Aircraft reliability distribution method and system and electric vertical take-off and landing aircraft

Abstract

The invention relates to the technical field of aircrafts, and discloses an aircraft reliability distribution method and system and an electric vertical take-off and landing aircraft, and the method comprises the steps: obtaining evaluation factors of aircraft reliability distribution indexes, the evaluation factors comprising complexity, technology maturity, fault criticality, maintainability and operation environment; constructing an expert group decision model, generating a comment set containing the evaluation level corresponding to each evaluation factor based on a preset comment criterion, performing triangular fuzzy quantization processing on each comment set, and determining a judgment matrix; a whitening value of each evaluation matrix is calculated, and evaluation comprehensive information of each expert is determined based on each whitening value; and determining a reliability distribution index coefficient of the aircraft according to the evaluation comprehensive information. According to the method, fuzzy quantization processing is carried out on each expert set comment, the uncertainty and fuzziness of evaluation are avoided, and the accuracy of evaluation is improved; meanwhile, the accuracy of reliability distribution is improved by integrating the evaluation information of the experts.

Description

Technical Field

[0001] This application relates to the technical field of aircraft, and particularly to an aircraft reliability allocation method, system and electric vertical takeoff and landing aircraft. Background Art

[0002] Low-altitude aircraft include unmanned aerial vehicles, helicopters, and eVTOL (electric vertical takeoff and landing aircraft). Among them, eVTOL integrates technologies such as distributed electric propulsion, green new energy, new aviation materials, artificial intelligence, and 5G networks, and has the vertical takeoff and landing capabilities of helicopters and the efficient cruise characteristics of fixed-wing aircraft, enabling people and goods to flow quickly and operate flexibly in the low altitude of cities in a seamless and economical manner.

[0003] However, in the related art, when it comes to new types of aircraft such as eVTOL, due to the lack of similar product data in the market as a reference for the reliability allocation design of new aircraft, on the one hand, the existing reliability allocation methods are based on traditional aircraft design experience and lack reference value. On the other hand, the related art uses a single expert scoring method, which has strong evaluation subjectivity and ambiguity. In this way, it is easy to cause inaccurate reliability allocation. Therefore, there is an urgent need for a new aircraft reliability allocation scheme. Summary of the Invention

[0004] Embodiments of this application provide an aircraft reliability allocation method, system and electric vertical takeoff and landing aircraft to solve the technical problem of low accuracy of the aircraft reliability allocation method in the related art.

[0005] Embodiments of this application provide an aircraft reliability allocation method. The aircraft reliability allocation method includes: obtaining evaluation factors of the aircraft reliability allocation index, where the evaluation factors include complexity, technology maturity, failure hazard degree, maintainability, and operating environment; constructing an expert group decision-making model, and generating a comment set including the evaluation grades corresponding to each evaluation factor based on a preset comment criterion; performing fuzzy quantification processing on each comment set to determine a judgment matrix; calculating the whitening value of each judgment matrix, and determining the comprehensive evaluation information of each expert based on each whitening value; and determining the reliability allocation index coefficient of the aircraft according to the comprehensive evaluation information.

[0006] In an embodiment of the present application, after determining the reliability allocation index coefficient of the aircraft, the following steps are further included: determining the mean flight hours between failures allocated to each component in the aircraft according to the reliability allocation index coefficient; performing an upper-rounding operation on the mean flight hours between failures to determine the allocation threshold of each component at the mean flight hours between failures; determining the mean flight hours between failures of the entire aircraft according to the allocation thresholds of each component, performing verification based on the preset mean flight hours between failures and the mean flight hours between failures of the entire aircraft, and determining the reliability allocation index according to the verification result.

[0007] In an embodiment of the present application, performing verification based on the preset mean flight hours between failures and the mean flight hours between failures of the entire aircraft, and determining the reliability allocation index according to the verification result, includes: if the mean flight hours between failures of the entire aircraft is greater than or equal to the preset mean flight hours between failures, the verification is passed, and the allocation threshold of each component at the mean flight hours between failures is used as the reliability allocation index; if the mean flight hours between failures of the entire aircraft is less than the preset mean flight hours between failures, the verification fails, and the allocation threshold of each component at the mean flight hours between failures is adjusted until the verification is passed.

[0008] In an embodiment of the present application, determining the mean flight hours between failures allocated to each component in the aircraft includes: determining the failure rate of the entire aircraft according to the preset mean flight hours between failures; performing a weighted calculation based on the failure rate of the entire aircraft and the reliability allocation index coefficient of each component to determine the failure rate allocated to each component; calculating the reciprocal value according to the failure rate allocated to each component to determine the mean flight hours between failures allocated to each component.

[0009] In an embodiment of the present application, constructing an expert group decision-making model and generating a comment set including the evaluation grades corresponding to each evaluation factor based on a preset comment criterion includes: constructing an expert group decision-making model according to the expert group participating in the decision-making, the evaluation factor set, and the comment set; calling the expert group decision-making model, and determining, based on the preset comment criterion, the comment set of each expert including the evaluation grades corresponding to each evaluation factor, where the comment set consists of the evaluation grade and the comment, and there is a one-to-one correspondence between the evaluation grade and the comment.

[0010] In an embodiment of the present application, performing fuzzy quantification processing on each comment to determine a judgment matrix includes: based on the fuzzy mathematics theory, performing fuzzy quantification processing on each comment set through a triangular fuzzy function to determine the triangular fuzzy number of each comment set; performing triangular fuzzy number quantification on each comment of each expert to obtain a judgment matrix composed of the quantified comment information of multiple experts.

[0011] In an embodiment of the present application, the expression of the triangular fuzzy function is:

[0012]

[0013] In formula (1), d L is the lower limit of the triangular fuzzy number, d Z is the median value of the triangular fuzzy number, d R is the upper limit of the triangular fuzzy number, r is the evaluation grade in the comment set, max is the maximum value in the set, and min is the minimum value in the set.

[0014] In an embodiment of the present application, the whitenization value of each judgment matrix is calculated, and the comprehensive evaluation information of each expert is determined based on the whitenization values, including: normalizing the judgment matrix to determine the normalized element value of each element; normalizing the normalized element value of each element to determine the normal whitenization value; determining the whitenization value of each element of the judgment matrix according to the normal whitenization value; performing a meta-summation process on the whitenization values of each element of the judgment matrix, and averaging the meta-summation value according to the number of each expert to determine the comprehensive evaluation information.

[0015] In an embodiment of the present application, determining the whitenization value of each element of the judgment matrix according to the normal whitenization value includes: determining a first difference between the upper limit maximum value and the lower limit minimum value of any element in the judgment matrix; determining a whitenization increment value according to the product of the first difference and the normal whitenization value; and determining the whitenization value of each element in the judgment matrix by summing the whitenization increment value and the lower limit minimum value of any element.

[0016] In an embodiment of the present application, normalizing the normalized element value of each element to determine the normal whitenization value includes: determining the lower limit value, median value, and upper limit value of the currently arbitrary element in the judgment matrix after normalization processing; calculating the ratio of the median value to the second difference between the median value and the lower limit value to determine a first normalization value; calculating the ratio of the upper value to the third difference between the upper value and the lower limit value to determine a second normalization value; calculating a fourth difference between the square of the second normalization value and the first normalization value; and determining the normal whitenization value by dividing the product of the fourth difference and the first normalization value by the fifth difference between the second normalization value and the first normalization value.

[0017] In an embodiment of the present application, determining the reliability allocation index coefficient of the aircraft according to the comprehensive evaluation information includes: calculating the meta-product of the comprehensive evaluation information corresponding to multiple experts for any component, and determining it as the weight coefficient of the current arbitrary component; determining the reliability allocation index coefficient of any component in the aircraft according to the ratio between the weight coefficient of the current arbitrary component and the sum of the weight coefficients of all components.

[0018] The embodiment of the present application further provides an aircraft reliability allocation system, and the aircraft reliability allocation system includes: a factor determination module configured to obtain the evaluation factors of the aircraft reliability allocation index; a model construction module configured to construct an expert group decision-making model and generate a comment set including the evaluation levels corresponding to each of the evaluation factors based on a preset comment criterion; a matrix determination module configured to perform fuzzy quantification processing on each of the comment sets to determine a judgment matrix; an evaluation comprehensive module configured to calculate the whitening value of each of the judgment matrices and determine the comprehensive evaluation information of each expert based on the whitening values; a distribution coefficient determination module configured to determine the reliability allocation index coefficient of the aircraft according to the comprehensive evaluation information.

[0019] In an embodiment of the present application, the aircraft reliability allocation system further includes: a calculation module configured to determine the average flight hours between failures allocated to each component in the aircraft according to the reliability allocation index coefficient; a verification module configured to perform an upper rounding operation on the average flight hours between failures to determine the allocation threshold of each component at the average flight hours between failures, determine the average flight hours between failures of the entire aircraft according to the allocation thresholds of each component, perform verification based on a preset average flight hours between failures and the average flight hours between failures of the entire aircraft, and determine the reliability allocation index according to the verification result.

[0020] The embodiment of the present application further provides an electric vertical takeoff and landing aircraft, and the electric vertical takeoff and landing aircraft includes the aircraft reliability allocation method according to any one of the above embodiments.

[0021] In the solutions implemented by the aircraft reliability allocation method, system, and electric vertical takeoff and landing aircraft provided above, the aircraft reliability allocation method adopts the knowledge and experience of multiple experts by constructing an expert group decision-making model, reducing the subjectivity and one-sidedness of a single expert's decision-making. At the same time, a comment set including evaluation grades is generated based on a preset comment criterion, ensuring the unity and objectivity of the expert evaluation criteria. The comment sets of each expert are subjected to fuzzy quantification processing through triangular fuzzy numbers, avoiding the uncertainty and ambiguity of evaluation and improving the accuracy of evaluation. Finally, the whitening values of each judgment matrix of multiple experts are calculated, and the comprehensive evaluation information of each expert is determined based on the whitening values. The opinions of all experts are comprehensively considered to form a reliability evaluation of the overall aircraft, improving the accuracy of reliability allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 An exemplary system architecture diagram in a related art provided by an embodiment of the present application;

[0024] Figure 2 A flowchart of an aircraft reliability allocation method provided by an embodiment of the present application;

[0025] Figure 3 A complete flowchart of an aircraft reliability allocation method provided by an embodiment of the present application;

[0026] Figure 4 A structural diagram of an aircraft reliability allocation system provided by an embodiment of the present application;

[0027] Figure 5 A complete structural diagram of an aircraft reliability allocation system provided by an embodiment of the present application;

[0028] Figure 6 A structural diagram of an electronic device in an embodiment of the present application;

[0029] Figure 7 Another structural diagram of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0031] To enable those skilled in the art to better understand the improvements in the technical solutions provided by the present disclosure, the implementation scenarios and related information of the aircraft reliability allocation method in the related art are briefly introduced.

[0032] In the related art, as a new type of aircraft, eVTOL involves many new technologies, and there is a lack of similar product data in the market as a reference for the reliability design and allocation of eVTOL. At the same time, the existing reliability allocation methods are mostly based on the design experience of traditional aircraft and lack systematic processing for the specific requirements of eVTOL. For different development stages, traditional reliability allocation methods include the equal allocation method, the scoring allocation method, the proportional combination method, and the prediction allocation method. Among them, the scoring allocation method is used more frequently in the case of lack of data for newly developed aircraft models, but it is highly subjective, and the allocation results are easily affected by expert experience; the proportional combination method is an allocation method based on the failure ratio of similar aircraft models. As a new type of aircraft, eVTOL also does not have reliable data of similar aircraft models for reference, resulting in limitations of this method in the initial stage of eVTOL design; in addition, the equal allocation method has large allocation differences and does not conform to the actual situation; the prediction allocation method requires suppliers to provide actual data, which is not conducive to the reliability design in the preliminary and detailed stages.

[0033] To solve the above technical problems, the present application provides an aircraft reliability allocation method, system and electric vertical takeoff and landing aircraft to solve the above problems. Please refer to Figure 1 , Figure 1 which is an exemplary system architecture diagram in the related art provided by the embodiments of the present application. As Figure 1 shown, the aircraft 100 includes a carrier (i.e., the fuselage) and a load. Those skilled in the art should understand that any embodiment of the aircraft described herein is applicable to any aircraft (such as an unmanned aircraft, also known as a drone, such as a manned aircraft, an electric vertical takeoff and landing aircraft, etc.). In some embodiments, the load can be directly located on the aircraft without a carrier. The aircraft may include a processor, a memory, a power mechanism, a sensing system, and a communication system. These components are interconnected through a bus system and / or other forms of connection mechanisms (not shown). The load may include a photographing device, etc.

[0034] The power mechanism may include one or more rotors, propellers, blades, engines, motors, wheels, bearings, magnets, nozzles. For example, the rotor of the power mechanism may be a self-fastening rotor, a rotor assembly, or other rotor power unit. The aircraft may have one or more power mechanisms. All the power mechanisms may be of the same type. Optionally, one or more power mechanisms may be of different types. The power mechanism may be mounted on the aircraft by suitable means, such as through a support element (such as a drive shaft). The power mechanism may be mounted at any suitable position of the aircraft 100, such as the top, bottom, front, rear, side, or any combination thereof.

[0035] In some embodiments, the power mechanism enables the aircraft to take off vertically from a surface or land vertically on a surface without any horizontal movement of the aircraft 100 (such as without taxiing on a runway). Optionally, the power mechanism may allow the aircraft 100 to hover at a preset position and / or direction in the air. One or more power mechanisms may be controlled independently of other power mechanisms.

[0036] The sensing system may include one or more sensors to sense the spatial orientation, velocity, and / or acceleration of the aircraft 100 (such as rotation and translation with respect to up to three degrees of freedom). One or more sensors include any of the sensors described above, including GPS sensors, motion sensors, inertial sensors, proximity sensors, or imaging sensors. The sensing data provided by the sensing system may be used to track the spatial orientation, velocity, and / or acceleration of the target (as described below, using a suitable processing unit and / or control unit). Optionally, the sensing system may be used to collect data on the environment of the aircraft, such as climate conditions, potential obstacles to approach, locations of geographical features, locations of man-made structures, image information, etc.

[0037] The communication system 101 is capable of communicating with a control device 102 having a communication system via a wireless signal. The communication system 101 may include any number of transmitters, receivers, and / or transceivers for wireless communication. The communication may be one-way communication, so that data can be sent in one direction. For example, one-way communication may include only the aircraft 100 transmitting data to the control device 102, or vice versa. One or more transmitters of the communication system 101 may send data to one or more receivers of the communication system 101, and vice versa. Optionally, the communication may be two-way communication, so that data can be transmitted in both directions between the aircraft 100 and the control device 102. Two-way communication includes one or more transmitters of the communication system 101 sending data to one or more receivers of the communication system 101, and vice versa.

[0038] In some embodiments, the control device 102 may provide control data to one or more of the aircraft 100, the carrier, and the load, and receive information from one or more of the aircraft 100, the carrier, and the load (such as the position and / or motion information of the aircraft, the carrier, or the load, and the data sensed by the load, such as the image data captured by an imaging device such as a camera). In some embodiments, the control data of the control device may include instructions regarding position, motion, actuation, or the control of the aircraft, the carrier, and / or the load. For example, the control data may cause a change in the position and / or orientation of the aircraft (such as by controlling the power mechanism), or cause the movement of the carrier relative to the aircraft (such as by controlling the carrier). The control data of the control device may cause load control, such as controlling the operation of a camera or other imaging capture device (capturing still or moving images, zooming, turning on or off, switching shooting modes, changing image resolution, changing focal length, changing depth of field, changing exposure time, changing viewing angle or field of view). The control data transmitted and provided by the control device 102 may be used to track the status of one or more of the aircraft 100, the carrier, or the load. Optionally or simultaneously, each of the carrier and the load may include a communication module for communicating with the control device 102 so that the control device can communicate with or track the aircraft 100, the carrier, and the load individually.

[0039] In some embodiments, the aircraft 100 may communicate with other remote devices other than the control device 102, and the control device 102 may also communicate with other remote devices other than the aircraft 100. For example, the aircraft and / or the control device 102 may communicate with another aircraft or the carrier or load of another aircraft. When needed, the additional remote device may be a second control device or other computing device (such as a computer, desktop computer, tablet computer, smartphone, or other mobile device). The remote device may transmit data to the aircraft 100, receive data from the aircraft 100, transmit data to the control device 102, and / or receive data from the control device 102. Optionally, the remote device may be connected to the Internet or other telecommunication networks so that the data received from the aircraft 100 and / or the control device 102 can be uploaded to a website or server.

[0040] In some embodiments, the movement of the aircraft, the movement of the carrier, and the movement of the load relative to a fixed reference (such as the external environment), and / or the movement between each other can be controlled by a control device. The control device can be a remote control terminal located away from the aircraft, the carrier, and / or the load. The control device can be located on or attached to a support platform. Optionally, the control device can be handheld or wearable. For example, the control device can include a mobile phone, a tablet computer, a computer, glasses, gloves, a helmet, a remote control, a microphone, or any combination thereof. The control device can include a user interface, such as a keyboard, a mouse, a joystick, a touch screen, or a display. Any suitable user input can interact with the control device, such as manual input commands, voice control, gesture control, or position control (such as by the movement, position, or tilt of the control device).

[0041] The aircraft 100 can include one or more memories on which computer programs run by a processor are stored, such as program instructions for implementing various functions of the aircraft. It can include one or more computer program products, and the computer program products can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory can include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory can include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0042] The aircraft 100 can include one or more processors. The processor can be a central processing unit (CPU), an image processing unit (GPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and can control other components in the aircraft 100 to perform desired functions. For example, the processor can include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware finite state machines (FSMs), digital signal processors (DSPs), or combinations thereof. In this embodiment, the processor includes a field-programmable gate array (FPGA) or one or more ARM processors.

[0043] Please refer to Figure 2 As shown, it is a schematic flowchart of a method for allocating the reliability of an aircraft provided by an embodiment of the present application, which is described in detail as follows:

[0044] Step S210, obtain the evaluation factors of the aircraft reliability allocation index, where the evaluation factors include complexity, technology maturity, failure hazard degree, maintainability, and operating environment;

[0045] Among them, the reliability allocation indicators of the aircraft include, but are not limited to, the (Mean Flight Hours Between Failures, MFHBF) mean flight hours between failures. As an important parameter for evaluating the inherent reliability design level of the product, MFHBF is widely used in the reliability design of aircraft because it can provide an intuitive and quantifiable reliability standard.

[0046] Exemplarily, the evaluation factors affecting MFHBF include, but are not limited to, complexity, technology maturity, failure hazard degree, maintainability, and operating environment. For example, complexity reflects the refinement of system design and the possibility of potential errors; for example, technology maturity ensures that the adopted technology has been fully verified and can operate stably; for example, the failure hazard degree evaluates the degree of impact of failures on flight safety and provides a basis for risk management; for example, maintainability considers the maintainability of the system and directly affects the availability and operating cost of the equipment; for example, the operating environment affects the performance and adaptability of the equipment and ensures reliable operation under different conditions. Therefore, by comprehensively considering these five dimensions in this application, the reliability of eVTOL can be evaluated more comprehensively and scientifically, ensuring its safety and practicality in reality. At the same time, the comprehensiveness and accuracy of reliability allocation are ensured through multi-dimensional evaluation.

[0047] Step S220: Construct an expert group decision-making model, and generate a comment set including the evaluation levels corresponding to each of the evaluation factors based on a preset comment criterion.

[0048] Among them, an expert group decision-making model is constructed according to the expert group participating in the decision-making, the evaluation factor set, and the comment set; according to the expert group and the evaluation factor set, based on a preset comment criterion, a comment set of the evaluation levels corresponding to each evaluation factor of each expert is determined. The comment set consists of the evaluation level and the comment, and there is a one-to-one correspondence between the evaluation level and the comment.

[0049] Exemplarily, let N = {1, 2, …, n}, M = {1, 2, …, m}, G = {1, 2, …, g}, the eVTOL reliability index evaluation factor set is V = {v j}, j ∈ N; the expert group participating in the decision-making is E = {e i}, i ∈ M; the comment set is S = {s r}, r ∈ G; the <V, E, S> constitutes an expert group decision-making mathematical model.

[0050] It should be noted that the group decision-making model is used to reduce the bias of single expert judgment and improve the objectivity and accuracy of evaluation. For example, a jury composed of multi-disciplinary experts is formed; for each evaluation factor, corresponding evaluation grades are set according to the preset comment criteria (such as excellent, good, average, poor, bad); each grade corresponds to a descriptive comment so that experts can give evaluations more intuitively. The group decision-making and standardized comments improve the comprehensiveness and consistency of evaluation.

[0051] Step S230, perform fuzzy quantification processing on each of the comment sets to determine a judgment matrix;

[0052] Specifically, the qualitative comment set of experts is converted into quantitative values, that is, fuzzy quantification, which is achieved by defining a membership function. Each comment corresponds to a membership degree interval; then, according to the evaluation of each expert on each evaluation factor, a judgment matrix is constructed. The fuzzy mathematics method can handle the fuzziness and uncertainty in evaluation and convert subjective evaluation into objectively comparable values.

[0053] For example, other quantification techniques are adopted, such as the analytic hierarchy process, network analytic method, etc., and the most suitable quantification method is selected according to the specific situation.

[0054] Among them, based on the fuzzy mathematics theory, each of the comment sets is subjected to fuzzy quantification processing through a triangular fuzzy function to determine the triangular fuzzy number of each of the comment sets; for each expert regarding each of the comment sets, the triangular fuzzy number is determined to obtain a judgment matrix.

[0055] In an embodiment of the present application, the expression of the triangular fuzzy function is:

[0056]

[0057] In formula (1), d L is the lower limit of the triangular fuzzy number, d Z is the median of the triangular fuzzy number, d R is the upper limit of the triangular fuzzy number, r is the evaluation grade in the comment set, max is the maximum value in the set, and min is the minimum value in the set.

[0058] Exemplarily, a comment set is defined, for example: V = {v 1 , v 2 , …, v n}, where each comment represents an evaluation grade, such as "excellent", "good", "average", "poor", "bad". For each comment in the comment set, a triangular fuzzy number is assigned according to expert opinions; there are m evaluation indicators and corresponding n comment grades. For example, these matrices are combined by methods such as weighted average or fuzzy operations, such as fuzzy union, fuzzy intersection, etc., and a judgment matrix is obtained through fuzzy composition operation.

[0059] Step S240: Calculate the whitenized value of each evaluation matrix, and determine the comprehensive evaluation information of each expert based on the whitenized values.

[0060] Among them, the whitenized value refers to the process of converting fuzzy information into clear numerical values, which is achieved by calculating the weighted average of membership degrees. Based on the whitenized values of each evaluation matrix, the comprehensive evaluation information of each expert is summarized.

[0061] Step S250: Determine the reliability allocation index coefficient of the aircraft according to the comprehensive evaluation information.

[0062] Among them, the determination of the reliability allocation index coefficient is based on the comprehensive consideration of all evaluation factors to ensure that the allocation is both fair and reasonable.

[0063] In this embodiment, the evaluation factors are determined through five dimensions: complexity, technology maturity, failure hazard degree, maintainability, and operating environment to ensure the comprehensive evaluation of the aircraft's reliability; by constructing an expert group decision-making model and adopting the knowledge and experience of multiple experts, the subjectivity and one-sidedness of single-expert decision-making are reduced; at the same time, by invoking the expert group decision-making model, a comment set containing evaluation levels is generated based on the preset comment criteria to ensure the unity and objectivity of the expert evaluation criteria; through triangular fuzzy numbers, the fuzzy quantification process is performed on each expert's comment set to avoid the uncertainty and ambiguity of evaluation and improve the accuracy of evaluation; finally, a judgment matrix is composed of the evaluation quantification information of multiple experts, the whitenized value of each judgment matrix is calculated, the comprehensive evaluation information of each expert is determined based on the whitenized values, and the overall evaluation of the aircraft's reliability is formed by comprehensively considering the opinions of all experts, improving the accuracy of reliability allocation.

[0064] Optionally, in an embodiment, after determining the reliability allocation index coefficient of the aircraft, it further includes:

[0065] Step S260: Determine the average flight hours between failures allocated to each component in the aircraft according to the reliability allocation index coefficient.

[0066] Specifically, the reliability allocation index coefficient reflects the importance of each component in the overall reliability of the aircraft or system. Obtain the reliability allocation index coefficient of each component in the aircraft, and use the proportional allocation method or other allocation methods according to the reliability allocation index coefficient and the target value of the average flight hours between failures (MFHBF) of the entire aircraft to calculate the average flight hours between failures that should be allocated to each component. For example, other reliability allocation methods such as the scoring allocation method and the analytic hierarchy process allocation method can also be used to ensure that each component obtains reliability requirements that match its importance.

[0067] Step S270: Perform ceiling rounding operation on the mean flight hours between failures to determine the allocation threshold of each component at the mean flight hours between failures.

[0068] Specifically, through the ceiling rounding operation, ensure that the reliability requirement of each component is not lower than the calculated average value. Perform ceiling rounding operation on the mean flight hours between failures of each calculated component, that is, round up to the nearest integer or the specified precision value. The obtained rounded value is the allocation threshold of each component at the mean flight hours between failures. Through the ceiling rounding operation, the reliability requirements of each component are fully met, and at the same time, the convenience and accuracy of management are improved.

[0069] Step S280: Determine the mean flight hours between failures of the entire aircraft based on the allocation threshold of each component, perform verification based on the preset mean flight hours between failures (i.e., the preset overall MFHBF) and the mean flight hours between failures of the entire aircraft, and determine the reliability allocation index according to the verification result.

[0070] Specifically, by verifying the difference between the mean flight hours between failures of the entire aircraft and the preset value, the rationality and effectiveness of the reliability allocation index can be evaluated; if there is a difference, it is necessary to optimize the reliability allocation scheme by adjusting the allocation index; for example, calculate the MFHBF of the entire aircraft according to the allocation threshold of each component; then, compare the overall MFHBF with the preset overall MFHBF for verification; if the overall MFHBF is lower than the preset overall MFHBF, adjust the reliability allocation index coefficient, and re-perform the calculations in steps S260 and S270 until the verification requirements are met; by verifying and adjusting the reliability allocation index, ensure that the overall reliability level of the aircraft reaches the preset requirements, and improve the safety and reliability of the aircraft.

[0071] Through the above method, through reasonable allocation and verification, ensure that the reliability requirements of each component of the aircraft are met, and improve the overall reliability level of the aircraft.

[0072] Optionally, in an embodiment, performing verification based on the preset overall mean flight hours between failures and the mean flight hours between failures of the entire aircraft, and determining the reliability allocation index according to the verification result includes:

[0073] If the mean flight hours between failures of the entire aircraft is greater than or equal to the preset overall mean flight hours between failures, the verification passes, and use the current allocation threshold of each component at the mean flight hours between failures as the reliability allocation index;

[0074] If the mean flight hours between failures (MFHBF) of the entire aircraft is less than the preset MFHBF of the entire aircraft, the verification fails, and the allocation threshold of the MFHBF of each current component is adjusted until the verification passes.

[0075] Specifically, based on the allocation threshold of each component for the mean flight hours between failures (MFHBF), the MFHBF value of the entire aircraft is calculated. The calculated MFHBF value of the entire aircraft is compared with the preset MFHBF value of the entire aircraft. The preset MFHBF value of the entire aircraft is usually determined based on factors such as the design requirements of the aircraft, historical data, and industry standards. If the MFHBF value of the entire aircraft is greater than or equal to the preset MFHBF value of the entire aircraft, it is confirmed that the verification passes. If the MFHBF value of the entire aircraft is less than the preset MFHBF value of the entire aircraft, it is confirmed that the verification fails.

[0076] According to the analysis results, the MFHBF allocation threshold of each component is adjusted. The adjustment methods include but are not limited to increasing the MFHBF requirements of critical components, reducing the requirements of non-critical components, or reallocating the reliability weights of each component. After the adjustment is completed, the MFHBF value of the entire aircraft is recalculated and compared with the preset MFHBF value of the entire aircraft until the verification passes.

[0077] Through the above method, by continuously adjusting and optimizing the allocation threshold, the overall reliability of the aircraft is ensured to meet the design requirements, and the safety and reliability level of the aircraft are improved.

[0078] Optionally, in one embodiment, determining the mean flight hours between failures allocated to each component in the aircraft includes:

[0079] Determining the failure rate of the entire aircraft according to the preset mean flight hours between failures;

[0080] Based on the failure rate of the entire aircraft and the reliability allocation index coefficients of each component, a weighted calculation is performed to determine the failure rate allocated to each component;

[0081] Calculating the reciprocal value of the failure rate allocated to each component, and determining it as the mean flight hours between failures allocated to each component.

[0082] Specifically, the mean flight hours between failures (MFHBF) is an important indicator to measure the reliability of an aircraft, which reflects the frequency of failures during the flight of the aircraft. The failure rate describes the probability of a product failing per unit time after a certain moment when it has not failed yet. For an aircraft, the lower the failure rate, the higher its reliability. The failure rate of the entire aircraft is determined through the entire preset mean flight hours between failures.

[0083] For example, reliability allocation is the process of converting the reliability requirements of the entire system into the reliability requirements of each subsystem or unit. For an aircraft, the reliability of each component directly affects the overall reliability. Through weighted calculation, the failure rates of each component are more reasonably allocated to ensure that the overall reliability of the aircraft meets the requirements. There is a reciprocal relationship between the failure rate and the mean flight hours between failures. By calculating the reciprocal value of the failure rate of each component, the mean flight hours between failures of each component are obtained, thereby evaluating the reliability level of each component.

[0084] Through the above method, by calculating the mean flight hours between failures of each component, the reliability level of each component can be intuitively understood; at the same time, the overall reliability of the aircraft is ensured to meet the requirements, improving the safety and reliability level of the aircraft.

[0085] Optionally, in one embodiment, the whiteness value of each of the judgment matrices is calculated, and the comprehensive evaluation information of each expert is determined based on the whiteness values, including:

[0086] Among them, the judgment matrix is normalized to determine the normalized element value of each element; for example, normalization is to eliminate the influence of different dimensions and value ranges on the results, so that all scores can be compared on the same scale. For example, standard deviation and mean can also be used for normalization, which is applicable to data with a normal distribution. It is also possible to scale the data by moving the decimal point.

[0087] The normalized element values of each element are normalized to determine the normal whiteness value; among them, normalization is to ensure that the sum of the scoring weights of each expert is 1, ensuring the consistency of weights in the comprehensive evaluation. For example, normalization values can also be used without normalization if the relative importance of each expert's score does not need to be emphasized, ensuring the fairness and consistency of the evaluation.

[0088] According to the normal whiteness value, the whiteness value of each element of the judgment matrix is determined; specifically, since the normalization process fine-tunes the whiteness value as needed, the final whiteness value matrix of the judgment matrix.

[0089] The whiteness values of each element of the judgment matrix are subjected to element summation processing, and the element summation value is averaged according to the number of each expert to determine the comprehensive evaluation information. For example, element summation processing and mean calculation are to synthesize the scores of multiple experts into a unified evaluation result, so as to obtain a comprehensive evaluation of each index.

[0090] Through the above method, the evaluation information of each expert is synthesized through the whiteness function, and the distribution coefficient is calculated to complete the reliability allocation of the next level of the product, thereby reducing the subjectivity and ambiguity of individual expert decisions and improving the accuracy of reliability allocation.

[0091] Optionally, in one embodiment, determining the whitening values of the elements of the evaluation matrix according to the regular whitening value includes:

[0092] Determining a first difference between the upper maximum value and the lower minimum value of any element in the evaluation matrix;

[0093] Determining a whitening increment value according to the product of the first difference and the regular whitening value;

[0094] Determining the whitening values of the elements in the evaluation matrix by summing the whitening increment value and the lower minimum value of any element.

[0095] Specifically, traverse all the elements in the evaluation matrix, find the upper maximum value and the lower minimum value of each element in its column (or row), calculate the difference between the upper maximum value and the lower minimum value, which is the first difference. Determine a regular whitening value according to actual needs, which is usually a constant between 0 and 1 and is used to control the intensity of the whitening process. Multiply the first difference by the regular whitening value to obtain the whitening increment value. For each element in the evaluation matrix, add its lower minimum value to the whitening increment value to obtain the whitening value of this element. Traverse all the elements in the evaluation matrix and repeat the above steps to obtain the whitening values of all elements.

[0096] In the above manner, by calculating the first difference, it provides the necessary numerical range information for the whitening process; by determining the whitening increment value, it provides a suitable increment range for the whitening process, which helps to ensure that the whitened data fluctuates within a reasonable range; by calculating the whitening value, a new data that not only retains the important features of the original data but also undergoes appropriate whitening processing is obtained. This kind of whitening processing helps to eliminate the redundant information and correlation in the data and improve the independence and separability of the data.

[0097] Optionally, in one embodiment, normalizing the normalized element values of each element to determine the regular whitening value includes:

[0098] Determining the lower limit value, median value, and upper limit value after the normalization process of the current arbitrary element in the evaluation matrix;

[0099] Calculating the ratio of the median value to the second difference between the median value and the lower limit value to determine the first normalization value;

[0100] Calculating the ratio of the upper limit value to the third difference between the upper limit value and the lower limit value to determine the second normalization value;

[0101] Calculating the fourth difference between the square of the second normalization value and the first normalization value;

[0102] The product of the fourth difference and the first normalization value is divided by the fifth difference between the second normalization value and the first normalization value to determine the normal whitening value.

[0103] Specifically, any current element in the judgment matrix is normalized through data standardization or normalization methods to ensure that the data is distributed within a specific range (such as 0 to 1); in the normalized data, the lower limit value, median value, and upper limit value of the current element are determined. The lower limit value is the smallest value in the dataset, the median value is the value in the middle position of the dataset, and the upper limit value is the largest value in the dataset.

[0104] For example, by calculating the ratio between the median value and the lower limit value, the position and distribution characteristics of the middle value in the dataset relative to the minimum value are reflected. Similar to calculating the first normalization value, by calculating the ratio between the upper value and the lower limit value, and by calculating the difference between the square of the second normalization value and the first normalization value, the degree of difference in the position and distribution characteristics of the maximum value and the median value in the dataset relative to the minimum value is reflected. The calculation of the normal whitening value combines the information of the fourth difference, the first normalization value, and the second normalization value, aiming to reflect the complex relationship of the positions and distribution characteristics of different values in the dataset relative to the minimum value, and obtain a comprehensive index that takes into account both the data distribution characteristics and the data variation degree.

[0105] In the above manner, the normal whitening value is a comprehensive index that reflects the complex relationship of the positions and distribution characteristics of different values in the dataset relative to the minimum value, and can more comprehensively understand the data distribution characteristics and variation degree.

[0106] Optionally, in one embodiment, determining the reliability allocation index coefficient of the aircraft according to the comprehensive evaluation information includes:

[0107] Calculate the meta-product of the comprehensive evaluation information corresponding to multiple experts for any component to determine the weight coefficient of any current component;

[0108] According to the ratio between the weight coefficient of any current component and the sum of the weight coefficients of all components, determine the reliability allocation index coefficient of any component in the aircraft.

[0109] Specifically, comprehensive evaluation information about each component of the aircraft is collected from multiple experts, and the collected evaluation information is organized into an evaluation matrix. Here, each row represents an expert's evaluation of each component, and each column represents the evaluation of a component among all experts. For each component in the evaluation matrix, calculate its meta-product in all experts' evaluations. That is, multiply the evaluation values of each expert for this component to obtain a comprehensive product value. Use the calculated meta-product value as the weight coefficient of the current component, and this coefficient reflects the comprehensive importance of the component in all experts' evaluations. Calculate the sum of the weight coefficients of all components, and this sum reflects the overall level of all components in the aircraft.

[0110] For each component, calculate the ratio between its weight coefficient and the sum of the weight coefficients. This ratio reflects the degree of importance of this component relative to other components in the aircraft, and determine the reliability allocation index coefficient: use this ratio as the reliability allocation index coefficient of this component.

[0111] In the above way, by calculating the meta-product to determine the weight coefficient, comprehensively considering the evaluation information of multiple experts, the accuracy and reliability are improved based on the weight coefficient, and the overall reliability of the aircraft is optimized; according to the ratio between the weight coefficient and the sum of the weight coefficients to determine the reliability allocation index coefficient, ensuring the reasonable allocation of reliability among each component of the aircraft, not only improving the overall reliability of the aircraft, but also reducing the failure rate, thereby improving the safety and performance of the aircraft.

[0112] In some embodiments, taking the power system of the entire aircraft as an example, the allocation of the reliability allocation index is carried out, and the details are as follows:

[0113] Step 1: Determine the reliability allocation index

[0114] The mean flight hours between failures MFHBF refers to the probability of a failure occurring in the average flight hours. As an important parameter for evaluating the inherent reliability design level of a product, since it provides an intuitive and quantifiable reliability standard, it is widely used in the reliability design of aircraft. Against the background of the increasing popularity of urban air travel, MFHBF can reflect the stability and safety faced by eVTOL in actual operation. By increasing the MFHBF value, the flight accident risk of eVTOL can be effectively reduced, and the sense of security of passengers and crew can be improved; in addition, MFHBF helps manufacturers and operators to make reasonable plans when predicting maintenance requirements and formulating corresponding operation strategies, thereby effectively optimizing the overall performance and economy of eVTOL. Therefore, select MFHBF as the reliability design index of eVTOL, where MFHBF *The mean flight hours between failures (MFHBF) allocated to each component of the power system, and the MFHBF of the overall power system.

[0115] Step 2: Determine the evaluation factors for eVTOL reliability indicators

[0116] eVTOL is a complex aircraft that incorporates new technologies such as redundant flight control systems, multi-axis propeller power systems, and new energy battery components. Due to its design complexity, the maturity of the technologies used, the severity of hazards after system or equipment failures, as well as the maintainability of the aircraft and the operating environment, etc., all affect the inherent reliability, safety, and economy of eVTOL. Therefore, five dimensions of complexity, technology maturity, failure hazard degree, maintainability, and operating environment are selected as the eVTOL reliability evaluation factors.

[0117] Step 3: Establish an expert group decision-making model

[0118] Let N = {1, 2, …, n}, M = {1, 2, …, m}, G = {1, 2, …, g}. The eVTOL reliability indicator evaluation factor set is V = {v j} where j ∈ N; the group of experts participating in the decision-making is E = {e i} where i ∈ M; the evaluation set is S = {s r} where r ∈ G. Then <V, E, S> constitutes an expert group decision-making mathematical model.

[0119] For example, the reliability indicator evaluation factor set is V = {complexity, technology maturity, failure hazard degree, maintainability, operating environment}; the group decision-making model requires at least 5 experts. Taking a group decision-making panel of 5 experts as an example, then E = {e 1 , e 2 , e 3 , e 4 , e 5}; the evaluated grades are divided into 5 grades of "lower, low, average, higher, high", then the evaluation set is S = {s 1 , s 2 , s 3 , s 4 , s 5}.

[0120] Step 4: Determine the evaluation criteria

[0121] The evaluation criteria are the basis for experts to evaluate the product reliability. According to the scoring factor set, expert evaluation criteria are established from five dimensions of complexity, technology maturity, failure hazard degree, maintainability, and operating environment.

[0122] (1) Evaluation criteria for complexity factor

[0123] Experts evaluate the complexity of a product based on the number of its constituent units and the difficulty of its processing, testing, and assembly.

[0124] Table 1 Evaluation Criteria for Complexity Factors

[0125]

[0126] (2) Evaluation Criteria for Technology Maturity Factors

[0127] Experts evaluate the technical level of a product based on its current technical level and maturity.

[0128] Table 2 Evaluation Criteria for Technology Maturity Factors

[0129]

[0130] (3) Evaluation Criteria for Failure Hazard Degree Factors

[0131] Experts evaluate the hazard degree of a product based on the consequences of failures occurring in its constituent units.

[0132] Table 3 Evaluation Criteria for Failure Hazard Degree Factors

[0133]

[0134] (4) Evaluation Criteria for Maintainability Factors

[0135] Experts evaluate the maintainability of a product based on its maintainability accessibility, maintenance time, and the difficulty of disassembly and assembly.

[0136] Table 4 Evaluation Criteria for Maintainability Factors

[0137]

[0138] (5) Evaluation Criteria for Operating Environment Factors

[0139] Experts evaluate the degree of environmental impact on a product based on the working environment in which it is located.

[0140] Table 5 Evaluation Criteria for Operating Environment Factors

[0141]

[0142]

[0143] Step 5: Fuzzy quantification of the comment level variable

[0144] Regarding the problem of fuzziness and uncertainty in expert evaluation, according to the theory of fuzzy mathematics, the risk value is quantified through triangular fuzzy numbers. The comment level variable s r , r = (1, 2, …, 5) of the triangular fuzzy number can be expressed by the following formula:

[0145]

[0146] Then, according to Equation (1), the evaluation grade variable s in the evaluation set is calculated. r The corresponding triangular fuzzy number, d L is the lower limit of the triangular fuzzy number, d Z is the median of the triangular fuzzy number, d R is the upper limit of the triangular fuzzy number, r is the evaluation grade in the evaluation set, max is the maximum value in the set, and min is the minimum value in the set, as shown in Table 6.

[0147] Table 6 Triangular Fuzzy Numbers of Evaluation Grade Variables

[0148] <![CDATA[s r > Triangular fuzzy number <![CDATA[s 1 > (0,0,0.25) <![CDATA[s 2 > (0,0.25,0.5) <![CDATA[s 3 > (0.25,0.5,0.75) <![CDATA[s 4 > (0.5,0.75,1) <![CDATA[s 5 > (0.75,1,1)

[0149] Step 6: Establish the initial judgment matrix

[0150] Suppose there are m experts participating in the decision-making. Each expert evaluates and scores each device in the system with the evaluation grade variable in the evaluation set, and obtains m initial judgment matrices D (m) , D (p) , and each element of p ∈ [1, … m] is represented by the triangular fuzzy number .

[0151] Exemplarily, taking the reliability index MFHBF allocation of a certain eVTOL power system as an example, 5 electrical system design experts are invited to evaluate the complexity, technical maturity, failure hazard, maintainability, and operating environment of each component of the system based on their own design experience. The power system of a certain eVTOL has a high integration level and mainly includes three components: an L-28V conversion power supply, an R-28V conversion power supply, and a 28V emergency power supply. The evaluation results of a certain expert are shown in Table 7.

[0152] Table 7 Evaluation Results of a Certain Expert

[0153] Component L-28V conversion power supply R-28V conversion power supply 28V emergency power supply Complexity <![CDATA[s 4 > <![CDATA[s 4 > <![CDATA[s 3 > Technology maturity <![CDATA[s 3 > <![CDATA[s 3 > <![CDATA[s 3 > Fault hazard degree <![CDATA[s 5 > <![CDATA[s 5 > <![CDATA[s 4 > Maintainability <![CDATA[s 3 > <![CDATA[s 3 > <![CDATA[s 4 > Operating environment <![CDATA[s 4 > <![CDATA[s 4 > <![CDATA[s 3 >

[0154] Step 7: Synthesize Expert Judgment Information

[0155] Synthesize the m original judgment matrices D (p) , to aggregate the expert group judgment information and obtain the comprehensive result of expert information (F ij ), n×n , and the specific steps are as follows:

[0156] (1) Normalization processing of the initial judgment matrix:

[0157]

[0158] Among them, They are respectively the lower limit value, median value, and upper limit value after the normalization process of any current element in the judgment matrix.

[0159] (2) Calculate the normal whitenization value, that is

[0160]

[0161] where They are respectively the first normalization value, the second normalization value, and the normal whitenization value.

[0162] (3) Calculate the whitenization value of each element of D (p) That is

[0163]

[0164] where They are respectively the upper limit maximum value and the lower limit minimum value of any element in the judgment matrix; is the normal whitenization value, is the whitenization value of each element in the judgment matrix.

[0165] (4) Synthesize the expert judgment information

[0166]

[0167] where, in formula (7), F ij is the evaluation comprehensive information of the triangular fuzzy numbers of multiple experts (that is, the synthesized expert judgment information), D (p) , each element of p ∈ [1, … m] is represented by the triangular fuzzy number According to the above calculation steps, calculate the expert evaluation comprehensive information of each component of the power supply system, as shown in Table 8.

[0168] Table 8 Expert Comprehensive Evaluation Information of Each Component of the Power Supply System

[0169] Component L-28V conversion power supply R-28V conversion power supply 28V emergency power supply Complexity 0.7375 0.7375 0.6475 Technology maturity 0.6025 0.6025 0.6925 Fault hazard degree 0.7375 0.7375 0.8275 Maintainability 0.5125 0.5125 0.5575 Operating environment 0.7375 0.6925 0.6025

[0170] Step 8: Carry out the reliability index allocation

[0171] (1) Calculate the reliability index allocation coefficient C i :

[0172]

[0173] According to formulas (8) and (9), calculate the MFHBF allocation coefficients of each component of the power supply system, where the reliability index allocation coefficient C corresponding to the i-th power supply component i, the weight coefficient w of the reliability index allocation corresponding to the i-th power supply component i , as shown in Table 9.

[0174] Table 9 Allocation coefficients of mean flight hours between failures for each component of the power supply system

[0175]

[0176]

[0177] (2) Calculate MFHBF

[0178]

[0179] λ i = C i × λ S (11)

[0180]

[0181] In equations (10) to (12), λ s is the failure rate allocated to this system at the top level, and λ i is the failure rate allocated to each component of the system; MFHBF is the mean flight hours between failures of the entire power supply system, and MFHBF * is the mean flight hours between failures of each device in the power supply system.

[0182] From the allocation results of the aircraft-level top-level indicators, it can be seen that the MFHBF allocated to the power supply system is 966 FH. Calculated from formula (10), the failure rate λ of the power supply system S = 0.001035. According to formulas (12) and (13), the failure rates λ of each component of the power supply system are calculated i and MFHBF * values, as shown in Table 10.

[0183] Table 10 MFHBF allocation coefficients for each component of the power supply system

[0184] Component L-28V conversion power supply R-28V conversion power supply 28V emergency power supply <![CDATA[λ i > 0.000337 0.000337 0.000361 <![CDATA[MFHBF * > 2967.17 2967.17 2768.90

[0185] Step 9: Rounding and verification of reliability allocation results

[0186] (1) Rounding of reliability allocation results

[0187] After the calculation of reliability index allocation is completed, the allocation results need to be rounded. The rounding method is the ceiling method, that is, no matter how large the data after the decimal point is, it is rounded up by one and the integer is retained as the allocation index value.

[0188] According to the MFHBF calculated values of each component of the power supply system, round up using the ceiling method. Finally, the allocated requirement values designed for each component shall not be lower than the following values:

[0189] For example, the MFHBF value (i.e., mean flight hours between failures) of a power supply type of L-28V (i.e., 28V negative pole) conversion power supply should be greater than or equal to 2968 flight hours.

[0190] For example, the MFHBF value of a power supply type of R-28V (i.e., 28V positive pole) conversion power supply should be greater than or equal to 2968 flight hours.

[0191] For example, the MFHBF value of a power supply type of 28V emergency power supply should be greater than or equal to 2969 flight hours.

[0192] (2) Verification of reliability allocation results

[0193] After the allocated values are determined, it is also necessary to verify whether the reliability indicators of each allocated system and equipment can meet the reliability requirements of the upper-level system (i.e., the overall MFHBF of the power supply system) with a margin. If not, the allocated indicators need to be adjusted, or re-allocated until the indicator requirements are met. If the allocated indicators meet the requirements, the indicators allocated to some systems or equipment can also be appropriately adjusted according to experience or relevant data to make the allocated values more reasonable, but the adjustment cannot affect the indicators of the entire product.

[0194] According to the above verification method, the failure rate and MFHBF of the overall power supply system are obtained, and the results are as follows:

[0195]

[0196] Finally, the verification result is 966.19 FH, which is significantly greater than the required value of 996 FH of the preset power supply system, indicating that the allocation result meets the top-level indicator requirements.

[0197] In some embodiments, a reliability allocation system for an aircraft is provided. The reliability allocation system for an aircraft is used to execute the aircraft reliability allocation method provided in any one of the above embodiments. Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of the reliability allocation system for an aircraft provided in an embodiment of the present application. As shown in Figure 4 , the reliability allocation system for an aircraft includes a factor determination module 401, a model construction module 402, a matrix determination module 403, an evaluation integration module 404, and an allocation coefficient determination module 405, wherein:

[0198] The factor determination module 401 is configured to obtain evaluation factors for the aircraft reliability allocation indicators;

[0199] The model construction module 402 is configured to construct an expert group decision-making model and generate a comment set including the evaluation levels corresponding to each of the evaluation factors based on a preset comment criterion.

[0200] The matrix determination module 403 is configured to perform fuzzy quantification processing on each of the comment sets to determine a judgment matrix.

[0201] The evaluation integration module 404 is configured to calculate the whitenized value of each of the judgment matrices and determine the comprehensive evaluation information of each expert based on the whitenized values.

[0202] The allocation coefficient determination module 405 is configured to determine the reliability allocation index coefficient of the aircraft according to the comprehensive evaluation information.

[0203] Please refer to Figure 5 , which is a schematic structural diagram of an aircraft reliability allocation system provided by an embodiment of the present application. On the basis of the above embodiment, it further includes:

[0204] The calculation module 406 is configured to determine the average flight hours between failures allocated to each component in the aircraft according to the reliability allocation index coefficient.

[0205] The verification module 407 is configured to perform an upper rounding operation on the average flight hours between failures to determine the allocation threshold of each component for the average flight hours between failures, determine the average flight hours between failures of the entire aircraft according to the allocation thresholds of each component, perform verification based on a preset average flight hours between failures and the average flight hours between failures of the entire aircraft, and determine the reliability allocation index according to the verification result.

[0206] For the specific limitations of the aircraft reliability allocation system, reference can be made to the limitations on the aircraft reliability allocation method in the above text, which will not be elaborated here. Each module in the above aircraft reliability allocation system can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the electronic device in the form of hardware or independent of the processor, or stored in the memory in the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0207] In this embodiment, the aircraft reliability allocation system essentially sets multiple modules to execute the aircraft reliability allocation method in any of the above embodiments. The specific functions and technical effects can be referred to the above embodiments, which will not be elaborated here.

[0208] In one embodiment, an electric vertical takeoff and landing aircraft is provided, and the electric vertical takeoff and landing aircraft includes the aircraft reliability allocation system provided in any of the above embodiments.

[0209] Specific limitations on the electric vertical takeoff and landing aircraft can be referred to the limitations on the aircraft reliability allocation method in the above text, which will not be elaborated here. Each module in the above electric vertical takeoff and landing aircraft can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the electronic device in hardware form or independent of it, or stored in the memory of the electronic device in software form, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0210] In one embodiment, an electronic device is provided. The electronic device can be a server, and its internal structure diagram can be as Figure 6 shown. The electronic device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the server side of the above method.

[0211] In one embodiment, an electronic device is provided. The electronic device can be a client, and its internal structure diagram can be as Figure 7 shown. The electronic device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes non-volatile storage media and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the client side of the above method.

[0212] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are realized:

[0213] Obtain the evaluation factors for the reliability allocation index of the aircraft, where the evaluation factors include complexity, technical maturity, failure hazard, maintainability, and operating environment; construct an expert group decision-making model, generate the evaluation grades and comments for each of the evaluation factors based on preset comment criteria, with a one-to-one correspondence between the evaluation grades and the comments; perform fuzzy quantification processing on each of the comments to determine a judgment matrix represented by a relationship matrix of multiple experts; calculate the whitenization values of each of the judgment matrices, and determine the comprehensive evaluation information of each expert based on the whitenization values; determine the reliability allocation index coefficient of the aircraft according to the comprehensive evaluation information.

[0214] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0215] Obtain the evaluation factors for the reliability allocation index of the aircraft, where the evaluation factors include complexity, technical maturity, failure hazard, maintainability, and operating environment; construct an expert group decision-making model, generate the evaluation grades and comments for each of the evaluation factors based on preset comment criteria, with a one-to-one correspondence between the evaluation grades and the comments; perform fuzzy quantification processing on each of the comments to determine a judgment matrix represented by a relationship matrix of multiple experts; calculate the whitenization values of each of the judgment matrices, and determine the comprehensive evaluation information of each expert based on the whitenization values; determine the reliability allocation index coefficient of the aircraft according to the comprehensive evaluation information.

[0216] It should be noted that for the functions or steps that the above computer-readable storage medium or electronic device can achieve, reference can be made to the relevant descriptions on the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0217] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The above computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0218] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the above device and system can be divided into different functional units or modules to complete all or part of the functions described above.

[0219] The embodiments provided above are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; 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, and should all be included in the protection scope of the present application.

Claims

1. A method for allocating aircraft reliability, characterized in that: The method comprises: Obtaining an evaluation factor of the aircraft reliability allocation index; Constructing an expert group decision model, and generating a comment set including the evaluation level corresponding to each evaluation factor based on preset comment criteria; Performing fuzzy quantization processing on each of the comment sets to determine a judgment matrix; Calculating the whitening value of each of the evaluation matrices, and determining the comprehensive evaluation information of each expert based on each of the whitening values; The reliability allocation index coefficient of the aircraft is determined according to the comprehensive evaluation information.

2. The aircraft reliability allocation method according to claim 1, characterized in that: After determining the reliability allocation index coefficient of the aircraft, the method further includes: Determining the mean flight hours between failures allocated to each component in the aircraft according to the reliability allocation index coefficient; Perform an upper limit rounding operation on the mean flight hours between failures to determine a distribution threshold of each component in the mean flight hours between failures; The mean flight hours between failures of the entire aircraft is determined according to the allocation thresholds of each component, a verification is performed based on a preset mean flight hours between failures and the mean flight hours between failures of the entire aircraft, and a reliability allocation index is determined according to the verification result.

3. The aircraft reliability allocation method according to claim 2, characterized in that: Verification is performed based on a preset mean flight hours between failures and the mean flight hours between failures of the entire aircraft, and a reliability allocation index is determined according to the verification result, including: If the MTBF of the aircraft as a whole is greater than or equal to the preset MTBF, the verification is passed, and the allocation threshold of the MTBF of each component is used as the reliability allocation index; If the mean flight hours between failures of the aircraft as a whole is less than the preset mean flight hours between failures, the verification fails, and the allocation threshold of the mean flight hours between failures of each component is adjusted until the verification passes.

4. The aircraft reliability allocation method according to claim 3, characterized in that: Determine the mean flight hours between failures allocated to each component in the aircraft, including: Determining the overall failure rate of the aircraft according to the preset mean flight hours between failures; Determine the failure rate allocated to each component by performing weighted calculation based on the failure rate of the entire aircraft and the reliability allocation index coefficient of each component; Calculate the inverse value of the failure rate assigned to each component and determine the mean flight hours between failures assigned to each component.

5. The aircraft reliability allocation method according to claim 1, characterized in that: Construct an expert group decision model, and generate a comment set containing the evaluation level corresponding to each evaluation factor based on the preset comment criteria, including: Construct an expert group decision model based on the expert group involved in decision-making, the evaluation factor set and the comment set; The expert group decision model is called to determine a comment set of each expert containing an evaluation grade corresponding to each evaluation factor based on a preset comment criterion, wherein the comment set consists of the evaluation grade and the comment, and there is a one-to-one correspondence between the evaluation grade and the comment.

6. The aircraft reliability allocation method according to claim 1, characterized in that: Perform fuzzy quantization processing on each of the comment sets to determine a judgment matrix, including: Based on fuzzy mathematics theory, each comment set is subjected to fuzzy quantization processing by a triangular fuzzy function to determine a triangular fuzzy number of each comment set; The triangular fuzzy quantization of each expert's comment set is processed to determine a judgment matrix composed of the quantitative comment information of multiple experts.

7. The aircraft reliability allocation method according to claim 6, characterized in that: The expression of the triangular fuzzy function is: In formula (1), d L is the lower limit of the triangular fuzzy number, d Z is the median of the triangular fuzzy number, d R is the upper limit of the triangular fuzzy number, r is the evaluation level in the review set, max is the maximum value in the set, and min is the minimum value in the set.

8. The aircraft reliability allocation method according to any one of claims 1 to 7, characterized in that: Calculating the whitening value of each of the evaluation matrices, and determining the comprehensive evaluation information of each expert based on each of the whitening values, including: Performing normalization processing on the evaluation matrix to determine the normalized element value of each element; Normalizing the normalized element value of each element to determine a normalized whitening value; Determining the whitening value of each element of the judgment matrix according to the normal whitening value; The whitened values ​​of the elements of the evaluation matrix are summed up, and the summed values ​​are averaged according to the number of the experts to determine the comprehensive evaluation information.

9. The aircraft reliability allocation method according to claim 8, characterized in that: Determining the whitening value of each element of the evaluation matrix according to the normal whitening value includes: Determine a first difference between an upper maximum value and a lower minimum value of any element in the evaluation matrix; Determine a whitening increment value according to a product of the first difference value and the normal whitening value; The whitening value of each element in the evaluation matrix is ​​determined by summing the whitening increment value and the lower limit minimum value of any element.

10. The aircraft reliability allocation method according to claim 8, characterized in that: Normalizing the normalized element value of each element to determine a normalized whitening value, including: Determine the lower limit value, median value and upper limit value of any current element in the evaluation matrix after normalization processing; Calculate the ratio of the median value and the second difference between the median value and the lower limit value to determine a first normalized value; Calculate the ratio of the upper value and the third difference between the upper value and the lower limit value to determine a second normalized value; calculating a fourth difference between the square of the second normalized value and the first normalized value; The normalized whitening value is determined by multiplying the fourth difference value and the first normalized value by the fifth difference value between the second normalized value and the first normalized value.

11. The aircraft reliability allocation method according to any one of claims 1 to 7, characterized in that: Determining the reliability allocation index coefficient of the aircraft according to the comprehensive evaluation information includes: Calculate the element product of the comprehensive evaluation information corresponding to multiple experts for any component, and determine it as the weight coefficient of the current arbitrary component; The reliability allocation index coefficient of any component in the aircraft is determined according to the ratio between the current weight coefficient of any component and the sum of the weight coefficients of all components.

12. An aircraft reliability allocation system, characterized in that: include: a factor determination module configured to obtain an evaluation factor of the aircraft reliability allocation index; A model building module is configured to build an expert group decision model and generate a comment set including an evaluation grade corresponding to each evaluation factor based on a preset comment criterion; A matrix determination module is configured to perform fuzzy quantization processing on each of the comment sets to determine a judgment matrix; An evaluation and synthesis module is configured to calculate a whitening value of each of the evaluation matrices, and determine the evaluation and synthesis information of each expert based on each of the whitening values; The allocation coefficient determination module is configured to determine the reliability allocation index coefficient of the aircraft according to the comprehensive evaluation information.

13. An aircraft reliability allocation system as claimed in claim 12, characterized in that: Also includes: A calculation module is configured to determine the mean flight hours between failures allocated to each component in the aircraft according to the reliability allocation index coefficient; The verification module is configured to perform an upper limit rounding operation on the mean flight hours between failures, determine an allocation threshold of each component in the mean flight hours between failures, determine the mean flight hours between failures of the entire aircraft based on the allocation threshold of each component, perform a verification based on a preset mean flight hours between failures and the mean flight hours between failures of the entire aircraft, and determine a reliability allocation index based on the verification result.

14. An electric vertical take-off and landing aircraft, characterized in that: The electric vertical take-off and landing aircraft includes the aircraft reliability allocation method according to any one of claims 1 to 11.

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