Tracking result prediction method and device, electronic equipment, storage medium and program

Abstract

The embodiment of the invention discloses a tracking result prediction method and device, electronic equipment, a storage medium and a program. The method comprises the following steps: determining target actual state data of a target missile at a target moment and an actual motion data set of a target tracking object; determining future prediction state data of the target missile at a future moment based on the target actual state data, the actual motion data set and a pre-trained state determination model; wherein the state determination model is a pre-trained neural network model; and determining a prediction tracking result of the target missile on the target tracking object based on the future prediction state data and the actual motion data set. According to the technical scheme of the embodiment of the invention, the neural network model does not need numerical integral calculation and differential calculation, so that the introduction of accumulated errors is avoided, the calculation amount is reduced, and the efficiency and accuracy of determining the prediction state data are improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of aircraft control technology, and in particular to a tracking result prediction method, device, electronic device, storage medium and program. Background Art

[0002] The analysis of fluid dynamics during missile launch is an important part of missile launcher design. In the prior art, the motion equation is solved by numerical integration method or numerical differentiation method, and a simulation framework is built based on the solution to obtain the missile dynamics model. The missile dynamics model is used to predict the tracking results of the target tracking object during the missile's motion.

[0003] However, in the process of implementing the present invention, it was found that the prior art has at least the following technical problems: the use of numerical integration to solve the motion equation leads to the introduction of cumulative errors, resulting in poor accuracy of the predicted tracking results; the use of numerical differentiation methods has a large amount of calculations and low efficiency in determining the tracking results. Summary of the invention

[0004] The embodiments of the present invention provide a tracking result prediction method, device, electronic device, storage medium and program to achieve the purpose of improving the accuracy of predicting tracking results.

[0005] According to one aspect of the present invention, a tracking result prediction method is provided, comprising:

[0006] Determine the target actual state data of the target missile at the target time and the actual motion data group of the target tracking object;

[0007] Determine the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and a pre-trained state determination model; wherein the state determination model is a pre-trained neural network model;

[0008] Based on the future predicted state data and the actual motion data group, a predicted tracking result of the target missile on the target tracking object is determined.

[0009] According to another aspect of the present invention, a tracking result prediction device is provided, the device comprising:

[0010] A first data determination module is used to determine the target actual state data of the target missile at the target time and the actual motion data group of the target tracking object;

[0011] A second data determination module is used to determine the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and a pre-trained state determination model; wherein the state determination model is a pre-trained neural network model;

[0012] The prediction tracking result determination module is used to determine the prediction tracking result of the target missile on the target tracking object based on the future prediction state data and the actual motion data group.

[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 tracking result prediction 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 tracking result prediction method described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the tracking result prediction method according to any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention determines the target actual state data of the target missile at the target moment and the actual motion data group of the target tracking object; based on the target actual state data, the actual motion data group and a pre-trained state determination model, determines the future predicted state data of the target missile at the future moment; wherein the state determination model is a pre-trained neural network model; since the neural network model does not need to perform numerical integral calculation or numerical differential calculation, it avoids introducing cumulative errors, reduces the amount of calculation, and is conducive to improving the efficiency and accuracy of determining the predicted state data; and based on the future predicted state data and the actual motion data group, determines the predicted tracking result of the target missile for the target tracking object, thereby achieving the effect of improving the accuracy of the predicted tracking result.

[0020] 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

[0021] 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.

[0022] Figure 1 is a flow chart of a tracking result prediction method provided according to an embodiment of the present invention;

[0023] Figure 2 is a flowchart of another tracking result prediction method provided according to an embodiment of the present invention;

[0024] Figure 3 is a structural schematic diagram of a tracking result prediction device provided according to an embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of the structure of an electronic device for implementing the tracking result prediction method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 1 1 is a flow chart of a tracking result prediction method provided according to an embodiment of the present invention. This embodiment can be applied to predicting the tracking result of a target missile on a target tracking object. The method can be executed by a tracking result prediction device, which can be implemented in the form of hardware and / or software.

[0030] like Figure 1 As shown, the method of this embodiment may specifically include:

[0031] S110, determining the target actual state data of the target missile at the target time and the actual motion data group of the target tracking object.

[0032] Among them, the target missile is the missile for which the tracking result needs to be predicted. The target moment may be the moment when the target missile leaves the launch device. For example, in the scenario of launching a missile through the air, the target moment may be the moment when the target missile leaves the aircraft. In the scenario of launching through the roadbed, the target moment may be the moment when the target missile leaves the ground launch device. The target actual state data may be at least one of the speed data, position data and attitude data at the target moment. The actual motion data group may be an array composed of the actual motion point data of the target tracking object at different times, and the actual motion point data includes position data and speed data. It should be noted that those skilled in the art may pre-set the target actual motion point data corresponding to the target moment in the actual motion data group according to the actual application situation.

[0033] In a specific implementation, the target actual state data of the target missile at the target moment can be determined based on different launch modes of the target missile. For example, if the launch mode is air launch, the speed, position and attitude of the aircraft at the moment when the target missile leaves the aircraft can be determined according to the pre-set propulsion rules when the aircraft launches the target missile; the target actual state data of the target missile is determined based on the speed, position and attitude of the aircraft. Furthermore, the historical motion data of the target tracking object in the historical time period can be obtained in advance, and the historical motion data is used as the actual motion data group of the target tracking object.

[0034] S120, determining the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and the pre-trained state determination model.

[0035] The state determination model is a pre-trained neural network model. The future predicted state data is the predicted state data of the target missile at a future time after the target time. It should be noted that the time interval between the future time and the target time is determined by the trained state determination model.

[0036] In a specific implementation, the method for determining the future predicted state data may be: inputting the target actual state data and the target actual motion point data into a pre-trained state determination model, and the state determination model outputs the future predicted state data at a future moment.

[0037] S130: Determine the predicted tracking result of the target missile on the target tracking object based on the future predicted state data and the actual motion data group.

[0038] Optionally, based on the future predicted state data and the actual motion data group, the predicted tracking result of the target missile for the target tracking object is determined, including: determining the future actual motion point data based on the actual motion data group; when the future predicted state data and the future actual motion point data meet preset conditions, determining that the predicted tracking result of the target missile for the target tracking object is a tracking success; wherein the future predicted state data includes the future predicted position of the target missile at a future moment; the future actual motion point data includes the future object position of the target tracking object at a future moment; the preset condition includes that the distance between the future predicted position and the future object position is less than a preset distance threshold.

[0039] In a specific implementation, based on the time interval between the target moment and the future moment, the actual motion point data at the time interval from the generation moment of the target actual motion point data can be determined in the actual motion data group as the future actual motion point data. The future predicted state data can be compared with the future actual motion point data to determine the predicted tracking result based on the comparison result. The predicted tracking result includes tracking success and tracking failure.

[0040] Specifically, the distance between the future predicted position and the future object position can be determined. When the distance is less than a preset distance threshold, it means that the target missile will catch up with the target tracking object at a future time, and the predicted tracking result is determined to be a successful tracking. If the distance is greater than or equal to the preset distance threshold, it means that the target missile will not catch up with the target tracking object at a future time, and it can be determined whether other predicted moments in the future time period can be successfully tracked.

[0041] Furthermore, the height of the future predicted position is determined. When the height is less than a preset height threshold, it indicates that the target missile has not caught up with the target tracking object at the future time.

[0042] It should be noted that those skilled in the art may determine specific values ​​of the preset distance threshold and the preset height threshold according to actual application conditions, and this embodiment does not limit this.

[0043] This embodiment compares the distance between the future predicted position and the future object position with a preset distance threshold to quickly determine whether the target navigation is successfully tracked at the future moment.

[0044] Furthermore, the method provided in this embodiment also includes: when the future predicted state data and the future actual motion point data do not meet the preset conditions, determining the input data based on the future predicted state data and the actual motion data group, inputting the input data into the state determination model to obtain the latest predicted state data, and updating the future predicted state data based on the latest predicted state data; when the updated future predicted state data meets the preset conditions, determining that the predicted tracking result of the target missile for the target tracking object is a successful tracking; when the updated future predicted state data does not meet the preset conditions, repeating the above-mentioned operations of determining the input data, inputting the input data into the state determination model, and updating the future predicted state data based on the latest predicted state data obtained, until the preset stop condition is reached, the operation stop is repeated.

[0045] In order to improve the accuracy of the prediction and tracking results, it is possible to determine whether the target missile can be successfully tracked at other moments in the future time period. Specifically, based on the time interval between the future moment and the target moment, the actual motion point data whose time distance from the target actual motion point data is the time interval can be determined in the actual motion data group as the future actual motion point data corresponding to the future moment.

[0046] In a specific implementation, the future predicted state data and the future actual motion point data can be used as input data and input into the state determination model, the latest predicted state data can be output, and the future predicted state data can be updated to the latest predicted state data. In order to accurately determine the prediction and tracking results, it can be determined whether the updated future predicted state data and the future actual motion point data meet the preset conditions. If they do, it means that the tracking is successful. If not, the above steps can be repeated to determine the actual motion point data at the next moment of the time interval from the future moment, and based on the actual motion point data at the next moment of the time interval from the future moment and the updated future predicted state data, the predicted state data at the next moment of the time interval from the future moment is determined as the latest predicted state data to update the future predicted state data; and, based on the preset conditions, it is determined whether the target missile is tracked successfully, and the operation stop is repeated until the preset stop condition is reached.

[0047] This embodiment repeatedly executes the operations of determining input data, determining a model for the input data input state, and updating future predicted state data based on the latest predicted state data obtained. It can determine whether the target navigation tracks the target tracking object at multiple moments in the future time period to improve the accuracy of determining the predicted tracking results.

[0048] Optionally, the preset stop condition includes: the number of executions of the repeated operation reaches a preset number threshold; and / or the updated future prediction state data meets the preset condition.

[0049] Specifically, in the process of repeatedly executing the above-mentioned operations of determining input data, determining the input data input state model, and updating the future prediction state data based on the latest prediction state data obtained, if it is determined that the updated future prediction state data meets the preset conditions, the prediction tracking result can be determined to be a tracking success, and the repeated execution operation is stopped. If the number of executions of the repeated execution operation reaches a preset number threshold, the repeated execution operation can be stopped, and the prediction tracking result can be determined to be a tracking failure.

[0050] In this embodiment, by setting a preset stop condition, it is possible to avoid repeatedly executing the operations of determining input data, determining a model for the input data input state, and updating future prediction state data based on the latest prediction state data obtained, which is beneficial to improving the efficiency of determining prediction tracking results.

[0051] The technical solution of the embodiment of the present invention determines the target actual state data of the target missile at the target moment and the actual motion data group of the target tracking object; based on the target actual state data, the actual motion data group and a pre-trained state determination model, determines the future predicted state data of the target missile at the future moment; wherein the state determination model is a pre-trained neural network model; since the neural network model does not need to perform numerical integral calculation or numerical differential calculation, it avoids introducing cumulative errors, reduces the amount of calculation, and is conducive to improving the efficiency and accuracy of determining the predicted state data; and based on the future predicted state data and the actual motion data group, determines the predicted tracking result of the target missile for the target tracking object, thereby achieving the effect of improving the accuracy of the predicted tracking result.

[0052] Figure 2It is a flowchart of another tracking result prediction method provided by an embodiment of the present invention. In this embodiment, based on the above embodiment, optionally, the state determination model includes a state transition network; before determining the future predicted state data of the target missile at a future moment based on the target actual state data, the actual motion data group and the pre-trained state determination model, it also includes: obtaining the historical operation data set of the target missile; based on the historical operation data set and the reinforcement learning algorithm, training the state transition network to be trained, and obtaining the state transition network after the training as the state determination model. Among them, 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:

[0053] S210, obtaining a historical operation data set of the target missile; training the state transition network to be trained based on the historical operation data set and the reinforcement learning algorithm, and obtaining the state transition network after the training as a state determination model.

[0054] Among them, the historical operation data set is composed of a historical state data group and a plurality of historical motion data groups generated in multiple historical time periods; the historical state data group includes the starting real state data corresponding to the starting motion moment of the target missile and the remaining real state data corresponding to the remaining motion moment, and the historical motion data group includes at least one historical real motion point data of the target tracking object.

[0055] In order to improve the prediction accuracy, data cleaning processing can be performed on the data in the historical state data group and the historical motion data group, wherein the data cleaning operation includes at least one of missing value processing, abnormal value processing, erroneous data processing and duplicate data processing.

[0056] In this embodiment, the data in the historical state data group and the historical motion data group may be aligned according to the data generation time to establish a data correspondence between the training state data and the training motion point data at different data generation times.

[0057] Optionally, the state transfer network is a neural network. Based on the historical operation data set and the reinforcement learning algorithm, the implementation method of training the state transfer network to be trained 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 and the historical motion data group corresponding to the starting real state data, 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 sending 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 basic The operation of updating input data, outputting historical prediction state data and determining the current reward value based on historical prediction state data 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 prediction state data obtained in the current traversal, the remaining real state data corresponding to the starting real state data, and the historical motion 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 starting real state data is stopped; the state transition network after the training is completed constitutes a trajectory determination model.

[0058] The preset training condition includes the convergence of the loss function corresponding to the discriminator network. The preset end condition includes the number of repetitions of the operations of updating input data based on historical prediction state data, outputting historical prediction state data, and determining the current reward value reaching a preset number threshold. In this embodiment, the state transition network after training is used as the state determination model.

[0059] Furthermore, the adjustment parameters of the state transfer network are determined through the PPO (Proximal Policy Optimization) algorithm and the current reward value, and the network parameters of the state transfer network are adjusted based on the adjustment parameters to maximize the cumulative reward.

[0060] In this embodiment, the state determination model is trained by reinforcement learning, so that the trained state determination model has stronger generalization ability.

[0061] S220, determining the target actual state data of the target missile at the target time and the actual motion data group of the target tracking object.

[0062] In this embodiment, before determining the future predicted state data of the target missile after the target moment based on the target actual state data, the actual motion data group and the pre-trained state determination model, it also includes: determining the target environment data of the target area to which the target missile belongs at the target moment; determining the future predicted state data of the target missile at the future moment based on the target actual state data, the actual motion data group and the pre-trained state determination model, including: obtaining the future predicted state data of the target missile at the future moment based on the target actual state data, the target motion data corresponding to the target moment in the actual motion data group, the target environment data and the state determination model.

[0063] Optionally, the target environment data includes at least one of wind speed data, wind direction data and temperature data of a target area to which the target missile belongs at a target time. The target area is a pre-set flight area of ​​the target missile.

[0064] In a specific implementation, determining the target environment data of the target area to which the target missile belongs at the target time includes: obtaining weather forecast data of the target area at the target time, and determining the target environment data based on the weather forecast data.

[0065] S230, determining the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and the pre-trained state determination model.

[0066] S240: Determine the predicted tracking result of the target missile on the target tracking object based on the future predicted state data and the actual motion data group.

[0067] In this embodiment, when determining the future predicted state data, the influence of environmental factors of the target area is taken into consideration, which is beneficial to improving the accuracy of the determined future predicted state data.

[0068] Figure 3 1 is a schematic diagram of the structure of a tracking result prediction device provided according to an embodiment of the present invention, and the device is used to execute the tracking result prediction method provided by any of the above embodiments. The device and the tracking result prediction method of the above embodiments belong to the same inventive concept, and the details not described in detail in the embodiment of the tracking result prediction device can refer to the embodiment of the above tracking result prediction method. Figure 3 As shown, the device comprises:

[0069] The first data determination module 10 is used to determine the target actual state data of the target missile at the target time and the actual motion data group of the target tracking object;

[0070] The second data determination module 11 is used to determine the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and the pre-trained state determination model; wherein the state determination model is a pre-trained neural network model;

[0071] The prediction tracking result determination module 12 is used to determine the prediction tracking result of the target missile on the target tracking object based on the future prediction state data and the actual motion data group.

[0072] Based on any optional technical solution in the embodiments of the present invention, optionally, the device further includes:

[0073] A target environment data determination module is used to determine the target environment data of the target area to which the target missile belongs at the target time before determining the future predicted state data generated by the target missile after the target time based on the target actual state data, the actual motion data group and the pre-trained state determination model;

[0074] The second data determination module 11 includes:

[0075] The future predicted state data determination submodule is used to obtain the future predicted state data of the target missile at a future moment based on the target actual state data, the target motion data corresponding to the target moment in the actual motion data group, the target environment data and the state determination model.

[0076] Based on any optional technical solution in the embodiment of the present invention, optionally, the prediction and tracking result determination module 12 includes:

[0077] The first tracking result determination submodule is used to determine the future actual motion point data based on the actual motion data group; when the future predicted state data and the future actual motion point data meet the preset conditions, determine that the predicted tracking result of the target missile for the target tracking object is a tracking success;

[0078] Among them, the future predicted state data includes the future predicted position of the target missile at the future moment; the future actual motion point data includes the future object position of the target tracking object at the future moment; the preset condition includes that the distance between the future predicted position and the future object position is less than the preset distance threshold.

[0079] Based on any optional technical solution in the embodiment of the present invention, optionally, the prediction and tracking result determination module 12 further includes:

[0080] A future prediction state data updating submodule is used to determine input data based on the future prediction state data and the actual motion data group when the future prediction state data and the future actual motion point data do not meet the preset conditions, input the input data into the state determination model to obtain the latest prediction state data, and update the future prediction state data based on the latest prediction state data;

[0081] The second tracking result determination submodule is used to determine that the predicted tracking result of the target missile for the target tracking object is a tracking success when the updated future predicted state data meets the preset conditions;

[0082] The repeated execution submodule is used to repeatedly execute the above operations of determining input data, inputting data input state determination model, and updating future prediction state data based on the latest prediction state data obtained when the updated future prediction state data does not meet the preset conditions, until the repeated execution operation stops when the preset stop condition is reached.

[0083] Based on any optional technical solution in the embodiments of the present invention, optionally, the preset stop condition includes:

[0084] The number of executions of the repeated operation reaches a preset number threshold; and / or,

[0085] The updated future prediction status data meets the preset conditions.

[0086] Based on any optional technical solution in the embodiments of the present invention, optionally, the state determination model includes a state transition network; and the device further includes:

[0087] The historical operation data set acquisition module is used to acquire the historical operation data set of the target missile before determining the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and the pre-trained state determination model; wherein the historical operation data set is composed of a historical state data group and a plurality of historical motion data groups generated in multiple historical time periods;

[0088] The model training module is used to train the state transition network to be trained based on the historical operation data set and the reinforcement learning algorithm, and obtain the state transition network after training as the state determination model.

[0089] The technical solution of the embodiment of the present invention determines the target actual state data of the target missile at the target moment and the actual motion data group of the target tracking object; based on the target actual state data, the actual motion data group and a pre-trained state determination model, determines the future predicted state data of the target missile at the future moment; wherein the state determination model is a pre-trained neural network model; since the neural network model does not need to perform numerical integral calculation or numerical differential calculation, it avoids introducing cumulative errors, reduces the amount of calculation, and is conducive to improving the efficiency and accuracy of determining the predicted state data; and based on the future predicted state data and the actual motion data group, determines the predicted tracking result of the target missile for the target tracking object, thereby achieving the effect of improving the accuracy of the predicted tracking result.

[0090] It is worth noting that in the embodiment of the above-mentioned tracking result prediction 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.

[0091] Figure 4 : is a schematic diagram of the structure of an electronic device that implements the tracking result prediction method of an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can 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.

[0092] 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.

[0093] 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.

[0094] 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 dedicated 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 tracking result prediction method.

[0095] In some embodiments, the tracking result prediction 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 tracking result prediction method described above may be performed. Alternatively, in other embodiments, the processor 21 may be configured to execute the tracking result prediction method in any other appropriate manner (e.g., by means of firmware).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] This embodiment also provides a computer program product, including a computer program, which, when executed by a processor, implements the tracking result prediction method provided in any embodiment of the present application.

[0103] 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).

[0104] 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.

[0105] 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 tracking result prediction method, characterized in that: include: Determine the target actual state data of the target missile at the target time and the actual motion data group of the target tracking object; Determine the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and a pre-trained state determination model; wherein the state determination model is a pre-trained neural network model; Based on the future predicted state data and the actual motion data group, a predicted tracking result of the target missile on the target tracking object is determined.

2. The method according to claim 1, characterized in that Before determining the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and the pre-trained state determination model, the method further includes: Determine target environment data of a target area to which the target missile belongs at the target time; The method of determining the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and the pre-trained state determination model comprises: Based on the target actual state data, the target motion data corresponding to the target moment in the actual motion data group, the target environment data and the state determination model, the future predicted state data of the target missile is obtained.

3. The method according to claim 1, characterized in that: The step of determining the predicted tracking result of the target missile for the target tracking object based on the future predicted state data and the actual motion data group includes: determining future actual motion point data based on the actual motion data set; When the future predicted state data and the future actual movement point data meet a preset condition, determining that the predicted tracking result of the target missile on the target tracking object is a tracking success; Among them, the future predicted state data includes the future predicted position of the target missile at the future moment; the future actual motion point data includes the future object position of the target tracking object at the future moment; the preset condition includes that the distance between the future predicted position and the future object position is less than a preset distance threshold.

4. The method according to claim 3, characterized in that Also includes: In the case that the future predicted state data and the future actual motion point data do not meet the preset conditions, determining input data based on the future predicted state data and the actual motion data group, inputting the input data into the state determination model to obtain the latest predicted state data, and updating the future predicted state data based on the latest predicted state data; In the case where the updated future prediction state data satisfies the preset condition, determining that the prediction tracking result of the target missile for the target tracking object is a tracking success; When the updated future prediction state data does not meet the preset conditions, the above operations of determining the input data, inputting the data input state determination model, and updating the future prediction state data based on the latest prediction state data are repeatedly performed until the preset stop conditions are met.

5. The method according to claim 4, characterized in that The preset stop conditions include: The number of executions of the repeated operation reaches a preset number threshold; and / or, The updated future prediction state data meets the preset condition.

6. The method according to claim 1, characterized in that The state determination model includes a state transition network; before determining the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and the pre-trained state determination model, it also includes: Acquire a historical operation data set of the target missile; wherein the historical operation data set is composed of a historical state data set and a plurality of historical motion data sets generated in a plurality of historical time periods; Based on the historical operation data set and the reinforcement learning algorithm, the state transition network to be trained is trained, and the state transition network after training is obtained as the state determination model.

7. A tracking result prediction device, characterized in that: include: A first data determination module is used to determine the target actual state data of the target missile at the target time and the actual motion data group of the target tracking object; A second data determination module is used to determine the future predicted state data of the target missile at a future time based on the target actual state data, the actual motion data group and a pre-trained state determination model; wherein the state determination model is a pre-trained neural network model; The prediction tracking result determination module is used to determine the prediction tracking result of the target missile on the target tracking object based on the future prediction state data and the actual motion data group.

8. 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 tracking result prediction method according to any one of claims 1 to 6.

9. 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 tracking result prediction method according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the tracking result prediction method according to any one of claims 1 to 6.

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