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

Abstract

The invention belongs to the technical field of data processing, and particularly relates to an unmanned aerial vehicle state monitoring method and system based on multi-source data fusion, and the method comprises the steps: collecting a motor rotating speed control signal and acceleration data of an unmanned aerial vehicle, and calculating a thrust level feature and a torque imbalance feature; determining a structural stiffness coefficient according to the thrust level characteristic and the torque imbalance characteristic; constructing a recursive calculation logic by using the structural stiffness coefficient, determining an input item weight and a memory item weight, and generating a dynamic vibration reference in combination with a transient impact compensation item; and according to the difference between the actually measured vibration amplitude and the dynamic vibration reference, calculating an instantaneous structural anomaly index by combining the structural stiffness coefficient, and further identifying the fault of the unmanned aerial vehicle. The dynamic vibration reference is generated by using the structural rigidity coefficient, the monitoring sensitivity is adjusted, the problems of high-maneuverability misjudgment and strong wind leak detection are solved, and accurate monitoring is realized.

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 on-board sensor 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 subjected to centrifugal hardening effect, resulting in an increase in structural stiffness. When the UAV performs a sharp twisting action, the connecting parts will be subjected to a large asymmetric shear force, resulting in a temporary decrease in equivalent stiffness, which produces a shear softening effect. This real-time coupling change of 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: synchronously collecting, through an on-board data bus, a motor speed control signal of each motor of the UAV and three-axis acceleration raw data of an on-board inertial measurement unit, calculating thrust level features and torque imbalance features of the UAV based on the motor speed control signal; calculating a structural stiffness coefficient of the UAV according to the thrust level features and the torque imbalance features, the structural stiffness coefficient representing the conduction ability and response followability of the body to vibration at the current time; The variable parameter recursive calculation logic is constructed by using the structural stiffness coefficient, input item weights of thrust level characteristics and memory item weights of the dynamic vibration benchmark generated at the last moment are dynamically determined, and a transient impact compensation item calculated based on the instantaneous change rate of the thrust level characteristics and the structural stiffness coefficient is superimposed to generate the dynamic vibration benchmark at the current moment; Based on the three-axis acceleration original data, a measured vibration amplitude is obtained, and an instantaneous structural abnormality index is calculated according to the difference between the measured vibration amplitude and the dynamic vibration benchmark and the structural stiffness coefficient. According to the instantaneous structural abnormality index, a cumulative abnormality index is determined, and the state of the unmanned aerial vehicle is determined according to the cumulative abnormality index.

[0006] Preferably, the thrust level characteristics of the unmanned aerial vehicle are calculated, comprising: The mean value of the motor speed control signals of the four motors of the unmanned aerial vehicle is taken as the thrust level characteristics.

[0007] Preferably, the torque imbalance characteristics satisfy the expression: ; In the expression, is the torque imbalance characteristics at the current moment; is the thrust level characteristics at the current moment; is the motor speed control signal of the i-th motor at the current moment. is normalized to the interval [0, 1].

[0008] Preferably, the structural stiffness coefficient satisfies the expression: ; In the expression, is the structural stiffness coefficient at the current moment; is the thrust level characteristics at the current moment; is the torque imbalance characteristics at the current moment; is a preset structure sensitive factor; is a natural exponential function. Preferably, the dynamic vibration benchmark at the current moment satisfies the expression: ;

[0009] In the expression, is the dynamic vibration benchmark at the current moment; represents the dynamic vibration benchmark at the current moment; is the thrust level characteristics at the current moment; is the torque imbalance characteristics at the current moment. ​​​​​​​​a thrust level feature at the time instant; for controlling the vibration mapping coefficient; for a structure stiffness coefficient at the time instant; for the impact compensation coefficient; denotes a rate of instantaneous change of the thrust level feature at the time instant; is an absolute value sign; denotes an input term weight of the thrust level feature at the time instant; denotes a memory term weight of the dynamic vibration reference at the time instant; denotes a transient impact compensation term at the time instant.

[0010] Preferably, the measured vibration amplitude satisfies the expression: ; wherein, is a measured vibration amplitude at the time instant; is X-axis acceleration raw data output by an on-board inertial measurement unit at the time instant; is Y-axis acceleration raw data output by an on-board inertial measurement unit at the time instant; is Z-axis acceleration raw data output by an on-board inertial measurement unit at the time instant; is a gravitational acceleration; is an absolute value sign.

[0011] Preferably, the instantaneous structure abnormality index satisfies the expression: ; wherein, is an instantaneous structure abnormality index at the time instant; is a measured vibration amplitude at the time instant; is a dynamic vibration reference at the time instant; is a structure stiffness coefficient at the time instant.

[0012] Preferably, the determining the cumulative abnormality index according to the instantaneous structure abnormality index comprises: constructing a sliding time window, and taking a mean value of the instantaneous structure abnormality index within the sliding time window as the cumulative abnormality index.

[0013] Preferably, the determining the UAV state according to the cumulative abnormality index comprises: If the cumulative anomaly index exceeds a preset safety threshold, the drone is determined to have a structural fault.

[0014] 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.

[0015] 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 the processor for convenient use.

[0016] The beneficial effects of this invention are as follows: This invention collects motor speed control signals and acceleration data from the UAV, calculates thrust level characteristics and torque imbalance characteristics, and transforms single-channel data into mechanical characteristics reflecting the overall stress state of the fuselage; it determines the structural stiffness coefficient based on the thrust level characteristics and torque imbalance characteristics, capturing the dynamic changes in stiffness caused by centrifugal hardening and shear softening effects; it constructs a variable-parameter recursive calculation logic using the structural stiffness coefficient, dynamically determines the input term weight and memory term weight, and generates a dynamic vibration benchmark by combining it with a transient impact compensation term, enabling the 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 combination 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

[0017] Figure 1 This is a flowchart illustrating the UAV status monitoring method based on multi-source data fusion in this invention; Figure 2 A schematic diagram illustrating the changes in thrust level characteristics and torque imbalance characteristics; Figure 3 This is a schematic diagram illustrating the variation of the structural stiffness coefficient. Figure 4 A schematic diagram comparing the dynamic vibration reference with the measured vibration amplitude; Figure 5 This is a schematic diagram illustrating the changes in the cumulative abnormal index. Detailed Implementation

[0018] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0019] The specific implementation of the present application will be described in detail below with reference to the accompanying drawings.

[0020] The embodiment of the present application discloses a UAV state monitoring method based on multi-source data fusion, with reference to Figure 1 , comprising steps S1-S4: S1, synchronously collecting the motor speed control signal of each motor of the UAV and the three-axis acceleration raw data of the airborne inertial measurement unit through the airborne data bus, and calculating the thrust level feature and the torque imbalance feature of the UAV based on the motor speed control signal.

[0021] It should be noted that the motor speed control signal is generated by the flight control system of the UAV, which is the excitation source of the system, and the vibration response is generated by the fuselage structure under the excitation, which is the output of the system. At the same time, the motion of the quadrotor UAV is realized by the differential control of the four motors, and the single motor data cannot reflect the overall stress state of the fuselage, therefore, the present application converts it into the mechanical feature reflecting the overall physical state of the fuselage.

[0022] Specifically, the motor speed control signal of each motor of the quadrotor UAV and the three-axis acceleration raw data of the airborne inertial measurement unit are synchronously collected through the airborne data bus, and all the collected data are time-stamped and resampled. In this embodiment, the uniform sampling frequency is 100 Hz, and in other embodiments, the implementer can set the uniform sampling frequency according to the output frequency of the UAV flight control log and the computing performance of the airborne processor.

[0023] The resampled motor speed control signal is normalized to linearly map the original pulse width value of the motor speed control signal to the closed interval of 0 to 1, so as to eliminate the dimensional difference caused by different electronic speed controller protocols.

[0024] Further, based on the normalized motor speed control signal, the thrust level feature and the torque imbalance feature of the UAV are calculated:

[0025]

[0026] In the formula, is the thrust level feature at the moment, which represents the thrust level of the UAV in the horizontal direction at the moment. is the torque imbalance feature at the moment, which represents the torque imbalance of the UAV at the moment. 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Specifically, based on the thrust level characteristics and torque imbalance characteristics, the structural stiffness coefficient of the UAV is calculated:

[0031] 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 torque imbalance characteristic at the moment; is a preset structure sensitivity factor used to adjust the sensitivity of the model to the maneuvering torque, the structure sensitivity factor is related to the material of the fuselage, when the material of the fuselage is more flexible, the value is larger, the experience value is 2, in other embodiments, the implementer can set the value according to the test result of the elastic modulus of the actual fuselage material is a natural exponential function.

[0032] In the formula, The term reflects the stiffness enhancement effect brought by the thrust, and the square term is used because the centrifugal force is proportional to the square of the rotation speed, when the thrust level characteristic increases, the non-linear growth is reflected, which reflects the hardening nonlinearity at high rotation speed; The term reflects the stiffness attenuation effect brought by the torque, and the exponential decay function reflects the rapid softening characteristics of the structure under large torque, when the torque imbalance characteristic increases, rapidly attenuates.

[0033] Exemplarily, Figure 3 is a schematic diagram of the change of the structure stiffness coefficient, from Figure 3 it can be seen that under the centrifugal hardening effect, the structure stiffness coefficient rises, and under the shear softening effect, the structure stiffness coefficient significantly decreases.

[0034] S3, using the structure stiffness coefficient to construct a variable parameter recursive calculation logic, dynamically determining the input term weight of the thrust level characteristic and the memory term weight of the dynamic vibration reference generated at the last moment, while superimposing the transient impact compensation term calculated based on the instantaneous change rate of the thrust level characteristic and the structure stiffness coefficient, to generate the dynamic vibration reference at the current moment.

[0035] ​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.

[0036] Specifically, based on the control input and structural stiffness coefficient, the dynamic vibration reference at each moment is calculated iteratively:

[0037] 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.

[0038] 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 adjusted to counteract the oscillations of the physical system.

[0039] 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.

[0040] 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.

[0041] Specifically, the measured vibration amplitude is obtained based on the raw triaxial acceleration data collected from the airborne inertial measurement unit:

[0042] In the formula, for The measured vibration amplitude at any given time; for The raw X-axis acceleration data output by the airborne inertial measurement unit at all times; is the Y-axis acceleration raw data output by the airborne inertial measurement unit at the moment; is the Z-axis acceleration raw data output by the airborne inertial measurement unit at the moment; is the gravitational acceleration; is the absolute value symbol. The present application calculates the Euclidean norm of the three-axis acceleration and subtracts the static gravitational acceleration component, thereby stripping out the dynamic acceleration amplitude caused only by the body oscillation.

[0043] Exemplarily, Figure 4 is a comparison diagram of the dynamic vibration reference and the measured vibration amplitude, it can be seen that in the high maneuvering stage, the dynamic vibration reference can automatically follow the measured vibration amplitude, effectively offsetting the impact interference brought by normal flight maneuvering, and in the fault occurrence area, the two are significantly separated.

[0044] 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 has strong determinacy, at this time the fitting degree of the dynamic vibration reference to the physical reality is extremely high, and the small difference between the measured vibration amplitude and the dynamic vibration reference is extremely likely to imply that the fuselage has structural cracks or looseness, therefore, the difference needs to be highly sensitive to achieve accurate capture of small faults; 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 amount of random nonlinear oscillation, and there is a non-fault physical deviation between the measured vibration amplitude and the dynamic vibration reference, at this time the sensitivity to the difference should be reduced to relax the tolerance range of normal maneuvering oscillation and avoid false positives. Therefore, the present application introduces the structural stiffness coefficient as a confidence weight to construct an instantaneous structural abnormality index that automatically adjusts the sensitivity according to the stress state.

[0045] Specifically, the instantaneous structural abnormality index is calculated according to the difference between the measured vibration amplitude and the dynamic vibration reference and the structural stiffness coefficient:

[0046] In the formula, is the instantaneous structural abnormality index at the moment, the larger the value, the higher the abnormality degree; is the measured vibration amplitude at the moment; is the dynamic vibration reference at the moment; is the original physical residual error between the measured vibration amplitude and the dynamic vibration reference at the moment; is The structural stiffness coefficient of the moment is taken as a confidence weighting factor. When the structural stiffness coefficient is large, the original physical residual is amplified , realizing high-sensitivity monitoring. When the structural stiffness coefficient is small, the original physical residual is suppressed , realizing high-robustness monitoring.

[0047] Further, in order to avoid single-point noise interference, a sliding time window is constructed, and an accumulated anomaly index is obtained according to the mean value of the instantaneous structural anomaly index in the sliding time window:

[0048] In the formula, is the accumulated anomaly index of the moment, representing the average anomaly level of the recent airframe structure; is the length of the sliding time window, and the empirical value is 50. ; is the instantaneous structural anomaly index of the moment. In response to the accumulated anomaly index exceeding a preset safety threshold , it is determined that the unmanned aerial vehicle has a structural failure and an alarm is triggered, wherein the safety threshold

[0049] is set by the implementer according to the historical data statistical distribution when the unmanned aerial vehicle is normally flying, for example, taking 1.5 times of the 99% quantile of the historical data. Exemplarily, is a cumulative anomaly index change schematic diagram, and the cumulative anomaly index maintains at a low level during the high-maneuvering flight stage, and after the failure occurs, the cumulative anomaly index rapidly breaks through the safety threshold and triggers an alarm.

[0050] Figure 5 The embodiment of the application also discloses an unmanned aerial vehicle state monitoring system based on multi-source data fusion, comprising a processor and a memory, and the memory stores computer program instructions, which realize the unmanned aerial vehicle state monitoring method based on multi-source data fusion according to the application when the computer program instructions are executed by the processor.

[0051] The above system also comprises a communication bus and a communication interface and other components familiar to those skilled in the art, and the settings and functions thereof are known in the art, so they will not be described here.

[0052] In the description of the present specification, the meaning of "a plurality of", "several" is at least two, for example, two, three or more, etc., unless otherwise explicitly specifically limited.

[0053] In the description of the present specification, the meaning of "a plurality of", "several" is at least two, for example, two, three or more, etc., unless otherwise explicitly specifically limited.

[0054] ​While the specification has illustrated and described various embodiments of the application, it will be clear to those of ordinary skill in the art that various changes, modifications, and substitutions can be made thereto without departing from the spirit and scope of the application. It is to be understood that in some instances the foregoing description has been presented in terms of processes, elements and / or items. It is to be appreciated that a process can include additional or fewer processes, elements and / or items. It is to be understood that the use of "including", "comprising", or "having" the recited elements or processes can mean that there are additional elements or processes. It is to be understood that the use of "comprising" or "including" one or more elements or processes does not preclude the presence or addition of one or more other elements or processes.

Claims

1. A method for monitoring the state of a UAV based on multi-source data fusion, characterized in that, The method comprises: synchronously collecting motor speed control signals of each motor of the unmanned aerial vehicle and three-axis acceleration raw data of an on-board inertial measurement unit through an on-board data bus, and calculating thrust level characteristics and torque imbalance characteristics of the unmanned aerial vehicle based on the motor speed control signals; calculating a structural stiffness coefficient of the unmanned aerial vehicle according to the thrust level characteristics and the torque imbalance characteristics, the structural stiffness coefficient representing the conduction ability and response followability of the fuselage to vibration at the current time; constructing a variable-parameter recursive calculation logic using the structural stiffness coefficient, dynamically determining the input item weight of the thrust level characteristics and the memory item weight of a dynamic vibration reference generated at the previous time, and simultaneously superimposing a transient impact compensation item calculated based on the instantaneous change rate of the thrust level characteristics and the structural stiffness coefficient to generate the dynamic vibration reference at the current time; obtaining a measured vibration amplitude based on the three-axis acceleration raw data, calculating an instantaneous structural abnormality index according to the difference between the measured vibration amplitude and the dynamic vibration reference and the structural stiffness coefficient; determining a cumulative abnormality index according to the instantaneous structural abnormality index, and determining the state of the unmanned aerial vehicle according to the cumulative abnormality index. 2.The UAV state monitoring method based on multi-source data fusion of claim 1, wherein, The method for calculating the thrust level characteristics of the unmanned aerial vehicle comprises: taking the average of the motor speed control signals of the four motors of the unmanned aerial vehicle as the thrust level characteristics. 3.The UAV state monitoring method based on multi-source data fusion of claim 1, wherein, The torque imbalance characteristics satisfy the expression: ； wherein is the torque imbalance feature at the time instant; is the thrust level feature at the time instant; is normalized to the first motor at the time instant. The motor speed control signal at the time instant is the first 4.The method of claim 1, wherein, The structural stiffness coefficient satisfies the expression: ； wherein is the structural stiffness coefficient at the instant of time; is the thrust level characteristic at the instant of time; is the torque unbalance characteristic at the instant of time; is a preset structural sensitivity factor; is a natural exponential function. 5.The method of claim 1, wherein, The dynamic vibration reference at the current time satisfies the expression: ； wherein is a dynamic vibration reference at the time instant; denotes a dynamic vibration reference at the time instant; is a thrust level characteristic at the time instant; is a control vibration mapping coefficient; is a structural stiffness coefficient at the time instant; is an impact compensation coefficient; denotes a rate of instantaneous change of the thrust level characteristic at the time instant; is an absolute value sign; denotes an input term weight of the thrust level characteristic at the time instant; denotes a memory term weight of the dynamic vibration reference at the time instant; denotes a transient impact compensation term at the time instant. 6.The method of claim 1, wherein, The measured vibration amplitude satisfies the expression: ； In the formula, is the measured vibration amplitude at the moment; is the X-axis acceleration raw data output by the airborne inertial measurement unit at the moment; is the Y-axis acceleration raw data output by the airborne inertial measurement unit at the moment; is the Z-axis acceleration raw data output by the airborne inertial measurement unit at the moment; is the gravitational acceleration; is the absolute value symbol. 7.The method of claim 1, wherein, The instantaneous structural abnormality index satisfies the expression: ； In the formula, is the instantaneous structure abnormality index at the moment; is the measured vibration amplitude at the moment; is the dynamic vibration reference at the moment; is the structure stiffness coefficient at the moment. 8.The method of claim 1, wherein, The method for determining the cumulative abnormality index according to the instantaneous structural abnormality index comprises: constructing a sliding time window, and taking the average of the instantaneous structural abnormality index in the sliding time window as the cumulative abnormality index. 9.The UAV state monitoring method based on multi-source data fusion of claim 1, wherein, The method for determining the state of the unmanned aerial vehicle according to the cumulative abnormality index comprises: in response to the cumulative abnormality index exceeding a preset safety threshold, determining that the unmanned aerial vehicle has a structural failure.

10. The unmanned aerial vehicle state monitoring system based on multi-source data fusion, characterized in that, The method comprises: a processor and a memory, the memory storing computer program instructions, when the computer program instructions are executed by the processor, realizing the unmanned aerial vehicle state monitoring method based on multi-source data fusion according to any one of claims 1-9.

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