EVTOL safety seat system of heavy-load unmanned aerial vehicle

Abstract

The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an eVTOL safety seat system of a heavy-load unmanned aerial vehicle, and the system comprises a sensing layer which is used for firstly measuring data of a safety seat and then collecting the measured data; the decision-making layer is used for performing real-time analysis and decision-making according to the data acquired by the sensing layer, generating a corresponding instruction and diagnosing whether the unmanned aerial vehicle has a fault or not; and the execution layer is used for correspondingly controlling the unmanned aerial vehicle according to the instruction of the decision layer. According to the invention, various sensors work cooperatively, environment information can be captured comprehensively, and the influence of loss or error of a single signal is reduced.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, specifically to an eVTOL safety seat system for heavy-duty UAVs. Background Technology

[0002] With the acceleration of urbanization, traditional ground traffic congestion is becoming increasingly serious, giving rise to eVTOL technology, which promises to build an efficient urban air transport network. Heavy-payload eVTOL drones can take off and land vertically in low-altitude environments, performing tasks such as cargo transport and medical relief supply delivery, effectively improving transportation efficiency and reducing transport time. However, due to the complex flight environment of eVTOL, including obstacles such as buildings and power lines at low altitudes, and significant weather influences, coupled with the fact that the current technology is not yet fully mature, safety issues are prominent. The safety seat system, as a key subsystem for ensuring personnel safety, directly affects the safety of eVTOL flight.

[0003] However, existing child seat systems have the following problems: Inconsistent safety standards: Currently, there is a lack of unified safety standards and technical specifications globally for heavy-duty eVTOL child safety seat systems. Different countries and regions have their own standards, leaving manufacturers confused during the design and production process. For example, in some European countries, the impact resistance standards for child safety seats focus on vertical impact resistance; while in some Asian countries, in addition to vertical impact, there are also strict requirements for horizontal impact. This makes it difficult for eVTOL systems operating across regions to meet the standards of different regions, hindering the globalization of the industry.

[0004] Poor adaptability to complex environments: eVTOL primarily flies in low-altitude, complex environments, requiring the safety seat system to cope with various interferences. In urban environments, dense high-rise buildings can easily obstruct and reflect signals, leading to errors or loss of sensor signals in the control system. For example, when flying in urban canyon areas (narrow passages between tall buildings), GPS signals may be severely interfered with, preventing the safety seat system from accurately acquiring location information. This can impact emergency response, such as the inability to precisely plan the landing point after safe ejection.

[0005] Insufficient system reliability: The heavy-duty eVTOL child safety seat system integrates multiple electronic devices and complex algorithms, resulting in high system complexity. Failure in any component can cause the entire system to fail. For example, if the core computing unit of the control system experiences a hardware failure, it may be unable to process data such as the drone's attitude and speed from sensors in a timely manner. This could lead to an inability to accurately determine whether to activate the child safety seat ejection procedure when the drone becomes uncontrollable, or, if activated, to precisely control the ejection direction and force, endangering passenger safety.

[0006] Emergency response capabilities need improvement: In the event of an emergency during eVTOL flight, such as power system failure or severe bird strike, the safety seat system needs to respond quickly. However, current systems are insufficient in terms of emergency response speed and decision-making accuracy. Taking power system failure as an example, the delay between detecting the failure and the safety seat initiating ejection is significant, potentially missing the optimal escape window. Furthermore, the algorithms for deciding the ejection timing and landing point are not intelligent enough to comprehensively consider complex environmental factors and the real-time status of the drone, meaning that even after ejection, the drone may still face danger, such as ejecting into water or densely populated areas.

[0007] Weak integration with the overall UAV system: The safety seat system lacks sufficient coordination with other eVTOL systems, such as avionics, power systems, and flight control systems. Delayed and inaccurate information transmission between systems affects overall safety. For example, when the flight control system detects an impending UAV stall, it may fail to quickly transmit this critical information to the safety seat system, preventing the safety seat from preparing for an emergency. Alternatively, when the safety seat system initiates ejection, it may fail to promptly notify the flight control system to make corresponding adjustments, potentially adversely affecting the flight stability of the remaining UAV structure and leading to secondary accidents.

[0008] Based on the above, an eVTOL safety seat system for heavy-duty unmanned aerial vehicles is invented. Summary of the Invention

[0009] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution: An eVTOL safety seat system for heavy-duty unmanned aerial vehicles, comprising: The perception layer is used to first measure the data of the child safety seat, and then collect the measured data; The decision-making layer is used to perform real-time analysis and decision-making based on the data collected by the perception layer, generate corresponding instructions, and diagnose whether the drone has any faults. The execution layer is used to control the drone according to the instructions of the decision-making layer.

[0010] As a preferred embodiment of the eVTOL safety seat system for a heavy-duty unmanned aerial vehicle (UAV) according to the present invention, the perception layer includes: The sensor module is used to detect the acceleration and angular velocity of the child safety seat, the weight of the passenger and the force on the seat belt, the surrounding environmental parameters, and the distance between the child safety seat and surrounding objects. The data acquisition module is used to collect the detection data from the sensor module and perform preliminary filtering and preprocessing.

[0011] As a preferred embodiment of the eVTOL safety seat system for a heavy-duty UAV according to the present invention, the sensor module includes: The inertial measurement unit module is used to measure the acceleration and angular velocity of the child safety seat in real time to determine the seat's attitude and motion state. The pressure sensor module is used to monitor the passenger's weight and the force on the seat belt, so as to adjust the ejection parameters according to the actual situation of the passenger when the emergency procedure is activated, ensuring that the ejection process is safe and reliable; An environmental sensor module is used to monitor surrounding environmental parameters; The proximity sensor module is used to detect the distance between the seat and surrounding objects.

[0012] As a preferred embodiment of the eVTOL safety seat system for a heavy-duty unmanned aerial vehicle (UAV) according to the present invention, the decision-making layer includes: The emergency decision-making module is used to perform real-time analysis and decision-making based on the data transmitted by the perception layer. When an emergency is detected in the drone, it can assess the current situation and determine whether to activate the emergency procedure of the safety seat. If it decides to activate, it will calculate the best ejection time, ejection direction and landing point based on the drone's real-time status, passenger information and surrounding environment information, and generate corresponding control commands. The fault diagnosis module is used to monitor the working status of each module. Once a fault is detected in a module, the fault location and isolation are immediately performed, and an attempt is made to repair itself. If the repair is not possible, the fault information will be reported to the UAV's maintenance system, and corresponding fault-tolerant measures will be taken to ensure that the system can continue to operate under partial fault conditions.

[0013] As a preferred embodiment of the eVTOL safety seat system for a heavy-duty unmanned aerial vehicle (UAV) according to the present invention, the emergency decision-making module includes: The data receiving and preprocessing module is used to first receive the data transmitted by the perception layer, and at the same time receive the data transmitted by the UAV avionics system and flight control system; then, it performs secondary verification on the received data, removes obvious outliers, and unifies the data format and timestamp to ensure the consistency of subsequent analysis. The emergency state identification module first extracts key feature parameters from the received data; then, it inputs the feature parameters into the trained fault classification model to identify whether the current situation is an emergency and the type of emergency.

[0014] As a preferred embodiment of the eVTOL safety seat system for a heavy-duty unmanned aerial vehicle (UAV) according to the present invention, the emergency decision-making module further includes: The risk level assessment module is used to calculate the risk level for identified emergency situations by combining real-time environmental information and passenger information through a risk assessment matrix. The emergency response matching module is used to call up a matching basic plan from the preset emergency response plan library based on the risk level and emergency status type; at the same time, it will also dynamically adjust the basic plan based on real-time data.

[0015] As a preferred embodiment of the eVTOL safety seat system for a heavy-duty unmanned aerial vehicle (UAV) according to the present invention, the emergency decision-making module further includes: The decision verification and optimization module is used to quickly simulate and verify the effect of the initial emergency decision through simulation. If the simulation results show that the decision can reduce the risk of passenger injury or death to below the preset safety threshold, the decision is confirmed to be effective and control instructions are output. If the simulation finds potential risks, it returns to the emergency measure matching stage, adjusts the plan parameters and re-verifies until the safety requirements are met. The instruction generation and transmission module is used to convert the emergency decision into specific control instructions after confirming its validity. It transmits the instructions to the execution layer through an encrypted communication protocol and simultaneously feeds back the decision results to the UAV avionics system so that other systems can cooperate. At the same time, the instructions contain timestamps and check codes to ensure that the execution layer can accurately receive and execute them and can trace the source and generation logic of the instructions.

[0016] As a preferred embodiment of the eVTOL safety seat system for a heavy-duty unmanned aerial vehicle (UAV) according to the present invention, the fault diagnosis module includes: The real-time status acquisition and baseline establishment module is used to continuously collect operational data from the decision-making level and related systems; at the same time, it collects historical normal operation data and establishes the normal fluctuation range of each parameter through statistical analysis. The anomaly detection and preliminary location module compares the real-time collected data with the baseline. If a parameter exceeds the normal range, it is marked as an anomaly point, and the fault range is narrowed down through multi-parameter correlation analysis. At the same time, it matches the corresponding fault type by combining the fault code library. The deep diagnostic module is used to perform in-depth detection on the initially located fault area and calls dedicated diagnostic subroutines.

[0017] As a preferred embodiment of the eVTOL safety seat system for a heavy-duty UAV described in this invention, the fault diagnosis module further includes: The fault level assessment and impact analysis module is used to classify faults into levels based on their severity and scope of impact, namely minor faults, moderate faults, and severe faults; at the same time, it analyzes the potential impact of faults on the system. The fault tolerance strategy execution and system reconfiguration module is used to initiate corresponding fault tolerance measures for different levels of faults; minor faults are resolved through software compensation; moderate faults activate redundant resources; and severe faults trigger system reconfiguration. The fault recording and reporting module is used to write fault information into the local fault log and upload it to the aircraft maintenance system via encrypted communication. At the same time, it feeds back key data in the fault handling process to the emergency decision-making algorithm to help it adjust its decision-making strategy in the system degraded state.

[0018] As a preferred embodiment of the eVTOL safety seat system for a heavy-duty unmanned aerial vehicle (UAV) according to the present invention, the execution layer includes: The ejection module is used to eject the safety seat from the aircraft at a predetermined speed and angle after receiving an ejection command from the decision-making level. The airbag module is used to inflate and deploy the airbag after the child safety seat deploys, providing cushioning protection for the passenger. The attitude control module is used to adjust the flight attitude of the safety seat according to the attitude adjustment instructions sent by the decision-making level after the safety seat is ejected, so as to keep it stable and fly towards the predetermined landing point. The landing cushioning module is designed to absorb the impact force during landing when the child safety seat approaches the ground, reducing the risk of injury to the passenger.

[0019] Compared with existing technologies: 1. Advantages of matching the perception layer with existing problems To address the issue of poor adaptability to complex environments, the perception layer employs a diverse combination of sensors and a data preprocessing mechanism to provide an effective solution. Multiple sensors work together to comprehensively capture environmental information, reducing the impact of single signal loss or error. Meanwhile, the filtering and preprocessing functions of the data acquisition module reduce noise interference with sensor data, improve data quality, provide a more reliable basis for subsequent decision-making, and enhance the system's stability in complex environments. 2. Advantages of matching the decision-making level with existing problems To address the issue of insufficient system reliability, the fault diagnosis module at the decision-making level plays a crucial role. This module continuously performs self-checks, enabling it to promptly detect and isolate faults. Through self-repair or fault-tolerant measures, it maintains system operation and prevents overall failure due to a single component failure. For example, if a hardware failure occurs in the core computing unit, the fault diagnosis module can quickly locate the fault and activate backup computing resources to ensure timely processing of sensor data, guarantee the accuracy of the ejection program's judgment and control, and improve system reliability. To address the need for improved emergency response capabilities, the emergency decision-making module, based on real-time analysis of multi-source data, can quickly assess emergency situations and generate response strategies. Through machine learning and deep learning training, it can quickly identify failure modes and, combined with environmental and aircraft status information, shorten decision-making time and improve decision accuracy, thus solving the problems of slow emergency response speed and inaccurate decision-making. For example, in the event of a power system failure, the algorithm can quickly determine the optimal ejection timing and landing point, reducing delays and avoiding missing the best escape opportunity. 3. Advantages of matching the execution layer with existing problems The coordinated operation of various devices at the execution level further enhances the effectiveness of emergency response; the reliable power source and precise control of the ejection module ensure that the safety seat can be accurately ejected according to the decision command in an emergency; the airbag module precisely controls inflation based on parameters to adapt to the needs of different ejection heights; the attitude control module ensures stable flight of the seat in the air; and the adaptive adjustment of the landing buffer module reduces the impact force during landing. All of these make emergency response measures more precise and effective, enhance the system's protective capabilities in emergency situations, and work together with the decision-making level to improve emergency response capabilities. At the same time, the close cooperation between the execution layer, the decision-making layer, and the perception layer forms a complete response chain, enabling the entire safety seat control system to operate efficiently from perception and decision-making to execution when facing various emergency situations. This indirectly enhances the synergy potential with the overall aircraft system and provides support for solving the problem of weak synergy. 4. Advantages of matching the overall architecture with security standards The overall system architecture is designed with a certain degree of flexibility and scalability. The modular design of the perception layer, decision layer, and execution layer allows the system to be adjusted and configured according to the safety standards of different regions. For example, in response to the differences in the requirements for impact direction in different regions, the corresponding impact resistance standards can be met by adjusting the weight of impact direction consideration in the emergency decision module and the parameter settings of the ejection module and buffer module in the execution layer. This alleviates the problems caused by inconsistent safety standards to a certain extent and enhances the adaptability and versatility of the system. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall framework of the present invention; Figure 2 This is a schematic diagram of the sensor module framework of the present invention; Figure 3 This is a schematic diagram of the emergency decision-making module framework of the present invention; Figure 4 This is a schematic diagram of the fault diagnosis module framework of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0022] This invention provides an eVTOL safety seat system for heavy-duty unmanned aerial vehicles (UAVs). Please refer to [link / reference]. Figures 1-4 ,include: The perception layer is used to first measure the data of the child safety seat, and then collect the measured data; The decision-making layer is used to perform real-time analysis and decision-making based on the data collected by the perception layer, generate corresponding instructions, and diagnose whether the drone has any faults. The execution layer is used to control the drone according to the instructions of the decision-making layer.

[0023] The sensing layer includes: The sensor module is used to detect the acceleration and angular velocity of the child safety seat, the weight of the passenger and the force on the seat belt, the surrounding environmental parameters, and the distance between the child safety seat and surrounding objects. The data acquisition module is used to collect the detection data from the sensor module and perform preliminary filtering and preprocessing to remove noise interference and improve data quality.

[0024] The sensor module includes: The inertial measurement unit module is used to measure the acceleration and angular velocity of the safety seat in real time to determine the seat's attitude and motion state; it can quickly capture changes when the drone experiences abnormal roll or acceleration, providing basic data for subsequent decision-making. The pressure sensor module is used to monitor the passenger's weight and the force on the seat belt, so as to adjust the ejection parameters according to the actual situation of the passenger when the emergency procedure is activated, ensuring that the ejection process is safe and reliable; The environmental sensor module is used to monitor surrounding environmental parameters; under adverse weather conditions, such as high temperature, high humidity or abnormal air pressure, the protective measures of the safety seat can be adjusted according to the environmental data, such as adjusting the inflation pressure of the airbag. The proximity sensor module is used to detect the distance between the seat and surrounding objects (such as cabin walls, other seats, etc.); in the event of violent shaking or tilting of the drone, it prevents the safety seat from colliding with other objects and causing secondary injuries.

[0025] The decision-making body includes: The emergency decision-making module is used to perform real-time analysis and decision-making based on the data transmitted by the perception layer. When an emergency is detected in the drone, such as a power system failure or severe damage to the fuselage, it can assess the current situation and determine whether to activate the emergency procedure for the safety seat. If the decision is made to activate it, it will calculate the best ejection timing, ejection direction and landing point based on the drone's real-time status, passenger information and surrounding environment information, and generate corresponding control commands. The fault diagnosis module is used to monitor the working status of each module. Once a fault is detected in a module, the fault location and isolation are immediately performed, and an attempt is made to repair itself. If the repair is not possible, the fault information will be reported to the UAV's maintenance system, and corresponding fault-tolerant measures will be taken to ensure that the system can continue to operate under partial fault conditions. For example, when a sensor fails, the system can make an estimate based on data from other sensors to maintain basic functions.

[0026] The emergency decision-making module includes: The data receiving and preprocessing module first receives data transmitted from the perception layer, and simultaneously receives data such as flight altitude, speed, attitude, and fault warnings transmitted from the UAV avionics system and flight control system. Then, it performs secondary verification on the received data, removes obvious outliers, and standardizes the data format and timestamp to ensure consistency in subsequent analysis. For example, if the flight speed data at a certain moment suddenly exceeds the normal flight range without a reasonable explanation, the algorithm will mark it as an anomaly and temporarily remove it to avoid interfering with decision-making. The emergency state identification module first extracts key feature parameters from the received data, such as the rate of change of the UAV's attitude angle, the magnitude of sudden speed changes, and the type of fault signal. Then, the feature parameters are input into a trained fault classification model (built based on a deep neural network) to identify whether the current state is in an emergency and the type of emergency. For example, when the output power of the UAV's power system is detected to drop sharply beyond a preset threshold and the rate of change of attitude angle exceeds the safe range, the model will determine it as a "power system failure emergency state". If a signal of severe damage to the fuselage structure is received, it will be determined as a "fuselage disintegration risk state". The risk level assessment module is used to calculate the risk level of identified emergency situations by combining real-time environmental information (such as flight altitude, distribution of surrounding obstacles, terrain type, etc.) and passenger information (weight, seat belt status, etc.) through a risk assessment matrix. The horizontal axis of the risk assessment matrix represents the severity of the emergency situation (e.g., power system failure can be divided into three levels: minor, moderate, and severe), and the vertical axis represents the environmental hazard coefficient (e.g., high hazard coefficient for densely populated areas, low hazard coefficient for open suburbs). The intersection point is the corresponding risk level (e.g., four levels: extremely high, high, moderate, and low). For example, a severe power system failure occurring over a densely populated city would be assessed as "extremely high" risk, while a minor power system failure occurring in a remote mountainous area might be assessed as "moderate" risk. The emergency response matching module is used to retrieve a matching basic plan from a pre-set emergency response library based on the risk level and emergency status type. The emergency response library contains standard operating procedures for different scenarios, such as "extremely high risk level + power system failure" corresponding to "immediately start the ejection procedure + prioritize open landing point"; "medium risk level + slight attitude abnormality" corresponding to "temporarily not eject, continuous monitoring + adjust safety seat cushioning parameters"; at the same time, it will also dynamically adjust the basic plan based on real-time data. For example, for passengers with larger body weight, the ejection force will be automatically increased by 10% in the ejection plan; if a small obstacle is detected at the landing point, the ejection direction will be finely adjusted to avoid the obstacle. The decision verification and optimization module is used to quickly simulate and verify the effectiveness of the initial emergency decision through simulation. Based on the current drone status, environmental parameters, and emergency measures, the simulation predicts the safety seat's trajectory, passenger stress, and potential risks within the next 3-5 seconds. If the simulation shows that the decision can reduce the risk of passenger injury or death to below a preset safety threshold, the decision is confirmed as effective and a control command is output. If the simulation identifies potential risks (such as a possible collision with a building after ejection), the module returns to the emergency measure matching stage, adjusts the scheme parameters (such as changing the ejection angle), and re-verifies until the safety requirements are met. For example, the initial ejection trajectory might pass through a tall building; after the simulation identifies a collision risk, the algorithm adjusts the ejection direction to avoid the building, and outputs the final command after successful verification. The instruction generation and transmission module is used to convert the emergency decision into specific control instructions after confirming its validity. These instructions include the launch time, launch angle, force parameters, airbag inflation time and pressure value, and attitude control adjustment range. The module transmits the instructions to the execution layer via an encrypted communication protocol and simultaneously feeds back the decision results to the UAV avionics system so that other systems can coordinate (e.g., the flight control system adjusts the remaining fuselage attitude to create favorable conditions for launch). The instructions also include timestamps and checksums to ensure that the execution layer can accurately receive and execute the instructions and trace their source and generation logic.

[0027] The fault diagnosis module includes: The real-time status acquisition and baseline establishment module is used to continuously collect operational data from the decision-making layer and related systems, including the computing load of the central processing unit (CPU), memory usage, data transmission rates of each interface, and communication latency with the perception and execution layers. At the same time, it collects historical normal operation data and establishes the normal fluctuation range (i.e., baseline) of each parameter through statistical analysis. For example, the normal CPU load baseline is set to 30%-70%, and the communication latency baseline is set to no more than 50 milliseconds, providing a reference standard for subsequent fault diagnosis. The anomaly detection and preliminary location module compares real-time collected data with a baseline. If a parameter exceeds the normal range, it is marked as an anomaly. Multi-parameter correlation analysis is used to narrow down the fault range. For example, when the CPU load suddenly rises to 95% and the memory usage rate spikes simultaneously, it is initially judged that the problem may be a failure of the computing unit or a software program. If only the communication delay exceeds the baseline and continues to rise, the focus is on troubleshooting the data transmission link (such as a high-speed data bus). At the same time, it combines a fault code library (preset characteristic parameter combinations corresponding to common faults) to match the corresponding fault type. For example, "communication delay exceeding the standard + data packet loss rate of 10%" corresponds to the fault code "bus interface loose". The deep diagnostic module is used to perform in-depth detection on the initially located fault area and call dedicated diagnostic subroutines. For example, for CPU anomalies, it verifies the stability of the CPU under high load by running the built-in stress test program; for communication link faults, it sends test data packets and analyzes the feedback results to determine whether it is a hardware interface fault or a protocol compatibility issue; if a sensor data is detected to be continuously abnormal (such as a pressure sensor value that remains unchanged), it will combine the state information of the perception layer to confirm whether it is a fault of the sensor itself or a fault of the link transmitted to the decision layer, and eliminate false alarms through cross-validation. For example, when a parameter briefly exceeds the baseline but quickly recovers, and other related parameters are normal, it is determined to be transient interference rather than a fault. The fault level assessment and impact analysis module is used to classify faults according to their severity and scope of impact, into minor faults (such as data drift from a non-critical sensor that does not affect core functions), moderate faults (such as degradation of some communication links, leading to a decrease in data update frequency), and severe faults (such as CPU main core failure, affecting the operation of emergency decision-making algorithms). Simultaneously, it analyzes the potential impact of faults on the system. For example, a failure in the control command transmission link of the ejection unit in the execution layer may lead to delays in emergency operations, directly threatening passenger safety; while a failure of an environmental sensor only affects environmental adaptability fine-tuning and has a relatively small impact on core functions. The fault tolerance strategy execution and system reconfiguration module is used to initiate corresponding fault tolerance measures for different levels of faults. Minor faults are resolved through software compensation, such as calling data from other similar sensors for interpolation estimation when an environmental sensor fails. Moderate faults activate redundant resources, such as switching to a backup bus when a communication link degrades. Severe faults trigger system reconfiguration, such as immediately activating the backup core and loading a simplified version of the emergency decision algorithm when the CPU main core fails, ensuring that core functions are not interrupted. The fault recording and reporting module is used to write information such as the fault occurrence time, type, location result, handling measures, and recovery status into the local fault log and upload it to the aircraft maintenance system via encrypted communication. The log content includes detailed parameter change curves, such as CPU load fluctuation graphs before and after the fault, providing a basis for subsequent maintenance. For serious faults, it simultaneously sends prompt information (such as audible and visual alarms + fault code display) to the cockpit alarm system to remind maintenance personnel to carry out timely repairs. At the same time, it feeds back key data in the fault handling process to the emergency decision-making algorithm to assist it in adjusting decision-making strategies in the system degradation state. For example, when the communication link is degraded, the algorithm automatically reduces the dependency weight of non-critical data.

[0028] The execution layer includes: The ejection module is used to eject the safety seat from the aircraft at a predetermined speed and angle after receiving the ejection command from the decision-making level; its ejection power can be gunpowder propulsion, electromagnetic drive, etc., to ensure that the safety seat can be reliably ejected under various circumstances. The airbag module is used to inflate and deploy the airbag after the safety seat ejects, providing cushioning protection for the passenger. The inflation volume and deployment time of the airbag are precisely controlled by the decision-makers based on parameters such as aircraft altitude and speed. For example, during low-altitude ejection, the airbag inflates and deploys more quickly to cope with the shorter landing time; during high-altitude ejection, the airbag is controlled to inflate in stages to avoid airbag rupture due to changes in air pressure. The attitude control module is used to adjust the flight attitude of the safety seat according to the attitude adjustment instructions sent by the decision-making level after the safety seat is ejected, so as to keep it stable and fly towards the predetermined landing point; for example, when the safety seat tilts in the air, the thrusters ignite to generate reverse thrust and correct the seat attitude. The landing cushioning module is designed to absorb the impact force during landing when the child safety seat approaches the ground, reducing the risk of injury to the passenger. The stiffness and cushioning travel of the landing cushioning module can be adaptively adjusted according to the speed of the child safety seat and the terrain of the landing point.

[0029] In practical use, the specific steps are as follows: S1: The sensor module detects the acceleration and angular velocity of the child safety seat, the weight of the passenger and the force on the seat belt, the surrounding environmental parameters, and the distance between the child safety seat and surrounding objects. After detection, the data acquisition module collects the detection data from the sensor module and performs preliminary filtering and preprocessing. S2: The emergency decision-making module performs real-time analysis and decision-making based on the data transmitted from the perception layer. When an emergency is detected in the drone, it assesses the current situation and determines whether to activate the child safety seat emergency procedure. If activation is decided, it calculates the optimal ejection timing, ejection direction, and landing point based on the drone's real-time status, passenger information, and surrounding environment information, and generates corresponding control commands. Simultaneously, the fault diagnosis module monitors the operational status of each module. If a module malfunctions, it immediately locates and isolates the fault and attempts self-repair. If repair is unsuccessful, the fault information is reported to the drone's maintenance system, and appropriate fault-tolerant measures are implemented to ensure the system can continue operating even under partial fault conditions. S3: Upon receiving the ejection command from the decision-making level, the ejection module can eject the safety seat from the aircraft at a predetermined speed and angle. After ejection, the airbag module will inflate and deploy the airbag to provide cushioning protection for the passenger. At the same time, the attitude control module will work according to the attitude adjustment command sent by the decision-making level to adjust the flight attitude of the safety seat to maintain stability and fly towards the predetermined landing point. Then, the landing cushioning module will absorb the impact force during landing as the safety seat approaches the ground, reducing the injury to the passenger.

[0030] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. An eVTOL safety seat system for a large-payload unmanned aerial vehicle (UAV), characterized in that, include: The perception layer is used to first measure the data of the child safety seat, and then collect the measured data; The decision-making layer is used to perform real-time analysis and decision-making based on the data collected by the perception layer, generate corresponding instructions, and diagnose whether the drone has any faults. The execution layer is used to control the drone according to the instructions of the decision-making layer.

2. The eVTOL safety seat system for a large-payload unmanned aerial vehicle according to claim 1, characterized in that, The sensing layer includes: The sensor module is used to detect the acceleration and angular velocity of the child safety seat, the weight of the passenger and the force on the seat belt, the surrounding environmental parameters, and the distance between the child safety seat and surrounding objects. The data acquisition module is used to collect the detection data from the sensor module and perform preliminary filtering and preprocessing.

3. The eVTOL safety seat system for a large-payload unmanned aerial vehicle according to claim 2, characterized in that, The sensor module includes: The inertial measurement unit module is used to measure the acceleration and angular velocity of the child safety seat in real time to determine the seat's attitude and motion state. The pressure sensor module is used to monitor the passenger's weight and the force on the seat belt, so as to adjust the ejection parameters according to the actual situation of the passenger when the emergency procedure is activated, ensuring that the ejection process is safe and reliable; An environmental sensor module is used to monitor surrounding environmental parameters; The proximity sensor module is used to detect the distance between the seat and surrounding objects.

4. The eVTOL safety seat system for a large-payload unmanned aerial vehicle according to claim 1, characterized in that, The decision-making body includes: The emergency decision-making module is used to perform real-time analysis and decision-making based on the data transmitted by the perception layer. When an emergency is detected in the drone, it can assess the current situation and determine whether to activate the emergency procedure of the safety seat. If it decides to activate, it will calculate the best ejection time, ejection direction and landing point based on the drone's real-time status, passenger information and surrounding environment information, and generate corresponding control commands. The fault diagnosis module is used to monitor the working status of each module. Once a fault is detected in a module, the fault location and isolation are immediately performed, and an attempt is made to repair itself. If the repair is not possible, the fault information will be reported to the UAV's maintenance system, and corresponding fault-tolerant measures will be taken to ensure that the system can continue to operate under partial fault conditions.

5. The eVTOL safety seat system for a large-payload unmanned aerial vehicle according to claim 4, characterized in that, The emergency decision-making module includes: The data receiving and preprocessing module is used to first receive the data transmitted by the perception layer, and at the same time receive the data transmitted by the UAV avionics system and flight control system; then, it performs secondary verification on the received data, removes obvious outliers, and unifies the data format and timestamp to ensure the consistency of subsequent analysis. The emergency state identification module first extracts key feature parameters from the received data; then, it inputs the feature parameters into the trained fault classification model to identify whether the current situation is an emergency and the type of emergency.

6. The eVTOL safety seat system for a large-payload unmanned aerial vehicle according to claim 5, characterized in that, The emergency decision-making module also includes: The risk level assessment module is used to calculate the risk level for identified emergency situations by combining real-time environmental information and passenger information through a risk assessment matrix. The emergency response matching module is used to call up a matching basic plan from the preset emergency response plan library based on the risk level and emergency status type; at the same time, it will also dynamically adjust the basic plan based on real-time data.

7. The eVTOL safety seat system for a large-payload unmanned aerial vehicle according to claim 6, characterized in that, The emergency decision-making module also includes: The decision verification and optimization module is used to quickly simulate and verify the effect of the initial emergency decision through simulation. If the simulation results show that the decision can reduce the risk of passenger injury or death to below the preset safety threshold, the decision is confirmed to be effective and control instructions are output. If the simulation finds potential risks, it returns to the emergency measure matching stage, adjusts the plan parameters and re-verifies until the safety requirements are met. The instruction generation and transmission module is used to convert the emergency decision into specific control instructions after confirming its validity. It transmits the instructions to the execution layer through an encrypted communication protocol and simultaneously feeds back the decision results to the UAV avionics system so that other systems can cooperate. At the same time, the instructions contain timestamps and check codes to ensure that the execution layer can accurately receive and execute them and can trace the source and generation logic of the instructions.

8. The eVTOL safety seat system for a large-payload unmanned aerial vehicle according to claim 4, characterized in that, The fault diagnosis module includes: The real-time status acquisition and baseline establishment module is used to continuously collect operational data from the decision-making level and related systems; at the same time, it collects historical normal operation data and establishes the normal fluctuation range of each parameter through statistical analysis. The anomaly detection and preliminary location module compares the real-time collected data with the baseline. If a parameter exceeds the normal range, it is marked as an anomaly point, and the fault range is narrowed down through multi-parameter correlation analysis. At the same time, it matches the corresponding fault type by combining the fault code library. The deep diagnostic module is used to perform in-depth detection on the initially located fault area and calls dedicated diagnostic subroutines.

9. The eVTOL safety seat system for a heavy-load unmanned aerial vehicle according to claim 8, characterized in that, The fault diagnosis module also includes: The fault level assessment and impact analysis module is used to classify faults into levels based on their severity and scope of impact, namely minor faults, moderate faults, and severe faults; at the same time, it analyzes the potential impact of faults on the system. The fault tolerance strategy execution and system reconfiguration module is used to initiate corresponding fault tolerance measures for different levels of faults; minor faults are resolved through software compensation; moderate faults activate redundant resources; and severe faults trigger system reconfiguration. The fault recording and reporting module is used to write fault information into the local fault log and upload it to the aircraft maintenance system via encrypted communication. At the same time, it feeds back key data in the fault handling process to the emergency decision-making algorithm to help it adjust its decision-making strategy in the system degraded state.

10. The eVTOL safety seat system for a large-payload unmanned aerial vehicle according to claim 1, characterized in that, The execution layer includes: The ejection module is used to eject the safety seat from the aircraft at a predetermined speed and angle after receiving an ejection command from the decision-making level. The airbag module is used to inflate and deploy the airbag after the child safety seat deploys, providing cushioning protection for the passenger. The attitude control module is used to adjust the flight attitude of the safety seat according to the attitude adjustment instructions sent by the decision-making level after the safety seat is ejected, so as to keep it stable and fly towards the predetermined landing point. The landing cushioning module is designed to absorb the impact force during landing when the child safety seat approaches the ground, reducing the risk of injury to the passenger.

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