Pilot state monitoring method based on flight simulation of unmanned aerial vehicle and related device
By constructing a simulation environment that includes typical UAV missions and failure scenarios, and simultaneously collecting and analyzing pilots' operational, physiological, and psychological data, the problem of existing systems' inability to assess pilots' overall condition is solved, achieving efficient multi-dimensional assessment and optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FOURTH MILITARY MEDICAL UNIVERSITY
- Filing Date
- 2026-02-02
- Publication Date
- 2026-05-05
AI Technical Summary
Existing drone simulation systems struggle to synchronously and quantitatively monitor the physiological and psychological state of drone pilots under realistic mission and failure scenarios, and lack the ability to comprehensively assess pilots' cognitive abilities in complex missions.
A simulation environment is constructed that includes typical UAV missions and preset fault scenarios. Flight data, operational data, and physiological data are collected simultaneously. Through data fusion analysis, the pilot's operational performance, physiological state, and psychological cognition are analyzed to generate a comprehensive evaluation report.
It enables multi-dimensional, real-time, and objective assessment of pilot status, improving human-machine collaboration efficiency and safety, and supporting pilot selection, training, and human-machine interface optimization.
Smart Images
Figure CN121982954A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of aviation flight training technology, and relates to a pilot status monitoring method and related device based on UAV flight simulation. Background Technology
[0002] Unmanned Aerial Vehicle (UAV) systems have been widely used in various fields such as military reconnaissance, civilian surveying and mapping, logistics transportation, and emergency rescue. With the increasing complexity of application scenarios, higher demands are being placed on the cognitive abilities, emergency response capabilities, and human-machine collaboration efficiency of UAV pilots (operators). Against this backdrop, UAV simulation technology has become a key tool for pilot training, system verification, and human factors assessment.
[0003] Currently, the development of existing UAV simulation system technology mainly focuses on the following aspects: First, in terms of aircraft body simulation, the technology is relatively mature. By establishing a high-precision six-degree-of-freedom (6-DoF) flight dynamics model and combining it with aerodynamics, propulsion systems, and automatic control algorithms, the flight attitude, trajectory, and response characteristics of UAVs can be simulated with high realism. Such systems can simulate various flight environments and system failures, such as engine failure and control surface jamming, providing an effective platform for the verification of flight control algorithms. Second, in terms of mission environment and visual simulation, existing technologies, by integrating Geographic Information Systems (GIS) and 3D rendering engines, can construct highly realistic virtual environments that include terrain, landforms, buildings, and weather effects. This provides pilots with an immersive first-person perspective (FPV) or third-person perspective, assisting in mission planning (such as area search and target tracking) and visual perception training. However, despite the significant progress made in aircraft dynamics and visual simulation, the design philosophy of existing technologies usually treats the pilot as an ideal, steady-state "operator" model.
[0004] Existing simulation systems primarily record flight parameters and operational command logs to evaluate external operational performance such as flight trajectory tracking accuracy and mission completion time. However, the operational performance of UAV pilots, especially when handling typical high-pressure, high-load missions and sudden malfunctions, is closely related to their internal physiological and psychological states (such as psychological load, situational awareness, and stress response). Traditional simulation systems struggle to reproduce the impact of these complex cognitive states on operations and lack platforms for synchronously collecting and correlating flight operation data with real-time pilot physiological indicators. Assessments of pilot performance often rely on post-flight subjective questionnaires (such as the NASA-TLX scale), which are inherently lagging and subjective, making it difficult to quantify changes in pilot cognitive load during dynamic missions in a real-time and objective manner. Furthermore, the challenges pilots face in real missions extend beyond flight control; they involve a comprehensive cognitive process encompassing information integration, decision-making, and handling of emergencies. Existing fault injection functions are primarily used to verify the reliability of the aircraft itself, rather than specifically for assessing human-in-the-loop response capabilities. Due to the lack of fault settings that are deeply coupled with the mission scenario and can be precisely controlled in terms of trigger timing, as well as the ability to record multimodal data synchronously, existing systems are unable to scientifically study the pilot's complete cognitive chain from fault identification and diagnosis to decision-making intervention, and are also unable to objectively assess the impact of different human-machine interface (HCI) designs or operating procedures on pilot performance.
[0005] In summary, there is an urgent need for a method that can construct realistic mission and failure scenarios and simultaneously, quantitatively, and multidimensionally monitor and evaluate the operational performance, physiological state, and psychological cognition of UAV pilots. This would provide indispensable data support and technical means for the scientific selection, targeted training, human-machine interface optimization, and verification of emergency response procedures for UAV pilots. Summary of the Invention
[0006] The purpose of this invention is to provide a pilot status monitoring method and related device based on UAV flight simulation, so as to solve the technical problem in the prior art that it is difficult to synchronously and quantitatively monitor and evaluate the physiological and psychological state of UAV pilots under realistic mission and fault scenarios.
[0007] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a pilot status monitoring method based on UAV flight simulation, comprising the following steps: Configure a simulation environment that includes typical drone mission scenarios and preset fault scenarios; When pilots train in the simulation environment, flight data, pilot operation data, and pilot physiological data are collected simultaneously. The system triggers a fault simulation at a preset time and records data on the pilot's identification, diagnosis, and handling of the fault. The collected flight data, pilot operation data, pilot physiological data, and pilot's fault identification, diagnosis, and handling process data are aligned and fused along the timeline. Based on the fused data, the pilot's operational performance, physiological state, and psychological cognition are analyzed to generate a comprehensive evaluation report.
[0008] Furthermore, the preset fault scenarios include at least one of engine degradation, in-flight engine failure, control surface jamming, sensor failure, or data link interruption.
[0009] Furthermore, the pilot's physiological data includes at least heart rate, heart rate interval, fixation point coordinates, pupil diameter, and blink frequency.
[0010] Furthermore, the operational performance analysis process specifically includes: Calculate the fault identification time: The fault identification time is the time from the moment the fault occurs to the time when the first alarm area is reached or the fault is mentioned. Assessing the correctness of a decision: Whether the correct emergency procedures were initiated within a reasonable timeframe; Calculate operational efficiency: The operational efficiency is expressed as the ratio of ineffective operations during the disposal period.
[0011] Furthermore, the analysis process of the aforementioned physiological state specifically includes: Heart rate response: Calculate the average heart rate increase relative to baseline during the stress response period; Visual attention allocation: Analyze the percentage of time the gaze point spends on each interface element during the stress response period.
[0012] Furthermore, the analytical process of psychological cognition specifically includes: Psychological load: The psychological load index during the decision-making and handling period was calculated based on pupil diameter and heart rate; Situational awareness score: After the fault is triggered but before the simulation ends, the simulation is paused and probe questions are posed to the pilot. The pilot's situational awareness is assessed based on the correctness of the probe questions and the reaction time.
[0013] Secondly, the present invention provides a system for implementing the above-mentioned pilot status monitoring method based on UAV flight simulation, comprising: The UAV flight simulation module is used to simulate the flight dynamics model of the UAV and inject system faults; The mission planning and monitoring simulation module provides a human-machine interface for mission planning and flight monitoring and records operation logs. The flight visual simulation module is used to generate a visual scene synchronized with the UAV's status and visualize mission and fault information; The pilot physiological and psychological monitoring module is used to collect pilots' physiological signals and assess their psychological load and situational awareness.
[0014] Furthermore, the pilot's physiological and psychological monitoring module includes: Physiological signal acquisition unit, used to collect pilot physiological data; The psychological assessment unit is used to calculate the psychological load index and assess situational awareness through probe questions.
[0015] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the pilot status monitoring method based on UAV flight simulation as described above.
[0016] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the pilot status monitoring method based on UAV flight simulation as described above.
[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention discloses a pilot status monitoring method and related device based on UAV flight simulation. By configuring a simulation environment including typical tasks and preset fault scenarios, it can highly reproduce real flight conditions and effectively improve the pilot's ability to cope with various complex situations. By synchronously collecting flight, operation, and physiological data, it can comprehensively and multi-dimensionally grasp the pilot's status information during training, providing rich data support for subsequent accurate analysis. This invention is the first to deeply integrate high-fidelity UAV flight simulation, typical / fault mission scenarios, and multimodal physiological and psychological monitoring, creating a near-realistic UAV pilot workload and stress assessment environment. It achieves multi-dimensional synchronous quantitative measurement of pilot operational performance, physiological state, and psychological cognition, overcoming the one-sidedness of traditional subjective assessment and providing objective data support for human factors evaluation. It can be used for UAV pilot selection and training, human-machine interface (HCI) design and evaluation, mission planning and operation procedure optimization, and verification of special fault handling procedures, significantly improving the human-machine collaborative efficiency and safety of UAV systems. Attached Figure Description
[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the overall architecture of the UAV flight monitoring simulation system according to an embodiment of the present invention; Figure 3 This is a flowchart of the drone pilot monitoring and evaluation method according to an embodiment of the present invention. Detailed Implementation
[0020] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.
[0021] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.
[0022] See Figure 1 This invention discloses a pilot status monitoring method based on UAV flight simulation, comprising the following steps: S1, configured with a simulation environment that includes typical UAV mission scenarios and preset fault scenarios; In this step, typical mission scenarios (such as complex environment search) and preset fault scenarios (such as triggering a data link interruption at minute N) can be selected or configured. The preset fault scenarios include at least one of engine degradation, in-flight engine failure, control surface jamming, sensor failure, or data link interruption.
[0023] S2, When the pilot is training in the simulation environment, flight data, pilot operation data and pilot physiological data are collected simultaneously. In this step, the simulation is initiated, and the pilot executes the mission through the mission planning and monitoring simulation module; the simulation is paused during mission execution to allow for real-time assessment of the pilot's perception and understanding of the current situation; the simulation is then stopped, ending the simulation operation. Flight parameters, operational behaviors, raw physiological signals of the pilot, and real-time assessment records are recorded simultaneously.
[0024] S3 triggers a fault simulation at a preset time and records the pilot's data on the process of identifying, diagnosing and handling the fault. This step simulates the unpredictability of sudden malfunctions in real flight, comprehensively and realistically examining the pilot's emergency response speed when faced with a malfunction at a specific moment. By meticulously recording the pilot's data on malfunction identification, diagnosis, and handling procedures, their emergency response capabilities can be accurately analyzed.
[0025] S4 aligns and merges the collected flight data, pilot operation data, pilot physiological data, and pilot's fault identification, diagnosis, and handling process data along the timeline. Based on the merged data, it analyzes the pilot's operational performance, physiological state, and psychological cognition to generate a comprehensive evaluation report.
[0026] In this step, multiple types of data are aligned and integrated along a timeline, enabling a comprehensive assessment of the pilot's condition from operational, physiological, and psychological dimensions. Operational performance reflects flight skill level, physiological data reflects physical endurance, and psychological cognitive data reveals the thought process and decision-making process. Combining these three aspects provides a comprehensive and in-depth understanding of the pilot's overall performance during flight training.
[0027] See Figure 2 This invention discloses a system for implementing the above-mentioned pilot status monitoring method based on UAV flight simulation, comprising a UAV flight simulation module, a mission planning and monitoring simulation module, a flight visual simulation module, and a pilot physiological and psychological monitoring module. Details are as follows: The UAV flight simulation module is used to simulate the flight dynamics model of UAVs and inject system faults. Specifically, it can simulate the six-degree-of-freedom flight dynamics model of UAVs, airborne sensors, actuators, and system faults. It can simulate typical aircraft faults, including engine degradation, in-flight engine failure, control surface jamming, and sensor failure.
[0028] The mission planning and monitoring simulation module provides a human-machine interface for mission planning and flight monitoring and records operation logs. Specifically, it can provide a planning interface for typical missions (such as area search, target tracking, and formation flight) and simulate the human-machine interface for flight and mission monitoring of the ground control station (GCS). This allows pilots to issue mission commands and monitor flight and mission status. This module can record all operation logs.
[0029] The flight visual simulation module is used to generate a visual scene synchronized with the UAV's status and visualize mission and fault information. Specifically, it can generate first-person (pilot's perspective) and third-person visual scenes synchronized with the UAV flight simulation module's status, and can visualize mission elements (targets, threat areas) and fault prompts in the visual scene.
[0030] The pilot physiological and psychological monitoring module is used to collect pilots' physiological signals and assess their psychological load and situational awareness. Specifically, it consists of a physiological signal acquisition unit, a psychological load assessment unit, and a situational awareness assessment unit. The physiological signal acquisition unit connects to physiological sensors (such as electrocardiogram (ECG) / heart rate (HR) sensors, electrical activity of the skin (EDA) sensors, electroencephalogram (EEG) devices, and eye trackers) to collect pilots' physiological data in real time. The psychological load assessment unit constructs a psychological load assessment model based on subjective scales and objective physiological indicators. The situational awareness assessment unit uses global situational awareness assessment technology or probe questions based on mission-critical events to randomly pause and ask questions during the simulation, assessing the pilot's understanding of the current situation.
[0031] Example: This embodiment simulates a scenario where a drone operator is performing a target search mission within visual range, and the link between the ground control station and the drone is suddenly interrupted. The system will comprehensively monitor and evaluate the pilot's emergency response capabilities and physiological and psychological reactions under sudden failure.
[0032] I. System Configuration and Scenario Construction: refer to Figure 2 The system architecture shown is configured as follows: 1. Unmanned Aerial Vehicle (UAV) Flight Simulation Module: A six-degree-of-freedom nonlinear flight dynamics model was used to simulate the UAV as a medium-to-high altitude UAV.
[0033] A "data link failure" fault mode is preset. The fault parameters are set to trigger instantaneously at 180 seconds after the start of the simulation, resulting in the loss of downlink telemetry signals (UAV status feedback).
[0034] 2. Task Planning and Monitoring Simulation Module: The interface simulates a typical UAV ground station mission planning and flight monitoring software interface. The main interface includes flight parameter display (altitude, speed, attitude, etc.), map display area, and mission command buttons.
[0035] Mission Planning: Establish multiple waypoints on a certain area of the electronic map to form a search route. The mission objective is to locate the air balloons at predetermined positions within that area.
[0036] 3. Flight Visual Simulation Module: Based on a 3D engine, a 3D visual scene similar to the real training ground is constructed, including mountain, road, forest and building models.
[0037] A first-person perspective (FPV) video stream, simulated from an airborne gimbal on a drone, is used for target search.
[0038] 4. Pilot Physiological and Psychological Monitoring Module 1) Physiological signal acquisition unit (worn by pilots) The Polar H10 heart rate sensor collects inter-beat interval (IBI) data to calculate heart rate (HR) and heart rate variability (HRV, expressed as RMSSD).
[0039] The Tobii Pro Glasses 3 eye tracker collects fixation point coordinates, pupil diameter, and blink frequency.
[0040] 2) Psychological assessment unit Subjective assessment: Complete the simplified NASA-TLX workload table before and after the mission.
[0041] Objective assessment model: Preset psychological load algorithm, for example: psychological load index = 0.6 * (normalized pupil diameter change rate) + 0.4 * (normalized heart rate growth rate).
[0042] Situational awareness assessment: Thirty seconds after the fault is triggered, the simulation pauses, and the pilot is asked questions such as, "What was the remaining fuel level of the drone when the fault occurred?" and "In which segment of the search route was the drone at that time?" The pilot must answer verbally, and the accuracy and reaction time are recorded.
[0043] II. Simulation Operation and Monitoring Process: See Figure 3 Perform the following steps: 1. Scene Configuration and Preparation The operator is in position, wearing physiological sensors, and familiarizing themselves with the simulation interface. The experimenter loads the "VLOS target search - communication interruption" scenario configuration file in the background.
[0044] 2. Simulation startup and synchronous data acquisition.
[0045] The pilot initiates the simulation and maneuvers the drone to begin the search along a preset route. Flight data, operational data, and physiological data are collected simultaneously.
[0046] 3. Fault Injection and Pilot Intervention. When T_sim = T0 (e.g., 182 seconds after simulation starts): Send fault trigger commands to the flight simulation module and the visual simulation module.
[0047] The flight simulation module cut off downlink telemetry data, but the UAV automatically entered the preset failure protection logic.
[0048] The FPV image from the visual simulation module remains continuous.
[0049] The critical monitoring period (from T0 to T0+60 seconds) begins: a. Record the pilot's reaction time from the discovery of the malfunction to the first effective action.
[0050] b. A typical pilot's operation sequence may be: ① Confirm the fault → ② Check the downlink telemetry (confirm the UAV status) → ③ Try to switch the communication channel or restart the link → ④ After realizing that it cannot be restored in a short time, initiate the emergency return procedure.
[0051] c. An eye tracker records the switching of the fixation point.
[0052] d. The heart rate sensor records instantaneous changes in heart rate and HRV.
[0053] 4. Probe Evaluation. At T0+30s, the simulation pauses, and a probe question pops up. After the pilot answers, the simulation resumes. This step evaluates its ability to maintain situational awareness under pressure.
[0054] 5. Mission Completion and Data Collection. The simulation ends after the pilot successfully handles the malfunction (such as instructing the UAV to return automatically) or the mission times out. The pilot completes the post-mission NASA-TLX questionnaire.
[0055] III. Data Analysis and Comprehensive Evaluation: The data analysis platform performs the following processing: 1. Operational performance indicators: Fault identification time: The time from T0 to the first operation alarm area or mention of the fault.
[0056] Decision correctness: Whether the correct emergency procedures (such as returning to base) were initiated within a reasonable timeframe.
[0057] Operational efficiency: The percentage of invalid operations (such as repeatedly clicking invalid buttons) during the processing period.
[0058] 2. Physiological and psychological indicators: Heart rate response: Calculates the average heart rate increase relative to baseline during the stress response period.
[0059] Visual attention allocation: Analyzing the percentage of time a fixation point spends on each interface element during the stress response period. A skilled pilot should be able to quickly shift their attention from search tasks to status monitoring.
[0060] Psychological load: The psychological load index during the decision-making and handling period is calculated based on pupil diameter and heart rate.
[0061] Situational Awareness (SA) score: calculated based on the accuracy and speed of answering probe questions.
[0062] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used in the operation of a pilot status monitoring method based on UAV flight simulation.
[0063] This invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the pilot status monitoring method based on UAV flight simulation in the above embodiments.
[0064] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0065] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0066] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A pilot status monitoring method based on UAV flight simulation, characterized in that, Includes the following steps: Configure a simulation environment that includes typical drone mission scenarios and preset fault scenarios; When pilots train in the simulation environment, flight data, pilot operation data, and pilot physiological data are collected simultaneously. The system triggers a fault simulation at a preset time and records data on the pilot's identification, diagnosis, and handling of the fault. The collected flight data, pilot operation data, pilot physiological data, and pilot's fault identification, diagnosis, and handling process data are aligned and fused along the timeline. Based on the fused data, the pilot's operational performance, physiological state, and psychological cognition are analyzed to generate a comprehensive evaluation report.
2. The pilot status monitoring method based on UAV flight simulation according to claim 1, characterized in that, The preset fault scenarios include at least one of the following: engine degradation, in-flight engine failure, control surface jamming, sensor failure, or data link interruption.
3. The pilot status monitoring method based on UAV flight simulation according to claim 1, characterized in that, The pilot's physiological data includes at least heart rate, heart rate interval, fixation point coordinates, pupil diameter, and blink frequency.
4. The pilot status monitoring method based on UAV flight simulation according to claim 1, characterized in that, The operational performance analysis process specifically includes: Calculate the fault identification time: The fault identification time is the time from the moment the fault occurs to the time when the first alarm area is reached or the fault is mentioned. Assessing the correctness of a decision: Whether the correct emergency procedures were initiated within a reasonable timeframe; Calculate operational efficiency: The operational efficiency is expressed as the ratio of ineffective operations during the disposal period.
5. The pilot status monitoring method based on UAV flight simulation according to claim 1, characterized in that, The analysis process of the physiological state specifically includes: Heart rate response: Calculate the average heart rate increase relative to baseline during the stress response period; Visual attention allocation: Analyze the percentage of time the gaze point spends on each interface element during the stress response period.
6. The pilot status monitoring method based on UAV flight simulation according to claim 1, characterized in that, The analytical process of psychological cognition specifically includes: Psychological load: The psychological load index during the decision-making and handling period was calculated based on pupil diameter and heart rate; Situational awareness score: After the fault is triggered but before the simulation ends, the simulation is paused and probe questions are posed to the pilot. The pilot's situational awareness is assessed based on the correctness of the probe questions and the reaction time.
7. A system for implementing the pilot status monitoring method based on UAV flight simulation as described in any one of claims 1 to 6, characterized in that, include: The UAV flight simulation module is used to simulate the flight dynamics model of the UAV and inject system faults; The mission planning and monitoring simulation module provides a human-machine interface for mission planning and flight monitoring and records operation logs. The flight visual simulation module is used to generate a visual scene synchronized with the UAV's status and visualize mission and fault information; The pilot physiological and psychological monitoring module is used to collect pilots' physiological signals and assess their psychological load and situational awareness.
8. A pilot status monitoring system based on UAV flight simulation according to claim 7, characterized in that, The pilot physiological and psychological monitoring module includes: Physiological signal acquisition unit, used to collect pilot physiological data; The psychological assessment unit is used to calculate the psychological load index and assess situational awareness through probe questions.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the pilot status monitoring method based on UAV flight simulation as described in any one of claims 1-6.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the pilot status monitoring method based on UAV flight simulation as described in any one of claims 1-6.