Unmanned aerial vehicle propeller definite reliability modeling method and system, medium and program product

Abstract

The invention relates to the technical field of unmanned aerial vehicle design, in particular to an unmanned aerial vehicle propeller definite reliability modeling method and system, a medium and a program product. The method comprises the following steps: firstly, constructing a demand model, a system context, a use case and an activity diagram of an unmanned aerial vehicle propeller based on SysML, and defining a mathematical function relationship of core performance parameters such as thrust, torque and rotating speed; then statistical fitting is carried out in combination with experimental data, a joint performance margin equation is constructed, a definite reliability theory is introduced, and the reliability value of the propeller is calculated through a standard normal cumulative distribution function; and finally, carrying out constraint binding on the constraint equation and the system structure model to realize dynamic response and model linkage under parameter change. According to the method, integration of reliability analysis in the MBSE model is realized, and the automation degree, the evaluation precision and the engineering application efficiency of reliability modeling of the propeller system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) design, and particularly to a method, system, medium and program product for modeling the reliability of UAV propellers with certainty. Background Art

[0002] With the wide deployment of multi-rotor UAVs in scenarios such as urban delivery, inspection and monitoring, and emergency response, the structural complexity and the uncertainty of the operating environment are continuously increasing the requirements for the reliability of core components. Under the actual operating conditions of high intensity, multiple environments and multiple tasks, the reliability of the power system has become a bottleneck factor restricting the large-scale industrial application and the extension to high-end scenarios. The power system generally consists of propellers (rotors), brushless motors, electronic speed controllers (ESCs), batteries and control algorithms. In particular, the rotor is not only the direct component for generating lift, but also the combined action point of aerodynamic load, structural vibration and external disturbances. A large number of accident statistics show that rotor structural failure, dynamic balance disorder, aeroelastic coupling, and the resulting insufficient thrust or excessive airframe vibration are one of the main causes leading to UAV out-of-control and crash.

[0003] Traditional practices usually take blade geometric parameters (diameter, pitch, aspect ratio, etc.) as independent variables, and use Betz's criterion, blade element momentum theory (BEMT) or CFD iterative calculation to obtain the thrust-torque curve, and then check the safety margin through static tests and fatigue tests in the prototype stage. This process has a long design cycle and high iteration costs; more importantly, the interaction of the propeller at the system level (such as ESC current limiting, flight controller adjustment rate, airframe attitude disturbance) is weakened, and only the static strength or life estimation of isolated components can be obtained, and the reliability change cannot be reflected in real time under multiple operating conditions of the whole machine.

[0004] In addition, in engineering practice, FMEA / FMECA, fault tree analysis (FTA) or statistical manuals such as MIL-HDBK-217 are often used to deduce the failure rate of the propeller; or an empirical safety factor of 1.5 - 2.0 is used to cope with the load uncertainty. However, such methods do not deeply quantify the coupling effect between thrust, torque and rotational speed, and also lack the joint evaluation of model error and environmental disturbances (temperature, air pressure, airflow shear). The results often tend to be overly conservative or underestimate the risks, and it is difficult to support the high-speed iteration rhythm of UAV product updates.

[0005] Although in recent years, some studies have combined the finite element analysis, CFD, hardware-in-the-loop (HIL) testing of the propeller with digital twin to perform multi-physics field simulations on material fatigue and aerodynamic loads, most platforms focus on post-processing visualization or online fault diagnosis, lacking a unified modeling language and automatic parameter constraint mechanism for the early stage of design; within the dimension of systems engineering, the data interfaces between different simulation tools often rely on manual script maintenance, and it is difficult to form end-to-end model consistency.

[0006] Since its inception, Model-Based Systems Engineering (MBSE) has been vigorously promoted by institutions such as NASA, INCOSE, and Dassault. Its core concept is to use digital models to run through the entire life cycle of the system, unify the expression of requirements, structure, behavior, parameters, constraints, and verification activities, and ensure information consistency in a single source of truth. MBSE provides a new paradigm for solving the problems of "coupled complexity, diverse mechanisms, scattered data, and fragmented models" in the reliability assessment of UAV power systems. However, having only the MBSE framework is not sufficient to directly output reliability indicators; it is necessary to further incorporate the concepts of uncertainty modeling and confidence into the model. The domestic academic community has been paying increasing attention to MBSE. Many universities and research institutes have successively constructed single-aircraft-level MBSE frameworks and conducted pilot projects in the directions of avionics, engine control, and UAV overall design.

[0007] The Chinese invention patent application (Publication No.: CN114781183 A, Publication Date: July 22, 2022) proposes that the MBSE method can achieve through modeling of the perception system from a logical model to a physical simulation model, and construct system reliability measurement indicators through a state transition model and a parameter model. However, it mainly focuses on intelligent perception systems and has not covered the modeling requirements of power execution devices such as UAV propellers with clear mechanical output characteristics (such as thrust, torque, and rotational speed). In addition, the Chinese invention patent application (Publication No.: CN116776656 A, Publication Date: September 19, 2023) points out that traditional methods mostly start from the perspective of single components or single functions, making it difficult to systematically evaluate the reliability transfer and interactive failure paths between different functions of unmanned systems, and unable to accurately depict the influence degree of each module on reliability in the "perception - decision - execution" chain. And by constructing a multi-layer hypernetwork and a Petri net to formalize the interactive relationship between the perception, collaboration, and drive layers, and defining reliability measurement indicators in the functional dimension, but this method focuses more on the task completion ability assessment of complex unmanned systems, does not introduce a physical parameter margin model based on statistical distribution characteristics, and does not consider the quantitative impact of model errors on reliability results. Summary of the Invention

[0008] To solve the above technical problems, the objective of the present invention is to provide a method for modeling the assured reliability of an unmanned aerial vehicle (UAV) propeller based on MBSE. This method embeds the assured reliability theory into the system structure model in the form of constraint relationships, and proposes a unified modeling framework applicable to a wide range of physical entity systems. This method not only reflects the dynamic coupling relationship between performance parameters and the system structure, but also realizes the visual deduction of the system reliability characteristics under multiple working conditions. Through the synergistic effect of the model hierarchy, parameter dependence, and constraint propagation mechanism, the applicability and engineering feasibility of the assured reliability theory in the MBSE scenario are further verified, demonstrating the scientificity, generality, and rigor of this methodology.

[0009] To achieve the above objective, the present invention adopts the following technical solutions: A method for modeling the assured reliability of an unmanned aerial vehicle (UAV) propeller based on MBSE is executed according to the following steps: 1) Generation of the requirements analysis model: Determine the propeller in the UAV power system as the target research unit; input the propeller parameters in the requirements analysis module to generate the requirements analysis model; 2) System context and use case modeling: Draw the UAV system context model at the system level, and define the interface relationships between the propeller and the flight control module, electronic speed controller, motor, and battery components; draw the context model and flight use case diagram at the power subsystem level to indicate the closed-loop execution logic of the propeller to achieve attitude adjustment by adjusting the thrust and angular velocity; 3) Construction of performance functions and statistical distributions: Collect the experimental data of the thrust F and torque τ of the propeller at different rotational speeds; after removing outliers using the BOX-COX transformation, establish the thrust function and torque function; 4) Setting of failure thresholds: Set the thrust threshold F th , torque threshold τ th and rotational speed threshold ω th respectively according to the flight mission requirements; 5) Calculation of the joint margin: Calculate the dimensionless performance margins m F , m τ , m ω of thrust, torque, and rotational speed through Monte Carlo simulation; obtain the weights m F , m τ , w ω based on min-max normalization and variance analysis; calculate the joint margin M: ; 6) Calculation of the assured reliability: Substitute the performance fluctuation variance σ m ² and the model error variance σ e ² into ; where Φ N (.)is the standard normal cumulative distribution function, and M th is the combined margin threshold, and the propeller confidence reliability R MB is obtained.

[0010] Preferably, the propeller parameters in step 1) include the propeller diameter, pitch, central aperture, material, weight, self-locking mechanism, blade section thickness, factory balance, and parameters of the adaptation platform.

[0011] Preferably, in step 3), the square of the rotational speed ω 2 is fitted to a normal distribution to obtain the mean μ and the standard deviation σ. The normal distribution fitting uses maximum likelihood estimation and verifies the normality through the Shapiro-Wilk test; the thrust function is PF(ω 2 ) = CFω 2 , and the torque function is Pτ(ω 2 ) = Cτω 2 . The coefficients CF and Cτ are obtained through least squares linear fitting, and the goodness of fit R² is not less than 0.98.

[0012] Preferably, the thresholds Fth, τth, and ωth in step 4) are jointly determined by the hover thrust demand, the minimum torque start value, and the thrust decay inflection point; and / or, the number of Monte Carlo simulations in step 5) is not less than 1000 times, and the simulation results are filtered for abnormal samples using a 95% confidence interval; the weight calculation further includes performing a principal component analysis on the normalized features, and when the contribution rate of the first principal component is greater than 85%, the load coefficients are used to correct the weights.

[0013] Preferably, the formula for calculating the statistical fluctuation variance σ m 2 in step 6) is as follows: ; where: σ m ² is the variance of the comprehensive or combined margin; w i is the weight of the i-th performance index, reflecting the influence degree of this index on the overall margin; σ m,i ² is the variance of the i-th performance index itself, that is, the variance of its performance margin; is the summation of n indicators; and / or, the model error variance σ e²Based on expert experience and comprehensive evaluation of historical model-measured deviations, the value range is 0.01-0.10.

[0014] Preferably, the method also includes MBSE constraint binding integration: the mathematical equations of steps 3)-6) are inherited and bound to the propeller module in the internal structure diagram through SysML constraint blocks to achieve real-time automatic refresh of reliability when parameters are updated; the constraint binding relationship supports automatic synchronous update in Magic Draw, Cameo Systems Modeler or MBSE tools.

[0015] Furthermore, the present invention also provides a model-based UAV propeller reliability modeling system, which implements the method described, including: A. Demand analysis module, used to receive propeller geometry, material and assembly parameters and generate a demand analysis model; B. System context modeling module, used to build SysML context model of device hierarchical relationship and interface interaction; C. Use case and activity modeling module, used to define propeller attitude adjustment use cases and draw corresponding activity diagrams; D. Performance modeling module, including: D1. Data acquisition unit, used to obtain the thrust and torque experimental data of the propeller at different speeds; D2. Parameter fitting unit, used to fit the normal distribution of the square of the speed ω² and obtain the coefficient C F , C τ ; E. Threshold setting unit, used to store and call thrust, torque and speed fault thresholds; F. a joint margin calculation unit, used for calculating the joint margin M based on dimensionless conversion, weight allocation and Monte Carlo simulation; G. Reliability evaluation unit, used to execute the confidence reliability formula and output RMB; H.MBSE constraint binding unit, used to establish inheritance and constraint binding between performance equations and propeller structure models in the MBSE tool environment, and to achieve real-time update of parameter changes; I. Processor, used to run the instructions of the above modules / units.

[0016] Preferably, the performance modeling module further includes an outlier processing subunit for performing a BOX-COX transformation and eliminating data points that deviate from ±3σ; and / or, the joint margin calculation unit is configured to call three algorithms, namely, minimum-maximum normalization, variance analysis and principal component analysis, when obtaining weights and automatically select a weight set with the smallest error; and / or, the reliability assessment unit is configured to send a maintenance warning signal to the flight control system when RMB is lower than a set threshold.

[0017] Furthermore, the present invention also provides a computer-readable storage medium having a computer program or instruction stored thereon, and the method is implemented when the computer program or instruction is executed by a processor.

[0018] Furthermore, the present invention also provides a computer program product, comprising a computer program or instructions, which implement the method when executed by a processor.

[0019] The present invention adopts the above technical solution, and by embedding the mathematical model of assured reliability in the MBSE framework, the full process model driving from demand to reliability of UAV propellers is realized, and the assured reliability theory is used in the system engineering framework to accurately model and express the research object of UAV propellers with quantifiable physical performance parameters (such as thrust, torque and speed). Specifically, the following significant technical effects can be obtained: 1. Model consistency and data integration: The three core performance equations of thrust, torque and speed are bound to the propeller structure model in the form of SysML constraint blocks. Any changes in input parameters (such as propeller diameter, pitch, material density) can automatically update the reliability results in <0.5 s, avoiding data fragmentation and manual synchronization errors caused by the traditional "CAD-simulation-table" separation. The demand layer, function layer, structure layer and parameter layer are kept "single source" and unified, significantly improving the model version traceability and configuration accuracy.

[0020] 2. Improved reliability quantification accuracy: Establishing the confidence reliability R by combining the margin M with the standard normal cumulative distribution function Φ MB , for the first time, considering the statistical volatility variance σ m ² and model error variance σ e ², compared with the method using only empirical safety factors, the mean square error of reliability assessment is reduced by about 28%. The coupling setting of equipment-level failure threshold and mission-level thrust demand can accurately locate the failure risk section and avoid redundant weight caused by overly conservative design.

[0021] 3. Rapid simulation and decision support for multiple working conditions: Built-in Monte Carlo simulation and weight adaptive algorithm can output margin distribution curve within 3 seconds under 1000 random working condition simulations, providing real-time and callable reliability boundary for the closed-loop design of flight control algorithm. MB When it is lower than the set threshold, a maintenance warning instruction can be immediately sent to the upper flight control system to provide a quantitative basis for preventive maintenance decisions.

[0022] In summary, the present invention has established a new paradigm for the reliability design of propellers at the system engineering level, achieving a technical breakthrough in the trinity of high-precision quantification, rapid iteration, and real-time monitoring, and significantly improving the safety, economy, and engineering development efficiency of the UAV power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a structural diagram of the self-locking propeller of the rotor UAV system of the present invention.

[0024] Figure 2 It is a system context modeling diagram of the UAV system.

[0025] Figure 3 It is a subsystem context modeling diagram of the UAV power system.

[0026] Figure 4 It is a use case diagram of the UAV propeller for flight.

[0027] Figure 5 It is an activity logic diagram of the UAV system level.

[0028] Figure 6 It is an activity logic diagram of the power module subsystem level.

[0029] Figure 7 It is a fitting value diagram of the rotational speed fitted to a normal distribution by the BOX-COX method.

[0030] Figure 8 For C F Fitting value diagram.

[0031] Figure 9 For C τ Fitting value diagram. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] Next, in combination with the embodiments of the present invention, the technical solutions in the embodiments will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] Select the research unit The rotor UAV system usually consists of a power module, a networking communication module, a flight control module, a core computing module, and a sensing module. Among them, the power system consists of a battery, an electronic speed controller, a motor, and a propeller, and receives and executes the control instructions issued by the flight control module to achieve flight actions.

[0034] Such as Figure 1As shown in the figure, the research object of the present invention is the DJI-1045 self-locking propeller, which is an optimized design for a multi-rotor platform with a specification of 10 × 4.5 inches (1045). It adopts a spline self-locking fastening structure, enabling quick installation and removal without tools.

[0035] 2. UAV System Modeling 2.1 Requirement Analysis Modeling As shown in Table 1, based on the design parameters, material requirements, rotational attitude control, structural characteristics, etc. of the propeller of the DJI-1045 UAV, a requirement analysis model is constructed.

[0036] Table 1 Propeller Requirement Analysis Table

[0037] 2.2 System Context To reasonably construct the reliability model of the propeller confidence, as Figure 2 、 Figure 3 shown, it is necessary to reasonably set the logical interaction between each system level and the interaction relationship between the propeller and each system level as well as the other components within the power system, and draw a reasonable system context.

[0038] 2.3 Use Case The present invention focuses on a key use case in the UAV power system, that is, the propeller dynamically adjusts the flight attitude by adjusting the thrust and angular velocity. This use case is the core execution link in the flight control closed-loop, directly related to flight stability and mission response capabilities.

[0039] 2.4 Activity Diagram As Figure 5 、 Figure 6 shown, the present invention constructs an activity logic diagram of the propeller attitude change in the power module at the system level and an activity logic diagram of the propeller executing attitude change in the subsystem level of the power module.

[0040] 2.5 System Port Definition The definition of the system port is the key mechanism to realize the dynamic coupling between UAV systems and is of great significance for modeling the relevance between system elements. As Figure 7 shown, the present invention can achieve the structured integration and relationship modeling of each UAV system module through the module diagram (Block Definition Diagram, BDD) and internal block diagram (Internal Block Diagram, IBD) in SysML. Each part of the system is no longer an isolated functional unit, but constitutes a highly coupled and collaborative overall system.

[0041] During the modeling process, when the input variables at the system level and subsystem level change, it is no longer a single module that responds to the change. Instead, through the establishment of ports and connection relationships, real-time linkage and collaborative response of each module of the system are achieved. This design method embodies the concept of "integration-based" in Model-Based Systems Engineering (MBSE), effectively supporting the simulation and verification of the dynamic behavior of complex systems.

[0042] 3. Construction of Margin Equation 3.1 Selection of Performance Parameters Based on the physical experimental data of DJI-1045 type self-locking propellers, the present invention established a dynamic fitting model for thrust and torque response. The square of the rotational speed was selected as the independent variable to fit the variation laws of thrust and torque.

[0043] The aerodynamic theory of propellers shows that when the intake velocity and aerodynamic load are constant, the thrust F, torque τ and the square of the rotational speed are in a proportional relationship, that is ; the torque also approximately follows 's law.

[0044] Therefore, the present invention takes thrust (F), torque (τ) and the square of the rotational speed ( ) as the core modeling parameters, systematically describes the aerodynamic performance and dynamic response laws of this type of propeller under typical working conditions, and establishes a basic mathematical model suitable for aerodynamic modeling and simulation optimization of rotary-wing aircraft, providing theoretical support for related flight control algorithms and system design.

[0045] 3.2 Construction of Performance Function P n refers to the function that maps the key performance indicators of the propeller at different rotational speeds to a mathematical expression. is the corresponding performance parameter.

[0046] For the DJI-1045 model, based on aerodynamic theory and experimental data, we respectively constructed: (1) Thrust function: ， (2) Torque function: ， (3) Rotational speed function: 。

[0047] Based on the physical experimental data, first, for the independent variable rotational speed Perform a normal distribution fitting to determine its mean μ = 794.448 and standard deviation σ = 339.283. These distribution parameters will serve as the statistical boundary basis for the normal distribution input of relevant variables in the subsequent parameter diagrams. As Figure 7 shown, the detailed calculation process is as follows: The rotational speed data of the experiment is fitted into normal distribution data values by removing outliers through the BOX-COX method. The transformation coefficient is 0.341, and the calculation results are mean μ = 794.448 and standard deviation σ = 339.283.

[0048] Furthermore, by performing a linear fitting on the experimental data, the coefficients C F = 1.99 and C τ = 3.5E-5 that conform to the formula expression are calculated. As Figure 8 、 Figure 9 shown, the detailed calculation process is as follows: By sorting out the rotational speed squared data and the thrust and torque data, two constant coefficients C F = 1.99 and C τ = 3.5E-5 are obtained by using the linear fitting method.

[0049] The experimental data is shown in Table 2.

[0050]

[0051] 3.3 Fault Threshold Setting As shown in Table 3, corresponding fault thresholds are set for each performance parameter. The thresholds are based on task requirements or design specifications and are determined by the minimum safe lift, minimum torque start value, and minimum energy efficiency respectively.

[0052] Table 3 Corresponding Fault Threshold Table for Parameter Setting

[0053] Calculated as:

[0054] 3.4 UAV Propeller Joint Margin Construction , where , , .

[0055] m n represents the performance margin result of the nth selected performance parameter.

[0056] The defining equation for this step eliminates the influence of the units of the parameters themselves, achieving a dimensionless unified measurement between parameters of different dimensions. The calculation gives: m F = 0.3109 > 0, m τ = 0.0282 > 0, m ω = 0.6824 > 0.

[0057] Detailed calculation process: By defining the constraint formula and inputting the value of the square of the rotational speed, use Monte Carlo simulation to calculate 1000 times and take the best value.

[0058] Weight reflects the importance of different performance parameters to the overall performance margin of the system, and the weights satisfy the normalization condition: ， The calculation gives: w F = 0.3637, w τ = 0.3589, w ω = 0.2774.

[0059] Detailed calculation process: 1. Use Min - Max Scaling to normalize each column of features (thrust, torque, RPM²) to [0, 1]: ; 2. Calculate the variance of each column ; 3. Calculate the weights

[0060] ; Then obtain the specific values.

[0061] 3.5 Construction of the assured reliability of the UAV propeller based on the model The formula is expressed as; ; where Φ N (.) is the standard normal cumulative distribution function, M th is the joint margin threshold.

[0062] The calculation result is R MB = 0.9838.

[0063] Detailed calculation process: 1) Calculate the statistical fluctuation variance of the performance parameters : ; is the variance of the comprehensive or combined margin (i.e., the overall variance after weighted synthesis); is the weight of the i-th performance indicator, reflecting the impact of this indicator on the overall margin; is the variance of the i-th performance indicator itself, that is, the variance of its performance margin; is to sum up n indicators; Inputting all the calculated data after sorting can obtain the result.

[0064] 2) Model error variance Reflects the structural simplification, parameter uncertainty and modeling error of the MBSE model. Through expert experience, a reliable degree judgment is made to prevent over-optimism. Here, the value is 0.04. Because the UAV propeller belongs to a simple physical entity.

[0065] Finally, the result is calculated: σ m = 0.3247, σ e ≈ 0.04.

[0066] 4. Simultaneous equations of parameter diagrams In the third step of modeling, all the defined performance equations are established with the propeller module in the IBD diagram in the form of a Constraint Block to establish a generalization and binding relationship, so as to realize the dynamic integration of the performance analysis model and system components in the MagicDraw tool.

[0067] Through this method, when facing different working conditions or scenarios of performance parameter changes, there is no need to rebuild a new system model. Just modify the parameter values, and the model can automatically reflect its dynamic response on each system component. At the same time, any change in a parameter value can be globally mapped to the relevant constraint expressions through the binding mechanism to ensure real-time linkage and dynamic coupling among the internal modules of the system. This modeling method not only greatly improves the modeling efficiency and system reusability, but also effectively supports the model consistency management and system-level performance verification under multiple performance conditions.

[0068] The foregoing is a description of embodiments of the present invention. Through the above description of the disclosed embodiments, those skilled in the art can implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined in the present invention can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown in the present invention, but will conform to the widest scope consistent with the principles and novel points disclosed in the present invention.

Claims

1. A method for modeling the assured reliability of an unmanned aerial vehicle propeller based on MBSE, characterized in that, Follow these steps: 1) Demand analysis model generation: The propeller in the UAV power system is determined as the target research unit; the propeller parameters are input into the demand analysis module to generate the demand analysis model; 2) System context and use case modeling: Draw the drone system context model at the system level to define the interface relationship between the propeller and the flight control module, ESC, motor, and battery components; draw the context model and flight use case diagram at the power subsystem level to indicate the closed-loop execution logic of the propeller to achieve attitude adjustment by adjusting thrust and angular velocity; 3) Performance function and statistical distribution construction: The experimental data of propeller thrust F and torque τ at different speeds were collected; after removing abnormal values using BOX-COX transformation, thrust function and torque function were established; 4) Fault threshold setting: Set the thrust threshold F, torque threshold τ, and rotational speed threshold ω respectively according to the flight mission requirements. th , torque threshold τ th , and rotational speed threshold ω th ; 5) Joint margin calculation: Calculating the dimensionless performance margins \(m\) of thrust, torque, and rotational speed through Monte Carlo simulation F , \(m\) τ , \(m\) ω ; Obtaining the weights \(w\) through min-max normalization and variance analysis F , \(w\) τ , \(w\) ω ; Obtaining the combined margin \(M\) through calculation: ； 6) Assurance reliability calculation: Substitute the performance fluctuation variance σ m 2 and the model error variance σ e 2 into ； where Φ N (.) is the standard normal cumulative distribution function, and M th is the combined margin threshold, and the propeller confidence reliability R MB is obtained.

2. The method according to claim 1, wherein The propeller parameters in step 1) include propeller diameter, pitch, center hole diameter, material, weight, self-locking mechanism, blade section thickness, factory balance and parameters of the adaptation platform.

3. The method according to claim 1, wherein In step 3), the square of the rotational speed ω² is fitted to a normal distribution to obtain the mean μ and the standard deviation σ. The normal distribution fitting uses maximum likelihood estimation and verifies the normality through the Shapiro-Wilk test; the thrust function is P F (ω 2 ) = C F ω 2 , and the torque function is P τ (ω 2 ) = C τ ω 2 . The coefficients C F and C τ are obtained through least squares linear fitting, and the goodness of fit R 2 is not less than 0.

98.

4. The method according to claim 1, wherein The threshold F in step 4) th , τ th , ω th are jointly determined by the hovering thrust requirement, the minimum torque starting value, and the thrust decay inflection point; and / or, the number of Monte Carlo simulations in step 5) is not less than 1000 times, and the simulation results are filtered for abnormal samples using a 95% confidence interval; the weight calculation further includes performing a principal component analysis on the normalized features, and when the contribution rate of the first principal component is greater than 85%, the load coefficients are used to correct the respective weights.

5. The method according to claim 1, wherein Step 6) Calculate the statistical fluctuation variance σ m ² of the performance parameter, and the formula is as follows: ； in: σ m ² is the variance of the combined or integrated margin; w i is the weight of the i-th performance metric, reflecting the degree of influence of this metric on the overall margin; σ m,i ² is the variance of the i-th performance metric itself, that is, the variance of its performance margin; To sum over n indices; and / or, the model error variance σ e ² is comprehensively evaluated based on expert experience and historical model-measured deviation, and the value range is 0.01 - 0.

10.

6. The method according to claim 1, wherein The method also includes MBSE constraint binding integration: the mathematical equations of steps 3)-6) are inherited and bound to the propeller module in the internal structure diagram through SysML constraint blocks to achieve real-time automatic refresh of reliability when parameters are updated; the constraint binding relationship supports automatic synchronous update in Magic Draw, Cameo Systems Modeler or MBSE tools.

7. A model-based reliability modeling system for drone propellers, characterized in that, The system implements the method described in any one of claims 1 to 6, including: A. Demand analysis module, used to receive propeller geometry, material and assembly parameters and generate a demand analysis model; B. System context modeling module, used to build SysML context model of device hierarchical relationship and interface interaction; C. Use case and activity modeling module, used to define propeller attitude adjustment use cases and draw corresponding activity diagrams; D. Performance modeling module, including: D1. Data acquisition unit, used to obtain the thrust and torque experimental data of the propeller at different speeds; D2. A parameter fitting unit, configured to perform a normal distribution fitting on the square of the rotational speed ω² and obtain coefficients C F and C τ ; E. Threshold setting unit, used to store and call thrust, torque and speed fault thresholds; F. a joint margin calculation unit, used for calculating the joint margin M based on dimensionless conversion, weight allocation and Monte Carlo simulation; G. Reliability evaluation unit, used to execute the confidence reliability formula and output RMB; H.MBSE constraint binding unit, used to establish inheritance and constraint binding between performance equations and propeller structure models in the MBSE tool environment, and to achieve real-time update of parameter changes; I. Processor, used to run the instructions of the above modules / units.

8. The system according to claim 7, wherein The performance modeling module further includes an outlier processing subunit for performing BOX-COX transformation and eliminating data points that deviate from ±3σ; and / or, the joint margin calculation unit is configured to call three algorithms of minimum-maximum normalization, variance analysis and principal component analysis when obtaining weights and automatically select the weight set with the smallest error; and / or, the reliability assessment unit is configured to send a maintenance warning signal to the flight control system when RMB is lower than a set threshold.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instruction is executed by a processor, it implements the method according to any one of claims 1-6.

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