A rocket launch key event identification method, device and computer equipment

Abstract

This invention provides a method, apparatus, and computer device for identifying critical events during rocket launches. It analyzes the radiation characteristics of rocket targets in infrared remote sensing images to determine critical events during the launch process, employing a long short-term memory network to pinpoint the timing of these critical events. This solution overcomes the challenge of identifying critical events that previously required specialized expertise, and its accuracy surpasses that of the latest existing technologies.

Description

Technical Field

[0001] This invention relates to the field of computers, and in particular to a method, apparatus, and computer equipment for identifying critical events during rocket launches. Background Technology

[0002] Computer vision technology is a field that uses intelligent imaging devices to acquire and process images. Object tracking, as a crucial component of computer perception systems, is a research hotspot in major laboratories both domestically and internationally. Object tracking primarily involves detecting and locating targets of interest in videos by linking their motion trajectories during movement and maintaining the identity information of each target throughout subsequent tracking. Object tracking algorithms are widely used in real-world scenarios, and their combination with other machine vision tasks yields significant application value and research significance.

[0003] During rocket launch, it is necessary to monitor the separation of the second stage rocket. During the rocket launch and ascent, due to the high viewing angle and wide range of remote sensing images, the targets presented in the infrared remote sensing images are very weak. The outline, texture and shape features of the targets in the images are often not obvious, and they are very similar to the noise generated by the sensors during the imaging process, making it difficult to track and identify the rocket separation images. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus and computer equipment for identifying critical events in rocket launches, which realizes automated identification and solves the difficulty that only professionals can identify critical events.

[0005] In a first aspect, embodiments of the present invention provide a method for identifying critical events during rocket launches, comprising:

[0006] Target characteristics are extracted from the target objects in the infrared remote sensing image to obtain the radiation characteristic information of the target mapped in the infrared remote sensing image;

[0007] The obtained radiation characteristic information is correlated with the launch time sequence of the target object, and a radiation characteristic curve of the target object during the launch process is plotted.

[0008] The radiation characteristic curve is input into a pre-trained long short-term memory network, and the output predicted value is compared with the true value to obtain anomaly scores for event discrimination.

[0009] As an optional approach, the step of inputting the radiation characteristic curve into a pre-trained long short-term memory network, comparing the output predicted value with the true value to obtain anomaly scores for event discrimination, includes:

[0010] Add an attention module to the long short-term memory network;

[0011] The obtained predicted value is sent to the next memory unit. The error value between the predicted value and the true value is calculated at each step, and the time when the critical event occurs is the one with the largest error value.

[0012] As an optional approach, before extracting target features from the infrared remote sensing image, the method further includes:

[0013] The infrared remote sensing image is processed to obtain the grayscale value of the target object;

[0014] Interference information in the infrared remote sensing image is eliminated by subtracting the background from consecutive frame data to obtain a clean target object.

[0015] As an optional approach, each structural unit in the Long Short-Term Memory (LSTM) network includes three distinct gate structures: a forget gate, a memory gate, and an output gate. These gates control the retention and transmission of information within the LTM network, reflecting the cellular state C. t and output signal h t It includes the following three stages:

[0016] In the forgetting phase, the forgetting gate consists of a Sigmoid neural network layer and a bitwise multiplication operation;

[0017] f t =σ(W f ·[h t-1 ,x t ]+b f );

[0018] In the selective memory stage, the memory gate functions in the opposite way to the forgetting gate, determining the newly input information x. t and h t-1 Information is retained in the middle;

[0019] i t =σ(W i ·[h t-1 ,x t ]+b i );

[0020]

[0021] Adding the results from the two steps above will give you C, which will be passed to the next state. t ;

[0022] In the output phase, the output of the current state is determined, and the input signal x is given at time t. t The corresponding output signal will then be calculated using the following formula;

[0023] o t =σ(W o [h t-1,x t ]+b o ;

[0024] h t =o t *tanh(C t ).

[0025] As an alternative, the key events are the rocket's stage separation and engine ignition.

[0026] Secondly, embodiments of the present invention provide a rocket launch critical event identification device, comprising:

[0027] The extraction module is used to extract the target characteristics of the target object in the infrared remote sensing image and obtain the radiation characteristic information of the target mapped in the infrared remote sensing image;

[0028] The plotting module is used to correlate the acquired radiation characteristic information with the emission timing of the target object and plot the radiation characteristic curve of the target object during the emission process.

[0029] The identification module is used to input the radiation characteristic curve into a pre-trained long short-term memory network, output the predicted value and compare it with the true value to obtain an anomaly score for event discrimination.

[0030] As an optional solution, the recognition module is specifically used to add an attention module to the long short-term memory network, and the obtained predicted value is sent to the next memory unit. The error value between the predicted value and the true value is calculated at each step, and the time when the critical event occurs is the one with the largest error value.

[0031] As an optional solution, a processing module is also included, which processes the infrared remote sensing image to obtain the grayscale value of the target object before extracting the target characteristics of the target object from the infrared remote sensing image, and eliminates the interference information in the infrared remote sensing image by subtracting the background from the continuous frame data to obtain a clean target object.

[0032] Thirdly, an embodiment of the present invention provides a computer device, characterized in that it includes:

[0033] At least one processor; and

[0034] A memory communicatively connected to the at least one processor; wherein,

[0035] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the rocket launch critical event identification method described above.

[0036] Fourthly, in this embodiment of the invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the above-described rocket launch key event identification method.

[0037] This invention provides a method, apparatus, and computer device for identifying critical events during rocket launches. It analyzes the radiation characteristics of rocket targets in infrared remote sensing images to determine critical events during the launch process, employing a long short-term memory network to pinpoint the timing of these critical events. This solution overcomes the challenge of identifying critical events that previously required specialized expertise, and its accuracy surpasses that of the latest existing technologies. Attached Figure Description

[0038] Figure 1 This is a flowchart of a method for identifying key events during rocket launches, provided in an embodiment of the present invention.

[0039] Figure 2 This is a schematic diagram illustrating the effect of the frame difference method in a rocket launch key event identification method provided in an embodiment of the present invention.

[0040] Figure 3 This is a schematic diagram of radiation characteristic curves in a rocket launch key event identification method provided in an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of the structure of a long short-term memory network in a rocket launch critical event identification method provided in an embodiment of the present invention;

[0042] Figure 5 This is a unit structure diagram of a rocket launch key event identification method provided in an embodiment of the present invention;

[0043] Figure 6 This is a schematic diagram of the network detection effect in a rocket launch key event identification method provided in an embodiment of the present invention;

[0044] Figure 7 This is a schematic diagram illustrating the detection accuracy in a rocket launch critical event identification method provided in an embodiment of the present invention;

[0045] Figure 8 This is a structural block diagram of a rocket launch critical event identification device provided in an embodiment of the present invention;

[0046] Figure 9 This invention provides a structural block diagram of a computer device in an embodiment of the invention. Detailed Implementation

[0047] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0048] The terms "first," "second," "third," "fourth," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0049] Combination Figure 1 As shown, this embodiment of the invention provides a method for identifying critical events during rocket launches, including:

[0050] S101. Extract target characteristics from the infrared remote sensing image to obtain the radiation characteristic information of the target mapped in the infrared remote sensing image.

[0051] S102. The obtained radiation characteristic information is matched with the launch time sequence of the target object, and a radiation characteristic curve of the target object during the launch process is plotted.

[0052] S103. Input the radiation characteristic curve into the pre-trained long short-term memory network, and compare the output predicted value with the true value to obtain anomaly scores for event discrimination.

[0053] This invention provides a method for identifying critical events during rocket launches. It analyzes the radiation characteristics of the rocket target in infrared remote sensing images to determine critical events during the launch process, and employs a long short-term memory network to determine the timing of these critical events. This method automates the process, overcoming the challenge that only professionals can identify critical events, and also surpasses the accuracy of the latest existing technologies.

[0054] Furthermore, this embodiment of the invention also provides a method for identifying critical events in rocket launches, including:

[0055] S201. Extract target characteristics from the infrared remote sensing image to obtain the radiation characteristic information of the target mapped in the infrared remote sensing image.

[0056] Because remote sensing images have a high viewing angle and wide range, targets appearing in infrared remote sensing images during rocket launch are very small. The target's outline, texture, and shape are often indistinct, and their characteristics are very similar to the noise generated by the sensor during imaging. At this time, the only characteristics of the target in the image are its grayscale and motion characteristics. Currently, most rockets use either hot or cold separation for stage separation. Hot separation refers to the high-temperature exhaust gas from the first-stage rocket engine pushing the second-stage rocket apart; the first-stage engine ignites when the connecting parts are released. In cold separation, the force separating the two stages is the thrust of the retro-rocket mounted on the second-stage rocket; the first-stage rocket engine ignites only after the two stages have separated. It can be observed that regardless of the separation method, the engine ignites, a characteristic that is highly sensitively captured in infrared remote sensing images. Therefore, analyzing key events based on the target's radiation characteristics is more reasonable and accurate.

[0057] Combination Figure 2 As shown, the radiation characteristic extraction method only processes the image, obtaining only the target's grayscale values. During emission, the target is interfered with by background elements such as clouds and noise. Therefore, the first step is to use background subtraction for consecutive frames to minimize background interference. Since the background does not change significantly in a short time, a clean target can be obtained by subtracting between frames.

[0058] S202. The obtained radiation characteristic information is matched with the launch time sequence of the target object, and a radiation characteristic curve of the target object during the launch process is plotted.

[0059] Combination Figure 3 As shown, the target was obtained through this method. Since the target will produce obvious ignition when the event occurs, the target will become significantly larger. Therefore, the calculated gray value of the target will change significantly, which will be more obvious in the radiation characteristic map.

[0060] S203. Input the radiation characteristic curve into the pre-trained long short-term memory network, and compare the output predicted value with the true value to obtain anomaly scores for event discrimination.

[0061] Specifically, in order to improve the accuracy of target event recognition, an attention mechanism is added to the Long Short-Term Memory (LSTM) network to focus on salient features. Specifically, an attention module is added to the LSM network, and the obtained predicted value is sent to the next memory unit. The error value between the predicted value and the true value is calculated at each step. The time when the error value is the largest is the moment when the critical event occurs. The critical event can be the interstage separation of the rocket and the engine ignition operation.

[0062] In some embodiments, before extracting target features from the infrared remote sensing image, the method further includes:

[0063] The infrared remote sensing image is processed to obtain the grayscale value of the target object;

[0064] Interference information in the infrared remote sensing image is eliminated by subtracting the background from consecutive frame data to obtain a clean target object.

[0065] While traditional RNNs (Recurrent Neural Networks) can acquire temporal information about the target, they can only remember the most recent temporal information, and they suffer from problems such as vanishing and exploding gradients during the learning process. Therefore, Long Short-Term Memory (LSTM) networks are used for training.

[0066] Combination Figure 4 As shown in the embodiments of the present invention, the following is obtained: Figure 3 After generating a large number of radiation characteristic curves, the radiation characteristic curves are fed into the Long Short-Term Memory network for training.

[0067] Figure 5 The diagram shows the structure of each structural unit in the Long Short-Term Memory (LSTM) network. Each structural unit includes three different gate structures: a forget gate, a memory gate, and an output gate, which control the information retention and transmission within the LTM network, reflecting the cell state C. t and output signal h t It includes the following three stages:

[0068] In the forgetting phase, the forgetting gate consists of a Sigmoid neural network layer and a bitwise multiplication operation;

[0069] f t =σ(W f ·[h t-1 ,x t ]+b f ;

[0070] In the selective memory stage, the memory gate functions in the opposite way to the forgetting gate, determining the newly input information x. t and h t-1 Information is retained in the middle;

[0071] it =σ(W i ·[h t-1 ,x t ]+b i );

[0072]

[0073] Adding the results from the two steps above will give you C, which will be passed to the next state. t ;

[0074] In the output phase, the output of the current state is determined, and the input signal x is given at time t. t The corresponding output signal will then be calculated using the following formula;

[0075] o t =σ(W o [h t-1 ,x t ]+b o ;

[0076] h t =o t *tanh(C t ).

[0077] To better illustrate the effects of the method provided in the embodiments of the present invention, and to verify the feasibility of the method through experiments, corresponding parameters are provided for auxiliary explanation, which are introduced below.

[0078] The parameters for this experiment are as follows:

[0079] Test set: Simulation dataset

[0080] Training parameters: Epoch = 200, BatchSize = 64, eval_batch_size = 64

[0081] Figure 6 The image shows the network detection results, demonstrating a relatively good detection performance. It can be seen that the anomaly scores are higher at frames 900 and 2250, which are also the points where the rocket's first and second stage separations occur. Figure 7 The detection accuracy graph shows the results from three aspects: accuracy, recall, and F1 score, demonstrating the feasibility of the method of this invention.

[0082] This invention provides a method for identifying critical events during rocket launches. It analyzes the radiation characteristics of the rocket target in infrared remote sensing images to determine critical events during the launch process and employs a Long Short-Term Memory (LSTM) network to determine the timing of these critical events. This method overcomes the challenge of identifying critical events that requires specialized expertise and offers superior accuracy compared to the latest existing technologies.

[0083] Knot Figure 8 As shown, this embodiment of the invention provides a rocket launch critical event identification device 800, comprising:

[0084] Extraction module 801 is used to extract target characteristics from the target object in the infrared remote sensing image and obtain the radiation characteristic information of the target mapped in the infrared remote sensing image;

[0085] The plotting module 802 is used to correlate the acquired radiation characteristic information with the emission timing of the target object and plot the radiation characteristic curve of the target object during the emission process.

[0086] The identification module 803 is used to input the radiation characteristic curve into a pre-trained long short-term memory network, output the predicted value and compare it with the true value to obtain an anomaly score for event discrimination.

[0087] As an optional solution, the recognition module 803 is specifically used to add an attention module to the long short-term memory network, and the obtained predicted value is sent to the next memory unit. The error value between the predicted value and the true value at each step is calculated, and the time when the critical event occurs is the one with the largest error value.

[0088] As an optional solution, a processing module 804 is also included, which processes the infrared remote sensing image to obtain the grayscale value of the target object before extracting the target characteristics of the target object from the infrared remote sensing image, and eliminates the interference information in the infrared remote sensing image by subtracting the background from the continuous frame data to obtain a clean target object.

[0089] This invention provides a rocket launch critical event identification device. It analyzes the radiation characteristics of a rocket target in infrared remote sensing images to determine critical events during the launch process, and employs a long short-term memory network to determine the timing of these critical events. This technology overcomes the difficulty of identifying critical events that only professionals can perform, and its identification accuracy surpasses that of the latest existing technologies.

[0090] Accordingly, according to embodiments of the present invention, the present invention also provides a computer device, a readable storage medium, and a computer program product.

[0091] Figure 9 This is a schematic diagram of the structure of a computer device 12 provided in an embodiment of the present invention. Figure 9 A block diagram of an exemplary computer device 12 suitable for implementing embodiments of the present invention is shown. Figure 9 The computer device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0092] like Figure 9 As shown, computer device 12 is represented in the form of a general-purpose computing device. Computer device 12 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. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0093] The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0094] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0095] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0096] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 9 Not shown; usually referred to as a "hard drive"). Although Figure 9Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0097] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0098] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with computer device 12, and / or with any device that enables computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0099] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the rocket launch key event identification method provided in the embodiments of the present invention.

[0100] This invention also provides a non-transitory computer-readable storage medium storing computer instructions, on which a computer program is stored, wherein the program, when executed by a processor, is the rocket launch key event identification method provided in all embodiments of this application.

[0101] The computer storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0102] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0103] The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. The computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "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 standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described rocket launch key event identification method.

[0105] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0106] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method of rocket launch key event recognition, characterized by, The method comprises the following steps: target characteristic extraction is performed on a target object in an infrared remote sensing image to obtain radiation characteristic information of the target object mapped in the infrared remote sensing image; the radiation characteristic information obtained is corresponded to a launch time sequence of the target object, and a radiation characteristic curve of the target object in a launch process is drawn; the radiation characteristic curve is input into a pre-trained long short-term memory network, and a predicted value is compared with a true value to obtain an anomaly score for event discrimination; wherein, an attention module is added in the long short-term memory network; the predicted value obtained is sent to a next memory unit, an error value is calculated for each step of the predicted value and the true value, and the maximum error value obtained is a critical event occurrence time, and the critical event is a stage separation of a rocket and an engine ignition operation.

2. The rocket launch key event recognition method of claim 1, wherein, Before the target characteristic extraction is performed on the target object in the infrared remote sensing image, the method further comprises the following steps: a gray value of the target object is obtained by processing the infrared remote sensing image; interference information in the infrared remote sensing image is eliminated by using a continuous frame data background subtraction method to obtain a pure target object.

3. A rocket launch key event recognition apparatus characterized by comprising: The method comprises the following steps: a target characteristic extraction module is configured to perform target characteristic extraction on a target object in an infrared remote sensing image to obtain radiation characteristic information of the target object mapped in the infrared remote sensing image; a drawing module is configured to correspond the radiation characteristic information obtained to a launch time sequence of the target object, and draw a radiation characteristic curve of the target object in a launch process; an identification module is configured to input the radiation characteristic curve into a pre-trained long short-term memory network, compare a predicted value with a true value to obtain an anomaly score for event discrimination, and comprises an attention module added in the long short-term memory network, a predicted value obtained is sent to a next memory unit, an error value is calculated for each step of the predicted value and the true value, and the maximum error value obtained is a critical event occurrence time.

4. The rocket launch key event recognition apparatus according to claim 3, characterized by, The method further comprises a processing module configured to, before the target characteristic extraction is performed on the target object in the infrared remote sensing image, obtain a gray value of the target object by processing the infrared remote sensing image, and eliminate interference information in the infrared remote sensing image by using a continuous frame data background subtraction method to obtain a pure target object.

5. A computer device, comprising: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the rocket launch critical event identification method in any one of claims 1 to 2. The computer instructions are used to enable the computer to perform the rocket launch critical event identification method in any one of claims 1 to 2.

6. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, ​

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