Method, device and equipment for processing fault data in airport during pilot training based on data platform, and medium

Abstract

The invention discloses an in-airport fault data processing method, device and equipment during pilot training based on a data platform, and a medium, and the method comprises the steps: traversing a plurality of adjustment devices on a target training flight device, and setting a plurality of abnormal values for each adjustment device; binding the adjusting equipment, the abnormal value corresponding to the adjusting equipment and the standard processing action of the abnormal value under the adjusting equipment to obtain a fault processing data set corresponding to each adjusting equipment; adjusting the preset neural network according to a prediction processing action output by the preset neural network and a standard processing action corresponding to the prediction processing action; obtaining a target prediction processing action corresponding to the target abnormal value of the target fault equipment; and judging whether the to-be-corrected fault processing action is qualified or not. The invention belongs to the field of aviation data processing. According to the invention, the flight training efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of aviation data processing, and in particular to a method, device, equipment and medium for processing airport fault data during pilot training based on a data center. Background Art

[0002] Pilot training is a systematic process designed to develop pilots' flying skills, aviation knowledge, and emergency response capabilities. This process is usually divided into several stages and varies depending on the pilot's ultimate goal (such as commercial pilot, private pilot, military pilot, etc.).

[0003] Usually, pilots who need training will be trained in flight training equipment. When the simulation equipment fails, the instructor will usually provide guidance and handle it. This training method has low training efficiency and high labor costs. Therefore, an efficient training method is urgently needed. Summary of the invention

[0004] The present invention solves the technical problem of low training efficiency of fault handling training in the prior art by providing a method, device, equipment and medium for processing airport fault data during pilot training based on a data center, and achieves the technical effect of improving the training efficiency of fault handling training.

[0005] In a first aspect, the present invention provides a method for processing airport fault data during pilot training based on a data center, the method comprising: Traversing a plurality of adjustment devices on the target training flight device, and setting a plurality of abnormal values ​​for each adjustment device, wherein when an adjustment device is at an abnormal value corresponding to the adjustment device, the adjustment device is in a fault state; For each adjustment device, the adjustment device and an abnormal value corresponding to the adjustment device, as well as the standard processing action of the abnormal value under the adjustment device, are bound to obtain a fault processing data set corresponding to each adjustment device, where each standard processing action includes a processing time limit, an adjustment target, and a body posture amplitude; Inputting the fault processing data sets corresponding to the plurality of adjustment devices into the preset neural network, and adjusting the preset neural network according to the predicted processing action output by the preset neural network and the standard processing action corresponding to the predicted processing action; When the target flight equipment is in a training state, the target abnormal value of the target faulty equipment is input into the adjusted preset neural network to obtain a target prediction processing action corresponding to the target abnormal value of the target faulty equipment; The to-be-corrected fault processing action of the first target is obtained, and the difference between the target predicted processing action and the to-be-corrected fault processing action is determined to determine whether the to-be-corrected fault processing action is qualified.

[0006] Further, the preset neural network is adjusted according to the predicted processing action output by the preset neural network and the standard processing action corresponding to the predicted processing action, including: adjusting a neural network parameter of a preset neural network according to a difference between a predicted processing action and a standard processing action corresponding to the predicted processing action; When the maximum iteration parameter is reached or the preset training requirement is met, the latest neural network parameters are saved, and the preset neural network under the latest neural network parameters is used as the adjusted preset neural network.

[0007] Further, judging whether the fault handling action to be corrected is qualified includes: When the difference between the target predicted processing action and the to-be-corrected fault processing action is greater than or equal to the difference threshold, the to-be-corrected fault processing action is unqualified; When the difference between the target predicted processing action and the to-be-corrected fault processing action is less than the difference threshold, the to-be-corrected fault processing action is qualified.

[0008] Further, determining the difference between the target prediction processing action and the to-be-corrected fault processing action includes: , in, is the difference between the i-th target prediction processing action and the i-th fault processing action to be corrected, a and b are both preset weights, Predict the processing time limit of the processing action for the i-th target, is the processing time limit of the i-th fault processing action to be corrected, The adjustment target for the i-th target prediction processing action, is the adjustment target of the i-th fault handling action to be corrected, The body posture magnitude vector for the i-th target prediction processing action, is the body posture amplitude vector of the i-th fault handling action to be corrected.

[0009] Furthermore, the method further comprises: According to the importance of several adjustment devices, they are sorted in descending order to obtain a standard sorting; When the number of target faulty devices is greater than 1, obtaining the adjustment order of the first target; According to the adjustment sequence of the first target and the standard ranking, it is determined whether the adjustment sequence of the first target is qualified.

[0010] Further, judging whether the adjustment sequence of the first target is qualified according to the adjustment sequence of the first target and the standard ranking includes: According to the adjustment sequence of the first target, determining whether the descending order of the adjustment sequence of the first target can correspond to the standard order; If so, the adjustment sequence of the first target is qualified; if not, the adjustment sequence of the first target is unqualified.

[0011] Furthermore, the missing judgment of the fault processing data set also includes: , in, is the sequence vector of the fault processing dataset, is the mean of the sequence vector, f is the variance of the sequence vector, c and d are both preset weights, is the weighted density of the sequence vector of the fault processing dataset.

[0012] In a second aspect, the present invention provides an airport fault data processing device during pilot training based on a data center, the device comprising: A traversal module is used to traverse a plurality of adjustment devices on the target training flight device, and set a plurality of abnormal values ​​for each adjustment device, wherein when an adjustment device is at an abnormal value corresponding to the adjustment device, the adjustment device is in a fault state; A binding module is used to bind, for each adjustment device, the adjustment device and an abnormal value corresponding to the adjustment device, and a standard processing action of the abnormal value under the adjustment device, to obtain a fault processing data set corresponding to each adjustment device, wherein each standard processing action includes a processing time limit, an adjustment target, and a body posture amplitude; An adjustment module, used to input the fault processing data sets corresponding to the plurality of adjustment devices into a preset neural network, and adjust the preset neural network according to the predicted processing action output by the preset neural network and the standard processing action corresponding to the predicted processing action; A prediction module, used for inputting a target abnormal value of a target faulty device into an adjusted preset neural network when the target flight device is in a training state, and obtaining a target prediction processing action corresponding to the target abnormal value of the target faulty device; The judgment module is used to obtain the to-be-corrected fault processing action of the first target, and to determine whether the to-be-corrected fault processing action is qualified based on the difference between the target predicted processing action and the to-be-corrected fault processing action.

[0013] In a third aspect, the present invention provides an electronic device, comprising: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to execute to implement the method for processing airport fault data during pilot training based on the data center as provided in the first aspect.

[0014] In a fourth aspect, the present invention provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for processing airport fault data during pilot training based on a data middle station as provided in the first aspect.

[0015] One or more technical solutions provided in the present invention have at least the following technical effects or advantages: The present invention provides a method for processing fault data in an airport during pilot training based on a data middle station, the method comprising: traversing a plurality of adjustment devices on a target training flight device, setting a plurality of abnormal values ​​for each adjustment device, wherein when the adjustment device is under the abnormal value corresponding to the adjustment device, the adjustment device is in a fault state; for each adjustment device, binding the adjustment device and an abnormal value corresponding to the adjustment device, and a standard processing action for the abnormal value under the adjustment device, to obtain a fault processing data set corresponding to each adjustment device, wherein each standard processing action includes a processing time limit, an adjustment target, and a body posture amplitude; inputting the fault processing data sets corresponding to the plurality of adjustment devices into a preset neural network, and adjusting the preset neural network according to a predicted processing action output by the preset neural network and a standard processing action corresponding to the predicted processing action; when the target flight device is in a training state, inputting the target abnormal value of the target fault device into the adjusted preset neural network, and obtaining a target predicted processing action corresponding to the target abnormal value of the target fault device; obtaining a fault processing action to be corrected of the first target, and judging whether the fault processing action to be corrected is qualified by comparing the difference between the target predicted processing action and the fault processing action to be corrected. The present invention first trains the neural network to obtain the predicted processing action, and compares the predicted processing action with the fault processing action to be corrected, so as to better guide the first goal, ensure the training effect of the first goal, save labor costs, and improve training efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A schematic flow chart of a method for processing airport fault data during pilot training based on a data center provided by the present invention. DETAILED DESCRIPTION

[0018] The embodiment of the present invention solves the technical problem of low training efficiency of fault handling training in the prior art by providing a method for processing airport fault data during pilot training.

[0019] The technical solution of the present invention is to solve the above technical problems, and the overall idea is as follows: A method for processing fault data in an airport during pilot training based on a data middle station, the method comprising: traversing a number of adjustment devices on a target training flight device, setting a number of abnormal values ​​for each adjustment device, wherein when an adjustment device is under an abnormal value corresponding to the adjustment device, the adjustment device is in a fault state; for each adjustment device, binding the adjustment device and an abnormal value corresponding to the adjustment device, and a standard processing action for the abnormal value under the adjustment device, to obtain a fault processing data set corresponding to each adjustment device, wherein each standard processing action includes a processing time limit, an adjustment target, and a body posture amplitude; inputting the fault processing data sets corresponding to the number of adjustment devices into a preset neural network, and adjusting the preset neural network according to the predicted processing action output by the preset neural network and the standard processing action corresponding to the predicted processing action; when the target flight device is in a training state, inputting the target abnormal value of the target fault device into the adjusted preset neural network, and obtaining the target predicted processing action corresponding to the target abnormal value of the target fault device; obtaining the fault processing action to be corrected of the first target, and judging whether the fault processing action to be corrected is qualified based on the difference between the target predicted processing action and the fault processing action to be corrected.

[0020] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0021] First of all, the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0022] The present invention provides Figure 1 The method for processing airport fault data during pilot training based on a data center station shown includes steps S11-S15: Regarding step S11, several adjustment devices on the target training flight device are traversed, and several abnormal values ​​are set for each adjustment device. When an adjustment device is at an abnormal value corresponding to the adjustment device, the adjustment device is in a fault state.

[0023] The target training flight device refers to the flight device (or aircraft) used to train pilots. There are several adjustment devices on the aircraft. The adjustment equipment refers to the equipment on the flight device that needs to be adjusted by the pilot, such as the landing gear system, instrument panel, electrical system and environmental control system. It is understandable that the adjustment equipment may fail. When a failure occurs, the adjustment equipment needs to be adjusted in time to avoid causing greater losses.

[0024] The abnormal value of each adjustment device can be obtained based on the data center. When the adjustment device is at the abnormal value corresponding to the adjustment device, the adjustment device is in a fault state.

[0025] Regarding step S12, for each adjustment device, the adjustment device and an abnormal value corresponding to the adjustment device, as well as the standard processing action of the abnormal value under the adjustment device, are bound to obtain a fault processing data set corresponding to each adjustment device, and each standard processing action includes a processing time limit, an adjustment target, and a body posture amplitude.

[0026] The adjustment device and an abnormal value corresponding to the adjustment device are bound, and the binding is performed once each time the adjustment device has an abnormal value, so as to obtain a fault processing data set corresponding to the adjustment device.

[0027] The standard processing action refers to the reference action for processing the abnormal value under the adjustment device. Each standard processing action can be described in three aspects: processing time limit, adjustment target and body posture amplitude. The processing time limit refers to the reference time required to process the abnormal value. The adjustment target refers to adjusting the abnormal value of the adjustment device to a normal value. The body posture amplitude refers to the total adjustment actions performed within the processing time limit.

[0028] It can be understood that when the fault processing data of the fault processing data set is more continuous, it can be considered that the training effect of the preset training model in the following text is better. Therefore, the present invention also performs missing judgment on the fault processing data set, and also includes: , in, is the sequence vector of the fault processing dataset, is the mean of the sequence vector, f is the variance of the sequence vector, c and d are both preset weights, is the weighted density of the sequence vector of the fault processing dataset.

[0029] When the weighted density of the sequence vector of the fault processing data set is greater than the corresponding set threshold, it means that the more discontinuous the data in the fault processing data set is, the worse the training effect is when the fault processing data set is used for training. Therefore, when the weighted density of the sequence vector of the fault processing data set is greater than the corresponding set threshold, the fault processing data set can be filled in by interpolation to improve the training effect of the preset training neural network model.

[0030] Regarding step S13, the fault processing data sets corresponding to the plurality of adjustment devices are input into the preset neural network, and the preset neural network is adjusted according to the predicted processing action output by the preset neural network and the standard processing action corresponding to the predicted processing action.

[0031] After the fault processing data sets corresponding to several adjustment devices are input into the preset neural network, it includes: adjusting the neural network parameters of the preset neural network according to the difference between the predicted processing action and the standard processing action corresponding to the predicted processing action; when the maximum iteration parameter is reached or the preset training requirements are met, saving the latest neural network parameters, and using the preset neural network under the latest neural network parameters as the adjusted preset neural network.

[0032] The maximum iteration parameter or the preset training requirement can be determined according to the actual situation and is not limited here. The preset neural network is used to predict the processing action under the abnormal value of the adjustment device to obtain the corresponding predicted processing action. The neural network parameters may include weights, biases, learning rates, activation functions, and the number and size of hidden layers.

[0033] Regarding step S14, when the target flight equipment is in a training state, the target abnormal value of the target faulty equipment is input into the adjusted preset neural network to obtain the target prediction processing action corresponding to the target abnormal value of the target faulty equipment.

[0034] When the target flight equipment is in the training state, it means that the flight equipment is in the simulation state. At this time, the target flight equipment can simulate the abnormal value of the adjustment equipment (it should be noted that at this time, the target flight equipment is in the simulation state and is not in the actual flight process).

[0035] The target abnormal value of the target faulty device is input into the adjusted preset neural network to obtain the target prediction processing action corresponding to the target abnormal value of the target faulty device.

[0036] Regarding step S15, the to-be-corrected fault processing action of the first target is obtained, and the difference between the target predicted processing action and the to-be-corrected fault processing action is determined to determine whether the to-be-corrected fault processing action is qualified.

[0037] The first target refers to the pilot who is under training guidance. The pilot is in the target flight equipment and adjusts the target abnormal value of the target faulty equipment to obtain the first target's pending fault processing action. The pending fault processing action also includes a processing time limit, an adjustment target, and a body posture range.

[0038] Determine whether the fault handling action to be corrected is qualified, including: when the difference between the target prediction processing action and the fault handling action to be corrected is greater than or equal to the difference threshold, the fault handling action to be corrected is unqualified; when the difference between the target prediction processing action and the fault handling action to be corrected is less than the difference threshold, the fault handling action to be corrected is qualified.

[0039] Determine the difference between the target prediction processing action and the fault processing action to be corrected, including: , in, is the difference between the i-th target prediction processing action and the i-th fault processing action to be corrected, a and b are both preset weights, Predict the processing time limit of the processing action for the i-th target, is the processing time limit of the i-th fault processing action to be corrected, The adjustment target for the i-th target prediction processing action, is the adjustment target of the i-th fault handling action to be corrected, The body posture magnitude vector for the i-th target prediction processing action, is the body posture amplitude vector of the i-th fault handling action to be corrected.

[0040] The present invention takes the processing time limit as the core, and normalizes the body posture amplitude vector and the adjustment target respectively. The three parameters after processing do not retain the dimension, but only retain the parameters themselves. The present invention takes the processing time limit as the axis, reflects the changes of the body posture amplitude vector and the adjustment target compared with time, and simultaneously incorporates the processing time limit, the body posture amplitude vector and the adjustment target as indicators for evaluating the target prediction processing action and the fault processing action to be corrected, so as to achieve consistency judgment, and can more accurately reflect whether the fault processing action to be corrected is qualified.

[0041] The method also includes: sorting a number of adjustment devices in descending order according to their importance to obtain a standard sorting; when the number of target faulty devices is greater than 1, obtaining the adjustment order of the first target; and judging whether the adjustment order of the first target is qualified according to the adjustment order of the first target and the standard sorting.

[0042] When the number of target faulty devices is greater than 1, it means that multiple faulty devices need to be adjusted continuously, but the importance of different adjustment devices is different. Therefore, the faulty devices need to be adjusted in sequence.

[0043] Specifically, it includes: judging whether the descending order of the adjustment order of the first target can correspond to the standard order according to the adjustment order of the first target; if so, the adjustment order of the first target is qualified; if not, the adjustment order of the first target is unqualified.

[0044] For example, the standard order is A, B, C, D, E, F, G (A, B, C, D, E, F, G are all adjustment devices), The adjustment order of the first target is A, C, D. ACD belongs to the order of ABCD and can correspond to the standard sorting, so the adjustment order of the first target is qualified.

[0045] In summary, the present invention provides a method for processing fault data in an airport during pilot training based on a data middle station, the method comprising: traversing a number of adjustment devices on a target training flight device, setting a number of abnormal values ​​for each adjustment device, wherein when the adjustment device is under the abnormal value corresponding to the adjustment device, the adjustment device is in a fault state; for each adjustment device, binding the adjustment device and an abnormal value corresponding to the adjustment device, and the standard processing action of the abnormal value under the adjustment device, to obtain a fault processing data set corresponding to each adjustment device, each standard processing action including a processing time limit, an adjustment target, and a body posture amplitude; inputting the fault processing data sets corresponding to the number of adjustment devices into a preset neural network, and adjusting the preset neural network according to the predicted processing action output by the preset neural network and the standard processing action corresponding to the predicted processing action; when the target flight device is in a training state, inputting the target abnormal value of the target fault device into the adjusted preset neural network, and obtaining the target predicted processing action corresponding to the target abnormal value of the target fault device; obtaining the fault processing action to be corrected of the first target, and judging whether the fault processing action to be corrected is qualified by comparing the difference between the target predicted processing action and the fault processing action to be corrected. The present invention first trains the neural network to obtain the predicted processing action, and compares the predicted processing action with the fault processing action to be corrected, so as to better guide the first goal, ensure the training effect of the first goal, save labor costs, and improve training efficiency.

[0046] Based on the same inventive concept, the present invention provides an airport fault data processing device during pilot training based on a data center, the device comprising: A traversal module is used to traverse a plurality of adjustment devices on the target training flight device, and set a plurality of abnormal values ​​for each adjustment device, wherein when an adjustment device is at an abnormal value corresponding to the adjustment device, the adjustment device is in a fault state; A binding module is used to bind, for each adjustment device, the adjustment device and an abnormal value corresponding to the adjustment device, and a standard processing action of the abnormal value under the adjustment device, to obtain a fault processing data set corresponding to each adjustment device, wherein each standard processing action includes a processing time limit, an adjustment target, and a body posture amplitude; An adjustment module, used to input the fault processing data sets corresponding to the plurality of adjustment devices into a preset neural network, and adjust the preset neural network according to the predicted processing action output by the preset neural network and the standard processing action corresponding to the predicted processing action; A prediction module, used for inputting a target abnormal value of a target faulty device into an adjusted preset neural network when the target flight device is in a training state, and obtaining a target prediction processing action corresponding to the target abnormal value of the target faulty device; The judgment module is used to obtain the to-be-corrected fault processing action of the first target, and to determine whether the to-be-corrected fault processing action is qualified based on the difference between the target predicted processing action and the to-be-corrected fault processing action.

[0047] Based on the same inventive concept, the present invention provides an electronic device, comprising: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to execute to implement the method for processing airport fault data during pilot training based on the data center as provided above.

[0048] Based on the same inventive concept, the present invention also provides a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the airport fault data processing method for pilot training based on the data middle station as provided above.

[0049] Since the electronic device introduced in this embodiment is an electronic device used to implement the information processing method in the embodiment of the present invention, based on the information processing method introduced in the embodiment of the present invention, a person skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present invention is not described in detail here. As long as the electronic device used by a person skilled in the art to implement the information processing method in the embodiment of the present invention, it belongs to the scope of protection of the present invention.

[0050] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0051] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0052] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0053] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0054] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0055] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for processing airport fault data during pilot training based on a data center, characterized in that: The method comprises: Traversing a plurality of adjustment devices on the target training flight device, and setting a plurality of abnormal values ​​for each adjustment device, wherein when an adjustment device is at an abnormal value corresponding to the adjustment device, the adjustment device is in a fault state; For each adjustment device, the adjustment device and an abnormal value corresponding to the adjustment device, as well as the standard processing action of the abnormal value under the adjustment device, are bound to obtain a fault processing data set corresponding to each adjustment device, where each standard processing action includes a processing time limit, an adjustment target, and a body posture amplitude; Inputting fault processing data sets corresponding to a plurality of adjustment devices into a preset neural network, and adjusting the preset neural network according to a predicted processing action output by the preset neural network and a standard processing action corresponding to the predicted processing action; When the target flight equipment is in a training state, the target abnormal value of the target faulty equipment is input into the adjusted preset neural network to obtain a target prediction processing action corresponding to the target abnormal value of the target faulty equipment; The to-be-corrected fault processing action of the first target is obtained, and the difference between the target predicted processing action and the to-be-corrected fault processing action is determined to determine whether the to-be-corrected fault processing action is qualified.

2. The method for processing airport fault data during pilot training based on a data center as claimed in claim 1, characterized in that: According to the predicted processing action output by the preset neural network and the standard processing action corresponding to the predicted processing action, the preset neural network is adjusted, including: adjusting a neural network parameter of the preset neural network according to a difference between a predicted processing action and a standard processing action corresponding to the predicted processing action; When the maximum iteration parameter is reached or the preset training requirement is met, the latest neural network parameters are saved, and the preset neural network under the latest neural network parameters is used as the adjusted preset neural network.

3. The method for processing airport fault data during pilot training based on a data center as claimed in claim 1, characterized in that: Determining whether the to-be-corrected fault processing action is qualified includes: When the difference between the target prediction processing action and the to-be-corrected fault processing action is greater than or equal to a difference threshold, the to-be-corrected fault processing action is unqualified; When the difference between the target prediction processing action and the to-be-corrected fault processing action is less than the difference threshold, the to-be-corrected fault processing action is qualified.

4. The method for processing airport fault data during pilot training based on a data center as claimed in claim 1, characterized in that: Determining the difference between the target prediction processing action and the to-be-corrected fault processing action includes: ， in, For the The target prediction processing action and the The difference between the fault handling actions to be corrected, a and b are both preset weights, Predict the processing time limit of the processing action for the i-th target, is the processing time limit of the i-th fault processing action to be corrected, The adjustment target for the i-th target prediction processing action, is the adjustment target of the i-th fault handling action to be corrected, The body posture magnitude vector for the i-th target prediction processing action, is the body posture amplitude vector of the i-th fault handling action to be corrected.

5. The method for processing airport fault data during pilot training based on a data center as claimed in claim 1, characterized in that: The method further comprises: According to the importance of several adjustment devices, they are sorted in descending order to obtain a standard sorting; When the number of target faulty devices is greater than 1, obtaining an adjustment order of the first target; According to the adjustment sequence of the first target and the standard ranking, it is determined whether the adjustment sequence of the first target is qualified.

6. The method for processing airport fault data during pilot training based on a data center as claimed in claim 5, characterized in that: Judging whether the adjustment sequence of the first target is qualified according to the adjustment sequence of the first target and the standard ranking includes: According to the adjustment sequence of the first target, determining whether the descending order of the adjustment sequence of the first target can correspond to the standard order; If so, the adjustment sequence of the first target is qualified; if not, the adjustment sequence of the first target is unqualified.

7. The method for processing airport fault data during pilot training based on a data center as claimed in claim 1, characterized in that: Deletion judgment of the fault processing data set also includes: ， in, is the sequence vector of the fault processing dataset, is the mean of the sequence vector, is the variance of the sequence vector, c and d are preset weights, is the weighted density of the sequence vector of the fault processing dataset.

8. The device for processing airport fault data during pilot training based on the data center is characterized by: The device comprises: A traversal module is used to traverse a plurality of adjustment devices on the target training flight device, and set a plurality of abnormal values ​​for each adjustment device, wherein when an adjustment device is at an abnormal value corresponding to the adjustment device, the adjustment device is in a fault state; A binding module is used to bind, for each adjustment device, the adjustment device and an abnormal value corresponding to the adjustment device, and a standard processing action of the abnormal value under the adjustment device, to obtain a fault processing data set corresponding to each adjustment device, wherein each standard processing action includes a processing time limit, an adjustment target, and a body posture amplitude; An adjustment module, used to input the fault processing data sets corresponding to the plurality of adjustment devices into a preset neural network, and adjust the preset neural network according to the predicted processing action output by the preset neural network and the standard processing action corresponding to the predicted processing action; A prediction module, used for inputting a target abnormal value of a target faulty device into an adjusted preset neural network when the target flight device is in a training state, and obtaining a target prediction processing action corresponding to the target abnormal value of the target faulty device; The judgment module is used to obtain the to-be-corrected fault processing action of the first target, and to judge whether the to-be-corrected fault processing action is qualified based on the difference between the target predicted processing action and the to-be-corrected fault processing action.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute to implement the method for processing in-airport fault data during pilot training based on a data center as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to implement the method for processing airport fault data during pilot training based on a data middle platform as described in any one of claims 1 to 7.

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