Method and device for predicting icing potential of an aircraft in flight based on fuzzy logic, and medium

Abstract

The embodiment of the specification provides a method and device for predicting aircraft icing potential based on fuzzy logic and a medium, the method comprising: establishing a fuzzy logic membership function about atmospheric temperature, relative humidity and cloud top temperature according to atmospheric temperature, relative humidity, cloud top temperature, air vertical velocity and atmospheric layer liquid water content; establishing a fuzzy logic membership function about air vertical velocity and atmospheric layer liquid water content and obtaining a SCIP algorithm equation; obtaining parameters of a future period or historical prediction, substituting into and solving the SCIP algorithm equation to obtain an aircraft icing index; determining an icing degree of the aircraft in the future period according to the icing index and an icing degree judgment threshold, and planning to determine an aircraft flight route. The two elements of air vertical velocity and atmospheric layer liquid water content affecting the aircraft icing in the air are added in the CIP algorithm, so that the generalization performance and anti-fitting performance of the diagnosis algorithm are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aircraft safety, and particularly relates to a method and device for predicting icing potential of an aircraft in the air based on fuzzy logic and a medium. BACKGROUND

[0002] When an aircraft is operating in a cloud layer rich in supercooled water droplets, the supercooled water droplets attached to the surface of the aircraft body will freeze into ice at some parts, which seriously affects the aerodynamic characteristics of the aircraft, causes the aircraft to increase in drag, decrease in lift, and reduce the control rate of the horizontal tail and vertical tail, and in severe cases, causes the aircraft to lose control and cause a flight accident. Therefore, accurately predicting the icing trend of the aircraft in the air is one of the most effective measures to reduce the icing of the aircraft.

[0003] In the prior art, the use of a numerical prediction model combined with an icing diagnosis algorithm to determine icing occurrence has become the mainstream method for icing prediction. Currently, the aircraft icing index Ic algorithm recommended by the International Civil Aviation Organization is commonly used in China. However, the Ic icing index algorithm used in China only considers the influence of atmospheric temperature and relative humidity, and does not consider the influence of other meteorological elements and aircraft parameters. Compared with other aircraft, civil aircrafts have high flight speed, high cruising altitude, and long range, and are greatly affected by the vertical speed of the aircraft and the liquid water content of the atmosphere. The commonly used icing prediction algorithm has a large number of false positives in business operation, which can easily cause the icing potential of the aircraft in the flight process to be not predicted in time, and in civil aircraft test flights, flight safety often needs to avoid any potential icing airspace. False positives of icing weather can cause a waste of a large amount of airspace resources and affect the progress and efficiency of test flights. SUMMARY

[0004] To overcome the problems in the related art, the present disclosure provides a method and device for predicting icing potential of an aircraft in the air based on fuzzy logic and a medium, to solve the technical problem of being able to predict icing potential in advance before aircraft test flight in the related art.

[0005] One or more embodiments of the present specification provide a method for predicting icing potential of an aircraft in the air based on fuzzy logic, comprising:

[0006] establishing a fuzzy logic membership function about atmospheric temperature, relative humidity, and cloud top temperature according to the atmospheric temperature, the relative humidity, and the cloud top temperature;

[0007] determining a CIP algorithm equation according to the fuzzy logic membership function of the atmospheric temperature, the relative humidity, and the cloud top temperature;

[0008] establishing a fuzzy logic membership function about the vertical speed of the airflow and the liquid water content of the atmosphere;

[0009] The CIP algorithm equation is obtained by substituting the established fuzzy logic membership function of the air current vertical speed and the atmospheric layer liquid water content into the CIP algorithm equation;

[0010] According to the predicted demand, the atmospheric temperature, the cloud top temperature, the relative humidity, the air current vertical speed and the atmospheric layer liquid water content of the future period or the historical forecast are obtained, and the SCIP algorithm equation is substituted and solved to obtain the aircraft icing index.

[0011] According to the icing index and the icing degree judgment threshold, the icing degree of the aircraft in the future period is determined, and the flight route of the aircraft is planned.

[0012] One or more embodiments of the present specification provide a fuzzy logic-based aircraft in-flight icing potential prediction device, comprising

[0013] The first function establishment module is configured to establish a fuzzy logic membership function about the atmospheric temperature, the relative humidity and the cloud top temperature according to the atmospheric temperature, the relative humidity and the cloud top temperature.

[0014] The CIP algorithm equation establishment module is configured to determine the CIP algorithm equation according to the fuzzy logic membership function of the atmospheric temperature, the relative humidity and the cloud top temperature established by the first function establishment module.

[0015] The second function establishment module is configured to establish a fuzzy logic membership function about the air current vertical speed and the atmospheric layer liquid water content.

[0016] The SCIP algorithm equation establishment module is configured to obtain the SCIP algorithm equation by substituting the established fuzzy logic membership function of the air current vertical speed and the atmospheric layer liquid water content into the CIP algorithm equation.

[0017] The parameter acquisition and calculation module is configured to obtain the atmospheric temperature, the cloud top temperature, the relative humidity, the air current vertical speed and the atmospheric layer liquid water content of the future period or the historical forecast, substitute and solve the SCIP algorithm equation to obtain the aircraft icing index.

[0018] The prediction module is configured to determine the icing degree of the aircraft in the future period according to the icing index and the icing degree judgment threshold, and plan the flight route of the aircraft.

[0019] One or more embodiments of the present specification provide a computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the steps of the fuzzy logic-based aircraft in-flight icing potential prediction method described above.

[0020] The application discloses a fuzzy logic-based aircraft in-flight icing potential prediction method and device and a medium. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the one or more embodiments of the present specification or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present specification, and other drawings can be obtained by those skilled in the art without creative labor.

[0022] Figure 1 A flowchart of a fuzzy logic-based aircraft in-flight icing potential prediction method provided by the one or more embodiments of the present specification is shown in the figure.

[0023] Figure 2 A membership function relationship diagram of different meteorological elements in the fuzzy logic-based aircraft in-flight icing potential prediction method provided by the one or more embodiments of the present specification is shown in the figure. The lower left figure is a fuzzy logic membership function relationship diagram of air vertical velocity, the upper right figure is a fuzzy logic membership function relationship diagram of relative humidity, and the lower right figure is a fuzzy logic membership function relationship diagram of atmospheric temperature and atmospheric liquid water content.

[0024] Figure 3 A ROC curve diagram of CIP algorithm icing potential index under different icing degrees in a comparison case of the fuzzy logic-based aircraft in-flight icing potential prediction method provided by the one or more embodiments of the present specification is shown in the figure.

[0025] Figure 4 A ROC curve diagram of SFIP algorithm icing potential index under different icing degrees in a comparison case of the fuzzy logic-based aircraft in-flight icing potential prediction method provided by the one or more embodiments of the present specification is shown in the figure.

[0026] Figure 5 A ROC curve diagram of SCIP algorithm icing potential index under different icing degrees in a comparison case of the fuzzy logic-based aircraft in-flight icing potential prediction method provided by the one or more embodiments of the present specification is shown in the figure.

[0027] Figure 6A TSS curve diagram of the icing potential index of the CIP algorithm under different icing degrees in the case of comparing the aircraft in-flight icing potential prediction method based on fuzzy logic provided by one or more embodiments of the present specification is shown in the following figure:

[0028] Figure 7 A TSS curve diagram of the icing potential index of the SFIP algorithm under different icing degrees in the case of comparing the aircraft in-flight icing potential prediction method based on fuzzy logic provided by one or more embodiments of the present specification is shown in the following figure:

[0029] Figure 8 A TSS curve diagram of the icing potential index of the SCIP algorithm under different icing degrees in the case of comparing the aircraft in-flight icing potential prediction method based on fuzzy logic provided by one or more embodiments of the present specification is shown in the following figure:

[0030] Figure 9 A structural schematic diagram of the aircraft in-flight icing potential prediction device based on fuzzy logic provided by one or more embodiments of the present specification is shown in the following figure:

[0031] Figure 10 A structural schematic diagram of the computer provided by one or more embodiments of the present specification is shown in the following figure. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be described clearly and completely in conjunction with the accompanying drawings of the present specification. Obviously, the described embodiments are only part of the embodiments of the present specification, not all. Based on one or more embodiments of the present specification, all other embodiments obtained by those skilled in the art without creative labor should fall within the protection scope of the present invention.

[0033] The present invention will be described in detail below in conjunction with the specific embodiments and the accompanying drawings.

[0034] Method embodiments

[0035] According to the embodiments of the present application, a fuzzy logic-based aircraft in-flight icing potential prediction method is provided, as shown in the following figure: Figure 1 The flowchart of the fuzzy logic-based aircraft in-flight icing potential prediction method provided by the present embodiment is shown in the following figure. According to the aircraft in-flight icing potential prediction method of the present embodiment, the method comprises the following steps:

[0036] Step S1, according to the atmospheric temperature, relative humidity and cloud top temperature, the fuzzy logic membership function about the atmospheric temperature, relative humidity and cloud top temperature is established.

[0037] Step S2, determining the CIP algorithm equation according to the atmospheric temperature, relative humidity and cloud top temperature fuzzy logic membership function;

[0038] Step S3, establishing the fuzzy logic membership function about the air vertical velocity and atmospheric liquid water content;

[0039] Step S4, substituting the established fuzzy logic membership function of the air vertical velocity and atmospheric liquid water content into the CIP algorithm equation to obtain the SCIP algorithm equation;

[0040] Step S5, according to the prediction requirement, obtaining the atmospheric temperature, cloud top temperature, relative humidity, air vertical velocity and atmospheric liquid water content of the future period or historical forecast, substituting and solving the SCIP algorithm equation to obtain the aircraft icing index;

[0041] S6, determining the icing degree of the aircraft in the future period according to the icing index and the icing degree judgment threshold, and planning to determine the flight route of the aircraft.

[0042] In this embodiment, the atmospheric temperature, relative humidity, cloud top temperature, air vertical velocity and atmospheric liquid water content are all the atmospheric temperature, relative humidity, cloud top temperature, air vertical velocity and atmospheric liquid water content under the preset time and preset flight height of the aircraft.

[0043] The aircraft in-flight icing diagnosis method based on fuzzy logic provided in this embodiment quantitatively considers the influence of atmospheric temperature, relative humidity and cloud top temperature on aircraft icing during flight, and also considers the influence of air vertical velocity and atmospheric liquid water content on aircraft icing under different environmental conditions. By improving the traditional CIP algorithm, the air vertical velocity and atmospheric liquid water content, which are two elements affecting in-flight icing, are added to the CIP algorithm to obtain a prediction SCIP equation, which is used to improve the generalization performance and anti-fitting performance of the diagnosis algorithm.

[0044] The aircraft icing diagnosis method improves the rationality and accuracy of the aircraft icing diagnosis method. The disclosure of the method can be used by the flight organization team to reasonably arrange the test flight task according to the airspace icing condition, which is of great significance to the test flight safety and airspace resource optimization.

[0045] In some embodiments, reference can be made to FIG. 2, which is a membership function relationship diagram for different meteorological elements, wherein, Figure 2 The upper left graph in FIG. 2 is a fuzzy logic membership function relationship diagram of atmospheric liquid water content, the lower left graph is a fuzzy logic membership function relationship diagram of air vertical velocity, the upper right graph is a fuzzy logic membership function relationship diagram of relative humidity, and the lower right graph is a fuzzy logic membership function relationship diagram of atmospheric temperature and atmospheric liquid water content. The fuzzy logic membership functions of the atmospheric temperature, relative humidity, cloud top temperature, air vertical velocity and atmospheric liquid water content are specifically as follows:

[0046] Atmospheric temperature fuzzy logic membership function M T :

[0047]

[0048] Relative humidity fuzzy logic membership function M RH :

[0049]

[0050] Airflow vertical velocity fuzzy logic membership function M ω :

[0051]

[0052] Atmospheric layer liquid water content fuzzy logic membership function M CLW :

[0053]

[0054] Cloud top temperature fuzzy logic membership function M CTT :

[0055]

[0056] In the embodiment, the CIP algorithm equation in step S2 is specifically:

[0057] CIP = M T × M RH × M CTT (6);

[0058] Wherein, M T , M RH , M CTT are the fuzzy logic membership functions of atmospheric temperature, relative humidity and cloud top temperature respectively.

[0059] In the embodiment, the cloud top temperature cannot be directly obtained through historical or predicted data analysis, and it is necessary to firstly determine whether there is a cloud layer. In the embodiment, the relative humidity threshold method is used to determine the relative humidity of the atmospheric layer at the target height. When the relative humidity of the atmospheric layer at the target height is greater than 85%, it is determined that there is a cloud top, and when the relative humidity of the atmospheric layer at the target height is less than 85%, it is determined that there is no cloud top. When there are two or more layers of clouds, the cloud top temperature will be calculated respectively.

[0060] The SCIP algorithm equation obtained in step S4 is specifically:

[0061]

[0062] In the formula, M ω , MCLW are the fuzzy logic membership functions of air temperature, relative humidity, air vertical velocity and atmospheric liquid water content, respectively; a, b are the influence coefficients of air vertical velocity and atmospheric liquid water content on the icing potential, a = 0.4, b = 0.25.

[0063] In this embodiment,

[0064] The advantages of the method of the present application are illustrated below through specific comparative cases.

[0065] Comparative Example 1: Based on the traditional CIP algorithm, the in-flight icing potential value is calculated using the same environmental parameters.

[0066] Comparative Example 2: Based on the SFIP algorithm, the in-flight icing potential value is calculated using the same environmental parameters. In this comparative example, the SFIP equation algorithm is:

[0067] SFIP = M T × (a×M w +b×M CLW +c×M RH )(8);

[0068] Wherein, M T , M RH , M ω , M CLW are the fuzzy logic membership functions of air temperature, relative humidity, air vertical velocity and atmospheric liquid water content, respectively; a, b, c represent the weight coefficients assigned to the corresponding parameter membership functions, and the weights are assigned to each membership function, a = 0.2, b = 0.45, c = 0.35.

[0069] This embodiment: In the CIP algorithm diagnosis equation, two elements affecting in-flight icing, vertical velocity and liquid water content in the cloud, are added to obtain the SCIP algorithm, and the in-flight icing potential value is calculated using the same environmental parameters.

[0070] The case background is: 878 civil aviation aircraft voice reports collected nationwide from March 1, 2021 to December 31, 2021 are selected, of which 613 are icing aircraft reports and 265 are non-icing aircraft reports. ERA5 data simulation is used to further verify the superiority of the SCIP index by using ROC score and TSS score, wherein,

[0071] TSS = POD Y +POD N -1 = POD Y -POFD

[0072] Wherein, PODY is defined as the ratio of icing observation events correctly diagnosed as icing to all icing observation events reported by the aircraft; PODN is defined as the ratio of events correctly diagnosed as non-icing to the total number of non-icing events reported by the aircraft; POFD is defined as the ratio of false positive events to the total number of non-icing events.

[0073] By setting different thresholds for icing occurrence, these icing potentiality diagnoses can be converted into "accurate / inaccurate" diagnoses. For each threshold, a corresponding PODY and POFD can be determined. A curve defined by a set of PODY and POFD values at a range of different thresholds is called an ROC curve. The area enclosed by the ROC curve and the curve of x-axis and y=1 is defined as ROCA. When the ROCA of a diagnosis algorithm is closer to 1, it indicates that the diagnosis effect of the algorithm is better.

[0074] Figures 3-5 The ROC curves corresponding to CIP, SFIP and SCIP respectively, as shown in the figure, for different degrees of icing, SCIP all show better performance than CIP and SFIP, and the ROCA scores in Table 1 also verify this point.

[0075] Table 1, CIP, SFIP, SCIP index ROCA score table

[0076]

[0077] Figures 6-8 The TSS scores of CIP, SFIP and SCIP at different thresholds respectively, as shown in the figure, the maximum TSS all appear at the threshold of 0.1; the maximum TSS scores of SCIP at the threshold of 0.1 for light, moderate and heavy icing are 0.422, 0.375 and 0.479 respectively, obviously the accuracy of predicted icing degree is better than that of CIP and SFIP indexes.

[0078] The example illustrates that SCIP has better real-time prediction accuracy during aircraft flight compared with the current icing algorithm, and at the same time, it is obviously helpful for accurate planning and safety protection in a short time in the future when the flight task is carried out. However, the prediction of in-flight icing before the flight or test flight cannot be realized, and in turn the icing prediction of the present technology cannot be realized.

[0079] Compared with the prior art, the above technical scheme has the following technical effects: by introducing the vertical velocity and the atmospheric liquid water content two elements, and giving the optimal influence coefficient, the overfitting of icing diagnosis is avoided, the generalization of the icing diagnosis model is increased, so as to improve the prediction accuracy of aircraft icing and reduce the false alarm of icing prediction.

[0080] Device embodiment

[0081] According to the embodiment of the present application, a fuzzy logic-based aircraft in-flight icing potential prediction device is provided, which comprises Figure 9 As shown in the figure, the fuzzy logic-based aircraft in-flight icing potential prediction device provided by the embodiment is a schematic diagram, and the aircraft in-flight icing potential prediction device according to the embodiment of the present application comprises:

[0082] The first function establishing module is configured to establish fuzzy logic membership functions of atmospheric temperature, relative humidity and cloud top temperature according to the atmospheric temperature, the relative humidity and the cloud top temperature;

[0083] The CIP algorithm equation establishing module is configured to determine the CIP algorithm equation according to the fuzzy logic membership functions of the atmospheric temperature, the relative humidity and the cloud top temperature established by the first function establishing module;

[0084] The second function establishing module is configured to establish fuzzy logic membership functions of air flow vertical speed and atmospheric layer liquid water content;

[0085] The SCIP algorithm equation establishing module is configured to substitute the established fuzzy logic membership functions of the air flow vertical speed and the atmospheric layer liquid water content into the CIP algorithm equation to obtain a SCIP algorithm equation;

[0086] The parameter obtaining and calculating module is configured to obtain the atmospheric temperature, the cloud top temperature, the relative humidity, the air flow vertical speed and the atmospheric layer liquid water content in a future period or a historical prediction, substitute them into the SCIP algorithm equation and solve the SCIP algorithm equation to obtain an aircraft icing index;

[0087] The prediction module is configured to determine an icing degree of the aircraft in a future period according to the icing index and an icing degree judgment threshold, and plan a flight route of the aircraft.

[0088] The fuzzy logic-based aircraft in-flight icing diagnosis device provided by the embodiment quantitatively considers the influences of the atmospheric temperature, the relative humidity and the cloud top temperature on aircraft icing in flight, and also considers the influences of the air flow vertical speed and the atmospheric layer liquid water content on aircraft icing in a divided environment. By improving the traditional CIP algorithm, the air flow vertical speed and the atmospheric layer liquid water content, two elements influencing in-flight icing, are added to the CIP algorithm to obtain a prediction SCIP equation, which is used to improve the generalization performance and the anti-fitting performance of the diagnosis algorithm.

[0089] In the embodiment, the fuzzy logic membership functions of the atmospheric temperature, the relative humidity, the cloud top temperature, the air flow vertical speed and the atmospheric layer liquid water content established by the first function establishing module are as follows:

[0090] The atmospheric temperature fuzzy logic membership function M T :

[0091]

[0092] Relative humidity fuzzy logic membership function M RH :

[0093]

[0094] Airflow vertical velocity fuzzy logic membership function M ω :

[0095]

[0096] Atmospheric liquid water content fuzzy logic membership function M CLW :

[0097]

[0098] Cloud top temperature fuzzy logic membership function M CTT :

[0099]

[0100] In this embodiment, the CIP algorithm equation establishing module determines that the CIP algorithm equation is specifically:

[0101] CIP = M T × M RH × M CTT (6);

[0102] Wherein, M T , M RH , M CTT are the fuzzy logic membership functions of atmospheric temperature, relative humidity and cloud top temperature respectively.

[0103] In this embodiment, the cloud top temperature cannot be directly obtained through analysis data, and cloud layer judgment needs to be performed. The cloud top judgment module is further set in this embodiment: used for judging the relative humidity of the atmospheric layer at the target height through the relative humidity threshold method, when the relative humidity of the atmospheric layer at the target height is greater than 85%, it is judged that the cloud top is present, when the relative humidity of the atmospheric layer at the target height is less than 85%, it is judged that the cloud top is not present; when two or more layers of clouds are present, the cloud top temperature will be calculated respectively.

[0104] The SCIP algorithm equation establishing module obtains the SCIP algorithm equation specifically as:

[0105]

[0106] In the formula, M ω , M CLWare the fuzzy logic membership functions of the vertical airspeed and the atmospheric liquid water content; a and b are the influence coefficients of the vertical airspeed and the atmospheric liquid water content on the icing potential, a = 0.4 and b = 0.25.

[0107] The embodiment of the application is a device corresponding to the above-mentioned method embodiment, and the specific operations of each module processing step can be understood with reference to the description of the method embodiment, which will not be repeated here.

[0108] As shown in Figure 10 The application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method for predicting the icing potential of an aircraft in the air based on fuzzy logic in the above-mentioned embodiment, or the computer program is executed by a processor to implement the method for predicting the icing potential of an aircraft in the air based on fuzzy logic in the above-mentioned embodiment, and the computer program is executed by the processor to implement the following method steps:

[0109] Step S1: establishing fuzzy logic membership functions about the atmospheric temperature, the relative humidity and the cloud top temperature according to the atmospheric temperature, the relative humidity and the cloud top temperature.

[0110] Step S2: determining a CIP algorithm equation according to the fuzzy logic membership functions of the atmospheric temperature, the relative humidity and the cloud top temperature.

[0111] Step S3: establishing fuzzy logic membership functions about the vertical airspeed and the atmospheric liquid water content.

[0112] Step S4: substituting the established fuzzy logic membership functions of the vertical airspeed and the atmospheric liquid water content into the CIP algorithm equation to obtain a SCIP algorithm equation.

[0113] Step S5: obtaining the atmospheric temperature, the cloud top temperature, the relative humidity, the vertical airspeed and the atmospheric liquid water content in a future period or a historical forecast according to a prediction requirement, substituting the SCIP algorithm equation and solving the SCIP algorithm equation to predict an aircraft icing index.

[0114] S6: determining the icing degree of the aircraft in the future period according to the icing index and an icing degree judgment threshold, and planning to determine the flight route of the aircraft.

[0115] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in each embodiment provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but 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 (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0116] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, it is described more simply, and the relevant part can be referred to the part of the method embodiment. The above-described device and system embodiments are only illustrative, and the units described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0117] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can still be modified, or some or all of the technical features can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and the contents not described in detail in the specification of the present application are the known technology of those skilled in the art.

Claims

1. A method for predicting the potential for in-flight icing of an aircraft based on fuzzy logic, characterized in that, The application comprises the following steps: According to the atmospheric temperature, relative humidity and cloud top temperature, a fuzzy logic membership function about the atmospheric temperature, relative humidity and cloud top temperature is established; The CIP algorithm equation is determined according to the fuzzy logic membership function of the atmospheric temperature, relative humidity and cloud top temperature; A fuzzy logic membership function about the air vertical velocity and atmospheric liquid water content is established; The SCIP algorithm equation is obtained by substituting the established fuzzy logic membership function of the air vertical velocity and atmospheric liquid water content into the CIP algorithm equation; According to the predicted demand, the atmospheric temperature, cloud top temperature, relative humidity, air vertical velocity and atmospheric liquid water content of the future period or historical forecast are obtained, substituted into and solved by the SCIP algorithm equation to obtain the aircraft icing index; According to the icing index and icing degree judgment threshold, the icing degree of the aircraft in the future period is determined, and the flight route of the aircraft is planned; The CIP algorithm equation is specifically as follows: CIP = M T x M RH x M CTT where M T is the atmospheric temperature fuzzy logic membership function, M RH is the relative humidity fuzzy logic membership function, M CTT is the cloud top temperature fuzzy logic membership function; The SCIP algorithm equation is specifically as follows: where M ω is the fuzzy logic membership function of the vertical airspeed, M CLW is the fuzzy logic membership function of the atmospheric liquid water content, w is the vertical airspeed; a and b are the influence coefficients of the vertical airspeed and the liquid water content on the icing potential, a = 0.4 and b = 0.

25.

2. The fuzzy logic based aircraft in-flight ice accretion potential prediction method of claim 1, wherein, It further comprises a judgment step of whether there is a cloud layer according to historical or predicted data analysis, which is specifically as follows: The relative humidity threshold method is used to judge the relative humidity of the target height atmospheric layer. When the relative humidity of the target height atmospheric layer is greater than 85%, it is judged as a cloud top. When the relative humidity of the target height atmospheric layer is less than 85%, it is judged as no cloud top.

3. The fuzzy logic based aircraft in-flight ice accretion potential prediction method according to any one of claims 1-2, characterized in that, The fuzzy logic membership functions of the atmospheric temperature, relative humidity, cloud top temperature, air vertical velocity and atmospheric liquid water content are specifically as follows: Atmospheric temperature fuzzy logic membership function M T : Relative humidity fuzzy logic membership function M RH : Airflow vertical velocity fuzzy logic membership function M ω : Atmospheric liquid water content fuzzy logic membership function M CLW : Cloud top temperature fuzzy logic membership function M CTT : Wherein, T is the atmospheric temperature, RH is the relative humidity, CLW is the atmospheric liquid water content, CTT is the cloud top temperature, and w is the air vertical velocity.

4. Apparatus for predicting the potential for ice accretion on an aircraft in flight based on fuzzy logic, characterised in that, The application comprises the following steps: The first function establishment module is used to establish a fuzzy logic membership function about the atmospheric temperature, relative humidity and cloud top temperature according to the atmospheric temperature, relative humidity and cloud top temperature; The CIP algorithm equation establishment module is used to determine the CIP algorithm equation according to the fuzzy logic membership function of the atmospheric temperature, relative humidity and cloud top temperature established by the first function establishment module; The second function establishment module is used to establish a fuzzy logic membership function about the air vertical velocity and atmospheric liquid water content; The SCIP algorithm equation establishment module is used to obtain the SCIP algorithm equation by substituting the established fuzzy logic membership function of the air vertical velocity and atmospheric liquid water content into the CIP algorithm equation; The parameter acquisition and calculation module is used to obtain the atmospheric temperature, cloud top temperature, relative humidity, air vertical velocity and atmospheric liquid water content of the future period or historical forecast, substitute them into and solve the SCIP algorithm equation to obtain the aircraft icing index; The prediction module is used to determine the icing degree of the aircraft in the future period according to the icing index and icing degree judgment threshold, and plan the flight route of the aircraft; The CIP algorithm equation establishment module determines the CIP algorithm equation specifically as follows: CIP = M T x M RH x M CTT where M T , M RH , and M CTT are the fuzzy logic membership functions for atmospheric temperature, relative humidity, and cloud top temperature, respectively. The SCIP algorithm equation establishment module obtains the SCIP algorithm equation specifically as follows: where M ω , M CLW are the fuzzy logic membership functions of the vertical airspeed and the atmospheric liquid water content, respectively, w is the vertical airspeed; a, b are the influence coefficients of the vertical airspeed and the atmospheric liquid water content on the icing potential, a = 0.4, b = 0.

25.

5. The fuzzy logic based aircraft in-flight ice accretion potential prediction apparatus of claim 4 wherein, It further comprises Cloud top judging module: used for judging the relative humidity of the target height atmosphere layer by the relative humidity threshold method, when the relative humidity of the target height atmosphere layer is greater than 85%, it is judged as a cloud top, and when the relative humidity of the target height atmosphere layer is less than 85%, it is judged as no cloud top.

6. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 5. The computer program, when executed by a processor, implements the steps of the method for predicting the potential of aircraft icing in the air based on fuzzy logic according to any one of claims 1 to 3.

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