Aircraft motion trail determination method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses an aircraft motion trail determination method and device, electronic equipment and a storage medium. The method comprises the following steps: determining target environment data of a to-be-predicted flight area corresponding to a target aircraft in a to-be-predicted time period; wherein the to-be-predicted time period comprises at least one to-be-predicted moment; based on the target environment data, the initial state data of the target aircraft, a preset aircraft action scheme and a pre-trained trajectory determination model, determining prediction state data corresponding to each to-be-predicted moment of the to-be-predicted time period of the target aircraft; wherein the trajectory determination model is obtained by performing reinforcement learning on a neural network model in advance; and based on the prediction state data corresponding to each to-be-predicted moment, generating a prediction motion track of the target aircraft in the to-be-predicted time period. According to the technical scheme provided by the embodiment of the invention, the accuracy of predicting the motion trail can be improved, the flexibility is high, and various prediction requirements of a user in practical application can be conveniently met.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of aircraft control technology, and in particular to a method, device, electronic device and storage medium for determining an aircraft motion trajectory. Background Art

[0002] At present, a fixed-wing aircraft dynamics model is usually built through simulation to predict the motion trajectory of the aircraft during flight.

[0003] However, in the process of implementing the present invention, it is found that the prior art has at least the following technical problems: Since the flight state and trajectory of the aircraft are affected by the ambient weather, there is uncertainty. The fixed-wing aircraft dynamics model can only predict the flight state and trajectory in a fixed environment, which has poor flexibility and cannot meet the user's various prediction needs in practical applications. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, electronic device and storage medium for determining the motion trajectory of an aircraft, so as to achieve the purpose of improving the accuracy of the predicted motion trajectory, generate corresponding predicted motion trajectories for different time periods to be predicted, and have strong flexibility, so as to facilitate meeting the various prediction needs of users in practical applications.

[0005] According to one aspect of the present invention, a method for determining a motion trajectory of an aircraft is provided, comprising:

[0006] Determine target environment data of a flight area to be predicted corresponding to a target aircraft within a time period to be predicted; wherein the time period to be predicted includes at least one time to be predicted;

[0007] Based on the target environment data, the initial state data of the target aircraft, the preset aircraft action plan and the pre-trained trajectory determination model, the predicted state data corresponding to each of the to-be-predicted moments in the to-be-predicted time period of the target aircraft is determined; wherein the trajectory determination model is obtained by pre-reinforcement learning of a neural network model;

[0008] Based on the predicted state data corresponding to each of the to-be-predicted moments, a predicted motion trajectory of the target aircraft in the to-be-predicted time period is generated.

[0009] According to another aspect of the present invention, there is provided a device for determining a motion trajectory of an aircraft, the device comprising:

[0010] A target environment data determination module, used to determine the target environment data of the flight area to be predicted corresponding to the target aircraft within the time period to be predicted; wherein the time period to be predicted includes at least one time to be predicted;

[0011] A prediction state data determination module is used to determine the prediction state data corresponding to each of the to-be-predicted moments of the target aircraft in the to-be-predicted time period based on the target environment data, the initial state data of the target aircraft, a preset aircraft action plan and a pre-trained trajectory determination model; wherein the trajectory determination model is obtained by pre-reinforcement learning of a neural network model;

[0012] The predicted operation trajectory generation module is used to generate the predicted movement trajectory of the target aircraft in the time period to be predicted based on the predicted state data corresponding to each of the predicted moments.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the aircraft motion trajectory determination method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the aircraft motion trajectory determination method described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention is to determine the target environment data of the flight area to be predicted corresponding to the target aircraft within the time period to be predicted; wherein the time period to be predicted includes at least one moment to be predicted; and based on the target environment data, the initial state data of the target aircraft, the preset aircraft action plan and the pre-trained trajectory determination model, determine the predicted state data corresponding to each moment to be predicted of the target aircraft in the time period to be predicted; wherein the trajectory determination model is obtained by pre-reinforcement learning of the neural network model; thereby, the predicted state data is determined taking into account the target environment data of the flight area to be predicted, which is conducive to improving the accuracy of the determined predicted state data; finally, based on the predicted state data corresponding to each moment to be predicted, the predicted motion trajectory of the target aircraft in the time period to be predicted is generated, which further improves the accuracy of the predicted motion trajectory, and generates corresponding predicted motion trajectories for different time periods to be predicted, which is flexible and convenient for meeting the various prediction needs of users in practical applications.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flow chart of a method for determining an aircraft motion trajectory provided according to an embodiment of the present invention;

[0022] Figure 2 is a flow chart of another method for determining a motion trajectory of an aircraft provided according to an embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of the structure of a device for determining a motion trajectory of an aircraft provided according to an embodiment of the present invention;

[0024] Figure 4 The present invention is a schematic diagram of the structure of an electronic device for implementing the method for determining the motion trajectory of an aircraft according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "etc." and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] It should be noted that the collection, collection, updating, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information and network security.

[0028] Figure 1 1 is a flow chart of a method for determining a motion trajectory of an aircraft provided according to an embodiment of the present invention. This embodiment is applicable to the case where the flight trajectory of an aircraft in a time period to be predicted is predicted by using the aircraft's state data. The method can be executed by an aircraft motion trajectory determination device, which can be implemented in the form of hardware and / or software.

[0029] like Figure 1 As shown, the method of this embodiment may specifically include:

[0030] S110, determining target environment data of a flight area to be predicted corresponding to a target aircraft within a time period to be predicted; wherein the time period to be predicted includes at least one time to be predicted.

[0031] Among them, the target aircraft is an aircraft whose motion trajectory needs to be predicted, and the time period to be predicted can be a future time period in which the target aircraft is expected to fly; the time period to be predicted can be divided into at least one moment to be predicted. Exemplarily, the time period to be predicted can be divided into equal time intervals, so that the moment corresponding to each division point is used as the moment to be predicted, and the length of the time interval is less than the length of the time period to be predicted. By determining the predicted state data of the target aircraft at each moment to be predicted, the predicted motion trajectory of the target aircraft in the time period to be predicted is formed. The target environment data includes at least one of the wind speed data, wind direction data and temperature data in the flight area to be predicted within the time period to be predicted. The flight area to be predicted is a predetermined aviation area that the target aircraft needs to fly in the time period to be predicted.

[0032] In this embodiment, weather forecast data of the flight area to be predicted within the time period to be predicted can be obtained, and the target environment data can be determined based on the weather forecast data. Specifically, the weather data corresponding to the time to be predicted in the weather forecast data can be extracted as the target environment data.

[0033] S120, determining predicted state data corresponding to each to-be-predicted moment in a to-be-predicted time period of the target aircraft based on the target environment data, the initial state data of the target aircraft, a preset aircraft action plan, and a pre-trained trajectory determination model.

[0034] The trajectory determination model is obtained by reinforcing the neural network model in advance. The preset aircraft action plan consists of preset action data of the joystick of the target aircraft corresponding to each time to be predicted. The initial state data may be state data corresponding to the preset starting position of the target aircraft entering the flight area to be predicted. The predicted state data is the state data obtained by predicting the target aircraft at the time to be predicted. The state data includes at least one of the position, attitude and speed of the target aircraft.

[0035] Optionally, the neural network model is a network model that uses a residual network model as a specific network framework and is constructed by connecting multiple layers of residual network models. Exemplarily, the neural network model can be constructed by connecting five layers of residual network models.

[0036] In this embodiment, based on the target environment data, the initial state data of the target aircraft, the preset aircraft action plan and the pre-trained trajectory determination model, the implementation method of determining the predicted state data corresponding to each to-be-predicted moment of the target aircraft in the to-be-predicted time period includes: traversing each to-be-predicted moment in the to-be-predicted time period in order from front to back; for the current to-be-predicted moment currently traversed, based on the target environment data, the initial state data of the target aircraft and the preset aircraft action plan, determining the current input data corresponding to the current to-be-predicted moment currently traversed, inputting the current input data into the pre-trained trajectory determination model, and obtaining the predicted state data corresponding to the current to-be-predicted moment.

[0037] Specifically, in order from the time of the to-be-predicted moment to the end, each to-be-predicted moment in the to-be-predicted time period is traversed in turn to determine the predicted state data corresponding to the current to-be-predicted moment that is currently traversed. Optionally, if the current to-be-predicted moment is the first to-be-predicted moment in the to-be-predicted time period, the initial state data, the moment environment data and the moment action data corresponding to the first to-be-predicted moment are used as current input data and input into the pre-trained trajectory determination model, and the predicted state data corresponding to the first to-be-predicted moment of the target aircraft is output. If the to-be-predicted moment of the current to-be-predicted state data is a to-be-predicted moment other than the first to-be-predicted moment in the to-be-predicted time period, the predicted state data corresponding to the previous to-be-predicted moment, the moment action data and the moment environment data of the current to-be-predicted moment of the target aircraft are used as current input data and input into the pre-trained trajectory determination model, and the predicted state data of the target aircraft at the current to-be-predicted moment is output.

[0038] In this embodiment, the predicted state data corresponding to the previous moment to be predicted is used as the input data corresponding to the current moment to be predicted, and the moment action data and moment environment data of the current moment to be predicted are combined, thereby taking into account the environmental information of the current moment to be predicted, which is beneficial to improve the accuracy of the predicted state data determined for each moment to be predicted.

[0039] S130: Generate a predicted motion trajectory of the target aircraft in the predicted time period based on the predicted state data corresponding to each predicted time moment.

[0040] Specifically, the predicted state data corresponding to each time to be predicted and the time sequence of the time to be predicted can be combined, and the predicted motion trajectory can be obtained based on the combined data. Exemplarily, data fitting can be performed on the combined data, and the obtained fitting trajectory can be determined as the predicted motion trajectory.

[0041] The technical solution of the embodiment of the present invention is to determine the target environment data of the flight area to be predicted corresponding to the target aircraft within the time period to be predicted; wherein the time period to be predicted includes at least one moment to be predicted; and based on the target environment data, the initial state data of the target aircraft, the preset aircraft action plan and the pre-trained trajectory determination model, determine the predicted state data corresponding to each moment to be predicted of the target aircraft in the time period to be predicted; wherein the trajectory determination model is obtained by pre-reinforcement learning of the neural network model; thereby, the predicted state data is determined taking into account the target environment data of the flight area to be predicted, which is conducive to improving the accuracy of the determined predicted state data; finally, based on the predicted state data corresponding to each moment to be predicted, the predicted motion trajectory of the target aircraft in the time period to be predicted is generated, which further improves the accuracy of the predicted motion trajectory, and generates corresponding predicted motion trajectories for different time periods to be predicted, which is flexible and convenient for meeting the various prediction needs of users in practical applications.

[0042] Figure 2 It is a flow chart of another method for determining the motion trajectory of an aircraft provided according to an embodiment of the present invention. On the basis of the above embodiment, optionally, the trajectory determination model includes a state transition network; the reinforcement learning process of the trajectory determination model includes: obtaining a historical operation data set of the target aircraft; wherein the historical operation data set is composed of a plurality of initial real state data, a plurality of action data groups, and a plurality of environment data groups; the action data group contains at least one historical action data, and the environment data group contains at least one historical environment data; based on the historical operation data set, the state transition network to be trained is trained, and the state transition network after the training is obtained as the trajectory determination model. The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here. Figure 2 As shown, the method includes:

[0043] S210, obtaining a historical operation data set of the target aircraft; wherein the historical operation data set consists of a plurality of initial real state data, a plurality of action data groups and a plurality of environment data groups; the action data group contains at least one historical action data, and the environment data group contains at least one historical environment data.

[0044] Optionally, the trajectory determination model includes a state transition network. The state transition network is a neural network. The historical operation data set may be actual operation data of the target aircraft in a historical flight time period. The historical environmental data may be at least one of the real wind direction, real wind speed and real temperature in the historical flight time period.

[0045] In a specific implementation, the real state data, real action data and environmental data of the target aircraft corresponding to the historical flight time period closest to the current moment can be obtained to form a historical operation data set. In order to facilitate the subsequent model training process, the data in the acquired historical operation data set can be preprocessed. Specifically, before training the state transition network to be trained based on the historical operation data set, it also includes: performing a data cleaning operation on the data in the historical operation data set, and updating the historical operation data set based on the data obtained after the data cleaning operation.

[0046] The data cleaning operation includes at least one of missing value processing, outlier processing, erroneous data processing and duplicate data processing.

[0047] In a specific implementation, the specific method of processing missing values ​​can be to fill the missing values ​​in the historical operation data set through the median, mean or mode of the data in the historical operation data set; or, the average of two adjacent values ​​adjacent to the missing value can be determined, and the average value can be filled as the missing value. For example, if there are missing values ​​in the state data group, the median, mean or mode of the state data group can be determined, and the missing state data can be filled with the median, mean or mode. The specific method of processing outliers can be to delete outliers or repair outliers based on the values ​​adjacent to the outliers. The method of processing duplicate data can be to delete duplicate data. The method of processing erroneous data can be to delete erroneous data.

[0048] This embodiment performs data cleaning operations on the data in the historical operation data set, thereby facilitating the effective completion of the model training process, which is beneficial to improving the accuracy of the model training results and reducing errors.

[0049] S220: Based on the historical operation data set, the state transition network to be trained is trained, and the state transition network after training is obtained as a trajectory determination model.

[0050] In this embodiment, in order to accurately and effectively train the model of the state transition network, the data in the historical operation data set can be aligned. Specifically, the data in the action data group and the environment data group are aligned based on the data generation time to establish the data correspondence between the historical action data and the historical environment data at different data generation times. The initial real state data is the state data generated at the initial data generation time of the historical flight time period.

[0051] In a specific implementation, the implementation method of training the state transfer network to be trained based on the historical running data set specifically includes: traversing each starting real state data; for each traversal of the starting real state data, determining the input data based on the starting real state data, the action data group corresponding to the starting real state data, and the environment data group, inputting the input data into the state transfer network to be trained, outputting the historical predicted state data corresponding to the input data, determining the current reward value corresponding to the input data based on the discriminator network to be trained, updating the input data based on the historical predicted state data, and transmitting the updated input data value to the state transfer network to be trained, outputting the historical predicted state data corresponding to the updated input data, and repeating the execution of updating the input data based on the historical predicted state data, Output the historical predicted state data and determine the current reward value, and stop the operation until the preset end condition is met. Update the network parameters of the state transition network to be trained based on the current reward values ​​obtained in the current traversal, and update the network parameters of the discriminator network to be trained based on the historical predicted state data obtained in the current traversal, the state data group corresponding to the initial real state data, the action data group and the environment data group, so as to determine the next reward value based on the discriminator network after the network parameters are updated in the next traversal operation, until the training is terminated when the preset training condition is met, and the traversal of the initial real state data is stopped; wherein the state data group is a data group in which the data generated at the time of initial data generation is the initial real state data; the trajectory determination model is composed of the state transition network after the training is completed.

[0052] It should be noted that the data generation time between the historical action data in each action data group and the data generation time between the historical environment data in each environment data group are all continuous time with equal time intervals. For example, if the action data group includes 10 historical real action data, the data generation time corresponding to each historical real action data is distributed with equal time intervals.

[0053] In this embodiment, each starting real state data can be traversed, and for the first starting real state data traversed, the historical action data and historical environment data corresponding to the data generation time of the starting real state data are determined in the action data group and the environment data group corresponding to the starting real state data. Exemplarily, if the starting real state data is data generated at the starting time of the historical flight time period, the historical action data and historical environment data generated at the starting time in the action data group and the environment data group corresponding to the starting real state data can be determined. The starting real state data, the historical action data and the historical environment data corresponding to the starting real state data are used as input data, input into the state transition network to be trained, and the historical predicted state data corresponding to the input data is output. Among them, the historical predicted state data obtained based on the starting real state data is the predicted data generated for the target aircraft at the next moment of the data generation time of the starting real state data. Based on the historical predicted state data and the pre-constructed discriminator network to be trained, the current reward value corresponding to the input data is determined.

[0054] The historical prediction state data, the historical action data corresponding to the historical prediction state data, and the historical environment data are updated as input data, and the updated input data number is input into the state transfer network to be trained, and the historical prediction state data corresponding to the updated input data is output. The operations of updating the input data based on the historical prediction state data, outputting the historical prediction state data, and determining the current reward value are repeatedly performed until the preset end condition is met, then the operation is stopped to end this traversal. Exemplarily, the preset end condition is that the number of repeated operations reaches a preset threshold number of times.

[0055] The adjustment parameters of the state transfer network can be determined through the PPO (Proximal Policy Optimization) algorithm and the current reward values ​​obtained by the current traversal, and the network parameters of the state transfer network can be adjusted based on the adjustment parameters to maximize the cumulative reward.

[0056] Furthermore, based on each historical predicted state data obtained by the current traversal, the state data group corresponding to the initial real state data, the action data group and the environment data group, the network parameters of the discriminator network to be trained are updated, so that in the next traversal operation, the next reward value is determined based on the discriminator network after the network parameters are updated, until the training ends when the preset training conditions are met, and the traversal of the initial real state data is stopped. Among them, the preset training conditions include the convergence of the loss function corresponding to the discriminator network.

[0057] In a specific implementation, the network parameters of the discriminator network to be trained are updated based on the various historical predicted state data obtained in the current traversal, the state data group corresponding to the initial real state data, the action data group and the environment data group, including: based on the generative network adversarial algorithm, the various historical predicted state data obtained in the current traversal, the state data group corresponding to the initial real state data, the action data group and the environment data group, the network parameters of the discriminator network to be trained are updated.

[0058] Specifically, each historical prediction state data currently obtained is traversed, and the currently traversed historical prediction state data, the historical real state data in the state data group corresponding to the historical prediction state data, the historical action data and the historical environment data are input into the discriminator network to be trained, and the loss function corresponding to the discriminator network is determined based on the output result of the discriminator network to be trained, and the network parameters of the discriminator network are adjusted based on the Adam optimization algorithm; and the next reward value corresponding to the state transfer network at the next traversal is determined by the discriminator network after adjusting the network parameters.

[0059] In a specific implementation, based on each historical prediction state data obtained in each traversal, the network parameters corresponding to the discriminator network are continuously adjusted until the training is completed when the preset training conditions are met, and the traversal of the state data group is stopped, and the trajectory determination model is formed by the state transition network after the training. Among them, the preset training conditions include the convergence of the loss function corresponding to the discriminator network. The loss function can be a cross entropy loss function.

[0060] This embodiment trains the discriminator network by generating a network adversarial algorithm, so that the discriminator network can more accurately distinguish between predicted state data and real state data, which is conducive to improving the accuracy of determining the trajectory determination model.

[0061] S230, determining target environment data of a flight area to be predicted corresponding to a target aircraft within a time period to be predicted; wherein the time period to be predicted includes at least one time to be predicted.

[0062] S240, based on the target environment data, the initial state data of the target aircraft, the preset aircraft action plan and the pre-trained trajectory determination model, determine the predicted state data corresponding to each predicted moment of the target aircraft in the predicted time period; wherein the trajectory determination model is obtained by pre-reinforcement learning of the neural network model.

[0063] S250: Generate a predicted motion trajectory of the target aircraft in the predicted time period based on the predicted state data corresponding to each predicted time moment.

[0064] In this embodiment, the trajectory determination model is trained by reinforcement learning so that the trajectory determination model can handle environments with high uncertainty and dynamics, so as to adapt to complex environments in the real world and accurately determine the trajectory of the aircraft.

[0065] Figure 3 1 is a schematic diagram of the structure of an aircraft motion trajectory determination device provided according to an embodiment of the present invention, and the device is used to execute the aircraft motion trajectory determination method provided in any of the above embodiments. The device and the aircraft motion trajectory determination method of the above embodiments belong to the same inventive concept, and the details not described in detail in the embodiment of the aircraft motion trajectory determination device can refer to the embodiment of the above aircraft motion trajectory determination method. Figure 3 As shown, the device comprises:

[0066] The target environment data determination module 10 is used to determine the target environment data of the flight area to be predicted corresponding to the target aircraft within the time period to be predicted; wherein the time period to be predicted includes at least one time to be predicted;

[0067] The prediction state data determination module 11 is used to determine the prediction state data corresponding to each to-be-predicted moment of the target aircraft in the to-be-predicted time period based on the target environment data, the initial state data of the target aircraft, the preset aircraft action plan and the pre-trained trajectory determination model; wherein the trajectory determination model is obtained by pre-reinforcement learning of the neural network model;

[0068] The predicted operation trajectory generating module 12 is used to generate the predicted movement trajectory of the target aircraft in the time period to be predicted based on the predicted state data corresponding to each predicted moment.

[0069] Based on any optional technical solution in the embodiment of the present invention, optionally, the predicted state data determination module 11 includes:

[0070] The to-be-predicted-moment traversal submodule is used to traverse each to-be-predicted moment in the to-be-predicted time period in order from the front to the back of the to-be-predicted moment;

[0071] The current input data determination submodule is used to determine the current input data corresponding to the current time to be predicted of the current traversal based on the target environment data, the initial state data of the target aircraft and the preset aircraft action plan, and input the current input data into the pre-trained trajectory determination model to obtain the predicted state data corresponding to the current time to be predicted.

[0072] On the basis of any optional technical solution in the embodiments of the present invention, optionally, the target environment data includes at least one of wind speed data, wind direction data and temperature data in the flight area to be predicted within the predicted time period; the preset aircraft action plan is composed of preset action data of the joystick of the target aircraft corresponding to each moment to be predicted.

[0073] Based on any optional technical solution in the embodiments of the present invention, optionally, the trajectory determination model includes a state transition network; and the device further includes:

[0074] A historical operation data set acquisition module, used to acquire the historical operation data set of the target aircraft; wherein the historical operation data set is composed of a plurality of initial real state data, a plurality of action data groups and a plurality of environment data groups; the action data group contains at least one historical action data, and the environment data group contains at least one historical environment data;

[0075] The trajectory determination model composition module is used to train the state transition network to be trained based on the historical operation data set, and obtain the state transition network after the training as the trajectory determination model.

[0076] Based on any optional technical solution in the embodiments of the present invention, optionally, the trajectory determination model composition module includes:

[0077] The current reward value determination submodule is used to traverse each starting real state data; for each traversal of the starting real state data, the input data is determined based on the starting real state data, the action data group corresponding to the starting real state data, and the environment data group, the input data is input to the state transfer network to be trained, and the historical prediction state data corresponding to the input data is output. Based on the discriminator network to be trained, the current reward value corresponding to the input data is determined, the input data is updated based on the historical prediction state data, and the updated input data value is input to the state transfer network to be trained, and the historical prediction state data corresponding to the updated input data is output, and the updating of the input data based on the historical prediction state data, the output of the historical prediction state data and the determination of the current reward value are repeated. The operation of the previous reward value is stopped until the preset end condition is met, and the network parameters of the state transition network to be trained are updated based on the current reward values ​​obtained in the current traversal, and the network parameters of the discriminator network to be trained are updated based on the historical predicted state data obtained in the current traversal, the state data group corresponding to the initial real state data, the action data group and the environment data group, so as to determine the next reward value based on the discriminator network after the updated network parameters in the next traversal operation, until the training is terminated when the preset training condition is met, and the traversal of the initial real state data is stopped; wherein the state data group is a data group whose data generated at the time of initial data generation is the initial real state data; the trajectory determination model is composed of the state transition network after the training is completed.

[0078] Based on any optional technical solution in the embodiments of the present invention, optionally, the current reward value determination submodule includes:

[0079] The discriminator network training unit is used to update the network parameters of the discriminator network to be trained based on the generation network adversarial algorithm, the various historical predicted state data obtained by the current traversal, the state data group corresponding to the initial real state data, the action data group and the environment data group.

[0080] Based on any optional technical solution in the embodiments of the present invention, optionally, the following further includes:

[0081] A data cleaning module is used to perform a data cleaning operation on the data in the historical operation data set before training the state transfer network to be trained based on the historical operation data set, and update the historical operation data set based on the data obtained after the data cleaning operation;

[0082] The data cleaning operation includes at least one of missing value processing, outlier processing, erroneous data processing and duplicate data processing.

[0083] The technical solution of the embodiment of the present invention is to determine the target environment data of the flight area to be predicted corresponding to the target aircraft within the time period to be predicted; wherein the time period to be predicted includes at least one moment to be predicted; and based on the target environment data, the initial state data of the target aircraft, the preset aircraft action plan and the pre-trained trajectory determination model, determine the predicted state data corresponding to each moment to be predicted of the target aircraft in the time period to be predicted; wherein the trajectory determination model is obtained by pre-reinforcement learning of the neural network model; thereby, the predicted state data is determined taking into account the target environment data of the flight area to be predicted, which is conducive to improving the accuracy of the determined predicted state data; finally, based on the predicted state data corresponding to each moment to be predicted, the predicted motion trajectory of the target aircraft in the time period to be predicted is generated, which further improves the accuracy of the predicted motion trajectory, and generates corresponding predicted motion trajectories for different time periods to be predicted, which is flexible and convenient for meeting the various prediction needs of users in practical applications.

[0084] It is worth noting that in the above-mentioned embodiment of the aircraft motion trajectory determination device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0085] Figure 41 is a schematic diagram of the structure of an electronic device that implements the method for determining the motion trajectory of an aircraft according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0086] like Figure 4 As shown, the electronic device 20 includes at least one processor 21, and a memory connected to the at least one processor 21, such as a read-only memory (ROM) 22, a random access memory (RAM) 23, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 21 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 22 or the computer program loaded from the storage unit 28 to the random access memory (RAM) 23. In RAM23, various programs and data required for the operation of the electronic device 20 can also be stored. The processor 21, ROM22 and RAM23 are connected to each other through a bus 24. An input / output (I / O) interface 25 is also connected to the bus 24.

[0087] A number of components in the electronic device 20 are connected to the I / O interface 25, including: an input unit 26, such as a keyboard, a mouse, etc.; an output unit 27, such as various types of displays, speakers, etc.; a storage unit 28, such as a disk, an optical disk, etc.; and a communication unit 29, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 29 allows the electronic device 20 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0088] The processor 21 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 21 executes the various methods and processes described above, such as the aircraft motion trajectory determination method.

[0089] In some embodiments, the aircraft motion trajectory determination method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 28. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 20 via the ROM 22 and / or the communication unit 29. When the computer program is loaded into the RAM 23 and executed by the processor 21, one or more steps of the aircraft motion trajectory determination method described above may be performed. Alternatively, in other embodiments, the processor 21 may be configured to perform the aircraft motion trajectory determination method in any other appropriate manner (e.g., by means of firmware).

[0090] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0092] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0093] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0094] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0095] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0096] This embodiment also provides a computer program product, including a computer program, which, when executed by a processor, implements the method for determining the motion trajectory of an aircraft as provided in any embodiment of the present application.

[0097] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0098] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0099] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining an aircraft motion trajectory, characterized in that: include: Determine target environment data of a flight area to be predicted corresponding to a target aircraft within a time period to be predicted; wherein the time period to be predicted includes at least one time to be predicted; Based on the target environment data, the initial state data of the target aircraft, the preset aircraft action plan and the pre-trained trajectory determination model, the predicted state data corresponding to each of the to-be-predicted moments in the to-be-predicted time period of the target aircraft is determined; wherein the trajectory determination model is obtained by pre-reinforcement learning of a neural network model; Based on the predicted state data corresponding to each of the to-be-predicted moments, a predicted motion trajectory of the target aircraft in the to-be-predicted time period is generated.

2. The method according to claim 1, characterized in that The step of determining the predicted state data corresponding to each of the to-be-predicted moments in the to-be-predicted time period of the target aircraft based on the target environment data, the initial state data of the target aircraft, a preset aircraft action plan, and a pre-trained trajectory determination model comprises: According to the order of the time of the to-be-predicted moment from the front to the back, traverse each to-be-predicted moment in the to-be-predicted time period in turn; For the current moment to be predicted of the current traversal, based on the target environment data, the initial state data of the target aircraft and the preset aircraft action plan, the current input data corresponding to the current moment to be predicted of the current traversal is determined, and the current input data is input into a pre-trained trajectory determination model to obtain the predicted state data corresponding to the current moment to be predicted.

3. The method according to claim 1, characterized in that The target environment data includes at least one of wind speed data, wind direction data and temperature data in the flight area to be predicted within the predicted time period; the preset aircraft action plan is composed of preset action data of the joystick of the target aircraft corresponding to each predicted moment.

4. The method according to claim 1, characterized in that: The trajectory determination model includes a state transition network; The reinforcement learning process of the trajectory determination model includes: Acquire a historical operation data set of the target aircraft; wherein the historical operation data set consists of a plurality of initial real state data, a plurality of action data groups and a plurality of environment data groups; the action data group contains at least one historical action data, and the environment data group contains at least one historical environment data; Based on the historical operation data set, the state transition network to be trained is trained, and the state transition network after training is obtained as the trajectory determination model.

5. The method according to claim 4, characterized in that The training of the state transition network to be trained based on the historical operation data set, and obtaining the state transition network after training as the trajectory determination model, includes: Traverse each starting real state data; For the starting real state data of each traversal, determine the input data based on the starting real state data, the action data group and the environment data group corresponding to the starting real state data, input the input data to the state transition network to be trained, output the historical prediction state data corresponding to the input data, determine the current reward value corresponding to the input data based on the discriminator network to be trained, update the input data based on the historical prediction state data, and send the updated input data value to the state transition network to be trained, output the historical prediction state data corresponding to the updated input data, and repeat the updating of the input data based on the historical prediction state data, outputting the historical prediction state data and determining the current reward value. The operation of the previous reward value is stopped until the preset end condition is met, the network parameters of the state transition network to be trained are updated based on the current reward values ​​obtained by the current traversal, and the network parameters of the discriminator network to be trained are updated based on the historical predicted state data obtained by the current traversal, the state data group corresponding to the initial real state data, the action data group and the environment data group, so as to determine the next reward value based on the discriminator network after the network parameters are updated in the next traversal operation, until the training ends when the preset training condition is met, and the traversal of the initial real state data is stopped; wherein, the state data group is a data group whose data generated at the time of initial data generation is the initial real state data; The trajectory determination model is composed of a state transition network after training.

6. The method according to claim 5, characterized in that The updating of the network parameters of the discriminator network to be trained based on each historical predicted state data obtained by the current traversal, the state data group corresponding to the initial real state data, the action data group and the environment data group comprises: Based on the generative network adversarial algorithm, the various historical predicted state data obtained by the current traversal, the state data group corresponding to the initial real state data, the action data group and the environment data group, the network parameters of the discriminator network to be trained are updated.

7. The method according to claim 4, characterized in that Before training the state transfer network to be trained based on the historical operation data set, the method further includes: Performing a data cleaning operation on the data in the historical operation data set, and updating the historical operation data set based on the data obtained after the data cleaning operation; The data cleaning operation includes at least one of missing value processing, outlier processing, erroneous data processing and duplicate data processing.

8. An aircraft motion trajectory determination device, characterized in that: include: A target environment data determination module, used to determine the target environment data of the flight area to be predicted corresponding to the target aircraft within the time period to be predicted; wherein the time period to be predicted includes at least one time to be predicted; A prediction state data determination module is used to determine the prediction state data corresponding to each of the to-be-predicted moments of the target aircraft in the to-be-predicted time period based on the target environment data, the initial state data of the target aircraft, a preset aircraft action plan and a pre-trained trajectory determination model; wherein the trajectory determination model is obtained by pre-reinforcement learning of a neural network model; The predicted operation trajectory generation module is used to generate the predicted movement trajectory of the target aircraft in the time period to be predicted based on the predicted state data corresponding to each of the predicted moments.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the aircraft motion trajectory determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the aircraft motion trajectory determination method according to any one of claims 1 to 7 when executed.

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