In the realm of modern surveying and mapping, the integration of UAV drone technology has revolutionized data acquisition processes, particularly in complex and remote environments such as aerospace landing sites. As a researcher focused on project risk management, I have observed that UAV drone-based surveying for these sites involves extensive coverage, high technical demands, significant resource consumption, and challenging戈壁滩 environments. Effective identification of risk scenarios and hierarchical management of risk elements are crucial. Traditional risk identification methods often fall short in addressing the multifaceted and interconnected nature of risks in such projects. In this article, I explore the application of Hierarchical Holographic Modeling (HHM) as a systematic approach to risk identification for UAV drone surveying and mapping in aerospace landing sites, emphasizing the need for comprehensive risk analysis to ensure mission success.
The use of UAV drones for surveying and mapping combines platforms, remote sensing sensors, geographic information systems (GIS), and computer processing to accurately measure and collect geospatial data, producing outputs like Digital Elevation Models (DEM) and Digital Orthophoto Maps (DOM). This “UAV drone +” approach offers mobility, stereoscopic capabilities, and intelligence, making it a trending development in surveying, with applications in基础测绘, emergency response, and major engineering projects. For crewed space mission recovery, precise terrain information of landing areas is vital for decision-making and rapid mobilization. UAV drone aerial photography enables efficient reconnaissance of aerospace landing sites, but the project faces broad scope, technical difficulties, resource intensity, and environmental complexities. This necessitates a robust risk identification framework to address diverse risk sources and interconnected factors.
Risk identification is the foundation of project risk management, defined by the Project Management Institute (PMI) as the process of determining potential risk events that could impact a project. Common methods include expert surveys, checklists, WBS-RBS, fault trees, and historical data analysis. However, these methods often have limitations in capturing the交叉interactions of multiple risk factors in UAV drone surveying scenarios. Risks here are not merely单一failures but arise from the interplay of personnel, technology, equipment, environment, and management. For instance, factors like operator error and equipment malfunction can combine to cause data quality issues, crashes, or injuries. Moreover, risk identification must balance multiple, often unquantifiable objectives—e.g., in setting control point布设视角, one must trade off scanning speed and accuracy. This “multi-input-multi-output” problem in complex risk contexts requires a holistic approach.
Hierarchical Holographic Modeling (HHM) provides a systematic theoretical framework for such analysis. Developed by Haimes et al., HHM aims to study a system’s intrinsic and extrinsic characteristics through multiple perspectives, dimensions, and levels. The term “hierarchical” refers to understanding the system from different layers, from macro to micro, while “holographic” denotes analyzing system risks from diverse angles. HHM decomposes complex systems into complementary, collaborative层次, each representing a specific视角structure. By identifying risks across all层次, a nearly complete set of risk factors is obtained. This method is particularly effective for systems with multiple elements, objectives, constraints, and stakeholders, as in UAV drone surveying projects. HHM has been applied in defense infrastructure, space missions, supply chains, and information security for risk identification.
The risk identification process based on HHM involves several steps. First,系统多视角分解: using qualitative methods like historical data review, expert surveys, and brainstorming to analyze risk sources, identifying 4–10 key perspectives for a “holographic” view. Second, constructing the HHM framework by decomposing the system into 3–5 levels of subsystems per perspective, represented via树状图or matrices. Third, risk identification and情景分析: leveraging the HHM framework to systematically identify risks through data analysis and case studies, with each sub-item representing risk scenarios. This ensures completeness by capturing interactions between factors through子模型(HHS) and risk交互矩阵. Iterative refinement maximizes risk factor identification.
For UAV drone surveying in aerospace landing sites, the workflow includes preparation, fieldwork, and indoor processing stages. The goal is to generate DOM and DEM data with accuracy better than 0.2 meters. Preparation involves collecting historical data, site reconnaissance, and technical design. Fieldwork includes control point布设and measurement, using UAV drone aerial photography—with two schemes for control point measurement based on network coverage. Indoor processing covers data handling, like aerial triangulation, DEM/DOM generation, and验收. This流程highlights the integration of UAV drone operations with traditional surveying steps, increasing risk exposure.
Risk源analysis reveals five primary risk categories: personnel, technology, equipment, environment, and management. Personnel risks involve专业团队structure, operator skills, and safety in remote areas. Technology risks relate to data quality, algorithm failures, and compliance with UAV drone standards. Equipment risks encompass UAV drone performance, maintenance, and calibration issues. Environment risks include extreme weather, terrain challenges, and electromagnetic interference. Management risks cover coordination,流程standardization, and保密requirements. These categories form the basis for HHM application.
Using HHM, I first conducted单一视角risk identification. Based on literature review and expert input, I defined five一级风险: personnel (A), technology (B), equipment (C), environment (D), and management (E). Through semantic clustering of 44 initial risk factors, I derived 16二级风险factors and iterated to build a three-level HHM model. This framework provides a structured view of risk elements, as summarized in the following table:
| Primary Risk | Secondary Risk | Risk Points |
|---|---|---|
| Personnel Risk (A) | Team Structure and Division Risk (A1) | Professional配置, multi-role协作, team training |
| Operational Capability and Experience Risk (A2) | UAV drone pilot skills, data processor proficiency, emergency response | |
| Health and Adaptability Risk (A3) | Physiological stress, psychological pressure, medical support | |
| Team Collaboration Risk | Quality and efficiency of technical handovers | |
| Technology Risk (B) | Data Collection Risk (B1) | Route planning algorithms, sensor issues, RTK signal quality |
| Route Planning Risk | Airspace conflicts, terrain obstacles | |
| Design Accuracy Risk | Resolution, flight height, accuracy allocation coordination | |
| Data Processing Risk (B2) | Image matching, orthorectification errors, multispectral data fusion | |
| Equipment Risk (C) | UAV Drone System Performance Risk (C1) | Power systems, propulsion, payload compatibility |
| Auxiliary Equipment Performance Risk (C2) | Ground station, transport vehicles, backup power | |
| Maintenance System Reliability Risk (C3) | Spare parts inventory, on-site repair capability, calibration cycles | |
| Environment Risk (D) | Extreme Weather Risk (D1) | Sudden strong winds, sandstorms |
| Terrain and Geomorphology Risk (D2) | Surface reflection, control point scarcity, complex地貌 | |
| Electromagnetic Environment Risk (D3) | Magnetic interference, satellite signal loss | |
| Management Risk (E) | Organizational Level Risk (E1) | Workflow standardization, resource调度 |
| Coordination Level Risk (E2) | Airspace approval, resource allocation | |
| Emergency Management Mechanism Risk (E3) | Contingency plans, risk预警, evacuation routes |
This HHM model captures the hierarchical nature of risks in UAV drone surveying. For example, under personnel risk, team structure issues can directly impact data collection quality, while under technology risk, design accuracy flaws may lead to rework. The UAV drone’s role is central here, as its performance intertwines with all categories—highlighting why “UAV drone” must be a recurring keyword in risk analysis.
Next, I performed多视角交叉risk identification to uncover interactions between primary risks. By analyzing子模型from different perspectives, I constructed risk交互矩阵. For instance, considering personnel risk (A) and technology risk (B), I developed an “A-B” HHM sub-model, where subsystems like A1 (team structure) intersect with B1 (data collection) to generate risk scenarios such as “采集任务交接盲区” (handover盲区in collection tasks). This交叉analysis yielded 19 interactive risk factors after consolidation, as shown in the table below:
| Risk Factor | Risk Scenario Description |
|---|---|
| Collection Task Handover盲区 | Unclear division between data collectors and UAV drone operators leads to parameter errors during complex route switches, causing invalid data batches. |
| Technical Misuse in Complex Conditions | Inexperienced operators misuse automated collection tech in strong winds or弱纹理areas, resulting in severe data quality degradation. |
| Personnel State-Induced Collection Consistency Risk | Fatigue or dehydration in extreme environments causes注意力分散, leading to incomplete coverage and undetected data gaps. |
| Raw Data Transfer断裂 | Poor communication between field and indoor teams causes loss or errors in关键data like flight logs, disrupting processing. |
| Lack of On-Site Quality Control Ability | Field personnel cannot preliminarily assess data quality, failing to spot “soft faults” like lens污点, leading to defective data. |
| Emergency Data Processing Decision Errors | Under time pressure,疲惫project heads may opt for “forced modeling” of subpar data instead of re-flights, compromising accuracy. |
| Ambiguous Data Transmission Responsibility | No designated person for daily data backup and encryption causes漏传or formatting errors without backups. |
| Insecure Transmission Operations | Use of unsafe public networks for data transfer by technicians risks敏感data theft or leakage. |
| Operational Negligence Due to Fatigue | Post-intensive work, fatigue leads to误删or overwriting of原始data without effective backups. |
| Disconnect Between Quality Standards and Skills | Management sets strict standards, but field staff lack skills to execute them (e.g., judging image拉线), allowing劣质data to proceed. |
| Misjudgment of Equipment Performance Boundaries | UAV drone pilots misjudge performance limits (e.g., wind resistance) in extreme climates, causing飞行accidents. |
| Rescue Failure in Complex Terrain | Contingency plans忽略terrain constraints; after a UAV drone crash in戈壁, rescue vehicles cannot reach, and lack of repair capability fails the mission. |
| UAV Drone Damage in Extreme Weather | Sudden strong winds cause instability, loss of control, or crashes of the UAV drone. |
| Ambiguous Equipment Maintenance Responsibility | Unclear assignment of maintenance duties for UAV drones, cameras, and bases leads to设备带病work and reduced reliability. |
| Human Error in Data Compliance | Fatigued data processors violate保密rules by using unauthorized devices or networks, causing serious breaches. |
| Automated Technology Collection Failure | Reliance on automated terrain-following tech fails in剧烈terrain (e.g., deep valleys), with no manual oversight, causing crashes or incomplete data. |
| Mismatch Between Control Point Measurement and Processing Standards | Field teams use varying GNSS receivers for control points, and indoor processing lacks correction, causing aerial triangulation偏差. |
| Chain Reaction from Inadequate Personnel Health保障 | Contingency plans lack health measures for extreme weather (e.g., sandstorms), leading to health issues, operational errors, and project中断. |
| Compliance Conflict with Data Auto-Erase | UAV drone security settings (设备) auto-format SD cards when connecting to untrusted devices, conflicting with retention rules and risking data loss. |
These交叉risks underscore the complexity of UAV drone surveying projects, where factors like management lapses can amplify technical or environmental threats. For example, “UAV drone damage in extreme weather” (D1-C1 interaction) highlights how environment and equipment risks converge, necessitating integrated mitigation strategies.
To generate a comprehensive risk清单, I combined单一and交叉perspectives, clustering and integrating factors to produce 28 initial risk elements. These were then optimized using Risk Priority Number (RPN) methodology to filter out low-probability or low-impact risks. The RPN formula is central here:
$$ RPN = O \times S \times D $$
where \( O \) is occurrence likelihood, \( S \) is severity, and \( D \) is detection difficulty. By calculating average RPN values and排序, I identified 20 key risk factors for the final list. This process emphasizes the quantitative aspect of risk assessment in UAV drone operations, complementing HHM’s qualitative insights. The optimized risk清单is presented below:
| Risk Type | Risk Factor | Typical Risk Scenario | Typical Consequence |
|---|---|---|---|
| Personnel Risk | Team Structure and Division Risk | Mismatch between professional配置and project needs | Poor technical design; delays in quality and进度 |
| Operational Capability and Experience Risk | Inexperienced UAV drone pilots; unskilled data processors | Poor data collection quality; crashes; orthoimage correction errors | |
| Health and Adaptability Risk | Personnel不适应戈壁environment;失联in remote areas | Collection delays; illness or casualties | |
| Technology Risk | Route Planning Risk | Poor route planning | Terrain affects flight height or overlap rates |
| Design Accuracy Risk | Inappropriate resolution, flight height, accuracy allocation | Data collection rework | |
| Data Collection Risk | Poor data collection quality | Data processing difficulties | |
| Data Processing Risk | Weak-texture image matching failure; insufficient orthorectification精度 | Missing feature points; DEM deviations from actual elevation | |
| Equipment Risk | UAV Drone System Performance Risk | Insufficient endurance; weak抗风ability; battery thermal管理failure; high-temperature camera noise; payload switch死机 | Crashes or loss of control; collection failure |
| Auxiliary Equipment Performance Risk | Poor ground station耐候性; transport vehicle防护defects | System宕机in temperature extremes;设备damage from戈壁碎石 | |
| Maintenance System Reliability Risk | Insufficient spare parts (e.g., motors, propellers); lack of on-site repair capability | Inadequate equipment保障 | |
| Equipment Calibration偏差Risk | Failed calibration of core sensors | Systematic errors causing aerial triangulation failure | |
| Environment Risk | Extreme Weather Risk | Sudden wind changes during UAV drone flights | Image distortion; UAV drone失控; crashes |
| Terrain and Geomorphology Risk | Weak-texture戈壁地貌 | Data processing difficulties | |
| Electromagnetic Environment Risk | EM interference affecting UAV drone operation | UAV drone loss of control | |
| Meteorological Condition Risk | Adverse weather during transits over special terrain; prolonged bad weather halting projects | Casualties; equipment loss; project delays | |
| Management Risk | Organizational Level Risk | Poor execution of standardized workflows; resource调度issues | 违规operations in unsupervised scenarios |
| Coordination Level Risk | Poor communication between field and indoor teams; airspace conflicts | Coordination failures; delays in spare parts/补给; project delays from airspace issues | |
| Emergency Management Mechanism Risk | Lack of contingency plans; delayed risk预警;失效evacuation routes (e.g., covered by sandstorms) | Ineffective response to crashes or失联 | |
| Quality Management Risk | Poor成果精度(required <0.2m); data compliance issues | Non-compliant products | |
| Confidentiality Management Risk | Weak保密awareness; lack of保密measures | Data leakage or泄密 |
This risk清单demonstrates how UAV drone-centric factors permeate all categories, from system performance to operational coordination. Each risk factor can be modeled further using quantitative approaches. For instance, the probability of UAV drone crashes due to extreme weather might be estimated via historical data, with severity assessed through cost impacts. Let \( P_{crash} \) denote crash probability, \( C_{damage} \) repair costs, and \( T_{delay} \) project delay time. A simplified risk score \( RS \) could be:
$$ RS = P_{crash} \times (C_{damage} + \alpha \cdot T_{delay}) $$
where \( \alpha \) is a weighting factor for time value. Such formulas help prioritize risks, but HHM ensures they are identified comprehensively first.
To further refine risk management, I applied ABC classification to the 20 key risks based on their综合得分值from RPN analysis. ABC分类法follows the Pareto principle, categorizing risks into high (A), medium (B), and low (C) priority based on cumulative percentages. The results are tabulated below:
| Risk Level | Risk Factor | Comprehensive Score | Cumulative Percentage (%) |
|---|---|---|---|
| A (High Risk) | Data Processing Risk | 0.281 | 9.43 |
| Design Accuracy Risk | 0.214 | 16.62 | |
| Route Planning Risk | 0.210 | 23.67 | |
| Organizational Level Risk | 0.207 | 30.61 | |
| Auxiliary Equipment Performance Risk | 0.200 | 37.33 | |
| Maintenance System Reliability Risk | 0.189 | 43.67 | |
| Emergency Management Mechanism Risk | 0.177 | 49.61 | |
| UAV Drone System Performance Risk | 0.164 | 55.12 | |
| Data Collection Risk | 0.144 | 59.95 | |
| Quality Management Risk | 0.139 | 64.62 | |
| Confidentiality Management Risk | 0.136 | 69.18 | |
| B (Medium Risk) | Coordination Level Risk | 0.136 | 73.75 |
| Equipment Calibration偏差Risk | 0.133 | 78.21 | |
| Health and Adaptability Risk | 0.132 | 82.65 | |
| Operational Capability and Experience Risk | 0.116 | 86.54 | |
| Team Structure and Division Risk | 0.104 | 90.03 | |
| C (Low Risk) | Meteorological Condition Risk | 0.102 | 93.45 |
| Extreme Weather Risk | 0.068 | 95.74 | |
| Terrain and Geomorphology Risk | 0.066 | 97.95 | |
| Electromagnetic Environment Risk | 0.061 | 100.00 |
The classification shows that 55% of risks (11 factors) are A-class, accounting for 69.18% of cumulative risk value—highlighting the “vital few” that demand focused management. These primarily involve technology and management aspects, such as data processing and organizational issues, directly tied to UAV drone operations. B-class risks (25% of factors, 20.85% value) include personnel and equipment calibration, requiring monitoring to prevent escalation. C-class risks (20% of factors, 9.97% value) are environmental, like weather and terrain, which can be mitigated through proactive measures. This stratification supports differentiated response strategies, ensuring resources are allocated efficiently to safeguard UAV drone mission success.
In conclusion, the application of HHM in risk identification for UAV drone surveying and mapping at aerospace landing sites offers a holistic and systematic approach. By leveraging多视角decomposition and交叉analysis, HHM captures both单一and interactive risks, surpassing traditional methods that often overlook complex factor interplay. The integration of UAV drone technology into surveying introduces unique challenges, but through HHM, risks related to personnel, technology, equipment, environment, and management are thoroughly identified and categorized. The use of RPN and ABC classification further refines this process, enabling prioritized risk management. This framework not only enhances risk identification accuracy but also lays a foundation for subsequent assessment and mitigation, ultimately contributing to the safe and efficient execution of UAV drone-based测绘tasks in critical aerospace environments. As UAV drone adoption grows, such methodologies will be indispensable for navigating the intricate risk landscapes of modern projects.