Low-altitude low-speed hanging chair circulating system

Abstract

The invention relates to the technical field of chairlift systems, and discloses a low-altitude low-speed chairlift circulating system which comprises an intelligent edge calculation module which is deployed on a chairlift, a supporting tower and a station and is used for monitoring the system operation state and environmental data in real time, making an autonomous decision and cooperating with a central control system; the rescue cooperation system module comprises an unmanned aerial vehicle and a ground robot and can work cooperatively to achieve rapid evacuation and rescue of hanging seat passengers; and a security data management module. Autonomous emergency response and distributed cooperation are realized through intelligent edge computing, and the security and fault tolerance of the system are improved; the unmanned aerial vehicle and the ground robot are cooperated to accelerate rescue and optimize emergency processing; the block chain ensures that the data is safe, transparent and traceable; self-rescue and emergency efficiency is improved through augmented reality and self-learning scheduling optimization; the system expansibility, the maintenance efficiency and the operation transparency are improved through the modular design and a multi-party cooperation mechanism.

Description

Technical Field

[0001] The invention relates to the technical field of chair suspension systems, in particular to a low-altitude slow-speed chair suspension circulation system. Background Art

[0002] Low-altitude slow-speed chairlift circulation systems are widely used in amusement parks, ski resorts and tourist attractions, mainly for sightseeing transportation of passengers. Such systems need to provide a safe and smooth riding experience under complex terrain and weather conditions. However, with the diversification of usage environments and climate change, the emergency response and rescue efficiency of existing technologies in the face of emergencies are increasingly challenged.

[0003] The existing low-altitude slow-speed chairlift circulation system uses a central control system to uniformly monitor and manage the chairlift's operating status and fault handling. When a system failure or weather change occurs, the central system collects data through sensors and issues dispatch instructions to take corresponding measures, such as slowing down or stopping operations. In addition, traditional emergency rescue mainly relies on ground rescue teams to use ropes, ladders and other tools to safely evacuate passengers. In terms of data storage, most systems use a centralized database to record operating data and fault logs for subsequent reference and maintenance.

[0004] The existing technology has the following problems: emergency response relies on the central system, resulting in delayed response or failure to respond in time when the system fails, affecting safety and fault tolerance; traditional rescue methods are inefficient, especially in complex terrains where it is difficult to quickly evacuate passengers; centralized data storage is susceptible to single point failures, and data transparency and traceability are poor; emergency handling lacks flexibility and cannot dynamically adjust plans according to actual conditions; the system is inconvenient to expand and maintain, and it is difficult to meet the needs of complex environments. These problems restrict the improvement of low-altitude slow-speed chairlift systems in terms of safety, emergency efficiency, and scalability. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a low-altitude slow-speed chair suspension circulation system, which solves the problems of delayed emergency response, low rescue efficiency, insufficient data security and transparency, poor emergency handling flexibility, and low system scalability and maintenance efficiency in the prior art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a low-altitude slow-speed chair suspension circulation system, comprising: Intelligent edge computing modules, deployed on chairlifts, support towers and sites, are used to monitor system operating status and environmental data in real time, make autonomous decisions, and work in collaboration with the central control system; The rescue coordination system module, including drones and ground robots, can work together to achieve rapid evacuation and rescue of chairlift passengers; The security data management module records and manages the system's operating data, fault information, and rescue process based on blockchain technology to ensure data security, transparency, and non-tamperability; Augmented reality guidance module, used to provide visual self-rescue and rescue guidance for passengers and rescue personnel; The self-learning scheduling optimization module continuously optimizes the system operation and emergency response through machine learning algorithms, achieving adaptive scheduling and efficient rescue.

[0007] Preferably, the intelligent edge computing module includes: The edge computing device submodule is deployed on the chairlift, support tower and site to collect multi-dimensional data including meteorological data, cable tension and chairlift vibration; The autonomous decision-making submodule is used to decide whether to slow down, stop or partially isolate the chairlift based on preset safety policies and real-time monitoring data; The distributed collaboration submodule uses distributed algorithms to realize information sharing and collaboration among multiple edge computing devices, so as to achieve local isolation of faults and autonomous recovery of the system.

[0008] Preferably, the rescue coordination system module includes: The drone rescue submodule has automatic obstacle avoidance and autonomous positioning capabilities, and can carry rescue equipment and reach the faulty chairlift for rescue; A ground robot rescue submodule that can move along the chairlift track or support structure and provide a soft ladder or rescue rope; The collaborative scheduling submodule is used to achieve coordinated operations between drones and ground robots, and real-time scheduling through the central command system or edge computing devices.

[0009] Preferably, the drone rescue submodule includes: Multi-rotor drones with high-precision positioning systems and load-carrying rescue equipment; The rescue rope subsystem can adjust the length and tension of the rope according to the height of the chairlift and wind speed to ensure the safe transfer of passengers to the ground.

[0010] Preferably, the security data management module includes: The blockchain data storage submodule is used to distribute and encrypt the system's operating data and emergency processing data to ensure that the data cannot be tampered with; The event tracing submodule is used to record the detailed process of each failure, rescue operation and emergency handling, and generate traceable operation records.

[0011] Preferably, the blockchain data storage submodule uses a hash function H(x) to encrypt the data to form an unalterable data chain, satisfying the following formula: H(x)=Hash(d1∥d2∥…∥d n ); Among them, d1, d2, ..., d n is the data collected by each node, and Hash(·) is the hash function.

[0012] Preferably, the augmented reality guidance module includes: a passenger self-rescue AR guidance submodule, an AR display device installed inside the hanging chair, capable of providing passenger self-rescue guidance information in real time; The rescuer AR assistance submodule is used to provide rescuers with visual data on the chair structure, passenger health information, and rescue path.

[0013] Preferably, the AR display device has a dynamic path guidance function, and can adjust the emergency guidance content according to the real-time status and environmental conditions of the hanging chair to meet the following navigation formula: ; Among them, P t is the target path position displayed by the AR device, P0 is the initial position, and v(t) is the real-time speed of the chairlift.

[0014] Preferably, the self-learning scheduling optimization module includes: The self-learning algorithm submodule uses deep learning algorithms to train a large amount of historical data and continuously optimize the system's emergency plans and scheduling strategies; The dynamic emergency plan generation submodule automatically generates or adjusts emergency plans based on real-time monitoring data.

[0015] Preferably, the self-learning algorithm submodule predicts the system state and optimizes the scheduling based on the neural network model f(x,θ), satisfying the following formula: f(x,θ)=σ(W·x+b); Among them, x is the input system state data, θ={W,b} is the model parameter, and σ(·) is the activation function.

[0016] The present invention provides a low altitude slow speed chair suspension circulation system. It has the following beneficial effects: 1. The present invention realizes the autonomous emergency response capability of the chairlift system through the intelligent edge computing module and distributed collaboration mechanism. When the system detects an emergency, the edge computing device can make independent decisions to slow down or stop the chairlift to ensure the safety of passengers. At the same time, the distributed collaboration mechanism allows multiple nodes to share information, isolate faults locally, and prevent a single device failure from causing the entire system to stop operating. This autonomous and collaborative working mechanism greatly improves the security and fault tolerance of the system.

[0017] 2. The present invention integrates a rescue system in which drones and ground robots work together, which can evacuate passengers quickly and safely. In emergency situations, the drone can quickly reach the faulty chairlift for rescue, while the ground robot provides auxiliary rescue equipment under complex terrain conditions to ensure smooth rescue operations. Through the dynamic priority system of AI, the health status of passengers is monitored in real time, and rescue tasks are automatically optimized according to priority, which greatly shortens the rescue time and significantly improves the rescue success rate.

[0018] 3. The present invention uses blockchain technology to store and manage the system's operating data, rescue process and emergency handling records. The tamper-proof and distributed storage characteristics of blockchain technology ensure the transparency and security of all operations and data. All parties can access system data in real time to ensure the traceability of responsibility after an incident occurs, thereby improving the transparency and credibility of the entire system.

[0019] 4. Through the augmented reality guidance system, the present invention enables passengers to quickly obtain visual self-rescue guidance when encountering emergencies, and rescue personnel can also understand the status of the hanging chair and the best rescue path in real time through AR technology, which greatly improves the efficiency of self-rescue and rescue. At the same time, the self-learning scheduling optimization module uses machine learning algorithms to continuously optimize emergency plans and system scheduling, so that the system can dynamically adjust rescue plans and scheduling strategies according to real-time conditions, thereby improving the system's adaptability.

[0020] 5. The present invention adopts modular design, so that each functional module can operate independently, which is convenient for system upgrade, maintenance and expansion. In addition, through the data sharing mechanism of blockchain, system operation information can be synchronized with manufacturers, operators, regulators and other parties in real time, which improves operational transparency and collaboration efficiency. This design not only ensures the high scalability and maintenance convenience of the system, but also can adapt to the development of future technologies and changes in demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a system architecture diagram of the present invention; Figure 2 It is a structural schematic diagram of the intelligent edge computing module of the present invention; Figure 3 It is a structural schematic diagram of the rescue cooperation system module of the present invention; Figure 4 It is a structural schematic diagram of the security data management module of the present invention; Figure 5 It is a structural schematic diagram of the augmented reality guidance module of the present invention; Figure 6 It is a structural diagram of the self-learning scheduling optimization module of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Please see attached Figure 1 - Attachment Figure 6 The present invention relates to a low-altitude slow-speed chairlift circulation system, which aims to provide an intelligent, multi-level emergency handling system to ensure that various emergencies can be handled efficiently and safely during the operation of the chairlift system. The system significantly improves safety and emergency response capabilities through edge computing, collaborative rescue of drones and ground robots, blockchain-based data management, augmented reality (AR) guidance, and self-learning scheduling.

[0024] 1. System overall architecture The low altitude slow chair suspension circulation system of the present invention is composed of five main modules, namely: Intelligent edge computing module Rescue coordination system module Security Data Management Module Augmented reality guidance module Self-learning scheduling optimization module Each module assumes different functions during the normal operation and emergency response of the system, and works closely together during the system operation to form a complete intelligent emergency response system.

[0025] 2. Intelligent edge computing module The intelligent edge computing module is one of the core modules in the present invention, which aims to improve the autonomy and response speed of the system through distributed computing capabilities, especially in the case of central system failure or large network delay, to ensure the timeliness of emergency processing.

[0026] 2.1 Edge computing device submodule Edge computing devices are deployed on the chairlift, support tower and site, and each device has a certain local computing and storage capacity. The edge devices collect the following data in real time: The operating status of the chairlift (including speed, acceleration, tilt angle, etc.) Cable tension and vibration frequency Environmental data, such as weather conditions (wind speed, humidity, temperature, etc.) The collected data will be processed locally and judged according to the system's preset safety strategy. When the chairlift operation is abnormal (such as sudden speed changes or excessive cable tension), the edge device can autonomously make emergency decisions, such as reducing the chairlift's operating speed or triggering emergency braking.

[0027] 2.2 Autonomous decision-making submodule Edge computing devices can make autonomous decisions in a short time based on preset safety policies. For example, when the wind speed exceeds the safety range, the edge device can determine whether the chairlift should be slowed down or shut down. In addition, edge computing devices have a certain self-learning ability and can optimize algorithms based on historical data to improve the accuracy of anomaly detection and emergency response.

[0028] 2.3 Distributed Collaboration Submodule Each edge computing device can communicate with adjacent devices to form a distributed collaborative network. Through distributed algorithms, such as the blockchain consensus mechanism, each edge node shares status information to achieve local isolation of faults and self-recovery of the system. For example, when a support tower detects that the structural vibration exceeds the standard, the edge device of the support tower can share data with adjacent devices and coordinate to slow down the speed of the chairlift in the local area to avoid shutdown of the entire system.

[0029] 3. Rescue coordination system module This module is designed to achieve rapid rescue of trapped passengers through the collaboration of drones and ground robots. In particular, when there is a serious failure in the hanging chair system, drones and ground robots can respond quickly and evacuate passengers safely.

[0030] 3.1 Drone rescue submodule The drone swarm in the present invention is a multi-rotor drone with autonomous positioning and obstacle avoidance capabilities, capable of carrying rescue equipment (such as a rescue chair or a suspension rope) and quickly reaching the faulty hanging chair. The drone uses a high-precision positioning system to ensure accurate positioning of the hanging chair in complex environments and dynamically adjusts according to the position of the passengers.

[0031] The drone is equipped with a rescue rope subsystem that can adjust the length and tension of the rope in real time according to the height of the hanging chair and wind speed to ensure the safe landing of passengers.

[0032] 3.2 Ground Robot Rescue Submodule The ground robot is deployed near the chairlift track and can move along the chairlift track or support structure. When the chairlift is low to the ground or the terrain is complex, the ground robot can provide a soft ladder, rescue rope or rappelling equipment to help passengers evacuate safely.

[0033] 3.3 Collaborative Scheduling Submodule Drones and ground robots are dispatched through a central control system or edge computing device. The rescue process is optimized by AI algorithms to ensure that high-risk passengers are rescued first. The AI ​​dispatch system evaluates the malfunction of the chairlift, the health of passengers, and weather changes in real time, and dynamically adjusts the task order of the rescue equipment.

[0034] 3.4 AI rescue priority submodule The AI ​​algorithm automatically evaluates the priority of rescue by monitoring the health status of passengers, the position of the chairlift and the type of fault in real time. The health monitoring equipment can detect physiological indicators such as the heart rate and body temperature of passengers. When an abnormality is detected, the AI ​​system will prioritize the rescue by drones or ground robots.

[0035] 4. Security Data Management Module The security data management module uses blockchain technology to distribute and encrypt the system's operating data and emergency processing data to ensure the data's immutability and transparency.

[0036] 4.1 Blockchain data storage submodule This module uses blockchain technology to store the system's operating data in a distributed manner. The data of each chairlift node (including operating status, fault information, meteorological data, etc.) forms multiple data blocks in the system, which are encrypted by the hash function H(x). The specific encryption formula is as follows: H(x)=Hash(d1∥d2∥…∥d n ); Among them, d1, d2, ..., d n is the data collected by each node, and Hash(·) is the hash function. This formula ensures that each data block is unique and cannot be tampered with.

[0037] 4.2 Event sourcing submodule Every emergency operation, troubleshooting and rescue process will be recorded through blockchain technology to generate traceable operation records. Every event in the system can be traced back to the responsible party through blockchain to ensure transparency and fairness.

[0038] 4.3 Multi-party collaboration submodule This module allows equipment manufacturers, operators, rescue teams and regulators to share system operation data and fault information through blockchain. Multiple parties access data in real time through the blockchain network to ensure the transparency and security of system operation.

[0039] 5. Augmented reality guidance module The augmented reality guidance module provides passengers and rescuers with dynamic and visual self-rescue and rescue guidance through AR technology.

[0040] 5.1 Passenger Self-rescue AR Guidance Submodule When the chairlift fails, the AR display device installed inside the chairlift will automatically start to provide passengers with self-rescue guidance information. The AR system detects the status of the chairlift in real time through cameras and sensors, and provides dynamic guidance based on actual conditions. For example, if the wind speed is high nearby, the AR system will show how to use the emergency descent device and ensure a safe landing.

[0041] 5.2 AR assistance submodule for rescuers Rescuers are equipped with head-mounted AR devices to view the chairlift's structural diagram, passenger location, health status, and rescue path in real time. The system automatically displays the chairlift's structural defects, passengers' physiological status, and provides rescuers with the best operating procedures.

[0042] 5.3 Dynamic Path Guidance Submodule The AR display device has a dynamic path guidance function, which adjusts the emergency guidance content according to the real-time status of the chairlift. The dynamic path is calculated using the following formula: ; Among them, P t is the target path position displayed by the AR device, P0 is the initial position, and v(t) is the real-time speed of the chairlift. Through this formula, the AR system can continuously update the path information to ensure that passengers can evacuate safely.

[0043] 6. Self-learning scheduling optimization module This module uses machine learning algorithms to continuously optimize the system's operation and emergency response, achieving adaptive scheduling and efficient rescue of the system.

[0044] 6.1 Self-learning algorithm submodule The self-learning algorithm trains the system model through historical data and continuously optimizes the emergency plan. The algorithm is based on the neural network model f(x,θ), and its prediction model formula is as follows: f(x,θ)=σ(W·x+b); Among them, x is the input system state data, θ={W,b} is the model parameter, and σ(·) is the activation function.

[0045] Through this formula, the self-learning algorithm can predict the future state of the system and adjust the operating parameters and rescue strategy of the chairlift.

[0046] 6.2 Dynamic emergency plan generation submodule The AI ​​system can automatically generate or adjust emergency plans based on real-time monitoring of the chairlift status, passenger status, weather conditions, etc. The self-learning algorithm optimizes the plan based on historical fault handling success rate, response time and other data to ensure maximum efficiency in plan execution.

[0047] Example 1: Intelligent emergency decision-making based on edge computing Specific functions and effects: In Example 1, the system deploys an intelligent edge computing module. The edge computing device monitors the operating status of the chairlift in real time, such as chairlift speed, cable tension, and support tower vibration. When the wind speed exceeds the critical value, the system can autonomously decide to slow down or shut down in an emergency, thereby preventing failures before they occur. This module allows the chairlift to rely on local computing to complete emergency response in the event of network delays or central system failures.

[0048] Comparative Example 1: Conventional Central Control System In Comparative Example 1, the chairlift system relies on the central control system for centralized processing of all data and emergency decision-making. If the central system is delayed or fails, the entire system will find it difficult to quickly respond to emergencies, resulting in inefficient emergency handling.

[0049] Example 2: Rescue collaboration system based on drones and ground robots Specific functions and effects: In Example 2, the rescue coordination system module is activated, and the drone and ground robot cooperate in rescue. When the system detects that the chairlift is faulty and requires emergency evacuation, the drone carries rescue ropes and seats, can quickly reach the fault location, and give priority to rescuing passengers at health risk. At the same time, the ground robot provides auxiliary rescue equipment in complex terrain to ensure that passengers can evacuate safely. The AI ​​system dynamically optimizes rescue scheduling based on passenger health and weather conditions.

[0050] Comparative Example 2: Traditional ground manual rescue In Comparative Example 2, the chairlift system relies on manual rescue, and rescuers must manually operate ladders or ropes from the ground for rescue. This method not only takes more time, but is also difficult to rescue in complex terrains, and manual scheduling is inefficient, making it difficult to flexibly adjust the rescue order according to the actual health status of the passengers.

[0051] Example 3: Secure data management and event tracing based on blockchain Specific functions and effects: In Example 3, blockchain technology is used to store and manage the system's operating data, rescue operation records, and emergency response procedures. Through blockchain technology, all data can be stored synchronously across multiple nodes to ensure data transparency and immutability. The event tracing function allows for responsibility tracing after an accident occurs, and allows operators and regulators to understand the system status in real time through a sharing mechanism.

[0052] Comparative Example 3: Centralized Data Management In Comparative Example 3, the system uses a traditional centralized database for data storage and management. This method is susceptible to single point failures, and there is a risk of data being tampered with or lost. It cannot ensure data transparency, and it is difficult to trace events afterwards.

[0053] Comparative Experimental Design In order to verify the advantages of the low-altitude slow-speed chairlift circulation system of the present invention in emergency handling and rescue, this paper designs an experiment to simulate the failure scenario of the chairlift system and compare the performance of different systems in terms of emergency response time, rescue time, data transparency, etc. The experiment will compare the performance of the three embodiments and the corresponding comparative examples, and test the response time when the wind speed is too high, the passenger evacuation time, and the transparency and traceability of the system data.

[0054] Experimental conditions: Experimental location: A hanging chair tourist attraction Environment: Wind speed 25-30 km / h Chairlift capacity: 4 passengers per chairlift System failure simulation: The wind speed suddenly exceeds the safety threshold, triggering a chairlift failure and initiating emergency rescue procedures Experimental indicators: emergency response time, passenger rescue time, data traceability transparency Experimental data table:

[0055] According to the table: the intelligent edge computing module of Example 1 can respond quickly when the wind speed exceeds the safety range, with an average response time of 1.5 seconds, while the central control system of Example 1 has a high delay and a response time of 10 seconds due to the need to centrally process data; the drone and ground robot collaborative system of Example 2 shows excellent rescue efficiency, with an average rescue time of 6.5 minutes and a 100% rescue success rate. In contrast, the manual rescue of Example 2 takes up to 18 minutes and a success rate of only 85% due to complex terrain and inflexible manual scheduling; the data management system based on blockchain technology in Example 3 has high transparency, ensuring that all operations can be tracked and verified, and data transparency reaches 100%. The centralized data management system in Example 3 has low transparency, and there is a risk of data loss and tampering, with a data transparency of only 60%; the system in the embodiment shows high reliability in all indicators, especially in emergency response and data management, the stability of system operation and fault handling capabilities are much higher than traditional systems. The experimental results show that the low-altitude slow chair circulation system of the present invention has significant advantages in emergency response, rescue efficiency and data management transparency. Through intelligent edge computing, drone and ground robot collaborative rescue and blockchain-based data management, the system can provide fast, efficient and safe emergency response solutions in complex environments and sudden failures. Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Low altitude slow chair circulation system, characterized by: include: Intelligent edge computing modules, deployed on chairlifts, support towers and sites, are used to monitor system operating status and environmental data in real time, make autonomous decisions, and work in collaboration with the central control system; The rescue coordination system module, including drones and ground robots, can work together to achieve rapid evacuation and rescue of chairlift passengers; The security data management module records and manages the system's operating data, fault information, and rescue process based on blockchain technology to ensure data security, transparency, and non-tamperability; Augmented reality guidance module, used to provide visual self-rescue and rescue guidance for passengers and rescue personnel; The self-learning scheduling optimization module continuously optimizes the system operation and emergency response through machine learning algorithms, achieving adaptive scheduling and efficient rescue.

2. The low altitude slow chair suspension circulation system according to claim 1 is characterized in that: The intelligent edge computing module includes: The edge computing device submodule is deployed on the chairlift, support tower and site to collect multi-dimensional data including meteorological data, cable tension and chairlift vibration; The autonomous decision-making submodule is used to decide whether to slow down, stop or partially isolate the chairlift based on preset safety policies and real-time monitoring data; The distributed collaboration submodule uses distributed algorithms to realize information sharing and collaboration among multiple edge computing devices, so as to achieve local isolation of faults and autonomous recovery of the system.

3. The low altitude slow chair suspension circulation system according to claim 1, characterized in that: The rescue coordination system module includes: The drone rescue submodule has automatic obstacle avoidance and autonomous positioning capabilities, and can carry rescue equipment and reach the faulty chairlift for rescue; A ground robot rescue submodule that can move along the chairlift track or support structure and provide a soft ladder or rescue rope; The collaborative scheduling submodule is used to achieve coordinated operations between drones and ground robots, and real-time scheduling through the central command system or edge computing devices.

4. The low altitude slow chair suspension circulation system according to claim 3 is characterized in that: The drone rescue submodule includes: Multi-rotor drones with high-precision positioning systems and load-carrying rescue equipment; The rescue rope subsystem can adjust the length and tension of the rope according to the height of the chairlift and wind speed to ensure the safe transfer of passengers to the ground.

5. The low altitude slow chair suspension circulation system according to claim 1, characterized in that: The security data management module includes: The blockchain data storage submodule is used to distribute and encrypt the system's operating data and emergency processing data to ensure that the data cannot be tampered with; The event tracing submodule is used to record the detailed process of each failure, rescue operation and emergency handling, and generate traceable operation records.

6. The low altitude slow chair suspension circulation system according to claim 1, characterized in that: The blockchain data storage submodule uses the hash function H(x) to encrypt the data to form an unalterable data chain that satisfies the following formula: H(x)=Hash(d1∥d2∥…∥d n ); Among them, d1, d2, ..., d n is the data collected by each node, and Hash(·) is the hash function.

7. The low altitude slow chair suspension circulation system according to claim 1, characterized in that: The augmented reality guidance module includes: a passenger self-rescue AR guidance submodule, an AR display device installed inside the hanging chair, which can provide passengers with self-rescue guidance information in real time; The rescuer AR assistance submodule is used to provide rescuers with visual data on the chair structure, passenger health information, and rescue path.

8. The low altitude slow chair suspension circulation system according to claim 7, characterized in that: The AR display device has a dynamic path guidance function, and can adjust the emergency guidance content according to the real-time status and environmental conditions of the hanging chair to meet the following navigation formula: ; Among them, P t is the target path position displayed by the AR device, P0 is the initial position, and v(t) is the real-time speed of the chairlift.

9. The low altitude slow chair suspension circulation system according to claim 1, characterized in that: The self-learning scheduling optimization module includes: The self-learning algorithm submodule uses deep learning algorithms to train a large amount of historical data and continuously optimize the system's emergency plans and scheduling strategies; The dynamic emergency plan generation submodule automatically generates or adjusts emergency plans based on real-time monitoring data.

10. The low altitude slow chair suspension circulation system according to claim 9, characterized in that: The self-learning algorithm submodule predicts the system state and optimizes the scheduling based on the neural network model f(x,θ), satisfying the following formula: f(x,θ)=σ(W·x+b); Among them, x is the input system state data, θ={W,b} is the model parameter, and σ(·) is the activation function.

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