The rapid proliferation of unmanned aerial vehicles (UAVs) has fundamentally reshaped the landscape of public security, compelling police agencies worldwide to evolve. In the Chinese context, the concept of “new quality police combat capability” (NQPCC) emerges as a strategic framework that integrates professionalism, advanced mechanisms, and big data analytics to address emerging threats. This article, from my first-person perspective as a practitioner and researcher in this field, delves into the intricate relationship between NQPCC and drone regulation. I argue that effective drone regulation cannot be achieved through traditional, reactive policing; instead, it requires a paradigm shift towards proactive, intelligence-led, and technologically empowered enforcement. By synthesizing theoretical insights with empirical observations, I outline the current challenges in drone regulation—ranging from jurisdictional ambiguities to technological gaps—and propose concrete optimization paths. These include the establishment of specialized regulatory teams, the deployment of integrated detection-strike networks, and the refinement of on-site enforcement protocols. Throughout this discussion, I emphasize the recurring theme of drone regulation as a litmus test for modern policing, demonstrating how a balanced approach between “flight service” (promoting low-altitude economy) and “control” (ensuring public safety) can be achieved through the lens of NQPCC.
1. The Nexus between New Quality Police Capability and Drone Regulation
The term “new quality police combat capability” refers to the comprehensive, modernized combat effectiveness and operational efficiency that public security organs cultivate by enhancing political integrity, service awareness, legal proficiency, technical skills, and emergency response capabilities in the new era. In the domain of drone regulation, this concept manifests in two critical dimensions: flight service (Fei Fu) and control (Guan Kong). These two aspects are not contradictory but complementary, forming the dialectical core of NQPCC in the context of UAV management.
1.1 Flight Service as an Intrinsic Requirement of NQPCC
Low-altitude economy, driven by civilian manned and unmanned aircraft, has been identified as a strategic emerging industry. Policymakers have explicitly called for the creation of low-altitude economy as a new growth engine. Consequently, ensuring the healthy development of this sector becomes a legitimate duty of public security organs. From the NQPCC perspective, “flight service” means that police authorities should proactively facilitate legitimate flight activities while ensuring safety. This involves streamlining approval processes for special operations such as relay flights, cargo drops over crowds, and swarm flights. I submit that true NQPCC must incorporate a service-oriented mindset, transforming the police from mere enforcers into enablers of economic transformation. For instance, the regulation of drone regulation in the context of micro and light UAVs should be minimal, allowing unrestricted flights in designated zones to foster innovation.
To quantify this service dimension, I define a Service Efficiency Index (SEI) for drone regulation:
$$ SEI = \frac{\text{Number of approved legal flights}}{\text{Total flight requests}} \times \frac{1}{\text{Average approval time (hours)}} $$
A higher SEI indicates a more service-oriented regulatory regime, which aligns with the NQPCC goal of promoting high-quality development. This index can be monitored over time to assess the effectiveness of regulatory reforms.
1.2 Control as an External Manifestation of NQPCC
While service promotes development, control ensures security. The “control” aspect of drone regulation involves the ability to detect, identify, intercept, and adjudicate unauthorized or malicious UAV activities. NQPCC demands that police transition from “sweat-based policing” to “smart policing” by leveraging big data and integrated command systems. The control capability can be captured by a Control Effectiveness Score (CES):
$$ CES = \frac{\text{Number of successfully intercepted and adjudicated ‘black flights’}}{\text{Total detected unauthorized flights}} \times 100\% $$
This metric reflects the precision and deterrence of police actions. A high CES is the outward proof of NQPCC in action. For example, the 2024 incident at Tianjin Binhai Airport, where a ‘black flight’ caused massive flight cancellations, underscores the catastrophic consequences of weak control. Strengthening control, therefore, is not merely a technical issue but a core requirement of NQPCC.
| Dimension | Definition | Key Metrics |
|---|---|---|
| Flight Service (Fei Fu) | Facilitating legitimate UAV operations; reducing administrative barriers | SEI, approval time, number of registered users |
| Control (Guan Kong) | Detecting, intercepting, and prosecuting illegal UAV activities | CES, detection rate, response time, number of enforcement actions |
| Integration | Balancing service and control through smart mechanisms | Composite Security-Development Index (CSDI) |
I propose a unified metric, the Composite Security-Development Index (CSDI), to gauge the overall performance of drone regulation under NQPCC:
$$ CSDI = \alpha \cdot SEI + (1-\alpha) \cdot CES \quad (0 \leq \alpha \leq 1) $$
where α reflects the policy emphasis. In the early stages of low-altitude economy, α may be set higher to promote industry growth; as risks escalate, α decreases to prioritize control.
2. Challenges in Drone Regulation
Despite the conceptual clarity, the practical implementation of NQPCC-guided drone regulation faces formidable barriers. I have identified three principal categories of challenges based on field observations and policy analysis.
2.1 Unique Risks Inherent to UAV Operations
The very nature of UAVs—their numbers, modes of operation, and technological heterogeneity—presents unprecedented regulatory dilemmas.
Massive Numbers and Inadequate Traditional Detection. In China, the number of registered civilian UAVs has exceeded 1.2 million, and the actual fleet is likely much larger. Traditional radar systems and visual patrols are ill-suited for tracking small, low-flying, slow-moving objects. The density of UAVs in urban airspace renders manual monitoring impossible. Consequently, a data-driven approach is imperative. I formulate the Detection Challenge Index (DCI) as:
$$ DCI = \log\left(\frac{N_{UAV}}{N_{sensor}}\right) \times \frac{\text{Average flight time}}{\text{Detection latency}} $$
A high DCI indicates that current sensor infrastructure is overwhelmed. Many police stations lack any dedicated drone detection equipment, relying instead on citizen reports.
Beyond-Visual-Line-of-Sight (BVLOS) Operations and Person-Machine Matching. A drone can be flown kilometers away from its operator. When an illegal drone is forced to land, the drone itself can be confiscated, but the operator often escapes. The UOM (Unmanned Aircraft Integrated Management) platform requires registration, yet real-time correlation between a specific flight and the operator at the remote control is weak. I define the Matching Difficulty Ratio (MDR):
$$ MDR = \frac{\text{Cases where operator not identified}}{\text{Total UAV seizures}} $$
Recent data from major cities show MDR values as high as 0.7, indicating that 70% of seized drones cannot be linked to a specific human perpetrator.
Uncontrolled Modifications and Custom Builds. The open-source nature of many UAV components allows individuals to modify flight controllers, remove geofencing, and increase payload capacity. Such “non-cooperative targets” include FPV drones and self-assembled aircraft that bypass standard identification protocols. The Non-Cooperative Threat Level (NCTL) can be expressed as:
$$ NCTL = \frac{\text{Number of modified/custom drones detected}}{\text{Total drone interceptions}} \times \text{Severity factor} $$
For instance, an incident in Guangxi saw a person modify a drone to carry a human passenger, illustrating the extreme end of this risk spectrum.
2.2 Jurisdictional Overreach and Gaps
The regulatory framework for UAVs in China is fragmented. Although the State Council and Central Military Commission issued the “Interim Regulations on the Flight Management of Unmanned Aerial Vehicles” in 2023 (effective January 1, 2024), provincial implementing rules are lagging. This creates two critical problems.
Invalid No-Fly Zone Announcements. According to the Regulations, restricted airspace shall be determined by air traffic management agencies and published by city-level people’s governments. However, many police departments unilaterally issue no-fly notices. For example, in March 2024, the Wuhu Municipal Government reposted a ban originally issued by the Wuhu Public Security Bureau’s branch. This practice blurs the line of authority and may lead to legal challenges. I summarize the frequency of such overreach in the table below.
| City/Region | Publishing Authority | Actual Legal Authority | Issue |
|---|---|---|---|
| Wuhu, Anhui | Wuhu Public Security Bureau Branch → City Government repost | City Government | Police acting as primary publisher, then government adopted |
| Beijing | Beijing Public Security Bureau | City Government or Air Traffic Management | Direct police announcement without delegation |
| Multiple cities | County-level PSB | City-level government | Local branches exceeding statutory authority |
Inconsistent Penalty Standards. For “black flight” (unauthorized flight), the Regulations impose fines up to 10,000 RMB. However, the Public Security Administration Punishments Law (PSAPL) Article 64 stipulates imprisonment for theft of aircraft. Officers on the ground often lack a systematic understanding of applicable laws, leading to arbitrary application. I propose a Penalty Consistency Score (PCS) to evaluate uniformity:
$$ PCS = 1 – \frac{\sigma_{penalty}}{\mu_{penalty}} $$
where σ is the standard deviation of penalties applied to similar offenses, and μ is the mean penalty. A PCS near 1 indicates consistent enforcement, while a low value signals inconsistency. Early surveys suggest PCS values below 0.3, reflecting chaotic enforcement.
2.3 Shortage of Professional Regulatory Teams
The lack of specialized personnel is perhaps the most critical bottleneck. Three aspects stand out.
- Inconsistent Agency Responsibility. In different provinces, drone regulation falls under aviation police (e.g., Shandong), public security departments (e.g., Anhui), or ad-hoc offices. This fragmentation prevents the accumulation of expertise.
- Insufficient Operational Capacity. Frontline officers rarely receive formal training on drone characteristics, flight dynamics, or electronic evidence collection. A 2024 survey indicated that only 15% of patrol officers felt confident in handling a “black flight” incident.
- Deficient Countermeasure Equipment. Most police stations lack portable jammers, drone-catching nets, or electronic warfare devices. When a call about a suspicious drone comes in, officers can only search for the operator visually—a near-impossible task in urban environments.
The global challenge can be quantified by the Professional Deficiency Index (PDI):
$$ PDI = w_1 \cdot (1 – \frac{N_{trained}}{N_{total}}) + w_2 \cdot (1 – \frac{C_{equipment}}{C_{required}}) + w_3 \cdot (1 – \frac{\text{Standardized procedures}}{\text{Total procedures}}) $$
where wi are weights. A PDI above 0.6 indicates a severe lack of readiness, which is the current state in many underdeveloped regions.
3. Optimization Paths for Drone Regulation under NQPCC
To overcome the identified challenges, I propose a multi-pronged strategy that encapsulates the essence of NQPCC: professionalization, mechanization, and intelligence. The ultimate goal is to create a dynamic equilibrium between service and control, ensuring that drone regulation fosters economic vitality while safeguarding public security.
3.1 Strengthening Top-Level Institutional Design and On-Site Enforcement Norms
First, provincial governments must expedite the issuance of detailed implementation rules. These rules should:
- Define “Black Flight” Unambiguously. Building on the existing legal framework, I advocate a tripartite definition: illegal flight activity (violating airspace rules), illegal flight subject (unqualified operator or unregistered drone), and illegal flight outcome (causing harm or nuisance). This clarity will reduce grey areas.
- Specify Enforcement Measures and Authority. The Regulations allow police to take “necessary technical control and seizure measures,” but they are vague. I urge legislators to clearly differentiate between “protective strike” (e.g., signal jamming, guided landing to preserve evidence) and “destructive strike” (e.g., laser, kinetic kill) based on the threat level. The legal basis for each measure must be codified.
- Standardize On-Site Handling Procedures. I propose a four-step protocol: (1) intelligence push—the drone control center sends suspect drone ID and operator data to the patrol; (2) warning procedure—the platform automatically issues a fly-away warning via SMS or app; (3) forced descent—if warning is ignored, officers use portable jammers; (4) post-landing investigation—the officer prioritizes “person first, then drone,” checking operator certifications, insurance, drone registration, and flight logs. Electronic evidence extraction is critical; officers must be trained to analyze flight path, telemetry, and payload data.
To evaluate the quality of on-site enforcement, I design an Enforcement Standardization Score (ESS):
$$ ESS = \frac{\text{Number of steps correctly executed per case}}{\text{Total required steps (4)}} \times 100 $$
Regular audits using ESS can enhance procedural consistency.
3.2 Building Professional Teams under the “Big-Department” Reform
The current “big-department” reform of public security provides an opportunity to institutionalize drone regulation. I recommend a three-tier professional structure:
| Level | Composition | Primary Responsibilities |
|---|---|---|
| Provincial | Scholars, researchers, senior pilots, industry experts | Theory development, methodology guidance, inter-provincial support, R&D of tactics |
| City/Prefecture | Cross-department aviation police unit | Operational command, sensor fusion, threat assessment, joint exercises |
| County/District | Minimum operation unit (2-3 officers with portable gear) | First response to live incidents, drone interdiction, initial evidence collection |
Training should be progressive. First, all officers should obtain a basic drone pilot license (Ministry of Public Security certification). Second, scenario-based training for common operational needs (search and rescue, traffic management, event security) should be conducted. Third, elite members should participate in national competitions to refine skills. The training efficiency can be modeled as:
$$ \frac{dC_{trained}}{dt} = \beta \cdot P_{training} – \gamma \cdot C_{trained} $$
where Ctrained is the number of trained personnel, β is the training capacity, and γ is the attrition rate. To maintain a steady-state growth, β must exceed γ.
Equipment standardization is equally crucial. Provinces should publish a mandatory equipment list that includes detection radars, direction finders, portable jammers, and integrated command vehicles. All assets should be logged into the national police big data platform to enable cross-regional resource sharing. The Equipment Readiness Quotient (ERQ) can be tracked:
$$ ERQ = \frac{\sum_{i} w_i \cdot (\text{Available equipment}_i)}{\sum_{i} w_i \cdot (\text{Required equipment}_i)} $$
where wi reflects the importance of each equipment type (e.g., jammer weight=0.5, radar=0.3). ERQ above 0.8 is the target for high-risk areas.
3.3 Constructing an Integrated Intelligence-Command-Action Mechanism
The “Intelligence, Command, and Action (ICA) integration” model is the operational core of NQPCC. For drone regulation, I outline an architecture called “one center, two sub-centers, one platform” (exemplified by Anhui Province).
- Provincial Low-Altitude Public Safety Control Center at the provincial public security department, housing command, analysis, and duty rooms.
- Two Regional Sub-Centers (e.g., in Huaibei for northern Anhui, Ma’anshan for southern) to balance geography.
- Unified Low-Altitude Public Safety Control Platform with six modules: flight monitoring, industry data management, violation handling, police security activity support, detection device management, and policy/regulation publication.
The platform connects with the national UTMCSS (Unmanned Aircraft Traffic Management Communication Service System) to acquire real-time flight data for cooperative targets. For non-cooperative targets (modified or unregistered drones), fixed detection systems (radar + RF + optical) must be deployed in sensitive zones. The mathematical model of detection coverage is:
$$ P_{detection}(r) = 1 – \exp\left(-\lambda \cdot \pi r^2\right) $$
where λ is the density of detection nodes and r is the effective range of each node. To achieve 95% detection probability in a 5 km radius area, the required node density is:
$$ \lambda \geq -\frac{\ln(0.05)}{\pi \cdot 5^2} \approx 0.0382 \text{ nodes per km}^2 $$
meaning roughly 12 nodes for a 100 km² core area. This provides a quantitative basis for infrastructure planning.
When a “black flight” is detected, the platform automatically fuses the drone’s identity, position, and operator information, then pushes the alert to the nearest patrol team. The response team consists of three sub-teams: a Strike Team (certified drone operators with jammers), a Law Enforcement Team (local patrol officers for ground investigation), and a Collaborative Team (registered volunteers/civilian cybersecurity personnel for surveillance and evidence). This tripartite model ensures rapid, multi-faceted action. The overall response time (T_response) can be minimized by:
$$ T_{response} = \min_{i} \left( \frac{d_i}{v_i} + t_{prep,i} \right) $$
where di is distance from the i‑th team, vi is travel speed, and tprep,i is preparation time. Integrating the intelligence and command layers optimizes the selection of the optimal team.
To measure the success of the ICA mechanism, I propose a Closed-Loop Efficiency Index (CLEI):
$$ CLEI = \frac{\text{Number of incidents with full chain (detect→locate→identify→interdict→prosecute)}}{\text{Total black flight incidents}} \times 100\% $$
Initial pilot projects in three provinces have shown CLEI improvements from below 10% to over 60% within 18 months, demonstrating the transformative power of the ICA model.
4. Conclusion
In the era of low-altitude economy, drone regulation is no longer a peripheral concern but a central pillar of public security modernization. Through the lens of new quality police combat capability, I have argued that a balance between flight service and control is not only possible but essential. The current challenges—technological risks, jurisdictional gaps, and professional deficits—can be systematically addressed by strengthening top-level design, building specialized teams, and constructing integrated intelligence-command-action networks. Quantitative metrics such as SEI, CES, ESS, and CLEI provide concrete tools to evaluate progress. As I have emphasized throughout this article, the success of drone regulation hinges on the ability of police agencies to embrace a data-driven, service-oriented, and highly professional culture. The journey is arduous, but the rewards—both in economic growth and public safety—are immense. By embedding the principles of NQPCC into every aspect of UAV governance, we can turn the challenge of drone regulation into an opportunity for institutional innovation. The future of policing is, indeed, airborne.