Intelligent management method and system for battery and motor of unmanned aerial vehicle

Abstract

The invention discloses an intelligent management method for a battery and a motor of an unmanned aerial vehicle, and relates to the field of intelligent system engineering, and the method comprises the steps: collecting and preprocessing flight data in real time, analyzing the data to predict the health state of the battery and the motor, determining whether to start energy recovery or not according to the health state, and storing electric energy generated by energy recovery into the battery. The use of the battery pack is optimized by applying a quantum algorithm, high energy efficiency is ensured, and an analysis report is generated after flight to assist in subsequent maintenance decision making. Through quantum calculation, accurate monitoring of the health states of the battery and the motor is realized, flight faults are effectively prevented, and the system reliability is improved. The quantum algorithm optimizes the battery management strategy, prolongs the service life of the battery, and enhances the energy management efficiency.

Description

Technical Field

[0001] The present invention relates to the field of intelligent system engineering, and in particular to an intelligent management method and system for batteries and motors of unmanned aerial vehicles. Background Art

[0002] With the rapid progress of microelectronics, wireless communications, sensor networks and AI technologies, drones, as high-tech products, have shown great potential in the military, logistics, agriculture and environmental protection fields. Drone performance and endurance are the research focus, and the battery and motor status directly affect flight efficiency and safety. Traditional management strategies are based on static thresholds, lack adaptability to complex environments, and are difficult to meet high-performance requirements. Early battery management systems relied only on voltage and current monitoring to estimate power and control charging and discharging, but the accuracy was insufficient and could not accurately assess health status or predict life. Motor management uses a fixed mode without considering actual working conditions such as temperature and load changes, which can easily lead to overheating, reduced efficiency and even failure, highlighting the limitations of traditional methods. Therefore, innovative technologies are urgently needed to improve the intelligent management level of drone core components to adapt to the changing flight environment and ensure stable and efficient operation performance. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides an intelligent management method and system for drone batteries and motors to solve the problems of low drone flight efficiency, poor endurance, high maintenance cost, and low system intelligence level.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides an intelligent management method for batteries and motors of a drone, which includes collecting flight data in real time and performing preprocessing;

[0007] Extract features from the collected flight data, build a prediction model, and output whether the battery and motor have performance degradation;

[0008] Whether to start the energy recovery mechanism is determined by the results of whether the battery and motor have performance degradation;

[0009] The converted electrical energy is stored in the drone’s battery to provide additional energy for subsequent flights;

[0010] Use quantum algorithms to globally optimize battery packs to ensure the proper allocation of battery resources;

[0011] Generate reports based on flight mission reports to provide data support for subsequent maintenance plans.

[0012] As a preferred solution of the intelligent management method of the drone battery and motor of the present invention, wherein: the flight data of the drone during flight is collected, including battery status data, motor status data, flight status data and environmental status data;

[0013] Define real-time data: S i (t) = [S B (t), S M (t), S F (t), S E (t)];

[0014] Among them, S i (t) is the real-time data read by the sensor at time t, S B (t) indicates battery status data, S M (t) represents the motor status data, S F (t) represents the flight status data, S E (t) represents environmental status data.

[0015] Preprocess the extracted information, the expression is:

[0016]

[0017] Among them, C i (t) represents the sensor data after preprocessing, S i (t) represents the real-time data collected by the sensor at time point t, μ i (t) represents the average value of real-time data, σ i (t) represents the fluctuation degree of real-time data, k i Indicates the threshold value;

[0018] According to real-time data S i (t) and real-time data μ i (t) The difference between the average values ​​is used to determine whether it is less than the threshold k i , when the difference is less than the threshold, C i (t) = S i (t), when the difference is greater than the threshold, C i (t) = μ i (t).

[0019] As a preferred solution of the intelligent management method of the drone battery and motor of the present invention, the feature extraction of the pre-processed information is performed by a quantum computer, and the expression is:

[0020] C(t)=[C B (t),C M (t),C F (t),CE (t)];

[0021] Among them, C B (t) represents the battery status data after extraction, C M (t) represents the extracted motor status data, C F (t) represents the extracted flight status data, C E (t) represents the environmental status data after extraction;

[0022] Build a prediction model and output whether the battery and motor have performance degradation. The expression is:

[0023]

[0024] Where P(C(t)) indicates whether the battery and motor have performance degradation based on the preprocessing results, α j The weight coefficient representing the importance of data, σ j (t) represents the standard deviation, ψ j (C(t)) represents the quantum state of the system under the sensor data configuration t, |ψ j (C(t))| 2 represents the probability density of the system;

[0025] The value range of P(C(t)) is [0,1]. When the value is equal to 0, it indicates that the battery and motor have no performance degradation. When the value is equal to 1, it indicates that the battery and motor have performance degradation.

[0026] As a preferred solution of the intelligent management method of the drone battery and motor of the present invention, the intelligent judgment of whether to perform energy recovery is made according to whether the output battery and motor are decayed, and the expression is:

[0027]

[0028] Wherein, E(P(C(t))) indicates whether energy recovery is performed, and θ indicates the energy recovery threshold;

[0029] The judgment is made based on whether the motor has performance value and the energy recovery threshold. If it is greater than the threshold, E(P(C(t)))=1, and energy recovery will be turned on. If it is less than the threshold, E(P(C(t)))=0, indicating that energy recovery is not performed.

[0030] As a preferred solution of the intelligent management method of the drone battery and motor of the present invention, wherein: after determining and confirming that energy recovery is to be performed, the generator installed on the drone is used to convert the kinetic energy during the flight into electrical energy;

[0031] Through energy conversion and storage technology, the recovered electrical energy is stored in the drone’s battery for use in subsequent flights.

[0032] As a preferred solution of the intelligent management method of the drone battery and motor of the present invention, wherein: the data of the battery health status, energy recovery efficiency, and battery charge and discharge status are integrated into the same data structure;

[0033] Design an optimization algorithm based on quantum computing, which is expressed as:

[0034]

[0035] Among them, QO(Ψ(C(t)), P(C(t))) represents the optimization solution after the optimization algorithm is calculated, QO represents the quantum optimization function, Ψ(C(t)) represents the quantum state function, Ω represents the set of all states, |Ψ(C(t))| 2 represents the modulus of the quantum state function, L(C(t)) represents the deviation between the sensor data and the average value, d(C(t)) represents the integration step, and Ψ represents the wave function of the quantum system;

[0036] The results of the optimization algorithm are used to solve the global optimization problems of the UAV battery pack’s charging and discharging strategy, working mode, and energy distribution. The expression is:

[0037]

[0038] Where D(QO) represents the optimal solution, η represents the degree of attention paid to energy consumption cost during the optimization process; E(x) represents the filter function, X represents the set of all decision variables, and x represents a single decision variable.

[0039] As a preferred solution of the intelligent management method of the drone battery and motor of the present invention, wherein: using sensors and communication modules, real-time flight data and data on the efficiency of the energy recovery system are collected;

[0040] After the flight, the drone will upload the flight data collected during the flight to the data center, allowing the system to generate a flight report containing a flight overview, equipment status, environmental impact, abnormal events, suggestions and predictions, providing data support for subsequent maintenance plans and algorithm optimization.

[0041] In a second aspect, the present invention provides an intelligent management system for drone batteries and motors, comprising:

[0042] Deep analysis and predictive modeling module: conducts in-depth analysis of the collected flight data, builds a predictive model, and outputs the results of whether the battery and motor have performance degradation;

[0043] Energy management decision module: determines whether to start the energy recovery mechanism based on the output prediction results;

[0044] Energy recovery and reuse module: stores the converted electrical energy in the drone's battery to provide additional energy for the next flight;

[0045] Global optimization and strategy formulation module: Use quantum algorithms to perform global optimization of battery packs to ensure the rational allocation of battery resources;

[0046] Data analysis and report generation module: Generates a flight report after the drone completes the flight mission and sends it to the operation and maintenance personnel for subsequent maintenance.

[0047] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the intelligent management method for drone batteries and motors as described in the first aspect of the present invention is implemented.

[0048] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the intelligent management method for drone batteries and motors as described in the first aspect of the present invention is implemented.

[0049] The beneficial effects of the present invention are as follows: through real-time data collection and preprocessing, the accuracy of flight data and the anti-interference ability of the system are ensured, laying the foundation for safe flight. The use of quantum computing for feature extraction and prediction enables accurate monitoring of the health status of batteries and motors, effectively preventing flight failures and improving system reliability. The energy recovery mechanism automatically starts when performance degradation is detected, converting kinetic energy into electrical energy storage, significantly improving energy efficiency and endurance. Quantum algorithms optimize battery management strategies, extend battery life, and enhance energy management efficiency. The generation of flight mission reports supports refined maintenance and flight strategy optimization, reduces maintenance costs, and improves overall operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 This is a flow chart of the intelligent management method and system for drone batteries and motors in Example 1.

[0052] Figure 2This is a diagram for determining whether the energy recovery mechanism of the drone is turned on in Example 1. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0056] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides an intelligent management method for drone batteries and motors, comprising the following steps:

[0057] S1. Use sensors installed on the drone to collect flight data of the drone during flight, including battery status data, motor status data, flight status data and environmental status data;

[0058] Define real-time data: S i (t) = [S B (t), S M (t), S F (t), S E (t)];

[0059] Among them, S i (t) is the real-time data read by the sensor at time t, S B (t) indicates battery status data, S M (t) represents the motor status data, S F (t) represents the flight status data, S E (t) represents environmental status data.

[0060] The data is transmitted to the data center in real time through the Internet of Things technology for preliminary cleaning and preprocessing to remove noise and ensure data quality. The extracted information is preprocessed, and the expression is:

[0061]

[0062] Among them, C i (t) represents the sensor data after preprocessing, S i (t) represents the real-time data collected by the sensor at time point t, μ i (t) represents the average value of real-time data, σ i (t) represents the fluctuation degree of real-time data, k i Indicates the threshold value;

[0063] According to real-time data S i (t) and real-time data μ i (t) The difference between the average values ​​is used to determine whether it is less than the threshold k i , when the difference is less than the threshold, C i (t) = S i (t), when the difference is greater than the threshold, C i (t) = μ i (t).

[0064] S2. Using the powerful parallel processing capability of quantum computers, the preprocessed information is used for feature extraction. The expression is:

[0065] C(t)=[C B (t),C M (t),C F (t),C E (t)];

[0066] Among them, C B (t) represents the battery status data after extraction, C M (t) represents the extracted motor status data, C F (t) represents the extracted flight status data, C E (t) represents the environmental status data after extraction;

[0067] Through the quantum neural network learning algorithm, a large amount of data sets can be processed in a short time, a prediction model can be built, and the results of whether the battery and motor have performance degradation can be output. The expression is:

[0068]

[0069] Where P(C(t)) indicates whether the battery and motor have performance degradation based on the preprocessing results, α j The weight coefficient representing the importance of data, σ j (t) represents the standard deviation, ψ j (C(t)) represents the quantum state of the system under the sensor data configuration t, |ψ j (C(t))| 2represents the probability density of the system;

[0070] The value range of P(C(t)) is [0,1]. When the value is equal to 0, it indicates that the battery and motor have no performance degradation. When the value is equal to 1, it indicates that the battery and motor have performance degradation.

[0071] S3. Intelligently determine whether to perform energy recovery based on whether the output battery and motor are declining. When it is detected that the battery and motor performance decline reaches a certain threshold, the intelligent management system will automatically activate the energy recovery mechanism, which is expressed as:

[0072]

[0073] Wherein, E(P(C(t))) indicates whether energy recovery is performed, and θ indicates the energy recovery threshold;

[0074] Whether the motor performance is degraded is determined based on the value and the energy recovery threshold. If it is greater than the threshold, E(P(C(t)))=1, and energy recovery will be turned on. If it is less than the threshold, E(P(C(t)))=0, indicating that energy recovery is not performed.

[0075] S4. After confirming that energy recovery is to be performed, the lightweight and highly efficient generator installed on the drone will start to operate, converting the kinetic energy generated by the descent during flight into electrical energy. The principle is that the generator captures the kinetic energy generated by the movement of the drone and converts it into electrical energy through the principle of electromagnetic induction;

[0076] The use of efficient energy conversion and storage technology can ensure the effect of energy conversion. The converted electrical energy will be stored through advanced energy storage devices such as solid-state electrolyte batteries. The stored energy can be used to replenish the power of the main battery, extend the flight time of the drone and its ability to perform tasks, and achieve efficient energy reuse. The BMS will monitor the battery's charge and discharge status in real time to ensure the safe operation of the battery, and regularly check the component status of the energy recovery and reuse system to ensure its long-term stable operation.

[0077] S5. Use the fast search characteristics of quantum computing to solve multivariable optimization problems and find the best solution to meet the flight mission requirements while maximizing battery life and improving energy efficiency;

[0078] Integrate data on battery health status, energy recovery efficiency, and battery charge and discharge status into the same data structure;

[0079] Design an optimization algorithm based on quantum computing, which is expressed as:

[0080]

[0081] Among them, QO(Ψ(C(t)), P(C(t))) represents the optimization solution after the optimization algorithm is calculated, QO represents the quantum optimization function, Ψ(C(t)) represents the quantum state function, Ω represents the set of all states, |Ψ(C(t))| 2 represents the modulus of the quantum state function, L(C(t)) represents the deviation between the sensor data and the average value, d(C(t)) represents the integration step, and Ψ represents the wave function of the quantum system;

[0082] Using the fast search characteristics of quantum computing to solve multivariable optimization problems, we can find the best solution to meet the flight mission requirements while maximizing battery life and improving energy efficiency.

[0083] The results of the optimization algorithm are used to solve the global optimization problems of the UAV battery pack’s charging and discharging strategy, working mode, and energy distribution. The expression is:

[0084]

[0085] Where D(QO) represents the optimal solution, η represents the degree of attention paid to energy consumption cost during the optimization process; E(x) represents the filter function, X represents the set of all decision variables, and x represents a single decision variable.

[0086] According to the changes in the real-time flight environment and mission requirements, the flight strategy and energy management plan will be dynamically adjusted, the flight environment changes will be monitored in real time, and the latest mission requirements sent by the mission center will be received. According to environmental changes and mission updates, the quantum optimization algorithm will be re-run to dynamically adjust the charging and discharging strategy, working mode and energy allocation plan. The adjusted strategy will be input into the UAV flight control system, and the execution effect will be monitored in real time to ensure the effectiveness and safety of the strategy.

[0087] S6. During the entire flight, the drone and system will use sensors and communication modules to collect real-time data including flight path, altitude, speed, GPS coordinates, battery power, motor temperature, environmental parameters, flight data of abnormal conditions and efficiency of the energy recovery system. In order to protect sensitive information, all collected data will be encrypted before uploading to the cloud. Advanced encryption algorithms are used to ensure data security. Under the premise of ensuring data integrity, flight data will be compressed to reduce file size, speed up data transmission speed, reduce bandwidth consumption and transmission time. After the drone completes the mission and lands safely, it will upload the encrypted and compressed data packets to the designated data center server through a wireless network connection. The server is located in a secure data center. These data centers have strong computing power and high availability and can handle large amounts of data;

[0088] After the data arrives at the data center, it is first decrypted and decompressed to restore the original data format in preparation for analysis. The data center uses specialized software and complex algorithms to parse and analyze the data, identify patterns, trends and potential problems, and use the data to generate a flight report. The flight report will record the flight overview, equipment status, environmental impact, abnormal events, recommendations and predictions; this flight report will be sent to the maintenance team, data analysts and decision makers for subsequent hardware maintenance of the drone and mining of data value.

[0089] This embodiment also provides an intelligent management system for drone batteries and motors, including:

[0090] Deep analysis and predictive modeling module: conducts in-depth analysis of the collected flight data, builds a predictive model, and outputs the results of whether the battery and motor have performance degradation;

[0091] Energy management decision module: determines whether to start the energy recovery mechanism based on the output prediction results;

[0092] Energy recovery and reuse module: stores the converted electrical energy in the drone's battery to provide additional energy for the next flight;

[0093] Global optimization and strategy formulation module: Use quantum algorithms to perform global optimization of battery packs to ensure the rational allocation of battery resources;

[0094] Data analysis and report generation module: Generates a flight report after the drone completes the flight mission and sends it to the operation and maintenance personnel for subsequent maintenance.

[0095] This embodiment also provides a computer device, which is suitable for the intelligent management method of drone batteries and motors, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the intelligent management method of drone batteries and motors proposed in the above embodiment.

[0096] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0097] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the intelligent management method of the battery and the motor of the unmanned aerial vehicle proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0098] In summary, the present invention ensures the accuracy of flight data and the anti-interference ability of the system through real-time data collection and preprocessing, laying the foundation for safe flight. The use of quantum computing for feature extraction and prediction enables accurate monitoring of the health status of batteries and motors, effectively preventing flight failures and improving system reliability. The energy recovery mechanism automatically starts when performance degradation is detected, converting kinetic energy into electrical energy storage, significantly improving energy efficiency and endurance. Quantum algorithms optimize battery management strategies, extend battery life, and enhance energy management efficiency. The generation of flight mission reports supports refined maintenance and flight strategy optimization, reduces maintenance costs, and improves overall operating efficiency.

[0099] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of the intelligent management method of the battery and motor of a drone are provided.

[0100] In order to evaluate the advantages of the intelligent management method of the present invention over the traditional management method, the following experiment was conducted. The experiment was conducted in a controlled environment, and two types of drones were selected, equipped with the traditional management method (TMA) and the intelligent management method of the present invention (IMA). Ten flight tests were conducted for each type of drone, each flight time was 30 minutes, the flight altitude was maintained at 100 meters, and different environmental conditions were simulated in the flight area, such as wind speed, temperature changes, etc., to test the performance of the method under different conditions.

[0101] During the flight, the following data were collected and recorded in real time:

[0102] Battery voltage (V), motor speed (RPM), flight altitude (m), wind speed (m / s), temperature (℃)

[0103] At the same time, the remaining battery capacity percentage of the drone after the flight mission, the failure rate of the motor during flight, and the energy recovery efficiency were recorded.

[0104] Table 1 Comparison of flight results between traditional UAV and new UAV

[0105]

[0106] As can be seen from the table above, the average remaining battery capacity of the drone using the intelligent management method (IMA) of the present invention is 74% at the end of the flight, while the average remaining battery capacity of the drone using the traditional management method (TMA) is only 50%. This shows that IMA significantly improves the energy efficiency of drones and extends flight time.

[0107] In terms of motor failure rate during flight, the motor failure rate of IMA drones is 0%, while the motor failure rate of TMA drones is around 0.05% on average. This shows that IMA has effectively reduced the risk of motor failure and improved the reliability of drones through real-time monitoring and intelligent adjustment.

[0108] In terms of energy recovery efficiency, IMA drones all achieved an energy recovery efficiency of about 11%, while TMA drones, which are not equipped with energy recovery function, achieved this value of 0. This shows that IMA not only improves energy efficiency, but also further enhances the endurance of drones through energy recovery.

[0109] In summary, the intelligent management method of the present invention has shown significant innovation and advantages in energy management, fault prevention and energy recovery compared with traditional management methods. Through intelligent prediction and dynamic adjustment, it not only improves the flight efficiency of drones, but also reduces maintenance costs, providing strong technical support for the long-term operation of drones.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent management method for drone batteries and motors, characterized in that: include: Collect flight data in real time and pre-process it; Extract features from preprocessed flight data, build a prediction model, and output whether the battery and motor have performance degradation; Whether to start the energy recovery mechanism is determined by the results of whether the battery and motor have performance degradation; The converted electrical energy is stored in the drone’s battery to provide additional energy for subsequent flights; Use quantum algorithms to globally optimize the battery pack and dynamically adjust the charging and discharging strategies; After the drone completes its flight mission, a flight report is generated and sent to the operation and maintenance personnel for subsequent maintenance.

2. The intelligent management method of drone batteries and motors as claimed in claim 1, characterized in that: The flight data collected during the flight of the drone includes battery status data, motor status data, flight status data and environmental status data; Define real-time data: S i (t) = [S B (t), S M (t), S F (t), S E (t)]; Among them, S i (t) is the real-time data read by the sensor at time t, S B (t) indicates battery status data, S M (t) represents the motor status data, S F (t) represents the flight status data, S E (t) represents environmental status data. Preprocess the extracted information, the expression is: Among them, C i (t) represents the sensor data after preprocessing, S i (t) represents the real-time data collected by the sensor at time point t, μ i (t) represents the average value of real-time data, σ i (t) represents the fluctuation degree of real-time data, k i Indicates the threshold value; According to real-time data S i (t) and real-time data μ i (t) The difference between the average values ​​is used to determine whether it is less than the threshold k i , when the difference is less than the threshold, C i (t) = S i (t), when the difference is greater than the threshold, C i (t) = μ i (t).

3. The intelligent management method of drone batteries and motors as claimed in claim 2, characterized in that: The feature extraction of the pre-processed information using a quantum computer is expressed as: C(t)=[C B (t),C M (t),C F (t),C E (t)]; Among them, C B (t) represents the battery status data after extraction, C M (t) represents the extracted motor status data, C F (t) represents the extracted flight status data, C E (t) represents the environmental status data after extraction; Build a prediction model and output whether the battery and motor have performance degradation. The expression is: Where P(C(t)) indicates whether the battery and motor have performance degradation based on the preprocessing results, α j The weight coefficient representing the importance of data, σ j (t) represents the standard deviation, ψ j (C(t)) represents the quantum state of the system under the sensor data configuration t, |ψ j (C(t))| 2 represents the probability density of the system; The value range of P(C(t)) is [0,1]. When the value is equal to 0, it indicates that the battery and motor have no performance degradation. When the value is equal to 1, it indicates that the battery and motor have performance degradation.

4. The intelligent management method of drone batteries and motors as claimed in claim 3, characterized in that: The intelligent determination of whether to perform energy recovery is based on whether the output battery and motor are degraded, and the expression is: Wherein, E(P(C(t))) represents the result of determining whether to perform energy recovery, and θ represents the energy recovery threshold; The judgment is made based on whether the motor has performance value and the energy recovery threshold. If it is greater than the threshold, E(P(C(t)))=1, and energy recovery will be turned on. If it is less than the threshold, E(P(C(t)))=0, indicating that energy recovery is not performed.

5. The intelligent management method of drone batteries and motors as claimed in claim 4, characterized in that: After determining and confirming that energy recovery is to be performed, the kinetic energy during the flight is converted into electrical energy using a generator installed on the drone; Through energy conversion and storage technology, the recovered electrical energy is stored in the drone’s battery for use in subsequent flights.

6. The intelligent management method of drone batteries and motors as claimed in claim 5, characterized in that: The data of battery health status, energy recovery efficiency, and battery charge and discharge status are integrated into the same data structure; Design an optimization algorithm based on quantum computing, which is expressed as: Among them, QO(Ψ(C(t)), P(C(t))) represents the optimization solution after the optimization algorithm is calculated, QO represents the quantum optimization function, Ψ(C(t)) represents the quantum state function, Ω represents the set of all states, |Ψ(C(t))| 2 represents the modulus of the quantum state function, L(C(t)) represents the deviation between the sensor data and the average value, d(C(t)) represents the integration step, and Ψ represents the wave function of the quantum system; According to the results of the optimization algorithm, the charging and discharging strategy, working mode, and energy allocation of the drone battery pack are solved for global optimization, and the expression is: Where D(QO) represents the optimal solution, η represents the degree of attention paid to energy consumption cost during the optimization process; E(x) represents the filter function, X represents the set of all decision variables, and x represents a single decision variable.

7. The intelligent management method of drone batteries and motors as claimed in claim 6, characterized in that: Using sensors and communication modules, real-time data is collected on flight data and the efficiency of the energy recovery system; After the flight, the drone will upload the flight data collected during the flight to the data center, allowing the system to generate a flight report containing a flight overview, equipment status, environmental impact, abnormal events, recommendations and predictions.

8. An intelligent management system for drone batteries and motors, based on the intelligent management method for drone batteries and motors according to any one of claims 1 to 7, characterized in that: include: Deep analysis and predictive modeling module: conducts in-depth analysis of the collected flight data, builds a predictive model, and outputs the results of whether the battery and motor have performance degradation; Energy management decision module: determines whether to start the energy recovery mechanism based on the output prediction results; Energy recovery and reuse module: stores the converted electrical energy in the drone's battery to provide additional energy for the next flight; Global optimization and strategy formulation module: Use quantum algorithms to perform global optimization of battery packs to ensure the rational allocation of battery resources; Data analysis and report generation module: Generates a flight report after the drone completes the flight mission and sends it to the operation and maintenance personnel for subsequent maintenance.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent management method of the drone battery and motor are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the intelligent management method of the drone battery and motor are implemented as described in any one of claims 1 to 7.

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