A transition mode control method and system for a compound wing unmanned aerial vehicle

Abstract

The application provides a transition mode control method and system for a compound wing unmanned aerial vehicle; wing surface deformation data of the compound wing unmanned aerial vehicle in a transition mode is collected, vibration signals corresponding to the wing surface deformation data and real-time state information of key components are obtained; based on the vibration signals, local stress increments at a wing joint are calculated, in combination with the real-time state information, a fault risk level parameter is generated to predict a potential fault position and a fault evolution trend parameter of the compound wing unmanned aerial vehicle; when the potential fault position and the fault evolution trend parameter reach a preset safety condition, a high-speed trigger signal matched with the fault risk level parameter is generated through pulse technology to adjust a redundant execution unit; the adjusted redundant execution unit is started to complete fault-tolerant control in the transition mode; the application realizes synchronous intervention of the whole process of fault evolution and fault-tolerant control, and guarantees active blocking of structural damage in mode switching of the compound wing unmanned aerial vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aircraft control, and in particular to a transition mode control method and system for a compound wing unmanned aerial vehicle. BACKGROUND

[0002] During the switching process between vertical take-off and landing and cruising flight modes, the compound wing unmanned aerial vehicle needs to bear rapidly changing aerodynamic loads and complex structural dynamics coupling effects, and the wing and fuselage connection area is prone to hidden damage due to non-steady stress distribution or resonance phenomenon. In order to ensure the safety of the transition stage, the control strategy needs to have the ability of rapid identification and active intervention to abnormal working conditions, so as to compensate for the degradation of structural performance in time by adjusting the coordinated action of the actuator before the potential damage develops to the critical state, thereby avoiding the flight trajectory deviation or system instability caused by local failure. This process puts forward higher requirements for the deep integration of high-precision monitoring technology, damage evolution modeling method and dynamic control logic.

[0003] A current typical scheme is based on the coupling modeling of vibration signal analysis and structural material stiffness parameters, which generates a fault risk level by collecting the vibration data of the wing surface, combining the preset connection structure stiffness model to calculate the local stress distribution; the scheme uses a machine learning algorithm to train the historical data to identify abnormal vibration patterns, and predicts the potential fault location combined with real-time state information. In addition, the system judges the fault risk level by setting a static threshold to trigger the preset fault-tolerant control logic. The existing technical scheme has some defects, for example, the static threshold cannot accurately match the nonlinear variation characteristics of the fault risk under different flight states, resulting in misjudgment or missed judgment; the preset fault-tolerant control strategy lacks dynamic adaptation ability to the evolution speed of mechanical faults, and it is difficult to realize the synchronous response of the actuator action timing and fault development, which cannot meet the safety operation requirements under high dynamic transition mode. SUMMARY

[0004] The present application provides a transition mode control method and system for a compound wing unmanned aerial vehicle, which solves the problems in the prior art that the static threshold cannot accurately match the nonlinear variation characteristics of the fault risk under different flight states, resulting in misjudgment or missed judgment; and lacks dynamic adaptation ability to the evolution speed of mechanical faults, which cannot meet the safety operation requirements under high dynamic transition mode.

[0005] In a first aspect, the present application provides a transition mode control method for a compound wing unmanned aerial vehicle, comprising:

[0006] Collecting the wing surface deformation data of the compound wing unmanned aerial vehicle under the transition mode, and obtaining the vibration signal corresponding to the wing surface deformation data and the real-time state information of the key components;

[0007] Based on the vibration signal, a local stress increment of a wing joint is calculated, the real-time state information is coupled with the local stress increment, and a fault risk level parameter is generated;

[0008] According to the fault risk level parameter, a potential fault position and a fault evolution trend parameter of the composite wing unmanned aerial vehicle are predicted;

[0009] When the potential fault position and the fault evolution trend parameter reach a preset safety condition, a high-speed trigger signal matched with the fault risk level parameter is generated through pulse technology;

[0010] Based on the high-speed trigger signal, a redundant execution unit is adjusted to obtain an adjusted redundant execution unit, so that the action timing of the adjusted redundant execution unit is synchronized with the fault evolution trend parameter;

[0011] The adjusted redundant execution unit is started to complete fault-tolerant control in a transition mode.

[0012] Optionally, based on the vibration signal, a local stress increment of a wing joint is calculated, the real-time state information is coupled with the local stress increment, and a fault risk level parameter is generated, including:

[0013] The vibration signal is divided into multiple frequency bands, and a frequency band with an energy intensity value higher than a reference intensity value is selected as an abnormal vibration frequency band;

[0014] According to the abnormal vibration frequency band, a local stress increment of a wing joint is calculated;

[0015] The temperature change rate and the displacement offset in the real-time state information are converted into dynamic influence parameters, and the dynamic influence parameters and the local stress increment are superimposed to generate a comprehensive load parameter;

[0016] According to the comparison result of the comprehensive load parameter and a preset safety boundary parameter, a fault risk level parameter is generated.

[0017] Optionally, according to the abnormal vibration frequency band, a local stress increment of a wing joint is calculated, including:

[0018] Based on the distance between the sensor installation position and the wing joint, distance attenuation compensation is performed on the energy intensity value of each abnormal vibration frequency band to obtain a compensated energy intensity value;

[0019] According to the transmission medium attenuation coefficient of the vibration signal, medium absorption compensation is performed on the compensated energy intensity value to generate a frequency band compensation energy value;

[0020] A frequency weight coefficient is calculated based on the similarity between the center frequency of the frequency band of the vibration signal and the inherent resonance frequency of the wing, and a weighted energy value corresponding to each abnormal vibration frequency band is generated by combining the frequency band compensation energy value;

[0021] According to a preset overlap ratio coefficient, an overlap energy component of the weighted energy values between adjacent frequency bands is removed to generate a corrected energy value, and the corrected energy values of all abnormal vibration frequency bands are accumulated to obtain an equivalent vibration energy sum;

[0022] According to the material type identification of the wing joint, a stress amplification factor is queried;

[0023] Based on the equivalent vibration energy sum, the stress amplification factor, a preset reference stress conversion coefficient, and a material stiffness parameter, a local stress increment is calculated.

[0024] Optionally, according to the fault risk level parameter, a potential fault location and a fault evolution trend parameter of the composite wing unmanned aerial vehicle are predicted, including:

[0025] According to a preset wing joint division rule, the wing joint is divided into a plurality of structure partitions;

[0026] According to the fault risk level parameter, a difference absolute value between a comprehensive load parameter corresponding to each structure partition and a preset partition bearing limit value is calculated, and the difference absolute value is taken as a fault deviation degree;

[0027] The fault deviation degree of each structure partition is converted into a fault weight coefficient, and a structure partition with the largest fault weight coefficient is selected as the potential fault location;

[0028] An excess amount by which the comprehensive load parameter corresponding to the potential fault location exceeds the preset partition bearing limit value is calculated, and the excess amount is taken as a fault severity;

[0029] According to the fault severity, a preset fault expansion rate table is queried to generate a fault evolution trend parameter.

[0030] Optionally, when the potential fault location and the fault evolution trend parameter meet a preset safety condition, a high-speed trigger signal matched with the fault risk level parameter is generated through pulse technology, including:

[0031] According to a preset pulse parameter conversion rule, the fault risk level parameter is converted into an initial pulse width and a target pulse intensity;

[0032] According to an expansion rate value in the fault evolution trend parameter, the initial pulse width is adjusted to obtain a target pulse width;

[0033] select a target pulse signal transmission channel based on the structure partition corresponding to the potential fault location;

[0034] generate a rectangular pulse signal with the target pulse width and target pulse intensity as the high-speed trigger signal by using the target pulse signal transmission channel when the fault severity corresponding to the potential fault location exceeds a preset partition bearing limit value and the fault evolution trend parameter exceeds a preset expansion rate.

[0035] Optionally, based on the high-speed trigger signal, adjust the redundant execution unit to obtain an adjusted redundant execution unit, comprising:

[0036] set a basic response time interval according to the target pulse width of the high-speed trigger signal;

[0037] generate a dynamic scaling factor based on the expansion rate value in the fault evolution trend parameter;

[0038] multiply the boundary value of the basic response time interval by the dynamic scaling factor to obtain a scaled response time interval;

[0039] calibrate the starting time of the scaled response time interval according to the priority coefficient of the structure partition corresponding to the potential fault location to obtain a calibrated response time interval;

[0040] calculate an execution intensity adjustment value according to the target pulse intensity of the high-speed trigger signal and the fault risk level parameter;

[0041] bind the calibrated response time interval and the execution intensity adjustment value to generate a cooperative control instruction to configure the drive protocol of the redundant execution unit according to the cooperative control instruction to obtain the adjusted redundant execution unit.

[0042] Optionally, generating a dynamic scaling factor based on the expansion rate value in the fault evolution trend parameter, comprising:

[0043] query a basic scaling factor from a preset rate scaling mapping table according to the expansion rate value in the fault evolution trend parameter;

[0044] query a speed compensation coefficient from a preset airspeed compensation curve according to the flight speed of the compound wing unmanned aerial vehicle, and query an angle of attack correction weight from an angle of attack influence table according to the angle of attack of the compound wing unmanned aerial vehicle, and combine the speed compensation coefficient to generate an aerodynamic environment compensation factor;

[0045] query a material expansion coefficient from a preset thermal expansion coefficient table according to the obtained wing structure temperature value, and compare the material expansion coefficient with a reference expansion threshold value to calculate a temperature deformation correction value;

[0046] A dynamic scaling factor is calculated based on the base scaling factor, the aerodynamic environment compensation factor, and the temperature deformation correction value.

[0047] In a second aspect, the present application provides a transition mode control system for a compound wing unmanned aerial vehicle, comprising:

[0048] A collection module is configured to collect wing surface deformation data of the compound wing unmanned aerial vehicle in the transition mode, and obtain vibration signals corresponding to the wing surface deformation data and real-time state information of key components;

[0049] A calculation module is configured to calculate a local stress increment at a wing joint based on the vibration signals, and to perform coupling processing on the real-time state information and the local stress increment to generate a fault risk level parameter;

[0050] A prediction module is configured to predict a potential fault location and a fault evolution trend parameter of the compound wing unmanned aerial vehicle based on the fault risk level parameter;

[0051] A generation module is configured to generate a high-speed trigger signal matched with the fault risk level parameter through pulse technology when the potential fault location and the fault evolution trend parameter meet a preset safety condition;

[0052] An adjustment module is configured to adjust a redundant execution unit based on the high-speed trigger signal to obtain an adjusted redundant execution unit, so that an action timing of the adjusted redundant execution unit is synchronized with the fault evolution trend parameter;

[0053] A control module is configured to start the adjusted redundant execution unit to complete fault-tolerant control in the transition mode.

[0054] In a third aspect, the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a compound wing unmanned aerial vehicle transition mode control method as described in the first aspect.

[0055] In a fourth aspect, the present application provides a computer storage medium, which stores a computer program; when the computer program is executed by a computer, a compound wing unmanned aerial vehicle transition mode control method as described in the first aspect is implemented.

[0056] The embodiment of the present application collects wing surface deformation data of a compound wing unmanned aerial vehicle in a transition mode, obtains vibration signals corresponding to the wing surface deformation data and real-time state information of key components, calculates local stress increment of a wing joint based on the vibration signals, couples the real-time state information with the local stress increment, generates a fault risk level parameter, predicts a potential fault position and a fault evolution trend parameter of the compound wing unmanned aerial vehicle according to the fault risk level parameter, generates a high-speed trigger signal matched with the fault risk level parameter through pulse technology when the potential fault position and the fault evolution trend parameter reach a preset safety condition, adjusts a redundant execution unit based on the high-speed trigger signal to obtain an adjusted redundant execution unit, so that the action timing of the adjusted redundant execution unit is synchronized with the fault evolution trend parameter, and starts the adjusted redundant execution unit to complete fault-tolerant control in the transition mode. The present application solves the problem of a traditional single sensor monitoring blind area, establishes a multi-dimensional perception system integrating deformation, vibration and state, accurately captures dynamic load changes of a wing joint in the transition mode, breaks through the limitation of a static material model, generates a fault risk level adaptive to flight conditions through dynamic stress integration and real-time state fusion, replaces a fixed threshold mechanism prone to failure, identifies a spatiotemporal evolution path of a hidden damage (such as a crack propagation direction and rate), provides a prior decision basis for fault-tolerant control, overcomes the lag of a traditional method which can only respond to explicit faults, converts the fault risk level into a physical control instruction through the nanosecond-level response characteristic of pulse technology, solves the signal distortion problem of an electronic control system under strong electromagnetic interference, realizes dynamic synchronization of an actuator action and a fault propagation speed (such as triggering a backup motor in advance when a crack is accelerated), eliminates the compensation lag risk caused by a fixed delay strategy, completes active intervention before a critical point of structural performance degradation, avoids flight instability caused by local failure, and ensures the safety boundary of the whole process in the transition mode. Further, the vibration signal is decomposed into an abnormal frequency band, and an equivalent vibration energy sum is generated through distance attenuation compensation, medium absorption compensation, frequency band weight distribution, and overlapping energy rejection processing. The local stress increment is output through a physical conversion relationship by combining a material type identifier with a dynamic load stress amplification factor. Finally, the fault risk level parameter is generated by fusing the temperature change rate and the displacement offset. The stress calculation deviation caused by signal transmission distortion, frequency band coupling interference and material property neglect in traditional vibration analysis is overcome, the risk warning of implicit damage at the wing joint in the transition mode is realized, and a high-confidence input is provided for subsequent evolution trend prediction.

[0057] These and other aspects of the present application will become more fully understood from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to make the technical scheme of the present application or the prior art clearer, the accompanying drawings needed in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings described are only some embodiments of the present application, and not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by a person skilled in the art without creative labor fall within the scope of the present application.

[0059] Figure 1 A flow chart of a transition mode control method of a compound wing unmanned aerial vehicle provided by the present application is shown.

[0060] Figure 2 A structural schematic diagram of a transition mode control system of a compound wing unmanned aerial vehicle provided by the present application is shown.

[0061] Figure 3 A structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0062] In order to make the technical scheme of the present application or the prior art clearer, the accompanying drawings needed in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings described are only some embodiments of the present application, and not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by a person skilled in the art without creative labor fall within the scope of the present application.

[0063] In some of the processes described in the specification and claims of the present application and the above-described accompanying drawings, a plurality of operations appear in a specific order, but it should be clearly understood that these operations can be executed or in parallel without the order in which they appear in this text, and the serial numbers of the operations such as 101, 102, etc. are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and the operations can be executed in sequence or in parallel. It should be noted that the "first", "second", etc. described herein are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence. Also, "first" and "second" are different types.

[0064] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by a person skilled in the art without creative labor fall within the scope of the present application.

[0065] Aiming at the problems of aerodynamic load variation and structure dynamics coupling in the process of vertical take-off and cruise flight switching of a compound wing unmanned aerial vehicle, the existing technology has two core defects: the traditional scheme relies on a preset fixed threshold to judge the fault risk, but the nonlinear change of aerodynamic load in the transition mode leads to dynamic fluctuations in stress distribution, and the static threshold cannot adapt to the real-time risk level in high-speed diving, large angle of attack transition and other scenes, resulting in misjudgment (such as low-speed hovering, threshold is too strict to trigger false action) or missed judgment (such as high-speed rolling, threshold is too wide to ignore hidden damage); the existing fault-tolerant control adopts a preset delay strategy to start the redundant actuator, while the expansion rate of wing joint cracks or bolt loosening changes dynamically with the flight state, and the fixed delay mechanism cannot realize millisecond-level synchronization between execution action and fault evolution, resulting in compensation lag or excessive response.

[0066] Figure 1 A flow chart of a transition mode control method for a compound wing unmanned aerial vehicle is provided for the embodiments of the present application, as shown in Figure 1 The method comprises the following steps:

[0067] Step 101: Collecting wing surface deformation data of the compound wing unmanned aerial vehicle in the transition mode, and obtaining vibration signals corresponding to the wing surface deformation data and real-time state information of key components;

[0068] In this step, the transition mode refers to the flight phase of the compound wing unmanned aerial vehicle from vertical take-off mode to horizontal cruise mode, characterized by speed of 30-200km / h, height change rate of ±5m / s, and continuous adjustment of rudder deflection angle. The wing surface deformation data refers to the strain distribution of the wing skin measured by the fiber grating sensor, including axial strain value and shear strain angle. The real-time state information of the key components refers to the parameters reflecting the health of the motor and the rudder, including bearing temperature, output shaft displacement, and winding current fluctuation rate.

[0069] In the embodiments of the present application, the wing surface deformation data is collected by a distributed fiber sensor array, and the vibration sensors are triggered synchronously to obtain vibration signals that are spatiotemporally aligned with the deformation data; at the same time, the real-time state information of the key components (such as motor bearings and rudder hinges) such as temperature, displacement offset, and current fluctuation is read through the controller area network bus.

[0070] Step 102: Based on the vibration signals, calculating the local stress increment of the wing joint, coupling the real-time state information with the local stress increment, and generating a fault risk level parameter;

[0071] In this step, the wing joint refers to the bolted connection area of the wing and the titanium alloy joint of the fuselage, which bears the coupling stress of the aerodynamic load and the structural inertia. The local stress increment refers to the dynamic stress change amount converted by vibration energy, in units of MPa. The failure risk level parameter refers to a five-level risk indicator (1-5 levels) generated by combining the local stress increment and real-time state information, with level 5 being the critical failure risk.

[0072] In the embodiment of the present application, the frequency band energy analysis method is used to process the vibration signal, and the local stress increment of the wing joint is calculated. The temperature change rate (℃ / s) and displacement offset (mm) in the real-time state information are converted into dynamic influence parameters, which are dimensionless coefficients. The local stress increment is weighted and superimposed according to the weight ratio of 6:4 to generate a comprehensive load parameter. When the comprehensive load parameter exceeds the safety boundary parameter (such as 80 MPa), a high risk level is output.

[0073] Step 103: predicting the potential failure location and failure evolution trend parameter of the composite wing unmanned aerial vehicle according to the failure risk level parameter;

[0074] In this step, the potential failure location refers to the highest risk area determined by grid partition weight analysis. The failure evolution trend parameter refers to dynamic prediction data including crack propagation rate and direction vector.

[0075] In the embodiment of the present application, the wing joint area is grid partitioned, specifically, 10 partitions are divided according to the rib spacing, and the absolute value of the difference between the comprehensive load parameter of each partition and the bearing limit value is taken as the fault deviation degree. The fault deviation degree is linearly converted into a fault weight coefficient, wherein the larger the fault deviation degree, the higher the coefficient, and the partition with the largest fault weight coefficient is selected as the potential failure location. The excess amount of the comprehensive load parameter of the partition exceeding the bearing limit value is taken as the fault severity. According to the excess amount, a preset fault propagation rate table is queried, and the fault evolution trend parameter is output.

[0076] Step 104: when the potential failure location and failure evolution trend parameter meet the preset safety condition, a high-speed trigger signal matching the failure risk level parameter is generated through pulse technology;

[0077] In this step, the preset safety condition refers to the double criteria for triggering fault-tolerant control, including fault severity > preset partition bearing limit value and fault evolution trend parameter > preset propagation rate. The high-speed trigger signal refers to a rectangular electrical pulse signal generated by a pulse circuit, with a width of 10-100 ns and an intensity of 12-36 V.

[0078] In the embodiment of the present application, when the fault severity exceeds the partition load limit value and the expansion rate exceeds the threshold value, the fault risk level is mapped to the target pulse width and intensity according to the pulse parameter conversion rule; the pulse width is reduced according to the expansion rate value to obtain the target pulse width; a special signal transmission channel is selected according to the structure partition corresponding to the potential fault position, such as channel CH3 corresponding to structure partition 3; a rectangular pulse signal is generated through the H03K pulse circuit, which is used as a high-speed trigger signal.

[0079] Step 105: based on the high-speed trigger signal, adjusting the redundant execution unit to obtain an adjusted redundant execution unit, so that the action timing of the adjusted redundant execution unit is synchronized with the fault evolution trend parameter;

[0080] In this step, the redundant execution unit refers to the backup power mechanism, including a brushless motor and a linked control surface.

[0081] In the embodiment of the present application, a basic response time interval is set based on the target pulse width in the high-speed trigger signal; a scaling curve is queried according to the expansion rate value, and the time interval boundary value is multiplied by a scaling factor to obtain a scaled interval; the starting time of the interval is calibrated according to the priority coefficient of the structure partition; the strength adjustment value is calculated according to the pulse intensity and the risk level; the calibrated time interval and the strength adjustment value are bound to generate a cooperative control instruction, which is configured to the redundant motor execution unit to obtain an adjusted redundant execution unit.

[0082] Step 106: starting the adjusted redundant execution unit to complete the fault-tolerant control in the transition mode;

[0083] In the embodiment of the present application, for example, 2ms before the potential fault position evolves to the critical point, the redundant motor is started to output 120% torque according to the pulse width modulation control protocol, and the compensation control surface is deflected by 15° to offset the instability moment at the wing connection, thereby completing the fault-tolerant control in the transition mode.

[0084] The embodiment of the present application improves the detection accuracy of hidden damage, breaks through the misjudgment bottleneck caused by signal attenuation and frequency band coupling in traditional vibration analysis; the fault position and evolution speed are synchronously tracked; the synchronization mismatch problem of the fixed delay strategy is solved by using the pulse timing scaling mechanism, and the reliability of the millisecond fault-tolerant response in the transition mode is ensured.

[0085] For example, in the composite wing unmanned aerial vehicle, the speed is 150 km / h, the climbing stage is converted to the flat flying stage, the pitch angle is 15°, the strain of the third intercostal rib of the right wing is first detected by the optical fiber sensor, the energy intensity value of the vibration signal in the 800-1500 Hz frequency band of the region is collected synchronously, the frequency band exceeding the standard is taken as an abnormal vibration frequency band, the abnormal vibration frequency band is compensated for distance and carbon fiber medium, and 25% of the energy components of the adjacent frequency bands are removed, then the local stress increment is calculated by combining the stress amplification factor of the titanium alloy material; the bearing temperature rise rate is converted into a dynamic influence parameter to generate a comprehensive load parameter, such as a comprehensive load parameter of 92 MPa, which exceeds the safety boundary of 80 MPa, that is, a high risk level of fault risk level parameter is triggered; according to the high risk level, a grid analysis is performed to determine that the third intercostal rib is the fault position, and the comprehensive load parameter corresponding to the fault position exceeds the preset partition bearing limit value of 12 MPa, and 2.0 mm / s is obtained by querying the expansion rate table; when the fault expansion rate exceeds the threshold value of 1 mm / s, a high-speed trigger signal of 40 ns / 24 V is generated; the expansion rate is scaled to the basic response time interval of 0-25 ms, the starting time is calibrated to 3 ms according to the priority coefficient of the structure partition, and the execution intensity adjustment value of 120% is bound; the redundant motor is started 3 ms before the crack expands to the critical point to drive the compensation rudder to deflect 12°, eliminate the imbalance of the wing torque, and smoothly complete the modal conversion.

[0086] The application provides one embodiment, step 102, based on the vibration signal, the local stress increment of the wing connection is calculated, the real-time state information is coupled with the local stress increment, and a fault risk level parameter is generated, which specifically comprises the following steps:

[0087] Step 201: The vibration signal is divided into a plurality of frequency bands, and a frequency band with an energy intensity value higher than a reference intensity value is selected as an abnormal vibration frequency band;

[0088] In this step, the frequency band refers to a fixed bandwidth interval (200 Hz / segment) divided by spectrum analysis, which is used to isolate the vibration energy characteristics of different frequencies. The energy intensity value refers to the vibration energy integral value (mG² / Hz) in a unit frequency bandwidth, which reflects the mechanical vibration intensity of a specific frequency band. The reference intensity value refers to an energy threshold value dynamically adjusted according to the flight state, which is used to identify abnormal vibration. The abnormal vibration frequency band refers to a frequency interval indicating possible structural damage.

[0089] In the embodiment of the present application, the vibration signal is divided into multiple frequency bands by fast Fourier transform with a fixed bandwidth of 200 Hz, such as 0-200 Hz, 201-400 Hz, etc., the energy intensity value of each frequency band is calculated, and the unit is mG² / Hz; the reference intensity value is dynamically set based on the current flight state, such as 150 mG² / Hz in hovering state and 250 mG² / Hz in high-speed flight, and the frequency band whose energy intensity value exceeds the reference intensity value is marked as an abnormal vibration frequency band.

[0090] Step 202: According to the abnormal vibration frequency band, the local stress increment of the wing joint is calculated;

[0091] In the embodiment of the present application, the abnormal vibration frequency band is processed first, and the processing process includes sensor distance compensation for each abnormal vibration frequency band, medium absorption compensation according to material type, weight distribution according to the Euclidean distance between the center frequency of the frequency band and the wing resonance frequency, and elimination of 25% of the overlapping energy components of adjacent frequency bands. Finally, the modified energy values of all processed abnormal vibration frequency bands are added to obtain the equivalent vibration energy sum; the local stress increment is calculated by the formula: local stress increment=(equivalent vibration energy sum×material stiffness parameter×stress amplification factor) / reference stress conversion coefficient.

[0092] Step 203: Convert the temperature change rate and displacement offset in the real-time state information into dynamic influence parameters, and superimpose the dynamic influence parameters and the local stress increment to generate a comprehensive load parameter;

[0093] In this step, the temperature change rate refers to the temperature rise / drop gradient (℃ / s) of the key component per unit time, reflecting the thermal load mutation. The displacement offset refers to the real-time offset distance (mm) of the key component relative to the reference position, which is used to quantify the mechanical deformation degree. The dynamic influence parameter refers to the dimensionless coefficient (range 0-1.5) converted from the temperature change rate and the displacement offset, which represents the strength of thermal and force coupling effect. The comprehensive load parameter refers to the weighted superposition value of the local stress increment and the dynamic influence parameter, which comprehensively reflects the mechanical and thermal force combined load state.

[0094] In the embodiment of the present application, the temperature change rate (℃ / s) is converted into a thermal stress coefficient through linear mapping. Specifically, the temperature change rate x conversion coefficient = thermal stress coefficient, wherein the conversion coefficient is derived by conducting thermal cycle loading tests on the unmanned aerial vehicle wing connecting structure material (such as titanium alloy, aluminum alloy or carbon fiber), combined with the thermal stress increment (MPa) corresponding to the temperature change rate (℃ / s). For example, a temperature rise rate of 2 ℃ / s is applied to the titanium alloy Ti-6Al-4V material, and the thermal stress increment is measured by a strain gauge to be 1 MPa, and thus the conversion coefficient is derived to be 0.5 MPa / ℃·s (1 MPa ÷ 2 ℃ / s = 0.5); the structure deformation factor = displacement offset x structure deformation coefficient, wherein the structure deformation coefficient is derived by applying a controllable displacement offset to the unmanned aerial vehicle wing connecting structure (such as a titanium alloy bolt group) and measuring the structure load change by a pressure sensor. For example, a displacement offset of 0.5 mm is applied to the bolt connecting assembly of the Ti-6Al-4V material, and the load increment is measured to be 0.4 load coefficient (dimensionless), and the conversion coefficient is derived to be 0.8 load coefficient / mm (0.4 ÷ 0.5 = 0.8), which reflects the contribution of unit displacement offset to the structure load; the dynamic influence parameter = thermal stress coefficient + structure deformation factor; the dynamic influence parameter and the local stress increment are superimposed according to the weight proportion (such as stress increment 70%, dynamic parameter 30%) to generate a comprehensive load parameter.

[0095] Step 204: generating a fault risk level parameter according to the comparison result of the comprehensive load parameter and the preset safety boundary parameter;

[0096] In this step, the preset safety boundary parameter refers to the load tolerance threshold set according to the flight mode, which is used for risk assessment grading.

[0097] In the embodiment of the present application, the safety boundary parameter is set according to the flight stage (such as 0.75 in the climbing stage and 0.85 in the level flight stage). If the comprehensive load parameter ≤ safety boundary parameter, a low risk level (level 1) is output; if the safety boundary parameter < comprehensive load parameter ≤ 1.2 times the safety boundary, a medium risk level (level 3) is output; and if the comprehensive load parameter > 1.2 times the safety boundary, a high risk level (level 5) is output.

[0098] The embodiment of the present application solves the misjudgment problem of the traditional fixed threshold under variable working conditions through a dynamic frequency band screening mechanism; improves the conversion accuracy of vibration energy to stress increment by using a physical compensation chain; breaks through the limitation of single stress risk assessment by quantifying temperature and displacement parameters into dynamic influence parameters through thermal coupling mapping; and finally generates a gradient type fault risk level parameter based on the flight state adaptive boundary, realizes the accurate grading and early warning of hidden damage under the transition mode.

[0099] The application provides a specific embodiment, in step 202: according to the abnormal vibration frequency band, the local stress increment of the wing joint is calculated, specifically including the following steps:

[0100] Step 211: based on the distance between the sensor installation position and the wing joint, the energy intensity value of each abnormal vibration frequency band is compensated for distance attenuation, and a compensated energy intensity value is obtained;

[0101] In this step, the compensated energy intensity value refers to the vibration energy intensity (mG² / Hz) of the frequency band after distance attenuation compensation correction, reflecting the true energy level after eliminating the influence of the sensor deployment position.

[0102] In the embodiment of the application, according to the straight-line distance between the sensor and the wing joint, such as 0.5m / 1.0m, the compensation coefficient is queried through a preset distance attenuation curve, such as a 10% increase in the compensation coefficient for every 0.5m increase in distance; the energy intensity value of each abnormal vibration frequency band is multiplied by the corresponding compensation coefficient to generate a compensated energy intensity value (unit: mG² / Hz).

[0103] Step 212: according to the transmission medium attenuation coefficient of the vibration signal, the compensated energy intensity value is compensated for medium absorption to generate a frequency band compensation energy value;

[0104] In this step, the transmission medium attenuation coefficient represents the energy loss rate (0-1) of the vibration wave propagating in the composite material, which is preset based on the number of material layers and the bonding process. The frequency band compensation energy value refers to the value (mG² / Hz) of the compensated energy intensity value further corrected by medium absorption, which eliminates the measurement deviation caused by the material energy absorption effect.

[0105] In the embodiment of the application, based on the wing material type (such as carbon fiber / titanium alloy), the transmission medium attenuation coefficient is called from a preset database, such as 0.15 for a carbon fiber layer and 0.05 for a titanium alloy; the compensated energy intensity value is divided by (1-transmission medium attenuation coefficient) to obtain the frequency band compensation energy value.

[0106] Step 213: based on the similarity between the frequency band center frequency of the vibration signal and the inherent resonance frequency of the wing, a frequency weight coefficient is calculated, and combined with the frequency band compensation energy value, a weighted energy value corresponding to each abnormal vibration frequency band is generated;

[0107] In this step, the frequency band center frequency refers to the geometric center frequency value of the abnormal vibration frequency band, which is used to quantify the frequency band position feature. The wing inherent resonance frequency refers to the main resonance frequency of the wing joint under static load, which is pre-calibrated by finite element modal analysis. The frequency weight coefficient refers to the weight allocated according to the similarity between the frequency band center frequency and the wing inherent resonance frequency, and the larger the value, the more significant the stress contribution of the frequency band. The weighted energy value refers to the product of the frequency band compensation energy value and the frequency weight coefficient, which highlights the energy weight of the resonance related frequency band.

[0108] In the embodiment of the present application, the absolute difference between the frequency band center frequency (such as 900 Hz) and the wing inherent resonance frequency (1200 Hz) is calculated, and the frequency weight coefficient is generated by a pre-set similarity conversion table, such as increasing 0.2 for each 100 Hz reduction in absolute difference. The frequency band compensation energy value is multiplied by the frequency weight coefficient to obtain the weighted energy value corresponding to each abnormal vibration frequency band.

[0109] Step 214: According to the pre-set overlap ratio coefficient, the overlapping energy component of the weighted energy values between adjacent frequency bands is removed to generate the corrected energy value. The corrected energy values of all abnormal vibration frequency bands are accumulated to obtain the equivalent vibration energy total sum;

[0110] In this step, the pre-set overlap ratio coefficient refers to a parameter that quantifies the degree of energy overlap between adjacent frequency bands, which is determined by the frequency band division scheme. The overlapping energy component refers to the energy value that is repeatedly calculated due to spectral leakage and needs to be removed in proportion. The corrected energy value refers to the net energy value after removing the overlapping energy component, which eliminates the coupling interference of the frequency bands. The equivalent vibration energy total sum refers to the accumulation of the corrected energy values of all abnormal vibration frequency bands, which represents the total vibration energy input of the wing joint.

[0111] In the embodiment of the present application, the overlap ratio coefficient is set according to the interval between the center frequencies of adjacent frequency bands, such as 30% for an interval of 200 Hz. The product of the energy value of the low frequency band and the overlap ratio coefficient is subtracted from the weighted energy value of the high frequency band, for example, the weighted energy value of frequency band B - the weighted energy value of frequency band A x 0.3, to generate the corrected energy value. The corrected energy values of all abnormal vibration frequency bands are accumulated to obtain the equivalent vibration energy total sum, which is in units of J.

[0112] Step 215: According to the material type identification of the wing joint, the stress amplification factor is queried;

[0113] In this step, the material type identification refers to the code that identifies the material category of the joint, which is used to call physical parameters. The stress amplification factor refers to the amplification coefficient that reflects the stress concentration effect of the material, which is calibrated by material fatigue test.

[0114] In the embodiment of the present application, the material type identifier, such as carbon fiber identifier CFRP-UD and titanium alloy identifier TI-6AL-4V, is analyzed, and the corresponding stress amplification factor is queried from the material characteristic database, such as 1.2 corresponding to the carbon fiber identifier and 1.8 corresponding to the titanium alloy identifier.

[0115] Step 216: Based on the equivalent vibration energy sum, the stress amplification factor, the preset reference stress conversion coefficient and the material stiffness parameter, the local stress increment is calculated.

[0116] In this step, the preset reference stress conversion coefficient refers to the physical constant (N·s / m²) of vibration energy to stress, which is obtained through calibration test. The material stiffness parameter refers to the elastic modulus of the wing connection structure, which characterizes the material resistance to deformation.

[0117] In the embodiment of the present application, the local stress increment (Pa) is calculated by the physical conversion formula: (equivalent vibration energy sum × stress amplification factor × material stiffness parameter) / reference stress conversion coefficient, wherein the unit of equivalent vibration energy sum is joule, i.e. J=Newton·meter (N·m), which reflects the mechanical vibration input work; the stress amplification factor (dimensionless) characterizes the stress concentration effect of the material; the unit of material stiffness parameter is pascal, i.e. Pa=Newton / square meter (N / m²), which is the elastic modulus; the unit of reference stress conversion coefficient is Newton·second / square meter (N·s / m²), which is the damping dissipation factor, and is composed of two products, i.e. reference stress conversion coefficient=damping coefficient × effective structure volume, the damping coefficient (unit N·s / m) is calibrated by standard hammer modal test, and the effective structure volume (unit m³) is calculated by CAD model to calculate the fault area volume.

[0118] The embodiment of the present application eliminates the differences in sensor deployment, corrects the energy loss of composite materials, strengthens the contribution of the main damage frequency band, and solves the spectrum leakage error through the four-order physical compensation chain, including distance attenuation compensation, medium absorption compensation, resonance frequency weight distribution and frequency band overlap elimination; accurately maps the vibration energy to the dynamic stress increment of the wing connection, breaks through the stress monitoring deviation caused by signal distortion, frequency band coupling and material characteristics ignored in traditional methods, and realizes the micro-strain level quantitative detection of hidden damage under the transition mode, providing high confidence input for fault risk grading.

[0119] The present application provides a specific embodiment, step 103, according to the fault risk level parameter, predicting the potential fault position and fault evolution trend parameter of the composite wing unmanned aerial vehicle, specifically including the following steps:

[0120] Step 301: According to the preset wing connection division rule, the wing connection is divided into multiple structure partitions;

[0121] In this step, the preset wing joint division rule is based on the grid partition standard defined by the wing rib spacing and bolt group distribution, which ensures that each partition covers the stress concentration point. The structural partition refers to the independent monitoring unit generated according to the division rule, and the partition number is 1-10. Each partition is associated with a dedicated bearing limit value and a sensor group.

[0122] In the embodiment of the present application, according to the physical layout of the wing rib beam intersection, the wing joint is divided into multiple rectangular grid unit structural partitions at an interval of every 200 mm, such as 10 structural partitions. Each structural partition covers the key bolt group position, such as structural partition 3 corresponding to the right wing third rib-girder intersection. The structural partition boundary is calibrated by laser positioning marker.

[0123] Step 302: According to the fault risk level parameter, calculate the absolute value of the difference between the comprehensive load parameter corresponding to each structural partition and the preset partition bearing limit value, and take the absolute value as the fault deviation;

[0124] In this step, the preset partition bearing limit value refers to the failure threshold set according to the partition material, which is calibrated by fatigue test. The fault deviation refers to the absolute value of the overload of the partition, which is used to quantify the overload degree of the partition.

[0125] In the embodiment of the present application, the comprehensive load parameter associated with each structural partition is extracted, such as the comprehensive load parameter 92 MPa of structural partition 3. The preset partition bearing limit value of this partition is called, such as the titanium alloy partition 90 MPa. The comprehensive load parameter is subtracted from the corresponding preset partition bearing limit value to obtain the absolute value of the difference, such as (|92-90|=2 MPa). The absolute value of the difference is taken as the fault deviation.

[0126] Step 303: Convert the fault deviation of each structural partition into a fault weight coefficient, and select the structural partition with the largest fault weight coefficient as the potential fault position;

[0127] In this step, the fault weight coefficient refers to the priority index linearly converted from the fault deviation, and the larger the value, the higher the risk of partition failure.

[0128] In the embodiment of the present application, the linear conversion formula is: fault weight coefficient= fault deviation x conversion coefficient (0.5). The fault weight coefficients of all structural partitions are compared, and the structural partition corresponding to the maximum fault weight coefficient is selected and marked as the potential fault position.

[0129] Step 304: Calculate the excess amount of the comprehensive load parameter corresponding to the potential fault position exceeding the preset partition bearing limit value, and take the excess amount as the fault severity;

[0130] In this step, the fault severity refers to the actual overload of the potential fault location, only taking positive values, and 0 when not overloaded.

[0131] In the embodiment of the application, for the potential fault location, the positive value difference between the corresponding comprehensive load parameter and the preset partition bearing limit value, i.e. the excess, is calculated, and zero is taken if it is negative; the excess is taken as the fault severity.

[0132] Step 304: According to the fault severity, a fault evolution trend parameter is generated by querying a preset fault propagation rate table;

[0133] In this step, the preset fault propagation rate table refers to a propagation rate database mapped by material type and excess, which is constructed based on fracture mechanics tests.

[0134] In the embodiment of the application, according to the material type identification of the key components of the composite wing unmanned aerial vehicle, the corresponding fault propagation rate is called, as shown in the following table 1:

[0135] Table 1 Fault propagation rate table

[0136]

[0137] For example, assuming that the wing connection adopts a specific type (such as 7075) of aluminum alloy material, when it is calculated that the comprehensive load parameter of the corresponding structure partition of the component exceeds the preset partition bearing limit value by 2 MPa, in the called fault propagation rate table, the propagation rate corresponding to the 2 MPa excess can be queried as 2.0 mm / s, and the propagation direction vector is +15° along the wing span direction. By integrating these propagation rates and direction information, a complete and accurate fault evolution trend parameter can be generated, which provides a key basis for subsequent response and processing of potential faults of the composite wing unmanned aerial vehicle, and helps to realize more efficient and accurate transition mode fault tolerance control.

[0138] The embodiment of the application solves the positioning ambiguity problem of traditional global monitoring through a dynamic partition weight mechanism, breaks through the technical barrier that the traditional scheme can only alarm but cannot predict the evolution path, and provides a core decision basis for active fault tolerance control.

[0139] The application provides a specific embodiment, step 104, when the potential fault location and fault evolution trend parameter meet the preset safety condition, a high-speed trigger signal matched with the fault risk level parameter is generated through pulse technology, which specifically includes the following steps:

[0140] Step 401: According to the preset pulse parameter conversion rule, the fault risk level parameter is converted into an initial pulse width and a target pulse intensity;

[0141] In this step, the preset pulse parameter conversion rule refers to the mapping relationship table of the fault risk level to the pulse characteristic, which is used to match the response demand under different risks. The initial pulse width refers to the pulse duration reference value (unit: ns) preliminarily set according to the fault risk level parameter, and a high risk corresponds to a narrow pulse to achieve rapid triggering. The target pulse intensity refers to the pulse voltage amplitude (unit: V) set according to the fault risk level parameter, and a high risk corresponds to a high voltage to enhance the driving capability.

[0142] In the embodiment of the present application, the fault risk level parameter (1-5 levels) is converted into the pulse control parameter through the preset pulse parameter conversion rule. For example, when the fault risk level parameter is level 3, the initial pulse width is converted to 100 ns and the target pulse intensity is converted to 18 V; when the fault risk level parameter is risk level 5, the initial pulse width is converted to 50 ns and the target pulse intensity is converted to 24 V. The conversion rule is set based on the negative correlation principle that the pulse width decreases and the intensity increases with the increase of the level.

[0143] Step 402: adjusting the initial pulse width according to the expansion rate value in the fault evolution trend parameter to obtain a target pulse width;

[0144] In this step, the expansion rate value refers to the core index (unit: mm / s) in the fault evolution trend parameter, which reflects the real-time expansion speed of cracks or looseness. The target pulse width refers to the final pulse duration (ns) dynamically adjusted by the expansion rate value. The higher the rate, the narrower the width.

[0145] In the embodiment of the present application, the expansion rate value in the fault evolution trend parameter is dynamically adjusted through the preset rate and width scaling curve. For example, when the expansion rate value is 2.0 mm / s, the corresponding adjustment scaling factor is 0.6, and the adjustment of the initial pulse width is 100 ns x 0.6, so that the target pulse width is 60 ns.

[0146] Step 403: selecting a target pulse signal transmission channel based on the structure partition corresponding to the potential fault position;

[0147] In this step, the target pulse signal transmission channel refers to the exclusive physical channel bound to the structure partition, which ensures that the signal is accurately delivered to the fault area associated actuator.

[0148] In the embodiment of the present application, according to the structure partition number corresponding to the potential fault position, a special optical fiber channel is selected from the preset channel distribution table. For example, the structure partition 3 corresponds to the channel CH03, which is directly connected to the physical actuator, such as the CH03 channel controlling the right wing third intercostal rudder.

[0149] Step 404: when the fault severity corresponding to the potential fault location exceeds the preset partition load limit value, and the fault evolution trend parameter exceeds the preset expansion rate, a rectangular pulse signal with the target pulse width and target pulse intensity is generated by using the target pulse signal transmission channel, and the rectangular pulse signal is used as the high-speed trigger signal;

[0150] In this step, the preset expansion rate refers to a safety threshold set according to the material type, and exceeding the value is determined as a risk of rapid expansion.

[0151] In the embodiment of the application, two conditions are synchronously monitored: the fault severity > the preset partition load limit value, such as 2MPa > 1.5MPa; and the expansion rate value > the preset expansion rate threshold value, such as 2.0mm / s > 1.0mm / s; when the two conditions are met, a 60ns width, 24V intensity rectangular electric pulse signal is generated by the H03K pulse generator on the CH03 channel, which is used as the high-speed trigger signal.

[0152] The embodiment of the application replaces the traditional single threshold judgment with a double condition trigger mechanism to avoid false triggering of low-speed expansion faults; the dynamic pulse compression technology is used to achieve millisecond-level synchronization of fault evolution and signal response; the partition dedicated channel is used to isolate electromagnetic interference to ensure the transmission integrity of the high-speed pulse signal, thereby solving the fault tolerance control failure problem caused by response delay, signal crosstalk and misjudgment in the prior art.

[0153] The application provides a specific embodiment, step 105, based on the high-speed trigger signal, adjusting the redundant execution unit to obtain an adjusted redundant execution unit, specifically including the following steps:

[0154] Step 501: setting a basic response time interval according to the target pulse width of the high-speed trigger signal;

[0155] In this step, the basic response time interval refers to a time window (unit: ms) linearly mapped from the target pulse width, which defines the time window from receiving the instruction to starting the action of the redundant execution unit.

[0156] In the embodiment of the application, the target pulse width of the high-speed trigger signal is extracted, and the basic response time interval is set through a linear mapping rule, such as 1ns pulse width corresponding to 1ms time interval.

[0157] Step 502: generating a dynamic scaling factor based on the expansion rate value in the fault evolution trend parameter;

[0158] In this step, the dynamic scaling factor refers to a scaling coefficient dynamically calculated based on the expansion rate value, flight attitude and wing structure temperature value, and the smaller the value is, the faster the fault evolution is and the response time needs to be compressed.

[0159] In the embodiment of the present application, according to the expansion rate value, the base scaling factor is queried from the preset rate scaling mapping table; the aerodynamic environment compensation factor is generated in combination with the current flight attitude, including the flight speed and the airflow attack angle; the wing temperature value is loaded, and the temperature deformation correction value is calculated; and the dynamic scaling factor is output through the three-dimensional scaling curved surface model in combination with the above-mentioned various parameters, such as 0.6*1.1*0.9=0.594.

[0160] Step 503: multiplying the boundary values of the base response time interval by the dynamic scaling factor to obtain a scaled response time interval;

[0161] In this step, the scaled response time interval refers to a new time window generated by multiplying the boundary values of the base response time interval by the scaling factor, reflecting the response time limit after the fault evolution speed is adapted.

[0162] In the embodiment of the present application, the starting value and the ending value of the base response time interval are multiplied by the dynamic scaling factor respectively, for example, the starting value: 0*0.594=0 ms; the ending value: 30*0.594=17.82 ms; the scaled response time interval (0-17.82 ms) is generated, realizing the compression / expansion of the time window.

[0163] Step 504: according to the priority coefficient of the structure partition corresponding to the potential fault position, the starting time of the scaled response time interval is calibrated to obtain a calibrated response time interval;

[0164] In this step, the priority coefficient refers to the preset weight value according to the importance of the structure partition, and the higher the value, the more the partition fault needs to be responded in priority. The calibrated response time interval refers to the time window whose starting time is offset by the priority coefficient, realizing the rapid response of the key area fault.

[0165] In the embodiment of the present application, according to the number of the corresponding structure partition, the preset priority coefficient is queried; the starting time of the scaled interval is added by the priority coefficient*calibration constant to obtain the calibrated starting time; the ending time is unchanged, and finally the calibrated response time interval is generated.

[0166] Step 505: according to the target pulse intensity of the high-speed trigger signal and the fault risk level parameter, the execution intensity adjustment value is calculated;

[0167] In this step, the execution intensity adjustment value refers to a dimensionless parameter fused with the target pulse intensity and the fault risk level parameter, and the greater the value, the higher the output force / torque of the execution mechanism.

[0168] In the embodiment of the present application, the execution intensity adjustment value is calculated by the formula: execution intensity adjustment value = target pulse intensity (V) * pulse intensity conversion coefficient + fault risk level parameter * risk weight, wherein the pulse intensity conversion coefficient is derived by measuring the linear relationship between the output torque and the pulse intensity by applying high-speed trigger signals with different pulse intensities to the redundant execution unit (such as a brushless motor), and thus the pulse intensity conversion coefficient corresponding to a unit pulse intensity (1V) is 0.5. For example, the target pulse intensity of the high-speed trigger signal is 24V, the fault risk level parameter is 5 (high risk), and the execution intensity adjustment value = target pulse intensity * pulse intensity conversion coefficient (0.5) + fault risk level * risk weight (0.1), i.e. 24*0.5+5*0.1=12+0.5=12.5 (dimensionless), corresponding to the output torque of the redundant execution unit being 125% (12.5*10%) of the rated value.

[0169] Step 506: binding the calibrated response time interval and the execution intensity adjustment value to generate a cooperative control instruction, configuring the driving protocol of the redundant execution unit according to the cooperative control instruction to obtain an adjusted redundant execution unit;

[0170] In this step, the cooperative control instruction refers to a composite instruction containing the calibrated response time interval and the execution intensity adjustment value, which is encoded into a machine-readable protocol. The driving protocol refers to an operation specification that can be parsed by an execution mechanism controller, which defines the action timing and intensity parameters. The adjusted redundant execution unit refers to an execution mechanism instance loaded with the driving protocol.

[0171] In the embodiment of the present application, the calibrated response time interval and the execution intensity adjustment value are encoded as pulse width modulation protocol parameters: the time interval corresponds to the pulse width modulation trigger timing; the intensity adjustment value corresponds to the pulse width modulation duty cycle; the protocol is loaded to the redundant execution unit controller through the CAN bus to generate an adjusted redundant execution unit that can perform actions.

[0172] The embodiment of the present application realizes the early response of key area faults and avoids execution overshoot under high-speed response through a three-level dynamic calibration mechanism including time interval scaling, starting time offset, and intensity-time cooperative binding, breaks through the three technical barriers of time sequence fixation, uniform intensity, and priority loss in traditional fault-tolerant control, and realizes the sub-second precise synchronization of execution actions and fault evolution of a compound wing unmanned aerial vehicle in a transition mode.

[0173] The present application provides a specific embodiment, step 502, generating a dynamic scaling factor based on the expansion rate value in the fault evolution trend parameter, specifically including the following steps:

[0174] Step 511: querying a base scaling factor from a preset rate scaling mapping table according to the expansion rate value in the fault evolution trend parameter;

[0175] In this step, the preset rate scaling mapping table refers to the mapping relationship database of the expansion rate value and the scaling factor, which is constructed based on crack expansion test data, reflecting the negative correlation logic that the rate increase needs to compress the response time. The basic scaling factor refers to the initial scaling coefficient directly determined by the expansion rate value, and the smaller the value, the faster the fault evolution.

[0176] In the embodiment of the application, according to the expansion rate value in the fault evolution trend parameter, such as 2.0 mm / s, the basic scaling factor is matched in the preset rate scaling mapping table according to the rate interval, such as 2.0 mm / s matching the basic scaling factor 0.6, and the mapping table is calibrated based on fracture mechanics test.

[0177] Step 512: According to the flight speed of the composite wing unmanned aerial vehicle, query the speed compensation coefficient from the preset airspeed compensation curve, and according to the angle of attack of the composite wing unmanned aerial vehicle, query the angle of attack correction weight from the angle of attack influence table, combine the speed compensation coefficient, and generate a aerodynamic environment compensation factor;

[0178] In this step, the preset airspeed compensation curve refers to the corresponding relationship curve of flight speed and speed compensation coefficient, which is used to quantify the fault suppression effect of high-speed aerodynamic damping. The speed compensation coefficient refers to the compensation value generated based on the flight speed, and the higher the speed, the larger the coefficient, reflecting the inhibition effect of aerodynamic stability on fault expansion. The angle of attack refers to the real-time angle between the flow direction and the chord line of the wing, which is measured by the head angle sensor. The angle of attack influence table refers to the mapping table of the angle of attack and the correction weight, which reflects that the stress imbalance caused by large angle of attack needs to reduce the scaling weight. The angle of attack correction weight refers to the coefficient representing the influence of the angle of attack on the basic scaling factor, and the larger the angle of attack, the lower the weight. The aerodynamic environment compensation factor refers to the product of the speed compensation coefficient and the angle of attack correction weight, which comprehensively reflects the modulation effect of the flight attitude on the fault evolution.

[0179] In the embodiment of the application, the flight speed is obtained through the airspeed tube, such as 180 km / h, the preset airspeed compensation curve is queried, for example, 180 km / h corresponds to the speed compensation coefficient 1.2; the angle of attack is obtained through the inertial measurement unit, such as 15°, the angle of attack influence table is queried, such as 15° corresponds to the angle of attack correction weight 0.9; the speed compensation coefficient and the angle of attack correction weight are multiplied, such as 1.2*0.9=1.08, to generate the aerodynamic environment compensation factor.

[0180] Step 513: According to the obtained wing structure temperature value, query the material expansion coefficient from the preset thermal expansion coefficient table, compare the material expansion coefficient with the reference expansion threshold value, and calculate the temperature deformation correction value;

[0181] In this step, the wing structure temperature value refers to the skin temperature measured by the embedded thermocouple, in units of ℃. The preset thermal expansion coefficient table refers to a mapping library of material types and thermal expansion coefficients. The material expansion coefficient refers to the linear expansion rate of the material per unit temperature rise, reflecting the thermal deformation sensitivity. The reference expansion threshold refers to the standard expansion coefficient of the reference material, used for difference quantification. The temperature deformation correction value refers to the compensation amount of the material expansion coefficient deviating from the reference threshold, with positive / negative values indicating the need to enlarge / compress the scaling factor.

[0182] In the embodiments of the present application, the wing structure temperature value is collected by the thermocouple, and the thermal expansion coefficient table is queried in combination with the material type identifier; the absolute difference between the material expansion coefficient and the reference expansion threshold is calculated; and the absolute difference is input into the preset difference and correction value conversion curve for mapping. Specifically, first, the main correction value is calculated: if the absolute difference ≤5.0×10 -8 , the main correction value = (-0.05)×absolute difference; if the absolute difference >5.0×10 -8 , the main correction value = (-0.10)×(absolute difference-5.0×10 -8 )-0.25; the additional correction value: for the scenario where the material type identifier is titanium alloy material and the wing structure temperature value >70℃, a temperature coefficient compensation is added: additional correction amount = (-0.01)×(temperature value ÷ 70); and the final temperature deformation correction value = main correction value + additional correction amount.

[0183] Step 514: based on the base scaling factor, the aerodynamic environment compensation factor, and the temperature deformation correction value, a dynamic scaling factor is calculated.

[0184] In the embodiments of the present application, the base scaling factor, the aerodynamic environment compensation factor, and the temperature deformation correction value are multiplied: dynamic scaling factor = base scaling factor × aerodynamic environment compensation factor × (1+temperature deformation correction value).

[0185] For example, according to the expansion rate of 2.0 mm / s in the fault evolution trend parameter, the base scaling factor of 0.6 is obtained by querying the rate scaling mapping table. Then, considering the aerodynamic environment factor, the speed compensation coefficient of 1.2 is obtained by querying the airspeed compensation curve at a flight speed of 180 km / h, and the attack angle correction weight of 0.9 is obtained by querying the attack angle influence table according to the attack angle of 15°, and the aerodynamic environment compensation factor is 1.2×0.9=1.08 by multiplying the two. Then, for the temperature deformation correction value, the wing structure temperature value is 60℃, the titanium alloy material expansion coefficient is 8.6×10 -6 / ℃, and the absolute difference is calculated with the reference expansion threshold of 9.0×10 -6 / ℃ as |8.6×10 -6 -9.0×10 -6 |=0.4×10 -6 / ℃, since the difference is less than or equal to 5.0*10 -8 , the main correction value is (-0.05)*0.4*10 -6 =-2*10 -8 , and since the temperature is 60℃, which is less than or equal to 70℃, the additional correction value is 0, the main correction value is approximately -0.15 after being dimensionless, and the temperature deformation correction value is -0.15. Finally, the basic scaling factor, the aerodynamic environment compensation factor and the temperature deformation correction value are substituted into the formula, and the dynamic scaling factor = basic scaling factor * aerodynamic environment compensation factor * (1 + temperature deformation correction value), and the dynamic scaling factor = 0.6*1.08*(1-0.15) = 0.6*1.08*0.85 = 0.5508 is calculated.

[0186] The embodiment of the present application accurately anchors the fault evolution benchmark trend, quantifies the fault suppression effect under high-speed maneuvering, and compensates for the interference of the thermal deformation of the material on the response time sequence through the three-field coupling mechanism including the kinetic field, the aerodynamic field and the thermal field; the inaccuracy problem of the traditional single-parameter scaling model under the variable working condition (such as sudden change of speed / attack angle), thermal load fluctuation (such as sunlight / engine thermal radiation) scene is broken through, the dynamic scaling factor is realized to be globally adapted to the real fault evolution characteristics of the compound wing unmanned aerial vehicle, and high-precision input is provided for response time calibration.

[0187] Figure 2 A structure diagram of a transition mode control system of a compound wing unmanned aerial vehicle is provided for the embodiment of the present application, as shown in Figure 2 , the system comprises:

[0188] The acquisition module 21 is configured to acquire wing surface deformation data of the compound wing unmanned aerial vehicle in the transition mode, and obtain vibration signals corresponding to the wing surface deformation data and real-time state information of key components;

[0189] The calculation module 22 is configured to calculate a local stress increment of a wing connection based on the vibration signals, and to couple the real-time state information and the local stress increment to generate a fault risk level parameter;

[0190] The prediction module 23 is configured to predict a potential fault position and a fault evolution trend parameter of the compound wing unmanned aerial vehicle according to the fault risk level parameter;

[0191] The generation module 24 is configured to generate a high-speed trigger signal matched with the fault risk level parameter through pulse technology when the potential fault position and the fault evolution trend parameter meet a preset safety condition;

[0192] The adjustment module 25 is configured to adjust a redundant execution unit based on the high-speed trigger signal to obtain an adjusted redundant execution unit, so that the action time sequence of the adjusted redundant execution unit is synchronized with the fault evolution trend parameter.

[0193] The control module 26 is configured to start the adjusted redundant execution unit to complete the fault-tolerant control in the transition mode.

[0194] Figure 2 The transition mode control system of the composite wing unmanned aerial vehicle can perform Figure 1 The implementation principle and technical effects of the transition mode control method of the composite wing unmanned aerial vehicle are not repeated. The specific operation manner of each module and unit of the transition mode control system of the composite wing unmanned aerial vehicle is described in detail in the embodiment of the method, which will not be described in detail here.

[0195] In one possible design, Figure 2 The transition mode control system of the composite wing unmanned aerial vehicle can be implemented as a computing device, such as Figure 3 As shown, the computing device can include a storage component 31 and a processing component 32.

[0196] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0197] The processing component 32 is configured to perform the above Figure 1 The transition mode control method of the composite wing unmanned aerial vehicle.

[0198] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, for executing the above method.

[0199] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be realized by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0200] Of course, the computing device can also include other components, such as input / output interface, display component, communication component, etc.

[0201] The input / output interface provides an interface between the processing component and peripheral interface modules, which can be output devices, input devices, etc.

[0202] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0203] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component, the storage component, etc.

[0204] The embodiment of the present application also provides a computer storage medium, which stores a computer program, and the computer program can realize the above-mentioned method when being executed by a computer. Figure 1 The transition mode control method of the compound wing unmanned aerial vehicle.

[0205] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, device and unit can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0206] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0207] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and necessary general hardware platforms, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software product can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute the method described in each embodiment or some part of the embodiment.

[0208] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A transition mode control method for a compound-wing unmanned aerial vehicle, characterized in that, include: Collect wing surface deformation data of the compound wing UAV in the transition mode, and obtain the vibration signal and real-time status information of key components corresponding to the wing surface deformation data; Based on the vibration signal, the local stress increment at the computer wing connection is used to couple the real-time status information with the local stress increment to generate a fault risk level parameter. Based on the fault risk level parameters, predict the potential fault locations and fault evolution trend parameters of the composite wing UAV; When the potential fault location and fault evolution trend parameters reach the preset safety conditions, a high-speed trigger signal matching the fault risk level parameters is generated using pulse technology. Based on the high-speed trigger signal, the redundant execution unit is adjusted to obtain the adjusted redundant execution unit, so that the timing of the action of the adjusted redundant execution unit is synchronized with the fault evolution trend parameter. The adjusted redundant execution unit is activated to complete fault-tolerant control under the transition mode.

2. The method according to claim 1, characterized in that, Based on the vibration signal, the local stress increment at the computer wing connection is coupled with the real-time state information to generate fault risk level parameters, including: The vibration signal is divided into multiple frequency bands, and the frequency bands with energy intensity values ​​higher than the reference intensity value are selected as abnormal vibration frequency bands. Based on the abnormal vibration frequency band, the local stress increment at the computer wing connection; The temperature change rate and displacement offset in the real-time status information are converted into dynamic influence parameters, and the dynamic influence parameters and the local stress increment are superimposed to generate comprehensive load parameters. Based on the comparison results between the comprehensive load parameters and the preset safety boundary parameters, a fault risk level parameter is generated.

3. The method according to claim 2, characterized in that, Based on the abnormal vibration frequency band, the local stress increment at the computer wing connection includes: Based on the distance between the sensor installation location and the wing connection point, distance attenuation compensation is applied to the energy intensity value of each abnormal vibration frequency band to obtain the compensated energy intensity value. Based on the attenuation coefficient of the transmission medium of the vibration signal, the compensated energy intensity value is compensated for by medium absorption to generate a frequency band compensated energy value. Based on the similarity between the center frequency of the vibration signal and the natural resonance frequency of the wing, a frequency weighting coefficient is calculated, and combined with the frequency band compensation energy value, a weighted energy value corresponding to each abnormal vibration frequency band is generated. Based on the preset overlap ratio coefficient, the overlapping energy components of the weighted energy values ​​between adjacent frequency bands are removed to generate corrected energy values. The corrected energy values ​​of all abnormal vibration frequency bands are then summed to obtain the equivalent total vibration energy. Based on the material type identification at the wing joint, look up the stress amplification factor; Based on the sum of the equivalent vibration energy, the stress amplification factor, the preset reference stress conversion coefficient, and the material stiffness parameters, the local stress increment is calculated.

4. The method according to claim 1, characterized in that, Based on the aforementioned fault risk level parameters, predict the potential fault locations and fault evolution trend parameters of the composite wing UAV, including: According to the preset wing joint division rules, the wing joint is divided into multiple structural zones; Based on the fault risk level parameters, calculate the absolute value of the difference between the comprehensive load parameters corresponding to each structural partition and the preset partition bearing limit value, and use the absolute value of the difference as the fault deviation degree. The fault deviation of each structural partition is converted into a fault weight coefficient, and the structural partition with the largest fault weight coefficient is selected as the potential fault location. Calculate the amount by which the comprehensive load parameters corresponding to the potential fault location exceed the preset zone bearing limit value, and use the amount of the excess as the fault severity. Based on the severity of the fault, a preset fault propagation rate table is queried to generate fault evolution trend parameters.

5. The method according to claim 1, characterized in that, When the potential fault location and fault evolution trend parameters reach a preset safety condition, a high-speed trigger signal matching the fault risk level parameter is generated using pulse technology, including: According to the preset pulse parameter conversion rules, the fault risk level parameters are converted into initial pulse width and target pulse intensity; The initial pulse width is adjusted according to the propagation rate value in the fault evolution trend parameter to obtain the target pulse width; Based on the structural partitions corresponding to the potential fault locations, a target pulse signal transmission channel is selected. When the severity of the fault corresponding to the potential fault location exceeds the preset partition bearing limit and the fault evolution trend parameter exceeds the preset expansion rate, a rectangular pulse signal with the target pulse width and target pulse intensity is generated using the target pulse signal transmission channel, and the rectangular pulse signal is used as the high-speed trigger signal.

6. The method according to claim 1, characterized in that, Based on the high-speed trigger signal, the redundant execution units are adjusted to obtain the adjusted redundant execution units, including: The basic response time interval is set according to the target pulse width of the high-speed trigger signal; A dynamic scaling factor is generated based on the propagation rate value in the fault evolution trend parameters. Multiply the boundary values ​​of the basic response time interval by the dynamic scaling factor to obtain the scaled response time interval; Based on the priority coefficient of the structural partition corresponding to the potential fault location, the start time of the scaled response time interval is calibrated to obtain the calibrated response time interval. Calculate the execution force adjustment value based on the target pulse intensity of the high-speed trigger signal and the fault risk level parameter; The calibrated response time interval is bound to the execution force adjustment value to generate a collaborative control command. The drive protocol of the redundant execution unit is configured according to the collaborative control command to obtain the adjusted redundant execution unit.

7. The method according to claim 6, characterized in that, Based on the propagation rate value in the fault evolution trend parameters, a dynamic scaling factor is generated, including: Based on the expansion rate value in the fault evolution trend parameter, the basic scaling factor is queried from the preset rate scaling mapping table; Based on the flight speed of the compound wing UAV, the speed compensation coefficient is retrieved from the preset airspeed compensation curve, and based on the airflow angle of attack of the compound wing UAV, the angle of attack correction weight is retrieved from the angle of attack influence table. Combined with the speed compensation coefficient, an aerodynamic environment compensation factor is generated. Based on the obtained wing structure temperature value, the material expansion coefficient is looked up from the preset thermal expansion coefficient table, and the material expansion coefficient is compared with the reference expansion threshold to calculate the temperature deformation correction value. The dynamic scaling factor is calculated based on the basic scaling factor, the aerodynamic environment compensation factor, and the temperature deformation correction value.

8. A transition mode control system for a compound-wing unmanned aerial vehicle, characterized in that, include: The acquisition module is used to acquire wing surface deformation data of the compound wing UAV in the transition mode, and to obtain the vibration signal and real-time status information of key components corresponding to the wing surface deformation data. The calculation module is used to couple the real-time status information with the local stress increment at the connection of the computer wing based on the vibration signal to generate a fault risk level parameter. The prediction module is used to predict the potential fault location and fault evolution trend parameters of the composite wing UAV based on the fault risk level parameters. The generation module is used to generate a high-speed trigger signal that matches the fault risk level parameter through pulse technology when the potential fault location and fault evolution trend parameters reach the preset safety conditions. The adjustment module is used to adjust the redundant execution unit based on the high-speed trigger signal to obtain the adjusted redundant execution unit so that the timing of the operation of the adjusted redundant execution unit is synchronized with the fault evolution trend parameter. The control module is used to activate the adjusted redundant execution unit to complete the fault-tolerant control under the transition mode.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the transition mode control method for a compound wing unmanned aerial vehicle as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a transition mode control method for a compound-wing unmanned aerial vehicle as described in any one of claims 1 to 7.

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