Aircraft fault early warning method and system based on flight data

Abstract

The invention provides an aircraft fault early warning method and system based on flight data, and relates to the technical field of aircraft fault early warning. The method comprises the following steps: firstly, mining out an aircraft external data semantic vector and an aircraft internal data semantic vector corresponding to flight data to be processed; secondly, performing semantic enhancement operation on a pre-generated flight data noise vector based on an aircraft external data semantic vector, and outputting a flight data enhancement vector; then, according to the aircraft internal data semantic vector, mining an aircraft internal depth vector; and finally, according to the flight data reinforcement vector, carrying out association mining operation on the aircraft internal depth vector, outputting a target flight data semantic vector, and according to the target flight data semantic vector, analyzing aircraft fault early warning information. On the basis of the content, the problem that in the prior art, the reliability of aircraft fault early warning is relatively low can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of aircraft failure warning technology, and in particular, to an aircraft failure warning method and system based on flight data. Background Art

[0002] In the prior art, aircraft fault warning based on aircraft flight data generally includes a data collection stage and a data analysis stage. In the data collection stage, various data of the aircraft during flight are generally collected, such as environmental data such as weather conditions or status data such as the temperature of aircraft components. Then, in the data analysis stage, the collected data can be compared with the threshold set based on experience, so as to determine whether there may be a fault. However, the inventors have found that since the threshold is set based on experience, its reliability is not high. Moreover, the individual comparison of various data also ignores the correlation between the data, which will also lead to relatively low reliability. Summary of the invention

[0003] In view of this, the purpose of the present application is to provide an aircraft fault warning method and system based on flight data, so as to improve the problem of relatively low reliability of aircraft fault warning in the prior art.

[0004] To achieve the above objectives, this application adopts the following technical solutions:

[0005] An aircraft fault early warning method based on flight data, comprising:

[0006] Mining out the semantic vector of aircraft external data corresponding to the flight data to be processed, and mining out the semantic vector of aircraft internal data corresponding to the flight data to be processed, wherein the flight data to be processed includes aircraft internal data and aircraft external data, the aircraft internal data is used to reflect the status of each aircraft component of the target aircraft during the flight, and the aircraft external data is used to reflect the status of the environment in which the target aircraft is located during the flight;

[0007] The aircraft external data semantic vector is loaded so that the aircraft fault prediction network acquires the aircraft external data semantic vector, and a semantic mining unit included in the aircraft fault prediction network is used to perform a semantic enhancement operation on a pre-generated flight data noise vector based on the aircraft external data semantic vector, and output a flight data enhancement vector corresponding to the semantic mining unit;

[0008] Mining an aircraft interior depth vector corresponding to the semantic mining unit according to the aircraft interior data semantic vector;

[0009] According to the flight data enhancement vector corresponding to the semantic mining unit, an association mining operation is performed on the internal depth vector of the aircraft to output a target flight data semantic vector, and, based on the target flight data semantic vector, the aircraft fault warning information corresponding to the flight data to be processed is analyzed, wherein the aircraft fault warning information is used to reflect whether there is a fault in the target aircraft.

[0010] In a preferred embodiment of the present application, in the above-mentioned aircraft fault warning method based on flight data, the step of loading the aircraft external data semantic vector so that the aircraft fault prediction network obtains the aircraft external data semantic vector, and using the semantic mining unit included in the aircraft fault prediction network to perform a semantic enhancement operation on the pre-generated flight data noise vector based on the aircraft external data semantic vector, and outputting the flight data enhancement vector corresponding to the semantic mining unit includes:

[0011] The aircraft external data semantic vector is loaded so that the aircraft fault prediction network acquires the aircraft external data semantic vector, and a semantic mining unit included in the aircraft fault prediction network is used to mine a flight data noise mining vector according to a pre-generated flight data noise vector;

[0012] Forming a corresponding flight data mapping vector according to the flight data noise mining vector mapping;

[0013] Mapping the aircraft external data semantic vectors to form a first flight mapping vector and a second flight mapping vector;

[0014] A focused fusion operation is performed according to the flight data mapping vector, the flight first mapping vector and the flight second mapping vector, and a flight data enhancement vector corresponding to the semantic mining unit is obtained according to an output vector of the focused fusion operation.

[0015] In a preferred embodiment of the present application, in the above-mentioned aircraft fault warning method based on flight data, the step of loading the aircraft external data semantic vector so that the aircraft fault prediction network obtains the aircraft external data semantic vector, and using the semantic mining unit included in the aircraft fault prediction network to perform a semantic enhancement operation on the pre-generated flight data noise vector based on the aircraft external data semantic vector, and outputting the flight data enhancement vector corresponding to the semantic mining unit, also includes:

[0016] Determine a target parameter, and determine a corresponding number of initial parameters based on the target parameter, wherein the initial parameters are multiple;

[0017] Determining a corresponding initial flight data noise vector according to the initial parameters, wherein each vector parameter in the initial aircraft data noise vector is determined by a random number generator based on the initial parameters;

[0018] A flight data noise vector is obtained based on the determined initial flight data noise vectors.

[0019] In a preferred embodiment of the present application, in the above-mentioned aircraft fault warning method based on flight data, the step of mining the aircraft internal depth vector corresponding to the semantic mining unit according to the aircraft internal data semantic vector includes:

[0020] Determining a flight data noise vector of a semantic mining unit included in the aircraft fault prediction network;

[0021] Performing a convolution mining operation on the semantic vector of the aircraft interior data, and performing a vector fusion operation on the semantic vector of the aircraft interior data after the convolution mining operation and the flight data noise vector, and outputting the corresponding semantic vector of the aircraft interior data after the vector fusion operation;

[0022] The semantic vector of the aircraft interior data after the vector fusion operation is loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft interior data after the vector fusion operation and performs corresponding processing, and the output vector of the vector mining unit is subjected to a convolution mining operation to output a corresponding aircraft interior depth vector.

[0023] In a preferred embodiment of the present application, in the above-mentioned aircraft fault warning method based on flight data, the step of loading the semantic vector of the aircraft internal data after the vector fusion operation so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft internal data after the vector fusion operation and performs corresponding processing, and the step of performing a convolution mining operation on the output vector of the vector mining unit to output the corresponding aircraft internal depth vector includes:

[0024] The semantic vector of the aircraft internal data after the vector fusion operation is loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft internal data after the vector fusion operation, and the predetermined reference flight data semantic vector is loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the reference flight data semantic vector, wherein the reference flight data semantic vector is determined based on the semantic vectors of each training flight data mined by the semantic mining unit and the vector mining unit during the training optimization process, and each training flight data semantic vector is obtained by performing corresponding semantic enhancement operations and association mining operations on the corresponding training flight data after the training optimization is completed;

[0025] According to the reference flight data semantic vector, an association mining operation is performed on the aircraft internal data semantic vector after the vector fusion operation, and a corresponding aircraft internal data association vector is output;

[0026] The aircraft interior data association vector is used as the output vector of the vector mining unit, and a convolution mining operation is performed to output a corresponding aircraft interior depth vector.

[0027] In a preferred embodiment of the present application, in the above-mentioned aircraft fault warning method based on flight data, the step of performing an association mining operation on the aircraft internal data semantic vector after the vector fusion operation based on the reference flight data semantic vector and outputting the corresponding aircraft internal data association vector includes:

[0028] Determine an association parameter between the reference flight data semantic vector and the aircraft interior data semantic vector after the vector fusion operation, and based on the association parameter, perform association adjustment on the aircraft interior data semantic vector after the vector fusion operation, and output an aircraft interior data adjustment vector;

[0029] A vector fusion operation is performed on the aircraft interior data semantic vector after the vector fusion operation and the aircraft interior data adjustment vector, and a corresponding aircraft interior data association vector is output.

[0030] In a preferred embodiment of the present application, in the above-mentioned aircraft fault warning method based on flight data, the steps of performing an association mining operation on the aircraft internal depth vector according to the flight data enhancement vector corresponding to the semantic mining unit, outputting a target flight data semantic vector, and analyzing the aircraft fault warning information corresponding to the flight data to be processed according to the target flight data semantic vector include:

[0031] Determine an association parameter between the flight data enhancement vector corresponding to the semantic mining unit and the aircraft interior depth vector, and based on the association parameter, perform association adjustment on the aircraft interior depth vector, and output a corresponding adjusted aircraft interior depth vector;

[0032] Performing a vector fusion operation on the adjusted aircraft interior depth vector and the aircraft interior depth vector, and outputting a corresponding target flight data semantic vector;

[0033] A full connection operation is performed on the target flight data semantic vector to output a corresponding fully connected flight data semantic vector, and aircraft fault warning information corresponding to the flight data to be processed is analyzed based on the fully connected flight data semantic vector.

[0034] The present application also provides an aircraft fault warning system based on flight data, comprising:

[0035] A flight data semantic mining module, used to mine out the semantic vector of aircraft external data corresponding to the flight data to be processed, and to mine out the semantic vector of aircraft internal data corresponding to the flight data to be processed, wherein the flight data to be processed includes aircraft internal data and aircraft external data, the aircraft internal data is used to reflect the status of each aircraft component of the target aircraft during the flight, and the aircraft external data is used to reflect the status of the environment in which the target aircraft is located during the flight;

[0036] A vector semantic enhancement module is used to load the aircraft external data semantic vector so that the aircraft fault prediction network obtains the aircraft external data semantic vector, and uses the semantic mining unit included in the aircraft fault prediction network to perform a semantic enhancement operation on the pre-generated flight data noise vector based on the aircraft external data semantic vector, and output the flight data enhancement vector corresponding to the semantic mining unit;

[0037] A vector depth mining module, used to mine the aircraft interior depth vector corresponding to the semantic mining unit according to the aircraft interior data semantic vector;

[0038] The aircraft fault analysis module is used to perform an association mining operation on the aircraft internal depth vector according to the flight data enhancement vector corresponding to the semantic mining unit, output a target flight data semantic vector, and analyze the aircraft fault warning information corresponding to the flight data to be processed according to the target flight data semantic vector, wherein the aircraft fault warning information is used to reflect whether the target aircraft has a fault.

[0039] In a preferred embodiment of the present application, in the above-mentioned aircraft fault warning system based on flight data, the vector semantic enhancement module is specifically used for:

[0040] The aircraft external data semantic vector is loaded so that the aircraft fault prediction network acquires the aircraft external data semantic vector, and a semantic mining unit included in the aircraft fault prediction network is used to mine a flight data noise mining vector according to a pre-generated flight data noise vector;

[0041] Forming a corresponding flight data mapping vector according to the flight data noise mining vector mapping;

[0042] Mapping the aircraft external data semantic vectors to form a first flight mapping vector and a second flight mapping vector;

[0043] A focused fusion operation is performed according to the flight data mapping vector, the flight first mapping vector and the flight second mapping vector, and a flight data enhancement vector corresponding to the semantic mining unit is obtained according to an output vector of the focused fusion operation.

[0044] In a preferred embodiment of the present application, in the above-mentioned aircraft fault warning system based on flight data, the vector deep mining module is specifically used for:

[0045] Determining a flight data noise vector of a semantic mining unit included in the aircraft fault prediction network;

[0046] Performing a convolution mining operation on the semantic vector of the aircraft interior data, and performing a vector fusion operation on the semantic vector of the aircraft interior data after the convolution mining operation and the flight data noise vector, and outputting the corresponding semantic vector of the aircraft interior data after the vector fusion operation;

[0047] The semantic vector of the aircraft interior data after the vector fusion operation is loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft interior data after the vector fusion operation and performs corresponding processing, and the output vector of the vector mining unit is subjected to a convolution mining operation to output a corresponding aircraft interior depth vector.

[0048] The present application provides an aircraft fault warning method and system based on flight data. First, the aircraft external data semantic vector and the aircraft internal data semantic vector corresponding to the flight data to be processed are mined; secondly, the pre-generated flight data noise vector is semantically enhanced based on the aircraft external data semantic vector, and the flight data enhancement vector is output; then, the aircraft internal depth vector is mined based on the aircraft internal data semantic vector; finally, the aircraft internal depth vector is associated with the flight data enhancement vector, and the target flight data semantic vector is output, and the aircraft fault warning information is analyzed based on the target flight data semantic vector. Based on the above content, since the flight data enhancement vector carrying the semantic information of the aircraft external data and the aircraft internal depth vector carrying the semantic information of the aircraft internal data are associated with the mining operation, the semantic representation ability of the obtained target flight data semantic vector can be better, and therefore, the reliability of the aircraft fault warning information analyzed based on the target flight data semantic vector is higher. On the other hand, since the flight data enhancement vector not only carries the semantic information of the aircraft's external data, but also integrates the flight data noise vector, it can better simulate these real situations (the collected aircraft external data may ignore some noise and interference), thereby improving its performance in the actual environment. In this way, the representation ability of the aircraft data enhancement vector can be further improved, thereby improving the problem of relatively low reliability of aircraft fault warning in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings.

[0050] Figure 1 A structural block diagram of an electronic device provided in an embodiment of the present application.

[0051] Figure 2 A flowchart of an aircraft fault warning method based on flight data provided in an embodiment of the present application.

[0052] Figure 3 A block diagram of an aircraft fault warning system based on flight data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution 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 part of the embodiments of the present application, not 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.

[0054] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0055] like Figure 1 As shown, an embodiment of the present application provides an electronic device, wherein the electronic device may include a memory, a processor, and an aircraft fault warning system based on flight data.

[0056] In detail, the memory and the processor are electrically connected directly or indirectly to achieve data transmission or interaction. For example, the memory and the processor can be electrically connected through one or more communication buses or signal lines. The aircraft fault warning system based on flight data includes at least one software function module stored in the memory in the form of software or firmware. The processor is used to execute an executable computer program stored in the memory, for example, the software function module and computer program included in the aircraft fault warning system based on flight data, so as to implement the aircraft fault warning method based on flight data provided in an embodiment of the present application.

[0057] Optionally, the memory may be, but not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. Furthermore, the processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0058] Understandably, Figure 1 The structure shown is for illustration only, and the electronic device may also include Figure 1 More or fewer components as shown, or with Figure 1 The different configurations shown, for example, may also include a communication unit for exchanging information with other devices (such as various sensors, etc.).

[0059] Combination Figure 2 The embodiment of the present application also provides an aircraft fault warning method based on flight data that can be applied to the above electronic device. The method steps defined in the process related to the aircraft fault warning method based on flight data can be implemented by the electronic device.

[0060] The following will Figure 2 The specific process shown is explained in detail.

[0061] Step S110 , mining out the semantic vector of the aircraft external data corresponding to the flight data to be processed, and mining out the semantic vector of the aircraft internal data corresponding to the flight data to be processed.

[0062] In an embodiment of the present application, the electronic device can mine the semantic vector of the aircraft external data corresponding to the flight data to be processed, and mine the semantic vector of the aircraft internal data corresponding to the flight data to be processed. Wherein, the flight data to be processed includes aircraft internal data and aircraft external data, the aircraft internal data is used to reflect the state of each aircraft component of the target aircraft during the flight (such as the data sequence formed by the temperature and stress of each aircraft component collected), and the aircraft external data is used to reflect the state of the environment where the target aircraft is located during the flight (such as the data sequence formed by the temperature, humidity, pressure, etc. of the environment where the target aircraft is located). In addition, since the aircraft internal data and the aircraft external data can be data sequences, after the word embedding model is embedded to obtain the word embedding vector, the position of each word segment in the data sequence can also be embedded to obtain the corresponding position embedding vector, and then, each word embedding vector and each position embedding vector can be spliced ​​and fused to form a corresponding semantic vector, that is, the corresponding aircraft external data semantic vector and aircraft internal data semantic vector are formed respectively. In addition, the word embedding model can be formed through training and optimization together with the aircraft fault prediction network described later.

[0063] Step S120, loading the aircraft external data semantic vector so that the aircraft fault prediction network obtains the aircraft external data semantic vector, and utilizing the semantic mining unit included in the aircraft fault prediction network to perform semantic enhancement operations on the pre-generated flight data noise vector based on the aircraft external data semantic vector, and outputting the flight data enhancement vector corresponding to the semantic mining unit.

[0064] In an embodiment of the present application, after mining the corresponding aircraft external data semantic vector, the electronic device can load the aircraft external data semantic vector, so that the aircraft fault prediction network (a neural network) obtains the aircraft external data semantic vector, and uses the semantic mining unit included in the aircraft fault prediction network to perform semantic enhancement operations on the pre-generated flight data noise vector based on the aircraft external data semantic vector, and outputs the flight data enhancement vector corresponding to the semantic mining unit. In this way, the flight data enhancement vector can carry the semantic information in the aircraft external data semantic vector, and also add noise semantics to improve the representation of the real external environment by the flight data enhancement vector.

[0065] Step S130: mining the aircraft interior depth vector corresponding to the semantic mining unit according to the aircraft interior data semantic vector.

[0066] In the embodiment of the present application, after mining the corresponding aircraft interior data semantic vector, the electronic device can mine the aircraft interior depth vector corresponding to the semantic mining unit based on the aircraft interior data semantic vector. In other words, the aircraft interior data semantic vector can be deeply mined to obtain deep semantic information, that is, to obtain the aircraft interior depth vector, so that the semantics of the aircraft interior depth vector can be richer and more generalized.

[0067] Step S140, based on the flight data enhancement vector corresponding to the semantic mining unit, an association mining operation is performed on the aircraft internal depth vector, a target flight data semantic vector is output, and, based on the target flight data semantic vector, aircraft fault warning information corresponding to the flight data to be processed is analyzed.

[0068] In an embodiment of the present application, after obtaining the flight data enhancement vector and the aircraft interior depth vector, the electronic device can perform an association mining operation on the aircraft interior depth vector according to the flight data enhancement vector corresponding to the semantic mining unit, output a target flight data semantic vector (i.e., fuse the flight data enhancement vector into the aircraft interior depth vector so that the semantic representation capability of the obtained target flight data semantic vector is better), and, according to the target flight data semantic vector, analyze the aircraft fault warning information corresponding to the flight data to be processed. Among them, the aircraft fault warning information is used to reflect whether the target aircraft has a fault (in some other embodiments, when a fault exists, it can also include the corresponding fault type, such as the risk of cracks in the wing, such as engine failure, etc.).

[0069] Based on the above content, since, on the one hand, the flight data enhancement vector carrying the semantic information of the aircraft's external data and the aircraft's internal depth vector carrying the semantic information of the aircraft's internal data will be associated with mining operations, the semantic representation ability of the obtained target flight data semantic vector can be better, so the reliability of the aircraft fault warning information analyzed based on the target flight data semantic vector is higher. On the other hand, since the flight data enhancement vector not only carries the semantic information of the aircraft's external data, but also integrates the flight data noise vector, it can better simulate these real situations (the collected aircraft's external data may ignore some noise and interference), thereby improving its performance in the actual environment. In this way, the representation ability of the aircraft data enhancement vector can be further improved, so that the reliability of the analyzed aircraft fault warning information is improved, thereby improving the problem of relatively low reliability of aircraft fault warnings in the prior art.

[0070] It should be noted that for step S120, the specific method of performing semantic enhancement operation on the pre-generated flight data noise vector based on the aircraft external data semantic vector is not limited and can be selected according to actual needs. For example, in an alternative embodiment, in order to fully integrate the aircraft external data semantic vector and the aircraft data noise vector, the above-mentioned step S120 can further include step S121, step S122, step S123 and step S124, and the specific content of each step is described as follows.

[0071] Step S121, loading the aircraft external data semantic vector so that the aircraft fault prediction network obtains the aircraft external data semantic vector, and using the semantic mining unit included in the aircraft fault prediction network to mine the flight data noise mining vector based on the pre-generated flight data noise vector.

[0072] In an embodiment of the present application, the aircraft external data semantic vector can be loaded so that the aircraft fault prediction network obtains the aircraft external data semantic vector. In this way, the aircraft fault prediction network can be used for subsequent processing. For example, first, the semantic mining unit included in the aircraft fault prediction network can be used to mine the flight data noise mining vector based on the pre-generated flight data noise vector. Specifically, the flight data noise vector can be directly used as the corresponding flight data noise mining vector, or the flight data noise vector can be self-attention processed to obtain the corresponding flight data noise mining vector.

[0073] Step S122: forming a corresponding flight data mapping vector according to the flight data noise mining vector mapping.

[0074] In an embodiment of the present application, after obtaining the flight data noise mining vector, a corresponding flight data mapping vector can be formed according to the flight data noise mining vector mapping. Exemplarily, the flight data noise mining vector can be directly used as the corresponding flight data mapping vector, or the flight data noise mining vector can be multiplied with the first mapping matrix in the semantic mining unit to obtain the corresponding flight data mapping vector.

[0075] Step S123, mapping to form a first flight mapping vector and a second flight mapping vector according to the aircraft external data semantic vector.

[0076] In an embodiment of the present application, the first flight mapping vector and the second flight mapping vector can be mapped respectively according to the aircraft external data semantic vector. Exemplarily, the aircraft external data semantic vector can be directly used as the corresponding first flight mapping vector and second flight mapping vector, or the aircraft external data semantic vector can be multiplied with the second mapping matrix and the third mapping matrix in the semantic mining unit respectively to obtain the first flight mapping vector and the second flight mapping vector corresponding to the aircraft external data semantic vector.

[0077] Step S124, performing a focused fusion operation based on the flight data mapping vector, the flight first mapping vector and the flight second mapping vector, and obtaining a flight data enhancement vector corresponding to the semantic mining unit based on an output vector of the focused fusion operation.

[0078] In an embodiment of the present application, a focused fusion operation can be performed based on the flight data mapping vector, the first flight mapping vector, and the second flight mapping vector, and based on the output vector of the focused fusion operation, the flight data enhancement vector corresponding to the semantic mining unit can be obtained. Exemplarily, the flight data mapping vector and the first flight mapping vector can be multiplied to obtain a corresponding association parameter, and then the second flight mapping vector can be weighted based on the association parameter to obtain the corresponding output vector of the focused fusion operation. Then, the output vector of the focused fusion operation can be superimposed with the aircraft external data semantic vector to obtain the flight data enhancement vector corresponding to the semantic mining unit.

[0079] It should also be noted that for step S120, in order to ensure that the fusion based on the flight data noise vector can make the formed flight data enhancement vector have better generalization, the above step S124 may also include a step of generating the flight data noise vector, which may specifically include the following contents:

[0080] First, a target parameter can be determined, and a corresponding number of initial parameters can be determined based on the target parameter, wherein the initial parameters are multiple, that is, the target parameter can be an integer greater than 1, such as 1, 2, 3, 4, 5, 6, 7, 8, etc.;

[0081] Secondly, a corresponding initial flight data noise vector is determined according to the initial parameters, wherein each vector parameter in the initial aircraft data noise vector is determined by a random number generator based on the initial parameters, that is, the initial parameters can be used as random seeds of the random number generator, and then a corresponding series of random numbers are generated to simulate a corresponding probability distribution, such as a normal distribution, etc., and this series of random numbers can constitute a corresponding initial flight data noise vector;

[0082] Finally, a flight data noise vector can be obtained based on the determined initial flight data noise vectors; illustratively, the mean of each initial flight data noise vector can be calculated to obtain a corresponding mean vector as the corresponding flight data noise vector; or, each initial flight data noise vector can be used as a flight data noise vector, and then, based on each flight data noise vector, steps S121 to S124 can be executed respectively, so that each flight data noise vector corresponds to a flight data enhancement vector, and then, the flight data enhancement vector corresponding to each flight data noise vector can be superimposed or mean-calculated to obtain the flight data enhancement vector corresponding to the semantic mining unit.

[0083] It should be noted that for step S130, the specific method of mining the aircraft interior depth vector corresponding to the semantic mining unit is not limited and can be selected according to actual needs. For example, in an alternative embodiment, in order to make the generalization of the aircraft interior depth vector stronger, the above-mentioned step S130 can further include step S131, step S132 and step S133, and the specific content of each step is described as follows.

[0084] Step S131, determining the flight data noise vector of the semantic mining unit included in the aircraft fault prediction network.

[0085] In the embodiment of the present application, the flight data noise vector of the semantic mining unit included in the aircraft fault prediction network can be determined first. The flight data noise vector can be different from the flight data noise vector in step S120, such as being determined based on different initial parameters or probability distributions, so that different noises can be introduced into the aircraft internal data and the aircraft external data, which is easier to match with the actual situation and can also avoid introducing the same noise and causing a decrease in semantic representation ability.

[0086] Step S132, performing a convolution mining operation on the semantic vector of the aircraft interior data, and performing a vector fusion operation on the semantic vector of the aircraft interior data after the convolution mining operation and the flight data noise vector, and outputting the corresponding semantic vector of the aircraft interior data after the vector fusion operation.

[0087] In an embodiment of the present application, the aircraft internal data semantic vector may be subjected to a convolution mining operation, and a vector fusion operation may be performed on the aircraft internal data semantic vector after the convolution mining operation and the flight data noise vector (for example, operations such as vector superposition may be performed), and the corresponding aircraft internal data semantic vector after the vector fusion operation may be output.

[0088] Step S133, loading the semantic vector of the aircraft interior data after the vector fusion operation, so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft interior data after the vector fusion operation and performs corresponding processing, and performing a convolution mining operation on the output vector of the vector mining unit to output the corresponding aircraft interior depth vector.

[0089] In an embodiment of the present application, the semantic vector of the aircraft interior data after the vector fusion operation can be loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft interior data after the vector fusion operation and performs corresponding processing (such as deep mining), and the output vector of the vector mining unit is subjected to a convolution mining operation to output the corresponding aircraft interior depth vector.

[0090] It is understandable that, in the above step S133, the specific method of deep mining performed by the vector mining unit is not limited and can be selected according to actual needs. For example, in an alternative embodiment, in order to make the mined aircraft interior depth vector have better semantic representation ability, the above step S133 can further include step S133a, step S133b and step S133c, and the specific content of each step is as follows.

[0091] Step S133a, loading the semantic vector of the aircraft internal data after the vector fusion operation, so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft internal data after the vector fusion operation, and loading the predetermined reference flight data semantic vector, so that the vector mining unit corresponding to the semantic mining unit obtains the reference flight data semantic vector.

[0092] In an embodiment of the present application, the semantic vector of the aircraft internal data after the vector fusion operation can be loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft internal data after the vector fusion operation, and the predetermined reference flight data semantic vector is loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the reference flight data semantic vector. Wherein, the reference flight data semantic vector is determined based on the semantic vectors of each training flight data mined by the semantic mining unit and the vector mining unit during the training optimization process (corresponding to the target flight data semantic vector in step S140, such as the mean vector of each training flight data semantic vector), and each training flight data semantic vector is obtained by performing corresponding semantic enhancement operations and association mining operations on the corresponding training flight data after the training optimization is completed. It should be noted that during the training process, in the first round of iterations, the reference flight data semantic vector may not be used. In step S133b, the self-attention processing may be directly performed on the semantic vector of the aircraft internal data after the vector fusion operation to obtain the corresponding aircraft internal data association vector. In each round of iteration after the first round of iteration, the semantic vectors of the training flight data mined in the previous round of iteration can be averaged to obtain the semantic vector of the reference flight data corresponding to the current round of iteration.

[0093] Step S133b, based on the reference flight data semantic vector, performing an association mining operation on the aircraft internal data semantic vector after the vector fusion operation, and outputting a corresponding aircraft internal data association vector.

[0094] In the embodiment of the present application, the semantic vector of the aircraft interior data after the vector fusion operation can be used to perform an association mining operation based on the reference flight data semantic vector, and the corresponding aircraft interior data association vector can be output. In other words, the semantic information carried by the reference flight data semantic vector can be fused into the aircraft interior data semantic vector after the vector fusion operation, based on which the problem of excessive loss of the true semantics of the aircraft interior data due to the introduction of noise can be avoided.

[0095] Step S133c, using the aircraft interior data association vector as the output vector of the vector mining unit, and performing a convolution mining operation to output a corresponding aircraft interior depth vector.

[0096] In an embodiment of the present application, the aircraft interior data association vector can be used as the output vector of the vector mining unit, and a convolution mining operation can be performed (the parameters of the convolution operation are not specifically limited here) to output the corresponding aircraft interior depth vector.

[0097] It can be understood that, in the above step S133b, the specific manner of performing the association mining operation on the semantic vector of the aircraft internal data after the vector fusion operation is not limited. For example, in an alternative implementation, the above step S133b may further include the following contents:

[0098] First, an association parameter between the reference flight data semantic vector and the aircraft interior data semantic vector after the vector fusion operation can be determined (e.g., multiplying the reference flight data semantic vector and the aircraft interior data semantic vector after the vector fusion operation), and based on the association parameter, an association adjustment is performed on the aircraft interior data semantic vector after the vector fusion operation (e.g., weighting the aircraft interior data semantic vector after the vector fusion operation based on the association parameter), and an aircraft interior data adjustment vector is output;

[0099] Then, the aircraft internal data semantic vector after the vector fusion operation and the aircraft internal data adjustment vector can be subjected to a vector fusion operation (such as superimposing the aircraft internal data semantic vector after the vector fusion operation and the aircraft internal data adjustment vector) to output a corresponding aircraft internal data association vector.

[0100] It should be noted that the specific method of analyzing the aircraft fault warning information corresponding to the flight data to be processed is not limited and can be selected according to actual needs. For example, in an alternative implementation, the above step S140 may include the following:

[0101] First, the association parameter between the flight data enhancement vector corresponding to the semantic mining unit and the aircraft interior depth vector can be determined, and based on the association parameter, the aircraft interior depth vector is associated and adjusted to output the corresponding adjusted aircraft interior depth vector, as described above;

[0102] Secondly, the adjusted aircraft interior depth vector and the aircraft interior depth vector may be subjected to a vector fusion operation to output a corresponding target flight data semantic vector, as described above;

[0103] Then, the target flight data semantic vector can be fully connected to output the corresponding fully connected flight data semantic vector, and, based on the fully connected flight data semantic vector, the aircraft fault warning information corresponding to the flight data to be processed can be analyzed; illustratively, the fully connected flight data semantic vector can be processed based on the output function included in the aircraft fault prediction network to obtain the probability distribution of whether the target aircraft has a fault, and then the situation with a higher probability (the presence of a fault or the absence of a fault) can be used as the aircraft fault warning information. In addition, the output function can be a classification function, which can be different according to different classification requirements, and therefore, no specific limitation is made in the embodiments of the present application.

[0104] Combination Figure 3 The embodiment of the present application also provides an aircraft fault warning system based on flight data that can be applied to the above electronic equipment. The aircraft fault warning system based on flight data can include a flight data semantic mining module, a vector semantic enhancement module, a vector deep mining module and an aircraft fault analysis module.

[0105] In detail, the flight data semantic mining module can be used to mine the semantic vector of the aircraft external data corresponding to the flight data to be processed, and to mine the semantic vector of the aircraft internal data corresponding to the flight data to be processed, wherein the flight data to be processed includes aircraft internal data and aircraft external data, the aircraft internal data is used to reflect the status of each aircraft component of the target aircraft during the flight, and the aircraft external data is used to reflect the status of the environment in which the target aircraft is located during the flight. In an embodiment of the present application, the flight data semantic mining module can be used to perform Figure 2 As shown in step S110, the relevant contents of the flight data semantic mining module can refer to the description of step S110 in the previous text.

[0106] In detail, the vector semantic enhancement module can be used to load the aircraft external data semantic vector, so that the aircraft fault prediction network obtains the aircraft external data semantic vector, and uses the semantic mining unit included in the aircraft fault prediction network to perform semantic enhancement operations on the pre-generated flight data noise vector based on the aircraft external data semantic vector, and outputs the flight data enhancement vector corresponding to the semantic mining unit. In the embodiment of the present application, the vector semantic enhancement module can be used to perform Figure 2 As shown in step S120, the relevant contents of the vector semantic enhancement module can refer to the above description of step S120.

[0107] In detail, the vector deep mining module can be used to mine the aircraft interior deep vector corresponding to the semantic mining unit based on the aircraft interior data semantic vector. In the embodiment of the present application, the vector deep mining module can be used to perform Figure 2 As shown in step S130, the relevant contents of the vector deep mining module can refer to the above description of step S130.

[0108] In detail, the aircraft fault analysis module can be used to perform an association mining operation on the aircraft internal depth vector according to the flight data enhancement vector corresponding to the semantic mining unit, output a target flight data semantic vector, and, according to the target flight data semantic vector, analyze the aircraft fault warning information corresponding to the flight data to be processed, wherein the aircraft fault warning information is used to reflect whether the target aircraft has a fault. In an embodiment of the present application, the aircraft fault analysis module can be used to perform Figure 2 As shown in step S140, the relevant contents of the aircraft fault analysis module can refer to the above description of step S140.

[0109] It can be understood that, in an alternative embodiment, the vector semantic enhancement module is specifically used to: load the aircraft external data semantic vector so that the aircraft fault prediction network obtains the aircraft external data semantic vector, and utilizes the semantic mining unit included in the aircraft fault prediction network to mine out the flight data noise mining vector based on the pre-generated flight data noise vector; map the flight data noise mining vector to form a corresponding flight data mapping vector; map the aircraft external data semantic vector to form a first flight mapping vector and a second flight mapping vector; perform a focusing fusion operation based on the flight data mapping vector, the first flight mapping vector and the second flight mapping vector, and obtain the flight data enhancement vector corresponding to the semantic mining unit based on the output vector of the focusing fusion operation.

[0110] It can be understood that, in an alternative embodiment, the vector deep mining module is specifically used to: determine the flight data noise vector of the semantic mining unit included in the aircraft fault prediction network; perform a convolution mining operation on the aircraft internal data semantic vector, and perform a vector fusion operation on the aircraft internal data semantic vector and the flight data noise vector after the convolution mining operation, and output the corresponding aircraft internal data semantic vector after the vector fusion operation; load the aircraft internal data semantic vector after the vector fusion operation, so that the vector mining unit corresponding to the semantic mining unit obtains the aircraft internal data semantic vector after the vector fusion operation and performs corresponding processing, and perform a convolution mining operation on the output vector of the vector mining unit to output the corresponding aircraft internal depth vector.

[0111] In an embodiment of the present application, corresponding to the above-mentioned aircraft fault warning method based on flight data applied to the electronic device, a computer-readable storage medium is also provided, in which a computer program is stored, and when the computer program is run, each step of the aircraft fault warning method based on flight data is executed.

[0112] Among them, the steps executed when the aforementioned computer program is running will not be described one by one here, and reference may be made to the previous explanation of the aircraft fault warning method based on flight data.

[0113] In summary, the present application provides an aircraft fault warning method and system based on flight data. First, the aircraft external data semantic vector and the aircraft internal data semantic vector corresponding to the flight data to be processed are mined; secondly, the pre-generated flight data noise vector is semantically enhanced based on the aircraft external data semantic vector, and the flight data enhancement vector is output; then, the aircraft internal depth vector is mined based on the aircraft internal data semantic vector; finally, the aircraft internal depth vector is associated with the flight data enhancement vector, and the target flight data semantic vector is output, and the aircraft fault warning information is analyzed based on the target flight data semantic vector. Based on the above content, since the flight data enhancement vector carrying the semantic information of the aircraft external data and the aircraft internal depth vector carrying the semantic information of the aircraft internal data are associated with the mining operation, the semantic representation ability of the obtained target flight data semantic vector can be better, and therefore, the reliability of the aircraft fault warning information analyzed based on the target flight data semantic vector is higher. On the other hand, since the flight data enhancement vector not only carries the semantic information of the aircraft's external data, but also integrates the flight data noise vector, it can better simulate these real situations (the collected aircraft external data may ignore some noise and interference), thereby improving its performance in the actual environment. In this way, the representation ability of the aircraft data enhancement vector can be further improved, thereby improving the problem of relatively low reliability of aircraft fault warning in the prior art.

[0114] In several embodiments provided in the embodiments of the present application, it should be understood that the disclosed device and method can also be implemented in other ways. The device and method embodiments described above are merely schematic, for example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0115] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0116] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk. It should be noted that, in this article, the term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0117] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An aircraft fault early warning method based on flight data, characterized in that: include: Mining out the semantic vector of aircraft external data corresponding to the flight data to be processed, and mining out the semantic vector of aircraft internal data corresponding to the flight data to be processed, wherein the flight data to be processed includes aircraft internal data and aircraft external data, the aircraft internal data is used to reflect the status of each aircraft component of the target aircraft during the flight, and the aircraft external data is used to reflect the status of the environment in which the target aircraft is located during the flight; The aircraft external data semantic vector is loaded so that the aircraft fault prediction network acquires the aircraft external data semantic vector, and a semantic mining unit included in the aircraft fault prediction network is used to perform a semantic enhancement operation on a pre-generated flight data noise vector based on the aircraft external data semantic vector, and output a flight data enhancement vector corresponding to the semantic mining unit; Mining an aircraft interior depth vector corresponding to the semantic mining unit according to the aircraft interior data semantic vector; According to the flight data enhancement vector corresponding to the semantic mining unit, an association mining operation is performed on the internal depth vector of the aircraft to output a target flight data semantic vector, and, based on the target flight data semantic vector, the aircraft fault warning information corresponding to the flight data to be processed is analyzed, wherein the aircraft fault warning information is used to reflect whether there is a fault in the target aircraft.

2. The aircraft failure early warning method based on flight data according to claim 1, characterized in that: The step of loading the aircraft external data semantic vector so that the aircraft fault prediction network acquires the aircraft external data semantic vector, and using the semantic mining unit included in the aircraft fault prediction network to perform a semantic enhancement operation on the pre-generated flight data noise vector based on the aircraft external data semantic vector, and outputting the flight data enhancement vector corresponding to the semantic mining unit, includes: The aircraft external data semantic vector is loaded so that the aircraft fault prediction network acquires the aircraft external data semantic vector, and a semantic mining unit included in the aircraft fault prediction network is used to mine a flight data noise mining vector according to a pre-generated flight data noise vector; Forming a corresponding flight data mapping vector according to the flight data noise mining vector mapping; Mapping the aircraft external data semantic vectors to form a first flight mapping vector and a second flight mapping vector; A focused fusion operation is performed according to the flight data mapping vector, the flight first mapping vector and the flight second mapping vector, and a flight data enhancement vector corresponding to the semantic mining unit is obtained according to an output vector of the focused fusion operation.

3. The aircraft failure early warning method based on flight data according to claim 2 is characterized in that: The step of loading the aircraft external data semantic vector so that the aircraft fault prediction network acquires the aircraft external data semantic vector, and using the semantic mining unit included in the aircraft fault prediction network to perform a semantic enhancement operation on the pre-generated flight data noise vector based on the aircraft external data semantic vector, and outputting the flight data enhancement vector corresponding to the semantic mining unit, further includes: Determine a target parameter, and determine a corresponding number of initial parameters based on the target parameter, wherein the initial parameters are multiple; Determining a corresponding initial flight data noise vector according to the initial parameters, wherein each vector parameter in the initial aircraft data noise vector is determined by a random number generator based on the initial parameters; A flight data noise vector is obtained based on the determined initial flight data noise vectors.

4. The aircraft failure early warning method based on flight data according to claim 1, characterized in that: The step of mining the aircraft interior depth vector corresponding to the semantic mining unit based on the aircraft interior data semantic vector comprises: Determining a flight data noise vector of a semantic mining unit included in the aircraft fault prediction network; Performing a convolution mining operation on the semantic vector of the aircraft interior data, and performing a vector fusion operation on the semantic vector of the aircraft interior data after the convolution mining operation and the flight data noise vector, and outputting the corresponding semantic vector of the aircraft interior data after the vector fusion operation; The semantic vector of the aircraft interior data after the vector fusion operation is loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft interior data after the vector fusion operation and performs corresponding processing, and the output vector of the vector mining unit is subjected to a convolution mining operation to output a corresponding aircraft interior depth vector.

5. The aircraft failure early warning method based on flight data according to claim 4 is characterized in that: The steps of loading the semantic vector of the aircraft interior data after the vector fusion operation so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft interior data after the vector fusion operation and performs corresponding processing, and performing a convolution mining operation on the output vector of the vector mining unit to output the corresponding aircraft interior depth vector include: The semantic vector of the aircraft internal data after the vector fusion operation is loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft internal data after the vector fusion operation, and the predetermined reference flight data semantic vector is loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the reference flight data semantic vector, wherein the reference flight data semantic vector is determined based on the semantic vectors of each training flight data mined by the semantic mining unit and the vector mining unit during the training optimization process, and each training flight data semantic vector is obtained by performing corresponding semantic enhancement operations and association mining operations on the corresponding training flight data after the training optimization is completed; According to the reference flight data semantic vector, an association mining operation is performed on the aircraft internal data semantic vector after the vector fusion operation, and a corresponding aircraft internal data association vector is output; The aircraft interior data association vector is used as the output vector of the vector mining unit, and a convolution mining operation is performed to output a corresponding aircraft interior depth vector.

6. The aircraft failure early warning method based on flight data according to claim 5, characterized in that: The step of performing an association mining operation on the aircraft internal data semantic vector after the vector fusion operation based on the reference flight data semantic vector and outputting the corresponding aircraft internal data association vector comprises: Determine an association parameter between the reference flight data semantic vector and the aircraft interior data semantic vector after the vector fusion operation, and based on the association parameter, perform association adjustment on the aircraft interior data semantic vector after the vector fusion operation, and output an aircraft interior data adjustment vector; A vector fusion operation is performed on the aircraft interior data semantic vector after the vector fusion operation and the aircraft interior data adjustment vector, and a corresponding aircraft interior data association vector is output.

7. The aircraft failure warning method based on flight data according to any one of claims 1 to 6, characterized in that: The steps of performing an association mining operation on the aircraft internal depth vector according to the flight data enhancement vector corresponding to the semantic mining unit, outputting a target flight data semantic vector, and analyzing the aircraft fault warning information corresponding to the flight data to be processed according to the target flight data semantic vector include: Determine an association parameter between the flight data enhancement vector corresponding to the semantic mining unit and the aircraft interior depth vector, and based on the association parameter, perform association adjustment on the aircraft interior depth vector, and output a corresponding adjusted aircraft interior depth vector; Performing a vector fusion operation on the adjusted aircraft interior depth vector and the aircraft interior depth vector, and outputting a corresponding target flight data semantic vector; A full connection operation is performed on the target flight data semantic vector to output a corresponding fully connected flight data semantic vector, and aircraft fault warning information corresponding to the flight data to be processed is analyzed based on the fully connected flight data semantic vector.

8. An aircraft fault warning system based on flight data, characterized in that: include: A flight data semantic mining module, used to mine out the semantic vector of aircraft external data corresponding to the flight data to be processed, and to mine out the semantic vector of aircraft internal data corresponding to the flight data to be processed, wherein the flight data to be processed includes aircraft internal data and aircraft external data, the aircraft internal data is used to reflect the status of each aircraft component of the target aircraft during the flight, and the aircraft external data is used to reflect the status of the environment in which the target aircraft is located during the flight; A vector semantic enhancement module is used to load the aircraft external data semantic vector so that the aircraft fault prediction network obtains the aircraft external data semantic vector, and uses the semantic mining unit included in the aircraft fault prediction network to perform a semantic enhancement operation on the pre-generated flight data noise vector based on the aircraft external data semantic vector, and output the flight data enhancement vector corresponding to the semantic mining unit; A vector depth mining module, used to mine the aircraft interior depth vector corresponding to the semantic mining unit according to the aircraft interior data semantic vector; The aircraft fault analysis module is used to perform an association mining operation on the aircraft internal depth vector according to the flight data enhancement vector corresponding to the semantic mining unit, output a target flight data semantic vector, and analyze the aircraft fault warning information corresponding to the flight data to be processed according to the target flight data semantic vector, wherein the aircraft fault warning information is used to reflect whether the target aircraft has a fault.

9. The aircraft failure warning system based on flight data according to claim 8, characterized in that: The vector semantics enhancement module is specifically used for: The aircraft external data semantic vector is loaded so that the aircraft fault prediction network acquires the aircraft external data semantic vector, and a semantic mining unit included in the aircraft fault prediction network is used to mine a flight data noise mining vector according to a pre-generated flight data noise vector; Forming a corresponding flight data mapping vector according to the flight data noise mining vector mapping; Mapping the aircraft external data semantic vectors to form a first flight mapping vector and a second flight mapping vector; A focused fusion operation is performed according to the flight data mapping vector, the flight first mapping vector and the flight second mapping vector, and a flight data enhancement vector corresponding to the semantic mining unit is obtained according to an output vector of the focused fusion operation.

10. The aircraft failure warning system based on flight data according to claim 8, characterized in that: The vector deep mining module is specifically used for: Determining a flight data noise vector of a semantic mining unit included in the aircraft fault prediction network; Performing a convolution mining operation on the semantic vector of the aircraft interior data, and performing a vector fusion operation on the semantic vector of the aircraft interior data after the convolution mining operation and the flight data noise vector, and outputting the corresponding semantic vector of the aircraft interior data after the vector fusion operation; The semantic vector of the aircraft interior data after the vector fusion operation is loaded, so that the vector mining unit corresponding to the semantic mining unit obtains the semantic vector of the aircraft interior data after the vector fusion operation and performs corresponding processing, and the output vector of the vector mining unit is subjected to a convolution mining operation to output a corresponding aircraft interior depth vector.

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