Aircraft landing distance prediction method, equipment and product based on fuzzy reasoning

Abstract

The invention relates to the technical field of avionics, and particularly provides an aircraft landing distance prediction method, equipment and product based on fuzzy reasoning. The method comprises the following steps: when it is detected that an aircraft reaches a first preset height, starting a glide section distance prediction module to predict a landing distance; the glide section distance prediction module predicts the distance change of the aircraft in the glide stage through a numerical calculation mode; when it is detected that the aircraft reaches a second preset height, a leveling section distance prediction module is switched to carry out landing distance prediction; the leveling section distance prediction module predicts the distance deviation of the aircraft in the leveling stage through fuzzy reasoning; when it is detected that the airplane slides on the runway, a sliding distance prediction module is switched to carry out landing distance prediction; the taxiing distance prediction module predicts the landing point position of the aircraft through a numerical calculation mode. According to the method, the accuracy of numerical solution and the adaptability of fuzzy reasoning are combined, so that the accuracy and robustness of aircraft landing distance prediction are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of avionics technology, and particularly to an aircraft landing distance prediction method, device, and product based on fuzzy inference. Background Art

[0002] In the aviation field, takeoff and landing are key links in the flight process. Due to the uncertain factors such as variable meteorological conditions and interference from the signal environment during these two stages, pilots bear a huge workload when performing tasks, and at the same time, the probability of accidents relatively increases. In order to effectively reduce the risk of safety accidents during the landing stage of the aircraft and meet the urgent need to improve airspace operation efficiency, it is particularly important to accurately predict the aircraft landing distance.

[0003] In related technologies, a neural network model is usually used to fit the landing data of the aircraft, or the landing distance of the aircraft is predicted by solving the dynamic formula. However, neither the neural network model nor the solution method of the dynamic formula can dynamically respond to the influence of different external conditions on the aircraft landing distance, resulting in a low accuracy of the final prediction result. Summary of the Invention

[0004] In view of this, an exemplary embodiment of the present disclosure provides an aircraft landing distance prediction method, device, and product based on fuzzy inference to solve the problems existing in related technologies.

[0005] One aspect of the exemplary embodiment of the present disclosure provides a method for predicting an aircraft landing distance based on fuzzy inference, the method comprising:

[0006] When it is detected that the aircraft reaches a first preset height, start the glide segment distance prediction module to predict the landing distance; the glide segment distance prediction module predicts the distance change of the aircraft during the glide phase through a numerical calculation method based on the real-time position information and real-time speed information of the aircraft;

[0007] When it is detected that the aircraft reaches a second preset height, switch to the flare segment distance prediction module to predict the landing distance; the flare segment distance prediction module predicts the distance deviation of the aircraft during the flare phase through fuzzy inference based on the real-time speed deviation value and the forward direction deviation value;

[0008] When it is detected that the aircraft is taxiing on the runway, switch to the ground roll distance prediction module to predict the landing distance; the ground roll distance prediction module predicts the landing point position of the aircraft through a numerical calculation method based on the real-time position information and real-time speed information of the aircraft.

[0009] Another aspect of the exemplary embodiment of the present disclosure provides a system for predicting an aircraft landing distance based on fuzzy inference, comprising:

[0010] The descent distance prediction module is used to predict the distance change of the aircraft during the descent phase by means of numerical calculation based on the real-time position information and real-time speed information of the aircraft when it is detected that the aircraft reaches the first preset altitude.

[0011] The flare distance prediction module is used to predict the distance deviation of the aircraft during the flare phase by means of fuzzy inference based on the real-time speed deviation value and the forward direction deviation value when it is detected that the aircraft reaches the second preset altitude.

[0012] The taxiing distance prediction module is used to predict the landing point position of the aircraft by means of numerical calculation based on the real-time position information and real-time speed information of the aircraft when it is detected that the aircraft is taxiing on the runway.

[0013] The landing distance prediction system selection module is used to switch the distance prediction module based on a preset trigger condition during the landing process of the aircraft.

[0014] Another aspect of the exemplary embodiment of the present disclosure provides a computer device, including a memory, a processor, and a computer program stored on the memory, and the processor executes the computer program to implement the method described in the exemplary embodiment of the present disclosure.

[0015] Another aspect of the exemplary embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program / instruction is stored, and when the computer program / instruction is executed by a processor, the method described in the exemplary embodiment of the present disclosure is implemented.

[0016] Another aspect of the exemplary embodiment of the present disclosure provides a computer program product, including a computer program / instruction, and when the computer program / instruction is executed by a processor, the method described in the exemplary embodiment of the present disclosure is implemented.

[0017] As will be described in detail below, in a method for predicting the landing distance of an aircraft by fuzzy inference according to an embodiment of the present disclosure, when it is detected that the aircraft reaches a first preset altitude, the glide segment distance prediction module is activated to predict the landing distance; the glide segment distance prediction module predicts the distance change of the aircraft during the glide phase through numerical calculation based on the real-time position information and real-time speed information of the aircraft; when it is detected that the aircraft reaches a second preset altitude, the flare segment distance prediction module is switched to predict the landing distance; the flare segment distance prediction module predicts the distance deviation of the aircraft during the flare phase through fuzzy inference based on the real-time speed deviation value and the forward direction deviation value; when it is detected that the aircraft is taxiing on the runway, the ground roll distance prediction module is switched to predict the landing distance; the ground roll distance prediction module predicts the landing point position of the aircraft through numerical calculation based on the real-time position information and real-time speed information of the aircraft. The present disclosure applies different methods in different stages, combining the accuracy of numerical calculation and the adaptability of fuzzy inference, and can better cope with the complexity and uncertainty during the aircraft landing process to improve the accuracy and robustness of aircraft landing distance prediction. Description of the Drawings

[0018] By describing the embodiments of the present disclosure in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present disclosure will become more apparent. The drawings are used to provide a further understanding of the embodiments of the present disclosure, and constitute a part of the specification, and are used to explain the present disclosure together with the embodiments of the present disclosure, and do not constitute a limitation to the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

[0019] Figure 1 Schematic diagram of the flight trajectory of an aircraft under windy conditions during the glide segment provided for an exemplary embodiment of the present disclosure;

[0020] Figure 2 Schematic diagram of the flight trajectory of an aircraft under windy conditions during the flare segment provided for an exemplary embodiment of the present disclosure;

[0021] Figure 3 Flowchart of a method for predicting the landing distance of an aircraft by fuzzy inference provided for an exemplary embodiment of the present disclosure;

[0022] Figure 4 Block diagram of the structure of an electronic device provided for an exemplary embodiment of the present disclosure;

[0023] Figure 5 Schematic diagram of a computer program product provided for an exemplary embodiment of the present disclosure. Detailed Description of the Embodiments

[0024] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Instead, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0025] It should be understood that the various steps recited in the method embodiments of the present disclosure can be executed in a different order and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0026] As used herein, the term "including" and its variations are open-ended, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order of the functions performed by these devices, modules or units or their interdependent relationships.

[0027] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly stated in the context, it should be understood as "one or more".

[0028] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0029] It can be understood that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, usage scopes, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, an application program, a server, or a storage medium that performs the operations of the technical solutions of the present disclosure according to the prompt message.

[0031] As an optional but non-limiting implementation manner, in response to receiving an active request from a user, the manner of sending a prompt message to the user may be, for example, in the form of a pop-up window, and the prompt message may be presented in text in the pop-up window. In addition, the pop-up window may also carry a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device. It can be understood that the above notification and user authorization acquisition process is only illustrative and does not limit the implementation manner of the present disclosure, and other manners that meet relevant laws and regulations can also be applied to the implementation manner of the present disclosure.

[0032] In the aviation field, takeoff and landing are key links in the flight process. Due to the uncertain factors such as variable meteorological conditions and signal environment interference in these two stages, pilots bear a huge workload when performing tasks, and at the same time, it also leads to a relatively increased accident probability. In order to effectively reduce the risk of safety accidents during the landing stage of an aircraft and meet the urgent need to improve airspace operation efficiency, it is particularly important to accurately predict the landing distance of the aircraft.

[0033] In related technologies, a neural network model is usually used to fit the landing data of an aircraft, or the landing distance of the aircraft is predicted by solving dynamic formulas. However, whether it is a neural network model or a method for solving dynamic formulas, it is difficult to dynamically respond to the influence of different external conditions on the landing distance of the aircraft, resulting in a relatively low accuracy of the final prediction result.

[0034] Therefore, to solve the above problems, an exemplary embodiment of the present disclosure provides an aircraft landing distance prediction system based on fuzzy inference, which improves the prediction accuracy of the landing position of an aircraft under turbulent wind field conditions by combining numerical solution and fuzzy inference. Among them, the aircraft landing distance prediction system includes a glide segment distance prediction module, a flare segment distance prediction module, a rollout segment distance prediction module, and a landing distance prediction system selection module.

[0035] Since there is no fixed flare trajectory during the flare stage of an aircraft, it is difficult to directly calculate the required flare distance of the aircraft by numerical solution, and it is necessary to use fuzzy inference to predict the required distance during the flare stage of the aircraft. Therefore, the glide segment distance prediction module and the rollout segment distance prediction module use numerical solution methods for distance prediction, while the flare segment distance prediction module uses fuzzy inference to achieve more accurate distance prediction.

[0036] Exemplarily, Figure 1 FIG. is a schematic diagram of the flight trajectory of an aircraft under windy conditions in the glide segment provided by an exemplary embodiment of the present disclosure. As Figure 1 shown, the black line is the standard glide path under windless conditions, the red line is the landing trajectory 1 passing through wind field 1, and the green line is the landing trajectory 2 passing through wind field 2.

[0037] Exemplarily,Figure 2 The figure shows a schematic diagram of the flight trajectory of an aircraft during the flare phase under windy conditions provided by this disclosure. As Figure 2 shown, due to the influence of the wind field during the flare phase, there is a flare point offset distance between the actual flare point and the standard flare point, which in turn leads to an actual touchdown point deviation distance between the actual touchdown point and the standard touchdown point.

[0038] During the landing process of the aircraft, when its altitude exceeds 30 feet, the system will activate the descent distance prediction module for prediction. When the aircraft altitude drops below 30 feet, the system automatically switches to the flare distance prediction module. When the braking system of the aircraft starts to work, the system will switch to the ground roll distance prediction module. Through this phased prediction method, the system can achieve real-time and accurate prediction of the touchdown point position throughout the landing process of the aircraft, thereby improving the safety and reliability of landing.

[0039] Exemplarily, the descent distance prediction module may specifically include:

[0040] When the aircraft is in the descent phase, the descent distance prediction module calculates the descent distance of the aircraft by numerical solution and predicts its touchdown point position.

[0041] Specifically, assuming that the aircraft maintains its current speed during the descent, the descent distance of the aircraft in the three-axis directions is proportional to the three-axis ground speed of the aircraft. Therefore, the descent distances of the aircraft in the x-axis and y-axis can be respectively expressed as:

[0042]

[0043] where, X glide_distance represents the descent distance of the aircraft in the x-axis; Y glide_distance represents the descent distance of the aircraft in the y-axis; V x represents the ground speed in the forward direction; Vz represents the descent speed; Vy represents the lateral ground speed; H represents the current altitude of the aircraft.

[0044] At this time, the predicted touchdown point position of the aircraft = the current position of the aircraft + the descent distance of the aircraft + the reference flare distance of the aircraft + the reference ground roll distance of the aircraft.

[0045] Exemplarily, the flare distance prediction module may specifically include:

[0046] During the flare phase of an aircraft, the wind field has a significant impact on the flare distance of the aircraft. To determine the influence relationship between the wind field and the flare distance of the aircraft, first, a landing trajectory database of the aircraft under calm wind conditions is established. Secondly, the landing trajectory of the aircraft under calm wind conditions is used as a reference trajectory. During the flare phase, based on the comparison between the current state of the aircraft and its state under calm wind, the difference in the current state of the aircraft is quantified. Finally, a Mamdani-type fuzzy inference system is used to predict the flare distance of the aircraft.

[0047] Specifically, based on the analysis of the flight laws of the aircraft under windy conditions, it is considered that five factors, namely, wind field intensity, wind field duration, wind field direction, the current altitude of the aircraft, and the difference between the forward direction of the aircraft and the standard glide path, will affect the flare distance of the aircraft.

[0048] Among them, the wind field intensity directly affects the speed of the aircraft in the horizontal direction. A headwind will increase the ground speed of the aircraft, resulting in an increase in the flare distance. The wind field intensity also affects the descent speed of the aircraft. For example, a strong vertical wind shear may cause a sudden increase or decrease in the descent speed of the aircraft, thus affecting the flare distance.

[0049] The longer the wind field duration, the greater its cumulative impact on the aircraft. A long-term crosswind may cause the aircraft to deviate further on the runway, increasing the flare and ground roll distances.

[0050] The wind field direction will cause the aircraft to yaw during the flare process. For example, a headwind will increase the ground speed of the aircraft and extend the flare distance, while a tailwind will decrease the ground speed and shorten the flare distance.

[0051] The current altitude of the aircraft will affect the control of the descent speed. When the aircraft enters the flare phase at a higher altitude, it takes longer time and distance to adjust the descent speed and attitude for a safe landing.

[0052] The difference between the forward direction of the aircraft and the standard glide path requires the pilot to make corrections, which may cause the aircraft to deviate on the runway and increase the flare distance.

[0053] Therefore, to accurately predict the flare segment distance, this embodiment proposes a fuzzy inference subsystem. Among them, the number of inference rules is in a proportional relationship with the number of input variables and the number of membership functions. Therefore, in order to simplify the inference rules and improve the universality of the fuzzy inference subsystem, the above influencing factors are further extracted as the deviation of the aircraft's ground speed in the x-axis, the deviation of the ground speed in the z-axis, and the difference between the forward direction of the aircraft and the standard glide path.

[0054] Deviation of the ground speed in the x-axis: It reflects the influence of the wind field on the horizontal speed of the aircraft and is a comprehensive manifestation of the wind field intensity and direction.

[0055] Deviation of the ground speed in the z-axis: It reflects the speed of the aircraft's altitude change and is affected by the vertical component of the wind field and the current altitude of the aircraft.

[0056] The difference between the aircraft and the forward direction of the standard glide path: It reflects the deviation degree of the current position of the aircraft from the standard glide path and can reflect the position deviation of the aircraft in space.

[0057] By processing the deviation value between the current state of the aircraft and the reference trajectory through the fuzzy inference subsystem, the deviation degree of the aircraft's flare distance can be effectively predicted, thereby improving the accuracy of the landing point position prediction.

[0058] Based on various information items contained in the aircraft flight state data set, a fuzzy inference subsystem for predicting the aircraft's flare distance is established. This fuzzy inference system for predicting the aircraft's flare distance predicts the deviation degree of the aircraft's flare distance according to different wind field conditions.

[0059] Exemplarily, the input variables of the fuzzy inference subsystem include: the deviation of the aircraft's ground speed in the x-axis, the deviation of the aircraft's ground speed in the z-axis, and the difference between the aircraft and the forward direction of the standard glide path, and the output variable is the deviation of the aircraft's flare distance.

[0060] Furthermore, the input variables and output variables are respectively fuzzified to convert the accurate input and output values into fuzzy values for fuzzy inference. The fuzzification of the input variables refers to the process of converting the accurate numerical values input into the system into fuzzy sets. The fuzzification of the output variables is carried out after fuzzy inference and converts the inference result into a fuzzy set output.

[0061] In the establishment process of the fuzzy inference subsystem for predicting the aircraft's flare distance, a three-input single-output system is designed. Based on the definition of fuzzy sets, there are two cases for the fuzzy input variables of the ground speed deviation in the x-axis and the ground speed deviation in the z-axis: large and small; there are five cases for the fuzzy input variable of the difference between the aircraft and the forward direction of the standard glide path: the left deviation degree V3, the left deviation degree V2, the left deviation degree V1, the left deviation degree V0, and the right deviation degree V0. There are seven cases for the fuzzy output variable of the aircraft's flare distance offset: the left deviation degree V5, the left deviation degree V4, the left deviation degree V3, the left deviation degree V2, the left deviation degree V1, the left deviation degree V0, and the right deviation degree V0.

[0062] According to the above fuzzy input variables and fuzzy output variables, a fuzzy rule table under the headwind condition is established, which contains a total of 20 fuzzy rules, as shown in Table 1:

[0063] Table 1 Fuzzy inference rules under the headwind condition

[0064]

[0065]

[0066] Based on the above-mentioned fuzzy input and output quantities, a fuzzy rule table under headwind conditions is established, which contains a total of 24 fuzzy rules, as shown in Table 2:

[0067] Table 2 Fuzzy Inference Rules under Headwind Conditions

[0068]

[0069]

[0070] Furthermore, membership functions of input and output variables are established based on the relevant flight data characteristics under tailwind conditions; membership functions of input and output variables are established based on the relevant flight data characteristics under headwind conditions.

[0071] Then, based on the Mamdani fuzzy inference subsystem, fuzzy inference is carried out. The input variables are fuzzified through the membership functions of the input variables, and the fuzzified input variables are subjected to a maximum value operation through the inference rules to obtain the fuzzy output of each inference rule, and then the fuzzy set of the output variable is obtained. Among them, the Mamdani fuzzy inference subsystem is a fuzzy inference method that derives fuzzy output from fuzzy input according to fuzzy rules.

[0072] The specific fuzzy inference process may include: defining the fuzzy sets and membership functions of the input variables, and then calculating the membership degree values of the input exact numerical values in each fuzzy set according to the input exact numerical values.

[0073] Furthermore, fuzzy rule evaluation is carried out. The fuzzified input variables are applied to the antecedent part of the fuzzy rules, and the triggering degree of the rules is calculated through fuzzy logic operators (such as AND, OR).

[0074] Then, a maximum value operation is carried out. For each fuzzy rule, according to the membership degree values of the input variables and the triggering degree of the fuzzy rules, the fuzzy set of the output variable is calculated. The output of each rule is a fuzzy set, and the fuzzy state of the output variable is described through the membership function.

[0075] Finally, the synthesis of fuzzy outputs is carried out. The output fuzzy sets of all rules are aggregated, usually by methods such as taking the maximum value (max) or taking the average value (mean), etc., to obtain a comprehensive fuzzy output set, and the fuzzy output set reflects the comprehensive inference results of all rules.

[0076] After obtaining the fuzzy set of the output variable, the area center (centroid) method can be used to defuzzify the fuzzy output. Specifically, the center of the area surrounded by the membership function curve of the fuzzy set and the abscissa is obtained, and the abscissa value corresponding to this center is selected as the representative value of this fuzzy set. Through the defuzzification operation, the specific value of the aircraft's leveling distance deviation can be obtained.

[0077] Exemplarily, the aircraft flare distance deviation value can be expressed as:

[0078]

[0079] where x error represents the aircraft flare distance offset; represents the proportional value corresponding to each fuzzy rule.

[0080] At this time, the predicted landing point position of the aircraft = the aircraft flare start value + the flare distance deviation value + the aircraft reference flare distance + the aircraft reference ground roll distance.

[0081] Based on this, in the flare phase, by establishing specific fuzzy rules based on the input variables, output variables, and membership functions of the variables, and then through the fuzzification and fuzzy inference steps, the input information is transformed into a fuzzy description of the output to achieve fuzzy inference. Finally, based on the fuzzy description of the output obtained from the fuzzy inference operation processing, the fuzzy output is processed through the defuzzification operation in the state determination output module to obtain the aircraft flare distance offset. Therefore, the application of fuzzy inference can effectively handle the uncertainty and fuzziness in the flare phase, enabling the aircraft landing distance prediction system to better adapt to complex environmental conditions. Through fuzzification and fuzzy rules, complex precise numerical values are simplified into fuzzy sets that are easy to process, thereby simplifying the inference process. In addition, the fuzzy inference system has a good tolerance for input noise and errors, improving the robustness and adaptability of the system. Through the Mamdani fuzzy inference subsystem, a fuzzy output can be effectively derived from the fuzzy input, providing a flexible and powerful tool for the control and decision-making of complex systems.

[0082] Exemplarily, the ground roll distance prediction module may specifically include:

[0083] When the aircraft is in the ground roll phase, the ground roll distance prediction module calculates the ground roll distance of the aircraft through numerical solution and predicts the landing point position of the aircraft.

[0084] Exemplarily, according to the aircraft deceleration rule, the remaining ground roll distance of the aircraft can be expressed as:

[0085]

[0086] where V x represents the ground speed of the aircraft along the center line of the runway; V 0 represents the speed of the aircraft when it reaches the runway exit, generally taken as 10 knots; a represents the current deceleration rate of the aircraft.

[0087] At this time, the predicted landing point position of the aircraft = the current position of the aircraft + the remaining ground roll distance of the aircraft.

[0088] Exemplarily, the aircraft landing distance prediction system may further include a landing distance prediction system selection module for switching prediction algorithms according to preset trigger conditions during the aircraft landing process. For example, when the aircraft altitude is between 500 feet and 30 feet, switch to use the glide phase prediction module to predict the landing distance; when the aircraft altitude is between 30 feet and touchdown, switch to use the flare phase prediction module to predict the landing distance; when the aircraft is taxiing on the runway, switch to use the taxiing distance prediction module to predict the landing distance.

[0089] In one or more technical solutions provided by the exemplary embodiments of the present disclosure, the landing distance is predicted by numerical calculation during the glide and taxiing phases. The flight parameters in these two phases are relatively stable, and the descent trajectory and taxiing distance of the aircraft can be accurately predicted through an accurate numerical model. During the flare phase, due to the large influence of uncertain factors such as the wind field on the aircraft, a fuzzy inference subsystem is used to predict the landing distance. Fuzzy inference can effectively handle these uncertainty and ambiguity problems, improving the robustness and adaptability of the prediction.

[0090] Therefore, the aircraft landing distance prediction system based on fuzzy inference provided by the exemplary embodiments of the present disclosure can dynamically adjust different prediction methods according to real-time flight data and environmental conditions, thereby realizing the full-process real-time prediction of the aircraft landing process, providing more reliable and accurate landing reference information for the pilot, and helping to improve the safety and efficiency of landing.

[0091] Based on the above embodiments, the present disclosure also provides a fuzzy inference-based aircraft landing distance prediction method. Figure 3 The following is a flowchart of the fuzzy inference-based aircraft landing distance prediction method provided by an exemplary embodiment of the present disclosure. As Figure 3 shown, the method may include the following steps:

[0092] Step S310: When it is detected that the aircraft reaches the first preset altitude, start the glide segment distance prediction module to predict the landing distance; the glide segment distance prediction module predicts the distance change of the aircraft during the glide phase through numerical calculation based on the real-time position information and real-time speed information of the aircraft.

[0093] In the embodiment, the first preset altitude may be set to 500 feet - 30 feet. When it is detected that the aircraft reaches the first preset altitude, the glide segment distance prediction module will be started for prediction. Since the aircraft is less affected by the wind field during the glide phase, the distance change of the aircraft during the glide phase can be calculated by numerical calculation, and its landing point position can be predicted. At the same time, it is also necessary to obtain the current position value of the aircraft during the glide phase, as well as the reference flare distance and reference taxiing distance of the aircraft under no-wind conditions.

[0094] At this time, the predicted landing point position of the aircraft = the current position of the aircraft + the glide distance of the aircraft + the reference flare distance of the aircraft + the reference roll distance of the aircraft.

[0095] Step S320: When it is detected that the aircraft reaches the second preset height, switch the flare distance prediction module to predict the landing distance; the flare distance prediction module predicts the distance deviation of the aircraft during the flare phase through fuzzy inference based on the real-time speed deviation value and the forward direction deviation value.

[0096] In the embodiment, the second preset height can be set to 30 feet - until the wheels touch the ground. When the aircraft height is between 30 feet - until the wheels touch the ground, switch to use the flare phase prediction module to predict the landing distance.

[0097] Since there is no fixed flare trajectory during the flare phase of the aircraft, it is difficult to directly calculate the required flare distance of the aircraft through numerical calculation. Therefore, fuzzy inference is used to predict the flare distance deviation value of the aircraft during the flare phase. At the same time, it is also necessary to obtain the starting position value of the aircraft during the flare phase, as well as the reference flare distance and reference roll distance of the aircraft under windless conditions.

[0098] At this time, the predicted landing point position of the aircraft = the starting value of the flare + the flare distance deviation value + the reference flare distance of the aircraft + the reference roll distance of the aircraft.

[0099] Step S330: When it is detected that the aircraft is taxiing on the runway, switch the roll distance prediction module to predict the landing distance; the roll distance prediction module predicts the landing point position of the aircraft through numerical calculation based on the real-time position information and real-time speed information of the aircraft.

[0100] In the embodiment, when the aircraft is rolling on the runway, switch to use the roll distance prediction module to predict the landing distance. Since the aircraft is less affected by the wind field during the roll phase, the remaining roll distance of the aircraft can be calculated through numerical calculation and its landing point position can be predicted. At the same time, it is also necessary to obtain the current position value of the aircraft during the roll phase. At this time, the predicted landing point position of the aircraft = the current position of the aircraft + the remaining roll distance of the aircraft.

[0101] Based on this, by combining numerical calculation and fuzzy inference, the aircraft landing distance prediction method has both the accuracy of numerical calculation and the adaptability of fuzzy inference, so as to better cope with the complexity and uncertainty during the aircraft landing process, and then improve the accuracy and robustness of the aircraft landing distance prediction.

[0102] Based on the above embodiments, in another embodiment provided by the present disclosure, the above step S320 may include:

[0103] Obtain the real-time speed deviation value and the forward direction deviation value of the aircraft, and perform fuzzification processing on the real-time speed deviation value and the forward direction deviation value to obtain an input vector;

[0104] Obtain the pre-constructed fuzzy rules, and perform fuzzy inference on the input vector based on the fuzzy rules to obtain a fuzzy set, where the fuzzy set is used to characterize the distance deviation degree and deviation direction of the aircraft during the flare phase;

[0105] Perform defuzzification processing on the fuzzy set to obtain the distance deviation value of the aircraft during the flare phase.

[0106] In the embodiment, during the flare phase of the aircraft, the wind field has a greater impact on the flare distance of the aircraft. To determine the influence relationship between the wind field and the flare distance of the aircraft, first establish a landing trajectory database of the aircraft under windless conditions, and secondly use the landing trajectory of the aircraft under windless conditions as a reference trajectory. During the flare phase, compare the current state of the aircraft with its state under windless conditions to quantify the current state difference of the aircraft. Finally, use a Mamdani-type fuzzy inference system to predict the flare distance of the aircraft.

[0107] Specifically, based on the analysis of the flight laws of the aircraft under windy conditions, it is considered that five factors, namely, wind field intensity, wind field duration, wind field direction, current altitude of the aircraft, and the difference in the forward direction between the aircraft and the standard glide path, will affect the flare distance of the aircraft.

[0108] Among them, the wind field intensity directly affects the speed of the aircraft in the horizontal direction. A tailwind will increase the ground speed of the aircraft, resulting in an increase in the flare distance. The wind field intensity will also affect the descent speed of the aircraft. For example, strong vertical wind shear may cause a sudden increase or decrease in the descent speed of the aircraft, thereby affecting the flare distance.

[0109] The longer the wind field duration, the greater its cumulative impact on the aircraft. A long-term crosswind may cause the aircraft to deviate further on the runway, increasing the flare and roll distances.

[0110] The wind field direction will cause the aircraft to yaw during the flare process. For example, a tailwind will increase the ground speed of the aircraft and extend the flare distance, while a headwind will decrease the ground speed and shorten the flare distance.

[0111] The current altitude of the aircraft will affect the control of the descent speed. When the aircraft enters the flare phase at a higher altitude, it takes longer time and distance to adjust the descent speed and attitude for a safe landing.

[0112] The difference in the forward direction between the aircraft and the standard glide path will require the pilot to make corrections, which may cause the aircraft to deviate on the runway and increase the flare distance.

[0113] Therefore, to accurately predict the flare distance, this embodiment proposes a fuzzy inference subsystem. Among them, the number of inference rules is in a proportional relationship with the number of input variables and the number of membership functions. Therefore, in order to simplify the inference rules and improve the universality of the fuzzy inference subsystem, the above influencing factors are further extracted as the ground speed deviation of the aircraft on the x-axis, the ground speed deviation on the z-axis, and the difference between the aircraft and the forward direction of the standard glide path.

[0114] Ground speed deviation on the x-axis: It reflects the influence of the wind field on the horizontal speed of the aircraft and is a comprehensive manifestation of the wind field intensity and direction.

[0115] Ground speed deviation on the z-axis: It reflects the speed of the aircraft's altitude change and is affected by the vertical component of the wind field and the current altitude of the aircraft.

[0116] Difference between the aircraft and the forward direction of the standard glide path: It reflects the deviation degree of the current position of the aircraft from the standard glide path and can reflect the position deviation of the aircraft in space.

[0117] By processing the deviation value between the current state of the aircraft and the reference trajectory through the fuzzy inference subsystem, the deviation degree of the aircraft's flare distance can be effectively predicted, thereby improving the accuracy of the landing point position prediction.

[0118] Exemplarily, the input variables of the fuzzy inference subsystem include: the real-time speed deviation value and the forward direction deviation value of the aircraft, where the real-time speed deviation value includes the ground speed deviation of the aircraft on the x-axis and the ground speed deviation on the z-axis. The output variable is the flare distance deviation of the aircraft.

[0119] Furthermore, the input variables and the output variable are respectively fuzzified to convert the precise input and output values into fuzzy values for fuzzy inference. Input variable fuzzification refers to the process of converting the precise numerical values input into the system into fuzzy sets. Output variable fuzzification is carried out after fuzzy inference and refers to converting the inference result into a fuzzy set for output.

[0120] Furthermore, based on flight laws and simulated flight data, the fuzzy inference subsystem for the aircraft's flare distance can be designed with fuzzy rules for subsequent fuzzy inference processes.

[0121] Exemplarily, 20 fuzzy inference rules can be designed for the fuzzy inference subsystem of the aircraft's flare distance under the condition of a tailwind, and 24 fuzzy inference rules can be designed for the fuzzy inference subsystem of the aircraft's flare distance under the condition of a headwind. For details, please refer to the above text. Table 1 in the above text is the fuzzy inference rules under the condition of a tailwind, and Table 2 is the fuzzy inference rules under the condition of a headwind.

[0122] Then, based on the Mamdani fuzzy inference subsystem, fuzzy inference is carried out. The input variables are fuzzified through the membership functions of the input variables. The fuzzified input variables are subjected to a maximum operation according to the inference rules to obtain the fuzzy output of each inference rule, and then the fuzzy set of the output variable is obtained. Among them, the Mamdani fuzzy inference subsystem is a fuzzy inference method that derives fuzzy outputs from fuzzy inputs according to fuzzy rules.

[0123] The specific fuzzy inference process may include: defining the fuzzy sets and membership functions of the input variables, and then calculating the membership values of the input exact values in each fuzzy set according to the input exact values.

[0124] Furthermore, fuzzy rule evaluation is carried out. The fuzzified input variables are applied to the antecedent part of the fuzzy rules, and the triggering degree of the rules is calculated through fuzzy logic operators (such as AND, OR).

[0125] Then, a maximum operation is carried out. For each fuzzy rule, according to the membership values of the input variables and the triggering degree of the fuzzy rules, the fuzzy set of the output variable is calculated. The output of each rule is a fuzzy set, and the fuzzy state of the output variable is described through the membership function.

[0126] Then, the composition of the fuzzy outputs is carried out. The output fuzzy sets of all rules are aggregated, usually by methods such as taking the maximum (max) or taking the average (mean), etc., to obtain a comprehensive fuzzy output set, and the fuzzy output set reflects the comprehensive inference results of all rules.

[0127] Finally, defuzzification is performed on the said fuzzy output to obtain the distance deviation value of the aircraft during the flare phase.

[0128] Based on this, during the flare phase, by establishing specific fuzzy rules based on the input variables, output variables, and membership functions of the variables, and then through the fuzzification and fuzzy inference steps, the input information is transformed into a fuzzy description of the output to achieve fuzzy inference. Finally, based on the fuzzy description of the output obtained by the fuzzy inference operation processing, the fuzzy output is processed through defuzzification operation in the state determination output module to obtain the flare distance offset of the aircraft. Therefore, the application of fuzzy inference can effectively handle the uncertainty and fuzziness during the flare phase, enabling the aircraft landing distance prediction system to better adapt to complex environmental conditions. Through fuzzification and fuzzy rules, complex exact values are simplified into easily processed fuzzy sets, thereby simplifying the inference process. In addition, the fuzzy inference system has good tolerance to input noise and errors, improving the robustness and adaptability of the system. Through the Mamdani fuzzy inference subsystem, fuzzy outputs can be effectively derived from fuzzy inputs, providing a flexible and powerful tool for the control and decision-making of complex systems.

[0129] Based on the above embodiments, in another embodiment provided by the present disclosure, the above-mentioned fuzzification processing of the real-time speed deviation value and the forward direction deviation value to obtain an input vector may include:

[0130] Obtain a pre-constructed variable membership function;

[0131] Based on the real-time speed deviation value and the forward direction deviation value, calculate the membership degree values in each fuzzy set to form an input vector.

[0132] In the embodiment, a suitable variable membership function is selected for each fuzzy set. The variable membership function may include a triangular function or a trapezoidal function.

[0133] According to the actual values of the real-time speed deviation value and the forward direction deviation value, calculate their membership degree values in each fuzzy set, so as to perform fuzzification processing. The calculated membership degree values form an input vector, representing the membership degrees of the real-time speed deviation value and the forward direction deviation value in each fuzzy set.

[0134] Based on this, by obtaining the pre-constructed variable membership function, the accurate real-time speed deviation value and forward direction deviation value are converted into membership degree values in the fuzzy set, forming an input vector. This process simplifies the complex accurate values into easy-to-process fuzzy sets, thus simplifying the reasoning process. Secondly, under different flight conditions and environmental changes, fuzzy reasoning can dynamically adjust the membership function and fuzzy rules, and fuzzification processing can ensure that the adjusted fuzzy output can be effectively converted into specific values, thereby ensuring the stability and reliability of the prediction method during the entire flight process.

[0135] Based on the above embodiments, in another embodiment provided by the present disclosure, the above-mentioned defuzzification processing of the fuzzy set to obtain the distance deviation value of the aircraft during the flare stage may include:

[0136] Calculate the area center of the variable membership function curve of the fuzzy set and the enclosed area of the abscissa;

[0137] Determine the abscissa value corresponding to the area center as the distance deviation value of the aircraft during the flare stage.

[0138] In the embodiment, after obtaining the fuzzy set of the output variable, the area center (centroid) method can be used to defuzzify the fuzzy output. Specifically, find the center of the area enclosed by the membership function curve of the fuzzy set and the abscissa, and select the abscissa value corresponding to this center as the representative value of this fuzzy set. Through the defuzzification operation, the specific value of the aircraft flare distance deviation can be obtained.

[0139] Exemplarily, the aircraft flare distance deviation value can be expressed as:

[0140]

[0141] where x error represents the aircraft flare distance offset; represents the proportional value corresponding to each fuzzy rule.

[0142] Based on this, by calculating the area center of the variable membership function curve of the fuzzy set and the abscissa enclosed area, and then determining the distance deviation value of the aircraft during the flare phase, all possible values and their membership degrees in the fuzzy set can be comprehensively considered, so as to obtain a more accurate and stable prediction result, which helps to reduce the uncertainty caused by fuzziness and makes the prediction result closer to the actual situation.

[0143] Based on the above embodiments, in another embodiment provided by the present disclosure, the above step S310 may include:

[0144] Obtain the real-time position information and real-time speed information of the aircraft; the real-time position information includes the real-time altitude information of the aircraft, and the real-time speed information includes the ground speed in the forward direction, the descent speed, and the lateral ground speed;

[0145] Based on the ground speed in the forward direction, the descent speed, and the real-time altitude information, determine the glide distance of the aircraft in the horizontal direction;

[0146] Based on the ground speed in the forward direction, the lateral ground speed, and the real-time altitude information, determine the glide distance of the aircraft in the vertical direction.

[0147] In the embodiment, when the aircraft is in the glide phase, the glide section distance prediction module calculates the glide distance of the aircraft by means of numerical solution and predicts its landing point position.

[0148] Specifically, assuming that the aircraft maintains its current speed unchanged during the glide, the glide distances of the aircraft in the three axes are proportional to the ground speeds of the three axes of the aircraft. Therefore, the glide distances of the aircraft in the x-axis and y-axis can be respectively expressed as:

[0149]

[0150] where X glide_distance represents the glide distance of the aircraft in the x-axis; Y glide_distance represents the glide distance of the aircraft in the y-axis; V x represents the ground speed in the forward direction; Vz represents the descent speed; Vy represents the lateral ground speed; H represents the current altitude of the aircraft.

[0151] Based on this, by comprehensively considering the real-time altitude of the aircraft, the ground speed in the forward direction, and the descent speed to determine the glide distance in the horizontal direction, and by combining the ground speed in the forward direction, the lateral ground speed, and the real-time altitude information to determine the glide distance in the vertical direction, it is possible to more comprehensively reflect the actual motion state of the aircraft under complex flight conditions, and thus provide a more accurate prediction of the glide distance.

[0152] Based on the above embodiments, in another embodiment provided by the present disclosure, the above step S330 may include:

[0153] Obtain the real-time position information and real-time speed information of the aircraft, where the real-time speed information includes the ground speed of the aircraft along the runway centerline direction and the real-time deceleration rate;

[0154] Based on the ground speed of the aircraft along the runway centerline direction and the real-time deceleration rate, obtain the remaining ground roll distance of the aircraft through numerical calculation;

[0155] Based on the real-time position information and the remaining ground roll distance of the aircraft, predict the landing point position of the aircraft.

[0156] In the embodiment, when the aircraft is in the ground roll stage, the ground roll distance prediction module calculates the ground roll distance of the aircraft through numerical solution and predicts the landing point position of the aircraft.

[0157] Exemplarily, according to the aircraft deceleration rule, the remaining ground roll distance of the aircraft can be expressed as:

[0158]

[0159] where V x represents the ground speed of the aircraft along the runway centerline direction; V 0 represents the speed of the aircraft when it reaches the runway exit, generally taken as 10 knots; a represents the current deceleration rate of the aircraft.

[0160] Based on this, by obtaining the real-time position information and real-time speed information of the aircraft, including the ground speed of the aircraft along the runway centerline direction and the real-time deceleration rate, and then calculating the remaining ground roll distance of the aircraft, it is possible to more comprehensively reflect the actual motion state of the aircraft during the landing stage, and thus provide a more accurate prediction of the landing point.

[0161] In one or more technical solutions provided by the exemplary embodiments of the present disclosure, during the glide and ground roll stages, the landing distance is predicted through numerical solution. The flight parameters in these two stages are relatively stable, and the descent trajectory and ground roll distance of the aircraft can be accurately predicted through an accurate numerical model. During the flare stage, since the aircraft is greatly affected by uncertain factors such as the wind field, a fuzzy inference subsystem is used to predict the landing distance. Fuzzy inference can effectively handle these uncertainties and fuzzy problems, improving the robustness and adaptability of the prediction.

[0162] Therefore, the aircraft landing distance prediction method based on fuzzy inference provided in the exemplary embodiments of the present disclosure can dynamically adjust different prediction methods according to real-time flight data and environmental conditions, thereby achieving real-time prediction of the entire aircraft landing process, providing more reliable and accurate landing reference information for pilots, and helping to improve the safety and efficiency of landing.

[0163] The above mainly introduces the solutions provided by the exemplary embodiments of the present disclosure. It can be understood that, in order to implement the above functions, the electronic device includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed herein, the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving the hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0164] The exemplary embodiments of the present disclosure can divide the electronic device into functional units according to the above method examples. For example, each functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. It should be noted that the division of modules in the exemplary embodiments of the present disclosure is illustrative, only a logical function division, and there can be other division methods in actual implementation.

[0165] The exemplary embodiments of the present disclosure also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program that can be executed by the at least one processor, and when the computer program is executed by the at least one processor, it is used to cause the electronic device to execute the method according to the embodiments of the present disclosure.

[0166] The exemplary embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is used to cause the computer to execute the method according to the embodiments of the present disclosure when executed by a processor of the computer.

[0167] Figure 4The following is a block diagram of an electronic device provided for an exemplary embodiment of the present disclosure. Now, a block diagram of an electronic device 400 that can be a server or a client of the present disclosure will be described. It is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0168] As Figure 4 shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0169] A plurality of components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 can be any type of device that can input information into the electronic device 400. The input unit 406 can receive input digital or character information, and generate key signal inputs related to the user settings and / or function controls of the electronic device. The output unit 407 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 408 can include but is not limited to a magnetic disk, an optical disk. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0170] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above. Each of the various methods described above can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 400 via the ROM 402 and / or the communication unit 409.

[0171] Figure 5 A schematic diagram of a computer program product provided for an exemplary embodiment of the present disclosure. The exemplary embodiments of the present disclosure also provide a computer program product 500, including a computer program 501, wherein the computer program 501, when executed by a processor of a computer, is used to cause the computer to execute the method according to the embodiments of the present disclosure.

[0172] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0173] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0174] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device that provides machine instructions and / or data to a programmable processor (e.g., a magnetic disk, an optical disk, a memory, a programmable logic device (PLD)), including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal that provides machine instructions and / or data to a programmable processor.

[0175] For purposes of providing an interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0176] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0177] A computer system can include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0178] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present disclosure are executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user device, or other programmable devices. The computer program or instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another, for example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center integrating one or more available media. The available medium can be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it can also be an optical medium, such as a Digital Video Disc (DVD); it can also be a semiconductor medium, such as a Solid State Drive (SSD).

[0179] Although the present disclosure has been described in conjunction with specific features and their embodiments, it is obvious that various modifications and combinations can be made without departing from the spirit and scope of the present disclosure. Accordingly, this specification and the drawings are merely exemplary illustrations of the present disclosure defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present disclosure. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalent technologies, the present disclosure also intends to include these changes and modifications therein.

Claims

1. A fuzzy reasoning method for predicting aircraft landing distance, characterized in that: The method comprises: When it is detected that the aircraft has reached the first preset height, the glide distance prediction module is started to predict the landing distance; the glide distance prediction module predicts the distance change of the aircraft in the glide stage by numerical calculation based on the real-time position information and real-time speed information of the aircraft; When it is detected that the aircraft has reached the second preset altitude, the leveling distance prediction module is switched to perform landing distance prediction; the leveling distance prediction module predicts the distance deviation of the aircraft in the leveling stage through fuzzy reasoning based on the real-time speed deviation value and the forward direction deviation value; When it is detected that the aircraft is taxiing on the runway, the taxiing distance prediction module is switched to perform landing distance prediction; the taxiing distance prediction module predicts the landing point position of the aircraft through numerical calculation based on the real-time position information and real-time speed information of the aircraft.

2. The method according to claim 1, characterized in that The leveling distance prediction module predicts the distance deviation of the aircraft in the leveling stage through fuzzy reasoning based on the real-time speed deviation value and the forward direction deviation value, including: Acquiring a real-time speed deviation value and a forward direction deviation value of the aircraft, and performing fuzzy processing on the real-time speed deviation value and the forward direction deviation value to obtain an input vector; Acquire a pre-constructed fuzzy rule, perform fuzzy reasoning on the input vector based on the fuzzy rule, and obtain a fuzzy set, wherein the fuzzy set is used to characterize the distance deviation degree and deviation direction of the aircraft in the leveling phase; Defuzzification processing is performed on the fuzzy set to obtain a distance deviation value of the aircraft in the leveling phase.

3. The method according to claim 2, characterized in that The fuzzy processing is performed on the real-time speed deviation value and the forward direction deviation value to obtain an input vector, including: Get pre-built variable membership functions; Based on the real-time speed deviation value and the forward direction deviation value, the membership value in each fuzzy set is calculated to form an input vector.

4. The method according to claim 2, characterized in that: The defuzzification process is performed on the fuzzy set to obtain the distance deviation value of the aircraft in the leveling phase, including: Calculate the variable membership function curve of the fuzzy set and the area center of the area surrounded by the horizontal coordinate; The abscissa value corresponding to the center of the area is determined as the distance deviation value of the aircraft in the leveling phase.

5. The method according to claim 1, characterized in that The glide phase prediction module predicts the distance change of the aircraft in the glide phase by numerical calculation based on the real-time position information and real-time speed information of the aircraft, including: Acquire real-time position information and real-time speed information of the aircraft; the real-time position information includes real-time altitude information of the aircraft, and the real-time speed information includes ground speed in the forward direction, descent speed, and lateral ground speed; Determine the horizontal gliding distance of the aircraft based on the ground speed in the forward direction, the descent speed and the real-time altitude information; Based on the forward ground speed, lateral ground speed and real-time altitude information, the vertical glide distance of the aircraft is determined.

6. The method according to claim 1, characterized in that The taxiing distance prediction module predicts the landing point position of the aircraft by numerical calculation based on the real-time position information and real-time speed information of the aircraft, including: Obtaining real-time position information and real-time speed information of the aircraft, wherein the real-time speed information includes the ground speed and real-time deceleration rate of the aircraft along the runway centerline; Based on the ground speed and real-time deceleration rate of the aircraft along the runway centerline, the remaining rolling distance of the aircraft is obtained by numerical calculation; The landing point position of the aircraft is predicted based on the real-time position information and the remaining takeoff distance of the aircraft.

7. A fuzzy reasoning aircraft landing distance prediction system, characterized in that: include: A glide distance prediction module is used to predict the distance change of the aircraft in the glide stage by numerical calculation based on the real-time position information and real-time speed information of the aircraft when the aircraft reaches the first preset altitude; The leveling stage distance prediction module is used to predict the distance deviation of the aircraft in the leveling stage through fuzzy reasoning based on the real-time speed deviation value and the forward direction deviation value when the aircraft is detected to have reached the second preset altitude; The taxiing distance prediction module is used to predict the landing point of the aircraft by numerical calculation based on the real-time position information and real-time speed information of the aircraft when the aircraft is detected taxiing on the runway; The landing distance prediction system selection module is used to switch the distance prediction module based on preset trigger conditions during the aircraft landing process.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the method of claim 1.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the method of claim 1 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the method of claim 1 is implemented.

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