Flight control intelligent management and control method and system based on big data and medium

Abstract

The invention provides an intelligent flight control management and control method and system based on big data and a medium. The method comprises the following steps: acquiring action operation characteristic data and facial characteristic data of a pilot, flight attitude monitoring data, flight sensing parameter data and a flight task real-time monitoring data packet through a plurality of airborne platforms, identifying the real-time deviation degree of the flight task, if the deviation degree exceeds the standard, performing flight control operation compliance processing, and if the deviation degree exceeds the standard, performing flight control operation compliance processing; meanwhile, cockpit flight control risk identification is carried out, the timeliness performance of an engine is evaluated, and finally processing is carried out according to a flight task real-time deviation early warning index, an engine timeliness performance evaluation index, a cockpit flight control risk identification index and flight control operation compliance data. A flight control deviation risk evaluation index is obtained, and a deviation risk level and a risk alarm are obtained; therefore, deviation evaluation and risk management and control are carried out on the flight task condition, the engine performance, the cabin flight control condition and the flight control state of the flight control operation through the flight control airborne platform.

Description

Technical Field

[0001] The present application relates to the field of flight control, and specifically, to a flight control intelligent management and control method, system and medium based on big data. Background Art

[0002] The onboard computer system is the core of the aircraft flight control. It collects information and processes data through the onboard optoelectronic sensors, radars, communication navigation, infrared and other transmission interaction modules to achieve comprehensive monitoring of flight control items such as cockpit operation, whole machine condition, engine performance, flight instructions, etc. At present, the onboard computing platform in service usually adopts a private customized computing mode, which is focused and targeted, but it has shortcomings such as poor data exchange capability, insufficient computing and storage capacity, and low information interaction processing capability. It cannot meet the future aircraft flight control's processing needs for remote sensing capture, human-computer interaction, gesture recognition, emotion monitoring and other multi-data information, and cannot realize intelligent risk analysis and control of cockpit flight control, whole machine condition, engine, and mission execution through intelligent analysis and processing of multi-data information of flight control.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a flight control intelligent management and control method, system and medium based on big data, which can collect and analyze multiple information on flight mission status, engine performance, cockpit flight control status, pilot information capture and flight control status of flight control operations through the flight control airborne platform, so as to realize deviation assessment and risk management of flight control status.

[0005] The present application also provides a flight control intelligent management and control method based on big data, comprising the following steps:

[0006] The pilot's action operation feature data and facial feature data are obtained through the preset cockpit optoelectronic sensor, the flight attitude monitoring data is obtained through the preset flight control monitoring platform, the flight sensing parameter data is obtained through the preset airborne radar monitoring platform, and the flight mission real-time monitoring data package is obtained through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package;

[0007] Processing the flight sensing parameter data, flight attitude monitoring data and mission package dynamic monitoring data to obtain a real-time deviation warning index for the flight mission, and comparing the threshold to determine the real-time deviation of the flight mission;

[0008] If the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated and the operation procedure task package calibration data is extracted, and the flight attitude monitoring data, facial feature data and action operation feature data are combined for processing to obtain flight control operation compliance data;

[0009] Processing the action operation feature data in combination with the facial feature data to obtain a cockpit flight control risk identification index;

[0010] Obtain and process engine performance monitoring data through a preset engine performance testing platform to obtain an engine time-efficiency performance evaluation index;

[0011] The flight mission real-time deviation warning index, engine time performance evaluation index, cockpit flight control risk identification index and flight control operation compliance data are processed to obtain the flight control deviation risk evaluation index, and the deviation risk level of the real-time flight mission is obtained by threshold comparison and risk warning.

[0012] Optionally, in the flight control intelligent management and control method based on big data described in the present application, the pilot's action operation feature data and facial feature data are obtained through a preset cockpit photoelectric sensor, the flight attitude monitoring data is obtained through a preset flight control monitoring platform, the flight sensing parameter data is obtained through a preset airborne radar monitoring platform, and the flight mission real-time monitoring data packet is obtained through a preset airborne flight information monitoring platform, including task package dynamic monitoring data and operation procedure task package, including:

[0013] Acquire the pilot's action operation information and biometric facial recognition information during the preset flight period through the preset cockpit photoelectric sensor;

[0014] The motion operation information and the biological facial recognition information are identified through a preset cockpit information processing platform to obtain motion operation feature data and facial feature data;

[0015] The action operation feature data includes operation step compliance data and gesture information warning degree data, and the facial feature data includes visual attention distraction data, emotional tension data and language instruction data;

[0016] Obtain flight attitude monitoring data through the preset flight control monitoring platform, including real-time pressure altitude data and attitude capture data;

[0017] Acquire flight sensing parameter data through a preset airborne radar monitoring platform, including real-time obstacle approach rate data, target telemetry offset data, and multiple flight trajectory point recording data;

[0018] The real-time monitoring data package of the flight mission is obtained through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package. The mission package dynamic monitoring data includes multiple preset trajectory point dynamic data and preset flight altitude data.

[0019] Optionally, in the flight control intelligent management and control method based on big data described in the present application, the processing is performed according to the flight sensing parameter data, the flight attitude monitoring data and the task package dynamic monitoring data to obtain a real-time deviation warning index of the flight mission, and the threshold comparison is used to determine the real-time deviation of the flight mission, including:

[0020] According to the flight trajectory point record data of multiple nodes within the preset flight period and the preset trajectory point dynamic data of the corresponding nodes, combined with the real-time pressure altitude data and preset flight altitude data, as well as the real-time obstacle approach rate data and target telemetry offset data, a preset flight mission deviation assessment model is used for processing to obtain a real-time deviation warning index of the flight mission;

[0021] Compare the real-time deviation warning index of the flight mission with the preset mission deviation threshold, and determine the real-time deviation of the flight mission according to the threshold comparison result;

[0022] The calculation formula of the real-time deviation warning index of the flight mission is:

[0023]

[0024] in, is the real-time deviation warning index of the flight mission, g yi , g Ti They are respectively the flight trajectory point record data of the i-th node among the n nodes in the preset flight period and the dynamic data of the preset trajectory point, r f 、z p They are obstacle progression rate data and target telemetry offset data, respectively. e 、c D They are respectively the pressure altitude data, the preset flight altitude data, σ, λ, ω, μ, ρ i , i , γ i is the preset characteristic coefficient.

[0025] Optionally, in the flight control intelligent management and control method based on big data described in the present application, if the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated and the operation procedure task package calibration data is extracted, and the flight attitude monitoring data, facial feature data and action operation feature data are combined for processing to obtain flight control operation compliance data, including:

[0026] If the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated, and the calibration data of the operation procedure task package is extracted;

[0027] The operation program task package calibration data includes attitude calibration data of navigation mark points and operation instruction calibration data;

[0028] According to the attitude calibration data and the operation instruction calibration data, combined with the attitude capture data and the language instruction data, and the operation step compliance data, a preset flight control operation specification evaluation model is used for processing to obtain flight control operation compliance data;

[0029] The calculation formula of the flight control operation compliance data is:

[0030]

[0031] in, is the flight control operation compliance data, a q 、a R They are posture capture data and posture calibration data, respectively. v 、h L They are language instruction data and operation instruction calibration data, respectively. z is the operation step compliance data, and ζ1, ζ2, ζ3, η1, and η2 are preset characteristic coefficients.

[0032] Optionally, in the big data-based flight control intelligent management and control method described in the present application, the processing of the action operation feature data in combination with the facial feature data to obtain a cockpit flight control risk identification index includes:

[0033] The gesture information warning degree data is combined with the visual attention distraction degree data and the emotional tension degree data, and processed by a preset cockpit flight control state recognition model to obtain a cockpit flight control risk recognition index;

[0034] The calculation formula of the cockpit flight control risk identification index is:

[0035]

[0036] Among them, y τ is the cockpit flight control risk identification index, d m is the gesture information warning degree data, b e is the visual attention distraction data, u c For emotional tension data, υ is the preset characteristic coefficient.

[0037] Optionally, in the big data-based intelligent flight control management method described in the present application, the engine performance monitoring data is acquired and processed through a preset engine performance detection platform to obtain an engine time performance evaluation index, including:

[0038] Obtain engine performance monitoring data within a preset flight period through a preset engine performance testing platform;

[0039] The engine performance monitoring data includes command operation effectiveness data, fuel regulation efficiency data, turbine temperature change efficiency data and effective boost ratio data;

[0040] Processing the engine performance monitoring data through a preset engine performance evaluation model to obtain an engine time-efficiency performance evaluation index;

[0041] The calculation formula of the engine time performance evaluation index is:

[0042]

[0043] Among them, v δ is the engine time performance evaluation index, m p 、s x 、e t , k c They are respectively command operation effectiveness data, fuel regulation efficiency data, turbine temperature change efficiency data, and effective boost ratio data. θ1, θ2, θ3, ε, and φ are preset characteristic coefficients.

[0044] Optionally, in the big data-based flight control intelligent management and control method described in the present application, the real-time deviation warning index of the flight mission, the engine time performance evaluation index, the cockpit flight control risk identification index and the flight control operation compliance data are processed to obtain a flight control deviation risk evaluation index, and the deviation risk level of the real-time flight mission is obtained by threshold comparison and risk warning, including:

[0045] The flight mission real-time deviation warning index, the engine time performance evaluation index, the cockpit flight control risk identification index and the flight control operation compliance data are processed by a preset flight control deviation risk evaluation model to obtain a flight control deviation risk evaluation index;

[0046] Comparing the flight control deviation risk assessment index with a preset flight control deviation risk threshold, to obtain a corresponding deviation risk level of the real-time flight mission;

[0047] Issue a flight control risk warning according to the deviation risk level;

[0048] The calculation formula of the flight control deviation risk evaluation index is:

[0049]

[0050] in, is the flight control deviation risk assessment index, is the real-time deviation warning index of the flight mission, v δ is the engine time performance evaluation index, y τ is the cockpit flight control risk identification index, is the flight control operation compliance data, and δ, χ, ω, ζ are the preset characteristic coefficients.

[0051] In a second aspect, the present application provides a flight control intelligent management and control system based on big data, the system comprising: a memory and a processor, the memory storing a program of a flight control intelligent management and control method based on big data, and the program of the flight control intelligent management and control method based on big data is executed by the processor to implement the following steps:

[0052] The pilot's action operation feature data and facial feature data are obtained through the preset cockpit optoelectronic sensor, the flight attitude monitoring data is obtained through the preset flight control monitoring platform, the flight sensing parameter data is obtained through the preset airborne radar monitoring platform, and the flight mission real-time monitoring data package is obtained through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package;

[0053] Processing the flight sensing parameter data, flight attitude monitoring data and mission package dynamic monitoring data to obtain a real-time deviation warning index for the flight mission, and comparing the threshold to determine the real-time deviation of the flight mission;

[0054] If the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated and the operation procedure task package calibration data is extracted, and the flight attitude monitoring data, facial feature data and action operation feature data are combined for processing to obtain flight control operation compliance data;

[0055] Processing the action operation feature data in combination with the facial feature data to obtain a cockpit flight control risk identification index;

[0056] Obtain and process engine performance monitoring data through a preset engine performance testing platform to obtain an engine time-efficiency performance evaluation index;

[0057] The flight mission real-time deviation warning index, engine time performance evaluation index, cockpit flight control risk identification index and flight control operation compliance data are processed to obtain the flight control deviation risk evaluation index, and the deviation risk level of the real-time flight mission is obtained by threshold comparison and risk warning.

[0058] Optionally, in the flight control intelligent management and control system based on big data described in the present application, the pilot's action operation feature data and facial feature data are obtained through a preset cockpit photoelectric sensor, the flight attitude monitoring data is obtained through a preset flight control monitoring platform, the flight sensing parameter data is obtained through a preset airborne radar monitoring platform, and the flight mission real-time monitoring data packet is obtained through a preset airborne flight information monitoring platform, including task package dynamic monitoring data and operation procedure task package, including:

[0059] Acquire the pilot's action operation information and biometric facial recognition information during the preset flight period through the preset cockpit photoelectric sensor;

[0060] The motion operation information and the biological facial recognition information are identified through a preset cockpit information processing platform to obtain motion operation feature data and facial feature data;

[0061] The action operation feature data includes operation step compliance data and gesture information warning degree data, and the facial feature data includes visual attention distraction data, emotional tension data and language instruction data;

[0062] Obtain flight attitude monitoring data through the preset flight control monitoring platform, including real-time pressure altitude data and attitude capture data;

[0063] Acquire flight sensing parameter data through a preset airborne radar monitoring platform, including real-time obstacle approach rate data, target telemetry offset data, and multiple flight trajectory point recording data;

[0064] The real-time monitoring data package of the flight mission is obtained through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package. The mission package dynamic monitoring data includes multiple preset trajectory point dynamic data and preset flight altitude data.

[0065] In the third aspect, the present application also provides a readable storage medium, which stores a flight control intelligent management and control method program based on big data. When the flight control intelligent management and control method program based on big data is executed by a processor, the steps of the flight control intelligent management and control method based on big data as described in any one of the above items are implemented.

[0066] As can be seen from the above, the flight control intelligent management method, system and medium based on big data provided by the present application. The pilot's action operation feature data and facial feature data, flight attitude monitoring data, flight sensing parameter data and flight mission real-time monitoring data packet are obtained through multiple airborne platforms, and then the real-time deviation of the flight mission is identified. If it exceeds the standard, the flight control operation compliance is processed, and the cockpit flight control risk is identified at the same time, and the engine time performance is evaluated. Finally, according to the flight mission real-time deviation warning index, engine time performance evaluation index, cockpit flight control risk identification index and flight control operation compliance data, the flight control deviation risk evaluation index is obtained and the deviation risk level and risk warning are obtained; thereby, the flight control airborne platform collects and analyzes the flight mission status, engine performance, cockpit flight control status and flight control operation flight control status of multiple information, and realizes the deviation evaluation and risk control of the flight control status.

[0067] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0069] Figure 1 A flowchart of a flight control intelligent management and control method based on big data provided in an embodiment of the present application;

[0070] Figure 2 A flowchart of obtaining action operation feature data and facial feature data, flight attitude monitoring data, flight sensing parameter data, and flight mission real-time monitoring data packets for the big data-based flight control intelligent management and control method provided in an embodiment of the present application;

[0071] Figure 3 A flowchart of determining the real-time deviation of a flight mission according to a flight control intelligent management and control method based on big data provided in an embodiment of the present application;

[0072] Figure 4 A flowchart of obtaining flight control operation compliance data for the flight control intelligent management and control method based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0074] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0075] Please refer to Figure 1 , Figure 1 1 is a flow chart of a flight control intelligent management method based on big data in some embodiments of the present application. The flight control intelligent management method based on big data is used in an airborne terminal device, such as a computer, an airborne system, etc. The flight control intelligent management method based on big data includes the following steps:

[0076] S11. Acquire the pilot's action operation feature data and facial feature data through the preset cockpit photoelectric sensor, acquire the flight attitude monitoring data through the preset flight control monitoring platform, acquire the flight sensing parameter data through the preset airborne radar monitoring platform, and acquire the flight mission real-time monitoring data package through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package;

[0077] S12, processing the flight sensing parameter data, the flight attitude monitoring data and the mission package dynamic monitoring data to obtain a real-time deviation warning index for the flight mission, and comparing the threshold to determine the real-time deviation of the flight mission;

[0078] S13, if the real-time deviation of flight execution exceeds the standard, activating the operation procedure task package and extracting the operation procedure task package calibration data, and processing them in combination with the flight attitude monitoring data, facial feature data and action operation feature data to obtain flight control operation compliance data;

[0079] S14, processing the action operation feature data in combination with the facial feature data to obtain a cockpit flight control risk identification index;

[0080] S15, acquiring and processing engine performance monitoring data through a preset engine performance detection platform to obtain an engine time-efficiency performance evaluation index;

[0081] S16. Process the flight mission real-time deviation warning index, engine time performance evaluation index, cockpit flight control risk identification index and flight control operation compliance data to obtain a flight control deviation risk assessment index, and compare the threshold to obtain the deviation risk level of the real-time flight mission and issue a risk warning.

[0082] Among them, in order to realize the risk assessment, calculation and warning of flight mission status, engine performance, cockpit flight control status, pilot information capture and flight control operation status through the flight control airborne platform, it is necessary to use a variety of recording data platforms based on DPU to sense, capture, calculate and store various information parameters of the aircraft, obtain the pilot's action operation and facial feature data through the preset cockpit photoelectric sensor, obtain the flight attitude monitoring data through the preset flight control monitoring platform, obtain the flight ground and target sensing parameters through the preset airborne radar monitoring platform, and obtain the real-time mission monitoring data package of the aircraft during flight through the preset airborne flight information monitoring platform, including the monitoring data of the mission package and the corresponding operation procedure mission package. Then, the flight sensing parameter data, flight attitude monitoring data and mission package dynamic monitoring data obtained from various data platforms are processed to obtain a warning index reflecting the real-time deviation of the flight execution mission, and the real-time deviation of the flight mission is judged by threshold comparison. If the real-time deviation exceeds The calibration data is then activated and the calibration data is extracted from the calibration data. The data is combined with the flight attitude monitoring data, facial feature data and motion operation feature data obtained by the platform for processing to obtain the compliance data of the flight control operation, that is, when the flight mission deviates from the standard, the compliance status of the flight control operation is identified. At the same time, the flight control status of the cockpit pilot is processed according to the motion operation and facial feature data to obtain the cockpit flight control risk identification index to judge the cockpit pilot's condition risk. At the same time, the engine performance status is processed to obtain an evaluation index reflecting the engine's timeliness performance. Finally, according to the real-time deviation warning index of the flight mission, the evaluation index of the engine's timeliness performance, the cockpit flight control risk identification index and the flight control operation compliance data, the overall deviation risk evaluation index reflecting the flight control status and performance is obtained, and the deviation risk level of the real-time flight mission is obtained by threshold comparison. According to the risk level, a real-time alarm is issued to realize the technology of identifying and controlling multiple information such as cockpit flight control, whole machine condition, engine, and mission execution.

[0083] Please refer to Figure 2 , Figure 2It is a flowchart of obtaining action operation feature data and facial feature data, flight attitude monitoring data, flight sensing parameter data, and flight mission real-time monitoring data packet of the flight control intelligent management and control method based on big data provided by the embodiment of the present application. According to the embodiment of the present invention, the action operation feature data and facial feature data of the pilot are obtained through the preset cockpit photoelectric sensor, the flight attitude monitoring data is obtained through the preset flight control monitoring platform, the flight sensing parameter data is obtained through the preset airborne radar monitoring platform, and the flight mission real-time monitoring data packet is obtained through the preset airborne flight information monitoring platform, including task package dynamic monitoring data and operation procedure task package, specifically including:

[0084] S21, obtaining the pilot's action operation information and biometric facial recognition information within a preset flight period through a preset cockpit photoelectric sensor;

[0085] S22, performing information recognition on the motion operation information and the biological facial recognition information through a preset cockpit information processing platform to obtain motion operation feature data and facial feature data;

[0086] S23, the action operation feature data includes operation step compliance data and gesture information warning degree data, and the facial feature data includes visual attention distraction data, emotional tension data and language instruction data;

[0087] S24, obtaining flight attitude monitoring data through a preset flight control monitoring platform, including real-time pressure altitude data and attitude capture data;

[0088] S25, obtaining flight sensing parameter data through a preset airborne radar monitoring platform, including real-time obstacle approach rate data, target telemetry offset data, and multiple flight trajectory point recording data;

[0089] S26. Acquire a flight mission real-time monitoring data packet through a preset airborne flight information monitoring platform, including mission package dynamic monitoring data and an operation procedure mission package. The mission package dynamic monitoring data includes multiple preset trajectory point dynamic data and preset flight altitude data.

[0090] Among them, the preset cockpit photoelectric sensor is a sensing device that captures the cockpit pilot's operating posture, behavioral movements, facial expressions and voice, and then transmits the obtained action operation and biological facial recognition information to the preset cockpit information processing platform. The platform performs information recognition to obtain action operation feature data and facial feature data. The action operation feature data includes the compliance data of the pilot's operating steps and the warning degree data translated and recognized by the gesture information. When the pilot needs to issue multiple quick orders or emergency messages, he will transmit sign language information through gestures. The warning degree issued can be learned through the recognition information. The facial feature data includes the visual attention distraction data of the pilot's control, the facial The emotional tension data and the issued language command data are obtained through the preset flight control monitoring platform, and the monitoring data of the flight attitude include the real-time air pressure altitude and the captured data of the aircraft flight attitude. The flight sensing parameter data include the real-time target obstacle approach rate, the offset from the target telemetry distance and the marking record data of multiple flight trajectory points are obtained through the preset airborne radar monitoring platform. The real-time monitoring data packet of the flight mission is obtained through the preset airborne flight information monitoring platform, that is, the real-time instruction data packet of the flight mission, which includes the task package of dynamic monitoring preset data and operation procedures contained in the task package, and the dynamic monitoring data of the task package includes multiple preset trajectory point dynamic data and preset flight altitude data.

[0091] Please refer to Figure 3 , Figure 3 This is a flowchart of the method for intelligent flight control based on big data to determine the real-time deviation of the flight mission provided by the embodiment of the present application. According to the embodiment of the present invention, the flight sensing parameter data, flight attitude monitoring data and task package dynamic monitoring data are processed to obtain the real-time deviation warning index of the flight mission, and the real-time deviation of the flight mission is determined by threshold comparison, specifically including:

[0092] S31, according to the flight trajectory point record data of multiple nodes within the preset flight period and the preset trajectory point dynamic data of the corresponding node, combined with the real-time pressure altitude data and preset flight altitude data, as well as the real-time obstacle approach rate data and target telemetry offset data, processed by a preset flight mission deviation assessment model to obtain a real-time deviation warning index of the flight mission;

[0093] S32, comparing the real-time deviation warning index of the flight mission with a preset mission deviation threshold, and determining the real-time deviation of the flight mission according to the threshold comparison result;

[0094] The calculation formula of the real-time deviation warning index of the flight mission is:

[0095]

[0096] in, is the real-time deviation warning index of the flight mission, g yi , g Ti They are respectively the flight trajectory point record data of the i-th node among the n nodes in the preset flight period and the dynamic data of the preset trajectory point, r f 、z p They are obstacle progression rate data and target telemetry offset data, respectively. e 、c D They are respectively the pressure altitude data, the preset flight altitude data, σ, λ, ω, μ, ρ i , i , γ i is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset flight control monitoring platform database).

[0097] Among them, after obtaining the cockpit monitoring motion and facial feature data, flight attitude monitoring data, flight sensing parameter data and mission package dynamic monitoring data, the flight trajectory point record data of multiple nodes in the preset flight time period is combined with the preset trajectory point dynamic data of the corresponding nodes, as well as the real-time pressure altitude data and the preset flight altitude data, the real-time obstacle approach rate data, and the target telemetry offset data are calculated and processed through the calculation formula of the preset flight mission deviation evaluation model to obtain the flight mission real-time deviation warning index, that is, the real-time deviation result data of the flight mission is calculated, and compared with the preset mission deviation threshold, and the real-time deviation of the flight mission is judged according to the threshold comparison result, that is, the real-time deviation is judged by the threshold whether it exceeds the threshold limit requirement, so as to realize the judgment of the deviation of the flight mission execution.

[0098] Please refer to Figure 4 , Figure 4 A flowchart of obtaining flight control operation compliance data for a flight control intelligent management method based on big data provided in an embodiment of the present application. According to an embodiment of the present invention, if the real-time deviation of the flight exceeds the standard, the operation program task package is activated and the operation program task package calibration data is extracted, and the flight attitude monitoring data, facial feature data and action operation feature data are combined for processing to obtain flight control operation compliance data, specifically including:

[0099] S41. If the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated, and the calibration data of the operation procedure task package is extracted;

[0100] S42, the operation program task package calibration data includes the attitude calibration data of the navigation mark point and the operation instruction calibration data;

[0101] S43, processing the attitude calibration data and the operation instruction calibration data in combination with the attitude capture data and the language instruction data, and the operation step compliance data through a preset flight control operation specification evaluation model to obtain flight control operation compliance data;

[0102] The calculation formula of the flight control operation compliance data is:

[0103]

[0104] in, is the flight control operation compliance data, a q 、a R They are posture capture data and posture calibration data, respectively. v 、h L They are language instruction data and operation instruction calibration data, respectively. z is the operation step compliance data, ζ1, ζ2, ζ3, η1, η2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset flight control monitoring platform database).

[0105] Among them, if the real-time deviation of the aforementioned flight execution exceeds the threshold limit requirement, it is necessary to further judge the compliance of the flight control operation, and extract the calibration data in the operation program task package by activating the operation program task package, including the attitude calibration data of the navigation mark points of the task package and the calibration data of the operation instructions, that is, the operation program in the task package calibrates the attitude flight state and cockpit operation instructions of the route navigation points, and then combines the attitude capture data and language instruction data, as well as the operation step compliance data through the calculation formula of the preset flight control operation specification evaluation model to obtain the flight control operation compliance data.

[0106] According to an embodiment of the present invention, the step of processing the action operation feature data in combination with the facial feature data to obtain a cockpit flight control risk identification index specifically includes:

[0107] The gesture information warning degree data is combined with the visual attention distraction degree data and the emotional tension degree data, and processed by a preset cockpit flight control state recognition model to obtain a cockpit flight control risk recognition index;

[0108] The calculation formula of the cockpit flight control risk identification index is:

[0109]

[0110] Among them, y τ is the cockpit flight control risk identification index, d m is the gesture information warning degree data, b e is the visual attention distraction data, u cFor emotional tension data, υ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset cockpit information processing platform database).

[0111] Among them, since the cockpit pilot's driving feedback, personal status and behavioral emotions also greatly affect the flight control risk and the deviation of the flight mission, it is necessary to identify and judge the cockpit pilot's gesture information, visual attention and emotions to obtain the risk identification of the cockpit flight control. The cockpit flight control risk identification index is obtained by processing and calculating the gesture information warning degree data combined with the visual attention distraction data and emotional tension data through the formula of the preset cockpit flight control state recognition model.

[0112] According to an embodiment of the present invention, the engine performance monitoring data is acquired and processed through a preset engine performance detection platform to obtain an engine time performance evaluation index, specifically including:

[0113] Obtain engine performance monitoring data within a preset flight period through a preset engine performance testing platform;

[0114] The engine performance monitoring data includes command operation effectiveness data, fuel regulation efficiency data, turbine temperature change efficiency data and effective boost ratio data;

[0115] Processing the engine performance monitoring data through a preset engine performance evaluation model to obtain an engine time-efficiency performance evaluation index;

[0116] The calculation formula of the engine time performance evaluation index is:

[0117]

[0118] Among them, v δ is the engine time performance evaluation index, m p 、s x 、e t , k c They are respectively command operation effectiveness data, fuel regulation efficiency data, turbine temperature change efficiency data, effective boost ratio data, and θ1, θ2, θ3, ε, and φ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset engine performance detection platform database).

[0119] Among them, the engine is the core of the aircraft, and the aircraft flight control status is also restricted by the engine performance status. The engine time performance needs to be evaluated. The engine performance monitoring data within the preset flight period is obtained through the preset engine performance detection platform, including the data on the implementation effectiveness of the command operation, the efficiency data of the fuel regulation, the efficiency data of the temperature change before and after the turbine, and the effective compression ratio data of the engine. Then, it is processed through the calculation formula of the preset engine performance evaluation model to obtain the engine time performance evaluation index.

[0120] According to an embodiment of the present invention, the real-time deviation warning index of the flight mission, the engine time performance evaluation index, the cockpit flight control risk identification index and the flight control operation compliance data are processed to obtain the flight control deviation risk evaluation index, and the deviation risk level of the real-time flight mission is obtained by threshold comparison and risk warning, specifically including:

[0121] The flight mission real-time deviation warning index, the engine time performance evaluation index, the cockpit flight control risk identification index and the flight control operation compliance data are processed by a preset flight control deviation risk evaluation model to obtain a flight control deviation risk evaluation index;

[0122] Comparing the flight control deviation risk assessment index with a preset flight control deviation risk threshold, to obtain a corresponding deviation risk level of the real-time flight mission;

[0123] Issue a flight control risk warning according to the deviation risk level;

[0124] The calculation formula of the flight control deviation risk evaluation index is:

[0125]

[0126] in, is the flight control deviation risk assessment index, is the real-time deviation warning index of the flight mission, v δ is the engine time performance evaluation index, y τ is the cockpit flight control risk identification index, is the flight control operation compliance data, and δ, χ, ω, ζ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset flight control monitoring platform database).

[0127] Among them, finally, according to the obtained real-time deviation warning index of the flight mission, the evaluation index of the engine time performance, the cockpit flight control risk identification index and the flight control operation compliance data, the calculation formula of the preset flight control deviation risk assessment model is processed to obtain the overall deviation risk evaluation index reflecting the flight control status and performance, and the deviation risk level of the real-time flight mission is obtained through threshold comparison, and real-time alarm is performed according to the risk level. The preset flight control deviation risk threshold is a segmented threshold, corresponding to different threshold ranges, and each threshold range corresponds to a different deviation risk level. The deviation risk level is obtained according to the threshold range into which the threshold comparison result falls. For example, the deviation risk levels corresponding to the threshold ranges are divided into four levels, namely, level one to level four, among which level four is the highest. The threshold ranges from level one to level four are (0, 0.36], (0.36, 0.52], (0.52, 0.78], (0.78, 1.0], then the corresponding deviation risk level is obtained through the flight control deviation risk evaluation index to identify and alarm the flight control risk.

[0128] According to an embodiment of the present invention, it also includes:

[0129] According to the real-time deviation warning index of the flight mission, the cockpit flight control risk identification index and the flight control operation compliance data, the corresponding flight control correction plan information is obtained by querying the preset flight control correction information database;

[0130] Extract the correction parameter data according to the flight control correction scheme information, and generate a virtual enhanced image of the vision through the preset flight control virtual vision model in combination with the real-time visual image of the pilot;

[0131] Perform virtual visualization correction guidance operations on the pilot based on the virtual enhanced visual image, and obtain correction guidance operation information, including correction operation point trajectory information, correction operation step operation amount information and correction parameter information;

[0132] Obtain the visual focus trajectory information, control step action amount information and adjustment input parameter information after the pilot performs the correction operation, and compare it with the correction guidance operation information to obtain the flight control correction operation compliance assessment index;

[0133] The effectiveness of the flight control correction guidance operation is judged by comparing the flight control correction operation compliance assessment index with the preset correction operation compliance threshold;

[0134] The calculation formula of the flight control correction operation compliance assessment index is:

[0135]

[0136] Among them, f s is the flight control correction operation compliance assessment index, j t 、n p , lh They are respectively the visual focus trajectory information, the manipulation step action information, and the adjustment input parameter information. E 、n R , l U They are respectively the trajectory information of the correction operation point, the operation amount information of the correction operation steps, and the correction parameter information. φ1, φ2, φ3, ο1, ο2, and ο3 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset airborne flight information monitoring platform database).

[0137] Among them, when a deviation occurs in the flight mission, in order to obtain the corresponding adaptive flight control correction and regulation plan, the preset flight control correction information database is queried according to the real-time deviation warning index of the flight mission, the cockpit flight control risk identification index and the flight control operation compliance data, and the flight control correction plan and plan information are obtained. The correction parameter data, that is, the adjustment parameters of the correction plan, are extracted through the plan information, and the real-time visual image of the pilot obtained by the preset cockpit information processing platform is combined for visual virtual enhancement. The preset flight control virtual vision model of the cockpit information processing platform generates a virtual enhanced image of the flight control vision, that is, a virtual image of the flight control vision under the virtual enhancement technology. According to the generated image, the pilot is given a virtual visualization guidance correction operation, and the correction guidance operation information is obtained through the platform, including the trajectory information of the correction operation point, the correction The information on the operation amount of the deviation control steps and the input correction parameter information, that is, the guidance information on the operation trajectory, operation action amount and input instruction parameters of the platform guiding the correction operation, and then obtain the actual operation information of the operation trajectory, operation step action amount and input parameters after the pilot performs the actual correction operation under the virtual enhanced visualization, and compare and calculate with the correction guidance operation information to obtain the flight control correction operation compliance evaluation index, that is, obtain the pilot's correction operation compliance under the platform's virtual visualization correction adjustment operation guidance, and then compare it with the preset correction operation compliance threshold, and judge the effectiveness of the flight control correction guidance operation through the threshold result. If the flight control correction operation compliance evaluation index is greater than the threshold comparison requirement of the preset correction operation compliance threshold, the pilot's correction operation is compliant and effective, thereby realizing the operation guidance and correction effectiveness inspection of the pilot's control correction.

[0138] The second aspect of the present invention further discloses a flight control intelligent management and control system based on big data, including a memory and a processor, wherein the memory stores a flight control intelligent management and control method program based on big data, and when the flight control intelligent management and control method program based on big data is executed by the processor, the following steps are implemented:

[0139] The pilot's action operation feature data and facial feature data are obtained through the preset cockpit optoelectronic sensor, the flight attitude monitoring data is obtained through the preset flight control monitoring platform, the flight sensing parameter data is obtained through the preset airborne radar monitoring platform, and the flight mission real-time monitoring data package is obtained through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package;

[0140] Processing the flight sensing parameter data, flight attitude monitoring data and mission package dynamic monitoring data to obtain a real-time deviation warning index for the flight mission, and comparing the threshold to determine the real-time deviation of the flight mission;

[0141] If the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated and the operation procedure task package calibration data is extracted, and the flight attitude monitoring data, facial feature data and action operation feature data are combined for processing to obtain flight control operation compliance data;

[0142] Processing the action operation feature data in combination with the facial feature data to obtain a cockpit flight control risk identification index;

[0143] Obtain and process engine performance monitoring data through a preset engine performance testing platform to obtain an engine time-efficiency performance evaluation index;

[0144] The flight mission real-time deviation warning index, engine time performance evaluation index, cockpit flight control risk identification index and flight control operation compliance data are processed to obtain the flight control deviation risk evaluation index, and the deviation risk level of the real-time flight mission is obtained by threshold comparison and risk warning.

[0145] Among them, in order to realize the risk assessment, calculation and warning of flight mission status, engine performance, cockpit flight control status, pilot information capture and flight control operation status through the flight control airborne platform, it is necessary to use a variety of recording data platforms based on DPU to sense, capture, calculate and store various information parameters of the aircraft, obtain the pilot's action operation and facial feature data through the preset cockpit photoelectric sensor, obtain the flight attitude monitoring data through the preset flight control monitoring platform, obtain the flight ground and target sensing parameters through the preset airborne radar monitoring platform, and obtain the real-time mission monitoring data package of the aircraft during flight through the preset airborne flight information monitoring platform, including the monitoring data of the mission package and the corresponding operation procedure mission package. Then, the flight sensing parameter data, flight attitude monitoring data and mission package dynamic monitoring data obtained from various data platforms are processed to obtain a warning index reflecting the real-time deviation of the flight execution mission, and the real-time deviation of the flight mission is judged by threshold comparison. If the real-time deviation exceeds The calibration data is then activated and the calibration data is extracted from the calibration data. The data is combined with the flight attitude monitoring data, facial feature data and motion operation feature data obtained by the platform for processing to obtain the compliance data of the flight control operation, that is, when the flight mission deviates from the standard, the compliance status of the flight control operation is identified. At the same time, the flight control status of the cockpit pilot is processed according to the motion operation and facial feature data to obtain the cockpit flight control risk identification index to judge the cockpit pilot's condition risk. At the same time, the engine performance status is processed to obtain an evaluation index reflecting the engine's timeliness performance. Finally, according to the real-time deviation warning index of the flight mission, the evaluation index of the engine's timeliness performance, the cockpit flight control risk identification index and the flight control operation compliance data, the overall deviation risk evaluation index reflecting the flight control status and performance is obtained, and the deviation risk level of the real-time flight mission is obtained by threshold comparison. According to the risk level, a real-time alarm is issued to realize the technology of identifying and controlling multiple information such as cockpit flight control, whole machine condition, engine, and mission execution.

[0146] According to an embodiment of the present invention, the method of obtaining the pilot's action operation feature data and facial feature data through a preset cockpit photoelectric sensor, obtaining the flight attitude monitoring data through a preset flight control monitoring platform, obtaining the flight sensing parameter data through a preset airborne radar monitoring platform, and obtaining the flight mission real-time monitoring data packet through a preset airborne flight information monitoring platform, including the task package dynamic monitoring data and the operation procedure task package, specifically includes:

[0147] Acquire the pilot's action operation information and biometric facial recognition information during the preset flight period through the preset cockpit photoelectric sensor;

[0148] The motion operation information and the biological facial recognition information are identified through a preset cockpit information processing platform to obtain motion operation feature data and facial feature data;

[0149] The action operation feature data includes operation step compliance data and gesture information warning degree data, and the facial feature data includes visual attention distraction data, emotional tension data and language instruction data;

[0150] Obtain flight attitude monitoring data through the preset flight control monitoring platform, including real-time pressure altitude data and attitude capture data;

[0151] Acquire flight sensing parameter data through a preset airborne radar monitoring platform, including real-time obstacle approach rate data, target telemetry offset data, and multiple flight trajectory point recording data;

[0152] The real-time monitoring data package of the flight mission is obtained through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package. The mission package dynamic monitoring data includes multiple preset trajectory point dynamic data and preset flight altitude data.

[0153] Among them, the preset cockpit photoelectric sensor is a sensing device that captures the cockpit pilot's operating posture, behavioral movements, facial expressions and voice, and then transmits the obtained action operation and biological facial recognition information to the preset cockpit information processing platform. The platform performs information recognition to obtain action operation feature data and facial feature data. The action operation feature data includes the compliance data of the pilot's operating steps and the warning degree data translated and recognized by the gesture information. When the pilot needs to issue multiple quick orders or emergency messages, he will transmit sign language information through gestures. The warning degree issued can be learned through the recognition information. The facial feature data includes the visual attention distraction data of the pilot's control, the facial The emotional tension data and the issued language command data are obtained through the preset flight control monitoring platform, and the monitoring data of the flight attitude include the real-time air pressure altitude and the captured data of the aircraft flight attitude. The flight sensing parameter data include the real-time target obstacle approach rate, the offset from the target telemetry distance and the marking record data of multiple flight trajectory points are obtained through the preset airborne radar monitoring platform. The real-time monitoring data packet of the flight mission is obtained through the preset airborne flight information monitoring platform, that is, the real-time instruction data packet of the flight mission, which includes the task package of dynamic monitoring preset data and operation procedures contained in the task package, and the dynamic monitoring data of the task package includes multiple preset trajectory point dynamic data and preset flight altitude data.

[0154] According to an embodiment of the present invention, the processing of the flight sensing parameter data, the flight attitude monitoring data and the task package dynamic monitoring data to obtain the flight mission real-time deviation warning index, and the threshold comparison to determine the real-time deviation of the flight mission specifically includes:

[0155] According to the flight trajectory point record data of multiple nodes within the preset flight period and the preset trajectory point dynamic data of the corresponding nodes, combined with the real-time pressure altitude data and preset flight altitude data, as well as the real-time obstacle approach rate data and target telemetry offset data, a preset flight mission deviation assessment model is used for processing to obtain a real-time deviation warning index of the flight mission;

[0156] Compare the real-time deviation warning index of the flight mission with the preset mission deviation threshold, and determine the real-time deviation of the flight mission according to the threshold comparison result;

[0157] The calculation formula of the real-time deviation warning index of the flight mission is:

[0158]

[0159] in, is the real-time deviation warning index of the flight mission, g yi , g Ti They are respectively the flight trajectory point record data of the i-th node among the n nodes in the preset flight period and the dynamic data of the preset trajectory point, r f 、z p They are obstacle progression rate data and target telemetry offset data, respectively. e 、c D They are respectively the pressure altitude data, the preset flight altitude data, σ, λ, ω, μ, ρ i , i , γ i is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset flight control monitoring platform database).

[0160] Among them, after obtaining the cockpit monitoring motion and facial feature data, flight attitude monitoring data, flight sensing parameter data and mission package dynamic monitoring data, the flight trajectory point record data of multiple nodes in the preset flight time period is combined with the preset trajectory point dynamic data of the corresponding nodes, as well as the real-time pressure altitude data and the preset flight altitude data, the real-time obstacle approach rate data, and the target telemetry offset data are calculated and processed through the calculation formula of the preset flight mission deviation evaluation model to obtain the flight mission real-time deviation warning index, that is, the real-time deviation result data of the flight mission is calculated, and compared with the preset mission deviation threshold, and the real-time deviation of the flight mission is judged according to the threshold comparison result, that is, the real-time deviation is judged by the threshold whether it exceeds the threshold limit requirement, so as to realize the judgment of the deviation of the flight mission execution.

[0161] According to an embodiment of the present invention, if the real-time deviation of flight execution exceeds the standard, the operation procedure task package is activated and the operation procedure task package calibration data is extracted, and the flight attitude monitoring data, facial feature data and action operation feature data are combined for processing to obtain flight control operation compliance data, specifically including:

[0162] If the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated, and the calibration data of the operation procedure task package is extracted;

[0163] The operation program task package calibration data includes attitude calibration data of navigation mark points and operation instruction calibration data;

[0164] According to the attitude calibration data and the operation instruction calibration data, combined with the attitude capture data and the language instruction data, and the operation step compliance data, a preset flight control operation specification evaluation model is used for processing to obtain flight control operation compliance data;

[0165] The calculation formula of the flight control operation compliance data is:

[0166]

[0167] in, is the flight control operation compliance data, a q 、a R They are posture capture data and posture calibration data, respectively. v 、h L They are language instruction data and operation instruction calibration data, respectively. z is the operation step compliance data, ζ1, ζ2, ζ3, η1, η2 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset flight control monitoring platform database).

[0168] Among them, if the real-time deviation of the aforementioned flight execution exceeds the threshold limit requirement, it is necessary to further judge the compliance of the flight control operation, and extract the calibration data in the operation program task package by activating the operation program task package, including the attitude calibration data of the navigation mark points of the task package and the calibration data of the operation instructions, that is, the operation program in the task package calibrates the attitude flight state and cockpit operation instructions of the route navigation points, and then combines the attitude capture data and language instruction data, as well as the operation step compliance data through the calculation formula of the preset flight control operation specification evaluation model to obtain the flight control operation compliance data.

[0169] According to an embodiment of the present invention, the step of processing the action operation feature data in combination with the facial feature data to obtain a cockpit flight control risk identification index specifically includes:

[0170] The gesture information warning degree data is combined with the visual attention distraction degree data and the emotional tension degree data, and processed by a preset cockpit flight control state recognition model to obtain a cockpit flight control risk recognition index;

[0171] The calculation formula of the cockpit flight control risk identification index is:

[0172]

[0173] Among them, y τ is the cockpit flight control risk identification index, d m is the gesture information warning degree data, b e is the visual attention distraction data, u c For emotional tension data, υ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset cockpit information processing platform database).

[0174] Among them, since the cockpit pilot's driving feedback, personal status and behavioral emotions also greatly affect the flight control risk and the deviation of the flight mission, it is necessary to identify and judge the cockpit pilot's gesture information, visual attention and emotions to obtain the risk identification of the cockpit flight control. The cockpit flight control risk identification index is obtained by processing and calculating the gesture information warning degree data combined with the visual attention distraction data and emotional tension data through the formula of the preset cockpit flight control state recognition model.

[0175] According to an embodiment of the present invention, the engine performance monitoring data is acquired and processed through a preset engine performance detection platform to obtain an engine time performance evaluation index, specifically including:

[0176] Obtain engine performance monitoring data within a preset flight period through a preset engine performance testing platform;

[0177] The engine performance monitoring data includes command operation effectiveness data, fuel regulation efficiency data, turbine temperature change efficiency data and effective boost ratio data;

[0178] Processing the engine performance monitoring data through a preset engine performance evaluation model to obtain an engine time-efficiency performance evaluation index;

[0179] The calculation formula of the engine time performance evaluation index is:

[0180]

[0181] Among them, v δ is the engine time performance evaluation index, m p 、s x 、e t , k cThey are respectively command operation effectiveness data, fuel regulation efficiency data, turbine temperature change efficiency data, effective boost ratio data, and θ1, θ2, θ3, ε, and φ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset engine performance detection platform database).

[0182] Among them, the engine is the core of the aircraft, and the aircraft flight control status is also restricted by the engine performance status. The engine time performance needs to be evaluated. The engine performance monitoring data within the preset flight period is obtained through the preset engine performance detection platform, including the data on the implementation effectiveness of the command operation, the efficiency data of the fuel regulation, the efficiency data of the temperature change before and after the turbine, and the effective compression ratio data of the engine. Then, it is processed through the calculation formula of the preset engine performance evaluation model to obtain the engine time performance evaluation index.

[0183] According to an embodiment of the present invention, the real-time deviation warning index of the flight mission, the engine time performance evaluation index, the cockpit flight control risk identification index and the flight control operation compliance data are processed to obtain the flight control deviation risk evaluation index, and the deviation risk level of the real-time flight mission is obtained by threshold comparison and risk warning, specifically including:

[0184] The flight mission real-time deviation warning index, the engine time performance evaluation index, the cockpit flight control risk identification index and the flight control operation compliance data are processed by a preset flight control deviation risk evaluation model to obtain a flight control deviation risk evaluation index;

[0185] Comparing the flight control deviation risk assessment index with a preset flight control deviation risk threshold, to obtain a corresponding deviation risk level of the real-time flight mission;

[0186] Issue a flight control risk warning according to the deviation risk level;

[0187] The calculation formula of the flight control deviation risk evaluation index is:

[0188]

[0189] in, is the flight control deviation risk assessment index, is the real-time deviation warning index of the flight mission, v δ is the engine time performance evaluation index, y τ is the cockpit flight control risk identification index, is the flight control operation compliance data, and δ, χ, ω, ζ are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset flight control monitoring platform database).

[0190] Among them, finally, according to the obtained real-time deviation warning index of the flight mission, the evaluation index of the engine time performance, the cockpit flight control risk identification index and the flight control operation compliance data, the calculation formula of the preset flight control deviation risk assessment model is processed to obtain the overall deviation risk evaluation index reflecting the flight control status and performance, and the deviation risk level of the real-time flight mission is obtained through threshold comparison, and real-time alarm is performed according to the risk level. The preset flight control deviation risk threshold is a segmented threshold, corresponding to different threshold ranges, and each threshold range corresponds to a different deviation risk level. The deviation risk level is obtained according to the threshold range into which the threshold comparison result falls. For example, the deviation risk levels corresponding to the threshold ranges are divided into four levels, namely, level one to level four, among which level four is the highest. The threshold ranges from level one to level four are (0, 0.36], (0.36, 0.52], (0.52, 0.78], (0.78, 1.0], then the corresponding deviation risk level is obtained through the flight control deviation risk evaluation index to identify and alarm the flight control risk.

[0191] According to an embodiment of the present invention, it also includes:

[0192] According to the real-time deviation warning index of the flight mission, the cockpit flight control risk identification index and the flight control operation compliance data, the corresponding flight control correction plan information is obtained by querying the preset flight control correction information database;

[0193] Extract the correction parameter data according to the flight control correction scheme information, and generate a virtual enhanced image of the vision through the preset flight control virtual vision model in combination with the real-time visual image of the pilot;

[0194] Perform virtual visualization correction guidance operations on the pilot based on the virtual enhanced visual image, and obtain correction guidance operation information, including correction operation point trajectory information, correction operation step operation amount information and correction parameter information;

[0195] Obtain the visual focus trajectory information, control step action amount information and adjustment input parameter information after the pilot performs the correction operation, and compare it with the correction guidance operation information to obtain the flight control correction operation compliance assessment index;

[0196] The effectiveness of the flight control correction guidance operation is judged by comparing the flight control correction operation compliance assessment index with the preset correction operation compliance threshold;

[0197] The calculation formula of the flight control correction operation compliance assessment index is:

[0198]

[0199] Among them, f s is the flight control correction operation compliance assessment index, j t 、n p , lh They are respectively the visual focus trajectory information, the manipulation step action information, and the adjustment input parameter information. E 、n R , l U They are respectively the trajectory information of the correction operation point, the operation amount information of the correction operation steps, and the correction parameter information. φ1, φ2, φ3, ο1, ο2, and ο3 are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset airborne flight information monitoring platform database).

[0200] Among them, when a deviation occurs in the flight mission, in order to obtain the corresponding adaptive flight control correction and regulation plan, the preset flight control correction information database is queried according to the real-time deviation warning index of the flight mission, the cockpit flight control risk identification index and the flight control operation compliance data, and the flight control correction plan and plan information are obtained. The correction parameter data, that is, the adjustment parameters of the correction plan, are extracted through the plan information, and the real-time visual image of the pilot obtained by the preset cockpit information processing platform is combined for visual virtual enhancement. The preset flight control virtual vision model of the cockpit information processing platform generates a virtual enhanced image of the flight control vision, that is, a virtual image of the flight control vision under the virtual enhancement technology. According to the generated image, the pilot is given a virtual visualization guidance correction operation, and the correction guidance operation information is obtained through the platform, including the trajectory information of the correction operation point, the correction The information on the operation amount of the deviation control steps and the input correction parameter information, that is, the guidance information on the operation trajectory, operation action amount and input instruction parameters of the platform guiding the correction operation, and then obtain the actual operation information of the operation trajectory, operation step action amount and input parameters after the pilot performs the actual correction operation under the virtual enhanced visualization, and compare and calculate with the correction guidance operation information to obtain the flight control correction operation compliance evaluation index, that is, obtain the pilot's correction operation compliance under the platform's virtual visualization correction adjustment operation guidance, and then compare it with the preset correction operation compliance threshold, and judge the effectiveness of the flight control correction guidance operation through the threshold result. If the flight control correction operation compliance evaluation index is greater than the threshold comparison requirement of the preset correction operation compliance threshold, the pilot's correction operation is compliant and effective, thereby realizing the operation guidance and correction effectiveness inspection of the pilot's control correction.

[0201] A third aspect of the present invention provides a readable storage medium, which includes a flight control intelligent management and control method program based on big data. When the flight control intelligent management and control method program based on big data is executed by a processor, the steps of the flight control intelligent management and control method based on big data as described in any one of the above items are implemented.

[0202] The invention discloses a flight control intelligent management and control method, system and medium based on big data, which obtains the pilot's action operation feature data and facial feature data, flight attitude monitoring data, flight sensing parameter data and flight mission real-time monitoring data packet through multiple airborne platforms, and then identifies the real-time deviation of the flight mission. If it exceeds the standard, the flight control operation compliance is processed, and the cockpit flight control risk is identified at the same time, and the engine time performance is evaluated. Finally, according to the flight mission real-time deviation warning index, the engine time performance evaluation index, the cockpit flight control risk identification index and the flight control operation compliance data, the flight control deviation risk evaluation index is obtained, and the deviation risk level and risk warning are obtained; thereby, the flight control airborne platform collects and analyzes the flight mission status, engine performance, cockpit flight control status and the flight control state of the flight control operation, so as to realize the deviation evaluation and risk management of the flight control state.

[0203] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0204] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0205] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0206] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0207] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. The flight control intelligent management and control method based on big data is characterized by: The following steps are involved: The pilot's action operation feature data and facial feature data are obtained through the preset cockpit optoelectronic sensor, the flight attitude monitoring data is obtained through the preset flight control monitoring platform, the flight sensing parameter data is obtained through the preset airborne radar monitoring platform, and the flight mission real-time monitoring data package is obtained through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package; Processing the flight sensing parameter data, flight attitude monitoring data and mission package dynamic monitoring data to obtain a real-time deviation warning index for the flight mission, and comparing the threshold to determine the real-time deviation of the flight mission; If the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated and the operation procedure task package calibration data is extracted, and the flight attitude monitoring data, facial feature data and action operation feature data are combined for processing to obtain flight control operation compliance data; Processing the action operation feature data in combination with the facial feature data to obtain a cockpit flight control risk identification index; Obtain and process engine performance monitoring data through a preset engine performance testing platform to obtain an engine time-efficiency performance evaluation index; The flight mission real-time deviation warning index, engine time performance evaluation index, cockpit flight control risk identification index and flight control operation compliance data are processed to obtain the flight control deviation risk evaluation index, and the deviation risk level of the real-time flight mission is obtained by threshold comparison and risk warning.

2. The flight control intelligent management and control method based on big data according to claim 1 is characterized in that: The method of obtaining the pilot's action operation feature data and facial feature data through the preset cockpit photoelectric sensor, obtaining the flight attitude monitoring data through the preset flight control monitoring platform, obtaining the flight sensing parameter data through the preset airborne radar monitoring platform, and obtaining the flight mission real-time monitoring data package through the preset airborne flight information monitoring platform, including the task package dynamic monitoring data and the operation procedure task package, includes: Acquire the pilot's action operation information and biometric facial recognition information during a preset flight period through a preset cockpit photoelectric sensor; The motion operation information and the biological facial recognition information are identified through a preset cockpit information processing platform to obtain motion operation feature data and facial feature data; The action operation feature data includes operation step compliance data and gesture information warning degree data, and the facial feature data includes visual attention distraction data, emotional tension data and language instruction data; Obtain flight attitude monitoring data through the preset flight control monitoring platform, including real-time pressure altitude data and attitude capture data; Acquire flight sensing parameter data through a preset airborne radar monitoring platform, including real-time obstacle approach rate data, target telemetry offset data, and multiple flight trajectory point recording data; The real-time monitoring data package of the flight mission is obtained through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package. The mission package dynamic monitoring data includes multiple preset trajectory point dynamic data and preset flight altitude data.

3. The flight control intelligent management and control method based on big data according to claim 2 is characterized in that: The processing is performed according to the flight sensing parameter data, the flight attitude monitoring data and the task package dynamic monitoring data to obtain the flight mission real-time deviation warning index, and the threshold comparison is used to determine the real-time deviation of the flight mission, including: According to the flight trajectory point record data of multiple nodes within the preset flight period and the preset trajectory point dynamic data of the corresponding nodes, combined with the real-time pressure altitude data and preset flight altitude data, as well as the real-time obstacle approach rate data and target telemetry offset data, a preset flight mission deviation assessment model is used for processing to obtain a real-time deviation warning index of the flight mission; Compare the real-time deviation warning index of the flight mission with the preset mission deviation threshold, and determine the real-time deviation of the flight mission according to the threshold comparison result; The calculation formula of the real-time deviation warning index of the flight mission is: in, is the real-time deviation warning index of the flight mission, g yi , g Ti They are respectively the flight trajectory point record data of the i-th node among the n nodes in the preset flight period and the dynamic data of the preset trajectory point, r f 、z p They are obstacle progression rate data and target telemetry offset data, respectively. e 、c D They are respectively the pressure altitude data, the preset flight altitude data, σ, λ, ω, μ, ρ i , i , γ i is the preset characteristic coefficient.

4. The flight control intelligent management and control method based on big data according to claim 3 is characterized in that: If the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated and the operation procedure task package calibration data is extracted, and the flight attitude monitoring data, facial feature data and action operation feature data are combined for processing to obtain flight control operation compliance data, including: If the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated, and the calibration data of the operation procedure task package is extracted; The operation program task package calibration data includes attitude calibration data of navigation mark points and operation instruction calibration data; According to the attitude calibration data and the operation instruction calibration data, combined with the attitude capture data and the language instruction data, and the operation step compliance data, a preset flight control operation specification evaluation model is used for processing to obtain flight control operation compliance data; The calculation formula of the flight control operation compliance data is: in, is the flight control operation compliance data, a q 、a R They are posture capture data and posture calibration data, respectively. v 、h L They are language instruction data and operation instruction calibration data, respectively. z is the operation step compliance data, and ζ1, ζ2, ζ3, η1, and η2 are preset characteristic coefficients.

5. The flight control intelligent management and control method based on big data according to claim 4 is characterized in that: The step of processing the action operation feature data in combination with the facial feature data to obtain a cockpit flight control risk identification index includes: The gesture information warning degree data is combined with the visual attention distraction degree data and the emotional tension degree data, and processed by a preset cockpit flight control state recognition model to obtain a cockpit flight control risk recognition index; The calculation formula of the cockpit flight control risk identification index is: Among them, y τ is the cockpit flight control risk identification index, d m is the gesture information warning degree data, b e is the visual attention distraction data, u c For emotional tension data, υ is the preset characteristic coefficient.

6. The flight control intelligent management and control method based on big data according to claim 5 is characterized in that: The engine performance monitoring data is obtained and processed through a preset engine performance detection platform to obtain an engine time-efficiency performance evaluation index, including: Obtain engine performance monitoring data within a preset flight period through a preset engine performance testing platform; The engine performance monitoring data includes command operation effectiveness data, fuel regulation efficiency data, turbine temperature change efficiency data and effective boost ratio data; Processing the engine performance monitoring data through a preset engine performance evaluation model to obtain an engine time-efficiency performance evaluation index; The calculation formula of the engine time performance evaluation index is: Among them, v δ is the engine performance evaluation index, m p 、s x 、e t , k c They are respectively command operation effectiveness data, fuel regulation efficiency data, turbine temperature change efficiency data, and effective boost ratio data. θ1, θ2, θ3, ε, and φ are preset characteristic coefficients.

7. The flight control intelligent management and control method based on big data according to claim 6 is characterized in that: The real-time deviation warning index of the flight mission, the engine time performance evaluation index, the cockpit flight control risk identification index and the flight control operation compliance data are processed to obtain the flight control deviation risk evaluation index, and the deviation risk level of the real-time flight mission is obtained by threshold comparison and risk warning, including: The flight mission real-time deviation warning index, the engine time performance evaluation index, the cockpit flight control risk identification index and the flight control operation compliance data are processed by a preset flight control deviation risk evaluation model to obtain a flight control deviation risk evaluation index; Comparing the flight control deviation risk assessment index with a preset flight control deviation risk threshold, to obtain a corresponding deviation risk level of the real-time flight mission; Issue a flight control risk warning according to the deviation risk level; The calculation formula of the flight control deviation risk evaluation index is: in, is the flight control deviation risk assessment index, is the real-time deviation warning index of the flight mission, v δ is the engine performance evaluation index, y τ is the cockpit flight control risk identification index, is the flight control operation compliance data, and δ, χ, ω, ζ are the preset characteristic coefficients.

8. The flight control intelligent management and control system based on big data is characterized by: The invention comprises a memory and a processor, wherein the memory comprises a flight control intelligent management and control method program based on big data, and when the flight control intelligent management and control method program based on big data is executed by the processor, the following steps are implemented: The pilot's action operation feature data and facial feature data are obtained through the preset cockpit optoelectronic sensor, the flight attitude monitoring data is obtained through the preset flight control monitoring platform, the flight sensing parameter data is obtained through the preset airborne radar monitoring platform, and the flight mission real-time monitoring data package is obtained through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package; Processing the flight sensing parameter data, flight attitude monitoring data and mission package dynamic monitoring data to obtain a real-time deviation warning index for the flight mission, and comparing the threshold to determine the real-time deviation of the flight mission; If the real-time deviation of the flight exceeds the standard, the operation procedure task package is activated and the operation procedure task package calibration data is extracted, and the flight attitude monitoring data, facial feature data and action operation feature data are combined for processing to obtain flight control operation compliance data; Processing the action operation feature data in combination with the facial feature data to obtain a cockpit flight control risk identification index; Obtain and process engine performance monitoring data through a preset engine performance testing platform to obtain an engine time-efficiency performance evaluation index; The flight mission real-time deviation warning index, engine time performance evaluation index, cockpit flight control risk identification index and flight control operation compliance data are processed to obtain the flight control deviation risk evaluation index, and the deviation risk level of the real-time flight mission is obtained by threshold comparison and risk warning.

9. The flight control intelligent management and control system based on big data according to claim 8, characterized in that: The method of obtaining the pilot's action operation feature data and facial feature data through the preset cockpit photoelectric sensor, obtaining the flight attitude monitoring data through the preset flight control monitoring platform, obtaining the flight sensing parameter data through the preset airborne radar monitoring platform, and obtaining the flight mission real-time monitoring data package through the preset airborne flight information monitoring platform, including the task package dynamic monitoring data and the operation procedure task package, includes: Acquire the pilot's action operation information and biometric facial recognition information during a preset flight period through a preset cockpit photoelectric sensor; The motion operation information and the biological facial recognition information are identified through a preset cockpit information processing platform to obtain motion operation feature data and facial feature data; The action operation feature data includes operation step compliance data and gesture information warning degree data, and the facial feature data includes visual attention distraction data, emotional tension data and language instruction data; Obtain flight attitude monitoring data through the preset flight control monitoring platform, including real-time pressure altitude data and attitude capture data; Acquire flight sensing parameter data through a preset airborne radar monitoring platform, including real-time obstacle approach rate data, target telemetry offset data, and multiple flight trajectory point recording data; The real-time monitoring data package of the flight mission is obtained through the preset airborne flight information monitoring platform, including the mission package dynamic monitoring data and the operation procedure mission package. The mission package dynamic monitoring data includes multiple preset trajectory point dynamic data and preset flight altitude data.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a flight control intelligent management and control method program based on big data. When the flight control intelligent management and control method program based on big data is executed by a processor, the steps of the flight control intelligent management and control method based on big data as described in any one of claims 1 to 7 are implemented.

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