An adaptive aircraft anomaly monitoring method based on flight parameters

Abstract

The present invention discloses an aircraft abnormality adaptive monitoring method based on flight parameters, which comprises: obtaining the flight parameter information of the aircraft to be processed; the flight parameter information of the aircraft to be processed comprises a plurality of collected aviation flight parameter information; the collected aviation flight parameter information comprises a plurality of initial aircraft flight parameter information arranged in sequence; the initial aircraft flight parameter information comprises N initial aircraft flight parameter values; pre-processing the flight parameter information of the aircraft to be processed to obtain the flight parameter information of the target processed aircraft; the target processed aircraft flight parameter information comprises a plurality of target collected aviation flight parameter information; the target collected aviation flight parameter information comprises a plurality of target processed aircraft flight parameter values; each target collected aviation flight parameter information corresponds to one collected aviation flight parameter information; analyzing and processing the flight parameter information of the target processed aircraft to obtain the monitoring result information of the target aircraft.

Description

Technical Field

[0001] The present invention relates to the field of aircraft technology, and in particular to a method and device for adaptively monitoring aircraft abnormalities based on flight parameters. Background Art

[0002] Aircraft abnormality monitoring is one of the key steps of PHM technology. Its main purpose is to distinguish the relationship between the current state of the equipment and the normal state, so as to achieve timely fault alarm and early warning. Nowadays, the development of reliable aircraft abnormal state monitoring methods has become an urgent need for all parties in the aviation field. At present, monitoring the sensor data collected during the flight is a common method for aircraft monitoring. After a long period of time and multiple times of work, the dirt, wear and deformation of various components of the aircraft will deteriorate its performance, which will be manifested through the changes in aircraft performance parameters. Tracking and monitoring these parameters will help maintenance personnel to grasp the changes in aircraft performance and provide reliable data supplements for aircraft comprehensive performance evaluation. With the continuous increase in the number of aircraft in service and the types and frequencies of flight parameter data records, the scale of aircraft performance parameters recorded by the flight parameter system is getting larger and larger. If these multi-dimensional data can be fully utilized and key information that can reflect mission requirements and equipment status can be extracted from the flight parameter data set, it will effectively solve the data quality problems currently faced by aircraft performance monitoring, effectively improve the equipment status perception capability, and provide fast and comprehensive status information for subsequent support systems. At present, aircraft abnormality patterns are extremely complex, and the single parameter trend analysis commonly used in engineering can no longer meet actual needs. There are many types of flight parameter data, and its large volume and multi-dimensional characteristics also bring difficulties to the actual application of monitoring. In addition, although the amount of flight parameter data that can be collected is large, there are very few fault samples in the actual operation of the aircraft. The main data is still the monitoring results under its normal state, and the cost of obtaining fault samples is high. How to develop a scientific and effective abnormality monitoring method in the absence of fault samples is the key to the current aircraft operation and maintenance guarantee problem. A method for adaptive monitoring of aircraft abnormalities based on flight parameters is provided to fully explore the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and alarm of aircraft abnormal states. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide an aircraft abnormality adaptive monitoring method based on flight parameters, which is conducive to fully exploring the original data characteristics of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and alarm of aircraft abnormal status.

[0004] In order to solve the above technical problems, a first aspect of an embodiment of the present invention discloses an aircraft monitoring method based on flight parameters, the method comprising:

[0005] Acquire the aircraft flight parameter information to be processed; the aircraft flight parameter information to be processed includes a plurality of collected aviation flight parameter information; the collected aviation flight parameter information includes a plurality of initial aircraft flight parameter information arranged in sequence; the initial aircraft flight parameter information includes N initial aircraft flight parameter values;

[0006] Pre-processing the aircraft flight parameter information to be processed to obtain target processing aircraft flight parameter information; the target processing aircraft flight parameter information includes a plurality of target collected aviation flight parameter information; the target collected aviation flight parameter information includes a plurality of target processing aircraft flight parameter values; each of the target collected aviation flight parameter information corresponds to one of the collected aviation flight parameter information;

[0007] The flight parameter information of the target aircraft is analyzed and processed to obtain the target aircraft monitoring result information.

[0008] A second aspect of an embodiment of the present invention discloses an aircraft monitoring device based on flight parameters, the device comprising:

[0009] An acquisition module is used to acquire the aircraft flight parameter information to be processed; the aircraft flight parameter information to be processed includes a plurality of collected aviation flight parameter information; the collected aviation flight parameter information includes a plurality of initial aircraft flight parameter information arranged in sequence; the initial aircraft flight parameter information includes N initial aircraft flight parameter values;

[0010] The first processing module is used to pre-process the aircraft flight parameter information to be processed to obtain target processing aircraft flight parameter information; the target processing aircraft flight parameter information includes a plurality of target collected aviation flight parameter information; the target collected aviation flight parameter information includes a plurality of target processing aircraft flight parameter values; each of the target collected aviation flight parameter information corresponds to one collected aviation flight parameter information;

[0011] The second processing module is used to analyze and process the flight parameter information of the target aircraft to obtain the monitoring result information of the target aircraft.

[0012] The third aspect of the present invention discloses another aircraft monitoring device based on flight parameters, the device comprising:

[0013] A memory storing executable program code;

[0014] a processor coupled to the memory;

[0015] The processor calls the executable program code stored in the memory to execute part or all of the steps in the aircraft monitoring method based on flight parameters disclosed in the first aspect of the embodiment of the present invention.

[0016] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the aircraft monitoring method based on flight parameters disclosed in the first aspect of an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a schematic diagram of a scenario of an aircraft monitoring system based on flight parameters provided by an embodiment of the present invention;

[0019] Figure 2 It is a flowchart of a method for monitoring an aircraft based on flight parameters disclosed in an embodiment of the present invention;

[0020] Figure 3 It is a structural schematic diagram of an aircraft monitoring device based on flight parameters disclosed in an embodiment of the present invention;

[0021] Figure 4 It is a structural schematic diagram of another aircraft monitoring device based on flight parameters disclosed in an embodiment of the present invention;

[0022] Figure 5 It is a structural schematic diagram of a target feature extraction model disclosed in an embodiment of the present invention;

[0023] Figure 6 Schematic diagram of fault monitoring results for two groups of typical aircraft cases. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0026] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0027] In this application, the word "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0028] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data for processing by the computer device. The details will not be repeated here.

[0029] The embodiments of the present application provide an aircraft monitoring method, apparatus, computer device, and computer-readable storage medium based on flight parameters, which are described in detail below.

[0030] See also Figure 1 , Figure 1A schematic diagram of a scenario of an aircraft monitoring system based on flight parameters provided in an embodiment of the present application. The aircraft monitoring system based on flight parameters may include a computer device 100, in which an aircraft monitoring device based on flight parameters is integrated, such as Figure 1 Computer equipment in.

[0031] In the embodiment of the present application, the computer device 100 is mainly used to obtain the to-be-processed aircraft flight parameter information; the to-be-processed aircraft flight parameter information includes a plurality of collected aviation flight parameter information; the collected aviation flight parameter information includes a plurality of sequentially arranged initial aircraft flight parameter information; the initial aircraft flight parameter information includes N initial aircraft flight parameter values;

[0032] Pre-processing the aircraft flight parameter information to be processed to obtain target processing aircraft flight parameter information; the target processing aircraft flight parameter information includes a plurality of target collected aviation flight parameter information; the target collected aviation flight parameter information includes a plurality of target processing aircraft flight parameter values; each target collected aviation flight parameter information corresponds to one collected aviation flight parameter information;

[0033] The flight parameter information of the target aircraft is analyzed and processed to obtain the monitoring result information of the target aircraft.

[0034] It can fully explore the characteristics of the original data of aircraft flight parameters, improve the accuracy and efficiency of aircraft status monitoring, and realize adaptive monitoring and alarm of aircraft abnormal status.

[0035] In the embodiment of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiment of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0036] It is understandable that the computer device 100 used in the embodiments of the present application may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0037] Those skilled in the art will understand that Figure 1The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or less computer equipment as shown in Figure 1 Only one computer device is shown. It can be understood that the aircraft monitoring system based on flight parameters may also include one or more other services, which are not specifically limited here.

[0038] In addition, if Figure 1 As shown, the flight parameter-based aircraft monitoring system may also include a memory 200 for storing data, such as image data, location information, etc.

[0039] It should be noted that Figure 1 The scenario diagram of the aircraft monitoring system based on flight parameters shown is merely an example. The aircraft monitoring system based on flight parameters and the scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person of ordinary skill in the art can appreciate that with the evolution of the aircraft monitoring system based on flight parameters and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0040] The present invention discloses an aircraft abnormality adaptive monitoring method based on flight parameters, which is conducive to fully exploring the original data characteristics of aircraft flight parameters, improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and warning of aircraft abnormal status. The following are detailed descriptions.

[0041] Embodiment 1

[0042] See also Figure 2 , Figure 2 : is a flow chart of an aircraft monitoring method based on flight parameters disclosed in an embodiment of the present invention. Figure 2 The described aircraft monitoring method based on flight parameters is applied to a management system, such as a local server or a cloud server for management, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the aircraft monitoring method based on flight parameters may include the following operations:

[0043] 101. Obtain the flight parameter information of the aircraft to be processed.

[0044] In an embodiment of the present invention, the aircraft flight parameter information to be processed includes a number of collected aviation flight parameter information; the collected aviation flight parameter information includes a number of initial aircraft flight parameter information arranged in sequence; the initial aircraft flight parameter information includes N initial aircraft flight parameter values.

[0045] 102. Pre-process the flight parameter information of the aircraft to be processed to obtain the flight parameter information of the target aircraft.

[0046] In an embodiment of the present invention, the target processing aircraft flight parameter information includes a plurality of target collected aviation flight parameter information; the target collected aviation flight parameter information includes a plurality of target processing aircraft flight parameter values; each target collected aviation flight parameter information corresponds to one collected aviation flight parameter information.

[0047] 103. Analyze and process the flight parameter information of the target aircraft to obtain the monitoring result information of the target aircraft.

[0048] It should be noted that the aircraft flight parameter data contains a large amount of key information that reflects the working conditions and performance changes of the aircraft. If these data can be fully utilized, it will effectively solve the data quality problems currently faced by aircraft performance monitoring. At present, with the continuous increase in the number of aircraft in service and the types and frequencies of flight parameter data records, the scale of data obtained is getting larger and larger, but the distribution of data categories is extremely unbalanced. Specifically, there are very few fault samples in the actual operation of the aircraft, the main data is still the monitoring results under normal conditions, and the cost of obtaining fault samples is high. Therefore, it is of great significance to develop a scientific and effective abnormal monitoring method in the absence of fault samples. The aircraft monitoring method based on flight parameters of the present application fully mines the characteristics of the original data of aircraft flight parameters, iteratively selects the feature combinations selected for clustering, and constructs monitoring indicators that reflect the operating status. While improving the accuracy and efficiency of status assessment, it realizes adaptive monitoring and alarm of abnormal aircraft status.

[0049] It should be noted that the above-mentioned collected aviation flight parameter information representing the aircraft flight parameters includes aircraft performance parameters and flight system recording parameters, such as turbine after-temperature T1, vibration value g, low-pressure speed n1, high-pressure speed n2 are aircraft performance parameters, normal overload n y , airspeed v is the flight parameter.

[0050] It should be noted that before using the aircraft monitoring method based on flight parameters of the present application, the target feature extraction model needs to be trained, and the training method can be as follows: sort out the historical flight parameter data of the aircraft, and select the initial sorties from them as normal samples for training. At the same time, taking into account the differences in flight conditions and mission profiles, the training set data is divided into conditions, and each group of conditions is analyzed separately. Genetic algorithms are used for feature encoding and selection, and several feature parameters are selected as feature vectors for each group of samples. Furthermore, the model is trained using the feature vectors of each group of samples to obtain the target feature extraction model. Furthermore, the "normal sample" refers to the historical aircraft sortie data set arranged in chronological order, such as the initial j nromal The flight data is a normal sample, where j nromal=0.2×J, J is the total number of frames. Furthermore, the "genetic algorithm" includes steps such as gene encoding, population initialization, selection operation, crossover operation, mutation operation, and end condition judgment; the "encoding" refers to representing the selected feature combination with a binary string, 0 means not selecting the corresponding feature, and 1 means selecting the corresponding feature. The flight parameter-based aircraft monitoring method of the present application utilizes a genetic algorithm to automatically select a sensitive flight parameter combination that characterizes the abnormal state of the aircraft, and uses the over-limit index as the fitness function, which can improve the accuracy of state monitoring while reducing complex calculations.

[0051] like Figure 6 As shown, the aircraft monitoring method based on flight parameters of the present application is used to analyze that the probability of exceeding the limit of engine deflagration-related faults fluctuates within a small range in sorties 1-11, and increases sharply in the last group of sorties, which is consistent with the characteristics of sudden faults; engine wear-related faults have an obvious upward trend since sortie 6, which is consistent with the characteristics of gradual faults. And compared with the scheduled inspection sorties, aircraft afterburner deflagration faults can be discovered in the current sortie, and abnormal wear can be discovered 3 sorties in advance. First, sort out the historical flight parameter data of the aircraft, and select the initial sorties from them as normal samples for training. This example contains two groups of aircraft historical data, which are numbered 1# and 2#, respectively. Number 1# is for engine deflagration faults, with a total of 12 groups of sortie data, and number 2# is for engine wear faults, with a total of 9 groups of sortie data. Each group of sortie data consists of 30-dimensional flight parameters, including turbine after-temperature T1, vibration value g, low-pressure speed n1, high-pressure speed n2, normal overload n y , airspeed v, etc. Arrange the historical aircraft flight data set in chronological order, and regard the first 0.2*total flight data as normal samples, that is, regard the first 3 groups of No. 1# as normal, and the first 2 groups of No. 2# as normal. Then, according to the high-pressure rotor speed n2 and normal acceleration n yTwo indicators, the data is divided into 6 working conditions, each working condition is analyzed separately. The genetic algorithm is used to encode and select the features of the training set samples, and 3 feature parameters are selected as the feature vectors of each set of training data. First, the selected feature combination is represented by a {0,1} binary string, 0 means not to select the corresponding feature, and 1 means to select the corresponding feature. Then, the original feature combination can be represented by an m×n 01 matrix, where m refers to the number of populations and n refers to the number of original features. In this case, m=100 and n=30. When performing crossover and mutation operations of the genetic algorithm, the crossover rate is set to 0.7 and the mutation rate is set to 0.02. In accordance with the principle that the higher the fitness, the greater the selection probability, the iterative selection is continuously performed. Finally, when the genetic operation reaches the set maximum number of iterations, the algorithm ends. In this case, the maximum number of iterations is set to 1000. The feature vector obtained by the genetic algorithm is used to train the target feature extraction model. Then, the flight parameter information of the aircraft to be processed is pre-processed, and the target feature extraction model is used to map the target aircraft flight parameter information to the feature vector space, and the target feature information is determined by considering factors such as safety margin, and then the monitoring threshold is used for comparative analysis to obtain the fault monitoring results of the aircraft. Furthermore, the "safety margin" refers to the range of 3 standard deviations from the cluster center on each feature dimension (a, b, c). Furthermore, the above cluster center representation adopts the K-medoids algorithm, and K=1 is set to obtain the cluster center (h, k, l) of the normal sample distribution in the space O-p1p2p3, which is not limited in the embodiment of the present invention.

[0052] It can be seen that implementing the aircraft monitoring method based on flight parameters described in the embodiment of the present invention is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and alarm of abnormal aircraft status.

[0053] In an optional embodiment, the above-mentioned analysis and processing of the target aircraft flight parameter information to obtain the target aircraft monitoring result information includes:

[0054] The target feature extraction model is used to extract the feature of the target processing aircraft flight parameter information to obtain the target feature information; the target feature information includes M target feature values;

[0055] Based on the target feature information, the target aircraft monitoring result information is determined.

[0056] It should be noted that the above-mentioned target characteristic values ​​are characteristic parameters obtained after feature extraction of the flight parameters monitored by the aircraft and can be used for standardization processing to improve the reliability and accuracy of state monitoring, and the embodiments of the present invention do not limit this.

[0057] It can be seen that implementing the aircraft monitoring method based on flight parameters described in the embodiment of the present invention is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and alarm of abnormal aircraft status.

[0058] In another optional embodiment, based on the target feature information, the target aircraft monitoring result information is determined, including:

[0059] Determine whether there is a target feature value greater than or equal to a first monitoring threshold in the target feature information, and obtain a first feature determination result;

[0060] When the first characteristic judgment result is yes, the target aircraft monitoring result information is determined as the first aircraft monitoring state information;

[0061] When the first feature judgment result is no, the target aircraft monitoring result information is determined based on the second monitoring threshold and the target feature information.

[0062] It should be noted that the first monitoring threshold can be set by the user or automatically set by the system according to historical parameters, which is not limited in the embodiment of the present invention. Further, when there is a target characteristic value greater than or equal to the first monitoring threshold in the target characteristic information, it indicates that the aircraft is significantly abnormal, that is, the first aircraft monitoring status information indicates that the aircraft is significantly abnormal, which is not limited in the embodiment of the present invention. For example, the first monitoring threshold β lim =α*β normal , β normal is the benchmark over-limit index calculated using the normal flight times, α is the threshold ratio (its value can be between 0.5 and 1, which is not limited in the embodiment of the present invention), and the target characteristic value β j With β lim In contrast, if β j >β lim , the aircraft is considered to be in an abnormal state and an alarm is issued.

[0063] It can be seen that implementing the aircraft monitoring method based on flight parameters described in the embodiment of the present invention is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and alarm of abnormal aircraft status.

[0064] In yet another optional embodiment, based on the second monitoring threshold and the target characteristic information, determining the target aircraft monitoring result information includes:

[0065] Determine whether there is a target feature value greater than or equal to a second monitoring threshold in the target feature information, and obtain a second feature determination result;

[0066] When the second feature judgment result is yes, the number of target feature values ​​in the target feature information that are greater than or equal to the second monitoring threshold is counted to obtain the target feature quantity;

[0067] Based on the number of target features and M, the target aircraft monitoring result information is determined;

[0068] When the second characteristic judgment result is no, the target aircraft monitoring result information is determined as the fourth aircraft monitoring status information.

[0069] It should be noted that the second monitoring threshold can be set by the user or automatically set by the system according to historical parameters, which is not limited in the embodiment of the present invention. Further, the range of the first monitoring threshold can be [0.8, 1], which is not limited in the embodiment of the present invention. Further, the first difference threshold can be set by the user or automatically set by the system according to historical parameters, which is not limited in the embodiment of the present invention. Further, the range of the first difference threshold can be [0.6, 1], which is not limited in the embodiment of the present invention. Further, when there is a target feature value greater than or equal to the second monitoring threshold in the target feature information, and the target feature difference value is greater than or equal to the first difference threshold, it indicates that the aircraft is abnormal, that is, the second aircraft monitoring status information indicates that the aircraft is abnormal, which is not limited in the embodiment of the present invention. Further, the second difference threshold can be set by the user or automatically set by the system according to historical parameters, which is not limited in the embodiment of the present invention. Further, the range of the second difference threshold can be [0.5, 0.6], which is not limited in the embodiment of the present invention. Further, when the target feature difference value is greater than or equal to the second difference threshold, it indicates that the aircraft may be abnormal, that is, the third aircraft monitoring status information indicates that the aircraft may be abnormal, which is not limited in this embodiment of the present invention.

[0070] It should be noted that the fourth aircraft monitoring status information indicates that the aircraft status is normal, which is not limited in this embodiment of the present invention.

[0071] It should be noted that in this application, the first monitoring threshold is used to perform a simple threshold comparison analysis on the aircraft state for the first round of relatively prominent abnormal problems, thereby screening out prominent abnormal state points. Further, on the premise that no abnormality is found in the first monitoring threshold, the second monitoring threshold is then used for further state analysis. Considering the complexity of the aircraft working environment, the state represented by some flight parameter information may have local short-term abnormal data. Therefore, based on the analysis of the second monitoring threshold, the proportion of abnormal data in the entire data is further analyzed, and the first difference threshold is used to optimize and determine whether the aircraft state is abnormal. Further, if the above method still cannot determine the abnormal situation of the aircraft, the threshold of the analysis comparison is further relaxed, that is, the second difference threshold is used to finally determine whether the aircraft may have an abnormal situation. If it does not meet the comparison of the second difference threshold, it means that the aircraft state is normal, thereby ensuring the accuracy and reliability of the aircraft state analysis, which is not limited in the embodiment of the present invention.

[0072] It should be noted that the first monitoring threshold and the second monitoring threshold can also be determined based on the analysis of "operating condition division" and are constructed uniformly by comprehensively considering the probability and distance of the abnormal point exceeding the safety boundary and the proportion of samples of each operating condition, which is not limited in the embodiment of the present invention. Further "operating condition division" refers to considering different flight conditions and mission profiles, according to the high pressure rotor speed n2 and the normal acceleration n y Two indicators, the aircraft data is divided into M types of working conditions, each of which is analyzed separately. If the j-th group of flight data in the original aircraft data set is {P1, P2, …, P i} j ,i=1,2,…T j , j = 1, 2, ... J, where T j is the data length of flight j, and J is the total number of flights. i is the flight parameter feature set at the i-th moment, which contains the flight parameter feature p of k dimensions, that is, P i ={p1,p2,…,p k} i After dividing the working conditions, the data set can be expressed as is the data length of the mth operating condition of flight j, Furthermore, the above safety margin can be a safety margin defined by determining the safety margin using the 3σ rule, which is not limited in the embodiments of the present invention. Furthermore, the “safety margin” refers to the distribution boundary θ obtained by training with normal samples in the feature vector space O-p1p2p3, and the data distributed within this range is considered to be normal values. When the feature vector dimension is 3, the ellipsoid model can be used Approximate it. The center of the circle Q is the cluster center (h, k, l), and the radius of the ellipsoid is determined by the safety margin (a, b, c).

[0073] It can be seen that implementing the aircraft monitoring method based on flight parameters described in the embodiment of the present invention is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and alarm of abnormal aircraft status.

[0074] In an optional embodiment, the target aircraft monitoring result information is determined based on the target feature quantity and M, including:

[0075] Calculate the difference between the number of target features and M to obtain the target feature difference value;

[0076] Determine whether the target feature difference value is greater than or equal to a first difference threshold, and obtain a first difference determination result;

[0077] When the first difference determination result is yes, determining the target aircraft monitoring result information as the second aircraft monitoring status information;

[0078] When the first difference determination result is no, determining whether the target feature difference value is greater than or equal to a second difference threshold value, and obtaining a second difference determination result;

[0079] When the second difference determination result is yes, determining the target aircraft monitoring result information as the third aircraft monitoring status information;

[0080] When the second difference determination result is no, the target aircraft monitoring result information is determined as the fourth aircraft monitoring status information.

[0081] It should be noted that if Figure 5 The target feature extraction model includes a first convolution module, a second convolution module, a third convolution module, a fourth convolution module, a first normalization module, a first activation module, a second normalization module, a second activation module, a channel splitting module, a channel shuffling module, a first pooling module, a fusion module and a feature extraction network; wherein,

[0082] The input end of the first convolution module is configured as the model input of the target feature extraction model, the output end of the first convolution module is connected to the input end of the first pooling module; the output end of the first pooling module is connected to the input end of the channel splitting module; the output end of the channel splitting module is respectively connected to the input end of the second convolution module and the input end of the fusion module; the output end of the second convolution module is connected to the input end of the first normalization module; the output end of the first normalization module is connected to the input end of the third convolution module; the output end of the third convolution module is connected to the input end of the first activation module; the output end of the first activation module is connected to the input end of the fourth convolution module; the output end of the fourth convolution module is connected to the input end of the second normalization module; the output end of the second normalization module is connected to the input end of the second activation module; the output end of the second activation module is connected to the input end of the fusion module; the output end of the fusion module is connected to the input end of the channel shuffling module; the output end of the channel shuffling module is connected to the input end of the feature extraction network; the output end of the feature extraction network is configured as the model output of the target feature extraction model.

[0083] It should be noted that the above-mentioned channel shuffle module is constructed based on channel shuffle to ensure that the input of the group convolution adopted next comes from different groups, so that information can flow between different groups, which is not limited in the embodiment of the present invention.

[0084] It should be noted that the above-mentioned channel splitting module is constructed based on Channel Split to be divided into two branches with the same number of channels, which is not limited in the embodiment of the present invention.

[0085] It should be noted that the first convolution module and the third convolution module are 1×1 common convolutions, and their step sizes are both 2. Furthermore, the second convolution module and the fourth convolution module are 3×3 depth convolutions, and their step sizes are both 2, which are used to achieve feature dimensionality reduction and reduce the amount of calculation, which is not limited in the embodiment of the present invention.

[0086] It should be noted that the fusion module is constructed based on the Concat operation to double the number of channels and increase the width of the network, which is not limited in the embodiment of the present invention.

[0087] It should be noted that the above-mentioned first pooling module is a maximum pooling layer with a step size of 1, which is not limited in the embodiment of the present invention.

[0088] It should be noted that the above-mentioned first normalization module and second normalization module are constructed based on the batch normalization layer, which is not limited in the embodiment of the present invention.

[0089] It should be noted that the above-mentioned first activation module and second activation module are constructed based on the RELU activation function, which is not limited in the embodiment of the present invention.

[0090] It should be noted that the above feature extraction network is constructed based on a bidirectional long short-term memory network, which is not limited in the embodiment of the present invention. Further, the feature extraction network includes two independent LSTM networks, one responsible for processing the forward information of the sequence and the other for processing the reverse information, so that it can learn key features from past and future information, thereby more effectively capturing the dependency features in the time series.

[0091] It can be seen that implementing the aircraft monitoring method based on flight parameters described in the embodiment of the present invention is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and alarm of abnormal aircraft status.

[0092] In another optional embodiment, pre-processing the flight parameter information of the aircraft to be processed to obtain the flight parameter information of the target aircraft to be processed includes:

[0093] For any initial aircraft flight parameter information in any collected aircraft flight parameter information in the aircraft flight parameter information to be processed, determine whether the number of initial aircraft flight parameter values ​​in the initial aircraft flight parameter information is greater than 1, and obtain a first number determination result;

[0094] When the first quantity judgment result is yes, retain the first-ranked initial aircraft flight parameter value in the initial aircraft flight parameter information, and remove the other initial aircraft flight parameter values ​​except the first-ranked one from the initial aircraft flight parameter information to obtain updated initial aircraft flight parameter information, and end the judgment process corresponding to the first quantity judgment result;

[0095] When the first quantity judgment result is no, ending the judgment process corresponding to the first quantity judgment result;

[0096] The parameter correction model is used to correct the updated flight parameter information of the aircraft to be processed to obtain the flight parameter information of the target aircraft.

[0097] It should be noted that if the number of initial aircraft flight parameter values ​​in the above-mentioned initial aircraft flight parameter information is greater than 1, it means that more than one data is collected by the collection unit at the same time. In this case, the first collected data is selected as the standard data to maintain the consistency of data collection and improve the reliability of state recognition. This is not limited to the embodiments of the present invention.

[0098] It can be seen that implementing the aircraft monitoring method based on flight parameters described in the embodiment of the present invention is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and alarm of abnormal aircraft status.

[0099] In yet another optional embodiment, the updated flight parameter information of the aircraft to be processed is corrected using the parameter correction model to obtain the flight parameter information of the target aircraft to be processed, including:

[0100] For any collected aviation flight parameter information in the updated aircraft flight parameter information to be processed, sequentially determine an initial aircraft flight parameter information in the collected aviation flight parameter information as the aircraft flight parameter information to be corrected;

[0101] Determine whether the number of initial aircraft flight parameter values ​​in the aircraft flight parameter information to be corrected is greater than 0, and obtain a second number determination result;

[0102] When the second quantity judgment result is yes, the initial aircraft flight parameter value in the aircraft flight parameter information to be corrected is used to determine the target processed aircraft flight parameter value corresponding to the aircraft flight parameter information to be corrected;

[0103] When the second quantity judgment result is no, the flight parameter information of the aircraft to be corrected is calculated and processed using the parameter correction model to obtain the target processed aircraft flight parameter value corresponding to the flight parameter information of the aircraft to be corrected;

[0104] Among them, the parameter correction model is:

[0105]

[0106] Wherein, Y1 is the target processed aircraft flight parameter value corresponding to the aircraft flight parameter information to be corrected; Y0 is the initial aircraft flight parameter value of the initial aircraft flight parameter information corresponding to the previous order corresponding to the aircraft flight parameter information to be corrected; X1 is the collection time corresponding to the aircraft flight parameter information to be corrected; X0 is the collection time corresponding to the initial aircraft flight parameter information corresponding to the previous order corresponding to the aircraft flight parameter information to be corrected; X2 is the collection time corresponding to the initial aircraft flight parameter information corresponding to the next order corresponding to the aircraft flight parameter information to be corrected;

[0107] Determine whether the aircraft flight parameter information to be corrected is the last initial aircraft flight parameter information in the collected aviation flight parameter information, and obtain a sequential determination result;

[0108] When the result of the sequential judgment is no, triggering execution to sequentially determine one of the initial aircraft flight parameter information in the collected aviation flight parameter information as the aircraft flight parameter information to be corrected;

[0109] When the result of the sequence judgment is yes, the trigger is to execute the analysis and processing of the target aircraft flight parameter information to obtain the target aircraft monitoring result information.

[0110] It should be noted that the number of initial aircraft flight parameter values ​​in the above-mentioned aircraft flight parameter information to be corrected is greater than 0, indicating that there is a unique initial aircraft flight parameter value in the current aircraft flight parameter information to be corrected. If it is equal to 0, it indicates that the aircraft flight parameter information to be corrected is abnormal and needs to be corrected. Furthermore, in this application, the parameter correction model is used to correlate and correct the collection time and initial aircraft flight parameter value of the two initial aircraft flight parameter information in the upper and lower adjacent order, so as to improve the correlation and reliability of the correction. Furthermore, when the aircraft flight parameter information to be corrected is to determine the last initial aircraft flight parameter information in the collected aviation flight parameter information, X2=X1+1 time collection units are determined, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned 1 time collection unit can be set in minutes or seconds, which is not limited in the embodiment of the present invention.

[0111] It can be seen that implementing the aircraft monitoring method based on flight parameters described in the embodiment of the present invention is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and alarm of abnormal aircraft status.

[0112] Embodiment 2

[0113] See also Figure 3 , Figure 3 : is a schematic diagram of the structure of an aircraft monitoring device based on flight parameters disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 3 As shown, the device may include:

[0114] The acquisition module 201 is used to acquire the aircraft flight parameter information to be processed; the aircraft flight parameter information to be processed includes a plurality of collected aviation flight parameter information; the collected aviation flight parameter information includes a plurality of initial aircraft flight parameter information arranged in sequence; the initial aircraft flight parameter information includes N initial aircraft flight parameter values;

[0115] The first processing module 202 is used to pre-process the aircraft flight parameter information to be processed to obtain the target processing aircraft flight parameter information; the target processing aircraft flight parameter information includes a plurality of target collected aviation flight parameter information; the target collected aviation flight parameter information includes a plurality of target processing aircraft flight parameter values; each target collected aviation flight parameter information corresponds to one collected aviation flight parameter information;

[0116] The second processing module 203 is used to analyze and process the flight parameter information of the target aircraft to obtain the monitoring result information of the target aircraft.

[0117] It can be seen that the implementation Figure 3The described aircraft monitoring device based on flight parameters is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and warning of abnormal aircraft status.

[0118] In another optional embodiment, Figure 3 As shown, the first processing module 202 analyzes and processes the target aircraft flight parameter information to obtain the target aircraft monitoring result information, including:

[0119] The target feature extraction model is used to extract the feature of the target processing aircraft flight parameter information to obtain the target feature information; the target feature information includes M target feature values;

[0120] Based on the target feature information, the target aircraft monitoring result information is determined.

[0121] It can be seen that the implementation Figure 3 The described aircraft monitoring device based on flight parameters is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and warning of abnormal aircraft status.

[0122] In yet another optional embodiment, Figure 3 As shown, the first processing module 202 determines the target aircraft monitoring result information based on the second monitoring threshold and the target characteristic information, including:

[0123] Determine whether there is a target feature value greater than or equal to a second monitoring threshold in the target feature information, and obtain a second feature determination result;

[0124] When the second feature judgment result is yes, the number of target feature values ​​in the target feature information that are greater than or equal to the second monitoring threshold is counted to obtain the target feature quantity;

[0125] Based on the number of target features and M, the target aircraft monitoring result information is determined;

[0126] When the second characteristic judgment result is no, the target aircraft monitoring result information is determined as the fourth aircraft monitoring status information.

[0127] It can be seen that the implementation Figure 3 The described aircraft monitoring device based on flight parameters is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and warning of abnormal aircraft status.

[0128] In yet another optional embodiment, Figure 3 As shown, the first processing module 202 determines the target aircraft monitoring result information based on the second monitoring threshold and the target characteristic information, including:

[0129] Determine whether there is a target feature value greater than or equal to a second monitoring threshold in the target feature information, and obtain a second feature determination result;

[0130] When the second feature judgment result is yes, the number of target feature values ​​in the target feature information that are greater than or equal to the second monitoring threshold is counted to obtain the target feature quantity;

[0131] Based on the number of target features and M, the target aircraft monitoring result information is determined;

[0132] When the second characteristic judgment result is no, the target aircraft monitoring result information is determined as the fourth aircraft monitoring status information.

[0133] It can be seen that the implementation Figure 3 The described aircraft monitoring device based on flight parameters is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and warning of abnormal aircraft status.

[0134] In yet another optional embodiment, Figure 3 As shown, the first processing module 202 determines the target aircraft monitoring result information based on the target feature quantity and M, including:

[0135] Calculate the difference between the number of target features and M to obtain the target feature difference value;

[0136] Determine whether the target feature difference value is greater than or equal to a first difference threshold, and obtain a first difference determination result;

[0137] When the first difference determination result is yes, determining the target aircraft monitoring result information as the second aircraft monitoring status information;

[0138] When the first difference determination result is no, determining whether the target feature difference value is greater than or equal to a second difference threshold value, and obtaining a second difference determination result;

[0139] When the second difference determination result is yes, determining the target aircraft monitoring result information as the third aircraft monitoring status information;

[0140] When the second difference determination result is no, the target aircraft monitoring result information is determined as the fourth aircraft monitoring status information.

[0141] It can be seen that the implementation Figure 3 The described aircraft monitoring device based on flight parameters is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and warning of abnormal aircraft status.

[0142] In yet another optional embodiment, Figure 3 As shown, the second processing module 203 performs pre-processing on the flight parameter information of the aircraft to be processed to obtain the flight parameter information of the target aircraft to be processed, including:

[0143] For any initial aircraft flight parameter information in any collected aircraft flight parameter information in the aircraft flight parameter information to be processed, determine whether the number of initial aircraft flight parameter values ​​in the initial aircraft flight parameter information is greater than 1, and obtain a first number determination result;

[0144] When the first quantity judgment result is yes, retain the first-ranked initial aircraft flight parameter value in the initial aircraft flight parameter information, and remove the other initial aircraft flight parameter values ​​except the first-ranked one from the initial aircraft flight parameter information to obtain updated initial aircraft flight parameter information, and end the judgment process corresponding to the first quantity judgment result;

[0145] When the first quantity judgment result is no, ending the judgment process corresponding to the first quantity judgment result;

[0146] The parameter correction model is used to correct the updated flight parameter information of the aircraft to be processed to obtain the flight parameter information of the target aircraft.

[0147] It can be seen that the implementation Figure 3 The described aircraft monitoring device based on flight parameters is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and warning of abnormal aircraft status.

[0148] In yet another optional embodiment, Figure 3 As shown, the second processing module 203 uses the parameter correction model to correct the updated flight parameter information of the aircraft to be processed to obtain the flight parameter information of the target aircraft to be processed, including:

[0149] For any collected aviation flight parameter information in the updated aircraft flight parameter information to be processed, sequentially determine an initial aircraft flight parameter information in the collected aviation flight parameter information as the aircraft flight parameter information to be corrected;

[0150] Determine whether the number of initial aircraft flight parameter values ​​in the aircraft flight parameter information to be corrected is greater than 0, and obtain a second number determination result;

[0151] When the second quantity judgment result is yes, the initial aircraft flight parameter value in the aircraft flight parameter information to be corrected is used to determine the target processed aircraft flight parameter value corresponding to the aircraft flight parameter information to be corrected;

[0152] When the second quantity judgment result is no, the flight parameter information of the aircraft to be corrected is calculated and processed using the parameter correction model to obtain the target processed aircraft flight parameter value corresponding to the flight parameter information of the aircraft to be corrected;

[0153] Among them, the parameter correction model is:

[0154]

[0155] Wherein, Y1 is the target processed aircraft flight parameter value corresponding to the aircraft flight parameter information to be corrected; Y0 is the initial aircraft flight parameter value of the initial aircraft flight parameter information corresponding to the previous order corresponding to the aircraft flight parameter information to be corrected; X1 is the collection time corresponding to the aircraft flight parameter information to be corrected; X0 is the collection time corresponding to the initial aircraft flight parameter information corresponding to the previous order corresponding to the aircraft flight parameter information to be corrected; X2 is the collection time corresponding to the initial aircraft flight parameter information corresponding to the next order corresponding to the aircraft flight parameter information to be corrected;

[0156] Determine whether the aircraft flight parameter information to be corrected is the last initial aircraft flight parameter information in the collected aviation flight parameter information, and obtain a sequential determination result;

[0157] When the result of the sequential judgment is no, triggering execution to sequentially determine one of the initial aircraft flight parameter information in the collected aviation flight parameter information as the aircraft flight parameter information to be corrected;

[0158] When the result of the sequence judgment is yes, the trigger is to execute the analysis and processing of the target aircraft flight parameter information to obtain the target aircraft monitoring result information.

[0159] It can be seen that the implementation Figure 3 The described aircraft monitoring device based on flight parameters is conducive to fully exploring the characteristics of the original data of aircraft flight parameters, while improving the accuracy and efficiency of aircraft status monitoring, and realizing adaptive monitoring and warning of abnormal aircraft status.

[0160] Embodiment 3

[0161] See also Figure 4 , Figure 4 is a structural schematic diagram of another aircraft monitoring device based on flight parameters disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 4 As shown, the device may include:

[0162] A memory 301 storing executable program codes;

[0163] a processor 302 coupled to the memory 301;

[0164] The processor 302 calls the executable program code stored in the memory 301 to execute the steps in the aircraft monitoring method based on flight parameters described in the first embodiment.

[0165] Embodiment 4

[0166] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the aircraft monitoring method based on flight parameters described in the first embodiment.

[0167] Embodiment 5

[0168] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the aircraft monitoring method based on flight parameters described in Example 1.

[0169] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0170] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0171] Finally, it should be noted that the method for adaptively monitoring aircraft anomalies based on flight parameters disclosed in the embodiment of the present invention only discloses a preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions recorded in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An aircraft monitoring method based on flight parameters, characterized in that: The method comprises: Acquire the aircraft flight parameter information to be processed; the aircraft flight parameter information to be processed includes a plurality of collected aviation flight parameter information; the collected aviation flight parameter information includes a plurality of initial aircraft flight parameter information arranged in sequence; the initial aircraft flight parameter information includes N initial aircraft flight parameter values; Pre-processing the aircraft flight parameter information to be processed to obtain target processing aircraft flight parameter information; the target processing aircraft flight parameter information includes a plurality of target collected aviation flight parameter information; the target collected aviation flight parameter information includes a plurality of target processing aircraft flight parameter values; each of the target collected aviation flight parameter information corresponds to one of the collected aviation flight parameter information; Analyzing and processing the target aircraft flight parameter information to obtain target aircraft monitoring result information; The step of analyzing and processing the target aircraft flight parameter information to obtain the target aircraft monitoring result information includes: Using a target feature extraction model to extract features from the target processing aircraft flight parameter information to obtain target feature information; the target feature information includes M target feature values; Based on the target characteristic information, determining target aircraft monitoring result information; Wherein, determining the target aircraft monitoring result information based on the target feature information includes: Determine whether there is a target feature value greater than or equal to a first monitoring threshold in the target feature information, and obtain a first feature determination result; When the first feature judgment result is yes, determining the target aircraft monitoring result information as the first aircraft monitoring state information; When the first feature judgment result is no, determining the target aircraft monitoring result information based on the second monitoring threshold and the target feature information; Wherein, determining the target aircraft monitoring result information based on the second monitoring threshold and the target characteristic information includes: Determine whether there is a target feature value greater than or equal to a second monitoring threshold in the target feature information, and obtain a second feature determination result; When the second feature judgment result is yes, counting the number of the target feature values ​​in the target feature information that are greater than or equal to the second monitoring threshold to obtain the target feature quantity; Determining the target aircraft monitoring result information based on the target feature quantity and M; When the second feature judgment result is no, determining the target aircraft monitoring result information as fourth aircraft monitoring status information; Wherein, determining the target aircraft monitoring result information based on the target feature quantity and M includes: Calculate the difference between the target feature quantity and M to obtain a target feature difference value; Determine whether the target feature difference value is greater than or equal to a first difference threshold, and obtain a first difference determination result; When the first difference determination result is yes, determining the target aircraft monitoring result information as the second aircraft monitoring status information; When the first difference determination result is no, determining whether the target feature difference value is greater than or equal to a second difference threshold value, and obtaining a second difference determination result; When the second difference determination result is yes, determining the target aircraft monitoring result information as the third aircraft monitoring status information; When the second difference determination result is no, the target aircraft monitoring result information is determined as fourth aircraft monitoring status information.

2. The aircraft monitoring method based on flight parameters according to claim 1, characterized in that: The pre-processing of the to-be-processed aircraft flight parameter information to obtain the target processing aircraft flight parameter information includes: For any of the initial aircraft flight parameter information in any of the collected aircraft flight parameter information in the aircraft flight parameter information to be processed, determine whether the number of the initial aircraft flight parameter values ​​in the initial aircraft flight parameter information is greater than 1, and obtain a first number determination result; When the first quantity judgment result is yes, retain the initial aircraft flight parameter value ranked first in the initial aircraft flight parameter information, and remove the other initial aircraft flight parameter values ​​except the first one from the initial aircraft flight parameter information to obtain updated initial aircraft flight parameter information, and end the judgment process corresponding to the first quantity judgment result; When the first quantity judgment result is no, ending the judgment process corresponding to the first quantity judgment result; The updated flight parameter information of the aircraft to be processed is corrected using the parameter correction model to obtain the flight parameter information of the target aircraft to be processed.

3. The aircraft monitoring method based on flight parameters according to claim 2, characterized in that: The method of using the parameter correction model to correct the updated flight parameter information of the aircraft to be processed to obtain the flight parameter information of the target aircraft to be processed includes: For any of the collected aviation flight parameter information in the updated aircraft flight parameter information to be processed, sequentially determine one of the initial aircraft flight parameter information in the collected aviation flight parameter information as the aircraft flight parameter information to be corrected; Determine whether the number of the initial aircraft flight parameter values ​​in the to-be-corrected aircraft flight parameter information is greater than 0, and obtain a second number determination result; When the second number judgment result is yes, the initial aircraft flight parameter value in the aircraft flight parameter information to be corrected is used to determine the target processing aircraft flight parameter value corresponding to the aircraft flight parameter information to be corrected; When the result of the second number judgment is no, the flight parameter information of the aircraft to be corrected is calculated and processed by using a parameter correction model to obtain the target processing aircraft flight parameter value corresponding to the flight parameter information of the aircraft to be corrected; Wherein, the parameter correction model is: In the formula, Y1 is the target processing aircraft flight parameter value corresponding to the aircraft flight parameter information to be corrected; Y0 is the initial aircraft flight parameter value of the initial aircraft flight parameter information corresponding to the previous order corresponding to the aircraft flight parameter information to be corrected; X1 is the collection time corresponding to the aircraft flight parameter information to be corrected; X0 is the collection time corresponding to the initial aircraft flight parameter information corresponding to the previous order corresponding to the aircraft flight parameter information to be corrected; X2 is the collection time corresponding to the initial aircraft flight parameter information corresponding to the next order corresponding to the aircraft flight parameter information to be corrected; Determine whether the aircraft flight parameter information to be corrected is the last of the initial aircraft flight parameter information in the determined collected aviation flight parameter information, and obtain a sequential determination result; When the result of the sequential judgment is no, triggering the execution of sequentially determining one of the initial aircraft flight parameter information in the collected aviation flight parameter information as the aircraft flight parameter information to be corrected; When the result of the sequence judgment is yes, the analysis and processing of the flight parameter information of the target aircraft is triggered to obtain the monitoring result information of the target aircraft.

4. An aircraft monitoring device based on flight parameters, characterized in that: The device comprises: An acquisition module is used to acquire the aircraft flight parameter information to be processed; the aircraft flight parameter information to be processed includes a plurality of collected aviation flight parameter information; the collected aviation flight parameter information includes a plurality of initial aircraft flight parameter information arranged in sequence; the initial aircraft flight parameter information includes N initial aircraft flight parameter values; The first processing module is used to pre-process the aircraft flight parameter information to be processed to obtain target processing aircraft flight parameter information; the target processing aircraft flight parameter information includes a plurality of target collected aviation flight parameter information; the target collected aviation flight parameter information includes a plurality of target processing aircraft flight parameter values; each of the target collected aviation flight parameter information corresponds to one collected aviation flight parameter information; The second processing module is used to analyze and process the flight parameter information of the target aircraft to obtain the monitoring result information of the target aircraft; The step of analyzing and processing the target aircraft flight parameter information to obtain the target aircraft monitoring result information includes: Using a target feature extraction model to extract features from the target processing aircraft flight parameter information to obtain target feature information; the target feature information includes M target feature values; Based on the target characteristic information, determining target aircraft monitoring result information; Wherein, determining the target aircraft monitoring result information based on the target feature information includes: Determine whether there is a target feature value greater than or equal to a first monitoring threshold in the target feature information, and obtain a first feature determination result; When the first feature judgment result is yes, determining the target aircraft monitoring result information as the first aircraft monitoring state information; When the first feature judgment result is no, determining the target aircraft monitoring result information based on the second monitoring threshold and the target feature information; Wherein, determining the target aircraft monitoring result information based on the second monitoring threshold and the target characteristic information includes: Determine whether there is a target feature value greater than or equal to a second monitoring threshold in the target feature information, and obtain a second feature determination result; When the second feature judgment result is yes, counting the number of the target feature values ​​in the target feature information that are greater than or equal to the second monitoring threshold to obtain the target feature quantity; Determining the target aircraft monitoring result information based on the target feature quantity and M; When the second feature judgment result is no, determining the target aircraft monitoring result information as fourth aircraft monitoring status information; Wherein, determining the target aircraft monitoring result information based on the target feature quantity and M includes: Calculate the difference between the target feature quantity and M to obtain a target feature difference value; Determine whether the target feature difference value is greater than or equal to a first difference threshold, and obtain a first difference determination result; When the first difference determination result is yes, determining the target aircraft monitoring result information as the second aircraft monitoring status information; When the first difference determination result is no, determining whether the target feature difference value is greater than or equal to a second difference threshold value, and obtaining a second difference determination result; When the second difference determination result is yes, determining the target aircraft monitoring result information as the third aircraft monitoring status information; When the second difference determination result is no, the target aircraft monitoring result information is determined as fourth aircraft monitoring status information.

5. An aircraft monitoring device based on flight parameters, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the aircraft monitoring method based on flight parameters as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the aircraft monitoring method based on flight parameters as described in any one of claims 1-3.

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