Unmanned aerial vehicle state monitoring method and system based on multi-source data fusion

Abstract

The application belongs to the technical field of data processing, and particularly relates to a UAV state monitoring method and system based on multi-source data fusion, which comprises the following steps: collecting motor speed control signals and acceleration data of a UAV, calculating thrust level features and torque imbalance features; determining a structural stiffness coefficient according to the thrust level features and the torque imbalance features; constructing a recursive calculation logic by using the structural stiffness coefficient, determining input item weights and memory item weights, and generating a dynamic vibration benchmark in combination with a transient impact compensation item; calculating an instantaneous structural abnormality index according to the difference between a measured vibration amplitude and the dynamic vibration benchmark in combination with the structural stiffness coefficient, and then identifying a UAV fault. The application generates a dynamic vibration benchmark by using a structural stiffness coefficient and adjusts monitoring sensitivity, solves the problems of high-maneuvering misjudgment and strong-wind missed detection, and realizes accurate monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing. More particularly, the present application relates to a UAV state monitoring method and system based on multi-source data fusion. BACKGROUND

[0002] With the wide application of industrial UAVs in the fields of power inspection, emergency rescue and logistics transportation, its flight safety guarantee technology is increasingly valued. When the UAV is performing a task, it often needs to face complex environmental wind fields or perform emergency obstacle avoidance and high-speed maneuvering. In these high dynamic scenarios, the vibration data collected by the onboard sensors often presents strong non-stationarity and nonlinearity.

[0003] The existing UAV state monitoring technology mainly relies on linear discrimination methods based on threshold values or black box machine learning models. However, these methods usually regard the UAV body as a linear system with constant stiffness, ignoring the dynamic changes in the physical properties of the UAV under different force conditions. Since the UAV is a typical variable stiffness viscoelastic system, when the motor rotates at high speed and generates a large pulling force, the arm will be hard due to the centrifugal hardening effect, resulting in an increase in structural stiffness. When the UAV performs a sharp twisting action, the connecting parts bear a large asymmetric shear force, resulting in a temporary decrease in equivalent stiffness, which produces a shear softening effect. This real-time coupling change in stiffness and damping causes the same control input to produce completely different vibration responses under different flight actions. The existing technology cannot perceive this change at the physical level, resulting in misjudgment of normal structural responses as fault vibrations when performing high maneuvering actions, or missing small structural damage under strong wind interference. SUMMARY

[0004] To solve the technical problems of the existing technology that ignores the dynamic changes in the physical properties of the UAV with force conditions, resulting in high maneuvering misjudgment of faults or missing damage under strong wind, the present application provides solutions in the following aspects.

[0005] In a first aspect, the present application provides a UAV state monitoring method based on multi-source data fusion, comprising:

[0006] Synchronously collecting the motor speed control signals of each motor of the UAV and the three-axis acceleration raw data of the onboard inertial measurement unit through the onboard data bus, calculating the thrust level feature and the torque imbalance feature of the UAV based on the motor speed control signals;

[0007] According to the thrust level feature and the torque imbalance feature, calculating the structural stiffness coefficient of the UAV, the structural stiffness coefficient representing the conduction ability and response followability of the body to vibration at the current time;

[0008] A variable parameter recursive calculation logic is constructed using the structural stiffness coefficient to dynamically determine the input weights of the thrust level characteristics and the memory weights of the dynamic vibration reference generated in the previous moment. At the same time, the transient impact compensation term calculated based on the instantaneous rate of change of the thrust level characteristics and the structural stiffness coefficient is superimposed to generate the dynamic vibration reference at the current moment.

[0009] The measured vibration amplitude is obtained based on the original triaxial acceleration data, and the instantaneous structural anomaly index is calculated based on the difference between the measured vibration amplitude and the dynamic vibration reference and the structural stiffness coefficient.

[0010] The cumulative anomaly index is determined based on the instantaneous structural anomaly index, and the drone status is determined based on the magnitude of the cumulative anomaly index.

[0011] Preferably, the calculation of the thrust level characteristics of the UAV includes:

[0012] The average value of the motor speed control signals of the four motors of the UAV is used as the thrust level characteristic.

[0013] Preferably, the torque imbalance characteristic satisfies the expression:

[0014] ;

[0015] In the formula, for The characteristics of torque imbalance at any given moment; for The thrust level characteristics at any given moment; To normalize to The first interval Motor No. 1 The motor speed control signal at any given time.

[0016] Preferably, the structural stiffness coefficient satisfies the expression:

[0017] ;

[0018] In the formula, for The structural stiffness coefficient at time t; for The thrust level characteristics at any given moment; for The characteristics of torque imbalance at any given moment; The pre-defined structural sensitivity factor; It is a natural exponential function.

[0019] Preferably, the dynamic vibration reference at the current moment satisfies the expression:

[0020] ;

[0021] In the formula, for Dynamic vibration reference at any given moment; express Dynamic vibration reference at any given moment; for The thrust level characteristics at any given moment; To control the vibration mapping coefficient; for The structural stiffness coefficient at time t; The impact compensation coefficient; express The instantaneous rate of change of the thrust level characteristics at any given moment; It is the absolute value symbol; express Input weights for the thrust level characteristics at any given time; express Weights of memory terms for the dynamic vibration benchmark at any given time; express Transient impact compensation term.

[0022] Preferably, the measured vibration amplitude satisfies the expression:

[0023] ;

[0024] In the formula, for The measured vibration amplitude at any given moment; for The raw X-axis acceleration data output by the airborne inertial measurement unit at all times; for The raw Y-axis acceleration data output by the airborne inertial measurement unit at all times; for The raw Z-axis acceleration data output by the airborne inertial measurement unit at all times; It is the acceleration due to gravity; It is the absolute value symbol.

[0025] Preferably, the instantaneous structural anomaly index satisfies the expression:

[0026] ;

[0027] In the formula, for Instantaneous structural anomaly index at any given moment; for The measured vibration amplitude at any given moment; for Dynamic vibration reference at any given moment; for The structural stiffness coefficient at time t.

[0028] Preferably, determining the cumulative anomaly index based on the instantaneous structural anomaly index includes:

[0029] A sliding time window is constructed, and the mean of the instantaneous structural anomaly index within the sliding time window is used as the cumulative anomaly index.

[0030] Preferably, determining the drone status based on the magnitude of the cumulative anomaly index includes:

[0031] If the cumulative anomaly index exceeds a preset safety threshold, the drone is determined to have a structural fault.

[0032] Secondly, the present invention provides a UAV status monitoring system based on multi-source data fusion, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned UAV status monitoring method based on multi-source data fusion is implemented.

[0033] By adopting the above technical solution, the above-mentioned UAV status monitoring method based on multi-source data fusion is generated into a computer program and stored in a memory so that it can be loaded and executed by a processor. In this way, a terminal device can be made based on the memory and processor for convenient use.

[0034] The beneficial effects of this invention are as follows: By collecting motor speed control signals and acceleration data from the UAV, this invention calculates thrust level characteristics and torque imbalance characteristics, transforming single-channel data into mechanical characteristics reflecting the overall stress state of the fuselage; based on the thrust level characteristics and torque imbalance characteristics, it determines the structural stiffness coefficient, capturing the dynamic changes in stiffness caused by centrifugal hardening and shear softening effects; using the structural stiffness coefficient to construct a variable-parameter recursive calculation logic, it dynamically determines the input term weight and memory term weight, and combines it with a transient impact compensation term to generate a dynamic vibration benchmark, enabling this benchmark to quickly follow the control input under high stiffness, exhibit the hysteresis characteristics of energy dissipation under low stiffness, and effectively offset the transient impact interference caused by maneuvering; furthermore, based on the difference between the measured vibration amplitude and the dynamic vibration benchmark, it calculates the instantaneous structural anomaly index in conjunction with the structural stiffness coefficient, amplifying the difference to improve monitoring sensitivity when the stiffness is large, and suppressing the difference to enhance monitoring robustness when the stiffness is small, thereby effectively solving the problem of misjudgment during high-maneuvering flight and the problem of missed detection under strong wind interference, realizing the state monitoring of the UAV fuselage structure. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the UAV status monitoring method based on multi-source data fusion in this invention;

[0036] Figure 2A schematic diagram illustrating the changes in thrust level characteristics and torque imbalance characteristics;

[0037] Figure 3 This is a schematic diagram illustrating the variation of the structural stiffness coefficient.

[0038] Figure 4 A schematic diagram comparing the dynamic vibration reference with the measured vibration amplitude;

[0039] Figure 5 This is a schematic diagram illustrating the changes in the cumulative abnormal index. Detailed Implementation

[0040] 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, not all, of the embodiments of the present invention. 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.

[0041] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0042] This invention discloses a method for monitoring the status of unmanned aerial vehicles (UAVs) based on multi-source data fusion, with reference to... Figure 1 This includes steps S1-S4:

[0043] S1. The motor speed control signals of each motor of the UAV and the raw triaxial acceleration data of the airborne inertial measurement unit are synchronously collected through the airborne data bus. Based on the motor speed control signals, the thrust level characteristics and torque imbalance characteristics of the UAV are calculated.

[0044] It should be noted that the flight control system of the UAV generates motor speed control signals as the excitation source of the system, while the fuselage structure generates vibration response under the excitation as the system output. At the same time, the motion of the quadcopter UAV is achieved by differential control of the four motors. The data of a single motor alone cannot reflect the overall stress state of the fuselage. Therefore, this invention transforms it into mechanical characteristics that reflect the overall physical state of the fuselage.

[0045] Specifically, the motor speed control signals of each motor of the quadcopter drone and the raw triaxial acceleration data of the airborne inertial measurement unit are synchronously collected through the airborne data bus. All collected data are timestamped and resampled. In this embodiment, the uniform sampling frequency is 100Hz during resampling. In other embodiments, the implementer can set the uniform sampling frequency according to the output frequency of the drone flight control log and the computing performance of the airborne processor.

[0046] The resampled motor speed control signal is normalized to linearly map the original pulse width value of the motor speed control signal to a closed interval of 0 to 1, so as to eliminate the dimensional differences caused by different ESC protocols.

[0047] Furthermore, based on the normalized motor speed control signal, the thrust level characteristics and torque imbalance characteristics of the UAV are calculated:

[0048]

[0049]

[0050] In the formula, for The thrust level characteristics at any given time characterize the UAV's performance. The overall power load level at any given time; the larger this value, the higher the rotor speed and the greater the vertical pull on the fuselage. To normalize to The first interval Motor No. 1 The motor speed control signal at any given time; for The torque imbalance characteristics at time t, characterizing The degree of imbalance in the output of the four motors reflects the torque shear strength experienced by the fuselage. When the drone performs maneuvers such as yaw, roll, or pitch, the speed difference between the diagonal motors increases. and The difference increases, making Significantly increased.

[0051] For example, Figure 2 The diagram illustrates the changes in thrust level characteristics and torque imbalance characteristics. During rapid acceleration, the thrust level characteristics increase significantly, while during violent maneuvers, the torque imbalance characteristics exhibit high-frequency, large-amplitude oscillations.

[0052] S2. Based on the thrust level characteristics and torque imbalance characteristics, calculate the structural stiffness coefficient of the UAV. The structural stiffness coefficient characterizes the airframe's ability to transmit vibrations and its response tracking at the current moment.

[0053] It should be noted that the UAV fuselage is not a rigid body; its equivalent stiffness dynamically changes with the stress state. When the total thrust increases, the centrifugal tension on the arms and propellers increases, and the structure, like a taut string, experiences an increase in its natural frequency, enhancing its resistance to deformation and producing a centrifugal hardening effect. When the asymmetric torque increases, the fuselage joints experience enormous shear stress, leading to microscopic misalignment or slippage. This reduces the structure's ability to constrain vibrations, manifesting as a decrease in equivalent stiffness and a shear softening effect. Therefore, this invention determines the UAV's structural stiffness coefficient based on the thrust level characteristics and torque imbalance characteristics to accurately obtain the current physical properties of the fuselage.

[0054] Specifically, based on the thrust level characteristics and torque imbalance characteristics, the structural stiffness coefficient of the UAV is calculated:

[0055]

[0056] In the formula, for The structural stiffness coefficient at any given time, the larger the value, the stronger the structural stiffness coefficient of the fuselage. The stiffer the surface, the faster the vibration is transmitted and the stronger the response and tracking. for The thrust level characteristics at any given moment; for The characteristics of torque imbalance at any given moment; The preset structural sensitivity factor is used to adjust the model's sensitivity to maneuvering torque. It depends on the material of the body; the more flexible the material, the better. The larger the value, the more likely it is to be 2. In other embodiments, the implementer can set the value based on the elastic modulus test results of the actual fuselage material. ; It is a natural exponential function.

[0057] In the formula, The term reflects the stiffness enhancement effect brought about by thrust. The square term is used because centrifugal force is proportional to the square of the rotational speed. When the thrust is horizontally characteristic... When it increases, It exhibits nonlinear growth, reflecting the hardening nonlinearity at high speeds; The term reflects the stiffness decay effect caused by torque, and the exponential decay function reflects the rapid softening characteristics of the structure under large torques. When the torque is unbalanced... When it increases, Rapid decay.

[0058] For example, Figure 3 This is a schematic diagram illustrating the variation of the structural stiffness coefficient. Figure 3As can be seen, the structural stiffness coefficient increases under centrifugal hardening, while it decreases significantly under shear softening.

[0059] S3. Utilize the structural stiffness coefficient to construct a variable parameter recursive calculation logic, dynamically determine the input weights of the thrust level characteristics and the memory weights of the dynamic vibration reference generated in the previous moment, and simultaneously superimpose the transient impact compensation term calculated based on the instantaneous rate of change of the thrust level characteristics and the structural stiffness coefficient to generate the dynamic vibration reference at the current moment.

[0060] It should be noted that when an UAV's fuselage structure is excited, its physical response exhibits hysteresis and transient elasticity. Traditional linear filtering methods cannot reflect the differences in UAV response under different structural stiffness coefficients. Due to the dynamic changes in the structural stiffness coefficient, the actual fuselage vibration response does not follow the thrust level characteristics at a constant speed: when the structural stiffness coefficient is large, vibration transmission is rapid, and the system response is highly responsive to the excitation source, exhibiting a fast response; when the structural stiffness coefficient is small, energy dissipation is slow, and the system response exhibits significant hysteresis and tailing relative to the excitation source. Furthermore, sudden throttle changes can trigger transient impacts, and the smaller the structural stiffness coefficient, the more severe the structural vibration caused by the same impact force. Therefore, this invention utilizes the structural stiffness coefficient to construct a variable-parameter recursive calculation logic, dynamically determining the input weights of the thrust level characteristics and the memory weights of the model output values ​​from the previous moment. Simultaneously, it superimposes transient impact compensation terms calculated based on the instantaneous rate of change of the thrust level characteristics and the structural stiffness coefficient to generate a dynamic vibration benchmark, reflecting the vibration amplitude that a theoretically healthy fuselage should produce under the current control state.

[0061] Specifically, based on the control input and structural stiffness coefficient, the dynamic vibration reference at each moment is calculated iteratively:

[0062]

[0063] In the formula, for The dynamic vibration reference at a given moment reflects the dynamic vibration at that moment. Under constant control, the vibration that a healthy fuselage should theoretically produce, measured in units of... ; express Dynamic vibration reference at any given moment; for The thrust level characteristics at any given moment; To control the vibration mapping coefficient, the unit is... This is used to map the dimensionless thrust level characteristics to physical vibration amplitudes; the empirical value is 4. In other embodiments, the implementer can obtain the average vibration amplitude to the average throttle position by collecting the ratio of the average vibration amplitude to the average throttle position while the drone is hovering. ; for The structural stiffness coefficient at time t; The impact compensation coefficient is used to adjust the amplitude of the impact term. An empirical value of 0.5 is used. In other embodiments, the implementer can set this value based on experimental data from emergency stops of the UAV under no-load conditions. ; express The instantaneous rate of change of the thrust level characteristics at any given moment, i.e. , for The thrust level characteristics at any given moment; It is the absolute value symbol.

[0064] In the formula, and They are respectively The weights of the input terms and the weights of the memory terms at each moment determine the dynamic vibration reference. Response characteristics to the current control input, when When the value is large, the input term weights approach 1, making the dynamic vibration reference... Capable of quickly following the thrust level characteristics after physical mapping The change reflects the instantaneous response characteristics under high stiffness; when When the value is small, the weight of the memory term increases, making the dynamic vibration reference more accurate. Retain more values ​​from the previous moment, relative to thrust level characteristics. It exhibits significant lag, reflecting the lag characteristics of energy dissipation under low stiffness; for Transient impact compensation term at time; denominator This reflects the physical law that the smaller the structural stiffness, the more violent the structural vibration caused by the same impact force. When it decreases, this compensation item Increase, thereby improving the dynamic vibration reference. The amplitude is used to counteract the oscillations of the physical system.

[0065] It should be noted that this refers to the initial moment when the drone is powered on and in a stationary or smoothly hovering state. Specify dynamic vibration reference , The thrust level characteristic at the initial moment is the average value of the speed control signals of all motors at the initial moment.

[0066] S4. Based on the original triaxial acceleration data, obtain the measured vibration amplitude, calculate the instantaneous structural anomaly index according to the difference between the measured vibration amplitude and the dynamic vibration reference and the structural stiffness coefficient, determine the cumulative anomaly index according to the instantaneous structural anomaly index, and determine the UAV status according to the magnitude of the cumulative anomaly index.

[0067] Specifically, the measured vibration amplitude is obtained based on the raw triaxial acceleration data collected from the airborne inertial measurement unit:

[0068]

[0069] In the formula, for The measured vibration amplitude at any given moment; for The raw X-axis acceleration data output by the airborne inertial measurement unit at all times; for The raw Y-axis acceleration data output by the airborne inertial measurement unit at all times; for The raw Z-axis acceleration data output by the airborne inertial measurement unit at all times; It is the acceleration due to gravity; The symbol represents the absolute value. This invention extracts the dynamic acceleration amplitude caused solely by fuselage oscillation by calculating the Euclidean norm of the triaxial acceleration and subtracting the static gravitational acceleration component.

[0070] For example, Figure 4 The diagram shows a comparison between the dynamic vibration reference and the measured vibration amplitude. It can be seen that during the high-maneuver phase, the dynamic vibration reference can automatically follow the measured vibration amplitude, effectively offsetting the impact interference caused by normal flight maneuvers. However, in the fault location area, the two show a significant separation.

[0071] It should be noted that when the structural stiffness coefficient is large, the fuselage is in a stable response state dominated by centrifugal hardening. The vibration transmission law of the physical system is clear and highly deterministic. At this time, the dynamic vibration reference fits the physical reality to a very high degree. Even a small difference between the measured vibration amplitude and the dynamic vibration reference may indicate structural cracks or loosening in the fuselage. Therefore, it is necessary to maintain high sensitivity to this difference in order to accurately detect minor faults. However, when the structural stiffness coefficient is small, the fuselage is in a complex stress state dominated by shear softening. The vibration response contains a large number of random nonlinear oscillations. There is a natural non-fault-related physical deviation between the measured vibration amplitude and the dynamic vibration reference. In this case, the sensitivity to this difference should be reduced to broaden the tolerance range for normal maneuvering oscillations and avoid false alarms. Therefore, this invention introduces the structural stiffness coefficient as a confidence weight to construct an instantaneous structural anomaly index that automatically adjusts its sensitivity according to the stress state.

[0072] Specifically, the instantaneous structural anomaly index is calculated based on the difference between the measured vibration amplitude and the dynamic vibration reference, as well as the structural stiffness coefficient:

[0073]

[0074] In the formula, for The instantaneous structural anomaly index at a given time; the larger the value, the higher the degree of anomaly. for The measured vibration amplitude at any given moment; for Dynamic vibration reference at any given moment; for The original physical residual between the measured vibration amplitude at a given moment and the dynamic vibration reference. for The structural stiffness coefficient at time t is used as a confidence weighting factor when the structural stiffness coefficient... When the value is large, amplify the original physical residual. To achieve high-sensitivity monitoring, when the structural stiffness coefficient When the value is small, suppress the original physical residual. To achieve highly robust monitoring.

[0075] Furthermore, to avoid single-point noise interference, a sliding time window is constructed, and the cumulative anomaly index is obtained based on the mean of the instantaneous structural anomaly index within the sliding time window:

[0076]

[0077] In the formula, for The cumulative anomaly index at any given time represents the average level of anomalies in the fuselage structure in recent times; The length of the sliding time window is empirically set to 50. In other embodiments, the implementer can set it according to the sampling frequency and real-time requirements. ; for Instantaneous structural anomaly index at any given time.

[0078] In response to the cumulative anomaly index exceeding a preset safety threshold The system determines that the drone has a structural fault and triggers an alarm, including a safety threshold. The settings are determined by the implementers based on the historical data distribution of the drone during normal flight, for example, by taking 1.5 times the 99th percentile of the historical data.

[0079] For example, Figure 5The diagram illustrates the changes in the cumulative anomaly index. During the high-maneuver flight phase, the cumulative anomaly index remains at a low level. However, after a malfunction occurs, the cumulative anomaly index rapidly exceeds the safety threshold and triggers an alarm.

[0080] This invention also discloses a UAV status monitoring system based on multi-source data fusion, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the UAV status monitoring method based on multi-source data fusion according to this invention.

[0081] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0082] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise expressly and specifically defined.

[0083] While various embodiments of the invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of the invention.

Claims

1. A method for monitoring the condition of unmanned aerial vehicles (UAVs) based on multi-source data fusion, characterized in that, include: The motor speed control signals of each motor of the UAV and the raw triaxial acceleration data of the airborne inertial measurement unit are collected synchronously through the airborne data bus. Based on the motor speed control signals, the thrust level characteristics and torque imbalance characteristics of the UAV are calculated. Based on the thrust level characteristics and torque imbalance characteristics, the structural stiffness coefficient of the UAV is calculated. The structural stiffness coefficient characterizes the airframe's ability to transmit vibrations and its response tracking at the current moment. A variable parameter recursive calculation logic is constructed using the structural stiffness coefficient to dynamically determine the input weights of the thrust level characteristics and the memory weights of the dynamic vibration reference generated in the previous moment. At the same time, the transient impact compensation term calculated based on the instantaneous rate of change of the thrust level characteristics and the structural stiffness coefficient is superimposed to generate the dynamic vibration reference at the current moment. The measured vibration amplitude is obtained based on the original triaxial acceleration data, and the instantaneous structural anomaly index is calculated based on the difference between the measured vibration amplitude and the dynamic vibration reference and the structural stiffness coefficient. The cumulative anomaly index is determined based on the instantaneous structural anomaly index, and the drone status is determined based on the magnitude of the cumulative anomaly index.

2. The UAV status monitoring method based on multi-source data fusion according to claim 1, characterized in that, The calculated thrust level characteristics of the UAV include: The average value of the motor speed control signals of the four motors of the UAV is used as the thrust level characteristic.

3. The UAV status monitoring method based on multi-source data fusion according to claim 1, characterized in that, The torque imbalance characteristic satisfies the expression: ； In the formula, for The characteristics of torque imbalance at any given moment; for The thrust level characteristics at any given moment; To normalize to The first interval Motor No. 1 The motor speed control signal at any given time.

4. The UAV status monitoring method based on multi-source data fusion according to claim 1, characterized in that, The structural stiffness coefficient satisfies the following expression: ； In the formula, for The structural stiffness coefficient at time t; for The thrust level characteristics at any given moment; for The characteristics of torque imbalance at any given moment; The pre-defined structural sensitivity factor; It is a natural exponential function.

5. The UAV status monitoring method based on multi-source data fusion according to claim 1, characterized in that, The dynamic vibration reference at the current moment satisfies the expression: ； In the formula, for Dynamic vibration reference at any given moment; express Dynamic vibration reference at any given moment; for The thrust level characteristics at any given moment; To control the vibration mapping coefficient; for The structural stiffness coefficient at time t; The impact compensation coefficient; express The instantaneous rate of change of the thrust level characteristics at any given moment; It is the absolute value symbol; express Input weights for the thrust level characteristics at any given time; express Weights of memory terms for the dynamic vibration benchmark at any given time; express Transient impact compensation term.

6. The UAV status monitoring method based on multi-source data fusion according to claim 1, characterized in that, The measured vibration amplitude satisfies the expression: ； In the formula, for The measured vibration amplitude at any given moment; for The raw X-axis acceleration data output by the airborne inertial measurement unit at all times; for The raw Y-axis acceleration data output by the airborne inertial measurement unit at all times; for The raw Z-axis acceleration data output by the airborne inertial measurement unit at all times; It is the acceleration due to gravity; It is the absolute value symbol.

7. The UAV status monitoring method based on multi-source data fusion according to claim 1, characterized in that, The instantaneous structural anomaly index satisfies the expression: ； In the formula, for Instantaneous structural anomaly index at any given moment; for The measured vibration amplitude at any given moment; for Dynamic vibration reference at any given moment; for The structural stiffness coefficient at time t.

8. The UAV status monitoring method based on multi-source data fusion according to claim 1, characterized in that, The determination of the cumulative anomaly index based on the instantaneous structural anomaly index includes: A sliding time window is constructed, and the mean of the instantaneous structural anomaly index within the sliding time window is used as the cumulative anomaly index.

9. The UAV status monitoring method based on multi-source data fusion according to claim 1, characterized in that, The process of determining the drone status based on the magnitude of the cumulative anomaly index includes: If the cumulative anomaly index exceeds a preset safety threshold, the drone is determined to have a structural fault.

10. A UAV status monitoring system based on multi-source data fusion, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the UAV status monitoring method based on multi-source data fusion according to any one of claims 1-9.

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