adaptability and practicality in emergency missions within the China UAV drone ecosystem.
Theoretical Framework and Research Process
The proposed methodology integrates three established theoretical models to create a structured, user-driven, and conflict-resolving design pathway. The Analytic Hierarchy Process (AHP), developed by Saaty in the 1970s, is a quantitative decision-making method that transforms qualitative problems into a hierarchical model. It calculates the weight distribution of various elements through pairwise comparison matrices, allowing designers to identify and prioritize core design objectives and avoid functional excess. The process involves constructing a judgment matrix A, calculating priority weights, and performing a consistency check to ensure rational judgment.
The judgment matrix is defined as:
$$A_{n \times n} = \begin{bmatrix}
a_{11} & a_{12} & \cdots & a_{1n} \\
a_{21} & a_{22} & \cdots & a_{2n} \\
\vdots & \vdots & \ddots & \vdots \\
a_{n1} & a_{n2} & \cdots & a_{nn}
\end{bmatrix}$$
Where \( a_{ij} \) represents the relative importance of element \( i \) compared to element \( j \) based on the standard 1-9 scale. For a group of m experts, their judgments are aggregated using the geometric mean:
$$a_{ij}^{aggregated} = \left( \prod_{k=1}^{m} a_{ijk} \right)^{\frac{1}{m}}$$
The weight vector \( W_i \) for each element is then calculated using the geometric mean method:
$$W_i = \frac{\left( \prod_{j=1}^{n} a_{ij} \right)^{\frac{1}{n}}}{\sum_{i=1}^{n} \left( \prod_{j=1}^{n} a_{ij} \right)^{\frac{1}{n}}}, \quad i = 1,2,3, …, n$$
Consistency is verified using the Consistency Ratio (CR):
$$CI = \frac{\lambda_{max} – n}{n – 1}, \quad CR = \frac{CI}{RI}$$
where \( \lambda_{max} \) is the principal eigenvalue of the matrix, and RI is the Random Index. A CR value less than 0.1 is acceptable.
Quality Function Deployment (QFD) is a user demand-driven product development method, with the House of Quality (HoQ) as its core matrix tool. It translates subjective customer needs (the “whats”) into objective engineering characteristics (the “hows”). The relationship strength between needs and characteristics is quantified, and the importance of each technical characteristic is calculated as:
$$I_j = \sum_{i=1}^{n} (w_i \times r_{ij})$$
where \( I_j \) is the importance of technical characteristic \( j \), \( w_i \) is the weight of customer requirement \( i \) (from AHP), and \( r_{ij} \) is the relationship score between requirement \( i \) and characteristic \( j \).
TRIZ, developed by Altshuller, provides a systematic approach to innovation by resolving technical contradictions. It offers a contradiction matrix that links 39 improving and worsening parameters to 40 inventive principles, guiding designers toward innovative solutions.
The integrated research process for the China UAV drone design is as follows:
- Collect and systematize core user requirements from rescue scenarios via surveys and expert interviews. Employ AHP to calculate the weight distribution of these requirements.
- Construct the House of Quality (HoQ) using QFD. Input the weighted user requirements and relevant technical characteristics to derive prioritized design parameters.
- Identify technical contradictions from the correlation matrix (“roof”) of the HoQ. Apply the TRIZ contradiction matrix and inventive principles to resolve these conflicts.
- Synthesize the outputs from AHP, QFD, and TRIZ to generate and validate a conceptual design for the rescue drone.
User Demand Analysis and AHP Weighting for China UAV Drones
To ground the design in real-world needs, core requirements were gathered from stakeholders including emergency responders, UAV engineers, and safety managers. These were categorized into a three-level AHP hierarchy: the goal (optimal rescue drone design), criteria (System Communication F, Appearance Structure A, Rescue Mission S), and sub-criteria (specific demands).
| Criteria | Sub-Criteria (Demand Indicators) | Demand Description |
|---|---|---|
| System Communication (F) | F1: Real-time Data Transmission | Ensures continuous information flow for ground control decision-making. |
| F2: Positioning & Navigation System | Provides accurate, stable navigation for quick target arrival. | |
| F3: Remote Control | Enables operators to conduct search/rescue from a safe distance. | |
| F4: Fault Emergency Protection | Automatic return or safe landing in case of low power or system failure. | |
| Appearance Structure (A) | A1: Durable & Reliable | Withstands harsh rescue environments. |
| A2: Payload Capacity | Can carry essential supplies (first-aid kits, communication gear). | |
| A3: Flight Stability | Maintains stable flight for precise task execution. | |
| A4: Long Endurance | Capable of extended mission times. | |
| A5: Portability | Lightweight and easy to carry. | |
| Rescue Mission (S) | S1: Visibility & Identifiability | Highly visible for easy recognition by ground personnel and victims. |
| S2: Remote Rescue Guidance | Broadcasts voice or enables video call for remote instruction. | |
| S3: Multi-scenario Applicability | Adaptable to diverse rescue environments. | |
| S4: Rescue Supply Delivery | Precise payload delivery with adjustable methods. | |
| S5: Fast Response Speed | Minimizes preparation and deployment time. |
Expert pairwise comparisons were conducted, and the resulting judgment matrices were processed to calculate weights. All matrices passed the consistency check (CR < 0.1).
| Criteria | Weight | Sub-Criteria | Local Weight | Global Weight | Rank |
|---|---|---|---|---|---|
| System Communication (F) | 0.3249 | F1: Real-time Data Transmission | 0.2376 | 0.0772 | 7 |
| F2: Positioning & Navigation | 0.3154 | 0.1025 | 3 | ||
| F3: Remote Control | 0.2879 | 0.0935 | 4 | ||
| F4: Fault Protection | 0.1591 | 0.0517 | 10 | ||
| Appearance Structure (A) | 0.2863 | A1: Durable & Reliable | 0.1176 | 0.0337 | 13 |
| A2: Payload Capacity | 0.2564 | 0.0734 | 8 | ||
| A3: Flight Stability | 0.3697 | 0.1058 | 1 | ||
| A4: Long Endurance | 0.1703 | 0.0488 | 12 | ||
| A5: Portability | 0.0860 | 0.0246 | 14 | ||
| Rescue Mission (S) | 0.3888 | S1: Visibility & Identifiability | 0.1311 | 0.0510 | 11 |
| S2: Remote Rescue Guidance | 0.1345 | 0.0523 | 9 | ||
| S3: Multi-scenario Applicability | 0.2353 | 0.0915 | 5 | ||
| S4: Rescue Supply Delivery | 0.2643 | 0.1028 | 2 | ||
| S5: Fast Response Speed | 0.2347 | 0.0913 | 6 |
The analysis reveals that for a specialized China UAV drone, Rescue Mission demands are the most critical criterion (0.3888), followed by System Communication (0.3249) and Appearance Structure (0.2863). At the indicator level, Flight Stability (A3), Rescue Supply Delivery (S4), and Positioning & Navigation (F2) are the top three priorities, providing clear quantitative guidance for the design focus.
QFD: Translating Needs into Technical Parameters
With the weighted user requirements defined, the next step involves translating them into actionable engineering characteristics via the House of Quality. The technical characteristics for the China UAV drone were categorized as shown below.
| Primary Characteristic | Secondary Characteristic | Tertiary Characteristic |
|---|---|---|
| E1: Form & Structure | E11: Rotor Type | |
| E12: Folding/Stowing Mechanism | ||
| E13: Fuselage Form Factor | ||
| E14: Material | ||
| E15: Rapid Response Deployment | ||
| E2: Communication & Monitoring | E21: Remote Comm. System (Satellite, 5G, Radio) | |
| E22: Imaging System (IR, HD camera) | ||
| E23: Audio System (Loudspeaker, siren) | ||
| E3: Payload & Delivery | E31: Payload Mounting & Release Mechanism | |
| E32: Rescue Supply Container | ||
| E33: Endurance (Battery Life) | ||
| E4: Identification & Signaling | E41: Rescue Livery & Visual Markings | |
| E42: Lighting System | ||
| E5: Safety & Intelligent Control | E51: Fault Protection (Auto-return, Emergency Land) | |
| E52: Intelligent Obstacle Avoidance (AI pathing) | ||
| E53: Hovering Capability (Precision, Stability) |
The House of Quality was constructed by establishing relationship scores \( r_{ij} \) (Strong=5, Medium=3, Weak=1) between each user requirement (i) and technical characteristic (j). The absolute importance \( I_j \) for each technical characteristic was then calculated using the formula \( I_j = \sum (w_i \times r_{ij}) \). This process identified key design parameters such as Flight Stability (linked to E11, E13, E53), Rescue Supply Delivery (linked to E31, E32), and Positioning & Navigation (linked to E21, E52) as having the highest design priorities, confirming and refining the AHP results. The “roof” of the HoQ also highlighted correlations (positive and negative) between technical characteristics, revealing potential design conflicts to be addressed.
TRIZ-Based Contradiction Analysis and Resolution
The correlations identified in the QFD’s HoQ roof point to specific technical contradictions. These were formalized using TRIZ’s 39 Standard Engineering Parameters and resolved by consulting its contradiction matrix and 40 Inventive Principles. This step is crucial for innovating the China UAV drone design beyond incremental improvements.
| Conflict Identified | Contradiction Type | Improving Parameter | Worsening Parameter | Suggested Inventive Principles | Applied Design Solution |
|---|---|---|---|---|---|
| Folding Mechanism vs. Rapid Response | Technical | #36: System Complexity | #25: Time Loss | #6: Universality, #29: Pneumatics/Hydraulics | Use of compression/torsion springs to store mechanical energy for automatic, rapid unfolding upon release, simplifying deployment and saving time. |
| Rescue Container vs. Endurance | Technical | #1: Weight of Moving Object | #19: Energy Consumption of Moving Object | #12: Equipotentiality, #31: Porous Materials, #35: Parameter Changes | Use of lightweight composite materials (CFRP) for the container and fuselage; aerodynamic optimization to reduce drag, thereby preserving battery life. |
| Rescue Container vs. Stowage Mechanism | Technical | #36: System Complexity | #8: Volume of Stationary Object | #1: Segmentation, #16: Partial/Excessive Action | Modular, detachable container design that can be separated into smaller units for transport, reducing stowed volume and complexity. |
| Lighting System vs. Endurance | Technical | #18: Brightness (Illumination) | #19: Energy Consumption of Moving Object | #1: Segmentation, #19: Periodic Action, #32: Color Change | Implement zoned or modular LED lighting with dynamic brightness control and pulsed operation modes to provide necessary visibility while minimizing power drain. |
Comprehensive Drone Design Proposal
Synthesizing the outputs from the AHP, QFD, and TRIZ analyses, a comprehensive design for a next-generation China UAV drone is proposed. The design prioritizes flight stability, precise payload delivery, robust communication, and operational adaptability.
Form & Material: The drone features a streamlined carbon fiber composite fuselage (approx. 480x460x220mm) for high strength, corrosion resistance, and minimal weight, directly addressing the endurance and durability requirements quantified by AHP.
Rotor System: A quadcopter configuration with high-torque brushless motors and durable composite propellers ensures superior flight stability (the top-ranked requirement) and precise hovering capability (E53), essential for tasks like supply delivery.
Rescue Payload System: A dedicated, aerodynamically shaped rescue container (240x150x100mm), compatible with standard AEDs, is mounted via a magnetic interface for quick attachment/detachment. Delivery is achieved either via safe landing or, in complex terrains, via a lightweight, high-strength Dyneema® tether, ensuring versatility (S3, S4).
Identification & Lighting: High-visibility red livery and a rear-facing warning beacon fulfill the identifiability requirement (S1). A front-mounted, intelligent LED lighting system provides illumination for night operations while managing energy consumption as per TRIZ resolution.
Audio System: An integrated loudspeaker enables remote voice guidance (S2) for victims and crowd dispersal instructions, enhancing the drone’s role as a direct intervention tool.
Imaging & Sensing: The drone is equipped with a multi-sensor pod including a thermal imaging camera, a high-definition optical camera with zoom, and a laser rangefinder. This suite supports all-weather, day-night search and rescue, 3D mapping, and obstacle detection.
Communication & Control: It employs a robust dual-channel communication system combining O4-like low-latency HD video transmission with 4G/5G cellular backup, ensuring reliable real-time data flow (F1) and control (F3) even in challenging environments typical for China UAV drone operations.
Safety Systems: An integrated fault protection system (E51) monitors vital parameters, triggering automated responses like return-to-home or gentle landing in emergencies. AI-powered obstacle avoidance (E52) further ensures operational safety.
Modular Expansion: An external mounting interface allows for the flexible attachment of mission-specific modules (e.g., searchlights, gas sensors), enhancing the multi-scenario applicability (S3) of the platform.
Human-Drone Interaction: The design emphasizes quick deployment with a simple arm-folding mechanism and a one-button battery ejection system, minimizing response time (S5).
Design Evaluation
To validate the proposed design, a qualitative evaluation was conducted based on the five primary technical characteristic categories derived from the QFD process. Experts were asked to rate the design’s performance in each category on a scale of 1 to 5. The results, visualized below, indicate high overall satisfaction, with particularly strong scores in Safety & Intelligent Control and Communication & Monitoring—areas critical for a reliable China UAV drone. The evaluation confirms that the integrated AHP-QFD-TRIZ process successfully guided the development of a balanced and highly capable rescue drone design.
| Evaluation Category | Average Score (1-5) | Performance Level |
|---|---|---|
| Form & Structure Design (E1) | 4.5 | Excellent |
| Communication & Monitoring System (E2) | 4.7 | Excellent |
| Payload & Delivery System (E3) | 4.4 | Very Good |
| Identification & Signaling System (E4) | 4.3 | Very Good |
| Safety & Intelligent Control (E5) | 4.8 | Excellent |
| Overall Satisfaction | 4.5 | Excellent |
Conclusion
This study presents a systematic and integrated approach to the innovative design of rescue drones, specifically within the context of advancing China UAV drone technology for emergency response. By sequentially applying AHP, QFD, and TRIZ, the research transitions effectively from vague user needs to a concrete, optimized product concept. The AHP method provided a quantitative foundation for prioritizing often-conflicting user demands, with results highlighting flight stability, supply delivery, and navigation as paramount. The QFD process successfully translated these weighted needs into critical engineering parameters, while its correlation matrix explicitly revealed inherent technical contradictions. The application of TRIZ principles offered inventive pathways to resolve these conflicts, leading to practical design solutions such as spring-assisted unfolding, modular payloads, and intelligent lighting control. The final design proposal embodies these solutions, resulting in a drone that excels in integration, functional adaptability, and professional rescue capability. This structured methodology not only enhances the efficiency and objectivity of the design process but also serves as a robust reference framework for the future development of specialized, high-performance China UAV drone systems aimed at saving lives and mitigating disaster impacts. Future work will focus on refining the human-drone interactive systems and conducting physical prototyping and field testing to further validate the design’s performance in real-world rescue simulations.