Low-altitude aircraft operation and maintenance management system and method based on artificial intelligence

Abstract

The invention relates to the technical field of low-altitude aircrafts, and discloses a low-altitude aircraft operation and maintenance management system and method based on artificial intelligence, and the system comprises a battery state monitoring module, a data analysis management module, a fault diagnosis module, and a planning processing module. The data analysis management module is used for calculating a battery heat dissipation efficiency index and a health state index according to the monitoring data, and the fault diagnosis module is used for carrying out fault diagnosis on aircraft equipment according to the battery heat dissipation efficiency index and the health state index. And the planning processing module is used for carrying out emergency processing according to the fault diagnosis result, carrying out comprehensive evaluation and monitoring on the battery, realizing early detection and preventive maintenance of the battery problem, and reducing the problem of unmanned aerial vehicle crash possibly caused under the battery fault condition.

Description

Technical Field

[0001] This invention relates to the field of low-altitude aircraft technology, specifically to an artificial intelligence-based operation and maintenance management system and method for low-altitude aircraft. Background Technology

[0002] Low-altitude aircraft refer to aircraft operating in airspace below 1000 meters vertically (extending to 3000 meters in some areas), encompassing both manned and unmanned types, primarily used for passenger transport, cargo transport, or special operations. Core types include electric vertical takeoff and landing (eVTOL) aircraft, drones, helicopters, and light fixed-wing aircraft. eVTOLs achieve vertical takeoff and landing through electric propulsion, suitable for urban air traffic; drones are characterized by their small size and low cost, widely used in aerial photography, agricultural plant protection, and other fields; helicopters possess hovering capabilities, adapting to complex terrain; light fixed-wing aircraft are fast and have long range, but require runways for takeoff and landing. Key technologies include navigation and obstacle avoidance (LiDAR, visual sensors), flight control systems (automated control and weather monitoring), and power systems (battery endurance optimization). Low-altitude aircraft play a vital role in agriculture, logistics, and rescue operations; for example, drones are used for precise pesticide spraying and rapid delivery of medical supplies.

[0003] Battery defects in low-altitude aircraft are a key factor affecting their safety and range. As the main power source, lithium batteries are prone to sudden drops in power, overheating and runaway, or even fire and explosion when operating under high load or extreme temperatures. Existing low-altitude aircraft operation and maintenance management systems lack effective management and monitoring of batteries, which may lead to loss of control or crash in the air due to battery failure. Summary of the Invention

[0004] (I) Technical Problems Solved To address the shortcomings of existing technologies, this invention provides an artificial intelligence-based operation and maintenance management system and method for low-altitude aircraft. This system enables comprehensive evaluation and monitoring of batteries, allowing for early detection and preventative maintenance of battery problems. It also prevents batteries from being exposed to high temperatures for extended periods, reducing heat damage and extending battery life. Through automated monitoring and fault diagnosis, it reduces the need for manual intervention, improving the efficiency of operation and maintenance management. Furthermore, in the event of a fault, it helps the system determine whether the aircraft is ready for a safe return, allowing for dynamic adjustments to flight plans and reducing the risk of drone crashes due to battery failures.

[0005] (II) Technical Solution

[0006] To achieve the above objectives, the present invention provides the following technical solution: an artificial intelligence-based low-altitude aircraft operation and maintenance management system, including a battery status monitoring module, a data analysis and management module, a fault diagnosis module, and a planning and processing module;

[0007] The battery status monitoring module is connected to the data analysis and management module via a network, the data analysis and management module is connected to the fault diagnosis module via a network, and the fault diagnosis module is connected to the planning and processing module via a network.

[0008] The battery status monitoring module is used to monitor the status of the aircraft and acquire, record, and save the monitoring data.

[0009] The data analysis and management module is used to calculate the battery heat dissipation efficiency index and health status index based on the monitoring data.

[0010] The fault diagnosis module is used to diagnose faults in aircraft equipment based on battery heat dissipation efficiency index and health status index.

[0011] The planning and processing module is used to make emergency responses based on the fault diagnosis results.

[0012] Preferably, the monitoring data includes a temperature monitoring dataset, a voltage monitoring dataset, and a current monitoring dataset. The temperature monitoring dataset is acquired by connecting a temperature sensor to the battery status monitoring module, the voltage monitoring dataset is acquired by connecting a voltage sensor to the battery status monitoring module, and the current monitoring dataset is acquired by connecting a current sensor to the battery status monitoring module.

[0013] Preferably, the temperature monitoring dataset includes multiple battery temperature monitoring data, and the record storage expression for the temperature monitoring dataset is: W1, W2, W3, ..., W... n In the expression, W1 represents the first battery temperature monitoring data, W n This represents the nth battery temperature monitoring data, where n represents the total number of data points in the dataset.

[0014] The voltage monitoring dataset includes multiple battery voltage monitoring data points, and the record storage expression for the voltage monitoring dataset is: Y1, Y2, Y3, ..., Y... n In the expression, Y1 represents the first battery voltage monitoring data, Y n This represents the nth battery voltage monitoring data point, where n represents the total number of data points in the dataset.

[0015] The current monitoring dataset includes multiple battery current monitoring data points, and the record storage expression for the current monitoring dataset is: L1, L2, L3, ..., L n In the expression, L1 represents the first battery current monitoring data, L n This represents the nth battery current monitoring data point, where n represents the total number of data points in the dataset.

[0016] Preferably, the formula for calculating the battery heat dissipation efficiency index SRzs is as follows:

[0017]

[0018] In the calculation formula, SRzs represents the battery heat dissipation efficiency index, W i This represents the temperature monitoring data of the i-th battery. This represents the i-th ambient temperature data. This represents the sum of the differences between the ambient temperature data and the battery temperature monitoring data, starting from i=1 and ending at i=n.

[0019] W b The standard deviation between ambient temperature data and battery temperature monitoring data is represented by p, where p represents the battery's heat generation power. Represents the thermal resistance of the battery, indicating the resistance to temperature rise per unit power.

[0020] n represents the total number of data points.

[0021] Preferably, the formula for calculating the health status index JKzs is as follows:

[0022]

[0023] In the calculation formula, JKzs represents the health status index, and W i W represents the temperature monitoring data of the i-th battery. o Represents the standard battery temperature, W i -W o MaxW represents the difference between battery temperature monitoring data and standard data. c α1 represents the maximum permissible difference in battery temperature, and α1 represents the weight of the battery temperature data.

[0024] The reaction cell temperature is abnormal;

[0025] Y i Y represents the voltage monitoring data of the i-th battery. o Y represents the standard battery voltage. i -Y o MaxY represents the difference between battery voltage monitoring data and standard data. c α1 represents the maximum allowable difference in battery voltage, and α2 represents the weight of the battery voltage data.

[0026] The reaction cell voltage is abnormal;

[0027] L i L represents the current monitoring data of the i-th battery. o L represents the standard battery current. i -Lo MaxL represents the difference between battery current monitoring data and standard data. c α3 represents the maximum allowable difference in battery current, and α3 represents the weight of the battery current data.

[0028] This indicates an abnormal battery current.

[0029] Preferably, when the calculated value of the battery heat dissipation efficiency index SRzs exceeds the battery heat dissipation efficiency index threshold, it indicates that the current battery heat dissipation effect is poor, and a heat dissipation signal is sent to optimize the heat dissipation system. While sending the heat dissipation signal, the fault diagnosis module continuously monitors the battery heat dissipation efficiency index SRzs. If the battery heat dissipation efficiency index SRzs is still higher than the battery heat dissipation efficiency index SRzs within a preset time, a battery fault signal is sent to the planning and processing module.

[0030] Preferably, when the health status index JKzs exceeds the health status index threshold, it indicates that the current battery health status is poor, and a poor battery health status signal is sent to the planning and processing module.

[0031] Preferably, the planning and processing module calculates the current battery return capability upon receiving either a battery fault signal or a poor battery health signal, and performs emergency processing based on the battery return capability. The formula for calculating the battery return capability FHnl is as follows:

[0032]

[0033] In the calculation formula, FHnl represents the battery's return capability, C represents the battery's nominal total capacity, JKzs represents the battery's state of health index, and X... d Represents the current battery power consumption, SRzs represents the battery heat dissipation efficiency index, and X represents the current battery power consumption. j F represents the flight distance corresponding to the current battery consumption. j Represents the return distance;

[0034] C*JKzs-X d Represents the actual remaining usable electricity, (C*JKzs-X) d The effect of SRzs reaction heat dissipation on discharge efficiency;

[0035] Average power consumption per unit distance. This represents the theoretical total amount of electricity required for the return trip.

[0036] In the calculation formula, FHnl represents the battery return capability, and X d X represents the current amount of electricity consumed by the battery. j This represents the flight distance corresponding to the current battery consumption. Average power consumption over distance.

[0037] Preferably, when the battery return capability FHnl ≥ 1, the planning and processing module sends a return signal for the aircraft; when the battery return capability FHnl < 1, the planning and processing module sends a landing signal for the aircraft at the nearest landing site.

[0038] The AI-based operation and maintenance management method for low-altitude aircraft includes the following steps:

[0039] Step 1: Monitor the aircraft's status and collect monitoring data to form temperature monitoring dataset, voltage monitoring dataset, and current monitoring dataset, which are then recorded and saved.

[0040] Step 2: Calculate the battery heat dissipation efficiency index and health status index based on the temperature monitoring dataset, voltage monitoring dataset, and current monitoring dataset;

[0041] Step 3: Judge the battery's heat dissipation effect based on the battery heat dissipation efficiency index. If the current battery heat dissipation effect is poor, send a heat dissipation signal to the heat dissipation system for heat dissipation optimization.

[0042] Step 4: After heat dissipation optimization, continuously monitor the battery heat dissipation efficiency index to determine if a malfunction has occurred; Step 5: Based on the health status index, determine whether the aircraft equipment has a poor battery health status.

[0043] Step 6: If either battery malfunction or poor battery health is detected, assess the current battery return capability.

[0044] Step 7: Take emergency measures based on the battery's return-to-home capability.

[0045] Compared with existing technologies, this invention provides an artificial intelligence-based operation and maintenance management system and method for low-altitude aircraft, which has the following beneficial effects:

[0046] 1. This invention comprehensively evaluates and monitors batteries using multi-dimensional data such as temperature, voltage, and current, enabling early detection and preventative maintenance of battery problems. It continuously monitors the battery's heat dissipation efficiency index and can detect the real-time operating status of the cooling system. When the battery's heat dissipation efficiency index exceeds a threshold, indicating poor heat dissipation, the system immediately triggers heat dissipation optimization measures to prevent the battery from overheating due to poor heat dissipation, which could lead to serious problems such as thermal runaway, capacity decay, or fire and explosion. Timely heat dissipation prevents the battery from being in a high-temperature state for extended periods, reducing heat damage and extending battery life. Simultaneously, heat dissipation optimization measures can eliminate temporary problems, improve system judgment accuracy, reduce interference with flight plans, and provide real-time monitoring of health status indices. This enables automated monitoring and fault diagnosis, reduces the need for manual intervention, and improves the efficiency of operation and maintenance management.

[0047] 2. This invention comprehensively considers energy consumption, return distance, heat dissipation efficiency, and health status, and can reflect the battery's return capability in a relatively comprehensive way. At the same time, it reflects the negative impact of heat dissipation efficiency and health status on return capability. When the system receives a battery fault signal or a poor health status signal, it can quickly calculate the battery's return capability, help the system determine whether the aircraft has the conditions for a safe return, help the system dynamically adjust the flight plan, and reduce the problem of drone crashes that may be caused by battery failure. Attached Figure Description

[0048] Figure 1 This is a diagram illustrating the steps of the method of the present invention;

[0049] Figure 2 This is a schematic diagram of the system flow of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see Figure 1-2 The low-altitude aircraft operation and maintenance management system based on artificial intelligence includes a battery status monitoring module, a data analysis and management module, a fault diagnosis module, and a planning and processing module.

[0052] The battery status monitoring module is connected to the data analysis and management module via the network. The data analysis and management module is connected to the fault diagnosis module via the network. The fault diagnosis module is connected to the planning and processing module via the network.

[0053] The battery status monitoring module is used to monitor the status of the aircraft and acquire, record, and save the monitoring data.

[0054] The monitoring data includes temperature monitoring dataset, voltage monitoring dataset, and current monitoring dataset. The temperature monitoring dataset is obtained by connecting the temperature sensor to the battery status monitoring module, the voltage monitoring dataset is obtained by connecting the voltage sensor to the battery status monitoring module, and the current monitoring dataset is obtained by connecting the current sensor to the battery status monitoring module.

[0055] The temperature monitoring dataset includes multiple battery temperature monitoring data points. The record storage expression for the temperature monitoring dataset is: W1, W2, W3, ..., W... n In the expression, W1 represents the first battery temperature monitoring data, W nThis represents the nth battery temperature monitoring data, where n represents the total number of data points in the dataset.

[0056] The voltage monitoring dataset includes multiple battery voltage monitoring data points. The record storage expression for the voltage monitoring dataset is: Y1, Y2, Y3, ..., Y... n In the expression, Y1 represents the first battery voltage monitoring data, Y n This represents the nth battery voltage monitoring data point, where n represents the total number of data points in the dataset.

[0057] The current monitoring dataset includes multiple battery current monitoring data points. The record storage expression for the current monitoring dataset is: L1, L2, L3, ..., L n In the expression, L1 represents the first battery current monitoring data, L n This represents the nth battery current monitoring data point, where n represents the total number of data points in the dataset.

[0058] By using multi-dimensional data such as temperature, voltage, and current, a comprehensive evaluation of the battery can be conducted to achieve early detection of battery problems and preventive maintenance, reduce sudden failures and downtime, and provide a large data foundation for the analysis of battery heat dissipation efficiency index and health status index.

[0059] The data analysis and management module is used to calculate the battery heat dissipation efficiency index and health status index based on the monitoring data;

[0060] The formula for calculating the battery heat dissipation efficiency index SRzs is:

[0061]

[0062] In the calculation formula, SRzs represents the battery heat dissipation efficiency index, W i This represents the temperature monitoring data of the i-th battery. This represents the i-th ambient temperature data. This represents the sum of the differences between ambient temperature data and battery temperature monitoring data, starting from i=1 and ending at i=n, ​​reflecting the battery's heat dissipation requirements.

[0063] W b The standard deviation between ambient temperature data and battery temperature monitoring data is represented by p, where p represents the battery's heat generation power. Represents the thermal resistance of the battery, indicating the resistance to temperature rise per unit power.

[0064] n represents the total number of data points, used in the denominator to standardize the impact of multi-point data.

[0065] The battery heat dissipation efficiency index SRzs can evaluate the heat dissipation efficiency of the battery in real time, determine whether the battery is within a safe temperature range, avoid the risk of thermal runaway or fire due to overheating of the battery, and prevent the battery performance from deteriorating in low temperature environments, which would affect the flight range and safety of the aircraft. The formula incorporates thermal resistance, which can directly reflect the effectiveness of the heat dissipation system.

[0066] The formula for calculating the health status index JKzs is:

[0067]

[0068] In the calculation formula, JKzs represents the health status index, and W i W represents the temperature monitoring data of the i-th battery. o Represents the standard battery temperature, W i -W o MaxW represents the difference between battery temperature monitoring data and standard data. c α1 represents the maximum permissible difference in battery temperature, and α1 represents the weight of the battery temperature data.

[0069] Abnormal battery temperature is a warning sign. Excessive temperature may cause the battery to overheat or even catch fire, while excessively low temperature may affect battery performance.

[0070] Y i Y represents the voltage monitoring data of the i-th battery. o Y represents the standard battery voltage. i -Y o MaxY represents the difference between battery voltage monitoring data and standard data. c α1 represents the maximum allowable difference in battery voltage, and α2 represents the weight of the battery voltage data.

[0071] Abnormal battery voltage can indicate that a low voltage may indicate battery capacity decay or over-discharge, while a high voltage may indicate abnormal charging or overcharging.

[0072] L i L represents the current monitoring data of the i-th battery. o L represents the standard battery current. i -L o MaxL represents the difference between battery current monitoring data and standard data. c α3 represents the maximum allowable difference in battery current, and α3 represents the weight of the battery current data.

[0073] This reflects abnormal battery current. Excessive current may indicate battery overload or short circuit, while insufficient current may indicate degraded battery performance.

[0074] The health status index, which integrates temperature, voltage, and current deviations, reflects the overall health status of the battery, facilitating rapid assessment and decision-making. It provides real-time feedback on battery health, preventing flight accidents caused by battery failures and improving the safety and reliability of the aircraft.

[0075] The fault diagnosis module is used to diagnose faults in aircraft equipment based on battery heat dissipation efficiency index and health status index.

[0076] When the calculated value of the battery thermal efficiency index SRzs exceeds the battery thermal efficiency index threshold, it indicates that the current battery thermal performance is poor. A thermal signal is sent to optimize the thermal system. While sending the thermal signal, the fault diagnosis module continuously monitors the battery thermal efficiency index SRzs. If the battery thermal efficiency index SRzs is still higher than the battery thermal efficiency index SRzs within a preset time, a battery fault signal is sent to the planning and processing module.

[0077] By continuously monitoring the battery thermal efficiency index, the operating status of the cooling system can be detected in real time. When the battery thermal efficiency index exceeds the threshold, it indicates poor heat dissipation. The system will immediately trigger thermal optimization measures to prevent the battery from overheating due to poor heat dissipation, which could lead to serious problems such as thermal runaway, capacity decay, or fire and explosion. Timely heat dissipation prevents the battery from being in a high-temperature state for a long time, reduces heat damage to the battery, and extends battery life. At the same time, thermal optimization measures can eliminate temporary problems, improve the accuracy of system judgment, and reduce interference with flight plans.

[0078] When the health status index JKzs exceeds the health status index threshold, it indicates that the current battery health status is poor, and a poor battery health status signal is sent to the planning and processing module.

[0079] Real-time monitoring of health status indices enables automated monitoring and fault diagnosis, reducing the need for manual intervention and improving the efficiency of operation and maintenance management.

[0080] The planning and processing module is used to make emergency responses based on the fault diagnosis results;

[0081] Upon receiving either a battery fault signal or a poor battery health signal, the planning and processing module calculates the current battery return capability and takes emergency measures based on it. The formula for calculating the battery return capability FHnl is as follows:

[0082]

[0083] In the calculation formula, FHnl represents the battery's return capability, C represents the battery's nominal total capacity, JKzs represents the battery's state of health index, and X... dRepresents the current battery power consumption, SRzs represents the battery heat dissipation efficiency index, and X represents the current battery power consumption. j F represents the flight distance corresponding to the current battery consumption. j Represents the return distance;

[0084] C*JKzs-X d Represents the actual remaining usable electricity, (C*JKzs-X) d The effect of SRzs reaction heat dissipation on discharge efficiency;

[0085] Average power consumption per unit distance. This represents the theoretical total amount of electricity required for the return trip.

[0086] In the calculation formula, FHnl represents the battery return capability, and X d X represents the current amount of electricity consumed by the battery. j This represents the flight distance corresponding to the current battery consumption. Average power consumption over distance.

[0087] Taking into account energy consumption, return distance, heat dissipation efficiency, and health status, the formula can comprehensively reflect the battery's return capability. It also reflects the negative impact of heat dissipation efficiency and health status on the return capability. When the system receives a battery fault signal or a poor health status signal, the formula can quickly calculate the battery's return capability, helping the system determine whether the aircraft has the conditions for a safe return, helping the system to dynamically adjust the flight plan, and reducing the problem of drone crashes that may be caused by battery failure.

[0088] When the battery return capability FHnl≥1, the planning and processing module sends a return signal to the aircraft; when the battery return capability FHnl<1, the planning and processing module sends a landing signal to the nearest landing site.

[0089] The specific methods of the aforementioned AI-based low-altitude aircraft operation and maintenance management system are as follows:

[0090] Step 1: Monitor the aircraft's status and collect monitoring data to form temperature monitoring dataset, voltage monitoring dataset, and current monitoring dataset, which are then recorded and saved.

[0091] Step 2: Calculate the battery heat dissipation efficiency index and health status index based on the temperature monitoring dataset, voltage monitoring dataset, and current monitoring dataset;

[0092] Step 3: Judge the battery's heat dissipation effect based on the battery heat dissipation efficiency index. If the current battery heat dissipation effect is poor, send a heat dissipation signal to the heat dissipation system for heat dissipation optimization.

[0093] Step 4: After heat dissipation optimization, continuously monitor the battery heat dissipation efficiency index to determine if a malfunction has occurred; Step 5: Based on the health status index, determine whether the aircraft equipment has a poor battery health status.

[0094] Step 6: If either battery malfunction or poor battery health is detected, assess the current battery return capability.

[0095] Step 7: Take emergency measures based on the battery's return-to-home capability.

[0096] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based operation and maintenance management system for low-altitude aircraft, characterized in that, It includes a battery status monitoring module, a data analysis and management module, a fault diagnosis module, and a planning and processing module; The battery status monitoring module is connected to the data analysis and management module via a network, the data analysis and management module is connected to the fault diagnosis module via a network, and the fault diagnosis module is connected to the planning and processing module via a network. The battery status monitoring module is used to monitor the status of the aircraft and acquire, record, and save the monitoring data. The data analysis and management module is used to calculate the battery heat dissipation efficiency index and health status index based on the monitoring data. The fault diagnosis module is used to diagnose faults in aircraft equipment based on battery heat dissipation efficiency index and health status index. The planning and processing module is used to make emergency responses based on the fault diagnosis results.

2. The low-altitude aircraft operation and maintenance management system based on artificial intelligence according to claim 1, characterized in that: The monitoring data includes a temperature monitoring dataset, a voltage monitoring dataset, and a current monitoring dataset. The temperature monitoring dataset is acquired by connecting a temperature sensor to the battery status monitoring module, the voltage monitoring dataset is acquired by connecting a voltage sensor to the battery status monitoring module, and the current monitoring dataset is acquired by connecting a current sensor to the battery status monitoring module.

3. The low-altitude aircraft operation and maintenance management system based on artificial intelligence according to claim 2, characterized in that: The temperature monitoring dataset includes multiple battery temperature monitoring data points, and the record storage expression for the temperature monitoring dataset is: W1, W2, W3, ..., W n In the expression, W1 represents the first battery temperature monitoring data, W n This represents the nth battery temperature monitoring data, where n represents the total number of data points in the dataset. The voltage monitoring dataset includes multiple battery voltage monitoring data points, and the record storage expression for the voltage monitoring dataset is: Y1, Y2, Y3, ..., Y... n In the expression, Y1 represents the first battery voltage monitoring data, Y n This represents the nth battery voltage monitoring data point, where n represents the total number of data points in the dataset. The current monitoring dataset includes multiple battery current monitoring data points, and the record storage expression for the current monitoring dataset is: L1, L2, L3, ..., L n In the expression, L1 represents the first battery current monitoring data, L n This represents the nth battery current monitoring data point, where n represents the total number of data points in the dataset.

4. The low-altitude aircraft operation and maintenance management system based on artificial intelligence according to claim 3, characterized in that: The formula for calculating the battery heat dissipation efficiency index SRzs is as follows: In the calculation formula, SRzs represents the battery heat dissipation efficiency index, W i This represents the temperature monitoring data of the i-th battery. This represents the i-th ambient temperature data. This represents the sum of the differences between the ambient temperature data and the battery temperature monitoring data, starting from i=1 and ending at i=n. W b The standard deviation between ambient temperature data and battery temperature monitoring data is represented by p, where p represents the battery's heat generation power. Represents the thermal resistance of the battery, indicating the resistance to temperature rise per unit power. n represents the total number of data points.

5. The low-altitude aircraft operation and maintenance management system based on artificial intelligence according to claim 4, characterized in that: The formula for calculating the health status index JKzs is as follows: In the calculation formula, JKzs represents the health status index, and W i W represents the temperature monitoring data of the i-th battery. o Represents the standard battery temperature, W i -W o MaxW represents the difference between battery temperature monitoring data and standard data. c α1 represents the maximum allowable difference in battery temperature, and α1 represents the weight of the battery temperature data. 、 The reaction cell temperature is abnormal; Y i Y represents the voltage monitoring data of the i-th battery. o Y represents the standard battery voltage. i -Y o MaxY represents the difference between battery voltage monitoring data and standard data. c α1 represents the maximum allowable difference in battery voltage, and α2 represents the weight of the battery voltage data. The reaction cell voltage is abnormal; L i L represents the current monitoring data of the i-th battery. o L represents the standard battery current. i -L o MaxL represents the difference between battery current monitoring data and standard data. c α3 represents the maximum allowable difference in battery current, and α3 represents the weight of the battery current data. This indicates an abnormal battery current.

6. The low-altitude aircraft operation and maintenance management system based on artificial intelligence according to claim 5, characterized in that: When the calculated value of the battery thermal efficiency index SRzs exceeds the battery thermal efficiency index threshold, it indicates that the current battery thermal performance is poor. A thermal signal is sent to optimize the thermal system. While sending the thermal signal, the fault diagnosis module continuously monitors the battery thermal efficiency index SRzs. If the battery thermal efficiency index SRzs is still higher than the battery thermal efficiency index SRzs within a preset time, a battery fault signal is sent to the planning and processing module.

7. The low-altitude aircraft operation and maintenance management system based on artificial intelligence according to claim 6, characterized in that: When the health status index JKzs exceeds the health status index threshold, it indicates that the current battery health status is poor, and a poor battery health status signal is sent to the planning and processing module.

8. The low-altitude aircraft operation and maintenance management system based on artificial intelligence according to claim 7, characterized in that: Upon receiving either a battery fault signal or a poor battery health signal, the planning and processing module calculates the current battery return capability and performs emergency actions based on it. The formula for calculating the battery return capability FHnl is as follows: In the calculation formula, FHnl represents the battery's return capability, C represents the battery's nominal total capacity, JKzs represents the battery's state of health index, and X... d Represents the current battery power consumption, SRzs represents the battery heat dissipation efficiency index, and X represents the current battery power consumption. j F represents the flight distance corresponding to the current battery consumption. j Represents the return distance; C*JKzs-X d Represents the actual remaining usable electricity, (C*JKzs-X) d The effect of SRzs reaction heat dissipation on discharge efficiency; Average power consumption per unit distance. This represents the theoretical total amount of electricity required for the return trip. In the calculation formula, FHnl represents the battery return capability, and X d X represents the current amount of electricity consumed by the battery. j This represents the flight distance corresponding to the current battery consumption. Average power consumption over distance.

9. The low-altitude aircraft operation and maintenance management system based on artificial intelligence according to claim 8, characterized in that: When the battery return capability FHnl ≥ 1, the planning and processing module sends a return signal to the aircraft; when the battery return capability FHnl < 1, the planning and processing module sends a landing signal to the nearest landing site for the aircraft.

10. An AI-based operation and maintenance management method for low-altitude aircraft, based on any AI-based operation and maintenance management system for low-altitude aircraft as described in claims 1 to 9, characterized in that: Includes the following steps: Step 1: Monitor the aircraft's status and collect monitoring data to form temperature monitoring dataset, voltage monitoring dataset, and current monitoring dataset, which are then recorded and saved. Step 2: Calculate the battery heat dissipation efficiency index and health status index based on the temperature monitoring dataset, voltage monitoring dataset, and current monitoring dataset; Step 3: Judge the battery's heat dissipation effect based on the battery heat dissipation efficiency index. If the current battery heat dissipation effect is poor, send a heat dissipation signal to the heat dissipation system for heat dissipation optimization. Step 4: After heat dissipation optimization, continuously monitor the battery heat dissipation efficiency index to determine if a fault has occurred; Step 5: Determine whether the aircraft equipment has poor battery health based on the health status index; Step 6: If either battery malfunction or poor battery health is detected, assess the current battery return capability. Step 7: Take emergency measures based on the battery's return-to-home capability.

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