A multi-parameter load spectrum compiling method and device considering load working conditions

Abstract

This invention discloses a method and apparatus for compiling a multi-parameter load spectrum considering load conditions, belonging to the field of aero-engine load spectrum compilation technology. The method includes: dividing the flight profile into flight mission segments based on flight maneuvers and constructing a multi-parameter joint probability distribution model; generating load samples based on the multi-parameter joint probability distribution model and extracting the ultimate load values ​​of the load parameters; statistically analyzing all measured flight profiles to obtain the frequency and duration of various flight mission segments; merging low-frequency mission segments and using the frequency of the merged mission segments as the cycle frequency of the flight profile; randomly selecting mission segments from the simulation mission segment library according to the original proportions in each cycle and splicing them to obtain a mixed-frequency statistical result; and extracting mission segments from the simulation mission segment library based on the mixed-frequency statistical result to construct a comprehensive mission spectrum. This application achieves quantitative extraction of low-probability extreme events, solving the problem that traditional methods struggle to cover ultimate loads.

Description

Technical Field

[0001] This invention belongs to the field of aero-engine load spectrum compilation technology, specifically relating to a multi-parameter load spectrum compilation method and apparatus that considers load conditions. Background Technology

[0002] During service, aero engines not only bear conventional maneuvering and aerodynamic loads, but also experience extreme flight conditions such as spin and deep stall. Although the probability of these conditions occurring is extremely low (usually corresponding to low-probability events in statistics), the load amplitudes they cause are large and drastic, posing a severe challenge to the strength, fatigue life, and even overall safety of the engine's key load-bearing structures (such as the casing, mounting section, turbine load-bearing frame, etc.). Existing load spectrum compilation methods are mostly based on typical flight mission profiles or average usage conditions, constructing a program load spectrum by statistically analyzing the mean and amplitude distribution of various flight parameters. However, these methods have the following drawbacks: (1) They are difficult to effectively cover and characterize low-probability, high-risk extreme load events, resulting in insufficient coverage of extreme loads and damage authenticity in the compiled load spectrum; (2) They lack targeted processing methods for low-frequency flight mission segments (such as extreme states in specific maneuvers), often ignoring or simply merging them, resulting in an underestimation of damage contribution; (3) They do not fully consider the coupling characteristics of multi-parameter loads under extreme conditions, and simply using single-parameter extreme value combinations may lead to load state distortion.

[0003] Therefore, there is an urgent need for a method and apparatus for compiling multi-parameter load spectra that takes into account load conditions to solve the existing problems. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for compiling a multi-parameter load spectrum that considers load conditions, in order to solve the problems that existing load spectrum compilation methods are unable to effectively cover ultimate load events, lack processing of low-frequency task segments, and fail to maintain multi-parameter coupling relationships.

[0005] Firstly, this application provides a method for compiling a multi-parameter load spectrum considering load conditions. Taking flight mission segments as the basic compilation unit, the complex time-varying load history of aero-engines is decomposed into fifteen types of flight mission segments with clear physical meaning. By establishing a multi-parameter joint probability distribution model, the marginal distribution characteristics of load parameters under each condition and their coupling relationship are characterized. Based on Monte Carlo simulation technology, the ultimate load values ​​under each flight condition are identified. For low-frequency mission segments with a frequency of less than 5, a physical similarity merging strategy is proposed. Finally, a multi-parameter comprehensive mission spectrum covering the ultimate load state and maintaining equivalence with the measured spectrum damage is compiled.

[0006] In one implementation of the first aspect, based on the established multi-parameter joint probability distribution model, Monte Carlo simulation technology is used to generate a massive number of load samples that conform to statistical characteristics; using statistical methods... The criterion is to use the threshold of low-probability events as the quantile of the cumulative probability reaching 99.83% as the estimated value of the ultimate load;

[0007] For each flight condition, a Monte Carlo simulation is run independently to generate a load sequence with progressively increasing sample size. The estimated value of the target quantile and its 95% confidence interval are calculated. The minimum sample size required to achieve convergence is determined by the convergence criterion, and the ultimate load values ​​of each load parameter under that condition are extracted. This solves the technical problem that the ultimate load is difficult to obtain directly from limited measured data.

[0008] In one implementation of the first aspect, all measured flight profiles are statistically analyzed to obtain the frequency and duration of various flight mission segments. For low-frequency mission segments with a frequency of less than 5, they are grouped according to physical similarity: uniform level flight, accelerated level flight, and decelerated level flight are merged into the level flight category; climb, jump, and decelerated climb are merged into the climb category; and descent, dive, and decelerated descent are merged into the descent category.

[0009] The frequency of the merged mission segments is used as the cycle frequency of the flight profile. In each cycle, a corresponding number of mission segments are randomly selected from the simulation mission segment library according to the original proportion and spliced ​​together to ensure that low-frequency conditions are fully covered in multiple cycles and to avoid underestimation of damage contribution due to ignoring low-frequency conditions.

[0010] In one implementation of the first aspect, based on the mixing statistics results, task segments are extracted from the simulation task segment library to construct a comprehensive task spectrum. The extraction strictly follows the following four spectrum compilation principles:

[0011] The number of load cycles remains unchanged, meaning that the distribution of the number of main cycles and secondary cycles in various task segments is consistent with the measured statistics.

[0012] The correlation structure is preserved, and the extracted task segments are derived from the simulation task segment library that retains the original correlation, ensuring that the multi-parameter coupling relationship remains unchanged;

[0013] The time proportions are consistent, and the total duration of each type of task segment is the same as the proportion of each task segment in the measured spectrum.

[0014] Ultimate load values ​​are retained, and each type of task segment extraction includes at least one task segment instance that covers the ultimate load value for that working condition.

[0015] The extracted task segments are arranged in a random order to simulate the non-periodic nature of field use, and spliced ​​together to form a complete multi-parameter integrated task spectrum that considers low-frequency load conditions.

[0016] In one implementation of the first aspect, the rainflow counting method is used to compare the low-cycle fatigue damage of the compiled spectrum and the original spectrum, and the damage equivalence is verified by the consistency of the rainflow cumulative probability distribution; the extreme value coverage of each load parameter in the compiled spectrum and the original spectrum is compared to ensure that the ultimate load condition has been effectively included.

[0017] Secondly, this application provides a multi-parameter load spectrum compilation device that considers load conditions, including:

[0018] The multi-parameter joint probability distribution model construction module is used to divide the flight profile into flight mission segments based on flight actions and construct a multi-parameter joint probability distribution model.

[0019] The extraction module is used to generate load samples based on the multi-parameter joint probability distribution model and extract the ultimate load value;

[0020] The mixing statistics result acquisition module is used to statistically analyze all measured flight profiles and obtain the frequency and duration of various flight mission segments. The low-frequency mission segments are merged, and the frequency of the merged mission segments is used as the loop frequency of the flight profile. In each loop, mission segments are randomly selected from the simulation mission segment library according to the original ratio and spliced ​​to obtain the mixing statistics result.

[0021] The integrated task spectrum construction module is used to extract task segments from the simulation task segment library based on the mixing statistics results to construct an integrated task spectrum.

[0022] According to the above-mentioned technical solution proposed in this application, the following technical effects can be achieved: This multi-parameter load spectrum compilation method and device considering load conditions addresses the problem of load spectrum compilation for the ultimate load conditions of aero-engines. It uses flight mission segments as the basic compilation unit and considers low-frequency load conditions. By establishing a multi-parameter joint probability distribution model and using Monte Carlo simulation to identify ultimate loads, it achieves quantitative extraction of low-probability extreme events, solving the problem that traditional methods cannot cover ultimate loads. For low-frequency mission segments, a physical similarity merging strategy is proposed, ensuring complete coverage of low-frequency conditions in multiple cycles and avoiding underestimation of damage contribution. The damage consistency between the compiled spectrum and the measured spectrum is verified by comparing the cumulative probability of rainflow, providing a more accurate load input basis for aero-engine structural strength design, life assessment, and accelerated mission testing, and has significant engineering application value. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below only involve some embodiments of this disclosure and are not a limitation of this disclosure. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the method flow of an embodiment of this application; Figure 2 This is a convergence trajectory diagram of the ultimate load under accelerated level flight conditions according to an embodiment of this application; Figure 3 This is a diagram showing the convergence trajectory of the ultimate load under the descent left turn condition according to an embodiment of this application. Figure 4 This is an example of a multi-parameter reference spectrum under extreme load conditions according to an embodiment of this application; Figure 5 This is an example of a multi-parameter adjustment spectrum considering low-frequency load conditions in an embodiment of this application; Figure 6 This is a comparison diagram of the cumulative probability of normal overload rainflow in embodiments of this application; Figure 7 This is a comparison chart of the cumulative probability of high-pressure rotation speed rainflow in embodiments of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships, which may change accordingly when the absolute position of the described objects changes. It should be understood that the various steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0027] Firstly, according to one or more embodiments, a multi-parameter load spectrum compilation method considering load conditions is provided, solving the problem that existing load spectrum compilation methods are unable to effectively cover low-frequency ultimate load events, and providing more realistic load inputs for aero-engine structural strength design, life assessment, and accelerated mission testing; using flight mission segments as the basic spectrum compilation unit, the complex time-varying load history of aero-engines is decomposed into fifteen types of flight mission segments, and a multi-parameter joint probability distribution model is established; Monte Carlo simulation technology is used to identify the ultimate load values ​​under each flight condition, in order to... The probability criterion is used as a threshold for low-probability events to extract the ultimate load; for low-frequency task segments with fewer than 5 occurrences, they are merged according to physical similarity to ensure complete coverage of low-frequency conditions in multiple cycles; guided by four principles—consistent load cycle number, preservation of relevant structures, consistent time proportion, and retention of ultimate load values—a multi-parameter comprehensive task spectrum covering ultimate load states and damage equivalence is compiled; specifically, the following steps are included:

[0028] Step S1: Flight mission segment division and multi-parameter joint probability modeling: Based on flight maneuvers, the flight profile is divided into fifteen types of flight mission segments. Statistical modeling is performed on the maneuver load parameters and aerodynamic load parameters under each type of condition. The marginal distribution of each load parameter is fitted using a variational Bayesian-Gaussian mixture distribution, and the distribution of mission segment duration is fitted using an exponential distribution. A multi-parameter joint probability distribution model describing the correlation between parameters is constructed based on the Gaussian Copula function.

[0029] Step S2: Ultimate Load Identification Based on Monte Carlo Simulation: Based on the multi-parameter joint probability distribution model established in Step S1, a massive load sample is generated using Monte Carlo simulation technology, and statistical methods are employed to identify the ultimate load. The criterion is used as the threshold for low-probability events. The quantile with a cumulative probability of 99.83% is selected as the estimated value of the ultimate load. The minimum sample size required to achieve convergence is determined by the convergence criterion, and the ultimate load values ​​of each load parameter under each working condition are extracted.

[0030] Step S3: Flight mission segment mixing statistics and low-frequency mission segment processing: Statistical analysis of all measured flight profiles is performed to obtain the frequency and duration of various flight mission segments; for low-frequency mission segments with a frequency of less than 5, they are merged according to physical similarity, and the frequency of the merged mission segments is used as the loop frequency of the flight profile. In each loop, a corresponding number of mission segments are randomly selected from the simulation mission segment library according to the original proportion for splicing to ensure that low-frequency conditions are completely covered in multiple loops;

[0031] Step S4: Multi-parameter integrated task spectrum compilation: Based on the mixing statistics obtained in step S3, task segments are extracted from the simulation task segment library to construct an integrated task spectrum. The following four compilation principles are followed when extracting the segments: (1) the number of load cycles remains unchanged; (2) the correlation structure is maintained; (3) the time proportion is consistent; (4) the ultimate load value is retained. The extracted task segments are arranged in random order and spliced ​​together to form a complete multi-parameter integrated task spectrum considering low-frequency load conditions.

[0032] Step S5: Result Verification: The rainflow counting method is used to compare the low-cycle fatigue damage of the integrated task spectrum and the original spectrum. The consistency of the rainflow cumulative probability distribution is used to verify the damage equivalence. The extreme value coverage of each load parameter in the integrated task spectrum and the original spectrum is compared to ensure that the ultimate load condition has been effectively included. The original spectrum is extracted from the original data, and the data source for the integrated task spectrum is the simulation database.

[0033] According to one or more embodiments, the fifteen types of flight mission segments in step S1 include: constant speed level flight, accelerated level flight, decelerated level flight, climb, jump, decelerated climb, glide, dive, decelerated glide, horizontal left turn, horizontal right turn, ascending left turn, ascending right turn, descending left turn, and descending right turn; the maneuvering load parameters include normal overload coefficient, lateral overload coefficient, longitudinal overload coefficient, yaw rate, pitch rate, roll rate, yaw angular acceleration, pitch acceleration, and roll acceleration; the aerodynamic load parameters include high-pressure speed and low-pressure speed.

[0034] According to one or more embodiments, the variational Bayesian Gaussian mixture distribution in step S1 automatically determines the optimal number of mixture components by maximizing the lower bound of evidence, thereby achieving high-precision fitting of the multi-peak and skewed distribution characteristics of the load parameters; the correlation coefficient matrix of the Gaussian Copula function is obtained by a correlation coefficient calculation method based on the flight mission segment, ensuring that the joint distribution model accurately reproduces the coupling relationship between parameters.

[0035] According to one or more embodiments, the convergence criterion for the Monte Carlo simulation in step S2 is: the half-width of the 95% confidence interval of the target quantile estimate is less than 1% of the estimate; the extraction of the ultimate load value needs to be verified for its stability through multiple independent repeated runs.

[0036] According to one or more embodiments, such as Figure 5 As shown, the specific method of physical similarity merging in step S3 is as follows: uniform level flight, accelerated level flight, and decelerated level flight are merged into the level flight category; climb, jump, and decelerated climb are merged into the climb category; descent, dive, and decelerated descent are merged into the descent category; the frequency and total duration of each category after merging are the sum of the original categories.

[0037] According to one or more embodiments, the specific meanings of the four spectral compilation principles in step S4 are as follows:

[0038] (1) The number of load cycles remains unchanged: the distribution of the number of main cycles and secondary cycles in the extracted task segments is consistent with the measured statistics;

[0039] (2) Relevance structure preservation: The extracted task segments are derived from the simulation task segment library that preserves the original relevance, ensuring that the multi-parameter coupling relationship remains unchanged;

[0040] (3) Consistent time proportion: The total duration of each type of task segment is the same as the proportion of each task segment in the measured spectrum;

[0041] (4) Retention of ultimate load value: In the extraction of each type of task segment, there is at least one task segment instance that covers the ultimate load value of the working condition. The ultimate load value is taken from the identification result of step S2.

[0042] Secondly, according to one or more embodiments, a multi-parameter load spectrum compilation device considering load conditions is provided, comprising:

[0043] The multi-parameter joint probability distribution model construction module is used to divide the flight profile into flight mission segments based on flight actions and construct a multi-parameter joint probability distribution model.

[0044] The extraction module is used to generate load samples based on the multi-parameter joint probability distribution model and extract the ultimate load value;

[0045] The mixing statistics result acquisition module is used to statistically analyze all measured flight profiles and obtain the frequency and duration of various flight mission segments. Low-frequency mission segments are merged, and the frequency of the merged mission segments is used as the cycle frequency of the flight profile. In each cycle, mission segments are randomly selected from the simulation mission segment library according to the original proportion and spliced ​​to obtain the mixing statistics result. The generation method of the simulation mission segment library can refer to a multi-parameter load spectrum modeling method based on flight mission segments, or other feasible technical solutions.

[0046] The integrated task spectrum construction module is used to extract task segments from the simulation task segment library based on the mixing statistics results to construct an integrated task spectrum.

[0047] like Figure 1 , Figure 4 As shown, this embodiment is a multi-parameter load spectrum compilation method considering low-frequency load conditions, based on 100 measured flight profile data of a certain type of aero-engine, with a total flight time of To compile a multi-parameter load spectrum considering low-frequency load conditions, follow these steps:

[0048] Flight mission segmentation and multi-parameter joint probabilistic modeling were employed. Based on flight maneuvers, the middle segment of the flight profile was divided into fifteen types of flight mission segments. The specific segmentation method was based on three key parameters: barometric altitude and rate of change, Mach number, and heading angle. Automatic identification was achieved by detecting the cumulative changes and ranges of these parameters. The segmentation results are statistically shown in Table 1. The mixing frequency and mission segment length for each type of mission segment are as follows:

[0049] Table 1 Mixing Statistics for Flight Mission Segment

[0050]

[0051] For each type of mission segment, the maneuvering load parameters (normal overload Ny, lateral overload Nz, longitudinal overload Nx, yaw rate Ωx, pitch rate Ωy, roll rate Ωz, yaw angular acceleration ξx, pitch acceleration ξy, roll acceleration ξz) and aerodynamic load parameters (high pressure speed) are specified. Low pressure speed Statistical modeling was performed; the marginal distributions of each parameter were fitted using a variational Bayesian Gaussian mixture distribution, and a multi-parameter joint probability distribution model was constructed based on the Gaussian Copula function. The correlation coefficient matrix was calculated from the measured data.

[0052] Ultimate load identification based on Monte Carlo simulation: Based on the established joint probability distribution model, load samples are generated using Monte Carlo simulation; taking acceleration level flight and descent left turn conditions as examples, the maximum sampling size is set to... The 3σ probability criterion was used as the ultimate load threshold. After multiple independent repeated runs, all parameters met the convergence condition and the ultimate load values ​​remained stable before reaching the maximum sample size. The statistically calculated ultimate load values ​​of each parameter are shown in Table 2.

[0053] Table 2 Overload coefficients and speed limit load values ​​under different flight conditions

[0054]

[0055] Multi-parameter integrated task spectrum compilation: Based on the mixing statistics, task segments are extracted from the joint distribution model to construct the integrated task spectrum; the extraction strictly follows four compilation principles: (1) Load cycle number remains unchanged: ensure that the number of primary and secondary cycles in the extracted task segments is consistent with the measured statistical distribution; (2) Correlation structure is maintained: the task segments are from the simulation library that maintains the original correlation, ensuring that the multi-parameter coupling relationship remains unchanged; (3) Time proportion is consistent: the total duration of each type of task segment is consistent with the measured proportion in Table 1; (4) Limit load value is retained: in the extraction of each type of task segment, there is at least one task segment instance that covers the limit load value of the working condition, and the limit load value is taken from Table 2.

[0056] A low-frequency configuration method is defined, using the frequency of the merged mission segments as the cycle frequency of the flight profile. For example, if the frequency of level flight is 19, then in the first 17 cycles, one uniform level flight mission segment is extracted in each cycle (17 in total). In the 18th and 19th cycles, one acceleration level flight mission segment and one deceleration level flight mission segment are extracted respectively, ensuring that low-frequency conditions are fully covered in multiple cycles. The spectrum that does not consider low-frequency flight conditions is defined as the baseline spectrum, and the spectrum that considers low-frequency flight conditions is defined as the adjustment spectrum. The extracted mission segments are arranged in random order to simulate the non-periodicity of field use, and spliced ​​together to form a complete multi-parameter integrated mission spectrum that considers low-frequency load conditions.

[0057] Taking normal overload and high-pressure speed as examples, the original spectrum and the adjusted spectrum are tested for damage consistency, and the results are as follows: Figure 6 Comparison of normal overload rainflow accumulation probability and as shown in the figure Figure 7 A comparison chart of the cumulative probability of rainflow at high-pressure speeds is provided. The cumulative probability curves of rainflow in the integrated mission spectrum and the measured spectrum are basically coincident, indicating that the low-cycle fatigue damage to the engine caused by the two is basically the same. There are more cycles with larger amplitude values ​​in the integrated mission spectrum, which is due to the consideration of the ultimate load condition and meets the expected conservative requirements.

[0058] exist Figure 2In the figure, the blue and orange shaded areas represent the confidence intervals of the normal overload and high-pressure speed estimates, respectively. The green blocks represent the points where the ultimate load estimates begin to converge, and the red points represent the points with the maximum sample size. The number of samples where the normal overload begins to converge is 138,950, and the ultimate load is 1.30. The number of samples where the high-pressure speed begins to converge is 227,585, and the ultimate load is 99.75. After multiple independent repeated runs, the convergence conditions are met and the converged ultimate load values ​​are stable before the maximum sample size reaches 1×10⁶.

[0059] exist Figure 3 In the figure, the blue and orange shaded areas represent the confidence intervals of the normal overload and high-pressure speed estimates, respectively. The green blocks represent the points where the ultimate load estimates begin to converge, and the red points represent the points with the maximum sample size. The number of samples where the normal overload begins to converge is 23,590, and the ultimate load is 5.62. The number of samples where the high-pressure speed begins to converge is 138,950, and the ultimate load is 100.82. After multiple independent repeated runs, the convergence conditions are met and the converged ultimate load values ​​are stable before the maximum sample size reaches 1×10⁶.

[0060] exist Figure 6 , Figure 7 In the process, the multi-parameter integrated mission spectrum with flight mission segments as the spectrum compilation unit contains most of the load cycles in the flight profile. The cumulative probability comparison results show that the compiled spectrum and the measured spectrum are basically consistent in causing low-cycle fatigue damage to the engine, which verifies the accuracy of this spectrum compilation method. The accuracy of the normal overload coefficient of the maneuver load is higher than that of the high-pressure speed of the aerodynamic load.

[0061] The units involved in the embodiments of this disclosure can be implemented in software or in hardware. The names of the units are not, in some cases, limiting the scope of the unit itself.

[0062] The functions described above can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0063] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0064] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0065] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for compiling a multi-parameter load spectrum considering load conditions, characterized in that: include: Based on flight maneuvers, the flight profile is divided into flight mission segments, and a multi-parameter joint probability distribution model is constructed. Load samples are generated based on the multi-parameter joint probability distribution model, and the ultimate load values ​​of the load parameters are extracted. All measured flight profiles were statistically analyzed to obtain the frequency and duration of various flight mission segments. Low-frequency mission segments were merged, and the frequency of the merged mission segments was used as the cycle frequency of the flight profile. In each cycle, mission segments were randomly selected from the simulation mission segment library according to the original ratio and spliced ​​together to obtain the mixed frequency statistical results. Based on the mixing statistics, task segments are extracted from the simulation task segment library to construct a comprehensive task spectrum.

2. The method for compiling a multi-parameter load spectrum considering load conditions according to claim 1, characterized in that: The process of dividing the flight profile into flight mission segments based on flight maneuvers and constructing a multi-parameter joint probability distribution model includes: Based on flight maneuvers, the flight profile is divided into fifteen flight mission segments. Statistical modeling is performed on the maneuver load parameters and aerodynamic load parameters under each type of condition. The marginal distribution of each load parameter is fitted using a variational Bayesian-Gaussian mixture distribution, and the distribution of mission segment duration is fitted using an exponential distribution. A multi-parameter joint probability distribution model is constructed based on the Gaussian Copula function.

3. The method for compiling a multi-parameter load spectrum considering load conditions according to claim 1, characterized in that: The process of generating load samples based on the multi-parameter joint probability distribution model and extracting the ultimate load values ​​of the load parameters includes: Based on a multi-parameter joint probability distribution model, a massive number of load samples are generated using Monte Carlo simulation to determine the estimated value of the ultimate load. The minimum sample size required to achieve convergence is determined by the convergence criterion, and the ultimate load values ​​of each load parameter under each working condition are extracted.

4. The method for compiling a multi-parameter load spectrum considering load conditions according to claim 3, characterized in that: The estimated values ​​for determining the ultimate load include: In statistics The criterion, which serves as a threshold for low-probability events, selects the quantile with a cumulative probability of 99.83% as the estimated value of the ultimate load.

5. The method for compiling a multi-parameter load spectrum considering load conditions according to claim 3, characterized in that: The minimum sample size required to achieve convergence, as determined by the convergence criterion, includes: the half-width of the 95% confidence interval of the target quantile estimate is less than 1% of the estimate.

6. The method for compiling a multi-parameter load spectrum considering load conditions according to claim 1, characterized in that: The step of extracting task segments from the simulation task segment library to construct a comprehensive task spectrum includes: The load cycle number remains unchanged; The correlation structure is preserved; The time allocation is consistent; Ultimate load values ​​are retained.

7. The method for compiling a multi-parameter load spectrum considering load conditions according to claim 1, characterized in that: The process of merging low-frequency task segments includes merging low-frequency task segments with a frequency of less than 5 according to their physical similarity.

8. The method for compiling a multi-parameter load spectrum considering load conditions according to claim 7, characterized in that: The physical similarity merging process includes: merging uniform level flight, accelerated level flight, and decelerated level flight into the level flight category; Climbing, jumping, and deceleration climbing are combined into the climbing category; The descent, dive, and deceleration descent are combined into the descent category; After merging, the frequency and total duration of each category are the sum of the original categories.

9. The method for compiling a multi-parameter load spectrum considering load conditions according to claim 1, characterized in that: The method further includes: using the rainflow counting method to compare the low-cycle fatigue damage of the integrated task spectrum and the original spectrum, verifying the damage equivalence through the consistency of the rainflow cumulative probability distribution; and comparing the extreme value coverage of each load parameter in the integrated task spectrum and the original spectrum.

10. A multi-parameter load spectrum compilation device considering load conditions, characterized in that, include: The multi-parameter joint probability distribution model construction module is used to divide the flight profile into flight mission segments based on flight actions and construct a multi-parameter joint probability distribution model. The extraction module is used to generate load samples based on the multi-parameter joint probability distribution model and extract the ultimate load values ​​of the load parameters. The mixing statistics result acquisition module is used to statistically analyze all measured flight profiles and obtain the frequency and duration of various flight mission segments. The low-frequency mission segments are merged, and the frequency of the merged mission segments is used as the loop frequency of the flight profile. In each loop, mission segments are randomly selected from the simulation mission segment library according to the original ratio and spliced ​​to obtain the mixing statistics result. The integrated task spectrum construction module is used to extract task segments from the simulation task segment library based on the mixing statistics results to construct an integrated task spectrum.

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